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Article

Variation in the Soil Microbial Community of Reclaimed Land over Different Reclamation Periods

by
Huping Hou
1,
Chen Wang
1,
Zhongyi Ding
1,
Shaoliang Zhang
1,*,
Yongjun Yang
1,
Jing Ma
2,
Fu Chen
2 and
Jinrong Li
1
1
School of Environment Science & Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
Low Carbon Energy Institute, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(7), 2286; https://doi.org/10.3390/su10072286
Submission received: 4 May 2018 / Revised: 25 June 2018 / Accepted: 26 June 2018 / Published: 2 July 2018
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Abstract

:
Improvement of soil quality after land reclamation is a key concern in mining areas. However, the characteristics and internal mechanisms of variation of bacterial community structure over different reclamation periods are currently unclear. The recovery and evolution of soil microbial community structure are important indicators of the level of soil quality improvement of reclaimed soil. Therefore, this study investigated soil samples from coal gangue-filled land after reclamation periods of 1, 6, and 15 years. To accomplish this, 16S rRNA gene libraries were produced to determine the microbial community composition of the soils. In addition, various soil microbial community characteristics in the filled reclamation areas were compared with soil samples from areas unaffected by coal mining. The results showed the following: (1) The diversity and abundance of bacterial communities in reclaimed soils was slightly different from that of natural soils. However, the soil bacterial community structure was highly similar to natural soil after a 15-year reclamation period; therefore, the recovery of bacterial communities can be used as an indicator of the effects of rehabilitation. (2) Some soil physicochemical properties are significantly correlated with the main bacteria in the soil. (3) The dominant bacteria included members of the phyla Firmicutes and Proteobacteria, as well as members of the genera Bacillus, Enterococcus, and Lactococcus. Taken together, the results of this study indicated that the application of microbial remediation technology can be used to adjust the soil microbial community structure, improve soil quality, and shorten the soil recovery period.

1. Introduction

Soil is one of the most important but severely exploited natural resources [1]. Soil ecology in mining areas has been heavily disturbed by human activities. Compared with natural soils, mine soils are pedogenically young and often characterized by poor soil structure, a lack of distinctive soil horizons, and nutrient-deprived conditions; therefore, they are categorized as anthrosols [2,3]. Reclamation of land degraded by coal mining aims to revert the damaged ecosystem to a healthy state. This involves regeneration of mine soil fertility, improvement of soil characteristics, revegetation, enhancement of nutrient stock, and improved biomass productivity. Land reclamation and environmental restoration of mined areas are topics of great research interest [4,5].
Much emphasis has been placed on restoring and protecting aboveground vegetation during reclamation of land in mining areas; nevertheless, research on soil microorganisms and their ecological functions in these areas, are lacking. Soil microbial communities comprise an important component of the soil ecological system and are considerably affected by the microenvironment [6,7,8]. When the soil microenvironment changes, its microbial community can respond quickly and substantially [1,9]. In mining areas, soil is disturbed by mining excavation, topsoil stripping and backfill, revegetation. As a result, the effectiveness of reclamation activities remains unclear, because of the dramatic physical, chemical, and biological alterations to associated ecosystems. These man-made disturbances alter the soil microbial characteristics. Soil bacteria participate in the transformation of most nutrients in the soil, and are the predominant soil microorganisms, accounting for more than 95% of the total microbial population [10,11,12,13]. Therefore, the structural characteristics and variations in soil bacterial communities can reflect the effects of soil restoration of mined areas [14,15,16,17].
Many studies have reported changes in plant communities based on their visibility, and plants are usually used to predict the success of the restoration of degraded environments [18]. However, microorganisms may be considered the first responders upon disruption of land by mining. Analysis of the biomass and quantities of microorganisms in reclaimed soil has been conducted [19,20], and the total amount of microbes and percentage of actinomycetes and fungi among microorganisms have been found to gradually increase in reclaimed soil with increased reclamation years [21]. Some studies have assessed the influence of mining on microbial from the aspects of community diversity and microbial activity in soil [15,22]. However, few studies have reported the degree of recovery and effect of bacterial communities under reclamation management. Moreover, the activities of microorganisms in reclaimed soil have seldom been examined in terms of soil microbial community structure variations and their influence on coal gangue-filled reclaimed land over different reclamation periods [9].
Therefore, this study was conducted to assess variations in the soil microbial community of reclaimed land over different reclamation periods. To accomplish this, we compared reclaimed land and natural land to investigate the effects of farmland filled with coal gangue and the degree of recovery of microbial communities. High-throughput sequencing technology was applied to: (1) examine the structure and diversity of soil bacterial communities between reclamation soil and natural soil and to assess the degree of recovery of bacterial communities; (2) investigate correlations between the soil microbial community and soil properties in coal gangue-filled reclaimed land; and (3) identify the dominant or beneficial bacteria that adapt to the mining environment and have important effects on restoring soil fertility. The results of this study will serve as a useful reference for remediation and restoration technology in coal mining. We hypothesized that bacterial community structure differs between reclamation soil and natural soil, but that it will tend to become the same as that of natural soil as the reclaimed period increases. Moreover, we expect that soil properties can impact the distribution and composition of bacterial communities.

