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Article

Microbial Community and Their Potential Functions after Natural Vegetation Restoration in Gullies of Farmland in Mollisols of Northeast China

by
Ziliang Xiao
,
Shaoliang Zhang
*,
Pengke Yan
,
Jiping Huo
and
Muhammad Aurangzeib
School of Resources and Environment, Northeast Agricultural University, 600 Changjiang Rd, Harbin 150030, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(12), 2231; https://doi.org/10.3390/land11122231
Submission received: 30 October 2022 / Revised: 5 December 2022 / Accepted: 6 December 2022 / Published: 7 December 2022
(This article belongs to the Special Issue New Insights in Mollisol Quality and Management)
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Abstract

:
Although huge numbers of gullies have been widely formed and have severely decreased the quality of farmlands in mollisols, it is still unclear how the microbial community distributes after natural vegetation restoration (NVR), which highly relates to the ecological functions in the farmland. In this study, both the microbial community and their potential ecological functions after NVR were reviewed, together with the environmental factors relating to microbial evolution which were detected in two gullies of mollisols situated on farmland in Northeast China. The main results showed that NVR improved the microbial diversity and complexity of the co-occurrence network in gullies, and promoted bacterial community composition to be similar between the gully and deposition area. Moreover, the soil organic matter (SOM) regulated the microbial diversity by balancing soil available phosphorus (AP), soil moisture (SM), and pH, thus stimulating the key bacterial biomarkers of gullies (Rhizobiales, Microtrichales, TRA3-20) and regulating the bacterial composition, as well as indirectly enriching the function of bacteria to perform denitrification, C fixation, and phosphorus transport in gullies. In addition, abundant Dicotyledons in gullies mainly regulate the fungal community composition, and increased fungal richness in 0–20 cm soil depth, but decreased bacteria richness in 0–20 cm soil depth. Our findings revealed the repair mechanism of NVR on soil bacterial and fungal communities, especially on bacterial functionality, which should be given further attention in nutrient cycling across eroding mollisols in gullies.

1. Introduction

Erosion gullies widely distribute and take many areas of farmland, which severely decreases the soil fertility and connectedness of the fields; opposite to the above, natural vegetation restoration (NVR) can effectively fight against that development and even improve and sustain the stability of gullies [1]. A recent study has shown that erosion decreased microbial multifunctionality and microbial diversity in soils (Hao et al., 2022). However, NVR always synchronously changes the diversity, composition, and functionality of microbial communities, and this process typically associates with vegetation types and composition [2,3]. Microbial communities, as an important part of the ecosystem, can participate in various biogeochemical cycles and highly relates to soil functions, such as nutrient cycling and carbon (C) sequestration, and closely interact with plant communities [4]. NVR as a crucial practice to control the development of gully erosion has been widely adopted and could deeply change the eco-environment in farmland [5,6]. Previous studies provide important insights into NVR influencing the heterogeneity of soil microbial communities, and their functions in polluted soils or forest ecosystems [7,8,9]. However, it is still poorly understood how NVR influences the soil microbial community, and their functions in the eroded soils, especially in the soils of gullies, which has been highly eroded and finally recovered by various vegetation.
Long-term erosion increases the heterogeneity of soil nutrients in gullies, influences plant competition, and further changes the structure and spatial pattern of NVR [10]. As such, NVR improved the soil microbial phosphorus (P) transport, nitrogen (N), or C storage, which was closely associated with vegetation types and restoration time [3]. Additionally, the root exudates are determined by plant species and abundance, which change the soil properties, and further influence the microbial composition, especially for the abundance of parasitic and symbiotic microorganisms [8,11]. Moreover, the decomposition of plant residues can also alter soil nutrient composition, and thus affect microbial composition [8]. Previous study indicated that long-term erosion decreased the microbial multifunctionality and microbial diversity in the soil of farmland [12]. However, the gully has a lower altitude and a low content of nutrients compared with farmland, as well as the NVR, and their residues determined by the special hydrologic process, landscape, and microclimate are also different from farmland [13]. Thus, microbial communities and functions influenced by the NVR and the key driving factor in gullies could also be different from that in other environments, and needs to be verified.
Because soil microbial communities have diverse functions, previous soil studies usually assess microbial communities by their multifunctionality or enzyme activities [14,15,16]. However, these indexes cannot reveal the specific role of microbial communities in important ecological processes such as nutrient cycling. The FARPROTAX database was established to classify functional bacteria and the statistical abundance of functional groups in the marine ecosystem [17], and a recent study just showed it could also be used in the terrestrial ecosystem [4], while its applications in eroded soil were rare. In addition, functional differences between microbial communities are always determined by key members or groups, which were usually dominant in their microbial communities [18,19]. Therefore, many previous studies focus on the most abundant phyla or OTUs in the microbial communities to streamline the analysis [20]. However, such studies cut off the systematic linkage of microorganisms at different taxonomic levels and ignored the covariation trend of related species which with similar functional genes. Therefore, it is more reasonable to select the key microbes through the difference in microbial community composition at multiple taxonomic levels, such as the biomarkers selected by LEfSe analysis [21]. Furthermore, the study demonstrated that the topological properties of co-occurrence network were correlated with microbial community function [22], while the study on the co-occurrence network of microbial community in soil after erosion and NVR was rarely carried out. Thus, it is necessary to applicate more novel and effective analyses in the research of microbial communities in gullies after NVR, to clearly reveal the association between the dynamic of microbial community and microbial functions.
Mollisols are one of the most important soil resources for crop production in the world [23]. It is approximately 27 × 104 km2, across three provinces of Heilongjiang, Jilin, and Liaoning, in Northeast China, and plays a crucial role in food security [24]. However, long-term extensive and intensive farming and the significant inputs of fertilizers and tillage increased soil erosion and land degradation in the Mollisol region [10]. Additionally, above 29 × 104 erosion gullies were developed in this region, and most of them were formed in central areas of farmland, which strongly impacts national food security [23]. There is no artificial intervention in most of these gullies, and NVR was the single restoration treatment [25]. In this study, vegetation cover, soil nutrients, soil moisture, and microorganisms were investigated in the farmland and gullies of two sites to verify the following hypothesizes: (1) NVR changes the microbial diversity and network complexity in the gully, and is different from that in farmland in the Mollisol of Northeast China, a process which is related to soil properties; (2) NVR changes microbial functionality in the gully, is different from that in farmland, and the result differs from another environment; and (3) using the LEfSe analysis found the biomarkers and linking it with the microbial functions classified by the FAPROTAX database, could clearly reveal how the dynamic of the microbial community drives the metabolic functions and nutrient cycling.

