Science of the Total Environment 609 (2017) 2–10

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Soil bacterial community response to vegetation succession after fencing in the grassland of China Quanchao Zeng a,b, Shaoshan An a,b,⁎, Yang Liu a a b

College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, PR China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Shaanxi 712100, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Secondary succession after fencing significantly affected soil nutrients and plant coverage. • Succession had no influence on soil bacterial diversity. • Soil copiotrophic bacterial composition increased across succession. • Soil nutrients and soil bacterial composition altered incongruously.

a r t i c l e

i n f o

Article history: Received 20 January 2017 Received in revised form 11 July 2017 Accepted 11 July 2017 Available online xxxx Editor: D. Barcelo Keywords: Available nutrients HiSeq prosequencing Soil bacteria Vegetation succession Yunwu Mountain

a b s t r a c t Natural succession is an important process in terrestrial system, playing an important role in enhancing soil quality and plant diversity. Soil bacteria is the linkage between soil and plant, has an important role in aboveground community dynamics and ecosystem functioning in terrestrial ecosystems, driving the decomposition of soil organic matter and plant litter. However, the role of soil bacteria in the secondary succession has not been well understood, particularly in the degraded soil of Loess Plateau. In this study, we investigated soil nutrients and soil bacterial community during the secondary succession after a long-term fencing in the grassland, in the Yuwu Mountain, northwest China. The chronosequence included 1 year, 12 years, 20 years and 30 years. The soil bacterial community composition was determined by the Illumina HiSeq sequencing method. The data showed that soil bacterial diversity had no significant changes along the chronosequence, but soil bacterial community compositions significantly changed. Proteobacteria, Acidobacteria and Actinobacteria were the main phyla in all the samples across succession. With the accumulation of soil organic matter and nutrients, the relative abundance of Actinobacteria decreased, whereas Proteobacteria increased. These shifts may be caused by the increase of the available nutrients across the secondary succession. In the younger sites, soils were dominated by oligotrophic groups, whereas soil in the late-succession site were dominated by copiotrophic groups, indicating the dependence of soil bacterial community composition on the contents of soil available nutrients. © 2017 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author at: College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, PR China. E-mail addresses: [email protected] (S. An), [email protected] (Y. Liu).

http://dx.doi.org/10.1016/j.scitotenv.2017.07.102 0048-9697/© 2017 Elsevier B.V. All rights reserved.

Plant secondary succession is a common phenomenon in the degraded areas, and has been proved that it is a useful and effective

Q. Zeng et al. / Science of the Total Environment 609 (2017) 2–10

method to enhance soil quality and plant diversity in the world (Cheng et al., 2012; Knelman et al., 2015; Lozano et al., 2014; Van Hall et al., 2017; Zhang et al., 2016). Studying chronosequences improves well understand the function of underground and aboveground systems (Hannula et al., 2017). Plant is the driver of succession in restored areas, affecting soil chemical and physical properties through root growth and the input of plant litter and root exudates (Van Der Heijden et al., 2008). Some studies revealed that the long term plant succession had positive soil nutrients and enhanced plant diversity (Deng et al., 2014; Lozano et al., 2014; Sojneková and Chytrý, 2015). For example, Lozano et al. (2014) showed that a continuous increase in plant diversity and plant cover along a 84-year succession. The increase in plant diversity and cover with succession subsequently enhanced soil nutrients via root exudates and plant litter decomposition (Berg, 2014; Bertin et al., 2003). In turn, better soil nutrients improve the growth of plant and develop a sustainable system. Plant succession is essential for the interaction between plants and soil microorganism (Kuramae et al., 2011). In the below and above ground systems, soil nutrients and plant diversity along a succession have been well studied, but the role of soil bacteria is still far lag behind, especially in arid ecosystems (Cline and Zak, 2015; Lozano et al., 2014). Soil bacteria are the linkage between soil and plant, driving the decomposition of soil organic matter and plant litter. Thus, soil bacteria play vital roles in soil element cycling, especially for carbon cycling and global warming. Little changes in microbial processes would cause a central effect on the global fluxes of greenhouse gases (Singh et al., 2010). Therefore, understanding the variations along a succession is essential to model and manage the restoration of degraded environments (Kirk et al., 2004). Across the secondary succession, some researchers have reported soil microbial diversity changed with the changes of plant diversity in different areas (Cline and Zak, 2015; Cutler et al., 2014; Zhang et al., 2016). These studies were partly indicated the changes of microorganism across the succession, but the function of different microbial communities in the soil were still unknown. Because soil is complex, it varies from nutrients to plant species. Thus investigating the role of soil microorganisms across the succession in the dryland is necessary and will guide the management of degraded pasture.

