Dynamics of soil microbial recovery from cropland to orchard along a 20-year chronosequence in a degraded karst ecosystem

Science of the Total Environment 639 (2018) 1051–1059

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

Dynamics of soil microbial recovery from cropland to orchard along a 20-year chronosequence in a degraded karst ecosystem Hongkai Liao a,b, Chunli Zheng b, Juan Li c, Jian Long a,⁎ a b c

Guizhou Provincial Key Laboratory of Mountain Environment, Guizhou Normal University, Guiyang 550001, People's Republic of China Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, People's Republic of China School of Geographic and Environmental Sciences, Guizhou Normal University, Guiyang 550001, People's Republic of 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

• Soil nutrient contents significantly increased following cropland to orchard conversion. • Conversion of cropland to orchard changed soil microbial community composition, but not diversity. • Actinobacteria tended to increase their relative abundances in responding to land-use conversion. • Belowground microbial community reflects the ecological recovery status.

a r t i c l e

i n f o

Article history: Received 9 March 2018 Received in revised form 20 May 2018 Accepted 20 May 2018 Available online xxxx Editor: Elena PAOLETTI Keywords: Restoration Soil microbial communities Soil carbon Degraded karst High-throughput amplicon sequencing

a b s t r a c t The ‘Grain for Green’ project (GGP) is the largest ecological rehabilitation project in China. A large body of croplands has been abandoned or converted to shrubs or grasslands since 1999. Soil microbes are recognized as sensitive responders of environmental changes, therefore, they are considered as a key component of ecological rehabilitation. However, very limited field experiments have been conducted to investigate the responses of soil microorganisms to restoration projects, especially in karst regions of China. In order to evaluate the response of soil microbial community to ecological restoration, we determined soil microbial community composition by means of qPCR, PLFAs, and high-throughput amplicon sequencing following conversion of cropland to Chinese prickly ash (Zanthoxylum bungeanum Maxim) orchard (CP) along a 20-year chronosequence in a degraded karst ecosystem. Our results showed that soil nutrient contents significantly increased following cropland to CP conversion. qPCR results showed that the highest bacterial abundance was found in the 20-year CP, but bacterial abundance decreased during the first 5-year land-use conversion. Conversion of cropland to CP strongly impacted soil microbial community composition, despite the cropland sites having a long cultivation history (N50 years). However, soil bacterial diversity remained unchanged within a 20-year land-use conversion. Actinobacteria, Proteobacteria, and Acidobacteria were the main bacterial phyla in all land-use sites. In particular, various members of Actinobacteria (e.g., Solirubrobacteraceae) tended to increase their relative abundances in responding to land-use conversion, which may imply that the shifts of soil microbial communities associated with recovering of ecological conditions. Overall, given the rapid yet differential response to ecological restoration, investigation of the belowground microbial community can provide an effective way of assessing ecological recovery of restoration projects in the karst region. © 2018 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Guizhou Provincial Key Laboratory of Mountain Environment, Guizhou Normal University, Baoshan Road 116, Guiyang 550001, People's Republic of China. E-mail address: [email protected] (J. Long).

https://doi.org/10.1016/j.scitotenv.2018.05.246 0048-9697/© 2018 Elsevier B.V. All rights reserved.

