Research Article

Response of Soil Bacterial Community Structure to Land-use Conversion of Natural Forests in Maoershan National Forest Park, China  

Mu Peng1 , Syed Sadaqat Shah2 , Qiuyu Wang1 , Fanjuan Meng1
1 Colleague of Life Science, Northeast Forestry University, Harbin, 150040, China
2 Key Laboratory of Vegetation Ecology, Ministry of Education, Institute of Grassland Science, Northeast Normal University, Changchun 130024, China
Author    Correspondence author
Molecular Microbiology Research, 2017, Vol. 7, No. 2   doi: 10.5376/mmr.2017.07.0002
Received: 26 Jun., 2017    Accepted: 25 Jul., 2017    Published: 02 Aug., 2017
© 2017 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Peng M., Shah S.S., Wang Q.Y., and Meng F.J., 2017, Response of soil bacterial community structure to land-use conversion of natural forests in Maoershan National Forest Park, China, Molecular Microbiology Research, 7(2): 10-19 (doi: 10.5376/mmr.2017.07.0002)


To compare and evaluate the soil bacterial community composition in Maoershan National Forest Park, we analyzed soil samples from four replicated land-use types (hardwood forests, cultivated lands, settlement and slash lands) using a denaturing gradient gel electrophoresis (DGGE) method based on 16s rRNA gene fragments. Forty-two DGGE bands were successfully excised for sequencing. Our results revealed that the conversion of natural forest to other land-use types had a significant effect on the soil bacterial community. Bacteroidetes was absent in forest soils. Beta-Proteobacteria was unique to settlement soils, whereas Cyanobacteria and Verrucomicrobia were absent in agricultural soils. Additionally, Acidobacteria and Proteobacteria (α, β, γ, δ classes) were the dominant bacterial communities in all soils. Thus, conversion of the forest land into other land-use types resulted in changes in the bacterial communities which might affect the productivity of the soil ecosystem. Together these results suggested that the utility of using sequence-based approaches to analyze bacterial communities provides detailed information on individual bacterial community composition and permit the robust assessment of the biogeographical patterns.

Bacterial community structure; 16S rRNA; Forest; Conversion of land-use


In recent years, to meet various demands, natural forests have been converted to other land-use, such as agriculture, settlement and grassland. Changes in land-use may profoundly alter the microbial community in soil resulting in a loss of biodiversity, land degradation and nutrient exhaustion (Zhao et al., 2005; Costa et al., 2006; Upchurch et al., 2008; Ding et al., 2013). As we known, soil microorganisms are crucial for the function and stability of an ecosystem (Rogers and Tate Iii, 2001). This is because a high microbial diversity is critical for the sustainability of a soil ecosystem by altering immobilization of the essential nutrients and nutrient cycling (Li, 2012). Many studies have shown that the conversion of natural forest to other land-use types affected the soil microbial community composition and soil physicochemical properties (Grünzweig et al., 2003; Upchurch et al., 2008; Yu et al., 2011).


Maoershan National Forest Park is a natural forest area located in Shangzhi City in the southern part of Heilongjiang Province in Northeast China. It is comprised of  260 km2 land area, 80% of which was previously natural forests. Since the early 1990s, some were utilized for agricultural landscape and settlement. Thus, these converted lands provide ideal conditions for study the response of soil microbial diversity in the land-use conversion of the natural forests to other land-use types under boreal climates. Black soil is a typical boreal soil type in Northeast China (Zeng et al., 1983). It is considered one of the most fertile soils in China as it possesses a high microbial diversity due to the rich nutrient contents. However, diverse land-use types have been developed to meet needs for crops, fiber and food, which may change the quality of black soil (Yu et al., 2011). To date, little is known about how soil microbial diversity responds to natural forest land conversion in the black soil in Northeast China.


