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Article

Effects of Land Conversion on Soil Microbial Community Structure and Diversity in Songnen Plain, Northeast China

by
Tong Zhang
1,2,
Yufei Liu
1,2,
Xin Sui
1,2,
Beat Frey
3 and
Fuqiang Song
1,2,*
1
Heilongjiang Provincial Key Laboratory of Ecological Restoration and Resource Utilization for Cold Region, School of Life Sciences, Heilongjiang University, Harbin 150080, China
2
Engineering Research Center of Agricultural Microbiology Technology, Ministry of Education, Heilongjiang University, Harbin 150500, China
3
Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(17), 10767; https://0-doi-org.brum.beds.ac.uk/10.3390/su141710767
Submission received: 28 July 2022 / Revised: 18 August 2022 / Accepted: 24 August 2022 / Published: 29 August 2022
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Abstract

:
To feed the growing human population, natural grasslands are being converted to agricultural use at a massive scale. This conversion may have consequences for soil biodiversity, but its impact on the community assembly of differentially microbial groups remains largely unknown. Here, we selected the three typical land-use types: grassland, arable land (formerly grassland), and forest (formerly grassland) in the Songnen Plain, Northeastern China. Illumina MiSeq high-throughput sequencing technology based on bacterial 16S rRNA and fungal ITS rRNA was used to study the community structures and diversities of soil bacteria and fungi and to explore the drivers of these changes. The results showed that bacterial community diversity did not change after grassland conversion to forest and arable land, but affected bacterial community abundance at the phylum level. Actinomycetes and Proteobacteria were significantly reduced, Acidobacteria were significantly increased, and Gemmatimonadetes and Bacteroidetes were the most abundant in arable land. Land conversion had significant effects on both fungal community diversity and abundance. After the grassland was converted into forest, the fungal community diversity decreased, but the community abundance increased significantly, the Ascomycetes decreased significantly, and Basidiomycetes became the dominant phylum, especially white rot fungi. Interestingly, the fungal community diversity and community abundance increased significantly after grassland was converted to arable land, and the abundance of Zygomycota increased significantly but the dominant phylum was still Ascomycetes. Canonical correlation analysis (CCA) showed pH, MC, NO3−-N, TP, AP, and other soil factors are important factors affecting the composition of microbial communities. In the soil of this study area, the composition of bacterial communities is mainly driven by changes in pH and soil texture, and the composition of fungal communities is most closely related to changes in soil nutrient utilization. Changes in land-use patterns have an effect on the structure and diversity of microbial communities by changing the physical and chemical properties of the soil.

1. Introduction

Globally, land-use transformation dominated by human activities has a significant impact on the diversity and structure of soil microbial communities [1,2]. It can basically change soil functions and then affect soil fertility, thereby affecting the construction and function of soil microbial communities, and it can potentially affect soil microbial diversity and ecosystem functions [3,4]. The soil microbial community is the main driving force of ecosystem processes and has the functions of completing the decomposition of soil organic matter and plant litter and mediating the carbon (C) and nitrogen (N) biogeochemical cycles in terrestrial ecosystems [5,6]. In-depth understanding and quantification of land-use conversion are crucial in regulating the soil ecosystem and should be well managed to achieve sustainable development goals. Previous studies have shown that land-use conversion exerts massive and prolonged influences on soil biodiversity-ecosystem functioning [7,8].
For example, Jangid et al. [9] found that grassland conversion to arable land caused significant changes in bacterial and fungal abundance and diversity and determined that land-use change was the main determinant of microbial community composition. Wang et al. [10] found that after grassland was transformed into pine forest, the dominant soil bacterial phyla changed from Proteobacteria to Actinobacteria, the dominant fungal phyla changed from Ascomycota to Basidiomycota, and afforestation of grassland increased ECM (Ectomycorrhizal) fungi. Mendes et al. [11] found that the abundance of acidobacteria and chloroflexi in forest soil, actinobacteria in forest logging areas, and both nitrifying bacteria and thermophilus bacteria in arable land were all higher; therefore, the composition of bacterial and fungal communities would be changed significantly among different land-use types. There are many studies on the taxonomic and function of soil microorganisms in different land-use types; however, there is still little knowledge on how the microbial community is affected and the regulation when natural grassland converting into forest and arable land.
Heilongjiang Province, as China’s largest commercial grain production base, has fertile soil and a long history of farming. The soil’s physical and chemical properties and fertility are crucial to the sustainable development of agriculture [12]. Since the 1950s, with the increase in population, to solve the problems of food and clothing, the area has adopted land reclamation to obtain arable land. The original grassland was reclaimed into arable land, and the natural vegetation disappeared, resulting in seriously damaged soil and then it was hard to restore the original plant community in a short period [13]. At the beginning of the 21st century, the government realized the severity of the ecological and environmental problems and planted some grassland and abandoned land with forest to protect the fragile local ecological environment and promote sustainable and stable economic development [14,15]. In this paper, the western region of Heilongjiang Province was chosen as the study area. We aim to (a) determine how the natural grassland transformation into forest and arable land affects soil physico-chemical properties and microbial community structure and diversity; (b) and determine which soil physico-chemical properties are closely related to the changes in microbial community structure. The study could provide a deeper understanding of the changes in soil microorganisms and soil physico-chemical properties during long-term land-use change and provide a new perspective for natural grassland conservation and development.

