Long-term effects of vegetation and soil on the microbial communities following afforestation of farmland with Robinia pseudoacacia plantations

Geoderma 367 (2020) 114263

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Long-term effects of vegetation and soil on the microbial communities following afforestation of farmland with Robinia pseudoacacia plantations

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Miaoping Xua,b, Dexin Gaoa,b, Shuyue Fua,b, Xuqiao Lua,b, Shaojun Wua,b, Xinhui Hana,b, , Gaihe Yanga,b, Yongzhong Fenga,b a b

College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China The Research Center of Recycle Agricultural Engineering and Technology of Shaanxi Province, Yangling 712100, Shaanxi, China

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Yvan Capowiez

Soil microorganisms play an important role in the functional connection of nutrient cycling between plants and soil ecosystems, but the exact responses of microbial communities to change in plant characteristics and soil properties remain uncertain. We analyzed the soil microbial communities of afforested tree stands of varying ages to assess the effects of vegetation and soil quality on soil microbial communities. We studied Robinia pseudoacacia plantations in the Loess Plateau for three age classes (RP13, RP29, and RP44), and compared results from these classes to one farmland (FL) control. The responses of soil microbial communities to changes in understory flora and soil physicochemical properties, including the Shannon index, richness, coverage, and biomass were analyzed. Compared with FL, afforestation significantly increased the Shannon index, species richness, coverage, and biomass of understory plants, which were highest in RP29. The concentrations of soil organic carbon (SOC), total nitrogen (TN), nitrate nitrogen (NO3−-N), and ammoniacal nitrogen (NH4+-N) were highest in RP44. The soil microbial community diversity index was significantly affected by afforestation. The relative abundances of bacterial populations were also affected, with Proteobacteria being dominant in RP29 and Actinobacteria being dominant in FL. Similarly, in fungi, the relative abundance of Basidiomycota was highest in FL, while the relative abundances of Zygomycota were highest in RP29. Pearson correlation analysis revealed that the Shannon and Chao indices of soil fungi were significantly positively correlated with SOC and NH4+-N. RDA showed that SOC, NH4+-N, and soil water content (SWC) were significantly associated with soil bacterial community composition. Soil fungal community composition were significantly affected by the Shannon and Richness indexes of understory flora. The results showed that characteristics of understory flora, soil properties, and soil microbial community composition were all affected by afforestation. The composition of the fungal community was correlated with changes in the diversity of understory flora. The changes in nutrients such as organic carbon and inorganic nitrogen was the key to the changes of soil microorganisms as plant colonization.

Keywords: Robinia pseudoacacia Succession Soil bacteria and fungi Soil properties Understory flora

1. Introduction Afforestation is regarded as a land use pattern to improve soil quality, can have significant effects on aboveground and underground ecosystems and ecological benefits (Deng and Shangguan, 2017; Li et al., 2012; Wei et al., 2013). Soil microbial communities are one of the most important biotic components in terrestrial ecosystems, mediating the flow of matter and energy while regulating the cycling of nutrients in the ecosystem (Sparling and Searle, 1993; Urbanova et al., 2015).

Furthermore, afforestation changes the microenvironment, vegetation flora, and soil properties, while microbes respond strongly to these variables (Andruschkewitsch et al., 2014; Song et al., 2015). In turn, changes in flora, soil quality and microbial community and their interactions affect ecosystem stability during habitat restoration and afforestation (Lamb et al., 2011). Therefore, it is important to understand soil microbial community dynamics and its response to vegetation and soil variables during plantations growth. Recent studies have explored how variation in environment (such as

Abbreviations: Shannon (B) and Chao (B), Shannon and Chao indices of soil bacteria; Shannon (F) and Chao (F), Shannon and Chao indices of soil fungi; SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; NO3—N, nitrate-nitrogen; NH4+-N, ammonium-nitrogen; SWC, soil water content. Shannon: ShannonWiener diversity index; Richness, Margalef richness index; Coverage and Biomass, total coverage and biomass of understory vegetation; RP, Robinia pseudoacacia; FL, farmland; RDA, Redundancy analysis; NMDS, Non-metric multidimensional scaling; LSD, least significant difference ⁎ Corresponding author at: College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China. E-mail address: [email protected] (X. Han). https://doi.org/10.1016/j.geoderma.2020.114263 Received 18 July 2019; Received in revised form 6 February 2020; Accepted 10 February 2020 0016-7061/ © 2020 Elsevier B.V. All rights reserved.

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Table 1 The geographical information of the experiment sites in Robinia pseudoacacia plantations. Stand sites

Stand ages (year)

area m2

Gradient (°)

Slope aspect (°)

Altitude (m)

Main herbaceous types

FL RP13 RP29 RP44

0 13 29 44

20*30 50*150 50*300 100*100

0 30 40 30

347 297 303 64

1290–1300 1310–1320 1270–1280 1300–1310

Zea mays L. Astertataricus L. f. Tripolium vulgare Nees Poa sphondylodes Trin. Dendranthema indicum (L.) Des Moul. Poa sphondylodes Trin. Patrinia heterophylla Bunge Carex tristachya Thunb.

Site codes: FL is farmland; RP13, RP19, and RP44 are 13, 29 and 44 years of Robinia pseudoacacia L, respectively.