Soil microbes, especially bacteria, can easily be influenced by land-use change, which can significantly influence diversity, composition and abundance of bacterial community in soil (Suleiman et al., 2013). In turn, these changes can cause an imbalance of the soil ecosystem (Valpassos et al., 2001; Aboim et al., 2008; Jesus et al., 2009). Therefore, it is important to understand soil ecosystems by studying the bacterial community structure and its relationship with the changes in land-use. In our previous study, changes in land-use type were shown to affect the changes in enzyme activities and the physical and chemical properties of soils (Cong et al., 2013). However, the impact of conversion of natural forest to other land-use patterns on soil bacterial community have not been yet examined in the black soil of Northeast China.


In this study, we examined differences in bacterial communities across a series of replicated land-use types in the Maoershan National Forest Park, China. We used denaturing gradient gel electrophoresis (DGGE) method based on 16S rRNA genes to obtain differences in the bacterial communities across those land-use types. The aim of this study is to investigate the influence of land-use change on the structure of the bacterial communities of black soil and, more specifically, to examine how shifts in the abundance and composition of soil bacterial communities correspond to changes in land-use type.


1 Materials and Methods

1.1 Sampling site description and soil sampling

The experimental site is located in Maoershan National Forest Park (45°10′-45°35′N, 127°20′-127°45′E), Shangzhi City, Heilongjiang Province, China. This site has a boreal climate with a mean annual rainfall of 723 mm, and an average annual temperature of 2.4°C (Pan et al., 2007). The soil located here is classified as representative black soil. The present study is a continuation of our previous work, in which the physical and chemical properties of soil samples have been described (Cong et al., 2013).


Our goal was to select plots representative of the land-use types in this region. Therefore, we chose four typical land uses to provide an overall picture of natural forest land-use changeJug. These included (i) three forest sites [(FL-1), (FL-2) and (FL-3)], (ii) two agricultural sites [(AL-4) and (AL-5)], (iii) three settlement sites [(SEL-6), (SEL-7) and (SEL-8)], (iv) and two slash sites [(SLL-9) and (SLL -10)]. The detailed descriptions of all sites are listed in the Table 1. The forest sites consist of pine (Pinus koraiensis) with varying amounts of Juglans, Syringa reticulate species. The agricultural plots are used to support populations with annual crop rotation between soybean and maize using conventional tillage practices. The settlement plots are dominated by barnyard grass. And the slash plots mainly consist of pine and spruce species.



Table1 Description of sampling sites


In each site, a central point was selected, and four sampling points at 20 m from the central point were chosen to five samples per site with three replicates. Then the 15 soil cores from same site was placed in a bag and mixed by shaking, and sifting air dried samples through 4 mm mesh sieve to provide a homogenous, representative sample. Thus, ten composited samples were obtained and stored at -80°C until DNA extraction.


1.2 DNA extraction and purification

The total DNA was extracted from 0.5 g soil sample by using the method of Li and Jin (2006) with three replicates. Replicate DNA extracts were pooled. DNA quality was checked using 1% (w/v) agarose gel electrophoresis. DNA quantity was determined using a MECASYS-OPTIZEN 3220 UV (Mecasys Co., Ltd.) spectrophotometer and was diluted to 50 ng/μL. Pooled DNA was stored at -20°C for PCR amplification.


1.3 16s rRNA amplification and DGGE

Pooled DNA was amplified by universal 16S rRNA gene primers (GC933-954f5’-GCACAAGCGGTGGAGCATGTGG-3’; 1369-1388r5’-GCCCGGGAACGTATTCACCG-3’) (Yu and Morrison, 2004). The amplification mixture contained 1 μL of each sample of pooled DNA, 5 μL of 10 × PCR buffer, 1 μL of 10 mM dNTPs, 6 μL of 25 mM MgCl2, 1 μL of 10 μM GC933-954f primer, 1 μL of 10 μM 1 369~1 388r primer, and 1 U Taq DNA polymerase (Promega, Madison, Wisconsin, USA) in 20 μL volume. PCR amplification was conducted as follows: initial denaturation at 94°C for 5 min, then 10 cycles of 94°C for 30 s, 61°C for 30 s (-0.5°C at each cycle) and 72°C for 1 min, followed by 25 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 1 min.


