Next Article in Journal
Evaluation of Android and Apple Store Depression Applications Based on Mobile Application Rating Scale
Next Article in Special Issue
Effect Mechanism of Land Consolidation on Soil Bacterial Community: A Case Study in Eastern China
Previous Article in Journal
Influence of Maternal Active and Secondhand Smoking during Pregnancy on Childhood Obesity at 3 Years of Age: A Nested Case–Control Study from the Japan Environment and Children’s Study (JECS)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 23
10.3390/ijerph182312504
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Effects of Seven-Year Fertilization Reclamation on Bacterial Community in a Coal Mining Subsidence Area in Shanxi, China

by
Li Li
1,2,
Tingliang Li
1,2,*,
Huisheng Meng
1,2,
Yinghe Xie
1,2,
Jie Zhang
1,2 and
Jianping Hong
1,2,*
1
College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China
2
National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(23), 12504; https://doi.org/10.3390/ijerph182312504
Submission received: 21 October 2021 / Revised: 20 November 2021 / Accepted: 24 November 2021 / Published: 27 November 2021
(This article belongs to the Special Issue Soil Degradation, Soil Pollution and Ecological Restoration)
Browse Figures
Versions Notes

Abstract

:
The restoration of soil fertility and microbial communities is the key to the soil reclamation and ecological reconstruction in coal mine subsidence areas. However, the response of soil bacterial communities to reclamation is still not well understood. Here, we studied the bacterial communities in fertilizer-reclaimed soil (CK, without fertilizer; CF, chemical fertilizer; M, manure) in the Lu’an reclamation mining region and compared them with those in adjacent subsidence soil (SU) and farmland soil (FA). We found that the compositions of dominant phyla in the reclaimed soil differed greatly from those in the subsidence soil and farmland soil (p < 0.05). The related sequences of Acidobacteria, Chloroflexi, and Nitrospirae were mainly from the subsided soil, whereas those of Alphaproteobacteria, Planctomycetes, and Deltaproteobacteria were mainly derived from the farmland soil. Fertilization affected the bacterial community composition in the reclaimed soil, and bacteria richness and diversity increased significantly with the accumulation of soil nutrients after 7 years of reclamation (p < 0.05). Moreover, soil properties, especially SOM and pH, were found to play a key role in the restoration of the bacterial community in the reclaimed soil. The results are helpful to the study of soil fertility improvement and ecological restoration in mining areas.

1. Introduction

China is one of the largest coal-producing countries in the world [1]. However, the large-scale and high-intensity exploitation of coal resources has caused a series of ecological and environmental problems, such as soil erosion, declines in soil quality, aggravation of land degradation, and imbalance of the soil ecosystem [2,3,4]. In particular, land subsidence caused by underground coal mining can lead to drastic disturbances of soil structure and remarkable variation in soil microbial communities [5,6], which greatly reduce soil fertility, crop productivity, and the stability of the soil ecosystem [4,7,8]. These have serious impacts on the sustainable development of agriculture in mining areas. Land reclamation is an effective method to solve the conflict between coal mining and land resource protection and to alleviate the contradiction between humans and land in the coal mining area.
Restoring soil fertility is the emphasis for land reclamation and ecological restoration in coal mining subsidence areas. Soil microorganisms, one of the most important parts of the soil ecosystem, are essential in soil formation, nutrient cycling (such as carbon, nitrogen, and phosphorus), and ecological balance [9,10,11]. The abundance, diversity, and activity of soil bacteria can be used as effective indicators of soil quality due to their high sensitivity to environmental changes and soil nutrient status [12,13,14]. It has been reported that bacterial community stability in subsidence soil was dramatically disrupted by coal mining activities, resulting in reductions in total bacterial biomass and diversity [15,16,17,18]. To some extent, it is not only necessary to increase soil nutrients but also more important to restore soil microbial activities and communities for the sustainability of reclaimed soil ecosystems [19]. For a long time, research on reclaimed soil has mainly focused on the improvement of soil physicochemical status and vegetation characteristics [20,21]. However, there are few studies on microbial population, diversity, and function in reclaimed soil.
The sustainability of terrestrial agroecosystems depends to a great extent on soil bacterial diversity for sustaining soil biological activity and crop productivity [22,23]. Fertilization can effectively improve soil nutrient conditions and affect soil microbial communities [24,25]. Generally, the application of organic fertilizer is beneficial to soil microbial communities [26,27,28], while the long-term application of chemical fertilizer can decrease soil microbial diversity in farmland [24,29,30]. At present, fertilization is the most effective way to restore cultivated land and improve soil in mining subsidence areas. Different fertilization methods have different effects on soil physical and chemical properties and microbial community. Therefore, an in-depth study on the changes in microbial community composition in reclaimed soil caused by fertilization will help to further understand how soil fertility affects the changes in microbial communities during the restoration of disturbed land to farmland.
The present study was carried out to investigate the response of bacterial communities to land reclamation with different fertilizers. Illumina high-throughput sequencing technology was used to compare the bacterial community structure and diversity in reclaimed soil with those in adjacent unreclaimed soil (from adjacent farmland and subsided land) from a mining area (in the same edaphic-climatic area) in Shanxi Province, China. We hypothesized that fertilization activity in the process of reclamation could improve soil nutrients and increase bacterial community diversity, and the variation of bacterial community structure may be related to changes in physicochemical properties, such as pH, soil organic matter, available nitrogen, phosphorus, and potassium. This study could identify an effective and appropriate method for the rapid restoration of soil fertility and provide a valuable reference for the soil restoration of coal mining subsidence areas with similar climatic and soil conditions.

2. Materials and Methods

2.1. Experimental Site Description

The field experimental area is located in the Lu’an coal mine (36°28′12′′ N, 113°00′53′′ E), Xiangyuan county in Shanxi, China. This region has a warm and semi-humid continental monsoon climate with a frost-free period of 160 d. The average annual temperature is about 9.5 °C, with monthly mean minimum temperatures occurring in January (−8.1 °C) and monthly mean maximum temperatures in July (23.4 °C). The mean annual precipitation is approximately 532.8 mm and mainly occurs from July to September. The soil type of the research area is calcareous cinnamon soil with silty loam, which is classified as Luvisols according to the World Reference Base (FAO) system [31]. In this region, coal mining has triggered the goaf in underground mines and formed land subsidence since the 1970s. Land consolidation, including topsoil stripping, land leveling, and backfilling, was carried out using large loaders before the reclamation in the autumn of 2008. The leveled land was divided into separate plots for fertilizer reclamation.

2.2. Experimental Treatments and Soil Sampling

The field experiment in this reclaimed site included three fertilization amended treatments: without fertilizer (CK), chemical fertilizer (CF), and manure (M) treatment. The chemical fertilizer was applied as urea, calcium superphosphate, and potassium chloride in CF. Decomposed chicken manure (27.8% organic matter, 1.68% N, 1.54% P2O5, and 0.82% K2O) was provided as an organic amendment at a rate of 12,000 kg∙ha−1 in the M treatment. An equal amount of 201 kg N∙ha−1, 185 kg P2O5∙ha−1, and 98.5 kg K2O∙ha−1 was applied before corn sowing in the fertilizer treatments. The treatments were arranged in a randomized complete block design with three replicates, and the size of each plot was 100 m2. Maize (Zea mays L.) was continuously planted in each plot from 2009 to 2015. According to local farming practices, crops are sown on or about May 1 and harvested on October 1. In addition, two unreclaimed treatments (SU and FA) were selected as controls for the reclaimed site. SU is a neighboring unclaimed subsidence site, and its surface vegetation is sparse and naturally growing weed. FA is another adjacent farmland that has been disturbed by coal mining and has been planting maize using local traditional fertilizing practices for many years.
Soil samples were collected after the maize harvest in October 2015. A total of fifteen individual soil samples (5 treatments × 3 replicates) represent three reclaimed treatments (CK, CF, and M) and two controls (SU and FA). All the soil samples were taken using a hand auger (5 cm diameter) at a depth of 0–20 cm after the superficial vegetation was removed, and each sample was a composite of six subsamples randomly collected from the five treatments. After mixing thoroughly, the homogeneous composite soil samples were enclosed in sterile plastic bags and transferred to the laboratory on ice. The samples were sieved through a 2.0 mm mesh and immediately divided into two parts: one part was stored at −80 °C for further molecular analysis, and the other was air-dried for chemical determination.