2. Materials and Methods

2.1. Study Sites

In order to examine the change of microbiological properties after NVR in the gullies and their related environmental factors, one stable gully in the Guangrong site (47.355484° N, 126.831300° E) and another stable gully in the Yanmagou site (45.86310307° N, 126.93867445° E) were randomly selected and investigated in Mollisols of Northeast China. These gullies mainly distribute in the hydrologic lines at the junction of the slope bottom and the roadsides where the runoff is converged. We used the meteorological data (2005–2022) from the weather station at Harbin airport station and the weather station of Hailun city to calculate the following climatic characteristics. The data was downloaded from the website (https://rp5.ru/Weather_in_the_world, accessed on 2 October 2022). In the Guangrong site, the mean annual temperature, extreme maximum temperature, and extreme minimum temperature are 2.6 °C, 36.9 °C, and −39.4 °C, respectively. The annual precipitation is 500 to 600 mm, and nearly 90% of the precipitation is concentrated from May to September. Annual sunshine is 2600–2800 h, and the frost-free period is about 120 days. In the Yanmagou site, the mean annual temperature, extreme maximum temperature, and extreme minimum temperature are 5.4 °C, 37.4 °C, and −33.4 °C. The annual precipitation is 520 to 580 mm, and nearly 60% of the precipitation is concentrated in the summer. Annual sunshine is 2460–2786 h, the frost-free period is about 136 days. In two gullies, soil formation began in the Quaternary period by loess deposits under natural grasses, and now soils typically with high organic matter content (SOM) and are classified as Mollisols.
In the Guangrong site, most areas of farmland were covered by crops (soybean and maize), follows by the secondary forest and grassland. The forestland has composited of the species of Populus and Larix gmelinii, and the grassland-like deposition area was dominated by gramineous. The runoff transport soil from farmland and gullies to the deposition area beside the Hailun river. In the Yanmagou site, farmland is the only land use type, and the main crop is maize. In addition, a road occupies the location for soil deposition, and the runoff with soil is diverted by a semi-artificial drain beside the road. The gully in Yanmagou was bigger and wider than the gully in Guangrong, and the vegetation in the two gullies was dominated by Elymus dahuricus and Artemisia argyi (Table S1). In the Yanmagou gully, all locations of the gully were covered by long-term vegetation. However, the middle and tail of the Guangrong gully were covered by complex vegetation, while the head of the developing gully was covered by an amount of Elymus dahuricus.

2.2. Investigation of Plant Species and Diversity, and Soil Properties

Soil samples and vegetation information were investigated in the gullies, farmland, and deposition area in the Guangrong site and Yanmagou site during the harvest season of 2019 and 2021, respectively. Species and diversity of herbaceous plants were investigated by a plot (1 m × 1 m) in the positions of the gully side slope (sunny slope and shady slope) and gully bottom from the head to the middle to the tail of each gully, and each position with at least three replicates in the range of 6 m × 6 m. There were 90 groups of plant data collected. Plant abundance was the number of each plant species. Richness (Chao1) was calculated by Chao1 = Sobs + n1(n1 − 1)/2(n2 + 1), which was used to reduce the difference between Sobs and true richness [26]. Sobs is the observed species number; n1 is the number of singletons; n2 is the number of doubletons. Diversity (Shannon) was calculated by H = −Σ(Pi)(lnPi). Pi = ni/n means the abundance proportion of i species to all species. Five dicotyledonous species (Artemisia argyi, Agrimonia pilosa, Setaria pumila, Sonchus wightianus, Glycine soja) and one species of monocotyledon (Elymus dahuricus) were observed in both two gullies.
In the Guangrong site, 27 soil samples were collected from 0–20 cm, 20–40 cm and 40–60 cm soil depths in nine soil plots (6 m × 6 m). Three soil plots were located at the gully head, gully middle and gully tail respectively, and corresponding to the plant plots. Three soil plots were in the deposition area, and another three soil plots were in the farmland beside the gully. In the Yanmagou site, 12 soil samples were collected from the 0–20 cm soil depth in twelve soil plots (6 m × 6 m). Three soil plots are located at each of the gully head, gully middle and gully tail, corresponding to the plant plots. Because the deposition area has been cultivated as farmland, this area was not considered independently. Three soil plots located at the random location of the farmland beside the gully. Each soil sample was a mixture of five cores taken randomly from corresponding soil plots. All fresh soil samples were transported to the laboratory as soon as possible, and 10 g of each sample were stored in an ultra-low temperature freezer at −80 °C and then delivered for DNA extraction and PCR amplification. Moreover, 20–30 g of each sample was used to measure soil moisture (SM). The remaining samples were air dried to constant weight and sieved at 2 mm for analyzing soil organic matter (SOM), total nitrogen (TN), and total phosphorus (TP), or at 0.42 mm for analyzing soil available nitrogen (AN), available phosphorus (AP) and pH.
TN and soil total carbon (TC) were measured with an elemental analyzer (Euro Vector, EA300). Since the soils were free of carbonates, soil organic carbon (SOC) was assumed to be equivalent to TC [13], and SOM was calculated by SOM = 1.724 × SOC [27]. TP was measured by the molybdenum-blue method after digestion with concentrated HClO4–H2SO4. AN was analyzed by the Alkaline hydrolysis diffusion method. AP was determined by the Molybdenum antimony colorimetric method. SM was determined by the oven-drying method to constant weight at 105 °C. Soil pH was measured using a pH meter (Rex Electric Chemical PHS-25, Shanghai, China) with a 2.5:1 ratio of pure water to soil [27].