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1. Introduction In past several decades, sub-tropical and tropical regions have been experiencing massive land-use changes to meet the food demand from an ever-expanding population (Tripathi et al., 2016). Conversion of native vegetation to cropland or pasture has been recognized as one of the key factors affecting soil biodiversity and nutrient cycles (Foley et al., 2005; Reidsma et al., 2006; Jacobs et al., 2017; Peerawat et al., 2018). As a consequence, the negative impacts of land-use changes could lead to soil degradation and fragmentation of habitats, which in turn influence ecological balance and health (Foley et al., 2005). In 1999, the Chinese government launched the ‘Grain for Green’ Project (GGP) to recover degraded agricultural lands. GGP is one of the largest ecological projects in the world, which targeted N146,700 km2 with a budget of about 40 billion US dollars (Liu et al., 2016). Soil microbial communities not only make up the largest part of the global biological diversity (Tripathi et al., 2016), but they also play a central role in carbon (C), nitrogen (N), and phosphorus (P) cycling in each ecosystem (Su et al., 2015). On the other hand, soil microbial communities are quite sensitive to climate change (Bardgett et al., 2008; Castro et al., 2010), land -use conversion (Bossio et al., 2005; Yao et al., 2006; Lauber et al., 2008), or management regimes (Donnison et al., 2000; Pankhurst et al., 2002). A large body of research has shown that landuse conversion alters soil microbial biomass, activities, and microbial community composition as measured by phospholipid fatty acid (PLFA), community level catabolic profiles (Biolog™) and polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) (Xue et al., 2008; Bissett et al., 2011; Potthast et al., 2011). However, these methods had low resolution on microbial community structure at a fine taxonomic level. In recent years, novel high-throughput environmental DNA (eDNA) sequencing technologies provide an opportunity to gain insight into the soil microorganisms response to GGP at higher resolution and coverage. Some recent studies have applied high-throughput amplicon sequencing to analyze microbial communities in the core GGP region of the Loess Plateau. For example, Zhang et al. (2016a) found that microbial communities shift from Acidobacteria-dominant to Proteobacteria-dominant after the 30 years of land abandonment in the GGP region of Loess Plateau; Zeng et al. (2016) detected that mean annual precipitation was the main environmental driver controlling the soil microbial community composition. Despite many experiments conducted to target land use events, the impacts of GGP on soil microbial communities and its underlying mechanisms are still not well explored, especially in the karst mountainous regions of southwest China. The karst region in the southwest China is characterized by limestone and extremely fragile geological background that soil forming capacity of soluble bedrock is quite low, and soil is usually thin and coarse (Xiao et al., 2017). This region covers approximately 5.8% of the national land of China (Jiang et al., 2014). Owing to fragmented landscape, soils in this region are easy to degrade and erode as induced by human disturbances, particularly for conversion of natural vegetation to cropland (Tian et al., 2008; Li et al., 2017). Furthermore, inappropriate land-use regimes (e.g., intensive slope cultivation), leading to stony desertification became a serious environmental issue for local residents. With implementing of GGP, a large body of degraded croplands has been gradually abandoned or converted into woodland or grassland. Late studies have shown that GGP had positive impacts on soil carbon sequestration in the karst region (Liao et al., 2016; Xiao et al., 2017). Nevertheless, the responses of soil microbial communities to GGP are still not well explored in this unique fragmented landscape ecosystem. Thus, it is necessary to investigate the impacts of GGP on soil microbial communities, which is beneficial to ecological health and sustainable agriculture management in degraded karst ecosystems. In this study, in order to gain a comprehensive understanding of the impacts of ecological restoration projects on soil microbial communities, real-time quantitative PCR (qPCR), PLFAs, and high-throughput