2.3. Selected Soil Properties Analysis

Soil pH was measured with a soil–water mixture (1:1) using a glass combination electrode [32]. Soil organic matter (SOM) was measured according to the method described by Strickland and Sollins [33]. The Mason-jar diffusion method by Bremner [34] was used to determine the soil alkali-hydrolyzable nitrogen (AN). Available phosphorus (AP) was analyzed by resin extraction following a protocol modified from Hedley and Stewart [35]. Available potassium (AK) was extracted with ammonium acetate and determined by flame photometry [36].

2.4. DNA Extraction, PCR Amplification, Illumina MiSeq Sequencing, and Sequencing Data Processing

Soil microbial DNA was extracted from approximately 1 g of soil samples using the TIANamp Genomic DNA Kit (TIANGEN Biotech, Beijing, China, Cat. No.: DP304) according to the manufacturer’s instructions. The integrity of the extracted DNA was assessed by agarose gel electrophoresis (1%), and the concentration and purification of the DNA (2 μL) were determined using NanoDrop ND-1000 microspectrophotometry (NanoDrop Technologies, Wilmington, DE, USA). The bacterial primer set of forward primer 341F (5′-CCTACGGGNBGCASCAG-3′) and reverse primer 806R (5′-GACTACNVGGGTATCTAATCC-3′) was used to amplify the 16S rDNA gene sequence in the V3–V4 hypervariable region (465 bp). PCRs were carried out in triplicate, 25 μL reactions with 2.5 μL of Ex Taq buffer (Takara Bio Inc., Kusatsu, Japan, Takara code: RR001B), 1.5 μL of 2.5 mM Mg2+, 2 μL of 2.5 mM dNTPs, 0.25 μL Ex Taq DNA Polymerase (Takara Bio Inc., Kusatsu, Japan, Takara Code: RR001B), 16.75 μL of double-distilled water, 10 μM of each primer, and approximately 20 ng of DNA template. The amplification program consisted of an initial denaturation step of 94 for 2 min, followed by 30 cycles of denaturation at 94 for 30 s, annealing at 50 for 30 s, and elongation at 72 for 30 s, with a final extension at 72 for 5 min. Replicate reaction mixtures of the same sample were assembled within a PCR tube. After visualization on agarose gels (1% in TBE buffer) containing ethidium bromide, PCR products were purified using the QIAquick PCR Purification Kit (QIAGEN, Hilden, Germany, Cat. No.: 28106) and quantified with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Purified amplicons were pooled in equimolar concentrations and paired-end sequenced on the Illumina MiSeq TM System platform according to the manufacturer’s protocols.
Sequence analysis was conducted using quantitative insights into the microbial ecology (QIIME) pipeline (version 1.7.0), as previously described by Fadrosh et al. [37]. After the barcodes and primers were trimmed, and low-quality sequences were removed (<Q20), the remaining high-quality reads were clustered into operational taxonomic units (OTUs) based on their sequence similarity at 97%. Community richness and diversity indices based on the number of OTUs and rarefaction curves were obtained using the Mothur software (version 1.34.0, Pat Schloss, Michigan, USA). Prior to the data analysis, the alpha-diversity indices of the bacterial community, including Good’s coverage, Chao1, ACE, and the Shannon index, were calculated based on an appropriate subsample depth.

2.5. Statistical Analysis

Statistical analyses were performed by one-way analysis of variance (ANOVA) using the PASW Statistics program (version 18.0 for windows). The means were segregated using Duncan’s multiple comparison test with a significance level of p < 0.05. Pearson’s correlation analysis was conducted to evaluate the correlations between soil physicochemical and microbiological characteristics. To compare bacterial community structures across all soil samples, principal coordinate analysis (PCoA) and cluster analysis were performed based on the unweighted UniFrac distance matrix [38]. Redundancy analysis (RDA) was carried out to examine the relationship between abundant phyla (proteobacterial classes) and soil physicochemical characteristics [39].

3. Results

3.1. Soil Properties

The result showed that fertilizer reclamation clearly affected the soil nutrient amounts. As shown in Table 1, the subsidence soil showed the lowest nutrient amounts. The amounts of SOM in the SU treatment were 36.56% of that in the FA treatment, and AN, AP, and AK in the SU treatment accounted for 33.97%, 16.37%, and 61.14%, respectively. After the 7-year fertilizer reclamation, the amounts of SOM, AN, AP, and AK were consistently increased in the reclamation-treated soil compared with the subsidence soil. The SOM and AN amounts in the CF and M treatments were significantly higher than those in the CK treatment (p < 0.05). Significantly higher SOM and AN amounts (p < 0.05) were obtained in the M treatment compared with the CF treatment. The highest pH was observed in the subsidence soil, and fertilizer reclamation decreased the soil pH (Table 1). However, there was no significant difference in soil pH between CF treatment and M treatment.

3.2. Soil Bacterial Abundance and Diversity

A total of 138,894 high-quality sequences (average read length of 440 bp) were obtained from all the soil samples. These optimized sequences were clustered into OTUs by Mothur software. As shown in Table 2, the Sobs values in all treatments were in the range of 3672–11,030, and the lowest Sobs was found in the SU treatment. There was no significant difference in Sobs between the reclaimed soil (CK, CF, and M) and farmland soil (FA), but they were significantly higher than those in the subsided soil (SU). Venn analysis (Figure 1) showed that 805 Sobs were found in all five treatments, accounting for 7.2–21.92% of each treatment, respectively. A total of 3478 Sobs were detected in the CK, CF, and M treatments, which were 31.53–35.59% of their total number. In addition, 5131 Sobs were detected in the CF and M treatments, accounting for 46.52% and 46.56% of their total Sobs, respectively. The unique Sobs out of all the treatments accounted for 21.52–88.82%, indicating that there were differences in the composition of the soil bacterial community among the treatments.
The result of bacterial community diversity is presented in Table 2. The SU treatment showed the lowest Chao1, ACE, and Shannon index (alpha-diversity indices), which were significantly different from those in the reclaimed soil. After reclamation, soil bacterial diversity and abundance in the reclaimed soil were significantly higher than those in the subsidence soil (p < 0.05). However, Chao1 and ACE showed no significant difference among the CF, M, and FA treatments, which were significantly higher (p < 0.05) than those in the CK treatment. The Shannon index of the FA treatment was the highest (11.77) and significantly higher than those of the reclamation treatments (CK, CF, and M). In addition, the Shannon–Weiner curve showed similar trends (FA > CF > M > CK > SU) in terms of high species richness at 97% similarity (Figure S1). Good’s coverage values in all samples ranged from 91% to 97% at a similarity cutoff of 97%, indicating that the current numbers of sequence reads were sufficient to capture the bacterial diversity in these soils.