2. Material and Methods

2.1. Overview of Research Plots

The study site was located in the Meiris District (123°35′ E, 47°31′ N) of Qiqihar, western Heilongjiang Province, with an average elevation of 146 m. It belongs to a moderate-temperate continental monsoon climate; the average temperature is 2.3 °C and an average rainfall of 454 mm. The freezing period is in early November, and the thawing period is in early May. We selected three land-use types: natural grassland, with an area of approximately 800 km2, and the vegetation are mainly Leymus chinensis and Stipa baicalensis; arable land, cropland is transformed from the grassland 20 years ago, with an area of approximately 600 km2. The main crop is corn, which is mechanically arable land, maintained once a year. Stanley corn compound fertilizer (N + P2O5 + K2O ≧ 40%) is applied on a per acre basis in spring at 35 kg, and topdressing is at 15 kg per acre in mid-June. Forest, with an area of approximately 500 km2, is a Populus artificial pure forest that was transformed from grassland 20 years ago.

2.2. Sample Collection

In August 2018, three typical land-use patterns in grassland, arable land, and forest land were selected. In each land-use pattern, six 10 m × 10 m plots were set up. Five-point mixed sampling was used to collect soil samples from a depth of 0–20 cm. After passing through a 2-mm sieve, 1 kg of the sample was placed in a ziplock bag, stored in liquid nitrogen, transferred to the laboratory, and stored in a −80 °C refrigerator for DNA extraction and microbial analysis. The remaining soil samples were used to determine the physical and chemical properties of the soil after air-drying.

2.3. Physical and Chemical Analyses

The physical and chemical properties of soil were determined according to Wang et al. [10]. Determination of soil moisture content (MC): Place the fresh soil samples into the aluminum box, and place it in the oven that has been preheated to 105 °C ± 2 °C. Bake in a constant-temperature drying oven for 8 h, take out the sample, and make the weighing calculation. Before the pH test, the fresh soils were homogenized and soil/water mixture was rested prior to measuring. Soil pH was measured at a soil/water ratio of 1:2.5. Soil organic matter (SOC): The soil organic carbon content was determined by the Vario TOC instrument produced by the German Elementar company. Total nitrogen (TN): Weigh 0.25 g of soil sample through a 0.149-mm sieve, add 2 g of zinc sulfate and copper sulfate mixed accelerator, and 5 mL of concentrated H2SO4 for digestion. After digestion and constant volume filtration, measure with a continuous flow analyzer. Nitrate nitrogen (NO3−-N) and ammonium nitrogen (NH4+-N): Weigh 6 g of air-dried soil, add 10 mL of 1 mol·L−1 potassium chloride solution, shake for 1 h, filter, and measure with a continuous flow analyzer. Weigh 20 g of fresh soil to determine the soil microbial biomass carbon (MBC) and nitrogen (MBN) by using chloroform fumigation [16]. Weigh 0.25 g of air-dried soil to determine total phosphorus (TP) by using the sulfuric acid–perchloric acid solution-molybdenum antimony colorimetric method. Weigh 5 g of air-dried soil to determine available phosphorus, available phosphorus (AP): 0.5 mol·L−1 sodium bicarbonate extraction-molybdenum antimony colorimetric determination was used. Total potassium (TK) (0.25 g air-dried soil) and available potassium (AK) (5 g air-dried soil) were determined by atomic absorption spectrometry.

2.4. Soil DNA Extraction and Illumina MiSeq Sequencing

Genomic DNA was isolated from 0.5 g of each pooled soil sample from each sample plot (n = 18) with the PowerSoil DNA Isolation Kit according to the manufacturer’s instructions. The extracts of three technical repeats were mixed into a single DNA sample. Extracted genomic DNA was detected by 1% agarose gel electrophoresis. PCR was carried out on a GeneAmp 9700 PCR system. The primers used for bacterial DNA were 338F and 806R, targeting the V3-V4 region. Meanwhile, the primers for amplifying fungal DNA were ITS1F and ITS2F, targeting the ITS region. PCR products were quantified using the QuantiFluorTM-ST fluorometer, and the samples were adjusted as needed for sequencing. Sequencing was conducted by Shanghai Majorbio Bio-pharm Technology (Shanghai, China) using an Illumina MiSeq platform for pair-ended 300 bp [17]. Six barcode tag sequences and preprimer sequences were used to screen out valid sequences from the data. The raw sequence data were deposited into the Sequence Read Archive and the accession numbers were SRR6431651-6431666, SRR6431681-6431696, and SRR6431703-6431718.