amplicon sequencing were applied to study the responses of soil microbial communities along a 20-year land-use chronosequence in a typical karst GGP region. The objectives of this study were to 1) evaluate the patterns of changes in soil properties including soil carbon, nitrogen, and total phosphorus along a 20-year land-use chronosequence, 2) to investigate the soil microbial communities response to land-use change along a 20-year land-use chronosequence, and 3) to explore the indicator microbial taxa which significantly respond to ecological restoration project in karst region. 2. Materials and methods 2.1. Study sites and experimental designs This study was conducted at the Huajiang Karst Ecosystem Research Station (25°40′N, 105°39′E) in the karst region of Guizhou Province, southwest China. The altitude ranges from 500 to 1500 m above sea level. The climate is mountainous monsoon with a mean annual temperature of 17.5 °C. The mean precipitation is 1200 mm, of which 80% occurs between May and October. This region is characterized by the typical fragmented landscape. The soil type is limestone soil (calcareous lithosols, FAO). Before 1990, this region was seriously degraded due to intensive slope cultivation and deforestation activities. Since 1992, a large body of croplands has been converted to Chinese prickly ash (Z. bungeanum) orchards to suppress the process of stony desertification. Z. bungeanum is a deciduous tree species, which is commonly used for afforestation/plantation in the degraded karst region of Guizhou Province, due to its fast growth and adaptability to drought tolerance, poor habitat, and calcareous soil (Cheng et al., 2015). Until now, the plantation area of Chinese prickly ash has been N20 km2 in this region. The chronosequence approach is an effective way to explore the impacts of land-use conversion on edaphic features and soil microbial communities (Walker et al., 2010; Zhang et al., 2016a). We applied this method to evaluate the impacts of land-use conversion on soil microbial communities along a 20-year time scale. Croplands were chosen as a zero point (0-year) of the chronosequence, which were used to cultivate maize (Zea mays L.) and sweet potato (Ipomea batatas) for N50 years, and then 5-, 10-, and 20-year Chinese prickly ash orchards that were converted from the croplands were selected. 2.2. Soil sampling and analyses For each land conversion stage, three field replicates were established, and three 20 × 20 m plots were conducted in each field replicated in December 2016. In each plot, 10 soil samples were collected at 0 to 10 cm depth using a 5-cm soil auger. In our study, no spatial variations within plots were considered. Therefore, collected soil cores were pooled per plot to form one homogenized sample, and then transported to the laboratory within 12 h with ice. Plant tissues and stones were removed, then each sample was divided into two parts, one was immediately freeze-dried for DNA and PLFA extraction, and the remaining was air-dried for soil properties analyses. Soil pH was measured by 1:2.5 soil/water slurry with a pH monitor; SOC, TN, and total sulfur (TS) analyses were treated with 0.5 M HCl to remove carbonates, then measured by combustion method using a CNS analyzer (Vario Macro, Elementar, Germany). Total phosphorus (TP) content was determined following colorimetric procedures after wet digestion with H2SO4 and HClO4 (Parkinson and Allen, 1975). The available phosphorus (AP) was measured with 0.5 M NaHCO3 (pH 8.5) and colorimetric analysis. 2.3. Soil PLFA extraction and analysis Liquid extraction was performed with a chloroform–methanol–citrate buffer mixture (1:2:0.8) according to Wang et al. (2016a). Methyl

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nonadecanoate fatty acid (19:0) was added as internal standard to quantify the concentrations of phospholipids. PLFAs were quantified by a gas chromatograph equipped with a FID detector (GC-FID, Agilent Technologies, USA) and identified by a MIDI Sherlock Microbial Identification System (MIDI Inc., Newark, DE, USA). Gram-positive bacteria was indicated by the iso- and anteiso-branched fatty acids (except 10Mebranched PLFAs); Gram-negative bacteria was indicated by the cyclopropyl fatty acids; universal PLFAs were indicated by the saturated straight chain fatty acids; fungi were indicated by PLFA 18:2ω6c; PLFA 20:4ω6c was used to biomarker of protozoa (Potthast et al., 2011). Total microbial PLFAs concentrations were summed over each identified PLFA. 2.4. Soil DNA extraction and quantification Total DNA was extracted from 0.50 g freeze-dried using the FastDNA SPIN kit for soil (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer's instructions. DNA concentration was determined using a NanoDrop ND-2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). 2.5. Real-time quantitative PCR (qPCR) of the bacterial 16S rRNA gene qPCR was performed with a real-time PCR detection system (Light Cycle 480; Roche). Bacterial 16S rRNA gene abundance was measured using the primers 515F (GTGCCAGCMGCCGCGGTAA) and 907R (CCGT CAATTCCTTTGAGTTT) (Biddle et al., 2008). Each sample was prepared in three replicates in a 20 μL of reaction volume containing 10 μL Absolute SYBER Fluorescein Mix (Thermo Scientific, Grand Island, NY), 0.4 μL forward and reverse primer, 1 μL of 1:10 diluted DNA template, and 8.2 μL double ddH2O. The template-free control reactions contained 1 μL of ddH2O instead of DNA. Thermal conditions were set as follows: 5 min at 95 °C, followed by 45 cycles of 10s at 95 °C, 45 s at 53 °C, 45 s at 72 °C, and 15 s at 84 °C. Standard curves for qPCR were created using an up to 10-fold dilution series of PCR product containing a fragment with known 16S rRNA gene copy numbers. 2.6. PCR amplification of bacterial 16S rRNA gene and Illumina Miseq PE300 sequencing To amplify the bacterial fragments for sequencing, primers 515F (5′XXXXXXGTGCCAGCMGCCGCGG-3′) and R907 (5′-XXXXXXCCGTCAATT CMTTTRAGTTT-3′), which covered the V4-V5 regions of 16S rRNA genes and included sequencing adapters for the Illumina sequencing platform, were selected, where XXXXXX represents the six unique barcode sequences used for distinguishing samples. PCR was performed for 35 cycles using DreamTaq Green PCR Master Mix, which is commonly used for Illumina high-throughput sequencing of 16S rRNA gene (Xu et al., 2014; Xie et al., 2015). Each 50 μL reaction mixture contained 1 μL of template DNA, 25 μL of DreamTaq Green PCR Master Mix (2×) (Thermo Scientific, USA), 1 μL of 10-nM forward and reverse primer, respectively. The cycling program for PCR was as follows: 95 °C for 3 min followed by 35 cycles of 30 s at 95 °C, 30 s at 55 °C, 30 s at 72 °C, and a final extension of 5 min at 72 °C. Three tubes of amplicons were pooled and purified with a Universal DNA Purification Kit (Tiangen, China). The premixed samples were sent to Novogene (Beijing, China) for sequencing, where they were run on an Illumina MiSeq PE300 sequencing machine. 2.7. Data processing and analysis Raw reads were separated into corresponding samples according to the unique barcodes. Sequencing data were analyzed using QIIME (Caporaso et al., 2010). Forward and reverse reads were joined together using the Fast Length Adjustment of Short reads (FLASH) (Magoč and Salzberg, 2011) to downstream processing. Reads with a low (b20)