Denaturing gradient gel electrophoresis (DGGE) was performed in a DCode universal mutation detection system (Bio-Rad Co., USA). 5 μL PCR product and 5 μL loading buffer was added into an 8% acrylamide gel with a chemical gradient (35-60% denaturant) and electrophoresed for 7 h at 150 V. After electrophoresis, the gel was removed and placed in an acetic acid solution (1%) for 15 min and then was washed in double-distilled water (ddH2O). Next, it was stained with silver nitrate solution (0.2%) for 20 min then the stained gel was washed twice in ddH2O and was placed in a developing solution (3% sodium carbonated and 0.06% formaldehyde). The development was stopped with 1% acetic acid. After rinsing with ddH2O, the gel was dried and photographed. DGGE images were analyzed using Quantity One software (Bio-Rad, version 4.6.2, USA).


1.4 Sequencing analysis of DGGE bands

Forty-two bands were produced based presence or absence among all samples, and almost no variability in banding patterns was observed between replicates. They were marked and excised from the gel with a sharp razor blade, and eluted in 30 µL of sterile double distilled water at 100°C for 10 min and then centrifuged. The extracts were subjected to recover using the same set of corresponding primers under the same PCR conditions as for re-amplification. The DNA fragments were cloned into the plasmid pMD18-T vector (TaKaRa, Dalian, China) and subjected for sequencing analysis. The sequences of bands were analyzed for homology to nucleotide sequences by searching the National Center for Biotechnology Information (NCBI) website (http://www. using the BLAST search program.


1.5 Data analysis

To assess the bacterial communities, several parameters were calculated for each sample. Richness (R) was estimated by using the number of bands observed in a sample with the Shannon-Wiener diversity index (H') and equitability (E) was calculated using the following equations (Pielou, 1969; Rogers and Tate Iii, 2001; Costa et al., 2012):


H' = - ΣPi(lnPi)

E = H'/ln R


Where, Pi is the ratio of the peak value of a band and the sum of all peak values in all bands.


Hierarchical Cluster analysis based on the presence and absence of bands detected in all soil samples of the different land-use were performed using QuantityOne software (Bio-Rad, version 4.6.2, USA). A dendrogram according to 16S rRNA gene sequences was performed using MEGA4.0 software.


2 Results

2.1 Effect of different land-use on richness (R), Shannon-Wiener diversity index (H'), and equitability (E)

Species richness, Shannon-Wiener diversity index, and equitability for ten land sites were shown in the Table 2. A higher diversity and species richness was observed in Maoershan National Forest Park soils, varying from 47 in FL-1 to 68 in SLL-9. The equitability index varied from 0.69 to 0.79. And the Shannon-Wiener diversity index was comparable values in all soil samples, with the highest value in site SLL-9 (3.23) and lowest in FL-2 or SLL-10 (2.94).



Table 2  Species richness (R), Shannon-Wiener diversity index (H') and equitability (H) in soils from ten sites


2.2 Sequence analysis and clustering of 16s rRNA

In this study, the homology of forty-two sequences was 87%-99% with available data; most bands were affiliated to uncultured bacteria (Figure 1). Identified bacteria belonged to seven phyla (Figure 2). The most abundant sequences were from the phylum Proteobacteria including Alpha, Delta and Gamma classes (Figure 2). The DGGE bands including Band 3, Band 5, Band 8, Band 9, Band 14, Band 16, Band 17, Band 19, Band 20, Band 21, Band 22, Band 28, Band 37 and Band 39 were assigned to Proteobacteria, whereas the other DGGE bands had relatively highly homologous with Acidobacteria, Bacteroidetes, Actinobacteria, Gemmatimonadetes and Verrucomicrobia (Figure 1; Figure 2).