2. Materials and Methods

2.1. Study Area

The studied coal mining area (34°13′39″–34°26′16″ N, 117°06′21″–117°12′16″ E) is located in the Jiuli District, Xuzhou, Jiangsu Province, eastern China (Figure 1). The study area has a warm temperate zone with a semi-humid monsoon climate and four distinctive seasons. The area has an average annual temperature of 14 °C, an annual sunshine duration ranging from 2284 to 2495 h, an average annual frost-free period of 200 to 220 days, an average annual precipitation of 800 to 930 mm, and an average annual humidity of 72%. The region is characterized by abundant climatic resources, is suitable for growing crops, and is an important grain-producing area of northern China. The underground water level of normal paddy-upland rotation land in the area is 0.80 ± 0.20 m, while that of normal upland is below 1.0 m.
The studied area has been highly affected by coal mining. When the coal mine was exploited by well mining, the surface subsided from 0.5 to 1.5 m, with an average subsidence of 1.1 m. After mining operations ceased, 50 cm of topsoil was removed and homogenized, after which coal gangue was used to fill the subsidence site. Finally, the same topsoil as previously removed was backfilled above the coal gangue. Based on mining records, sampling points were identified in regions that were undisturbed by mining activities and regions that were reclaimed in 2016, 2012, and 2001. The undisturbed site was taken as control soil. The control points were located close (within 1 km) to the reclamation sample in an area with similar field conditions.
To determine the changes in the attributes of newly constructed ecosystems, all reclaimed soils were grouped in to three categories. Sample site S (116°48′44″ E, 34°48′47″ N) had a 1-year reclamation period and SC was the related control sample. Sample site M (117°23′48″ E, 34°21′25″ N) had a 6-year reclamation period and MC was the related control sample. Sample site L (117°08′21″ E, 34°25′24″ N) had a 15-year reclamation period and LC was the related control sample.
Despite limited sampling, we were able to obtain representative field conditions across a large test area. To eliminate interference by unrelated factors, the following requirements were followed when selecting the sampling site: (1) areas had to be planted with the same type of crop; (2) the locations of plots could not be too far apart and the geological conditions were required to be similar. Ordinary cinnamon soil develops from alluvial deposits of the Yellow River. At our test site, field crops with two harvests within a year, such as maize and soybeans, were planted in the reclaimed and control lands, and they grew well.

2.2. Soil Sampling

Soil samples were collected on a sunny day in May of 2016 between 9:00—11:00 a.m. To obtain samples from each reclamation region and control region, sampling sites were on open ground between plants. Sampling was performed in an S-shaped route, from which three soil cores (50.46 mm diameter) with a weight of approximately 1 kg, were collected at depths of 0–20 and 20–40 cm. A total of 36 Samples were transported directly to the laboratory on ice. After the soil was sieved through a 2-mm mesh and manually homogenized, all of the samples were split to obtain two subsamples. Subsamples used for microbial analysis were kept in a refrigerator at −20 °C before further processing. Subsamples intended for investigation of physicochemical properties and enzymatic activity analysis were naturally air dried and ground before soil analysis.

2.3. Soil Analysis

Soil parameters were measured following the methods of the Nanjing Agricultural College [23]. pH and Electric conductivity (EC) were determined in soil: H2O (1:5; w/v soil/water) suspensions using a pH Meter (model: FE20K, Mettler Toledo, Giessen, Germany) and TDR (model: TRIME-IPH, Aozuo Inc., Beijing, China). The Moisture Content (MoiC) and Bulk Density (BD) of the soil samples were measured by placing samples in stainless Kopecky cylinders, and then oven drying them at 105 °C for 48 h, after which the ratio of weight and volume of the soil was calculated. Nitrate nitrogen (N) was measured using the Kjeldahl method [24], available phosphorus (P) was determined using the Olsen method [25], rapidly available potassium (K) was evaluated using atomic absorption spectroscopy after extraction with ammonium hydrochloric acid, and total organic carbon (TOC) was measured using the k-dichromate oxidation method [26].

2.4. 16S rRNA Gene Sequencing of Soil Bacteria

2.4.1. DNA Extraction and PCR Amplification

The 16S rRNA gene of soil bacteria were sequenced within three days of sampling. The DNA of soil samples was extracted using the E.Z.N.A® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s protocols. The V4–V5 area of the 16S rRNA of soil bacteria was amplified by polymerase chain reaction (PCR). The applied degenerate primers included the 515F 5′-barcode-GTGCCAGCMGCCGCGG-3′ and 907R 5′–CCGTCAATTCMTTT RAGTTT-3′. The PCR amplification program was as follows: pre-degeneration at 95 °C for 2 min, degeneration at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s. The above steps were repeated for 25 cycles, followed by a final extension at 72 °C for 5 min. The PCR reaction was performed in triplicate in 20 μL mixtures, containing 4 μL of 5 × FastPfu buffer solution, 2 μL of 2.5 mM dNTP, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu polymerase, and 10 ng template DNA. The amplicons were extracted from 2% agarose gel, and were then purified using the AxyPrep DNA gel extraction kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions. Finally, samples were quantified using QuantiFluor™-ST (Promega, WI, USA).

2.4.2. Library Construction and Sequencing

Purified PCR products were quantified using the Qubit®3.0 (Promega Corporation, Madison, WI, USA) fluorometer, and amplicons with different sequences were mixed evenly. The collected DNA products were then used to establish the Illumina pair-end library with a “Y” shaped connector. An Illumina Nextera® XT Index Kit (Illumina, San Diego, CA, USA) was used to attach dual indices and Illumina sequencing adapters. Following the second PCR, samples were re-cleaned with AMPure XP beads (Beckman Coulter, Pasadena, CA, USA) and quantified. The amplicon library was then pair-end sequenced (2 × 250) using the Illumina MiSeq platform (Shanghai BIOZERON Co., Ltd., Shanghai, China) according to the standard scheme. The read raw data were then saved in the National Center for Biotechnology Information (NCBI) sequence read archive (SRA) database (Accession Number: SRP05270).