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The study is designed to enhance the understanding of the effect of grazing exclusion and restoration sequence on the function of soil bacteria. The Yunwu Mountain reserve, excluded from grazing since 1982, is the only remaining grassland region of the Loess Plateau. The longterm (30 years) fencing has made it possible to better understand the effects of grazing exclusion on both soil properties and soil bacteria. Therefore, this study focused on four different restoration ages (30 years, 20 years, 12 years and 1 year) along a successional gradient in northwest China. Thus, we tested the following hypotheses: the diversity of soil bacteria and copiotrophic communities increased as the increase of soil nutrients across the succession. This study strengthens the understanding of the restoration of soil C, N and P sequestration and the changes of soil bacterial communities in degraded grasslands on the Loess Plateau. 2. Methods and materials 2.1. Studied areas The experiment area is located in the Yunwu Mountain Reserve for Vegetation Protection and the Eco-environment in the city of Guyuan, Ningxia Province, China. Predominant soil of the experimental site is a loess based on the Chinese soil classification and Entisols, according to the U.S.A. taxonomy, respectively (Staff, 1999; Zeng et al., 2016). The primary plant species are herbaceous plants (i.e., Androsace erecta, Artemisia capillaries, Artemisia frigid, Artemisia sacrorum, Heteropappus altaicus, Lespedeza davurica, Potentilla acaulis, Stipa bungeana, Stipa grandis, Thymus mongolicus, etc.). The dominant plant species are Stipa bungeana, Thymus mongolicus, and Artemisia vestita. The reserve consists of three areas, including core area, buffer area and experimental area, which have comparatively similar geographical patterns and climate (Fig.1). The core area comprises approximately 1000 ha, which is totally enclosed and accounts for 25% of the total area. In the core area, the Stipa bungeana community is the most extensive; Stipa grandis and Stipa bungeana are the dominant grass species, and Thymus mongolicus and Artemisia sacrorum are the dominant forb species. The buffer area covers 1200 ha and also accounts for approximately 25% of the total area. The

Fig. 1. The information of sampling site in the Yunwu Mountain, the Loess Plateau.

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Table 1 Characteristics of the sites under different restoration ages.

1y 12 y 20 y 30 y

AGB (t/ha)

BGB (t/ha)

TB (t/ha)

R/S

Coverage%

Species richness

Shannon

0.2 ± 0.05b 0.1 ± 0.06b 0.5 ± 0.13a 0.4 ± 0.18a

0.2 ± 0.05b 0.3 ± 0.15b 0.9 ± 0.23a 0.9 ± 0.18a

0.4 ± 0.09a 0.4 ± 0.17a 1.4 ± 0.31b 1.3 ± 0.3b

0.9 ± 0.17a 0.4 ± 0.15b 0.6 ± 0.16b 0.5 ± 0.18b

31.1 ± 1.2d 51.6 ± 4.13c 80.1 ± 4.88b 89.7 ± 1.51a

12.4 ± 1.14b 15 ± 2.24b 27 ± 2.55a 25.6 ± 2.07a

2.2 ± 0.04b 2.3 ± 0.19b 2.9 ± 0.07a 2.8 ± 0.08a

Different lowercase letters indicated significant differences across succession (P b 0.05). AGB, above ground biomass; BGB, below ground biomass; TB, total biomass; R/S, root/shoot ratio.