Figure 1 Bacterial PCR-DGGE band patterns of bacterial 16S rRNA gene among soil samples. Forty-two bands (band number) were produced based presence or absence among all samples. As almost no variability in banding patterns was observed between replicates, thus, we pooled PCR products to DGGE. Sample numbers indicating the different soil sites were given above the figure. The description of sample number is shown in Table 1



Figure 2 Phylogenetic tree generated by using the Neighbor-Joining method of analysis of 16S rRNA gene sequences obtained from dominant DGGE bands. The bar represents an estimated 0.02 sequence divergence. The tree was constructed with MEGA4.0 software, using 1000 bootstrap for neighbor joining and ClustalW for sequence alignment

Note: Band 8 and Band 28 belong to β-proteobacteria and Cyanobacteria, respectively, which were not marked in Figure 1. The description of band number was listed in Figure 1


The hierarchical cluster analysis basing on the presence and absence of bands in samples from ten sites is depicted in the dendrogram of Figure 3. The samples tended to group in relation to the land-use type. The clustering analysis of DGGE band patterns identified two distinct clusters: Cluster I and Cluster II. Cluster I contained the soil samples from three forestry sites (FL-1, FL-2 and FL-3). The remaining soil samples were in Cluster II. Each cluster could be partitioned into sub-clusters which demonstrated the effect of different land-use on the bacterial communities. The results clearly demonstrated that DGGE profiles revealed marked differences in response to soil microbial communities under different land-use systems.



Figure 3 Cluster dendrogram based on banding patterns in samples from ten sites. The description of sample number is shown in Table 1


2.3 Effect of different land-use on bacterial community composition

At the phylum level, 7 major bacterial taxa present within most of the soils and, averaged across all of soil, the most abundant bacterial groups were the Proteobacteria, Acidobacteria and Verrucomicrobia (Figure 4). The relative abundances of the different taxa varied between and within land-use.



Figure 4 Bacterial community composition for soil samples from ten sites. Sample numbers indicating the different land uses are given below the graph. The description of sample number is shown in Table 1


The absence, presence and abundance of bands were considered in estimating relative changes and abundance of the bacterial community. The results of these changes from all soil samples are demonstrated in Figure 4. Bacteroidetes were not detected in forest soil (FL-1, FL-2 and FL-3). Beta-Proteobacteria was unique to the settlement soil (site 6, 7 and 8), whereas Cyanobacteria and Verrucomicrobia were absent in agricultural soil (AL-4 and AL-5). Additionally, Acidobacteria and Proteobacteria (α, β, γ, δ classes) were dominant bacterial communities in all soil samples.


3 Discussion

In this study, the result of DGGE analysis showed that land-use conversion had a significant effect on the bacterial community composition in black soil of boreal region in China. Conversion of natural forests to other land-use caused a negative response in soil physicochemical characteristics in previous study (Jaiyeoba, 2003; Su et al., 2004), and accordingly, probably impacts the composition of bacterial community in the soil.


The main bacterial genera detected in this work were similar to the general isolation pattern found in soil ecosystems (Tangjang et al., 2009). Bacterial diversity and richness found in the soil plots reflected the equilibrium between production and decomposition inside this system, as soil plots (FL-3, AL-4, AL-5, SEL-8, SLL-9) present a higher abundance of plant species and a leaf litter layer over the soil, thereby leading to increased richness and abundance of bacteria (Table 2). Richness, defined by the Shannon-Wiener diversity index, was the highest for SLL-9 and evenness was the greatest for FL-1. Thus, different land-use had no clear effect on these parameters. However, Costa et al. (2012) found that plant species substitution could change the quantity and quality of organic matter under decomposition, as the plant residues would be different.