2.5. Data Analysis

2.5.1. Raw Data Processing and Analysis of the Number of Sample Sequences

Quality filtering of the raw fastq file was conducted using QIIME 1.17 (http://www.wernerlab.org/software/macqiime) according to the following protocol: at any locus with an average quality score below 20, read data with 250 bp truncated using a 10 bp sliding window and truncated read data shorter than 50 bp were discarded. Barcodes were matched accurately. During primer matching, two nucleotides were mismatched and read data containing fuzzy characters were deleted. Only sequences with lengths greater than 10 bp were assembled according to the overlapping sequence and read data that could not be assembled were discarded. Clusters of operational taxonomic units (OTUs) with similarities of 97% were truncated using UPARSE version 7.1, and chimeric sequences were then authenticated and deleted by UCHIME. The genetic relationship of the system was established by analyzing every 16S rRNA gene sequence in the SILVA (SSU123) 16S rRNA database, generated using the RDP classifier. A confidence threshold of 70% was adopted for analyses.

2.5.2. Bioinformatic Analysis

Based on analysis by Mothur (ver. 1.21), α diversity analysis was conducted and the Shannon diversity index and Chao richness index were calculated using Equations (1) and (2).
H s h a n n o n = − ∑ i = 1 S o b s n i N ln n i N
S c h a o 1 = S o b s + n 1 ( n 1 − 1 ) 2 ( n 2 + 1 )
where Schao1 is the estimated number of OTUs, Sobs is the observed number of OTUs, ni is the number of sequences contained in the ith OTU, N is the total number of sequences, n1 is the number of OTUs that contained only one sequence, and n2 is the number of OTUs that contained two sequences.
β diversity analysis was conducted using Hierarchical clustering analysis to visualize differences between microbial communities. Unweighted Pair Group Means Analysis (UPGMA) was conducted based on unweighted and weighted UniFrac distances [27]. Qiime were used to calculated UniFrac distances and β diversity distance matrix based on Equation (3).
D B r a y − C u r t i s = 1 − 2 ∑ min ( S A , i , S B , i ) ∑ S A , i + ∑ S B , i
where SA,i is the number of sequences contained in the ith OTU in sample A, and SB,i is the number of sequences contained in the ith OTU in sample B.

2.5.3. Statistical Analysis

All of the data were subjected to analysis of variance (ANOVA) with SPSS 22.0 (SPSS Inc., Chicago, IL, USA). Significant effects were reported at the 0.05 level. Redundancy analysis (RDA) was conducted to investigate relationships between microbial community composition and soil properties using CANOCO 4.5 (CANOCO, Microcomputer Power Inc., Ithaca, NY, USA). Proximity value was calculated by dividing the sample data of reclaimed land by the sample data of their respective control land, to determine the degree of closeness and recovery degree of sample properties between reclaimed soil and control soil, which can exclude the interference caused by other factors unrelated to the results analysis.

3. Results

3.1. Soil Characteristics

Soil properties are shown in Table 1. The pH values of precipitate samples ranged from 6.56 to 6.69, indicating they were generally weakly acidic. The EC was in a normal range for saline soil. The BD values were slightly higher in reclaimed than in control soil. The reclaimed land is of larger soil porosity value, which has advantages for soil aeration, nutrient transport, microbial activity and thermal characteristics. The N and TOC content differed significantly between reclaimed and control soils. Sample L and LC had higher N, K and TOC levels. Additionally, the 0–20-cm soil layer had higher N, P, K and TOC contents than the 20–40-cm soil layer in sample M and L, while the opposite was true for sample S. The content of N, P, K and TOC differed at different soil depths. It indicated that nutrients for microorganisms may affect the vertical distribution of soil microorganisms.

3.2. 16S rRNA Gene Sequences and Taxa Characteristics

Illumina Miseq 16S rRNA gene libraries sequencing was performed on all samples using a similarity level of 97% (Table 2). Optimized data from 439938 effective sequences were obtained. Among these samples, S-2 sample had the minimum number of sequences (33,964) while LC-2 sample had the maximum (42,272). In the 0–20-cm soil layer, the number of bacterial sequences in the reclaimed soil samples was greater than that in the control samples, while the opposite was true observed in the 20–40-cm soil layer.
Biosystematic analysis of OTUs was performed at the phylum, class, order, family, genus and species levels, using a similarity level of 97%, and primary bacterial counts were conducted for all samples. In the high-throughput sequencing of bacteria in reclaimed land soils, a total of 29 phyla, 63 classes, 127 orders, 223 families, 488 genera, and 715 species were found. In the control soils, a total of 32 phyla, 70 classes, 140 orders, 244 families, 618 genera, and 943 species were found.