by titration of the digests with standardized 0.2 mol/L FeSO4. Soil nitrate + nitrogen (NO− 3 -N) and soil ammonia nitrogen (NH4 -N) extracted by 1 mol/L KCl, and the extraction were measured by a Seal AutoAnalyzer3 (Zeng et al., 2016).

role of the buffer area is to minimize the effects of human activities on the core area. Aside from the core and buffer areas, there is an experimental area that encompasses approximately 1800 ha and accounts for 45% of the total area. Crop cultivation and animal husbandry are practiced in the experimental area. The core area has been fenced since 1982 without any input of fertilizer (Deng et al., 2014). Soil pH varied from 8.0 to 8.6. The climate of the experimental site is semi-arid temperate continental monsoon climate. Mean annual temperature (MAT) is about 8 °C, and mean annual precipitation (MAP) is 371 mm (1991–2015), with 70% rainfall occurred in between June and September. Mean annual total sunshine hours, mean annual evaporation and average frost-free days per year were 2518 h, 1600 mm, and 137 days, respectively (Deng et al., 2014).

2.4. Soil NDA extraction and PCR amplification Soil DNA was extracted from 0.5 g of soil sample with the method of CTAB. DNA concentration and purity was monitored on 1% agarose gels. According to the concentration, DNA was diluted to 1 ng/μL using sterile water. The 16S rRNA V4 genes were amplified for each sample using primer sets of 515F (5′-GTG CCA GCM GCC GCG GTA A-3′) and 806R (5′-GGA CTA CHV GGG TWT CTA AT-3′) (Bergmann et al., 2011; Qu et al., 2016). All PCR reactions were carried out with Phusion® HighFidelity PCR Master Mix (New England Biolabs). Mix same volume of 1× loading buffer (contained SYB green) with PCR products and operate electrophoresis on 2% agarose gel for detection. Samples with bright main strip between 400 and 450 bp were chosen for further experiments. PCR products were mixed in equidensity ratios. Then, mixture of PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany).

2.2. Sampling method Soil samples were obtained during mid-August 2014 when the biomass had reached its peak (Sparks et al., 1996) for four different restoration ages on natural grasslands with grazing exclusion. The main characteristics of sample sites (i.e., the dominant species, plant coverage, species richness and Shannon-Wiener index) were shown in Table 1. The vegetation coverage changed from 31% (1-year-old restoration) to 89% (30-year-old restoration) (Table 1). The younger sample sites had similar species richness and Shannon-Wiener index, and significantly differed from the older sample sites. Five field replicates were established for each successional age, and three plots (20 × 20 m) were conducted in each field replicate. These plots were randomly arranged in the field. Soil samples were collected by using seven soil cores in each plot. Soil core was obtained by using a 5-cm diameter soil auger and composited. Soil samples were obtained from 0 to 20 cm depth. Roots, stones and animals were removed by hand and separated two parts. One part was air-dried and sieved through a 2-mm sieve for C, N and P analyses. Another part was stored at −80 °C for the extraction of DNA.

2.5. Illumina MiSeq sequencing Sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following manufacturer's recommendations and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina HiSeq 2500 platform and 250 bp paired-end reads were generated. All raw sequences have been deposited in the GenBank with the accession number of SRP110551. 2.6. Statistical and bioinformatics analysis Sequences analysis was performed by UPARSE software (UPARSE v7.0.1001, http://drive5.com/uparse/) (Edgar, 2013). Sequences with ≥ 97% similarity were assigned to the same OTUs (Stackebrandt and Goebel, 1994). Taxonomy was assigned to each OTU via the Ribosomal Database Project (RDP) classifier (Cole et al., 2009). Representative sequence for each OTU was screened for further annotation. OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analysis of alpha diversity and beta diversity were all performed basing on this output normalized data. Alpha diversity is applied in

2.3. Soil chemical properties Soil microbial biomass nitrogen (MBN) was determined by the fumigation-extraction method (Vance et al., 1987). The details were Concentrations of soil total N (TN) were determined by the Kjeldahl acid-digestion method (KDY-9830) after extraction with 0.02 mol/L sulfuric acid (Thomas et al., 1967). Soil organic carbon (SOC) was measured by a modified Mebius method (Ren et al., 2015). Briefly, 0.5000 g soil sample was digested with 5 ml of 0.8 mol/L K2Cr2O7 and 5 ml of concentrated H2SO4 at approximately 180 °C for 5 min, followed Table 2 Soil chemical properties under different succession stages.