2.3. DNA Extraction and PCR Amplification

Community DNA was extracted from 0.5 g soil using the E.Z.N.A.® soil Spin kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s instructions, DNA quantity and purity were determined using NanoDrop2000 UV-visible spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). PCR amplification of the V3-V4 variable region was amplified using 338F (5′-ACTCCTACGGGAGCAGCAG-3′) and 806R (5′-GGACTACHVGGGT-WTCTAAT-3′) primers3. The purified amplified fragments were constructed according to the standard operating procedures of the Illumina Miseq platform (Illumina, Sandiego, CA, USA). Sequencing was performed using Illumina’s MiSeq PE300 platform. The raw data is uploaded to the NCBI database. OTU clustering was performed according to 97% similarity, and single sequences and chimeras were removed in the clustering process. Species classification was annotated for each sequence using RDP Classifier and compared with the Silva database (SSU123). The comparison threshold was set at 70%.

2.4. Statistical Analysis

All statistical analyses were conducted in the R 4.0.2 and SPSS21 unless noted otherwise, and the graphic was completed in the software of Origin 2019b and R 4.0.2. The analysis of variance (ANOVA) adjusted by Bonferroni correction in SPSS21 were used to assess differences in soil properties, microbial alpha diversity, the bacterial abundance of metabolic function groups, the relative abundance of biomarkers, and topological parameters of the bacterial and fungal co-occurrence networks in farmland, gullies, deposition area and three soil depths. Correlation and significance between soil properties, soil diversity, the bacterial abundance of metabolic function groups, the relative abundance of biomarkers and the abundance of plants were determined by the spearman and person correlation analysis in the “corrplot” and “PerformanceAnalytics” packages. The partial correlation analysis in the SPSS21 has been used to analyze the correlation between plant diversity, plant richness, plant composition and microbial richness, and diversity, by the control of soil properties. Amos was used to build the structural equation model (SEM) for detecting the direct and indirect effect of NVR and soil physicochemical properties on soil microbial diversity.
The nonmetric multidimensional scaling (NMDS), based on the Bray-Curtis matrix, was used to visualize bacterial and fungal community structures. The analysis of similarities (ANOSIM) or Adonis analysis was used to determine the significant differences in bacterial and fungal community structures between areas. The Redundancy Analysis (RDA) was used to detect the correlations between plants, soil properties and microbial community composition in gullies. The NMDS and RDA were both carried by the “vegan” package. Linear discriminant analysis Effect Size (LEfSe) was carried out from the website (http://huttenhower.sph.harvard.edu/galaxy, accessed on 5 May 2022) to find the bacterial and fungal biomarkers of farmland and Gullies, and the threshold value is set to three.
Lastly, the “WGCNA” package based on Spearman’s correlation matrices of different areas were used to construct the bacterial and fungal co-occurrence network of soil samples in gully, farmland, and deposition area. OTUs with a relative abundance higher than 0.01% across all samples were used to calculate the pair-wised Spearman correlations between OTUS. Benjamini and Hochberg’s false discovery rate (FDR) was used to adjust the p values in the correlation matrices. Statistically robust correlations were identified when Spearman’s r values >0.9 and the FDR adjusted p < 0.01 and were incorporated into network analyses. The network topological characteristics of each group were implemented in the subgraph function via the igraph package. The Gephi software was used to extract node number, edge, average path length, betweenness centrality, closeness centrality, and degree centrality of each network (https://gephi.org, accessed on 5 April 2022). The FAPROTAX database was used to assign ecologically relevant functions to all detected OTUS, and all database and assignment instructions are available at (http://www.loucalab.com/archive/FAPROTAX/, accessed on 25 August 2022).

3. Results

3.1. Soil Physicochemical Properties

In the Guangrong site, the SOM, TN, TP, and pH in the gully were higher than that in farmland in 40–60 cm soil depth, and AP was significantly higher in the gully (39.52 ± 8.27 mg/kg) and deposition area (47.16 ± 11.22 mg/kg) than that in farmland (29.93 ± 4.98 mg/kg), while soil AN in farmland was higher than that in gully and deposition area. In the Yanmagou site, the SOM, TN, TP, AN, and pH were higher in the gully than that in the farmland in 0–20 cm soil depth. SM was significantly higher in the gully (29 ± 2%) than that in the farmland (24 ± 2%), while soil AP was higher in the farmland than that in the gully (Table 1).

3.2. Alpha(α)-Diversity of the Bacterial and Fungal Community

In the Guangrong site, the bacterial richness and bacterial diversity and fungal richness were higher in the gully than that in farmland in 0–20 cm, 20–40 cm and 40–60 cm soil depths respectively. Moreover, these three indexes in the gully were even higher than the deposition area in 0–20 cm soil depth. It was interesting that the microbial diversity and richness in gully and farmland both decreased by depths in 0–60 cm soil depths, but the microbial diversity and richness in deposition area were higher in 20–40 cm soil depth than that in the 0–20 cm and 40–60 cm soil depths (Figure 1a). In addition, in the Yanmagou site, the richness of bacteria and fungi was also higher in the gully than that in the farmland (Figure 1b). Moreover, from the SEM model, the SOM was mediated by NVR, and then SOM indirectly mediated bacterial richness and diversity by SM, pH, and AP, while directly mediating fungal richness and diversity. The total standard effects of SOM on bacteria richness, bacterial diversity, and fungal diversity were negative, while the effect on fungal richness was positive (Figure 1c, Table S2).