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average quality score and short reads (b100 nt) were removed (Kong, 2011). Sequence reads were clustered into operational taxonomic units (OTUs) based on 97% sequence similarity using uclust (Edgar, 2010), using an open reference OTU-picking strategy. Amplicons were chimera-screened against the GOLD data (Reddy et al., 2014) database and removed. The most abundant sequence within each OTU was considered as a representative sequence and was classified against the Greengenes reference database version 13_8 (McDonald et al., 2012) using uclust. The resultant OTU abundance tables were filtered to remove singletons and rarefied to 56,900 sequences per sample (OTU-tables 56,900) to ensure an equal sampling depth. Faith's index was used to determine the phylogenetic diversity (Faith, 1992). Alpha diversity metrics including Phylogenetic Diversity Whole Tree (PD whole tree), Shannon, and Chao1 were calculated using the OTU-tables 56,900 in QIIME. All statistical analyses were carried out using SPSS 16.0 (SPSS, Chicago, IL, USA). The differences in soil properties and alpha-diversity among samples were tested using a one-way analysis of variance (ANOVA) with Fisher's least significant difference (LSD) post hoc test. The significant differences were defined as p b 0.05. Beta-diversity of soil bacterial community was performed using R (version 3.3.2; http:// www.r-project.org/) with the “phyloseq” package (McMurdie and Holmes, 2013). The R VEGAN (Oksanen et al., 2013) Adonis method for permutational multivariate analysis of variance (PERMANOVA) (Anderson, 2001) was used to test significant differences in microbial community composition (both microbial PLFAs and 16S rRNA gene) along with a 20-year land-use chronosequence. DESeq2 was used to identify the OTU enrichment (Love et al., 2014) following land-use conversion. DESeq2 was run using Wald test, and adjusted P-value threshold of 0.01 was to calculate the log2flod change of CP vs. cropland.

3. Results 3.1. Soil physical and chemical properties Conversion of cropland to CP had significant impacts on soil nutrient concentrations in soil. Soil pH (F (3, 12) = 4.48, p = 0.04) was higher in the Chinese prickly ash orchards (CP) than in the cropland (Table 1). SOC(F(3,12) = 60.8, p b 0.001), TN(F(3,12) = 29.9, p b 0.001), TS (F(3,12) = 22.1, p b 0.001) concentrations, and C/N (F (3,12) = 24.3, p b 0.001) increased after conversion of cropland to CP, which was especially pronounced in the 20-year CP. Furthermore, the soil moisture content (SM) (F (3,12) = 18.6, p = 0.001) increased from 17.9% to 25% following land-use conversion. In contrast, soil available phosphorus (AP) concentration was highest in the cropland as compared with CP (Table 1).