1y 12 y 20 y 30 y

NO3-N

NH4-N

SOC

TN

TP

C:N

C:P

N:P

MBN

10.2 ± 1.1b 10.4 ± 1.7b 14.3 ± 2.1b 25.5 ± 7.6a

4.8 ± 0.7c 5.3 ± 0.2c 6.9 ± 0.8b 7.7 ± 0.5a

15.4 ± 0.2c 10.6 ± 0.2d 18.3 ± 1.4b 21 ± 0.5a

1.6 ± 0.07b 1.1 ± 0.08c 2.1 ± 0.18a 2.2 ± 0.08a

0.7 ± 0.13a 0.6 ± 0.02a 0.6 ± 0.02a 0.7 ± 0.01a

9.5 ± 0.5ab 10 ± 1.0a 8.9 ± 0.3b 9.6 ± 0.5ab

22 ± 4.2b 17 ± 0.9c 29.4 ± 2.6a 32.1 ± 0.6a

2.3 ± 0.36b 1.7 ± 0.1c 3.3 ± 0.35a 3.4 ± 0.16a

46 ± 9.6b 12.2 ± 5.3c 18.8 ± 3.8c 61.8 ± 8.2a

NO3-N means soil nitrate nitrogen; NH4-N means soil ammonia nitrogen; SOC means soil organic carbon; TN means soil total nitrogen; TP means soil total phosphorus; C:N means the ratio of SOC to TN; C:P means the ratio of SOC to TN; N:P ratio means the ratio of TN to TP; MBN means soil microbial biomass carbon. Different lowercase letters indicated significant differences across succession (P b 0.05).

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Table 3 Soil bacterial diversity indices across secondary succession.

1y 1 2y 20 y 30 y

Observed_species

Shannon

Simpson

Chao1

ACE

Goods_coverage

PD_whole_tree

3345 ± 128 3311 ± 84 3357 ± 86 3365 ± 142

9.6 ± 0.14 9.7 ± 0.03 9.7 ± 0.1 9.7 ± 0.09

0.996 ± 0 0.997 ± 0 0.997 ± 0 0.997 ± 0

3821 ± 169 3744 ± 113 3831 ± 103 3773 ± 282

3894 ± 149 3823 ± 126 3914 ± 124 3891 ± 286

0.986 ± 0 0.986 ± 0 0.985 ± 0 0.986 ± 0

201 ± 6 203 ± 5 202 ± 7 205 ± 9

All the indices were not significant across different succession ages (P N 0.05).

The vegetation restoration age had significant effects on the nutrient concentration in soil. The concentration of SOC increased with increase in the restoration age, and was significantly higher in the 20-year-old and 30-year-old restored grasslands than those in the other two grassland ages (Table 2). The pattern of TN concentration was similar to that of SOC concentration (Table 2). The range of concentration of TP was rather narrow (0.66–0.74 g/kg), with no significant difference among the restoration ages (Table 2). The soil C:N ratios ranged between 8.68 and 9.63, and there was no significant difference between restoration ages (P N 0.05). The soil C:P ratios in the 20-year-old and 30-year-old restoration ages were significantly higher than those in lower durations. The pattern of soil N:P ratios was similar to that for the soil C:P ratios (Table 2).