3.3. Composition of Bacterial and Fungal Community

The compositions of the bacterial community and fungal community were significantly different between farmland and gullies in both the Guangrong site and Yanmagou site (p < 0.01). However, in 0–60 cm, the compositions of the bacterial community and fungal community were similar between the gully and deposition areas in the Guangrong site (Figure 2). Interestingly, bacterial community composition in the 0–20 cm soil depth and fungal community composition in 0–20 cm, 20–40 cm, and 40–60 cm soil depths was significantly different between the gully and deposition area in the Guangrong site (p < 0.01). Moreover, the community composition of bacteria was similar in the 20–40 cm and 40–60 cm soil depths but differed from that in the 0–20 cm soil depth in the gully in the Guangrong site (Table S3).
LEfSe analysis detected the bacterial and fungal biomarkers in gullies and farmlands in both the Guangrong site and the Yanmagou site, and there were 13 bacterial biomarkers and 13 fungal biomarkers of gullies and farmland were detected in both two sites (Figure 3a,b). The biomarkers belonging to four bacterial taxa (TRA3-20, Rhizobiales, Chloroflexales, Microtrichales) and two fungal taxa (Serendipitaceae, Agaricales) had a higher relative abundance in gullies than that in farmland in both two sites, hence these seven taxa were selected as key bacterial or fungal biomarkers in of gullies. In addition, Gonitrichum and Tremellomycetes were selected as key fungal biomarkers in soils of farmland (Figure 4c).
Then, we found that in the Guangrong site, compared with the gully, the relative abundance of TRA3-20 and Microtrichales was higher, but for Rhizobiales and Chloroflexales was lower in the deposition area. Interestingly, the key fungal biomarkers of gullies were specificity abundant in gullies, such as the relative abundance of Serendipitaceae and Agaricales were much higher in gully than that in farmland and deposition area (Figure 4). In addition, the Serendipitaceae and Agaricales were also specificities abundant in gully rather than farmland in the Yanmagou site. And the Serendipitaceae was not even detected in farmland in the Yanmagou site (Figure S5).

3.4. The Co-Occurrence Network of Bacterial and Fungal Community

In the present study, we used the topological parameters of the co-occurrence network to assess soil microbial network size and complexity (Figure 5a,b). The higher numbers of nodes and edges and the longer average path length represent the greater network size. In addition, the higher betweenness and degree centrality, while the lower closeness centrality represents the greater network complexity. The difference in co-occurrence network between farmland, gully, and deposition area in the Guangrong site showed that the bacterial network has the smallest size in the gully than that in both farmland and deposition area while having similar complexity in the gully and farmland (Figure 5c). The size of the fungal network was bigger in the gully than that in farmland, while was smaller than that in the deposition area, as well as had greater complexity in the gully than that in both farmland and deposition area (Figure 5d). In addition, in the Yanmagou site, we found a similar size and complexity of bacterial co-occurrence network between farmland and gully and found a bigger size and more complex fungal co-occurrence network in gully than in farmland (Table S4).

3.5. The Bacterial Metabolic Functional Groups on Nitrogen and Carbon

Based on the FAPROTAX database, the bacterial abundance of different N and C metabolism functions was analyzed. The bacterial abundance of nitrate-denitrification, nitrite denitrification, nitrous denitrification, and aerobic ammonia oxidation were significantly higher in the gully than that in both the farmland and deposition area in the Guangrong site. However, the bacterial abundance of N fixation and nitrification was similar among gully, farmland, and deposition area. Moreover, the bacterial abundance of ureolysis and methanol oxidation was highest in farmland than that in the gully and deposition area (Figure 6). However, the bacterial abundance of aromatic compound degradation and cellulolysis were highest in the deposition area compared with gullies and farmland. It was interesting that the bacteria abundance of C fixation functions except for photoheterotrophy was highest in the gully rather than that in the farmland and deposition area of the Guangrong site (Figure S3a). In addition, the bacterial abundance of denitrifications and C fixations were all higher in the gully than in farmland in the Yanmagou site (Figures S3b and S4).
Based on the correlation analysis between the metabolism functions of N and C and the key bacterial biomarkers of the gully, we found the relative abundance of Microtrichales, Rhizobiales and TRA3-20 were significantly positively correlated with the bacterial abundance of nitrate denitrification function, nitrite denitrification, nitrous denitrification. Additionally, as the relationship between the relative abundance of Microtrichales and TRA3-20 and the bacterial abundance of aerobic ammonia oxidation, between the relative abundance of Rhizobiales and the bacterial abundance of N fixation, between the relative abundance of TRA3-20 and the bacterial abundance of nitrification were all significantly positive (Figure 7). Moreover, we found the relative abundance of Microtrichales, Rhizobiales and TRA3-20 were significantly positively correlated with the bacterial abundance of anoxygenic photoautotrophy, anoxygenic photoautotrophy S oxidizing, photoautotrophy, and photoheterotrophy. Furthermore, the relative abundance of Microtrichales and TRA3-20 was significantly positively correlated to the abundance of phototrophy (Figure S2).