2. Materials and methods

plant diversity, underground vegetation characteristics, pH, soil nutrient content, and soil water content) affects soil microbial communities on local scales, however the results are equivocal (Dai et al., 2017; Dang et al., 2018; Yao et al., 2018). Liu et al. (2010) indicted that soil microbial community diversity is significantly affected by plant biomass and flora richness. Further research revealed that the increase in vegetation coverage changes the soil water content and solar radiation, which in turn drives the diversity of soil microbial communities (Bakker et al., 2014; Daniel et al., 2015). Dang et al. (2018) and Williams et al. (2013) reported that soil microbial community composition is related to soil pH and inorganic nitrogen, indicating that microbial communities are more sensitive to soil acidification and nitrogen conversion. Furthermore, some studies have shown that soil bacterial community composition is more sensitive to understory vegetation than soil fungi on forest ecosystems in semi-arid regions (Dang et al., 2018). However, the opposite conclusions were found in study areas with more diversity and richness of understory vegetation communities (Li et al., 2017). Therefore, significant differences in plant communities and soil properties may partially explain microbial community changes in different study areas or forest ecosystems (Epelde et al., 2012; Li et al., 2017; Zhang et al., 2016a). However, the response of microbial communities to understory vegetation and soil variables during afforestation was unclear. Robinia pseudoacacia is an exotic species from North America that has become the most important introduced species in the afforestation in the ecologically fragile area of the Loess Plateau due to its high growth rate and ability to improve soil nutrient availability (Liang et al., 2018). Moreover, R. pseudoacacia has N2-fixing root nodules with belowground litter likely leading to high N inputs to the mineral soil (De Marco et al., 2013b). In this area, the use of R. pseudoacacia with strong nitrogen fixation ability in artificial afforestation has aided in ecosystem restoration, improved soil quality and regulated the microclimate of the Loess Plateau (Ren et al., 2016b). The composition and diversity of soil microbes are also affected by the changes of microhabitats under the forest. However, previous studies only observed changes in microbial biomass and activity (Hui et al., 2017; Wang et al., 2008). Furthermore, there is little in-depth study of the interaction between vegetation and soil variables and soil microbial communities, which is important for studying the restoration effects of afforestation on poor soils. We hypothesized that soil microbial community variables were related to the changes in soil properties and vegetation characteristics after afforestation. To verify the hypothesis, we evaluated understory flora, soil chemical properties over three timepoints in R. pseudoacacia plantations and compared them to farmland which was used as a control. Simultaneously, the 16S rRNA and ITS high-throughput sequencing technologies were used to examine the diversity and composition of soil bacterial and fungal communities, respectively. The objectives of this study were to (i) evaluate the changes in soil properties and soil microbial communities following afforestation; and (ii) explore the response of soil microbial communities to changes of soil properties and understory flora.

2.1. Study area The study was carried out in the Wuliwan catchment located in Ansai County of the hill-and-gully Loess plateau region (36°51′13.42″–36°52′17.36″N, 109°20′53.44″-109°21′26.64″E), which is characterized by a semiarid climate (Li et al., 2013). In this region the average annual temperature is 8.8 °C and the average annual precipitation is 510 mm, with rainfall mainly occurring from July to September (Ren et al., 2016a; Zhang et al., 2019a). There are 2415 h of sunshine and a frost-free period of 157 days per year (Zhang et al., 2019a). The soil is mainly composed of Huangmian soil (Calcaric Cambisols, HWSD), which develops by wind deposition of Loess parent material. It is characterized by its yellow granules, absence of bedding, silt loam texture, and overall looseness. The original crops in the area were Zea mays L., but most of the land has been planted with R. pseudoacacia. 2.2. Experimental design The experiment was conducted in July 2018. The R. pseudoacacia artificial forests with three age classes (13, 29, and 44 years) and farmland (FL) were selected. Detailed information about each age class of R. pseudoacacia plantation and FL is presented in Table 1. Each age class of R. pseudoacacia plantation and FL was represented by three independent replicate sites. The typical distance between different stand age classes was more than 2 km apart, and the replications of the same stand age class exceeded 1 km (Ren et al., 2016a). All selected lands are located at similar gradients, slopes, altitudes, and have similar farming practices with maize (Zea mays) in monoculture prior to afforestation. In addition, the farmland (FL) with the same farming practices and similar geomorphological characteristics was selected as the reference. After the maize was harvested, the straw was cut and made into animal feed. Three replicate plots (20 × 20 m) were randomly placed at each site for subsequent investigation and sampling. The replicate plots are adjacent and at least 100 m apart from each another. Overall, a total of 36 observations were collected (1 land use type × 3 age categories × 3 replicate sites × 3 plots + one farmland × 3 replicate sites × 3 plots). 2.3. Soil sampling and analysis of soil properties Soil samples were taken at a depth of 0–10 cm. After removing the litter layer and other debris, soil samples were taken from 10 points in an “S” shape using a soil auger (5 cm in diameter). The soil samples were homogenized to provide a final soil sample at each plot. All soil samples were quickly sieved with a 2 mm mesh to remove roots and other debris. A portion of each soil sample was immediately transported to the laboratory to determine the soil water content. One set of subsamples was stored at −80 °C for DNA extraction, and the remaining soil subsamples were air dried and stored at room temperature to assess the chemical properties of the soil. Soil pH was measured using a pH meter after shaking the soil water (1:5 w/v) suspension for 30 min. Soil water content was determined by 2

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containing each target gene. Finally, a total of the PCR products was achieved and an equal amount of PCR product from each sample was then transferred into a single tube and sent to Illumina’s MiSeq platform at the Major Biological Institute in Shanghai, China.

oven-drying to constant mass at 105 °C (Bao, 2000). The soil organic carbon (SOC) was determined by K2Cr2O7 oxidation method (Nelson and Sommers, 1982). Total nitrogen (TN) content was determined using the Kjeldahl method (Qiu et al., 2010). Total phosphorus (TP) was determined colorimetrically after wet digestion with HClO4-H2SO4. Ammonium-nitrogen (NH4+-N) and nitrate-nitrogen (NO3−-N) were measured using an AA3 continuous flow analytical system (AA3, Germany) with a 1 M KCl extraction.