2.5. Processing of Sequencing Data

First, the raw sequence files were analyzed using QIIME (version 1.9.1, Knight and Caporasolabs, La Jolla, CA, USA). Terminal sequences were truncated using Trimmomatic. FLASH (version 1.2.10, Baltimore, MD, USA) was used to merge the pair-reads into one read-through overlap with an error matching rate ≤ 0.1. Reads that could not be assembled were discarded. The chimeric sequences were identified and removed using UCHIME (RC EDGAR, 2011, USA) software. The operational taxonomic units (OTUs) with a 97% similarity cut-off were clustered using the UPARSE (RC EDGAR, 2013, USA) software. The representative sequence of each OTU was taxonomically classified by the Ribosomal Database Project (RDP) classifier against the SILVA (SSU123) database for 16S rRNA and the UNITE database for ITS rRNA using a confidence threshold of 85%.

2.6. Statistical Analyses

First, all the reads were normalized as the minimum number of the reads. The Mothur software was used to calculate the community diversity index (Chao1, Simpson, Shannon index) [18]. PCoA and h-cluster analysis are based on the Bray–Curtis matrix and were implemented using R software (vegan package) (version 3.2.0; R Development Core and Team, 2013). The bioenv method was used to test the soil environmental factors, and the environmental factors with significant differences were selected for Canonical Correlation Analysis (CCA) using R software (vegan package). One-way analysis of variance (ANOVA) was used to analyze the differences in the diversity of both soil bacterial and fungal communities among the arable land, grassland, and forest sites. Tukey’s HSD (honestly significant difference) test was used for multiple comparisons of soil bacterial and fungal relative abundance when the homogeneity of variance test was successful, and significance was observed at p < 0.05. One-way ANOVA and Tukey’s HSD test were conducted using SPSS 16.0 (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Physical and Chemical Properties of Soil in Different Land-Use Patterns

The physical and chemical properties of the soil of the three land-use patterns are shown in Table 1. The pH of all soils was relatively alkaline, with a significant difference between grassland and arable land (p < 0.05), with the lowest pH value in arable land and the highest pH value in grassland. The soil moisture contents of grassland and arable land were significantly lower than that of forest (Table 1, p < 0.05). The contents of microbial biomass carbon, microbial biomass nitrogen, total phosphorus, available phosphorus, and nitrate nitrogen in arable land were significantly higher than those in forest and grassland (p < 0.05); however, there was no significant difference in soil organic matter, total nitrogen, ammonium nitrogen, total potassium, or available potassium.

3.2. Effects of Land-Use Patterns on Microbial Alpha Diversity

There was no significant difference between the Shannon index and Simpson index of soil bacteria in the three land-use types (Table 2); however, the soil bacterial Chao1 index patterns were significantly different in the three land-use types (p < 0.05). Among them, the Chao1 index showed arable land > forest > grassland, and there were significant differences between grassland, forest, and arable land (p < 0.05). Our result indicated that land-use types did not change the soil bacteria community diversity; however, there was a significant effect on soil bacterial abundance.
The soil fungal Shannon index, Simpson index, and Chao1 index were significantly different (Table 2). The Shannon diversity index was arable land > grassland > forest; the Simpson index was forest > grassland > arable land; the Chao1 index was arable land > forest > grassland. Our result showed that fungal diversity was highest in arable land compared to grassland and forest, and the community richness of fungi was the highest in the arable land.

3.3. Effects of Land-Use Patterns on Beta Diversity of Soil Bacteria and Fungi

The beta diversity of the bacterial community is shown in Figure 1A,B. Soil bacterial community structures in forest and grassland were relatively similar but completely different from arable land (Figure 1A,B). Long-term changes in land use will lead to significant changes in bacterial community structure. The beta diversity of fungal communities is shown in Figure 1C,D, with significant differences between fungal community structures in different land-use patterns (PERMANOVA: r = 0.54, p < 0.01). This result indicates that the differences within samples are not significant, and the differences mainly come from the different samples. Long-term changes in land use will lead to significant changes in fungal community structure.