3.3. Dominant Bacteria in Soil Samples

The main bacterial phyla in soil samples were Firmicutes, Proteobacteria, Chloroflexi, Actinobacteria, Acidobacteria, and Gemmatimonadetes (Figure 2A). The dominant species in both the reclaimed and control lands were Firmicutes and Proteobacteria, which together comprised 80.48–99.89% of all of the bacteria in reclaimed land samples, and 67.70–83.15% in control samples, indicating a higher ratio of the dominant bacteria in reclaimed land than in control land. With the exception of Firmicutes, numbers of all bacteria in reclaimed land samples were lower than those in the control samples. The quantitative proportion of Firmicutes in reclaimed land samples was larger than in the control samples.
At the genus level, the main bacterial genera contained Bacillus, Enterococcus, Lactococcus, Paenibacillus, Cronobacter, Gemmatimonas, and Alkaliphilus (Figure 2B), while the dominant bacterial genera in both the reclaimed and control lands were Bacillus, Enterococcus, and Lactococcus, all three of which were Firmicutes, and these together constituted a relative proportion of 62.05–85.33% of the bacterial population in reclaimed land, and of 49.80–82.87% in the control land. The quantities of these three dominant bacterial genera in reclaimed land were larger than in the control land.
Dominant soil bacterial communities at the phylum and genus levels were further analyzed. They were Firmicutes, Proteobacteria, Bacillus, Enterococcus and Lactococcus. The proximity values of the OTUs in soil from reclaimed land to those in the control land was calculated to evaluate the variation of ratio of dominant bacteria with different reclamation periods (Table 3). In the 0–20-cm soil layer, for the Firmicutes, and the Bacillus specifically, sample S had a lower proximity value than sample M, which, in turn, had lower proximity value than sample L. In the same layer, for Enterococcus and Lactococcus, sample M had the lowest proximity value, followed by sample S, then sample L. With increasing reclamation period, the community structure of Firmicutes appeared to be closer to that found in control land. For Proteobacteria in the 0–20 cm soil layer, proximity value of sample S was greater than sample M, which was higher than sample L, indicating that the number of Proteobacteria decreased over time in reclaimed land filled by coal gangue, with the difference in number increasing in both reclaimed and control lands.

3.4. Diversity of Soil Bacterial Communities

Diversities in soil bacterial communities in the 0–20 cm and 20–40 cm soil layers were analyzed using the Shannon and Chao indexes, as shown in Figure 3. The Shannon index reflects the diversity of the soil microbial community, with a higher index indicating more species in the community. The Chao index reflects the soil microbial community richness, with a higher Chao value indicating more abundant species in the community. Relative to the control land, the Shannon diversity and Chao indexes in the reclaimed land were reduced by 33.39–66.94% and 34.75–85.32%, respectively, in the 0–20 cm soil layer, and by 13.41–65.62% and 12.43–84.80%, respectively, in the 20–40-cm soil layer. However, ANOVA indicated that Shannon index showed significant differences between L and LC in the 10–20-cm soil layer, so as between M and MC in the 20–40-cm soil layer. Therefore, the diversity and abundance of bacterial communities in reclaimed soils was slightly different from that of natural soils.
Different treatments of reclamation periods showed no significant differences. Moreover, proximity values of Shannon and Chao indices was calculated to compare diversities of microbial communities in reclaimed land after different reclamation periods (Table 4). In the 0–20-cm soil layer, with increasing reclamation period, the recovery degree of soil bacterial community diversity and richness in coal gangue-filled reclaimed land has improved. In the 20–40-cm soil layer, although these proximity values fluctuated, the diversity and richness of bacterial communities in samples from the 20–40-cm layer of soils with a 15-year reclamation period, were extremely close to levels in normal farmland.

3.5. Similarity among Soil Bacterial Communities

Operational taxonomic units with a similarity level of 97% were used for cluster analysis (Figure 4). When the clusters were divided into two categories (distance = 25), the reclaimed land samples S, M, and L were clustered together, as were SC, MC, and LC. These findings demonstrate that there was a considerable difference between the bacterial community structure of the reclaimed soil and the control land. The Euclidean distance between M-2 and L-1 was close (distance = 1), and the similarity of samples was high. Similarly, M-1, L-2 and LC-2 were close (distance = 1), indicating that these samples showed high similarity. Additionally, there was a strong similarity between bacterial community structures in land with 6-year and 15-year reclamation periods.

3.6. Correlation of Soil Bacterial Community and Dehydrogenase

Correlation analysis of soil dehydrogenase and the main bacteria phyla and genera was conducted, and the correlation coefficients are shown in Table 5. Soil dehydrogenase content was significantly negatively correlated with Firmicutes, and with several genera of this phylum, including Bacillus, Enterococcus, Lactococcus, Paenibacillus, and Cronobacter. Soil dehydrogenase content was also significantly positively correlated with Proteobacteria, Chloroflexi, Acidobacteria, and with Actinobacteria, as well as with Gemmatimonadetes and Gemmatimonas within the phylum Gemmatimonadetes.