2.7. Sequence data processing The data was demultiplexed and mass filtered. Read data was demultiplexed, quality filtered, and processed using QIIME (Caporaso et al., 2012). Based on the following three criteria. First, the 300 bp reading was truncated at any site with an average mass score of less than 20 within the 50 bp sliding window. Truncation readings shorter than 50 bp were then discarded; second, accurate barcode matching, dinucleotide mismatches in primer matching, and reads containing ambiguous characters were removed. Third, according to the overlapping sequence, only sequences overlapping more than 10 bp are assembled. Reads that cannot be assembled are discarded. Similar sequences with clusters of less than 3% dissimilarity were filtered out to eliminate negative interference from the data. Combined with the clustering molecules exhibiting 97% similarity, the 16S rRNA and ITS rRNA operational taxonomic units were selected for classification, and chimeric sequences were identified and removed using UCHIME. The complete data set was then sent to the National Biotechnology Information Center (NCBI) Sequence Read Archive (SRA) database and registered under the accession numbers of SRP175035 and SRP175098.

2.4. Plant investigation and analysis Along the soil sampling routes, six 1 × 1 m2 squares were randomly arranged in each plot to record the name, coverage, frequency and density of each species. And the aerial parts of all plants in each square were collected by shearing, which was dried to constant weight at 75 °C to constant weight to calculate aboveground biomass. We calculated the vegetation coverage (Coverage) by using the proportion of understory vegetation projected to the ground per unit area (Zhang et al., 2019a). The Shannon-Wiener diversity index (Shannon) and Margalef richness index (Richness) were calculated for each plot as previously described (Liu et al., 2018; Zhang et al., 2016a). The litter layer samples were collected from each study site using six spatially independent subsamples from litter traps without understory vegetation in each plot (Zhang et al., 2019a). The litter was dried to constant weight at 75 °C to constant weight to calculate litter biomass that was 0.21, 0.51, and 0.57 kg m2 from RP13 to RP44, respectively (Zhang et al., 2019b).

2.8. Statistical analyses Taxonomic alpha diversity was based on the diversity of bacterial communities in individual samples, using the Chao index that reflected the richness of the community and the Shannon index that took the uniformity of the community into account, was determined using Mothur software (version v.1.30.1). These indicators can accurately reflect the richness and uniformity of bacterial communities. A one-way analysis of variance (ANOVA) and multiple significant differences (p < 0.05) were used to assess the changes in soil properties (SOC, TN, NH4+-N, NO3−-N, TP, SWC, and pH), plant characteristics (Shannon index, species richness, coverage, and biomass) and soil microbial community diversity and composition. Non-metrical multidimensional scaling (NMDS) methods were used to assess the similarity of the microbial community composition at different stand ages. We normalized the data for Pearson correlation and redundancy analysis with logtransformation (Dang et al., 2018; Ren et al., 2016b). The Pearson correlation analysis was used to assess the relationships among dominant microflora, microbial community diversity, plant variables, and soil physicochemical properties. Redundancy analysis (RDA) was used to assess the relationships between plant characteristics and soil properties and the composition of microbiotas and to extract principle environmental factors (plant and soil properties) influencing the microbial communities.

2.5. Soil DNA extraction Soil DNA was extracted from each sample in triplicate using the E.Z.N.A Soil DNA Kit (OMEGA, USA) according to the manufacturer's instructions. First, the soil sample was loaded into the test tube and was treated with a special formulation buffer containing detergent. Subsequently, proteins, humic acid, and other contaminants were removed via precipitation by heating and freezing. The pure DNA was then eluted in water or a low strength ionic buffer. All the DNA extracted from the same soil sample was then combined and quantified using a spectrophotometer (NanoDrop2000, Thermo Scientific, Wilmington, DE, USA). Finally, the extracted soil DNA was stored at −80 °C until PCR amplification and analysis. 2.6. PCR amplification and 16S rRNA gene and ITS sequencing The abundance of the microbial community was estimated using PCR amplification techniques. Target genes were bacterial 16S rRNA and fungal ITS. The bacterial 16S rRNA was amplified using primers 338F (5′- ACTCCTACGGGAGGCAGCA-3′) and 806R (5′- GGACTACHVGGGTWTCTAAT -3′). The fungal ITS was amplified using primers ITS5F (5′- GGAAGTAAAAGTCGTAACAAGG -3′) and ITS1R (5′- GCTG CGTTCTTCATCGATGC -3′). The treated 16S rRNA mixture of bacteria contained 0.4 μl of the two primers, 0.4 μl of FastPfu polymerase, and 1.25 μl of template DNA (10 ng). These samples were denatured at 95° for 3 min and then amplification was performed using three steps of PCR for thirty cycles, denaturing at 95 °C for 30 s, annealing at 55 °C for 30 s, extending at 72 °C for 45 s, and further extending at 72 °C for 10 min. The ITS rRNA was performed in a 25 μl mixture, which contained 0.5 μl of each primer at 30 μmol.L-1, 1.5 μl of template DNA (10 ng), and 22.5 μl of Platinum PCR SuperMix. These samples were denatured at 95° for 2 min and then amplification was performed using three steps of PCR for thirty cycles, denaturing at 95 °C for 30 s, annealing at 55 °C for 30 s, extending at 72 °C for 30 s, and further extending at 72 °C for 5 min. Three independent PCR assays were performed on each DNA sample to provide replicates. The gene copy number was calculated by the standard curve method. The standard curve was formulated using a 10-fold diluted series of plasmids

3. Results 3.1. The properties of understory flora and soil The characteristics of understory plants varied significantly between different forest ages (Table 2). The canopy density and total aboveground plant coverage of R. pseudoacacia gradually increased over time as farmland slowly converted to forest (p < 0.05). The Shannon index and richness of plants were unimodal distributed, with the highest values occurring in RP29. The dominant species in the herb layer change significantly at different restoration stages (Table 1). Aster tataricus L.f., Tripolium vulgare Nees and Poa sphondylodes Trin. were the dominant species at the RP13, and T. vulgare Nees gradually disappeared at RP44; P. sphondylodes Trin gained growth advantage in the development process from the RP13 to RP29 restoration stages and 3