3.4. Analysis of Soil Bacterial and Fungal Community Structure in Different Land-Use Patterns

From the perspective of the overall bacterial community structure, all OTUs belong to 55 bacterial phyla. According to the relative abundance of all phyla levels of the three land-use patterns, the relative abundance of Actinobacteria in the original grassland soil was the highest than that of the two land-use types (Figure 2A), the relative abundance of the Proteobacteria was the highest in arable land soil (Figure 2B), and the relative abundance of the Acidobacteria was the highest in forest (Figure 2C).
From the perspective of the overall composition of the fungal community structure, all OTUs belong to 10 fungal phyla. From the relative abundance of all levels of the three land-use patterns, the dominant phyla in the sample were Ascomycota, Basidiomycota, Mortierellomycota, and Glomeromycota. The relative abundance of Ascomycota in grassland soil was highest (Figure 3A). Similar, the relative abundance of Ascomycota was also highest in the arable land, but the relative abundance decreased from the grassland to arable land (Figure 3B); however, the relative abundance of Basidiomycota was the highest in forest (Figure 3C).
A total of seven phyla had significant differences in the three land-use patterns (Figure 4A). These included Acidobacteria (p < 0.05), Proteobacteria (p < 0.05), Actinobacteria (p < 0.001), Gemmatimonadetes (p < 0.05), Bacteroidetes (p < 0.001), Planctomycetes (p < 0.001), and Latescibacteria (p < 0.01). The phyla abundance of Acidobacteria and Latescibacteria were higher in forest than in the other land-use patterns, while Proteobacteria, Bacteroidetes, and Gemmatimonadetes were higher than in the other land-use patterns. Actinobacteria and Planctomycetes were higher than other land-use patterns. There were four phyla significant differences between forest and grassland (Figure 4B). There were nine phyla significant differences between the forest and arable land (Figure 4C). There were seven bacterial phyla with significant differences between grassland and arable land (Figure 4D).
Based on the difference in the level abundance of the fungal community phyla in the three land-use patterns, there were four phyla with significant differences in the three land-use patterns: Ascomycota, Basidiomycota, Mortierellomycota, and Rozellomycota (Figure 5A). There were three phyla of significant differences between arable land and grassland (Figure 5B), which were Basidiomycota, Mortierellomycota, and Chytridiomycota; Basidiomycota, Mortierellomycota, and Chytridiomycota in the arable land were higher than in grassland. There were six phyla significant differences between arable land and forest, which were Basidiomycota, Ascomycota, Mortierellomycota, Glomeromycota, Chytridiomycota, and Rozellomycota; Basidiomycota in forest was higher than arable land, Ascomycota, Mortierellomycota, Glomeromycota, Chytridiomycota, and Rozellomycota were higher in the arable land than forest (Figure 5C). There were four phyla significant differences between grassland and forest, which were Basidiomycota, Ascomycota, Rozellomycota, and Kickxellomycota; the Basidiomycota in the forest was higher than in grassland, and the Ascomycota in the grassland was higher than in forest (Figure 5D).

3.5. Redundancy Analysis of Soil Bacterial and Fungal Communities and Physicochemical Properties in Different Land-Use Patterns

The results of CCA for bacteria are shown in Figure 6. The first axis of bacteria explained 29.6% and the second axis explained 21.1% of all information. pH, AP, MC, and NO3−-N are the main factors affecting the composition of bacterial communities. pH explained 39.69% of all information, while TP explained 87.18% of all information. AP and MC explained 72.95% and 87.74%, while NO3−-N explained 78.27% of all information (Figure 6A).
The results of CCA for fungi are shown in Figure 6B. The first sequence axis of fungi explained 15.9% of all information, and the second sequence axis of fungi explained 14.4% of all information. pH explained 39.69% of all information, while MC explained 87.74% of all information. NO3−-N and AP explained 87.18% and 72.95%, respectively.

4. Discussion

4.1. Impact of Land Use on Soil Bacterial and Fungal Community Diversity

The results of this study show that land-use patterns change the soil bacterial Chao1 indexes. After the primitive grassland was changed into forest land and arable land, the abundance of soil bacterial communities increased significantly; however, the diversity of soil bacterial communities did not change. These results agree with previous studies; after the natural grassland was reclaimed into arable land, the bacterial community diversity did not change, but the relative bacterial abundance changed; therefore, the change in soil bacterial community is not reflected in the difference in species composition, but mainly in the quantitative ratio of the dominant population [19]. That is to say, the effects of land-use changes on the structure of soil bacterial communities were more quantitative than qualitative.
Compared with the bacteria, the Shannon, Simpson, and Chao1 indexes of the three land-use soil fungi were significantly different. After the grassland was converted to forest and crop land, the abundance of the soil fungal community increased significantly (Table 2). This result may be due to the increase in soil fungal diversity after conversion to forest due to abundant litter. This result is consistent with previous research, where afforestation often stimulates the growth of soil fungal communities [20], while soil bacteria appear to be less sensitive to land use [21,22]. According to research reports, bacterial community structure, diversity, and biomass are more stable than fungi when the soil environment is disturbed [23]. This difference may be because bacteria can produce a wider range of metabolites to adapt to the new environment. In contrast, mycorrhizal fungi depend to a large extent on the presence of their hosts [24], so the structure and diversity of fungal communities have more dramatic changes based on land use.