3.7. Correlation of Soil Bacterial Community and Soil Variables

Correlations between soil properties and the abundant OTUs were analyzed using RDA. Eleven representative OTUs and eight soil properties were selected (Figure 5). The eigenvalues of axis 1 and axis 2 were found to be 69.2% and 12.8% respectively, explaining the relationships between the environment and 82% of the species. A significant difference (p < 0.05) between species and environment data was obtained through Monte Carlo permutation testing, with 499 permutations. F test (F = 5.343, p = 0.006) indicated that the analysis results were reliable. With the exception of P and K, the other soil properties were negatively correlated with axis 1 and negatively correlated with axis 2 (except for EC and MoiC).
A major difference in the soil properties of reclaimed and control lands was observed. Points representing reclaimed land mainly aggregated and were distributed to the right of axis 1, while those representing control land mainly aggregated and were distributed to the left.
The distribution of soil bacterial communities was significantly influenced by the MioC, TOC, EC, K, and P, whereas soil bacteria were only slightly affected by the pH and N content. The number of OTU1 and OTU2 representing Firmicutes were positively correlated with P and K, negatively correlated with other indicators of soil quality, and significantly negatively correlated with conductivity (Table 6). The Firmicutes were most abundant in sampling points in M and in the 20–40-cm soil layer of S. The relationships between soil quality and different Proteobacteria varied considerably.
OTU13 and OTU23 representing Chloroflexi were negatively affected by the MoiC and P. In particular, OTU23 was slightly negatively correlated with K, which distributed more in the control than in the reclaimed land. OTU51 (Acidobacteria) was negatively correlated with axis 1 and most affected by BD and MoiC and P had an inhibitory effect on it. OTU32 (Gemmatimonadetes), which were distributed in SC and MC samples, had a significant positive correlation with pH and TOC had little effect on it. OTU5 (Gammaproteobacteria), OTU15 (Alphaproteobacteria), OTU1 and OTU2 were distributed to the right of axis 1, closely aggregated with S, M, L sampling sites, indicating that there were more dominant bacterial communities in the reclaimed land than in the control land. OTU (Betaproteobacteria), which were distributed in the control land, were negatively influenced by the pH, MoiC and P.

4. Discussion

4.1. Comparative Structures of Soil Microbial Communities in Reclaimed and Control Lands

The distinct soil microbial composition may represent a core to maintain ecosystem functions during harsh conditions [28]. After severe disturbances, such as coal mining, land subsidence, and coal gangue filling and reclamation, soil ecology was seriously disturbed, and soil microbial diversity tended to decrease. Our study supported findings reported in other pyrosequencing studies in mining areas after phytoremediation, where increased microbial diversity followed improvements in soil conditions [29,30].
The bacterial community abundance of reclaimed soil was lower than that of control soil, which supports the findings of other researchers [21,31,32]. The bacterial community diversity in reclaimed land was also inferior to that of natural soils. Previous investigations of reclamation soil reported increased microbial diversity following the addition of compost, lime, and vegetation [33,34,35,36].
Comparison of the bacterial community compositions of the control and reclaimed lands indicated that while the dominant bacteria in soil bacterial communities were consistent, the structural proportions of the bacterial populations differed. Even after 15 years of reclamation, soil bacterial community structures in reclaimed land had not recovered completely to the levels found in control land, which is consistent with the results of previous studies [32,37,38]. Lewis (2012) reported that in the composition of microbial communities persists even after more than 20 years of recovery, revealing the significant negative effects of mining on the soil [38].

4.2. Variation in Microbial Community Structure after Different Reclamation Periods

The diversity of soil microorganisms represents the stability of microbial communities and reflects the effects of soil ecological mechanisms on communities [39]. We compared the degree of restoration of diversity and richness of the bacterial community following different reclamation period and found that samples with a 15-year reclamation periods were extremely close to those of normal farmland. These results supported the findings of a previous investigation of the effects of different reclamation methods and periods on soil microorganisms [13]. Variations in bacterial diversity in the 0–20-cm soil layer were more marked than those in the 20–40-cm layer, which may be because the soil environment in the 0–20-cm soil layer was more easily altered by artificial disturbance than that in the 20–40-cm soil layer.
Microbial community succession was found in reclaimed soils, and the ratios of dominant bacterial communities in the soil ecological system from reclaimed land successively changed. The role of the dominant bacterial community is crucial in recovering soil quality. Most Firmicutes have unique physiological structures, allowing them to adapt to the lack of water and extreme environment of mining areas; therefore, Firmicutes were more abundant in reclaimed land than in control land. Bacterial genera from Firmicutes, such as Bacillus, which are characterized by resistance to extreme environmental conditions and their ability to eliminate heavy metals from soil, and Enterococcus, which are characterized by rapid growth, became the dominant bacterial community members in reclamation soil. These findings indicate that Firmicutes made a major contribution to ecological remediation and re-establishment of soil ecology in coal mining subsidence areas, which supports the findings of Chen (2012) and Poncelet (2014) [30,40]. In addition, some bacteria such as Bacillus and Enterococcus, can grow and propagate in nutrient-depleted environments, playing an essential role in the recovery of soil fertility, and contributing to soil remediation and ecological improvement of coal mines after reclamation.
We speculate that samples of reclaimed land with unstable soil ecology lack some beneficial bacteria; namely, Proteobacteria. Previous studies have indicated that the resilience of a microbial community will be promoted by increasing the abundance of bacteria that can be classified as copiotrophs, such as members of Proteobacteria [41,42], as well as decreasing abundance of oligotrophs, such as many members of Acidobacteria [43]. This may be because higher proportions of Proteobacteria in soil communities have been associated with facilitated plant growth during remediation of contaminated soils, suggesting a positive feedback relationship between available carbon from root growth and members of this phylum [44].
Therefore, the predominant bacteria found in degraded soils often can adapt to ecological changes, improve the soil environment, and replenish soil that is relatively nutrient poor. He et al., (2017) [45] reported similar findings, stating that the dominant bacterial communities in reclaimed open-pit coal mining land were mostly chemoautotrophic or chemoheterotrophic, or that they were functional bacterial genera participating in the nitrogen cycle or in degradation of polycyclic aromatic hydrocarbon organic matter, making them beneficial for ecological remediation and recovery of fertility in polluted soil. Therefore, bio-organic fertilizers or specific microbial fertilizers can be applied to adjust the soil micro-ecological environment, and promote the succession of bacterial communities and the stability of soil ecosystems. This will facilitate the practice of soil remediation.
At present, numerous studies have analyzed the effect of compound microbial agents on the quality of reclaimed soil. For example, when continuously applying ripening biofertilizer to reclaimed soil for three years, total phosphorus, available phosphorus, and organic matter content in the soil increased by 7.1%, 27.9% and 30.2%, respectively, comparing to the control group. Moreover, the number of bacteria, fungi, and actinomycetes increased by 28.2%, 32.2% and 22.8% respectively [46]. Further, different microbial agents have been shown to have a great influence on the quantity and structure of microorganisms in the soil [47]. Hence, it is important to identify suitable and efficient bacterial strains to improve soil quality, which can promote the rapid restoration of soil function for the reclamation soil. Moreover, when applying bacterial fertilizer to reclaimed soil, managers should pay special attention to the influence of soil heavy metals on bacterial activity [48].