α-, β-, γ- and δ-Proteobacteria were found in all samples. The relative abundance of β- and δ-Proteobacteria had no significant difference among different succession ages, occupied 6% and 7% on average of all populations, respectively. Alpha-Proteobacteria was the most abundant in the Proteobacteria taxa, and significantly increased from 12% at 1-year site to 16% at 30-year site. γ-Proteobacteria showed an opposite trend with succession age increase. At the order level, Solirubrobacterales was the most abundant taxa for all groups, with the range from 10 to 13%. Subgroup_6 and Subgroup_4 were the dominant populations in the Acidobacteria phylum, and had no significant changes with the increase of succession ages. Gaiellales was another dominant group, significantly decreased from 9% at 1-year site to 7% at 30-year site. Rhizobiales dominated the α-Proteobacteria populations, indicating a significant increase at the first 20 years and a significant reduction in the last 10 years. Cluster analysis showed that 1-year site significantly differed from other year sites. 20-year and 30-year sites had similar community compositions (Fig. 2). Pairwise comparison between sites revealed that bacterial communities were unique to each site. This was confirmed by non-parametric multivariate statistical tests including ANOSIM, ADONIS and MRPP (Table 4). The NMDS plot clearly identified variations in bacterial community composition among different age sites. As expected, the close clustering of soil samples in 20-year site and 30-year site indicated similar bacterial community compositions. The samples in 1-year site were distinctly separated from other succession age sites (Fig. 3). These observations were confirmed that succession had significant effects on soil bacterial composition. LEfSe analysis showed that Gaiellales and Thermoleophilia (P b 0.05) significantly changed taxa in the youngest site (1-year site). In the 12-year site, Sphingomonadaceae, Sphingomonadales and α-Proteobacteria (P b 0.05) were predominated. Bacillales, Bacilli and Rhizobiales (P b 0.05) were predominated in 20-year site, whereas Xanthomonadaceae and Xanthomonadales (P b 0.05) were predominated in 30-year site (Fig. 4). DistLM analysis revealed that most important variations affecting soil bacterial community composition when considered individually (Table 5a); soil total nitrogen (9.71%), soil organic carbon (9.45%) and N:P ratio (9.36%) were the most closely correlated soil factors. The sequential model indicated that soil factors individually accounted for 26.3% of the total variation (Table 5b). Among the plant factors, plant coverage and Shannon index were the main factors, with the explanation of 33% in the sequential test.

3.2. Soil bacterial community response to succession age

4. Discussion

Vegetation succession ages had no significant effects on soil bacterial diversity (Table 3), but had significant effects on soil bacterial community structure. The relative abundance of bacterial community at phylum and class levels was showed in Fig. 2. The dominant groups across all the soil samples at the phylum level were Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Bacteroidetes, Chloroflexi, Firmicutes, Verrucomicrobia and Planctomycetes. With the increase of succession age, the relative abundance of Actinobacteria significantly decreased from 35 to 29%, Proteobacteria significantly increased from 28 to 35%. Succession age had no significant effects on the relative abundance of Acidobacteria and Planctomycetes, with the range from 12 to 13% and 2 to 3%, respectively. In the Proteobacteria,

4.1. Soil bacterial succession

analyzing complexity of species diversity for a sample through 6 indices, including Observed-species, Chao1, Shannon, Simpson, ACE, Goodcoverage. All these indices in our samples were calculated with QIIME 1.8.0 (http://qiime.org/tutorials/tutorial.html) and displayed with R software (Version 3.4.0). Beta diversity analysis was used to evaluate differences of samples in species complexity, Beta diversity on both weighted and unweighted UniFrac was calculated by QIIME 1.8.0. Several statistical analyses were performed separately on the soil property datasets using the statistical package for the social sciences (SPSS version 20.0 for Windows), including one-way ANOVA, Student's t-test, and S-K-N multiple range comparison (P = 0.05). Nonmetric multidimensional scaling (NMDS) was performed to distinguish the microbial distribution patterns of different succession age (Liang et al., 2015). Distance-based multivariate (DistLM) analysis was performed to determine the influence of environmental variables on bacterial community diversity with the computer program DISTLM_forward3 (Anderson, 2004). Non-parametric multivariate statistical tests including similarities (ANOSIM) (CLARKE, 1993), non-parametric multivariate analysis (ADONIS) (Anderson, 2001) and multiple response permutation procedure (MRPP) (Mielke and Berry, 2007) were used to test the significant differences of soil bacterial communities across succession. Only results with P ≤ 0.05 are reported as significant. 3. Results 3.1. Soil chemical properties and microbial biomass response to succession age