Table 1 Soil physical and chemical properties of cropland and Chinese prickly ash plantation with different stand ages. Values are mean ± SE (n = 3). Different letters represent significant differences (LSD post hoc test, p b 0.05).

pH SM% SOC(g kg−1) TN(g kg−1) TP(g kg−1) TS(g kg−1) AP(g kg−1) C/N

Cropland

CP5

CP10

CP20

7.18 ± 0.18b 17.9 ± 0.84b 15.83 ± 0.96c 2.35 ± 0.05c 0.53 ± 0.02b 0.17 ± 0.02c 13.1 ± 1.43a 6.73 ± 0.31c

7.67 ± 0.15a 19.4 ± 0.50b 23.53 ± 0.49b 3.25 ± 0.06b 0.60 ± 0.04b 0.28 ± 0.01b 8.59 ± 0.39b 7.25 ± 0.05bc

7.80 ± 0.05a 22.8 ± 0.58a 26.43 ± 0.95b 3.51 ± 0.19b 0.59 ± 0.03b 0.35 ± 0.05b 11.3 ± 0.67ab 7.54 ± 0.15b

7.82 ± 0.14a 25.0 ± 0.97a 36.7 ± 1.70a 4.18 ± 0.19a 0.69 ± 0.04a 0.43 ± 0.03a 11.7 ± 1.78ab 8.79 ± 0.08a

CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively. SM = soil moisture; SOC = soil organic carbon; TN = total nitrogen; TP = total phosphorus; TS = total sulfur; AP = Available phosphorus; C/N = SOC/TN ratio.

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Table 2 The mean value ± SE (n = 3) of soil microbial PLFAs (n mol g−1) for each Chinese prickly ash plantation age. Different letters represent significant differences (LSD post hoc test, P b 0.05).

Universal Gram-positive bacteria Gram-negative bacteria Fungi Protozoa Fungi: bacteria (%) Total PLFAs

Cropland

CP5

CP10

CP20

18.9 ± 3.61 27.6 ± 4.32

15.5 ± 1.61 22.1 ± 2.74

19.7 ± 2.82 26.9 ± 2.65

20.9 ± 2.32 33.2 ± 4.69

36.8 ± 8.36

30.9 ± 4.9

42.1 ± 5.09

49.4 ± 5.82

4.2 ± 1.42 0.87 ± 0.26 6.23 ± 0.95a 120.7 ± 23.9

2.53 ± 0.09 0.97 ± 0.21 4.92 ± 0.49ab 102.1 ± 12.8

3.53 ± 0.69 1.07 ± 0.13 5.04 ± 0.45ab 138.7 ± 15.7

2.87 ± 0.67 1.10 ± 0.06 3.39 ± 0.36b 148.6 ± 18

CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively.

Table 3 Adonis tests of the effect of land-use conversion on microbial PLFA composition and bacterial community composition estimated by MiSeq sequencing data. 16S rDNA

Plantation Time (T) Cropland vs CP

PLFA

R2

P-value

R2

P-value

0.2183 0.2766

0.001 0.001

0.2325 0.1736

0.005 0.034

Cropland was defined as the 0-year plantation of Chinese prickly ash orchard. CP refers Chinese prickly ash orchard. Values in bold indicate significant correlations at P b 0.05 level.

variation, respectively. Adonis test showed that land-use conversion significantly changed soil microbial community composition, and cropland had distinct microbial community composition with CP (Fig. 1 and Table 3).

3.2. Microbial biomass and soil microbial communities as estimated by PLFAs

3.3. Genomic analysis of soil bacterial communities and diversity

Analysis of the microbial PLFAs showed that there were no significant differences among different land-use conversion stages (Table 2). All the microbial groups including Gram-negative bacteria, Grampositive bacteria, universal, and soil protozoa followed the same trend with the total microbial PLFAs, except that relatively high soil fungi concentration was observed in the cropland. In addition, the fungi to bacteria ratio was significantly higher (LSD post hoc test, p b 0.05) in the cropland than 5-year CP. Principal component analysis (PCA) of the relative abundances PLFAs showed that CP plantation changed soil microbial community composition, with PC1 and PC2 explaining 35.4% and 26.5% of the