3.3. Soil Bacterial Taxa Community Composition

Based on the Illumina platform analysis, sequences from all soil samples were classified into 54 different phyla, 138 classes, 213 orders, 244 families, 423 genera, and 218 species (Figure S2). Proteobacteria were the most abundant phyla, accounting for 20.15%–31.66% in these five treatments (Figure 2a). Furthermore, classes of Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria were detected in this study, and Alphaproteobacteria was the most abundant phylum, accounting for 41.48% of total Proteobacteria sequences. Other predominant phyla were Actinobacteria (27.80%), Acidobacteria (7.96%), Bacteroidetes (7.42%), Chloroflexi (7.21%), and Gemmatimonadetes (6.62%), accounting for 81.30% of the bacterial sequences. Additionally, Firmicutes (2.81%), Planctomycetes (2.01%), Cyanobacteria (1.50%), Nitrospirae (1.25%), TM7 (1.07%), and Verrucomicrobia (1.01%) were present in soil samples with lower relative abundances, which occupied 9.65% of bacterial sequences (Figure S2 and Figure 2a).
The distribution of predominant bacterial phyla (classes) between the different treatments is illustrated in Figure 2a. Although similar main phyla (classes) existed in the selected treatments, the relative abundance of taxa in these treatments was different. No significant differences in the abundance of Gemmatimonadetes and Verrucomicrobia were observed in all treatments (Table S1). The highest relative abundance of Acidobacteria (9.37%), Chloroflexi (9.85%), and Nitrospirae (5.88%), as well as the lowest abundance of Alphaproteobacteria (4.16%), Gammaproteobacteria (3.85%), and Actinobacteria (16.12%), was detected in the SU treatment. Alphaproteobacteria (15.32%), Planctomycetes (2.44%), and Deltaproteobacteria (6.34%) showed the highest relative abundance in the FA treatment, while Firmicutes showed the lowest abundance. In the reclaimed soil, Bacteroidetes and Firmicutes were remarkably distinct (p < 0.05) in the CK treatment as compared with those in the CF and M treatments. Moreover, the respective abundances of the top 10 genera were examined to compare the distribution of bacterial genera in the different treatments (Figure 2b). It was found that the most bacterial taxa at the phylogenetic of genera differed greatly among the selected treatments, except for Streptomyces and Bacillus (Table S1). The lowest (p < 0.05) relative abundance of Sphingomonas (0.12%), Rhodoplanes (0.24%), Skermanella (0.09%), Steroidobacter (0.24%), and Lentzea (0.14%) was obtained in the SU treatment. The abundance of Kaistobacter in the M treatment (2.42%) was significantly (p < 0.05) higher than that in the FA treatment (1.62%). The highest (p < 0.05) abundance of Lentzea (1.16%) and Balneimonas (0.94%) was observed in the CF and FA treatments, respectively. Furthermore, the abundance of Balneimonas and Lentzea were significantly distinct (p < 0.05) between the CF and M treatments.
PCoA analysis based on the unweighted UniFrac distance metric was conducted to estimate β-diversity, which clearly revealed the variation of bacterial community among the treatments (Figure 3a). The first and second principal components explained 47.96% (PC1) and 11.25% (PC2) of the variance, respectively. As illustrated in the results of PCoA, the soil bacterial community in the SU treatment (with lower soil nutrient content) was separated from that in the CK, CF, M, and FA treatments (with higher soil nutrient content) along the PC1 axis. The soil bacterial community in the farmland soil (with higher soil OM, AN, and AP) was separated from that in the reclamation soil (CK, CF, and M treatments) along the PC2 axis. In addition, the CF and M treatments were clustered together and located in the first quadrant. It meant that the soil from the CF and M treatments had some of the same Sobs but at different levels. Moreover, Bray–Curtis analysis of dissimilarity (ANOSIM, R = 0.778, p = 0.001) showed that the differences between groups were larger than within groups, thus there was dissimilarity between the groups.
A hierarchical cluster analysis based on the beta distance matrix was conducted to compare the similarity of the soil bacterial community in the different treatments. The results showed that soil samples (three replicates) from the subsidence site were grouped together and differed from other samples (Figure 3b). The other samples were clustered into two main groups: one group consisting of reclaimed soil samples from the CK, CF, and M treatments and the other consisting of samples from the FA treatment. In addition, the cluster tree revealed that the bacterial communities in the soil of the CF treatment were similar to that of the M treatment, which was different from that of the CK treatment. Overall, the results of cluster analysis were in line with PCoA.

3.4. Relationship between Soil Properties and Bacterial Community Composition

The results of Pearson’s correlation analysis showed that soil properties were highly related to bacterial abundance and diversity. As shown in Table 3, soil pH showed significant negative correlations with Chao1 (r = −0.826, p < 0.01), ACE (r = −0.824, p < 0.01), and Shannon index (r = −0.745, p < 0.01). In contrast, SOM, AN, and AP showed significant positive correlations with Chao1 (r = 0.545–0.606, p < 0.05), ACE (r = 0.564–0.659, p < 0.05, p < 0.01), and Shannon index (r = 0.559–0.623, p < 0.05). AK was positively correlated with Chao1 (r = 0.644, p < 0.01) and ACE (r = 0.656, p < 0.01).
Results of the RDA analysis are shown in Figure 4. Selected soil property factors could explain 78.96% of the variation. The first and second axes of RDA explained 64.4% and 9.6% of total variation in our data, respectively. The bacterial community of the FA treatment was related to higher SOM, AN, and AP contents and lower pH, as shown by the vectors, while that of the SU treatment was associated with higher pH and lower soil nutrients (SOM, AN, and AP). As shown in Figure 4, the main group of phyla in the CK, M, and CF treatments was closer to that in the FA treatment along the first axis with decreasing pH and increasing soil nutrient contents. Furthermore, abundant phyla of the M and CF treatments were more alike their closer distance than the other treatments. The contributions of the selected physicochemical factors followed this trend: pH > SOM > AP > AN > AK, and their contributions were 39.34%, 27.59%, 18.27%, 13.80% and 11.27%. It is indicated that the structure of the bacterial community was closely correlated with soil properties and mainly shaped by soil pH and SOM. Pearson’s correlation was calculated between the most abundant bacterial phyla (Proteobacteria classes) and soil environmental factors (Table S2). We found that the relative abundance of dominant phyla (Proteobacteria classes), such as Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, and Planctomycetes, showed significantly negative correlations with soil pH and positive correlations with AK (p < 0.05; p < 0.01). Chloroflexi, Firmicutes, and Nitrospirae showed significantly positive correlations with pH and negative correlations with SOM, AN, and AK (p < 0.05; p < 0.01). Alphaproteobacteria and Planctomycetes were positively correlated with SOM and AN (p < 0.05; p < 0.01), and Actinobacteria was positively correlated with AN (p < 0.05). Additionally, Acidobacteria, Gemmatimonadetes, Verrucomicrobia, and TM7 had no significant correlation with the selected soil factors.