4. Discussion

4.1. Soil Microbial Community in Gullies after NVR

Microbial diversity and complexity of microbial co-occurrence in gullies were higher than in local farmland and similar to grass-covered deposition areas (Figure 1 and Figure 5). That suggested that NVR could repair the microbial community from the impact of high soil erosion, just like NVR did in other terrestrial ecosystems, which were disturbed by environmental pollution, overgrazing, and excessive deforestation [7,8,9,28]. However, the fungal community composition at 0–20 cm, 20–40 cm, and 40–60 cm in gullies were all significantly different from the deposition area (Table S3). Previous studies found that NVR repaired both the fungal community and bacterial community in many terrestrial ecosystems, however, limited publication relates to that NVR repairing on bacterial community, especially impacting the fungal community in the erosion environment [2,29,30]. Vegetation succession or plant type played an important role in the establishment of the microbial community [9,31]. In the present study, the riverain deposition area was dominant by multiple tall types of grass like Phragmites australis, while Dicotyledons like Artemisia argyi were abundant in gullies and such differences of vegetation presumably affected the fungal community composition.
As previously observed, NVR greatly improved soil quality and nutrient cycling rates by mediating microbial metabolisms [2]. In the present study, we found richer phototrophy functional bacteria in gullies after NVR than in farmland (Figure S3). Additionally, phototrophic bacteria could convert carbon dioxide into organic matter through photosynthesis progress [32]. Thus, the potential microbial C storage was higher in gullies than in farmland. Although the plant species were totally different between the present study and the previous study in degraded karst landscapes, it is obvious long-term NVR can increase the abundance of phototrophy functional bacteria in soil [33]. In addition, previous studies found high concentrations of some herbicides can also increase the phototrophy functional bacteria in soil [34]. In this study, the runoff from farmland transports many herbicides into gullies during crop growing season, and herbicides may also increase phototrophy functional bacteria in gully soil especially since the high density of vegetation could effectively intercept runoff.
In addition, compared with farmland, we found gullies after NVR has a similar abundance of nitrogen fixation bacteria, while the denitrification bacteria got higher (Figure 6 and Figure S4). Because the denitrification progress could convert nitrate or nitrite to nitrous oxide or nitrogen gas released into the air [35], the N storage potential caused by bacteria tended to get lower in gullies than that in the farmland. Previous studies found that exogenous nitrogen increased the denitrification rate or denitrification bacteria in both soil and river environmental conditions [36,37,38]. Because gullies are the channels of sediment, runoff and nutrient, the exogenous nitrogen from farmland presumably simulated the denitrification functional bacteria in gullies [39], thus further decreasing soil N storage potential and increasing the N leaching or emission.

4.2. NVR Influenced the Microbial Community in Gullies by Plant Community Composition

In the present study, we found the relative abundance of Dicotyledons not only was a major factor associated with the fungal composition in gullies, but was also positively associated with fungal richness, while negatively associated with bacterial richness in gullies (Table 2 and Table 3). This can attribute to the fact that root exudates and residues of the plants are the key factors that altered the composition of fungi and bacteria in soils, such as fungi, are typically presented as decomposers of plant residues and parasites of plant roots; in contrast, bacteria are mainly regulated by plant root secretions [40,41,42,43]. As well, we found that Serendipitaceae as a key fungal biomarker was enriched in gullies, and was positively associated with all five Dicotyledons species, which were observed in both two gullies (Figure 4 and Figure S1). Additionally, the Serendipitaceae family contains peculiar species of cultivable root-associated fungi involved in symbiotic associations with a wide range of plant species [44,45]. Thus, the symbiotic or parasitic between Dicotyledons and fungi could be an important factor influencing the microbial community in gullies under NVR.
In the present study, we found that plant diversity was positively associated with fungal richness, while negatively associated with bacterial richness in gullies (Table 4). However, the previous study in Poaceae dominated grassland showed a positive association between plant diversity and bacterial diversity [46]. In the present study, plant diversity increased with enriched Dicotyledons rather than Poaceae. Furthermore, plant diversity drives the plant functional diversity through litter input and root exudate, and then controls soil microbial diversity [47,48]. Thus, the increased litter input and exudate from Dicotyledons may cause different influences of plant diversity to microbial diversity between the previous study and the present study. In addition, we found plant diversity was regulated by SM, AN and AP in gullies, as we observed in a previous study about dozens of gullies [13]. Therefore, microbial diversity may drive by a path from soil AN, AP, and SM to plant diversity and then to the microbial diversity, as well this path was mediated by plant composition.

4.3. NVR Influenced the Microbial Community in Gullies by the Accumulation of SOM

A previous study observed that the large NPP increased over time under NVR in abandoned farmland, and then increased SOM by litter and roots [49]. As well, we found SOM accumulation in gullies after NVR in a previous study on dozens of gullies in the Mollisol region [13], as well as in the present study (Table 1). Moreover, we found soil properties, especially the SOM were the major factor that influenced bacterial community (Table 1, Table 2 and Table 4). That may be because SOM was an important nutrient resource for soil microorganisms [50], especially the heterotrophic microorganisms are sensitive to SOM dynamics [51,52]. As well, in the present study, we found the key biomarkers of gullies such as the Rhizobiales, were positively correlated with SOM (Figure 3). Additionally, Rhizobiales were reported as heterotrophic bacteria in previous studies [53,54]. Thus, NVR may influence and repair the bacterial community in gullies by the positive influence of SOM on heterotrophic bacteria.
In addition, SOM is always associated with multiple soil nutrients like P and N, as well SM and pH in soils, were reported to correlate with the microbial community [55,56]. Thus, we deduced that SOM improved after NVR, and then regulated soil microbial diversity by three paths via the association of SOM and other soil properties (Figure 1c). For the first path, SOM was negative to soil pH, and then positive to AP and further weakening the bacterial richness. This can attribute to the association between AP and bacterial richness being tight in soils with the limited P [57,58]. In the second path, SOM promoted the storage of water in soils, and then directly negatively associated with bacterial richness and further positively associated with bacterial diversity. This is possibly due to SM being a strong determinant factor influencing the activity and diversity of bacteria [59]. In the third path, SOM is directly positively associated with soil fungal richness, but negatively associated with fungal diversity. This means that SOM decreased the evenness, and finally decreased the diversity of fungi. That could attribute to the composition of saprophytic fungi in soil [60]. Generally, the NVR in gullies increased SOM, then SOM affected the soil pH, SM, and then indirectly regulated the bacterial diversity by the intermediate with soil AP, as well as SOM directly associated with fungal diversity.