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Table 2 Characteristics of understory vegetation with restoration stage. Stand sites

Canopy density

Shannon

Richness

Coverage (%)

Biomass (kg·m−2)

RP13 RP29 RP44

0.60 ± 0.03b 0.83 ± 0.03a 0.88 ± 0.03a

2.23 ± 0.50 2.68 ± 0.11 1.62 ± 0.37

4.23 ± 0.22 4.67 ± 0.33 4.01 ± 0.42

33.00 ± 4.32b 40.93 ± 4.04b 87.87 ± 1.97a

0.18 ± 0.03 0.22 ± 0.01 0.31 ± 0.06

Different letters indicate significant differences (p < 0.05) among different restoration stages based on a one-way ANOVA followed by an LSD test. Shannon, Richness, Coverage and Biomass denote the Shannon-Wiener diversity index and Margalef richness index, total coverage and biomass of understory vegetation in the different restoration stages, respectively. Table 3 Soil properties at different restoration stages. Stand sites

SOC (g·kg−1)

TN (g·kg−1)

TP (g·kg−1)

NO3−-N (mg·kg−1)

NH4+-N (mg·kg−1)

SWC (%)

pH

FL RP13 RP29 RP44 p

4.26 ± 0.44c 5.76 ± 0.29b 7.28 ± 0.24a 8.30 ± 0.35a < 0.001

0.30 ± 0.01d 0.44 ± 0.01c 0.67 ± 0.03b 0.94 ± 0.07a < 0.001

0.42 ± 0.42 ± 0.45 ± 0.47 ± 0.002

8.96 ± 0.20d 9.83 ± 0.03c 12.00 ± 0.13b 16.42 ± 0.40a < 0.001

1.52 ± 0.06b 1.99 ± 0.05a 2.08 ± 0.01a 2.11 ± 0.01a < 0.001

6.22 ± 0.68c 12.44 ± 0.83ab 11.56 ± 0.63b 13.91 ± 0.27a < 0.001

8.48 ± 8.39 ± 8.30 ± 8.30 ± 0.024

0.01c 0.01c 0.01b 0.01a

0.06a 0.03ab 0.03b 0.03b

The values are mean ± standard error. Different letters indicate significant differences (p < 0.05) among different restoration stages based on a one-way ANOVA followed by an LSD test. FL is farmland; RP13, RP19, and RP44 are 13, 29 and 44 years of Robinia pseudoacacia L, respectively. SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; NO3−-N: nitrate-nitrogen; NH4+-N: ammonium-nitrogen; SWC: soil water content.

back afforestation. The Shannon and Chao indices of soil fungal community in FL were lower than those in the other three forest stands. However, the diversity indices of soil microbial community did not change significantly among the three stand ages of R. pseudoacacia. According to all sequences, the dominant bacterial phyla (relative abundance greater than 1%) were as follows: Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, Gemmatimonadetes, Nitrospirae and Bacteroidetes with average proportions of 22.73, 22.69, 27.24, 11.42, 8.97, 2.35, and 1.37%, respectively (Fig. 2 and Table S1). The relative abundances of Actinobacteria, Acidobacteria, Proteobacteria, Chloroflexi, Gemmatimonadetes and Nitrospirae were significantly different in the four restoration stages. Above all, Acidobacteria, Proteobacteria and Nitrospirae were significantly more abundant in forests. Conversely, the abundance of Acidobacteria was significantly reduced during the conversion of farmland to forests (p < 0.001) (Table S1). Moreover, within the classification of the orders, Blastocatellales, Nitrospirales and Rhizobiales were more abundant after afforestation than those in the farmland, but the relative abundance of Acidimicrobiales, Frankiales and Propionibacteriales were reduced from farmland to forests (Table S1). Notably, the relative abundances of Acidobacteria and Gemmatimonadetes in RP29 were

Dendranthema indicum (L.) Des Moul. became a dominant species at the RP29; Carex tristachya Thunb. as the dominant species and completely occupied the herbaceous layer at the RP44. There were significant differences in soil properties at different restoration stages (Table 3). Comparing with FL, the concentrations of SOC, TN, NO3−-N, NH4+-N, and SWC after afforestation were significantly higher by 35.21–94.84%, 46.67–213.33%, 9.71–83.26%, 30.92–38.82% and 85.85–123.63%, respectively. And the concentrations of TN, TP and NO3−-N significantly increased from RP13 to RP44. In addition, the pH decreased in later stages of forest succession relative to farmland.

3.2. Changes of soil microbial community After quality sequencing and trimming, the diversities (bacterial: 751,458 sequences; fungi: 668,149 sequences) of the soil microbial communities were obtained using the 16S rRNA and ITS primer sets across per sample and cluster analysis. An OTU-level approach was performed to calculate soil bacterial and fungal alpha diversity (Shannon index and Chao index) (Fig. 1). The Shannon and Chao indices of bacterial community composition varied significantly in front-

Fig. 1. Diversity indices of bacterial(a) and fungal(b) community among different restoration stages in Robinia pseudoacacia plantations. Values are the mean ± standard error. Different letters denote statistically significant differences among different restoration stages (p < 0.05). FL: farmland; RP: Robinia pseudoacacia, respectively. 4

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Fig. 2. Relative abundance of the dominant groups of bacterial communities at the phylum level among the different restoration stages in Robinia pseudoacacia plantations. FL: farmland; RP: Robinia pseudoacacia, respectively.

communities of the RP13 and RP44 were similar and obviously distinct from those of the FL and RP29, which were in turn distinct from one another. And the fungal communities of the FL obviously distinct from those of the RP13, RP29 and RP44.