4. Discussion

4.1. Effects of Reclamation on Soil Properties and Bacterial Community

Coal mining disturbance and reclamation can change original soil physical and chemical characteristics, consequently affecting soil microbial community structure and diversity [16,40,41]. In general, coal mining activity and preliminary engineering reclamation, including stripping, reconstruction, and tillage, lead to the disturbance of vegetation and original surface soil structure, resulting in the degradation of soil bacterial diversity. In our study, the lower soil nutrients and bacterial community diversity shown in the subsided soil were mainly attributable to the deficient management and high erosion rates in the post-mined soil. After 7-year consecutive fertilizer reclamation, Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, Chloroflexi, and Gemmatimonadetes were the main bacterial phyla (Figure S2). In particular, Proteobacteria and Actinobacteria, as the copiotrophic groups living in nutrient-rich conditions, were the most dominant phyla in the reclaimed soil (Figure 2a), which is in agreement with previous studies [42,43,44]. This consistency of dominant phyla in different mining areas indicates that these bacteria play an important role in soil improvement in mining areas and have a wide range of adaptability to the soil environment of the mining area.
In this study, we found Alphaproteobacteria was the largest subgroup of Proteobacteria in the reclaimed soil. Moreover, the populations of Rhizobiales, Rhodospirillales, and Sphingomonadales affiliated with the alpha-subclass were also observed in significant proportions in the reclaimed soil (Table S1). The bacterial community of those functional species played an important role in C, N cycles and in maintaining the integrity of the coal mine ecosystem [45]. This is consistent with our findings of higher SOM and AN in the reclaimed soil. In contrast, we found Chloroflexi (9.85%) was the advantageous population in the subsidence soil (Table S1) and showed lower abundance in the reclaimed soil with improvement in soil nutrients. This is mainly because this bacterium is a kind of autotrophic bacterium that does not depend on the nutrient supply in the environment and has a survival advantage in barren soil [32]. We also found a higher abundance of Acidobacteria (9.37%) in the subsidence soil (Table S1). As a type of slow-growing oligotroph [46] with a rich diversity of metabolism and function, they prefer oligotrophic living environments with poor available carbon sources [47]. Our results demonstrated that the accumulation of soil available nutrients promoted copiotrophic bacteria but negatively affected the oligotrophic groups [8].
Reclamation significantly increased the nutrient contents (SOM, AN, AP, and AK) of reclaimed soil, improved the abundance and diversity of bacteria, and promoted the restoration of the soil microbial community in the mining area [2,16,18,48]. In the present study, bacterial communities were restored after 7 years of reclamation with the increase in soil fertility, but it still differed from the adjacent farmland soil. In the reclaimed soil, the bacterial community composition of the organic fertilizer and chemical fertilizer treatments showed higher similarity (PCoA and cluster analysis). We found that Actinomycetes in manure treatment were slightly more abundant than those in the chemical fertilizer treatment (Table S1). Furthermore, Lentzea (affiliated to Actinomycetales) in the manure treatment were also significantly higher than those in the chemical fertilizer treatment (Table S1). This may be because manure contains more carbon and nitrogen sources for the growth of Actinomycetes than chemical fertilizers. In addition, the organic matter in manure can improve the aggregation and water-holding capacity of the soil, which is beneficial to the growth of Actinomycetes. We also observed that soil treated with manure had a higher abundance of Balneimonas (affiliated with Rhizobiales) than soil treated with chemical fertilizer (Table S1), probably because the dramatic increase in organic matter in manure could promote the intensive reproduction of this microbial taxon [49].
The results of PCoA and cluster analysis confirmed the distinct difference in bacterial community composition existed between the reclaimed and subsided soil (Figure 3a,b). It should be noted that the CK treatment (planting maize without fertilization for 7 years) significantly increased SOM and AN compared with the SU treatment, which resulted in elevated bacterial richness and diversity indices. This is mainly because the input of previous maize residues and the exudates released by maize roots through consolidation directly provided the energy source for microorganisms [50]. Additionally, aboveground maize can also alter the soil bacterial community by affecting the quality and quantity of microbial metabolic substrates [51]. These results indicated that surface vegetation restoration was also an important factor affecting bacterial communities in the reclaimed soil. However, the influences of various plants on soil microorganisms and soil physicochemical properties are different [52,53]. Different results could be obtained if other plants were selected in the soil reclamation of the mining area in this study.
Fertilization can improve soil quality and accelerate soil maturation, which further affects the diversity and richness of bacteria [25]. The application of organic fertilizer can improve soil structure and function and promote microbial richness and abundance [26,27,28,45], which is consistent with our results. In our study, nutrient contents and bacterial richness (Chao1 and ACE) in soil treated with manure were higher than those treated with chemical fertilizer (Table 1 and Table 2), which may be related to the input of organic matter. In addition, it is probably because the original bacteria from manure contributed to the increase in bacterial species. Conversely, the CF treatment had remarkably higher bacterial diversity (Shannon index) than the M treatment (Table 2), mainly due to the mineral nutrient supplied by chemical fertilizer being sufficient to induce the corresponding bacterial population in the root system and increase the soil bacterial diversity. In addition, a dramatic increase in organic matter contained in mature crops could promote the intensive reproduction of some microbial groups (Proteobacteria, Actinobacteria, and Bacteroidetes), resulting in the decrease in soil bacterial diversity [49]. It has been reported that the long-term application of chemical fertilizer can destroy soil structure, cause soil acidification, reduce soil enzyme activity, and decrease microbial biomass and diversity [24,29,30]. In this study, fertilizers also significantly increased the abundance and diversity of soil bacteria, which may be related to the lower nutrient levels in the soil before fertilization treatment. It should be pointed out that, under equal nutrient conditions, much more manure will be applied than chemical fertilizer because of its fewer available nutrients. Therefore, the detrimental effects of heavy metal accumulation and antibiotic residues on soil bacteria caused by the extensive application of manure [54,55,56] cannot be ignored.