4.4. NVR Influence the Potential Microbial C, P and N Storage by Key Biomarkers

Microbial biomarkers were representative microbes of microbial composition in corresponding groups like regions or treatments [21,61], therefore, the microbial functions of key biomarkers of gullies after NVR (Rhizobiales, TRA3-20, Microtrichales and Chloroflexales) may implicate the dynamic mechanism of microbial functions in gullies. Generally, the heterotrophic bacteria, Rhizobiales, and TRA3-20 are associated with C or N metabolism [54,62]. As well, in the present study, we found Rhizobiales, Microtrichales, and TRA3-20 were significantly positively associated with the denitrification functional groups and phototrophy functional groups (Figure 7 and Figure S2). As well, the previous study also reported that Rhizobiales had the functions of denitrification and nitrification and promoted N2O simulated from agricultural soil [37], and some bacteria of the Rhizobiales order, like the Rhodoplanes, were phototrophic [63]. These cases confirmed the positive associations between biomarkers and metabolic functions in our study. Moreover, we found another key biomarker, the Microtrichales was positively correlated with soil AP (Figure 3). Additionally, the previous study in farmland after natural reforestation found that Microtrichales could accumulate P in soil by phosphate transporter strategy [64]. Therefore, the enriched Microtrichales may also have increased the microbial P storage potential in gullies after NVR. Generally, the enriched key biomarkers may act an important role in the dynamic of soil microbial N, P and C storage potential in gullies under NVR, especially the Rhizobiales and Microtrichales.

5. Conclusions

In the stable gullies of the eroded Mollisol farmland, long-term NVR repaired the microbial community, which was disturbed by high erosion. After NVR, the microbial diversity and richness and the complexity of the fungal co-occurrence network were both improved in stable gullies, as well as the potential of microbial denitrification, C fixation and P accumulation. The abundance of Dicotyledons influenced plant diversity and then mediated soil microbial diversity, composition, and key biomarker (Serendipitaceae) in gullies. SOM directly mediated fungal diversity and indirectly mediated bacterial diversity by pH, SM and AP, as well as associated with the bacterial composition and enriched key biomarker (Rhizobiales). The bacterial biomarkers (Rhizobiales, Microtrichales and TRA3-20) are positively associated with the higher potential of microbial denitrification, C fixation and P accumulation in gullies. Overall, this study demonstrated the NVR in stable gullies repaired the microbial diversity loss in high erosion and revealed the driving mechanism of microbial community properties and potential microbial functions about soil C, N, and P storage. These findings could provide a theoretical basis for the restoration of gullies based on vegetation restoration in the Mollisol region. In addition, the high potential functions of denitrification and phototrophy may increase soil nitrous oxide flux, while increasing carbon dioxide fixation in soil. It is necessary to measure greenhouse gases in gullies in future studies about the ecological function assessment of gullies and the eroded Mollisol region.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land11122231/s1. Table S1:The morphological characteristics and dominant plants in gullies at Guangrong and Yanmagou sites. Table S2: Total standard effect of SEM analysis indicates the influence of natural vegetation restoration (NVR) and soil physicochemical properties on the bacterial and fungal alpha diversity. Table S3: NMDS analysis of bacterial and fungal community composition in soil depths and positions in Guangrong site. Table S4. Figure S1: Pearson correlations between mutual fungal biomarkers and plants in gullies at Guangrong and Yanmagou sites. Figure S2: Pearson correlations between bacterial biomarkers and the function groups of carbon metabolic. Figure S3: The function groups of bacterial carbon metabolic differed among farmland, gully, and deposition area (DA) at Guangrong site. Figure S4: The function groups of bacterial denitrifications differed among farmland and gully at Yanmagou site. Error bars are double the standard error of the mean. Figure S5: The abundance of biomarkers of gullies and farmland differed among farmland and gully in Yanmagou site).

Author Contributions

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

Funding

This research was founded by the National Key Research and Development Program of China (2021YFD1500801) and the National Natural Science Foundation of China (41771313; 42177321).

Data Availability Statement

The data are not publicly available due to privacy.