The results of this study indicated that succession age had no significant effects on soil bacterial diversity. This was not in accord with previous studies (Li et al., 2014; Zhang et al., 2016). For example, Zhang et al. (2016) reported that soil bacterial community Shannon and ACE indices were significantly increased with the succession age and subsequently remained stable after 20 years in the grassland. Li et al. (2014) also found significant differences between soil bacterial diversity indices along a chronosequence of revegetation. In contrast, no changes were observed in alpha-diversity in secondary succession in a burned forest (Ding et al., 2017) and paddy field (Knelman et al., 2015),

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Fig. 2. Soil bacterial community compositions at the levels of phylum (a), class (b) and order (c) across the secondary succession in the Yuwu Mountain. Only significantly changed (P b 0.05) taxa were shown at the level of class and order levels.

Q. Zeng et al. / Science of the Total Environment 609 (2017) 2–10 Table 4 Significance of the effects of succession age on bacterial community structure using three non-parametric multivariate statistical methods. Bold values indicate a significance (P b 0.05). Group

MRPP

ANOSIM

ADONIS

A

P

R

P

F

P

20 y–30 y 20 y–12 y 1 y–30 y 1 y–20 y 1 y–12 y 12 y–30 y

0.11 0.12 0.16 0.14 0.17 0.09

0.006 0.003 0.008 0.011 0.009 0.005

0.56 0.70 0.85 0.75 0.88 0.52

0.012 0.008 0.008 0.01 0.007 0.012

3.31 3.62 5.02 4.49 5.17 2.75

0.001 0.009 0.001 0.001 0.005 0.001

which is consistent with our study. This result suggested that secondary succession with fencing for about 30 years had no significant changes in soil bacterial diversity (Table 3). Although soil bacterial community diversity indices were not changed, soil bacterial community compositions changed as expected. From the MDS analysis, soils in 20-year and 30-year sites grouped together and separated from the younger sites (12-year and 1-year sites). This result suggested soil bacterial compositions in 20-year site and 30-year site were similar, consistent with the results reported by Zhang et al. (2016) and Lozano et al. (2014). The Significance using by MRPP, ANOSIM and ADONIS also confirmed that plant succession significantly influenced soil bacterial structure. The results indicated that Proteobacteria, Actinobacteria and Acidobacteria were the most dominant phyla, and similar results were observed in soils under different environments (Koyama et al., 2014; Li et al., 2014; Liu et al., 2014; Zhang et al., 2016). Although Proteobacteria, Actinobacteria and Acidobacteria were the main groups in most soils, the relative abundances were greatly changed along environmental factors (Knelman et al., 2015; Tscherko et al., 2005). For example, the relative abundance of Acidobacteria decreased, but Proteobacteria increased along a 30-year succession in the grassland