4.2. Effects of Soil Properties on Bacterial Community Composition

Soil microorganisms are closely related to soil properties [13,16,25,57]. Many studies have reported that improved soil fertility resulted in higher bacterial abundance and diversity [45,58,59]. In this experiment, we found that Chao1, ACE, and Shannon index were positively correlated with SOM, AN, AP, and AK (p < 0.01 or p < 0.05). It is indicated that the accumulation of soil organic matter and nutrients could be a good explanation for the higher bacterial richness and diversity indices in the reclaimed soil (Table 3). We also found that most of the abundant phyla or classes were significantly correlated with one or more selected soil properties, emphasizing the critical role of soil organic matter and nutrients in shaping the abundance and diversity of the soil bacterial community.
Previous studies have indicated that pH, SOM, and AN are the main factors affecting the composition of bacterial communities in soil [60,61,62,63], which is consistent with our study. In this study, pH was the major factor affecting the bacterial community structure in the reclaimed soil. After 7 years of reclamation, soil pH was significantly reduced. Moreover, the application of chemical fertilizer and manure decreased the soil pH, but there was no significant difference with no fertilizer (CK). This may be due to the short reclamation time (≤7 years) resulting in the insufficient effect of fertilization on soil pH (Table 1). Furthermore, it may relate to our experimental calcareous soil [8]. In this experiment, bacterial diversity increased with the decrease in soil pH (Table 2). It was found that there was a positive correlation between soil pH and Nitrospirae because Nitrospirae are sensitive to soil acidity and usually have a high abundance in alkaline soil [8,64]. Moreover, the abundance of Actinobacteria and Bacteroidetes had a negative correlation with the soil pH (Table S2), indicating that the bacteria were most abundant in agricultural soil near neutral pH [63,64]. It was also found that pH had a closer relationship with Chao1, ACE, and Shannon index (Table 3) than soil nutrients (SOM, AN, and AP) did. It is indicated that the soil bacterial diversity and community structure are shaped more by changes in soil pH than by direct nutrient addition (Zhang et al. 2017). In addition, soil pH could affect the structure of soil bacterial communities by altering many other environmental factors [42,65].
SOM is another major factor that was found to influence the soil bacterial community. It was observed that there were significant positive correlations with SOM, Chao1, ACE, and Shannon index (p < 0.05, Table 3). These results suggested that the restoration of bacterial diversity occurred gradually with the accumulation of soil organic carbon. Soil organic matter is considered to be one of the most common indicators of soil quality. The increase in soil organic matter can promote soil aggregation and improve soil physical properties (e.g., soil structure, bulk density, and water storage) and nutrients, which contributed to the growth and restoration of bacteria in the reclaimed soil. In addition, as a reservoir of carbon and nitrogen sources, the successive decomposition of SOM can produce diverse substrates for microbiota, thus contributing to the improvement of bacterial community diversity [45]. It was also pointed out that SOM had a significant positive correlation with the abundance of Proteobacteria, Actinobacteria, and Bacteroidetes (p < 0.05, RDA). These copiotrophic bacteria have a higher abundance in reclaimed soil for their fast growth rates in nutrient-rich conditions [46].
The aim of reclamation in the mining land is to re-establish a productive, healthy, and sustainable ecosystem suitable for post-mining land use. The restoration of soil fertility by increasing soil nutrients and enriching microbial populations is an effective method for ecological restoration in mining areas [66]. However, the reestablishment of the soil microbial community in mining subsidence areas not only depends on the reclamation practices but also the reclamation time. In general, soil nutrients are gradually accumulated with the increase in reclamation time, and soil fertility can approach a relatively stable or predisturbance level after almost 20 years of reclamation [2,48,59]. Studies on mining reclamation suggest that the most important recovery phase of microbial community occurs between 5 and 20 years after reclamation, and the difference is mainly associated with several critical factors, including reclamation practice, soil properties, climatic conditions, and vegetation [16,18,51]. In our study, soil nutrient and bacterial community diversity were significantly improved after 7 years of reclamation but did not reach the level of adjacent farmland soil. Continuous monitoring of soil nutrients and microbial communities in selected sites will be still needed in the future.

5. Conclusions

In summary, coal mining subsidence greatly reduced soil fertility and changed the bacterial community. Reclamation could promote the soil bacterial diversity and community composition in coal mining areas by improving soil physical and chemical characteristics. According to our hypothesis, the soil bacterial richness and diversity were elevated with the improvement of soil nutrients after 7 years of reclamation, but it still did not reach the level of the adjacent farmland soil. Moreover, fertilization (organic fertilizer and chemical fertilizer) could significantly increase the soil bacterial abundance, while inorganic fertilizer had a more obvious impact on bacterial diversity. In addition, soil physicochemical factors affected by the soil fertilizer remediation, especially SOM and pH, are critical in shaping the main bacterial populations.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph182312504/s1, Figure S1: Shannon wiener curves of OUT cluster at 97% sequence identity across different soil samples, Figure S2: The composition of the bacterial community taxa in all soil samples. Table S1: Relative abundance of dominant bacterial community taxa in different treatment, Table S2: Pearson’s correlation coefficients between soil properties and dominant bacterial community taxa.