4.2. Effects of Land-Use Patterns on Soil Bacterial Community Composition

At the phyla level, the dominant phyla in the three types of soil are Proteobacteria, Acidobacteria, and Actinomyces, which can account for more than 80% of the total bacterial community in each soil sample. The community structure results are consistent with Zeng et al. [25] and Zhang et al. [26]. Moreover, this study also found that when the grassland was changed to forest and arable land, the abundance of its dominant phyla (Proteobacteria, Acidobacteria, and Actinomyces) changed significantly. This study found that the relative abundance of grassland soil Actinomycetes was the highest. After conversion to forest and cropland, the soil Actinomycete content decreased significantly. Several studies have shown that Actinomycetes are the most widely distributed in herbaceous vegetation soils, and their relative abundance is significantly higher than that of forests and cropland; actinomycetes are the dominant mycophytes in grassland soils [27,28]. Actinomyces can degrade cellulose and chitin, which is the main source of the soil nutrient supply. It can decompose more difficult-to-decompose organic carbon by infiltrating its hyphae into large plant tissues, and the spores produced can resist unfavorable external environmental conditions and are considered to be dominant in harsh and stressful soil conditions [29]. The relative abundance of Proteobacteria was the lowest in grassland soils. Liu et al. found that the relative abundance of Proteobacteria may be controlled by the difference in soil nutrients [30]. Soil total phosphorus is the main factor affecting the distribution of Proteobacteria, with an interpretation rate as high as 85.3%. Other studies have found that Proteobacteria are relatively abundant in nutrient-rich soils but also relatively abundant in soil that is nutritionally poor [17]. The relative abundance of Planctomycetes was highest in grasslands. Fei et al. found that there was a significant positive correlation between Planctomycetes and soil total nitrogen content [31]. The total nitrogen content of grassland was the highest among the three land patterns in this study; therefore, the relative abundance of Planctomycetes was the highest in grassland soil.
The results showed that the relative abundance of Acidobacteria was the highest after grassland was transformed into forest, and Acidobacteria became the dominant bacteria in the soil. Acidobacteria can grow in the medium based on plant polymer, which indicates that Acidobacteria plays an important role in the degradation of plant residues and forest litter [32]. Pankratov et al., found that although the Acidobacteria degradation function is not as good as other known cellulose-degrading bacteria, it has strong resistance to stress and can survive in cold northern soils, which plays an important role in cellulose degradation under these conditions [33]. Based on this study, there is less litter content in the grassland and arable land patterns, and the forest litter content is significantly higher than that in forest land and grassland; therefore, the content of insoluble matter in litter is also high. As a result, they are more susceptible to litter composition, making Acidobacteria more abundant in forests. It can be concluded that the abundance of Acidobacteria is mainly related to the composition and content of litter.
After the grassland was transformed into arable land, the soil Actinomycete, Proteobacteria, and content decreased, but Gemmatimonadetes and Bacteroidetes increased significantly. This indicated that the application of chemical fertilizers throughout the year would result in a change in dominant bacterial phyla composition. Clegg et al. found that the addition of inorganic nitrogen reduced the abundance of Actinomycetes compared with the non-fertilized grassland soil [34]. In this study, due to the application of chemical fertilizers throughout the year, the soil NO3−-N content increased, the soil structure changed, and the relative abundance of soil Actinomycetes decreased. Cui et al. and Liu et al. found that the relative abundance of Proteobacteria was more abundant in the soil [35,36].
Gemmatimonadetes is an alkalophilic microorganism and can produce spores, which can resist dehydration and adapt to drought and extreme environmental conditions. Some Gemmatimonadetes species have strong nitrogen-fixing effects and play an important role in the biological control of the production and release of plant hormones and soil-derived plant pathogens (such as fungi) [37,38]. Due to the effect of external nitrogen application, the available nitrogen content in the soil is high. Because Gemmatimonadetes has a strong nitrogen-fixing capacity, its content is highest in arable lands. Bacteroidetes are mainly anaerobic or facultative anaerobic bacteria and can be found in a variety of habitats, including soil, sediment, and seawater. Li et al. studied the black soil in arable land in Northeast China and found that Bacteroidetes was the dominant bacteria in the soil [39]. These phyla were found to be the most common flora in arable land soil. Turner et al. and Donn et al. also found that the abundance of Bacteroidetes was higher in the field soils of wheat and pea [40,41]. Bergkemper et al. found that the relative abundance of Bacteroidetes was positively related to available phosphorus, and available phosphorus may be one of the important factors affecting the bacterial community [42]. In this study, due to the application of chemical fertilizers to the arable land, the available phosphorus content was the highest, and the relative abundance of Bacteroidetes increased in the arable land soil.