Previous reports indicated that agricultural management actions such as fertilization, irrigation and herbicide could affect the soil bacterial community structure (Calderon et al., 2001; Shannon, 2001). Therefore, tillage is an important factor driving shifts in the soil microbial community structure. In the present study, it was shown that conversion of forest soil into agricultural land influenced the abundance of four bacterial phyla, e.g., Cyanobacteria, δ-Proteobacteria, Gemmatimonadetes and Bacteroidetes. Cyanobacteria have been found not detected after the conversion from natural forests to agricultural land. Generally, Cyanobacteria can affect the soil fertility and increase the contents of organic matter and nitrogen. These results suggested that deliberate management of soils have a negative impact on the bacterial community structure (Pankratova, 2006). However, δ-Proteobacteria and Gemmatimonadetes had significantly higher abundances in agricultural land compared to forest soils. In the study of Jangid et al. (2008), it was revealed that β-, δ-and γ-Proteobacteria were predominant in ungrazed and cropped soils, whereas the numbers of α-Proteobacteria decreased in the cropped soils. Furthermore, there was a minor difference between the agricultural soil from AL-4 (soybean) and AL-5 (maize), which may be correlated to their crop species. Some studies suggested plant species to be one of major factors influencing the microbial community structure (Calderon et al. 2001; Deangelis et al. 2009; Mendes et al. 2011; Underwood et al. 2009). However, Kielak et al. (2008) reported that plant species have only a minor influence on the soil bacterial community composition. Kuramae et al. (2012) also found that soil physicochemical factors, such as C: N, phosphate, and pH, were the main factors explaining the variation in bacterial communities, as opposed to the independent impact of vegetation type and land-use practices, but they also showed that exceptions were the pine forest and natural grassland plots.


In the present study, bacterial diversity in the settlement soil was higher than that of the other soils (Table 2). Three settlement soil samples, except for SEL-6, were distinguished by the presence of seven phyla. Particularly, β-Proteobacteria was only present in the settlement soil. In addition, δ-Proteobacteria was shown to be a more abundant bacteria compared to the other soils. Some possible explanations for these results are as follows. First, the abundance of bacteria may be due to the release of rubbish or overuse of chemicals in residential environments. Second, the settlement land has a greater human or animal activity than forest land; thus, residential soils are frequently exposed to contaminations and disturbances. Third, higher diversity in plant species may create an increased bacterial diversity. Moreover, previous studies found that there were more disturbances in residential soils (Mendes et al., 2011; Munir and Xagoraraki, 2011; Liu et al., 2012).


During the last decades, some of the forest land in Maoershan National Forest Park has been converted for wood production (Li, 2004). Generally, two soil management methods were used after a wood harvest. In one method, fire is used after a harvest; in the other method, fire is not used. In this study, there was little change in both the cutting-blank cutover land and the burnt land compared to forest soils. However, Bacteroidetes had not been observed in the soil of the slash land, indicating a lower impact of burnt management on microbial community composition. This finding was in contrast with the study by Rachid et al. (2013), who found that green management (no fire) had little influence on the bacterial community of soils. In contrast to burnt land, the phylum Bacteroidetes was found in the cutting-blank cutover land. This variation can be explained by the different abundance of litterfall in the two soils. Chave et al. (2010) showed that litterfall was the main pathway for nutrient and energy return to the soil. In our study, litterfall in the cutting-blank cutover land was higher than that of the burnt land (data not show). Moreover, removal of nutrients is common after using fire treatment (Yang, 1992). Therefore, the soil nutrient status and litterfall of cutting-blank cutover land may result to similar bacterial communities with that of forest soils.


The dendrogram constructed using the DEEG band profiles of soil bacterial communities from the ten plots revealed differences in the microbial community structure in relation to differing land-use (Figure 3). These results (two clusters) revealed that bacterial communities can change, mainly according to the type of vegetation. Forest system is composed of diverse plant species, while the conventional management of soils keeps them covered by a single annual or semi-perennial culture (Costa et al., 2012). Garbeva et al., (2004) affirmed that the type of vegetation cover is a factor determining the soil microbial community structure, as plants are the greatest providers of specific types of carbon and energy sources.


4 Conclusions

A molecular approach was used in this study, namely DGGE, to assess the bacterial community structure of ten different land-uses in the Northeast China, which might affect the productivity of soil ecosystem. The results showed that land-use change have significant impacts on soil microbial communities, which were likely to respond to these changes. Together these results suggested that the utility of sequence-based approaches to analyze bacterial communities provides detailed information on individual communities and permit the robust assessment of the biogeographical patterns exhibited by soil microbial communities.


Authors contributions

Mu Peng and Syed Sadaqat Shah wrote this paper, Qiuyu Wang analyzed the experiment data, Fanjuan Meng and designed this experiment.



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