Author Contributions

L.L., T.L., H.M. and J.H. participated in this study’s design, data collection and analyses. L.L. and J.Z. played a special role in writing the manuscript. T.L., Y.X. and J.H. provided scientific insights and detailed comments on the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. u1710255 and No. 41907215) and the Youth Science Foundation of Shanxi Province (No. 2013021032-2).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xu, Z.J.; Zhang, Y.; Yang, J.; Liu, F.W.; Bi, R.T.; Zhu, H.F.; Lv, C.J.; Yu, J. Effect of Underground Coal Mining on the Regional Soil Organic Carbon Pool in Farmland in a Mining Subsidence Area. Sustainability 2019, 11, 4961. [Google Scholar] [CrossRef] [Green Version]
  2. De Quadros, P.D.; Zhalnina, K.; Davis-Richardson, A.G.; Drew, J.C.; Menezes, F.B.; de O. Camargo, F.A.; Triplett, E.W. Coal mining practices reduce the microbial biomass, richness and diversity of soil. Appl. Soil Ecol. 2016, 98, 195–203. [Google Scholar] [CrossRef] [Green Version]
  3. Chen, Y.; Zhang, J.; Zhou, A.; Yin, B. Modeling and analysis of mining subsidence disaster chains based on stochastic Petri nets. Nat. Hazards 2018, 92, 19–41. [Google Scholar] [CrossRef]
  4. Ma, K.; Zhang, Y.X.; Ruan, M.Y.; Guo, J.; Chai, T.Y. Land Subsidence in a Coal Mining Area reduced Soil Fertility and Led to Soil Degradation in Arid and Semi-Arid Regions. Int. J. Environ. Res. Public Health 2019, 16, 3929. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Shi, P.L.; Zhang, Y.X.; Hu, Z.Q.; Ma, K.; Wang, H.; Chai, T.Y. The response of soil bacterial communities to mining subsidence in the west China Aeolian sand area. Appl. Soil Ecol. 2017, 121, 1–10. [Google Scholar] [CrossRef]
  6. Guo, Y.; Liu, X.; Tsolmon, B.; Chen, J.; Bao, Y. The influence of transplanted trees on soil microbial diversity in coal mine subsidence areas in the Loess Plateau of China. Glob. Ecol. Conserv. 2019, 21, e00877. [Google Scholar] [CrossRef]
  7. Guebert, M.D.; Gardner, T.W. Macropore flow on a reclaimed surface mine: Infiltration and hillslope hydrology. Geomorphology 2001, 39, 151–169. [Google Scholar] [CrossRef]
  8. Wang, X.Y.; Li, Y.; Wei, Y.; Meng, H.S.; Cao, Y.Z.; Lead, J.R.; Hong, J.P. Effects of fertilization and reclamation time on soil bacterial communities in coal mining subsidence areas. Sci. Total Environ. 2020, 739, 139882. [Google Scholar] [CrossRef]
  9. Brussaard, L.; de Ruiter, P.C.; Brown, G.G. Soil biodiversity for agricultural sustainability. Agr. Ecosyst. Environ. 2007, 121, 233–244. [Google Scholar] [CrossRef]
  10. Wagg, C.; Bender, S.F.; Widmer, F.; Heijden, M. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl. Acad. Sci. USA 2014, 111, 5266–5270. [Google Scholar] [CrossRef] [Green Version]
  11. Trap, J.; Bonkowski, M.; Plassard, C.; Villenave, C.; Blanchart, E. Ecological importance of soil bacterivores for ecosystem functions. Plant Soil 2016, 398, 1–24. [Google Scholar] [CrossRef]
  12. Romaniuk, R.; Giuffré, L.; Costantini, A.; Nannipieri, P. Assessment of soil microbial diversity measurements as indicators of soil functioning in organic and conventional horticulture systems. Ecol. Indic. 2011, 11, 1345–1353. [Google Scholar] [CrossRef]
  13. Singh, J.S.; Pandey, V.C.; Singh, D.P. Efficient soil microorganisms: A new dimension for sustainable agriculture and environmental development. Agric. Ecosyst. Environ. 2011, 140, 339–353. [Google Scholar] [CrossRef]
  14. Tu, J.; Qiao, J.; Zhu, Z.; Li, P.; Wu, L. Soil bacterial community responses to long-term fertilizer treatments in Paulownia plantations in subtropical China. Appl. Soil Ecol. 2018, 124, 317–326. [Google Scholar] [CrossRef]
  15. Anderson, J.D.; Ingram, L.J.; Stahl, P.D. Influence of reclamation management practices on microbial biomass carbon and soil organic carbon accumulation in semiarid mined lands of Wyoming. Appl. Soil Ecol. 2008, 40, 387–397. [Google Scholar] [CrossRef]
  16. Dangi, S.R.; Stahl, P.D.; Wick, A.F.; Ingram, L.J.; Buyer, J.S. Soil Microbial Community Recovery in Reclaimed Soils on a Surface Coal Mine Site. Soil Sci. Soc. Am. J. 2012, 76, 915–924. [Google Scholar] [CrossRef]
  17. Dimitriu, P.A.; Prescott, C.E.; Quideau, S.A.; Grayston, S.J. Impact of reclamation of surface-mined boreal forest soils on microbial community composition and function. Soil Biol. Biochem. 2010, 42, 2289–2297. [Google Scholar] [CrossRef]
  18. Cheng, Z.; Zhang, F.; Gale, W.J.; Wang, W.; Sang, W.; Yang, H. Effects of reclamation years on composition and diversity of soil bacterial communities in Northwest China. Can. J. Microbiol. 2018, 64, 28–40. [Google Scholar] [CrossRef] [Green Version]
  19. Kohler, J.; Caravaca, F.; Azcon, R.; Diaz, G.; Roldan, A. Suitability of the microbial community composition and function in a semiarid mine soil for assessing phytomanagement practices based on mycorrhizal inoculation and amendment addition. J. Environ. Manag. 2016, 169, 236–246. [Google Scholar] [CrossRef]
  20. Hahn, A.S.; Quideau, S.A. Long-term effects of organic amendments on the recovery of plant and soil microbial communities following disturbance in the Canadian boreal forest. Plant Soil 2012, 363, 331–344. [Google Scholar] [CrossRef] [Green Version]
  21. Mummey, D.L.; Stahl, P.D.; Buyer, J.S. Soil microbiological properties 20 years after surface mine reclamation: Spatial analysis of reclaimed and undisturbed sites. Soil Biol. Biochem. 2002, 34, 1717–1725. [Google Scholar] [CrossRef]
  22. Edmeades, D.C. The long-term effects of manures and fertilisers on soil productivity and quality: A review. Nutr. Cycl. Agroecosyst. 2003, 66, 165–180. [Google Scholar] [CrossRef]
  23. Torsvik, V.; Øvreås, L. Microbial diversity and function in soil: From genes to ecosystems. Curr. Opin. Microbiol. 2002, 5, 240–245. [Google Scholar] [CrossRef]
  24. Ding, J.; Jiang, X.; Ma, M.; Zhou, B.; Guan, D.; Zhao, B.; Zhou, J.; Cao, F.; Li, L.; Li, J. Effect of 35 years inorganic fertilizer and manure amendment on structure of bacterial and archaeal communities in black soil of northeast China. Appl. Soil Ecol. 2016, 105, 187–195. [Google Scholar] [CrossRef]
  25. Sun, L.; Xun, W.; Huang, T.; Zhang, G.; Gao, J.; Ran, W.; Li, D.; Shen, Q.; Zhang, R. Alteration of the soil bacterial community during parent material maturation driven by different fertilization treatments. Soil Biol. Biochem. 2016, 96, 207–215. [Google Scholar] [CrossRef]
  26. Gomez, E.; Ferreras, L.; Toresani, S. Soil bacterial functional diversity as influenced by organic amendment application. Bioresour. Technol. 2006, 97, 1484–1489. [Google Scholar] [CrossRef] [PubMed]
  27. Chang, E.H.; Chung, R.S.; Tsai, Y.H. Effect of different application rates of organic fertilizer on soil enzyme activity and microbial population. Soil Sci. Plant Nutr. 2007, 53, 132–140. [Google Scholar] [CrossRef]
  28. Zhuang, X.; Han, Z.; Bai, Z.; Zhuang, G.; Shim, H. Progress in decontamination by halophilic microorganisms in saline wastewater and soil. Environ. Pollut. 2010, 158, 1119–1126. [Google Scholar] [CrossRef]
  29. Zhong, W.; Gu, T.; Wang, W.; Zhang, B.; Lin, X.; Huang, Q.; Shen, W. The effects of mineral fertilizer and organic manure on soil microbial community and diversity. Plant Soil 2010, 326, 511–522. [Google Scholar] [CrossRef]
  30. Zhou, J.; Guan, D.W.; Zhou, B.K.; Zhao, B.S.; Ma, M.C.; Qin, J.; Jiang, X.; Chen, S.F.; Cao, F.M.; Shen, D.L.; et al. Influence of 34-years of fertilization on bacterial communities in an intensively cultivated black soil in northeast China. Soil Biol. Biochem. 2015, 90, 42–51. [Google Scholar] [CrossRef]
  31. Driessen, P.; Deckers, J.; Spaargaren, O.; Nachtergaele, F. Lecture Notes on the Major Soils of the World; FAO: Rome, Italy, 2001; pp. 265–270. [Google Scholar]