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(Li et al., 2014; Zhang et al., 2016). Our result was consistent with their observations. However, the relative abundance of Acidobacteria was not significantly changed along the vegetation succession, suggesting that soil nutrients had no significant effects on Acidobacteria. Among the Proteobacteria, the α-Proteobacteria significantly increased in relative abundance along the chronosequence, which is in accord with the observations that α-Proteobacteria is more abundant in higher nutrient soils (Freedman and Zak, 2015; Knelman et al., 2015). These changes may be explained by the variations of soil available nutrients and plant biomass. Soil nutrients and vegetation composition are two of the main drivers of soil communities (Bardgett, 2002; Bokhorst et al., 2017; Nielsen et al., 2010). Along a chronosequence, soil organic carbon, soil total nitrogen and other available nutrients were increased, which provided carbon and nitrogen sources for the growth of soil microorganisms (Cleveland et al., 2007). 4.2. Relations among plant, soil and bacterial succession As expected, our results showed that plant biomass, plant cover, plant diversity and some bacterial communities were increased across the succession, which is consistent with previous studies reporting that plant characteristics altered soil bacterial community compositions (Song et al., 2016; Zhang et al., 2016; Zhong et al., 2015). In general, microbes changed with the variations of plant and soil conditions. The increase of plant cover, plant biomass and plant diversity improves the input of organic matter via roots and litters, and subsequently enhances the accumulations of soil organic carbon, available N and P contents (Lozano et al., 2014; Pugnaire et al., 2004). Health soil conditions also enhanced the growth of plant and soil microorganisms along a succession (Tscherko et al., 2005). Plant influences soil bacteria by the changes in soil nutrient pools (Lozano et al., 2014; Zhang et al., 2016). Similarly, soil nutrient conditions had no significant effects on soil bacterial diversity, but strongly altered bacterial compositions (Leff et al., 2015). The bacterial community shifts corresponded to the

Fig. 3. The plot of Nonmetric Multidimensional Scaling (NMDS). Different colors represented different sample sites.

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Fig. 4. A linear discriminant analysis effect size (LEfSe) method identifies the significance of soil bacterial abundant taxa across the secondary succession. Different colors represented different succession ages.

variations in the relative abundances of main taxa. For example, in the younger sites, soil bacterial community was mainly dominated by Actinobacteria, whereas in the old-age sites, Proteobacteria was increased and became the most dominant phylum. This turnover was caused by the changes of soil nutrients across the secondary succession.

Proteobacteria is copiotrophic as previous studies reported, which grew rapidly when labile substrates were available (DeAngelis et al., 2015; Li et al., 2014; Zhang et al., 2016). The abundance of Proteobacteria are also positively correlated with soil C and N contents reported by some researchers (Goldfarb et al., 2011; Nemergut et al., 2010; Zhang et al.,

Table 5 DistLM analysis of multivariate species data from soil parameters for (a) each variable taken individually and (b) variables combined in sequence. Marginal tests

Sequential tests

Variable

F

P

Prop.

Variable

F

P

Prop.

Cumul.

Soil parameters TN SOC N:P MBN NH4-N C:P TP NO3-N C:N

1.9361 1.8778 1.8599 1.8175 1.7942 1.7712 1.5628 1.496 1.2922

0.001 0.002 0.002 0.002 0.004 0.005 0.016 0.041 0.107

9.71% 9.45% 9.36% 9.17% 9.06% 8.96% 7.99% 7.67% 6.70%

TN MBN NO3-N TP C:N NH4-N SOC N:P C:P

1.9361 1.923 1.6121 1.3655 1.1876 1.1162 0.95804 0.77371 1.0346

0.002 0.003 0.027 0.089 0.216 0.29 0.526 0.766 0.451

9.712% 9.175% 7.424% 6.149% 5.281% 4.923% 4.239% 3.489% 4.651%

10% 19% 26% 32% 38% 43% 47% 50% 55%

Plant parameters Coverage Species richness Shannon index BGB AGB TB R:S Plant C Plant N Plant P

2.1098 1.9666 2.0698 1.6449 1.7096 1.729 1.9821 1.8909 1.2283 1.5277

0.001 0.002 0.001 0.015 0.006 0.005 0.002 0.003 0.151 0.022

10.49% 9.85% 10.31% 8.37% 8.67% 8.76% 9.92% 9.51% 6.39% 7.82%

Coverage R:S AGB Plant C Plant P BGB Plant N Shannon index Species richness

2.1098 1.9433 1.6635 1.4482 1.2093 1.048 0.9986 0.95429 1.0436

0.002 0.003 0.009 0.04 0.182 0.416 0.51 0.539 0.428

0.10491 0.091821 0.075648 0.064063 0.052758 0.045565 0.043423 0.041655 0.045375

10% 20% 27% 34% 39% 43% 48% 52% 57%

Prop: percentage variance explained by that variable; Cum.: cumulative percentage of variance explained. NO3-N means soil nitrate nitrogen; NH4-N means soil ammonia nitrogen; SOC means soil organic carbon; TN means soil total nitrogen; TP means soil total phosphorus; C:N means the ratio of SOC to TN; C:P means the ratio of SOC to TN; N:P ratio means the ratio of TN to TP; MBN means soil microbial biomass carbon. AGB, above ground biomass; BGB, below ground biomass; TB, total biomass; R/S, root/shoot ratio. Significant values are shown in bold (P b 0.05).