qPCR analysis showed that bacterial 16S rRNA gene copy numbers changed significantly (LSD post hoc test, p b 0.05) during cropland to CP conversion (Fig. 2), and the highest 16S rRNA gene copy numbers were found in the 20-year CP. A total of 2,839,582 high-quality sequences remained from the complete data set, with sequences ranging from 56,963 to 361,286 per sample, of which 99,306 OTUs were identified. The most dominant phylum was Actinobacteria, which occupied from 34.1 to 44% of the total bacterial sequences (Fig. 3). Acidobacteria was the second dominant phylum, which accounted for 17–20.4% of the total bacterial sequences. Significant differences (LSD post hoc test, p b 0.05) in the relative abundance of Actinobacteria, Chloroflexi,

Fig. 1. Principal component analysis (PCA) ordination of microbial community composition using 28 individual phospholipid fatty acid (PLFA) biomarkers. CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively.

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Table 4 Effects of cropland and Chinese prickly ash plantation age on soil bacterial α-diversity. Values are mean ± SE (n = 3).

Observed species Chao1 PD whole tree Shannon

Cropland

CP5

CP10

CP20

13,101 ± 707

12,517 ± 187

12,550 ± 484

13,310 ± 802

33,578 ± 2512 33,546 ± 1091 34,190 ± 1897 37,181 ± 2675 752 ± 29.4 726 ± 7.96 733 ± 20.3 763 ± 37.5 11.4 ± 0.1 11.2 ± 0.06 11.2 ± 0.05 11.3 ± 0.15

CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively. All the α-diversity indices were not significant across different landuse conversion stages (LSD post hoc test, p N 0.05).

Fig. 2. Effects of Chinese prickly ash plantation age on the abundance of bacterial 16S rRNA gene copy numbers. Values are means ± SE (n = 3). Different letters indicate significant differences among different plantations ages at p b 0.05 level. CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively.

Beta-, Delta-, and Gammaproteobacteria were detected among different plantation stages. Actinobacteria were present in low abundance in the cropland, and the highest was found in the 20-year CP. The relative abundance of Actinobacteria was significantly correlated with SOC, TN, and TS concentrations (Table S1). On the contrary, the relative abundance of Chloroflexi showed a decreasing trend with cropland to CP conversion. Relatively high bacterial alpha-diversity indices (including Chao1, Shannon, and PD whole tree) were found in the 20-year CP plantation, although no significant differences were detected (Table 4). For soil bacterial beta-diversity, non-metric multi-dimensional scaling (NMDS) based on Bray-Curtis distance showed that land-use conversion changed soil bacterial community structure (P b 0.05), and the cropland clearly differed from CP (Table 3 and Fig. 4). Results of the Mantel test showed that soil properties, including C/N, SOC, SM, TS, and TN were positively correlated with soil bacterial community composition (Table S2 and S3).

Many OTUs, from several phyla, enriched significantly following cropland to CP conversion. There were a total of 520 enriched OTUs identified, of which 33% (173 OTUs) belonged to phylum Actinobacteria and 19% (100 OTUs) belonged to Planctomycetes, respectively (Fig. 5). At the family level, a large number of enriched OTUs with a wide range of microbial taxa were identified. We identified that members of Gaiellaceae, Solirubrobacteraceae, Gemmataceae, Acetobacteraceae, and Gemmataceae tended to increase their relative abundance in responding to cropland to CP conversion (Fig. 6). In particular, members of Solirubrobacteraceae and Acetobacteraceae were significantly enriched in each conversion stage as compared with cropland (Fig.S1). In contrast, members of Micromonosporaceae and Sinobacteraceae were more enriched in the cropland as compared with CP plantations (Fig. 6). 4. Discussion In our study, Chinese prickly ash orchard (CP) plantation had greater SOC and TN concentrations than that of the cropland. This finding was in consistent with that of Zheng et al. (2017), who found that long-term poplar plantation soils had greater SOC concentration as compared with cropland. The enrichments of SOC and TN could be attributed to the increased input of organic matter from both root exudates and leaves. Furthermore, the cessation of tillage may also contribute to the increases of SOC content through reducing soil disturbance and soil active carbon (e.g., particulate organic carbon) mineralization (Kantola et al., 2017). Interestingly, the concentration of SOC in the karst region

Fig. 3. Relative abundance of the soil bacterial communities at the phylum level. Values are means ± SE (n = 3). Different letters indicate significant differences among different plantations ages at p b 0.05 level. CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively.