  32. Li, X.Y.; Deng, Y.; Li, Q.; Lu, C.; Wang, J.; Zhang, H.; Zhu, J.; Zhou, J.; He, Z. Shifts of functional gene representation in wheat rhizosphere microbial communities under elevated ozone. ISME J. 2013, 7, 660–671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Strickland, T.C.; Sollins, P. Improved Method for Separating Light and Heavy-Fraction Organic Material from Soil. Soil Sci. Soc. Am. J. 1987, 51, 1390–1393. [Google Scholar] [CrossRef]
  34. Bremner, J.M. Nitrogen Total. In Methods of Soil Analysis Part 3: Chemical Methods; SSSA Book Series, 5; Sparks, D.L., Ed.; Soil Science Society of America: Madison, WI, USA, 1996; pp. 1085–1122. [Google Scholar]
  35. Hedley, M.J.; Stewart, J.W.B. Method to measure microbial phosphate in soils. Soil Biol. Biochem. 1982, 14, 377–385. [Google Scholar] [CrossRef]
  36. Mehlich, A. Mehlich 3 soil test extractant: A modification of Mehlich 2 extractant. Commun. Soil Sci. Plant. Anal. 1984, 15, 1409–1416. [Google Scholar] [CrossRef]
  37. Fadrosh, D.W.; Ma, B.; Gajer, P.; Sengamalay, N.; Ott, S.; Brotman, R.M.; Ravel, J. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2014, 2, 6. [Google Scholar] [CrossRef] [Green Version]
  38. Lozupone, C.; Lladser, M.; Knights, D.; Stombaugh, J.; Knight, R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 2011, 5, 169–172. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Sheik, C.S.; Mitchell, T.W.; Rizvi, F.Z.; Rehman, Y.; Faisal, M.; Hasnain, S.; McInerney, M.J.; Krumholz, L.R. Exposure of soil microbial communities to chromium and arsenic alters their diversity and structure. PLoS ONE 2012, 7, e40059. [Google Scholar] [CrossRef]
  40. Gasch, C.; Huzurbazar, S.; Stahl, P. Measuring soil disturbance effects and assessing soil restoration success by examining distributions of soil properties. Appl. Soil Ecol. 2014, 76, 102–111. [Google Scholar] [CrossRef]
  41. Poncelet, D.; Cavender, N.; Cutright, T.; Senko, J. An assessment of microbial communities associated with surface mining-disturbed overburden. Environ. Monit. Assess. 2013, 186, 1917–1929. [Google Scholar] [CrossRef]
  42. Rastogi, G.; Osman, S.; Vaishampayan, P.A.; Andersen, G.L.; Stetler, L.D.; Sani, R.K. Microbial diversity in uranium mining-impacted soils as revealed by high-density 16S microarray and clone library. Microb. Ecol. 2010, 59, 94–108. [Google Scholar] [CrossRef] [PubMed]
  43. Lauber, C.L.; Hamady, M.; Knight, R.; Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 2009, 75, 5111–5120. [Google Scholar] [CrossRef] [Green Version]
  44. Zhan, J.; Sun, Q.Y. Development of microbial properties and enzyme activities in copper mine wasteland during natural restoration. Catena 2014, 116, 86–94. [Google Scholar] [CrossRef]
  45. Li, Y.Y.; Chen, L.; Wen, H.; Zhou, T.; Zhang, T.; Gao, X. 454 Pyrosequencing Analysis of Bacterial Diversity Revealed by a Comparative Study of Soils from Mining Subsidence and Reclamation Areas. J. Microbiol. Biotechnol. 2014, 24, 313–323. [Google Scholar] [CrossRef] [Green Version]
  46. Kielak, A.; Pijl, A.S.; van Veen, J.A.; Kowalchuk, G.A. Phylogenetic diversity of Acidobacteria in a former agricultural soil. ISME J. 2009, 3, 378. [Google Scholar] [CrossRef] [PubMed]
  47. Ward, N.L.; Challacombe, J.F.; Janssen, P.H.; Henrissat, B.; Coutinho, P.M.; Wu, M.; Xie, G.; Haft, D.H.; Sait, M.; Badger, J.; et al. Three genomes from the phylum Acidobacteria provide insight into the lifestyles of these microorganisms in soils. Appl. Environ. Microbiol. 2009, 75, 2046–2056. [Google Scholar] [CrossRef] [Green Version]
  48. Li, Y.Y.; Wen, H.; Chen, L.; Yin, T. Succession of bacterial community structure and diversity in soil along a chronosequence of reclamation and re-vegetation on coal mine spoils in China. PLoS ONE 2014, 9, e115024. [Google Scholar] [CrossRef]
  49. Tian, W.; Wang, L.; Li, Y.; Zhuang, K.; Li, G.; Zhang, J.; Xiao, X.; Xi, Y. Responses of microbial activity, abundance, and community in wheat soil after three years of heavy fertilization with manure-based compost and inorganic nitrogen. Agric. Ecosyst. Environ. 2015, 213, 219–227. [Google Scholar] [CrossRef]
  50. Grayston, S.J.; Campbell, C.D.; Bardgett, R.D.; Mawdsley, J.L.; Clegg, C.D.; Ritz, K.; Griffiths, B.S.; Rodwell, J.S.; Edwards, S.J.; Davies, W.J.; et al. Assessing shifts in microbial community structure across a range of grasslands of differing management intensity using CLPP, PLFA and community DNA techniques. Appl. Soil Ecol. 2004, 25, 63–84. [Google Scholar] [CrossRef]
  51. Li, Y.Y.; Chen, L.; Wen, H. Changes in the composition and diversity of bacterial communities 13 years after soil reclamation of abandoned mine land in eastern China. Ecol. Res. 2015, 30, 357–366. [Google Scholar] [CrossRef]
  52. Fang, X.; Yu, D.; Zhou, W.; Zhou, L.; Dai, L. The effects of forest type on soil microbial activity in Changbai Mountain, Northeast China. Ann. Forest Sci. 2016, 73, 473–482. [Google Scholar] [CrossRef] [Green Version]
  53. Wei, Y.; Yu, L.F.; Zhang, J.C.; Yu, Y.C.; Deangelis, D.L. Relationship Between Vegetation Restoration and Soil Microbial Characteristics in Degraded Karst Regions: A Case Study. Pedosphere 2011, 21, 132–138. [Google Scholar] [CrossRef]
  54. Reichel, R.; Radl, V.; Rosendahl, I.; Albert, A.; Amelung, W.; Schloter, M.; Sören, T.B. Soil microbial community responses to antibiotic-contaminated manure under different soil moisture regimes. Appl. Microbiol. Biotechnol. 2014, 98, 6487–6495. [Google Scholar] [CrossRef]
  55. Sun, J.; Zhang, Q.; Zhou, J.; Wei, Q. Pyrosequencing technology reveals the impact of different manure doses on the bacterial community in apple rhizosphere soil. Appl. Soil Ecol. 2014, 78, 28–36. [Google Scholar] [CrossRef]
  56. Unger, I.M.; Goyne, K.W.; Kennedy, A.C.; Kremer, R.J.; McLain, J.; Williams, C.F. Antibiotic Effects on Microbial Community Characteristics in Soils under Conservation Management Practices. Soil Sci. Soc. Am. J. 2013, 77, 100. [Google Scholar] [CrossRef]
  57. Francioli, D.; Schulz, E.; Lentendu, G.; Wubet, T.; Buscot, F.; Reitz, T. Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies. Front. Microbiol. 2016, 7, 1446. [Google Scholar] [CrossRef] [Green Version]
  58. Cui, J.; Liu, C.; Li, Z.; Wang, L.; Chen, X.; Ye, Z.; Fang, C. Long-term changes in topsoil chemical properties under centuries of cultivation after reclamation of coastal wetlands in the Yangtze Estuary, China. Soil Tillage Res. 2012, 123, 50–60. [Google Scholar] [CrossRef]
  59. Sun, Y.G.; Li, X.; Mander, Ü.; He, Y.L.; Jia, Y.; Ma, Z.G.; Guo, W.Y.; Xin, Z.J. Effect of reclamation time and land use on soil properties in Changjiang River Estuary, China. Chin. Geogr. Sci. 2011, 21, 403. [Google Scholar] [CrossRef]
  60. Hartmann, M.; Frey, B.; Mayer, J.; Mäder, P.; Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 2015, 9, 1177–1194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Sun, R.; Zhang, X.X.; Guo, X.; Wang, D.; Chu, H. Bacterial diversity in soils subjected to long-term chemical fertilization can be more stably maintained with the addition of livestock manure than wheat straw. Soil Biol. Biochem. 2015, 88, 9–18. [Google Scholar] [CrossRef]
  62. Zeng, J.; Liu, X.; Ling, S.; Lin, X.; Chu, H. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 2016, 92, 41–49. [Google Scholar] [CrossRef]
  63. Zhang, Y.; Shen, H.; He, X.; Thomas, B.Z.; Lupwayi, N.; Hao, X.; Thomas, M.; Shi, X. Fertilization Shapes Bacterial Community Structure by Alteration of Soil pH. Front. Microbiol. 2017, 8, 1325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Wu, Y.; Zeng, J.; Zhu, Q.; Zhang, Z.; Lin, X. pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution. Sci. Rep. 2017, 7, 40093. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Rousk, J.; Baath, E.; Brookes, P.C.; Lauber, C.L.; Lozupone, C.; Caporaso, J.G.; Knight, R.; Fierer, N. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010, 4, 1340–1351. [Google Scholar] [CrossRef]