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2016). Proteobacteria include many N-fixing bacteria which can accumulate soil nitrogen contents (Spain et al., 2009). Acidobacteria is oligotrophic and fast-growing on a nutrient deficiency environment (Koyama et al., 2014). Some studies showed that a negative correlations between soil nutrients and the abundance of Acidobacteria (DeAngelis et al., 2015). These observations supported our hypothesis that the relative abundance of copiotrophs increased across the succession (Cleveland et al., 2007). N contents is the main factors affecting soil bacterial communities in our study and similar results were observed by Zhong et al. (2015) in a long-term N additions experiments and Leff et al. (2015) in grasslands across the globe. Yao et al. (2014) reported that soil NH+ 4 -N content positively correlated with the relative abundance of the main bacterial taxa on the Tibetan Plateau. Similar results were confirmed by the DistLM analysis in this study. DistLM analysis showed that soil total N, nitrate N and microbial biomass N were the main affecting factors on soil bacterial community, which was in agreement with previous studies (Leff et al., 2015; Yao et al., 2014; Zhang et al., 2016; Zhong et al., 2015). The relative abundance of Proteobacteria or α- Proteobacteria can reflect soil trophic status (Leff et al., 2015), with higher abundance in copiotrophic environments (Hartman et al., 2008). In this study, αProteobacteria were more abundant in the higher nutrient soils, which further supports this idea. 5. Conclusion After a long-term fencing in the grassland on the Loess Plateau, vegetation secondary succession had significant effects on soil nutrients and soil bacterial community compositions, except for community diversity. Bacterial community compositions transitioned from Actinobacteria-dominant to Proteobacteria-dominant along the succession. Soil organic carbon and total nitrogen contents showed a reduction as the fast growth of plants, whereas soil Proteobacteria showed an increase at the 12-year site. After about 12 years, soil bacterial community compositions remained stable, but soil nutrients continued to increase. These results indicated that the changes between soil nutrients and soil bacteria were incongruous. Soil available N contents were the main affecting factors on soil bacterial community compositions across succession in the dryland of the Loess Plateau. Acknowledgments This study was supported by the National Natural Science Foundation of China (41671280), Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (2015BAC01B01) and Special-Funds of Scientific Research Programs of State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau (A314021403-C6). We also thanked the anonymous reviewers to help improve the manuscript. References Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. Anderson, M.J., 2004. DISTLM v. 5: a FORTRAN computer program to calculate a distancebased multivariate analysis for a linear model. Department of Statistics, University of Auckland, New Zealand, p. 10. Bardgett, R.D., 2002. Causes and consequences of biological diversity in soil1. Zoology 105, 367–375. Berg, B., 2014. Decomposition patterns for foliar litter – a theory for influencing factors. Soil Biol. Biochem. 78, 222–232. Bergmann, G.T., Bates, S.T., Eilers, K.G., Lauber, C.L., Caporaso, J.G., Walters, W.A., et al., 2011. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biol. Biochem. 43, 1450–1455. Bertin, C., Yang, X., Weston, L.A., 2003. The role of root exudates and allelochemicals in the rhizosphere. Plant Soil 256, 67–83. Bokhorst, S., Kardol, P., Bellingham, P.J., Kooyman, R.M., Richardson, S.J., Schmidt, S., et al., 2017. Responses of communities of soil organisms and plants to soil aging at two contrasting long-term chronosequences. Soil Biol. Biochem. 106, 69–79.

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