4.3. Comparative Analysis of Bacterial Communities and Dehydrogenase Activity of Soil during Different Reclamation Periods

Soil enzyme activities play a pivotal role in soil biochemical processes and have often been used as indicators in the evaluation of soil recovery conditions in different ecosystems [40,49,50]. Previous research has shown that interactions between soil enzymes and bacteria can promote the mineralization and decomposition of organic matter and the cycling and transformation of nutrients in soil [51]. The results of our study demonstrate a relationship between the structure of the microbial community (both by PLFA and genomic approaches) and soil enzyme activities [42].
Based on the correlation analysis conducted in the current study (Table 5), a strong correlation was found between soil dehydrogenase and Firmicutes, Proteobacteria, Chloroflexi, Acidobacteria, as well as Bacillus, Enterococcus, Lactococcus, Paenibacillus, Cronobacter, and Alkaliphilus. This is because soil dehydrogenases are redox enzymes and are mainly derived from soil microorganisms, plant root exudates, and animal and plant residues [4]. The activity of dehydrogenases changes with changes in soil biota, thus, soil dehydrogenase activity can be used as an index of changes in bacterial community structure and soil fertility, as well as to provide a theoretical basis for improving the soil quality of reclaimed land. These findings are similar to those of Meng et al. [16], who reported that the soil enzyme activities of the main Proteobacteria in reclaimed land were highly correlated with the Olsen phosphorous value and alkaline phosphatase and could be used to measure the effectiveness of soil phosphorus by evaluating relationships between bacterial colonies and enzyme activity.

4.4. Comparative Analysis of Physicochemical Properties and Microbial Communities of Soil with Different Reclamation Periods

The soil microbial community composition and soil environment are inseparable. Soil properties affect the activity and diversity of the soil microbial community, while soil microorganisms gradually improve soil quality through decomposition of organic matter [52]. Interactions between the soil and its microorganisms drive the normal function of this ecosystem, and any modification in this relationship may influence microbial structures, which in turn affect soil ecological processes [53].
At the phylum level, different relationships between bacterial communities and the soil quality of reclaimed land were observed after different reclamation periods. As the reclamation period increased, the reclaimed land showed increasing P and K with increasing Firmicutes and Proteobacteria, indicating a direct correlation between increased availability of these minerals and better growth of Firmicutes and some Proteobacteria. The quantities of other bacterial phyla have been shown to be increased by increasing the TOC, BD, pH, and EC, which supports the findings of Nkongolo et al. (2016) [54].
Our research revealed that the distribution of soil bacterial communities was significantly influenced by the MioC, TOC, EC, K, and P, but that pH had no effect on the bacterial community. This may have occurred because the pH of our samples did not differ significantly. However, other studies have found a strong influence of pH on the composition of soil microbial communities [22,55,56,57].
Carter (1986) [58] reported that increasing soil organic matter content could significantly influence soil microbial diversity. Moreover, He et al. (2017) [45] found that the C:N of soil was the main factor affecting variations in soil bacterial colonies. The easy-available organic matter in the soil was beneficial for the potential development and activity of copiotrophs [42]. Other authors have observed a negative impact of salinity on bacterial diversity [59].
Gemmatimonadetes are suitable for survival under low soil moisture conditions [60]. Acidocbacillus is weakly positively correlated with pH. However, some studies have reported that the relative abundance of Acidocbacillus in soil was significantly negatively correlated with soil pH [61,62]. In addition, changes in land use patterns also have a significant impact on the distribution of Acidocbacillus [63].

5. Conclusions and Implications

The effects of various reclamation periods on diversity of soil bacterial community were investigated in a coal gangue-filled reclaimed land in a collapsed coal mining area in eastern China. The following conclusions were drawn:
The diversity and abundance of bacterial communities in reclaimed soils is slightly different from that of natural soils. After a 15-year reclamation period, the soil bacterial community structure was found to have a high similarity with natural soil. Additionally, microbial community succession was found in reclaimed soils, as indicated by changes in the ratios of dominant bacterial communities in the soil ecological systems of reclaimed land.
Monitoring of soil dehydrogenase activities can facilitate the evaluation of soil microbial community structures, and different soil characteristics affect the distribution of bacterial communities in different soil environments. Firmicutes, as well as Bacillus, Enterococcus, and Lactococcus, which are functional, were the dominant bacterial phyla in both the reclaimed and control lands. During reclamation, soil bacteria play an important role in recovery of the soil fertility in reclaimed land.
The results of this study provide some valuable practical implications for improving reclamation soil characteristics. Implementation of microbial reclamation technology can improve the fertility of reclaimed soil in mining areas, improve soil structure, and increase soil biological activity.