  66. Sheoran, V.; Sheoran, A.; Poonia, P. Soil Reclamation of Abandoned Mine Land by Revegetation: A Review. Int. J. Soil Sediment Water 2010, 3, 13. [Google Scholar]
Ijerph 18 12504 g001 550
Figure 1. OTU Venn analysis of different treatments. OTU: Operational Taxonomic Units; CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland.
Figure 1. OTU Venn analysis of different treatments. OTU: Operational Taxonomic Units; CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland.
Ijerph 18 12504 g001
Ijerph 18 12504 g002 550
Figure 2. (a) Relative abundance of the dominant bacteria phyla (top 10) in all different treatments; (b) relative abundance of the dominant bacteria genera (top 10) in all different treatments. Relative abundances (>1%) are based on the proportional frequencies of those DNA sequences that could be classified at the phylum (proteobacterial class) level. Sequences not classified to any known phylum and phylogenetic groups accounting for ≤1% of all classified sequences are summarized in the artificial group “others and unclassified”. CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland. TM7: the phylum candidatus Saccharibacteria.
Figure 2. (a) Relative abundance of the dominant bacteria phyla (top 10) in all different treatments; (b) relative abundance of the dominant bacteria genera (top 10) in all different treatments. Relative abundances (>1%) are based on the proportional frequencies of those DNA sequences that could be classified at the phylum (proteobacterial class) level. Sequences not classified to any known phylum and phylogenetic groups accounting for ≤1% of all classified sequences are summarized in the artificial group “others and unclassified”. CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland. TM7: the phylum candidatus Saccharibacteria.
Ijerph 18 12504 g002
Ijerph 18 12504 g003 550
Figure 3. (a) Principal coordinate analysis (PCoA) based on unweighted UniFrac distances of soil bacterial communities sampled from different treatments; (b) similarity trees based on Bray–Curtis distance indices were calculated by OTUs at a distance of 3% using the hierarchical clustering analysis of bacterial communities for soil samples. CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland.
Figure 3. (a) Principal coordinate analysis (PCoA) based on unweighted UniFrac distances of soil bacterial communities sampled from different treatments; (b) similarity trees based on Bray–Curtis distance indices were calculated by OTUs at a distance of 3% using the hierarchical clustering analysis of bacterial communities for soil samples. CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland.
Ijerph 18 12504 g003
Ijerph 18 12504 g004 550
Figure 4. Redundancy analysis (RDA) of abundant phyla (proteobacterial classes) and selected soil edaphic properties such as pH, SOM, AN, AP, and AK for individual samples from three sites. SOM: organic matter; AN: alkali-hydrolyzable nitrogen; AP: available phosphorus; AK: available potassium; CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland. TM7: the phylum candidatus Saccharibacteria. The red arrow: soil property factors; The blue arrow: the main group of bacteria phyla.
Figure 4. Redundancy analysis (RDA) of abundant phyla (proteobacterial classes) and selected soil edaphic properties such as pH, SOM, AN, AP, and AK for individual samples from three sites. SOM: organic matter; AN: alkali-hydrolyzable nitrogen; AP: available phosphorus; AK: available potassium; CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland. TM7: the phylum candidatus Saccharibacteria. The red arrow: soil property factors; The blue arrow: the main group of bacteria phyla.
Ijerph 18 12504 g004
Table
Table 1. Soil chemical characteristics in different treatments.
Table 1. Soil chemical characteristics in different treatments.
TreatmentspHSOM (g∙kg−1)AN (mg∙kg−1)AP (mg∙kg−1)AK (mg∙kg−1)
SU8.06 ± 0.06 a9.74 ± 0.28 e16.51 ± 0.58 e3.18 ± 0.12 c123.30 ± 4.61 b
CK7.91 ± 0.03 b11.37 ± 0.59 d24.68 ± 1.01 d4.65 ± 0.31 c135.30 ± 2.31 b
CF7.84 ± 0.02 bc14.06 ± 0.46 c27.13 ± 1.46 c17.75 ± 1.54 b233.57 ± 15.32 a
M7.76 ± 0.02 c18.15 ± 0.51 b35.60 ± 0.68 b19.46 ± 1.47 b236.36 ± 19.58 a
FA7.85 ± 0.03 bc26.64 ± 0.40 a48.59 ± 2.33 a35.60 ± 0.68 a201.7 ± 15.78 a
Values followed by different lowercase letters (a–e) are significantly different (p < 0.05) according to Duncan’s multiple comparison test; SOM: organic matter; AN: alkali-hydrolyzable nitrogen; AP: available phosphorus; AK: available potassium; CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland.
Table
Table 2. Estimated number of observed Sobs, coverage, richness, and diversity in different treatments.
Table 2. Estimated number of observed Sobs, coverage, richness, and diversity in different treatments.
TreatmentsSobsCoverageRichness and Diversity Indices
Chao1ACEShannon
SU3672 ± 195 b0.97 ± 0.01 a4455 ± 323 c4358 ± 319 c10.02 ± 0.24 d
CK10803 ± 106 a0.96 ± 0.02 a13864 ± 174 b14078 ± 242 b11.37 ± 0.05 c
CF11021 ± 731 a0.94 ± 0.01 ab15190 ± 463 a15621 ± 413 a11.60 ± 0.05 b
M11030 ± 337 a0.95 ± 0.00 a15330 ± 216 a15845 ± 170 a11.39 ± 0.12 c
FA9772 ± 288 a0.91 ± 0.02 b14430 ± 313 a14988 ± 302 a11.77 ± 0.04 a
Values followed by different lowercase letters (a–d) are significantly different (p < 0.05) according to Duncan’s multiple comparison test; Sobs: the species of OTU that can be detected; Coverage: Good’s nonparametric coverage estimator; ACE: abundance-based coverage estimator; Shannon: nonparametric Shannon diversity index. CK: reclaimed soil sampled in no-fertilizer treatment; CF: reclaimed soil sampled in chemical fertilizer treatment; M: reclaimed soil sampled in manure treatment. SU: subsided soil sampled in an adjacent site; FA: soil sampled in another adjacent farmland.
Table
Table 3. Pearson’s correlation coefficients between soil physicochemical characteristics and Sobs, and diversity indices.
Table 3. Pearson’s correlation coefficients between soil physicochemical characteristics and Sobs, and diversity indices.
PearsonSobsChao1ACEShannon
pH−0.768 **−0.826 **−0.824 **−0.745 **
SOM0.3990.545 *0.564 *0.616 *
AN0.4630.572 *0.647 **0.559 *
AP0.3770.606 *0.659 **0. 623 *
AK0.5080.644 **0.656 **0.409
** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level. pH: potential of hydrogen; SOM: organic matter; AN: alkali-hydrolyzable nitrogen; AP: available phosphorus; AK: available potassium.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, L.; Li, T.; Meng, H.; Xie, Y.; Zhang, J.; Hong, J. Effects of Seven-Year Fertilization Reclamation on Bacterial Community in a Coal Mining Subsidence Area in Shanxi, China. Int. J. Environ. Res. Public Health 2021, 18, 12504. https://doi.org/10.3390/ijerph182312504

AMA Style

Li L, Li T, Meng H, Xie Y, Zhang J, Hong J. Effects of Seven-Year Fertilization Reclamation on Bacterial Community in a Coal Mining Subsidence Area in Shanxi, China. International Journal of Environmental Research and Public Health. 2021; 18(23):12504. https://doi.org/10.3390/ijerph182312504

Chicago/Turabian Style

Li, Li, Tingliang Li, Huisheng Meng, Yinghe Xie, Jie Zhang, and Jianping Hong. 2021. "Effects of Seven-Year Fertilization Reclamation on Bacterial Community in a Coal Mining Subsidence Area in Shanxi, China" International Journal of Environmental Research and Public Health 18, no. 23: 12504. https://doi.org/10.3390/ijerph182312504

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

Article Metrics

Back to TopTop