Acknowledgments

Thanks to Li Yu and Xinrui Wang who joined in the investigation.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Alpha diversity of soil bacterial and fungal in farmland (FL), gully (GL) and deposition area (DA), and their response to the vegetation recovery and soil physicochemical properties. (a,b) are bacterial and fungal α diversity in the Guangrong site and Yanmagou site. Error bars are twice the standard error of the mean. Mean values with the same lower-case letter were not a significant difference at p < 0.05 among farmland, gully, and deposition area in each soil depth respectively. Mean values with the same capital letter were not a significant difference at p < 0.05 among depths in each area respectively; (c) is an SEM analysis indicates the influence of natural vegetation restoration (NVR) and soil physicochemical properties on the bacterial and fungal alpha diversity in both two sites in 0–20 cm, 20–40 cm and 40–60 cm soil depth (χ2 = 17.32, df = 20, p = 0.63). The solid line represents the significant relationship, while the dotted line means a non-significant relationship. The blue lines mean a negative relationship, while the red lines mean a positive relationship. The numbers inside the boxes of endogenous variables were R2.
Figure 1. Alpha diversity of soil bacterial and fungal in farmland (FL), gully (GL) and deposition area (DA), and their response to the vegetation recovery and soil physicochemical properties. (a,b) are bacterial and fungal α diversity in the Guangrong site and Yanmagou site. Error bars are twice the standard error of the mean. Mean values with the same lower-case letter were not a significant difference at p < 0.05 among farmland, gully, and deposition area in each soil depth respectively. Mean values with the same capital letter were not a significant difference at p < 0.05 among depths in each area respectively; (c) is an SEM analysis indicates the influence of natural vegetation restoration (NVR) and soil physicochemical properties on the bacterial and fungal alpha diversity in both two sites in 0–20 cm, 20–40 cm and 40–60 cm soil depth (χ2 = 17.32, df = 20, p = 0.63). The solid line represents the significant relationship, while the dotted line means a non-significant relationship. The blue lines mean a negative relationship, while the red lines mean a positive relationship. The numbers inside the boxes of endogenous variables were R2.
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Figure 2. Soil bacterial and fungal community composition affected by natural vegetation restoration and land use types at the Guangrong and Yanmagou sites. Non-metric Multidimensional scaling analysis (NMDS) plot based on the Bray-Curtis distances of bacterial (a,c) and fungal (b,d) among gully, farmland and deposition area at Guangrong site in 0–60 cm soil depth (a,b) and Yanmagou site in 0–20 cm soil depth (c,d).
Figure 2. Soil bacterial and fungal community composition affected by natural vegetation restoration and land use types at the Guangrong and Yanmagou sites. Non-metric Multidimensional scaling analysis (NMDS) plot based on the Bray-Curtis distances of bacterial (a,c) and fungal (b,d) among gully, farmland and deposition area at Guangrong site in 0–60 cm soil depth (a,b) and Yanmagou site in 0–20 cm soil depth (c,d).
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Figure 3. Mutual biomarkers of microbial lineages for bacteria and fungi were described from phylum to genus among farmland and gully at the Guangrong site (0–60 cm) and Yanmagou site in (0–20 cm) identified by linear discriminant analysis effect size (LEfSe), and their correlation with soil physicochemical properties. (a,b) are Cladograms of biomarkers in Guangrong and Yanmagou. The circle midpoints in cladograms represent the indicator of microbes with Linear discriminant analysis (LDA) score greater than 3, and the yellow dots represent the microbes without statistical difference between the farmland and gully. The green and red dots with radiation represent the biomarkers of the gully and farmland respectively. The Spearman correlation heatmap was used to analyze the relationship between soil physicochemical properties and biomarker microbes (c). The biomarkers with the same color diagram indicate the same phylum, and the same diagram shape indicates the same order. The key biomarkers are marked by the yellow star. * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 3. Mutual biomarkers of microbial lineages for bacteria and fungi were described from phylum to genus among farmland and gully at the Guangrong site (0–60 cm) and Yanmagou site in (0–20 cm) identified by linear discriminant analysis effect size (LEfSe), and their correlation with soil physicochemical properties. (a,b) are Cladograms of biomarkers in Guangrong and Yanmagou. The circle midpoints in cladograms represent the indicator of microbes with Linear discriminant analysis (LDA) score greater than 3, and the yellow dots represent the microbes without statistical difference between the farmland and gully. The green and red dots with radiation represent the biomarkers of the gully and farmland respectively. The Spearman correlation heatmap was used to analyze the relationship between soil physicochemical properties and biomarker microbes (c). The biomarkers with the same color diagram indicate the same phylum, and the same diagram shape indicates the same order. The key biomarkers are marked by the yellow star. * p < 0.05; ** p < 0.01; *** p < 0.001.
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Figure 4. The abundance of biomarkers of gullies and farmland differed among farmland, gully, and deposition area in the Guangrong site. Error bars are twice the standard error of the mean. Means with the same lowercase were not a significant difference at p < 0.05 among farmland, gully, and deposition area.
Figure 4. The abundance of biomarkers of gullies and farmland differed among farmland, gully, and deposition area in the Guangrong site. Error bars are twice the standard error of the mean. Means with the same lowercase were not a significant difference at p < 0.05 among farmland, gully, and deposition area.
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Land 11 02231 g005 550
Figure 5. The difference in co-occurrence patterns of soil bacterial and fungal are among farmland, gully, and deposition area (DA) in 0–60 cm soil depth. (a,b) are co-occurrence networks of soil bacteria and fungi. The nodes in the network are colored by modularity class. The numbers of nodes, edges and three centrality indexes (betweenness, degree and closeness) represent the soil bacterial (c) and fungal (d) co-occurrence patterns from farmland to gully to deposition area. Error bars are double the standard error of the mean. Mean values with the same lowercase are not significantly different at p < 0.05 among farmland, gully, and deposition area.