significantly lower than those in RP44 and RP13. Among the fungal community, taxonomic classification at the phylum level found that Ascomycota (48.99%), Basidiomycota (14.75%) and Zygomycota (8.97%) were dominant (Fig. 3 and Table S2). The relative abundances of Basidiomycota and Zygomycota were significantly different in the different restoration stages. In particular, Zygomycota was significantly more abundant in afforested sites than that in FL. In contrast, the abundance of Basidiomycota in farmland was higher than in the three restoration stages. Additionally, the dominant classes were Hypocreales (11.66%), Mortierellales (8.77%), and Agaricales (5.99%). Among them, the abundance of Mortierellales was significantly higher in the three RP forest stands than in the FL, but Agaricales revealed a reverse trend (Table S2). The relative abundance of Zygomycota in RP29 was higher than that of RP44 and RP13. The abundance of Basidiomycota in RP29 was lower than that in the RP44 and RP13. Non-metrical multidimensional scaling (NMDS) analysis showed that the similarity of soil community composition and structure among the four sites (Fig. 4). The analysis reinforced the idea that the bacterial

3.3. Soil microbial community response to plant characteristics and soil properties We used Pearson correlation analysis to assess the correlation of soil and plant variables with soil microbial community diversity (Table 4). The results showed that SOC, NH4+-H, SWC and Richness were significantly positively correlated with the Chao index of soil fungi, as well as SOC and NH4+-H were also significantly positively correlated with the Shannon index of soil fungi. While soil and plant variables were not significantly associated with the Shannon and Chao indices of soil bacteria. We used redundancy analysis (RDA) to assess the effects of plants and soil properties on soil bacterial communities at the order level (Fig. 5, Fig. 6 and Table 5). The two axes of soil properties and

Fig. 3. Relative abundance of the dominant groups of fungal communities at the phylum level among the different restoration stages in Robinia pseudoacacia plantations. FL: farmland; RP: Robinia pseudoacacia, respectively. 5

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Proteobacteria, Actinobacteria and Nitrospirae, and conversely, significantly and negatively correlated with the relative abundance of Acidobacteria. The two axes of soil properties and vegetation explained 54.19% and 70.46% of the total variation in soil fungal communities at the order level (Fig. 5b, Fig. 6b and Table 5). RDA analysis showed that pH had significant effects on fungal communities. Furthermore, plant Shannon and Richness were positively correlated with fungal communities (Fig. 5b and Table. 5). In addition, there was no significant correlation between canopy density and any quality of the soil fungal communities. Pearson correlation analysis indicated that the Shannon of aboveground vegetation was correlated positively with the relative abundance of Zygomycota, and negatively correlated with the abundance of Basidiomycota, but the Coverage revealed a reverse trend (Table S4). The relative abundance of Basidomycota was negatively correlated with the NH4+-N and SOC. In particular, the canopy density, plant coverage, and soil nutrients were interrelated (Table S5). 4. Discussion 4.1. Changes in understory flora and soil properties after afforestation The colonization and growth of R. pseudoacacia changed the microhabitat under the forest, including humidity, temperature and light conditions (Liu et al., 2018). After afforestation, the diversity of understory vegetation (Shannon and Richness index) of stands followed a parabolic distribution, with a significant peak in RP29 (Table 2). Our analysis was consistent with previous studies and showed that changes in micro-habitats under forests significantly affected understory flora (Meier et al., 2005). In addition, changes in the canopy during the growth of the stand significantly affected the temperature and illumination of the forest ecosystem (Zeng et al., 2018). The superior light conditions in the early stages of afforestation leads to the growth of large number of pioneer species and natural flora, which subsequently leads to a sharp increase in the diversity of species (Chiarucci and Dedominicis, 1995). Competition over growing space in young-middle aged forests leads to the elimination of some species (Li et al., 2013; Wei et al., 2013). Shade-tolerant species C. tristachya T. became the main species and achieved maximum coverage (Table 2). Some studies confirmed that the survival rate of shade-loving species is enhanced when canopy density is high, leading to the improvement of the understory (Bloor, 2003; Rijkers et al., 2000). However, the accumulation of canopy density from RP13 to RP44 stands leads to the gradual formation of dominant species and tends to be stabilized (Small and McCarthy, 2005). In the Loess Plateau, dense planting and the low availability of soil moisture and nutrients may explain fierce competition between understory plants. The cultivation and growth of exotic tree species and understory flora have profound effects on soil properties, such as changes in organic matter, inorganic nitrogen and soil water retention capacity (Lin et al., 2017). Afforestation using R. pseudoacacia has had significant effects on the soil investigated chemical properties of the hilly gully region (Table 3). After afforestation, the absorption of soil nutrients, dead leaves, and root litter of plants increases soil nutrient cycling (Solly et al., 2014). Most studies have demonstrated that ammoniumnitrogen and nitrate-nitrogen are the main nitrogen sources that can be directly used by N2-fixation trees. De Marco et al. (2013a) revealed that the growth of R. pseudoacacia promoted the accumulation of soil carbon and nitrogen in the organic layer. Our results showed that the concentrations of NO3−-N and NH4+-N changed significantly from farmland to afforestation, which was consistent with the conclusion of (Hawkins et al., 2000). In addition, Tobar et al. (1994) used N-15 markers to study the conversions of NO3−-N and NH4+-N in soil, which showed that ammonium-nitrogen was easily rejected by symbiotic rhizobia and weakened the acquisition of NH4+-N by hyphae under drought conditions. This conclusion was similar with our finding that

Fig. 4. Non-metrical multidimensional scaling (NMDS) of bacterial (a) and fungal(b) community composition among the different restoration stages in Robinia pseudoacacia plantations. FL: farmland; RP: Robinia pseudoacacia, respectively.