4.3. Effects of Land-Use Patterns on the Composition of Soil Fungal Communities

Among the three land-use patterns, the soil fungal groups were mainly Ascomycota, Basidiomycota, and Zygomycota. Ascomycota was the dominant phyla in grassland soil. Cao et al. also found that the grassland soil phylum Ascomycota accounted for the highest abundance, which was mainly due to the faster evolution rate of Ascomycota, drought resistance and radiation resistance, suitability for bare sand with a low vegetation canopy and harsh living environments such as land and grassland [43,44]. Zhang et al., found that Ascomycota was the dominant bacteria in the most primitive grassland, and its dominant orders are mainly Hypocreales and Sordariomycetes [45]. Its abundance in forest decreases significantly with aging. Most Sordariomycetes are saprophytic, usually found on feces or rotten plants. In our study area, animal dung was found in grazing grasslands, and animal and human dung in arable land lands are common fertilizers; therefore, Ascomycetes become the dominant fungi in grasslands and arable lands.
After the grassland was transformed into forest, Basidiomycota increased significantly in the forest and became the dominant phyla in the soil. This result is consistent with previous studies. After 29 years of pine planting in the wasteland, the relative abundance of Basidiomycota increased from 10.9% to 68.7% [29]. During the fungal succession of the Damma glacier forefield in central Switzerland, it was found that the community dominated by Ascomycota became a community dominated by Basidiomycota [46]. The Cortinarius and Suillus in Basidiomycota are common mycorrhizal fungi in forest soils, which can be symbiotic with Pinus sylvestris var. Mongolica and thus account for a large proportion of soil fungi [47]. Other Basidiomycota flora, especially white rot fungi, can breakdown litter with high lignin and aromatic substrates; however, only a small group of fungal groups have the ability to secrete enzymes that catalyze the degradation of complex macromolecules such as lignin, and they are largely confined to the Agaricus species in Basidiomycota [48,49]. In this study, litterfall increased significantly after grassland afforestation, requiring more decomposing bacteria, which is also the reason for the increase in soil Basidiomycota.
After the grassland was transformed into arable land, the dominant fungal phyla were still Ascomycetes, but the abundance of Zygomycota significantly increased. Part of Zygomycota is a saprophytic fungus that mainly decomposes plant litter and changes soil chemical properties. Qian et al., found that the relative abundance of soil Zygomycota increased after grass grew in apple orchards, indicating that grass would affect the relative abundance of soil Zygophyta [50]. This study found that the relative abundance of Zygomycota had a significant positive correlation with the soil nitrate–nitrogen content and with the increase in the soil nitrate–nitrogen content. The highest nitrate–nitrogen content in arable land in this study may cause an increase in Zygomycota. Although the species composition of soil fungi communities is similar between different land-use patterns, the relative abundance of soil fungal phyla and genera may be different because of different plant patterns and differences in the form and content of nutrients provided to the soil [51,52].

4.4. Effects of Soil Physical and Chemical Properties on Soil Microbial Community Composition

Land-use and management patterns will change the type of vegetation on the ground and then affect the physical and chemical properties of the soil [53,54]. Changes in soil physical and chemical properties will affect the structure and composition of soil microbial communities. Consistent with most other studies, pH is an important factor affecting soil microbial community structure. Barka et al. found that there was a significant positive correlation between Actinomycetes and soil pH [55]. Actinomycetes grew healthily in soils with a neutral pH and grew fastest between pH 6 and 9. Rousk et al., found that both bacterial and fungal communities were affected by soil pH, but bacterial communities were more affected by pH than were fungal communities, which may be due to the relatively narrow optimal pH range for bacterial growth, while the pH range for fungal growth is very wide [56]. Although soil pH has a direct impact on microbial community structure, soil pH can also indirectly change microbial communities through other variables, such as nutrient utilization and organic carbon content.
As an indispensable source of energy and nutrients for microorganisms, SOC plays an important role in shaping the microbial community and significantly changes the proportion of bacteria and fungi in the soil [57]; however, the SOC content in this study may not have caused changes in soil microbial communities. In this study, NO3−-N was the most important factor affecting the soil bacterial and fungal communities (Figure 6). Nitrogen restrictions are common in most terrestrial ecosystems and often lead to fierce competition between microorganisms and plants [58]. With the increase in nitrogen availability, the taxonomic and functional characteristics of soil microbial communities change, including the decrease in the relative abundance of mycorrhizal fungi and the slow growth of bacterial groups. Due to the low soil nitrogen content and low litter mass in forest, fungi appear to be the main decomposers of complex litter and soil organic matter and have largely affected related bacterial communities and their activities [59]. Soil moisture is also an important limiting factor that strongly affects soil microbial communities [60]. In this study, MC plays a key role in soil fungal diversity. Not only can it protect soil organic matter from decomposition and leaching by combining with aggregates, it can also provide a larger surface area for the growth of soil microorganisms [61].
In this study, the TP and AP contents of arable land and forest were significantly higher than those of grassland. The reason may be that the interception of rainwater by the forest canopy makes the surface runoff smaller, the soil surface organic matter and mineral nutrients are retained, and the loss is less. The artificial fertilization in the arable land compensates for the nutrients in the soil. Other studies have shown that under eutrophic conditions, the limiting effect of phosphorus on the original microbial community has been greatly reduced, and the metabolic activity of microorganisms has changed, which may change the species composition of microorganisms [62]; however, the grassland is not supplemented with external nutrients, and the growth of vegetation has absorbed phosphorus in the soil, which ultimately results in lower total phosphorus and available phosphorus in the soil. He et al. found that P is the most critical contributor to differences in fungal communities, and phosphorus in forest is usually less than that in managed ecosystems due to fertilization [63]; therefore, although the exact mechanism is not yet clear, P may be an important driving force for the construction of soil fungal communities across land-use types.