Author Contributions

H.H. and S.Z. designed the research; C.W., Z.D., Y.Y., J.M., F.C., Z.D. and J.L. performed the analysis based on the paper’s framework; and H.H. wrote the manuscript. All of the authors reviewed the manuscript and approved it for submission.

Funding

This study was supported by the Fundamental Research Funds for the Central Universities (No. 2017XKQY071, 2017).

Acknowledgments

This research was supported by the Fundamental Research Funds for the Central Universities (No. 2017XKQY071). We thank Jing Ma, research assistant at the Low Carbon Energy Institute of China University of Mining and Technology, for providing technical support for testing the physicochemical properties of soil. We also thank Shanghai LingEn Biological Technology Co., Ltd. for performing PCR amplification and Illumina sequencing of soil microorganisms. Finally, we thank Accdon (www.accdon.com) for its linguistic assistance during the preparation of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the study region and distribution of sampling sites.
Figure 1. Location of the study region and distribution of sampling sites.
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Figure 2. The main bacteria in soil samples and bacterial community relative abundance at (A) phylum level and (B) genus level. Phylogenetic groups accounting for <1% of all classified sequences are summarized in the artificial group “others”.
Figure 2. The main bacteria in soil samples and bacterial community relative abundance at (A) phylum level and (B) genus level. Phylogenetic groups accounting for <1% of all classified sequences are summarized in the artificial group “others”.
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Figure 3. Shannon (A,B) and Chao (C,D) indexes of soil bacterial communities in the 0–20 cm soil layer (A,D) and 20–40 cm soil layer (B,D) using a similarity level of 97%. Different letters indicate significant differences between samples (p < 0.05).
Figure 3. Shannon (A,B) and Chao (C,D) indexes of soil bacterial communities in the 0–20 cm soil layer (A,D) and 20–40 cm soil layer (B,D) using a similarity level of 97%. Different letters indicate significant differences between samples (p < 0.05).
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Figure 4. Cluster dendrogram representing the degree of association between sites. A lower Euclidean distance indicates a more significant association.
Figure 4. Cluster dendrogram representing the degree of association between sites. A lower Euclidean distance indicates a more significant association.
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Figure 5. Redundancy analysis of (A) OTUs and (B) soil properties.
Figure 5. Redundancy analysis of (A) OTUs and (B) soil properties.
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Table
Table 1. Soil physicochemical characteristics of the reclaimed and control sites.
Table 1. Soil physicochemical characteristics of the reclaimed and control sites.
Sample IDDepth (cm)pHEC (μS·cm−1)MoiC (%)BD (g·mL−1)N (mg·kg−1)P (g·kg−1)K (mg·kg−1)TOC (g·kg−1)
SS-10–206.56 ± 0.07 a663.05 ± 0.44 a0.37 ± 0.02 ac1.07 ± 0.03 c3.78 ± 1.94 bc10.68 ± 1.06 b9.85 ± 0.59 b13.05 ± 1.30 d
S-220–406.61 ± 0.03 a664.30 ± 0.62 a0.41 ± 0.05 a1.16 ± 0.13 c4.54 ± 3.63 bc10.19 ± 0.21 b9.02 ± 0.23 b12.95 ± 0.16 d
SCSC-10–206.63 ± 0.04 a663.05 ± 0.17 a0.24 ± 0.09 ac1.23 ± 0.27 b23.05 ± 9.86 a10.44 ± 0.37 b8.05 ± 2.59 bc19.94 ± 2.79 d
SC-220–406.65 ± 0.05 a664.20 ± 0.34 a0.3 ± 0.02 ac1.31 ± 0.21 b12.00 ± 4.79 bc10.93 ± 0.38 b8.01± 1.58 bc15.12± 6.29 d
MM-10–206.67 ± 0.03 a652.05 ± 1.56 a0.12 ± 0.01 b1.05 ± 0.13 c2.15 ± 0.55 b11.23 ± 0.94 ab6.39 ± 0.38 c15.78 ± 3.03 d
M-220–406.7 ± 0.02 a653.20 ± 1.47 a0.15 ± 0.06 bc1.13 ± 0.09 b1.85 ± 0.20 b12.15 ± 1.01 ab8.10 ± 0.79 bc14.84 ± 2.27 d
MCMC-10–206.69 ± 0.02 a662.70 ± 0.06 a0.13 ± 0.05 bc1.26 ± 0.24 b2.70 ± 0.72 bc12.15 ± 1.06 ab8.69 ± 1.32 b32.20 ± 2.31 c
MC-220–406.73 ± 0.03 a663.00 ± 0.61 a0.16 ± 0.02 bc1.34 ± 0.15 b2.88 ± 1.06 bc11.29 ± 1.07 ab7.07 ± 1.24 bc35.03 ± 5.39 bc
LL-10–206.60 ± 0.08 a655.75 ± 1.37 a0.12 ± 0.01 c1.41 ± 0.07 a7.34 ± 2.57 b13.86 ± 4.67 a14.19 ± 0.94 a54.25 ± 3.82 a