Table 4 Correlation analysis between soil and plant variables and soil microbial diversity. Bacteria Shannon (B)

Chao (B)

Fungi Shannon (F)

Chao (F)

Soil variables SOC TN TP NO3−-N NH4+-N SWC pH

−0.36 −0.158 0.005 −0.04 −0.508 −0.302 0.112

−0.309 −0.189 −0.12 −0.137 −0.436 −0.352 0.022

0.577* 0.432 0.127 0.304 0.768** 0.564 −0.432

0.593* 0.461 0.254 0.362 0.826** 0.683* −0.337

Plant variables Canopy density Shannon Richness Coverage Biomass

0.149 −0.531 −0.407 0.43 −0.193

0.089 −0.219 −0.465 0.16 −0.554

−0.096 −0.188 0.525 −0.095 −0.208

0.034 −0.374 0.623* 0.014 0.324

The different asterisks denote statistically significant differences among different restoration stages (* = p < 0.05, ** = p < 0.01). Shannon (B) and Chao (B): the Shannon and Chao indices of soil bacteria; Shannon (F) and Chao (F): the Shannon and Chao indices of soil fungi; SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; NO3−-N: nitrate-nitrogen; NH4+-N: ammonium-nitrogen; SWC: soil water content. Shannon, Richness, Coverage and Biomass denote the Shannon-Wiener diversity index and Margalef richness index, total coverage and biomass of understory vegetation in the different restoration stages, respectively.

vegetation explained 66.43% and 52.39% of the total variation in soil bacterial communities (Fig. 5a, Fig. 6a and Table 5). The SOC, NH4+-N, and SWC contents were significantly correlated with the composition of bacterial communities. Using Pearson correlation, the composition of soil bacterial communities was further analyzed at the phyla level (Table S3). The SOC, NO3−-N, NH4+-N, TN and SWC contents were significantly and positively correlated with the relative abundances of 6

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Fig. 5. Redundancy analysis (RDA) identified the relationship between dominant bacteria (a) and fungi (b) at the order level and soil properties. SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; NO3−-N: nitrate-nitrogen; NH4+-N: ammonium-nitrogen; SWC: soil water content.

the concentration of NO3−-N was significantly higher than that of NH4+-N (Table 3). Notably, the growth trend of soil TP was consistent with that of TN, NO3−-N, and NH4+-N, which may be due to the need for more P for root development of R. pseudoacacia to maintain its mutualistic relationship with nitrogen-fixing rhizobia (Carl et al., 2018; Xu et al., 2019). Our results showed that soil nutrient levels can be improved through afforestation in the Loess Plateau. Nevertheless, there was no significant difference in soil pH after afforestation, which is consistent with previous studies (Ren et al., 2017).

soil microbial communities changed significantly after afforestation, and no significant difference was noted in community diversities and compositions between stands of varying ages (Fig. 1). Our results were consistent with previous studies which have shown that the diversity and richness of soil fungal community after afforestation increased significantly compared with farmland (Lin et al., 2017; Zhang et al., 2016a). Our previous studies found that the conversion of land from agricultural cultivation to forests increased undergrowth vegetation and litter biomass and reduced soil disturbance (Ren et al., 2016b; Liu et al., 2018). Simultaneously, Zhang et al. (2019b) and Wang et al. (2010) found that the accumulation of litter and the growth of understory vegetation promoted the input of above-ground nutrients and increased the diversity of soil microbes. Furthermore, we found that changes in understory vegetation were similar to changes in the

4.2. Changes in soil microbial community diversity and composition following afforestation The results of our study indicated that the diversity and richness of

Fig. 6. Redundancy analysis (RDA) identified the relationship between dominant bacteria (a) and fungi (b) at the order level and understory vegetation characteristics. Shannon, Richness, Coverage and Biomass denote the Shannon-Wiener diversity index and Margalef richness index, total coverage and biomass of understory vegetation, respectively. 7

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Table 5 RDA and soil and plant variables contacted with soil bacterial and fungal abundances at the order level. Bacteria

Fungi

RDA1

RDA2

Explains (%)

p

RDA1

RDA2

Explains (%)

p

Soil variables SOC TN TP NO3−-N NH4+-N SWC pH

−0.815 −0.772 −0.741 −0.716 −0.944 −0.910 0.830

0.232 0.146 0.086 0.115 0.034 −0.221 −0.021

16.3 4.3 8.1 4.9 38.8 10.5 8.5

0.026* 0.542 0.130 0.396 0.004** 0.046* 0.134

−0.610 −0.577 −0.609 −0.529 −0.833 −0.750 0.867

0.194 0.421 0.532 0.592 −0.066 0.099 −0.230

7.6 6.5 6.6 11.9 8.2 6.2 26.3

0.328 0.442 0.466 0.092 0.354 0.464 0.002**

Plant variables Canopy density Shannon Richness Coverage Biomass

0.655 0.334 0.537 0.069 0.319

0.092 −0.115 −0.260 0.225 0.639

23.9 6.0 13.2 10.8 3.1

0.054 0.748 0.134 0.226 0.862

0.087 −0.906 −0.458 0.551 0.178

−0.110 −0.107 0.482 −0.239 0.312

6.5 23.4 23.3 16.0 16.2

0.688 0.020* 0.008** 0.134 0.120

The r2 values show the proportion of variance explained, and different asterisks denote statistically significant differences among different restoration stages (* = p < 0.05, ** = p < 0.01). SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; NO3−-N: nitrate-nitrogen; NH4+-N: ammonium-nitrogen; SWC: soil water content. Shannon, Richness, Coverage and Biomass denote the Shannon-Wiener diversity index and Margalef richness index, total coverage and biomass of understory vegetation in the four different restoration stages, respectively.