5. Conclusions

This study reveals the impacts of land-use type on various aspects of the soil microbial Community. Bacterial community diversity did not change after grassland conversion to forest and arable land, but affected bacterial community abundance at the phylum level. Actinomycetes and Proteobacteria were significantly reduced, Acidobacteria were significantly increased, and Gemmatimonadetes and Bacteroidetes were the most abundant in arable land. Land conversion had significant effects on both fungal community diversity and abundance. After the grassland was converted into forest, the fungal community diversity decreased, but the community abundance increased significantly, the Ascomycetes decreased significantly, and Basidiomycetes became the dominant phylum, especially white rot fungi. Interestingly, the fungal community diversity and community abundance increased significantly after grassland was converted to arable land, and the abundance of Zygomycota increased significantly, but the dominant phylum was still Ascomycetes. In arable land, an increase in the abundance of fungal communities can easily lead to a decrease in food production, but fungi can control crop diseases or prevent other fungi, which has two sides.
Changes in land use can affect soil quality and nutrients. pH, MC, NO3−-N, TP, AP, and other soil factors are important factors affecting the composition of microbial communities. In the soil of this study area, the composition of bacterial communities is mainly driven by changes in pH and soil texture, and the composition of fungal communities is most closely related to changes in soil nutrient utilization. Changes in land-use patterns have an effect on the structure and diversity of microbial communities by changing the physical and chemical properties of the soil. This study provides reliable data to guide land-use management and supervision by decision makers in this region.
It is hoped that multiple factors such as grassland vegetation amount, grazing, and forest litter content will be taken into account in future research in order to achieve more completeness and persuasiveness of the data.