L-220–406.63 ± 0.06 a657.25 ± 0.72 a0.14 ± 0.01 bc1.47 ± 0.23 a8.26 ± 5.72 b10.93 ± 0.56 b13.73 ± 0.92 a42.64 ± 0.52 b
LCLC-10–206.62 ± 0.05 a661.00 ± 0.50 a0.16 ± 0.06 d1.45 ± 0.20 a6.66 ± 2.41 b11.17 ± 1.10 b13.54 ± 0.39 a52.39 ± 3.48 a
LC-220–406.66 ± 0.02 a664.25 ± 0.81 a0.18 ± 0.04 d1.52 ± 0.14 a7.61 ± 3.26 b10.56 ± 0.56 b12.99 ± 1.16 a40.79 ± 9.12 b
Note: Mean + standard deviation followed by the same letter (a, b, c or d) indicates no significant difference between samples (p < 0.05). EC is electrical conductivity. MoiC is moisture content. BD is bulk density. N is nitrate nitrogen. P is available phosphorus. K is rapidly available potassium. TOC is total organic carbon.
Table
Table 2. Number of sequences and bases and average sequence length in samples.
Table 2. Number of sequences and bases and average sequence length in samples.
SampleSequencesBases (bp)Average Length (bp)
S-13758314,151,946376.545
S-23396412,786,037376.46
SC-13491013,143,259376.495
SC-23440712,953,865376.495
M-13880014,608,334376.505
M-23592513,523,191376.43
MC-13548713,360,703376.51
MC-23743814,099,242376.61
L-13698413,921,391376.415
L-23751314,123,077376.485
LC-13465913,046,710376.45
LC-24227215,916,010376.52
Table
Table 3. Proximity values of OTU numbers of dominant bacteria in soil samples.
Table 3. Proximity values of OTU numbers of dominant bacteria in soil samples.
Dominant BacteriaS-1S-2M-1M-2L-1L-2
PhylumFirmicutes2.561.723.262.247.141.04
Proteobacteria0.630.460.470.270.140.77
GenusBacillus2.731.733.043.398.551.04
Enterococcus3.251.853.024.539.121.02
Lactococcus3.001.722.955.009.451.07
Table
Table 4. Proximity values of OTU numbers of dominant bacteria in soil samples.
Table 4. Proximity values of OTU numbers of dominant bacteria in soil samples.
S-1S-2M-1M-2L-1L-2
Shannon0.330.610.650.340.670.87
Chao0.150.450.650.150.550.88
Table
Table 5. Correlation analysis of soil bacterial communities and dehydrogenase activity.
Table 5. Correlation analysis of soil bacterial communities and dehydrogenase activity.
PhylumFirmicutesProteobacteriaChloroflexiActinobacteriaAcidobacteriaGemmatimonadetes
−0.86 **0.839 **0.776 **0.706 *0.839 **0.664 *
GenusBacillusEnterococcusLactococcusPaenibacillusCronobacterGemmatimonasAlkaliphilus
−0.862 **−0.832 **−0.776 **−0.839 **−0.762 **0.629 *−0.818 **
Notes. ** and * indicate significance level at 1% and 5%, respectively.
Table
Table 6. Correlation analysis of OTUs and soil variables.
Table 6. Correlation analysis of OTUs and soil variables.
OTU1OTU2OTU5OTU10OTU15OTU13OTU23OTU14OTU35OTU51OTU32Axis1Axis2
pH−0.202−0.211−0.070−0.052−0.3260.0600.248−0.587 *−0.3610.0830.679 *−0.1537−0.2014
EC−0.599 *−0.580 *−0.703 *0.174−0.5040.2880.4860.2650.579 *0.3910.097−0.66180.2944
MoiC−0.0230.002−0.133−0.2760.073−0.285−0.2170.624 *0.621 *−0.214−0.366−0.01870.6842
BD−0.186−0.201−0.3000.555−0.1280.632 *0.496−0.495−0.3490.715 **−0.067−0.4383−0.7885
N−0.257−0.270−0.3360.105−0.3110.1730.2330.0280.3010.255−0.367−0.2695−0.035
P0.3930.4070.524−0.1700.081−0.284−0.231−0.307−0.465−0.4340.1600.5888−0.3995
K0.2600.2480.1420.4850.3890.398−0.005−0.111−0.3960.395−0.3670.0295−0.6226
TOC−0.146−0.138−0.2020.568−0.2030.5100.399−0.355−0.3390.4890.009−0.1618−0.7547
Notes: ** and * indicate significance level at 1% and 5%, respectively.

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MDPI and ACS Style

Hou, H.; Wang, C.; Ding, Z.; Zhang, S.; Yang, Y.; Ma, J.; Chen, F.; Li, J. Variation in the Soil Microbial Community of Reclaimed Land over Different Reclamation Periods. Sustainability 2018, 10, 2286. https://doi.org/10.3390/su10072286

AMA Style

Hou H, Wang C, Ding Z, Zhang S, Yang Y, Ma J, Chen F, Li J. Variation in the Soil Microbial Community of Reclaimed Land over Different Reclamation Periods. Sustainability. 2018; 10(7):2286. https://doi.org/10.3390/su10072286

Chicago/Turabian Style

Hou, Huping, Chen Wang, Zhongyi Ding, Shaoliang Zhang, Yongjun Yang, Jing Ma, Fu Chen, and Jinrong Li. 2018. "Variation in the Soil Microbial Community of Reclaimed Land over Different Reclamation Periods" Sustainability 10, no. 7: 2286. https://doi.org/10.3390/su10072286

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