Figure 5. The difference in co-occurrence patterns of soil bacterial and fungal are among farmland, gully, and deposition area (DA) in 0–60 cm soil depth. (a,b) are co-occurrence networks of soil bacteria and fungi. The nodes in the network are colored by modularity class. The numbers of nodes, edges and three centrality indexes (betweenness, degree and closeness) represent the soil bacterial (c) and fungal (d) co-occurrence patterns from farmland to gully to deposition area. Error bars are double the standard error of the mean. Mean values with the same lowercase are not significantly different at p < 0.05 among farmland, gully, and deposition area.
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Land 11 02231 g006 550
Figure 6. The microbial metabolic function groups of soil nitrogen cycling are differed among the farmland, gully, and deposition area. Error bars are twice of the standard error of the mean values. Mean values with the same lower case were no significant difference at p < 0.05 between farmland, gully, and deposition area (DA).
Figure 6. The microbial metabolic function groups of soil nitrogen cycling are differed among the farmland, gully, and deposition area. Error bars are twice of the standard error of the mean values. Mean values with the same lower case were no significant difference at p < 0.05 between farmland, gully, and deposition area (DA).
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Land 11 02231 g007 550
Figure 7. Pearson correlations between bacterial biomarkers and the functional groups of nitrogen metabolic and mutual plants at the Yanmagou site and Guangrong site. The key biomarkers are marked by the yellow star. * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 7. Pearson correlations between bacterial biomarkers and the functional groups of nitrogen metabolic and mutual plants at the Yanmagou site and Guangrong site. The key biomarkers are marked by the yellow star. * p < 0.05; ** p < 0.01; *** p < 0.001.
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Table
Table 1. Soil physicochemical properties in the Guangrong site and Yanmagou site.
Table 1. Soil physicochemical properties in the Guangrong site and Yanmagou site.
SiteDepthPositionsSOM
g/kg		TN
g/kg		TP
g/kg		AN
mg/kg		AP
mg/kg		SM
%		pH
us/cm
Guangrong0–20 cmFarmland24.90 (4.29) a1.58 (0.23) a0.40 (0.09) a139.83 (81.20) a30.41 (5.84) a0.2 (0.01) a6.63 (0.37) a
Deposition area18.88 (5.35) a1.33 (0.34) a0.47 (0.11) a90.02 (37.85) a51.53 (14.85) a0.19 (0.02) a7.28 (0.71) a
Gully24.54 (21.24) a1.46 (0.93) a0.51 (0.14) a70.84 (23.43) a39.38 (10.86) a0.16 (0.02) b6.74 (0.05) a
20–40 cmFarmland18.17 (3.91) a1.27 (0.10) a0.36 (0.03) a169.34 (181.63) a28.11 (2.28) b0.21 (0.03) a6.62 (0.14) a
Deposition area18.56 (0.97) a1.30 (0.02) a0.51 (0.10) a109.57 (34.98) a46.34 (11.27) a0.19 (0.03) a7.14 (0.60) a
Gully15.26 (5.50) a1.09 (0.24) a0.38 (0.09) a79.69 (25.93) a36.24 (7.35) ab0.21 (0.03) a6.8 (0.08) a
40–60 cmFarmland13.89 (1.08) a1.03 (0.1) a0.28 (0.04) a70.10 (17.99) a31.28 (7.21) a0.19 (0.01) a6.59 (0.15) a
Deposition area17.60 (3.78) a1.23 (0.15) a0.55 (0.15) a83.38 (22.69) a43.62 (10.38) a0.17 (0.05) a7.01 (0.52) a
Gully26.47 (29.05) a1.70 (1.37) a0.55 (0.28) a73.79 (10.92) a42.95 (8.23) a0.19 (0.03) a6.68 (0.05) a
Yanmagou0–20 cmFarmland38.84 (11.15) a1.80 (0.38) a0.44 (0.08) a24.03 (17.07) a19.54 (7.42) a0.24 (0.02) b5.94 (0.41) b
Gully45.57 (22.80) a1.97 (0.73) a0.45 (0.08) a31.50 (19.06) a19.43 (12.10) a0.29 (0.02) a6.62 (0.19) a
Note: The values in the brackets are the standard deviation of the mean values. The values with the same lower-case letters were not significant at p < 0.05 among areas of the same site and layers.
Table
Table 2. RDA analysis of soil bacterial and fungal community composition in gullies at Guangrong and Yanmagou sites.
Table 2. RDA analysis of soil bacterial and fungal community composition in gullies at Guangrong and Yanmagou sites.
GroupDepthAPSOMpHSMPlant
Composition
GuangrongBacteria0.810 *0.2020.647 *0.1150.3340.338
Fungi0.0030.2740.3700.0840.2240.888 **
YanmagouBacteria-0.2210.832 **0.860 **0.0690.542
Fungi-0.1430.0630.2280.0850.111
Note: plant composition was assessed by the relative abundance of Dicotyledon. * p < 0.05; ** p < 0.01.
Table
Table 3. Partial correlations between bacterial or fungal richness and plant community after control soil moisture (SM), available nitrogen (AN) and available phosphorus (AP) in 0–20 cm soil depth of gullies.
Table 3. Partial correlations between bacterial or fungal richness and plant community after control soil moisture (SM), available nitrogen (AN) and available phosphorus (AP) in 0–20 cm soil depth of gullies.
Bacterial Richness Fungal Richness
VariablesVariancep-ValueVariancep-Value
Control no variables
Plant diversity−0.6500.0220.5890.044
Plant richness−0.7820.0030.4180.176
Plant composition−0.7580.0040.6610.019
Control SM
Plant diversity−0.0190.9950.1370.689
Plant richness−0.3640.271−0.2710.421
Plant composition−0.2380.4800.2510.457
Control AN
Plant diversity−0.5050.1130.2750.414
Plant richness−0.6970.017−0.0350.918
Plant composition−0.6650.0260.4220.196
Control AN
Plant diversity−0.5540.0770.6500.030
Plant richness−0.7270.0110.4630.151
Plant composition−0.6930.0180.7340.010
Note: plant composition was assessed by the relative abundance of Dicotyledon.
Table
Table 4. Correlations between the alpha diversity of bacteria and fungi, vegetation composition, and soil properties in 0–20 cm and 20–60 cm soil depths of gullies.
Table 4. Correlations between the alpha diversity of bacteria and fungi, vegetation composition, and soil properties in 0–20 cm and 20–60 cm soil depths of gullies.
Alpha DiversityDepths (cm)Plant DiversityPlant RichnessPlant CompositionSOM
Bacterial Diversity0–20−0.32−0.39−0.48−0.06
Bacterial Diversity20–600.190.280.11−0.88 *
Bacterial Richness0–20−0.65 *−0.78 **−0.75 **−0.15
Bacterial Richness20–600.490.560.44−0.97 **
Fungal Diversity0–200.240.500.16−0.44
Fungal Diversity20–600.470.560.40−0.63
Fungal Richness0–200.58 *0.410.66 *0.46
Fungal Richness20–60−0.37−0.44−0.310.24
Note: plant composition was assessed by the relative abundance of Dicotyledon. * p < 0.05; ** p < 0.01.
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Xiao, Z.; Zhang, S.; Yan, P.; Huo, J.; Aurangzeib, M. Microbial Community and Their Potential Functions after Natural Vegetation Restoration in Gullies of Farmland in Mollisols of Northeast China. Land 2022, 11, 2231. https://doi.org/10.3390/land11122231

AMA Style

Xiao Z, Zhang S, Yan P, Huo J, Aurangzeib M. Microbial Community and Their Potential Functions after Natural Vegetation Restoration in Gullies of Farmland in Mollisols of Northeast China. Land. 2022; 11(12):2231. https://doi.org/10.3390/land11122231

Chicago/Turabian Style

Xiao, Ziliang, Shaoliang Zhang, Pengke Yan, Jiping Huo, and Muhammad Aurangzeib. 2022. "Microbial Community and Their Potential Functions after Natural Vegetation Restoration in Gullies of Farmland in Mollisols of Northeast China" Land 11, no. 12: 2231. https://doi.org/10.3390/land11122231

APA Style

Xiao, Z., Zhang, S., Yan, P., Huo, J., & Aurangzeib, M. (2022). Microbial Community and Their Potential Functions after Natural Vegetation Restoration in Gullies of Farmland in Mollisols of Northeast China. Land, 11(12), 2231. https://doi.org/10.3390/land11122231

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