acids and organic acids) and secondary metabolites (Kuzyakov et al., 2007). Soil organic carbon and other nutrient elements, understory flora, and above-ground residues differed significantly in afforested sites. Interestingly, the results of our study found that the coverage of understory flora and aboveground litter biomass was greatest in RP29 (Table 2). The results indicated that the abundance of Zygomycota was related to the coverage of herb and the enrichment of litter substrates (Aneja et al., 2006). Furthermore, the reduction in the abundance of Zygomycota in RP44 may be due to the decline in above- and underground productivity during the late growth of Robinia pseudoacacia. Previous studies have revealed that Basidiomycota can rapidly metabolize organic substrates deposited by litter on surface soil (Jones et al., 2009; Lamb et al., 2011). While Ascomycota is usually enriched in environments with high disturbances or strong human intervention, compared to virgin forest soils (Chen et al., 2017; Ma et al., 2013). Our results showed that the relative abundance of Ascomycota has a significant advantage (48.99%) over other fungal communities (Table S2). Notably, the relative abundances of Ascomycota and Basidiomycota have different trends (Fig. 3). The results implied that Basidiomycota was less competitive against established Ascomycota populations (Chee-Sanford, 2008; Mouhamadou et al., 2013). The NMDS analysis reinforced the idea that the microbial community composition of the FL was significantly different from that of the forestland due to the changes in the vegetation composition and soil nutrients, suggesting that soil microbes were sensitive to the changes in the flora and soil properties in the different restoration stages of R. pseudoacacia. Previous studies have shown that plant communities regulate soil microbial activity by affecting microclimate, root exudates, and litter decomposition (Prescott and Grayston, 2013). Simultaneously, changes in soil properties caused by plant growth may be closely related to the metabolism of soil microbial communities.

Shannon and Chao indices of subterranean fungal communities after afforestation, suggesting a correlation between the patterns of diversity in above- and underground ecosystems (Prescott and Grayston, 2013). Our results showed that the dominant groups in the soil bacterial community changed significantly from farmland to afforestation (Fig. 2 and Fig. 3). Significant changes in the dominant groups of bacteria may lead to differences in soil microbial community structure regardless of forest age (Fig. 2 and Fig. 3). Compared with FL, the abundances of Proteobacteria and Acidobacteria were significantly higher in the forests, but the abundance of Actinobacteria was significantly lower. Some studies have demonstrated that bacteria communities are associated with soil carbon and nitrogen cycling (Anderson et al., 2011; Nemergut et al., 2008). And the oligotrophic and copiotrophic patterns associated with ecosystem succession were often used to characterize soil bacterial communities (Garcia-Pausas and Paterson, 2011). Proteobacteria is commonly used as copiotrophic bacteria, due to its extracellular membrane consists of lipopolysaccharide that was involved in carbon conversion (Zhang et al., 2016b). Metabolism in Acidobacteria was affected by light intensity, and these groups were generally regarded as oligotrophic bacteria in previous studies (Jangid et al., 2008). Simultaneously, soil nutrients increased significantly after afforestation. Our results suggested that afforestation improved soil nutrients, which was reflected by increase in the abundance of copiotrophic bacteria. Interestingly, we found the abundance of Nitrospirae associated with soil nitrification was the highest in RP44 compared with other restoration stages (Fig. 2). Similarly, the abundance of Rhizobiales belonging to the Proteobacteria phylum associated with symbiotic nitrogen fixation was higher in RP44 than that in other restoration stages. This result suggests that the growth rate of R. pseudoacacia significantly affected the forest microenvironment by increasing the proportion of nitrogen-fixing bacteria during growth. This was consistent with previous studies that colonization of woody plants has been shown to enrich for Rhizobiales in successional environments (Lin et al., 2011). In addition, the relative abundance of Actinobacteria associated with the decomposition of cellulose and lignin was the largest in RP29 when compared to the other forest stands, which may be related to the decomposition of litter (Foulon et al., 2016). The abundance of Zygomycota was highest at RP29, while the abundance of Basidiomycota in the forest stands was lower than that in FL (Fig. 3). Some studies have shown that Zygomycota was significantly associated with litter decomposition in branches and leaves (Aneja et al., 2006; Deacon et al., 2006). Litter was an important source of carbon and nitrogen, releasing primary metabolites (sugars, amino

4.3. Soil microbial community response to understory flora and soil properties Our correlation analysis showed that the Shannon and Chao indices of soil fungal communities were significantly positively correlated with SOC and NH4+-H (Table 4). This result is consistent with the results of Zhang et al. (2016a) who found that the effect of soil nutrient supply is critical on soil fungal diversity and richness. We found that SWC was significantly positively correlated with the Chao index of soil fungi (Table 4). Bakker et al., (2014) found that many arbuscular mycorrhizal (AM) fungal species received significant regulation of soil moisture by 8

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rhizosphere soils not directly associated with plant roots or the rhizosphere (Patkowska, 2009; Qi et al., 2012). Some studies have confirmed that fungal communities are almost inseparably associated with understory flora, being heavily reliant on rotten detritus, litter, and symbiotic mycorrhizas, while overly dense herbs and litter may inhibit the growth of Basidiomycota (Uibopuu et al., 2012; Xiang et al., 2015). In addition, soil moisture is an important factor driving changes in soil microbial respiration and soil nutrients, which can affect soil microbial community structure (Drenovsky et al., 2004). Simultaneously, soil TP was closely related to TN, NO3−-N and NH4+-N those significantly correlated with nitrogen-fixing rhizobia (Table S5) is in line with the findings of Carl et al. (2018), revealing that phosphorus is a key driver for the growth and nitrogen cycles of R. pseudoacacia. In summary, our results found that high canopy closure and interspecific competition of plant species led to gradual decline in above- and underground productivity during the late growth stage. Importantly, soil microbial community composition and diversity were closely related to soil and plant variables. Thus, we should strengthen plantation management and adopt appropriate thinning measures in the future.