Author Contributions

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

Funding

This study received financial support from the the Natural Science Foundation of Heilongjiang Province (TD2019C002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Sequences generated in this study were submitted to the NCBI Sequence Read Archive (SRA) database under BioProject accession number PRJNA660096 (Bacterial SRA accession numbers from SRR12545003 to SRR12545020, Fungal SRA accession numbers from SRR12549501 to SRR12549518).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Soil bacterial community clustering tree (A), PCoA diagram (B), fungal community clustering tree (C), and PCoA diagram (D) in different land-use patterns. Note: the length of the branches in the community clustering tree represents the distance between the samples. Different groups can be presented in different colors. The X-axis and Y-axis in the PCoA diagram represent the two selected main coordinate axes, and the percentage represents the interpretation value of the main coordinate axis to the difference in sample composition; Color or shape points represent samples of different groups, the closer the two sample points are, the more similar the species composition of the two samples.
Figure 1. Soil bacterial community clustering tree (A), PCoA diagram (B), fungal community clustering tree (C), and PCoA diagram (D) in different land-use patterns. Note: the length of the branches in the community clustering tree represents the distance between the samples. Different groups can be presented in different colors. The X-axis and Y-axis in the PCoA diagram represent the two selected main coordinate axes, and the percentage represents the interpretation value of the main coordinate axis to the difference in sample composition; Color or shape points represent samples of different groups, the closer the two sample points are, the more similar the species composition of the two samples.
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Figure 2. Community structure composition at the level of bacterial phyla at different land uses patterns. Note: (A) Grassland; (B) arable land; (C) forest. The phylum abundance is less than 1% and merged into others.
Figure 2. Community structure composition at the level of bacterial phyla at different land uses patterns. Note: (A) Grassland; (B) arable land; (C) forest. The phylum abundance is less than 1% and merged into others.
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Figure 3. Community structure composition at the level of fungal phyla at different land uses patterns. Note: (A) Grassland; (B) arable land; (C) forest. The phylum abundance is less than 1% and merged into others.
Figure 3. Community structure composition at the level of fungal phyla at different land uses patterns. Note: (A) Grassland; (B) arable land; (C) forest. The phylum abundance is less than 1% and merged into others.
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Figure 4. Differences in the phylum level classification abundance of bacterial communities in different land-use patterns. Note: (A) Three different land use patterns; (B–D) Two different land use patterns. The Y-axis represents the name of a species at a certain taxonomic level, the X-axis represents the average relative abundance of different groups of species, and the columns of different colors represent different groups; the far right is the p-value, * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001.
Figure 4. Differences in the phylum level classification abundance of bacterial communities in different land-use patterns. Note: (A) Three different land use patterns; (B–D) Two different land use patterns. The Y-axis represents the name of a species at a certain taxonomic level, the X-axis represents the average relative abundance of different groups of species, and the columns of different colors represent different groups; the far right is the p-value, * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001.
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Figure 5. Differences in the phylum level classification abundance of fungal communities in different land-use patterns. Note: (A) Three different land use patterns; (B–D) Two different land use patterns. The Y-axis represents the name of a species at a certain taxonomic level, the X-axis represents the aver-age relative abundance of different groups of species, and the columns of different colors repre-sent different groups; the far right is the p-value, * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001.
Figure 5. Differences in the phylum level classification abundance of fungal communities in different land-use patterns. Note: (A) Three different land use patterns; (B–D) Two different land use patterns. The Y-axis represents the name of a species at a certain taxonomic level, the X-axis represents the aver-age relative abundance of different groups of species, and the columns of different colors repre-sent different groups; the far right is the p-value, * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001.
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Figure 6. CCA analysis of soil bacterial (A) and fungal (B) community structure and soil physical and chemical properties. Note: Arrows indicate the direction and magnitude of the environmental parameters associated with bacterial and fungal community structures, respectively. MC: moisture content; MBC: micro biomass carbon; MBN: microbiomass nitrogen: SOC: soil organic carbon: TN: total nitrogen; TP: total phosphorus; AP: available phosphorus; TK: total potassium; AK: available potassium.
Figure 6. CCA analysis of soil bacterial (A) and fungal (B) community structure and soil physical and chemical properties. Note: Arrows indicate the direction and magnitude of the environmental parameters associated with bacterial and fungal community structures, respectively. MC: moisture content; MBC: micro biomass carbon; MBN: microbiomass nitrogen: SOC: soil organic carbon: TN: total nitrogen; TP: total phosphorus; AP: available phosphorus; TK: total potassium; AK: available potassium.
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Table
Table 1. Physical and chemical properties of soil in different land-use patterns.
Table 1. Physical and chemical properties of soil in different land-use patterns.
Land-Use PatternspH ValueMC (%)MBC
/(mg/kg)		MBN
/(mg/kg)		SOC
/(g/kg)		TN
/(g/kg)
Forest9.09 ± 0.07 ab14.19 ± 0.52 a140.89 ± 36.51 b12.48 ± 3.43 b2.01 ± 0.51 a5.06 ± 2.03 a
Grassland9.23 ± 0.26 a13.66 ± 0.27 b154.65 ± 52.63 b9.33 ± 5.03 b2.17 ± 0.51 a5.29 ± 2.77 a
Arable land8.96 ± 0.11 b13.48 ± 0.82 b227.49 ± 69.93 a19.59 ± 6.33 a2.06 ± 0.08 a4.34 ± 1.11 a
Land-Use PatternsNH4+-N
/(mg/kg)		NO3−-N
/(mg/kg)		TP
/(g/kg)		TK
/(mg/kg)		AP
/(mg/kg)		AK
/(mg/kg)
Forest2.76 ± 0.42 a5.44 ± 2.41 b0.51 ± 0.05 b4.02 ± 1.31 a5.71 ± 1.74 b22.14 ± 6.73 a
Grassland2.37 ± 0.23 a2.93 ± 0.79 c0.43 ± 0.07 c3.24 ± 1.71 a4.51 ± 1.79 b20.09 ± 2.51 a
Arable land2.56 ± 0.35 a11.42 ± 2.33 a0.74 ± 0.07 a3.92 ± 1.61 a15.83 ± 5.82 a25.78 ± 4.35 a
Note: Mean values (means ± SD, n = 6) followed (a, b, c, ab) indicate significant difference between land-use types at the p < 0.05 level. MC: moisture content; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen: SOC: soil organic carbon: TN: total nitrogen; NH4+-N: Ammonium nitrogen; NO3−-N: Nitrate nitrogen; TP: total phosphorus; AP: available phosphorus; TK: total potassium; AK: available potassium.
Table
Table 2. Diversity index of soil bacterial and fungal communities in different land-use patterns.
Table 2. Diversity index of soil bacterial and fungal communities in different land-use patterns.
Land-Use PatternsLand-Use TypesShannonSimpsonChao1
BacterialForest6.34 ± 0.19 a0.0045 ± 0.0012 a2284.91 ± 139.51 ab
Grassland6.08 ± 0.30 a0.0077 ± 0.0041 a1990.98 ± 391.46 b
Arable land6.32 ± 0.49 a0.0126 ± 0.0199 a2408.77 ± 140.43 a
FungalForest2.73 ± 0.48 b0.1506 ± 0.0639 a358.88 ± 11.91 b
Grassland3.09 ± 0.46 b0.1205 ± 0.0808 a111.91 ± 24.81 c
Arable land4.04 ± 0.34 a0.0597 ± 0.0353 b468.18 ± 23.50 a
Note: Mean values (means ± SD, n = 6) followed (a, b, c, ab) indicate significant difference between land-use types at the p < 0.05 level.
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Zhang, T.; Liu, Y.; Sui, X.; Frey, B.; Song, F. Effects of Land Conversion on Soil Microbial Community Structure and Diversity in Songnen Plain, Northeast China. Sustainability 2022, 14, 10767. https://0-doi-org.brum.beds.ac.uk/10.3390/su141710767

AMA Style

Zhang T, Liu Y, Sui X, Frey B, Song F. Effects of Land Conversion on Soil Microbial Community Structure and Diversity in Songnen Plain, Northeast China. Sustainability. 2022; 14(17):10767. https://0-doi-org.brum.beds.ac.uk/10.3390/su141710767

Chicago/Turabian Style

Zhang, Tong, Yufei Liu, Xin Sui, Beat Frey, and Fuqiang Song. 2022. "Effects of Land Conversion on Soil Microbial Community Structure and Diversity in Songnen Plain, Northeast China" Sustainability 14, no. 17: 10767. https://0-doi-org.brum.beds.ac.uk/10.3390/su141710767

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