studying the mycorrhizal colonization rate of plants under different soil moisture. The Richness of plants was significantly correlated with the Chao index of fungi, indicating that the changes of understory vegetation richness may have a greater impact on the growth of soil fungal communities. However, the diversity of soil bacterial communities was not significantly associated with soil and plant variables. This result was consistent with the findings of Liu et al. (2018), who emphasized that the diversity of soil bacterial communities may lag behind changes in soil properties and plant characteristics. Soil bacterial community composition is sensitive to changes in soil properties, including changes in SOC, TN, and nitrate (Arenz et al., 2014; Marschner et al., 2004; Wan et al., 2015). In our study, SOC, TN, NH4+-N, NO3−-N and SWC were positively correlated with the abundances of Proteobacteria and Actinobacteria, and negatively correlated with the abundance of Acidobacteria (Table S3). Oligotroph and copiotroph were often used to measure the soil environment of ecosystems (Li et al., 2016; Maloney et al., 1997). Proteobacteria associated with the enrichment of organic carbon are regarded as copiotrophs (Ahn et al., 2016). In addition, Rhizobiales belonging to Alpha-proteobacteria were considered to be a type of Gram-negative bacteria that promotes plant abnormal growth and can provide nitrogen nutrition to the host (Hu et al., 2014). Therefore, increases in the abundance of Proteobacterial flora can promote the accumulation of soil nitrogen. Conversely, Acidobacteria may be richer in nutrient-poor soil environments and likely act as oligotrophs (Lin et al., 2011). Notably, NO3−-N and NH4+-H were positively correlated with the abundance of Nitrospirae (Table S3). The abundance of Nitrospirales was positively correlated with inorganic nitrogen concentration, implying that Nitrospirales oxidizes ammonium to nitrate through nitrification, and the suitable air tightness of soil improves the metabolism of nitrobacteria in restoration stages (Liu et al., 2018; Zhang et al., 2015). The results of our study are consistent with previous studies (Nemergut et al., 2008), suggesting that the suitable soil environment promotes nitrification of nitrobacteria. The pH of shallow soil was not associated with bacterial communities, which was consistent with previous studies (Hu et al., 2010; Li et al., 2013). RDA analysis indicated that SOC, NH4+-N, and SWC were the main soil factors affecting the soil bacterial community of R. pseudoacacia plantations at the bacterial order level (Fig. 5a and Table 4). This further confirmed that the composition of soil bacterial communities was closely related to changes in soil properties. Differently, RDA showed that pH was closely related to soil fungi at the order level (Fig. 5b and Table 4). Jansa et al. (2005) demonstrated that arbuscular mycorrhizal fungi were closely related to soil pH. The soil during the growth of the stand tends to be acidified to facilitate soil fungal growth (Patkowska, 2009). Afforestation improved soil nutrient content associated with the response of soil microorganisms with different nutrient requirements to plant colonization. Our results implied that, compared to soil fungal communities, the stability and diversity of soil bacterial communities are more sensitive to changes in soil properties which can thus be utilized to promote the availability of soil labile carbon and other nutrients (Garcia-Pausas and Paterson, 2011; You et al., 2014). Changes in soil microbial community composition can map the effect of afforestation on overall ecosystem composition and function. RDA and correlation analysis showed that fungi in shallow soil were more sensitive to changes in soil vegetation characteristics than soil bacteria (Fig. 6 and Table 4). The relative abundance of Zygomycota was positively correlated with the diversity and abundance of understory vegetation, whilst the opposite was true for Basidiomycota (Table S4). Previous studies identified that strains of Zygomycota are involved in symbiotic mycorrhizas with plants and aid in the decomposition of plant humic substances and litter (Osinska-Jaroszuk et al., 2015; Xu et al., 2012). Thus, the work highlights the fact that different factors may play important roles in the assembly of bacteria vs. fungi, supporting emerging understandings of this dynamic (Zeng et al., 2017). In contrast, some unicellular fungal biotas that are closely-linked to non-

5. Conclusions The results showed that the cultivation of R. pseudoacacia significantly changed the vegetation flora, soil properties and microbial communities in the Loess Plateau. The Proteobacteria of soil bacterial communities were significantly positively correlated with soil nutrient variables, while Acidobacteria inversed. The diversity and richness of soil fungal community were significantly higher than that of farmland, significantly regulated by SOC and NO3−-N variables. For the fungal community, the relative abundance of Zygomycota was positively correlated with the diversity and richness of understory plant community, while the opposite was true for Basidiomycota. The significant correlation between the composition and diversity of the fungal community and the diversity of the understory community indicated that the soil fungal community was more sensitive to the changes of plant colonization. It must be pointed out that future research should focus more on the relationship between soil microorganisms and plant diversity and litter properties. And our study should be extended to the mixed-forest ecosystems, where relationships between vegetation-soil feedback and microbial communities are more complex. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was financially supported by National Natural Science Foundation of China (No. 41877543). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoderma.2020.114263. References Ahn, J.-H., Lee, S.A., Kim, J.M., Kim, M.-S., Song, J., Weon, H.-Y., 2016. Dynamics of bacterial communities in rice field soils as affected by different long-term fertilization practices. J. Microbiol. 54 (11), 724–731. Anderson, C.R., Condron, L.M., Clough, T.J., Fiers, M., Stewart, A., Hill, R.A., Sherlock, R.R., 2011. Biochar induced soil microbial community change: Implications for biogeochemical cycling of carbon, nitrogen and phosphorus. Pedobiologia 54 (5–6), 309–320. Andruschkewitsch, M., Wachendorf, C., Sradnick, A., Hensgen, F., Joergensen, R.G.,

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