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Fig. 4. Non-metric multidimensional scaling (NMDS) ordination based on Bray-Curtis distance of bacterial communities at each plantation age. CP5, CP10, and CP20 represent 5, 10, and 20 years since plantation of Chinese prickly ash orchard, respectively.

maintained relatively higher level than other GGP regions of China (Zhang et al., 2016a; Zheng et al., 2017), although it had greater risks of loss due to disturbance. This phenomenon might be attributed to the high soil carbonate content, which is beneficial to the formation of OM-Ca2+-mineral complexation, which facilitates the accumulation of soil C in the karst region (Li et al., 2017). Additionally, compared with

Fig. 5. Log2-fold change in relative abundance of OTUs as compared with cropland. Each circle shows a single OTU and dotted and dashed lines represent increase or decreases of 2- and 10-fold, respectively. Yellow circles represent OTUs that are significantly enriched in their relative abundances, with grey circles showing OTUs have no significant difference between Chinese prickly ash orchard (CP) and cropland.

CP, significant higher soil pH and available P and lower C/N ratio were observed in the cropland. This phenomenon could be attributed to the absence of fertilization during the CP growth periods. In our study, soil microbial PLFAs were used to indicate soil microbial biomass. We did not see statistical differences for total PLFAs and different microbial groups, although the highest mean values of total PLFAs and different microbial PLFAs were detected in the 20-year CP plantation. Generally, the relatively low microbial PLFAs were found in the 5-year CP plantation, which may contribute to rebuilding soil microbial communities during the early establishment stage of CP. The fungi to bacteria ratio has been widely used to evaluate the responses of the soil microbial communities to soil C and N dynamics and environmental changes (Six et al., 2006; Potthast et al., 2011; Deng et al., 2016). Interestingly, we found a significantly higher fungi to bacteria ratios in the cropland rather than CP plantations. The results were inconsistent with Zhang et al. (2016b), who found that fungi to bacteria ratios increased following cropland to coniferous plantations conversions. However, our results were in agreement with Xiang et al. (2017), who showed that the fungi to bacteria ratios decreased following afforestation in the GGP region of Loess Plateau. On the other hand, the fungi to bacteria ratios could be also affected by soil environment. We did find that the fungi to bacteria ratios were negatively correlated with soil moisture (r = −0.591, p = 0.04), which was consistent with the results obtained from Deng et al. (2016) in afforestation in central China. Thus, our results suggest that it is worth noting that some other factors may also impact the fungi to bacteria ratio in the soil during land-use conversion. Relatively high soil bacterial diversity indices were detected in the 20-year cropland, although no significant differences were detected. In contrary, many studies have shown that soil bacterial diversity indices changed significantly during land-use conversion. For example, Zheng et al. (2017) found that soil bacterial diversity indices changed significantly after 20-year cropland to hybrid poplar conversion. Zhang et al. (2016a) observed that diversity index of the bacterial communities also changed pronouncedly (p b 0.05) during 30-year land abandonment. In Amazon rainforest region, Rodrigues et al. (2013) reported

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Fig. 6. Log2-fold change in relative abundance of OTUs as compared with cropland at the family level. Each circle shows a single OTU significantly increased or decreased (p b 0.01) in their relative abundance. f_ indicates unidentified bacteria at the family level.

that soil bacterial diversity increased following rainforest to agriculture land conversion. However, it is noteworthy that land-use change may not always alter soil bacterial diversity. For example, Jesus et al. (2009) observed that there was not a reduction in bacteria diversity even of conversion of long-standing primary forest to pasture or croplands. Our results suggest that conversion of cropland to CP for 20year did not change soil bacterial diversity. Conversion of cropland to CP significantly changed bacterial abundance and microbial community structure as revealed by quantitative real-time PCR, PLFAs, and Illumina sequencing. Both PLFAs and 16S rRNA gene data showed that conversion of cropland to CP significantly altered soil microbial community composition, whereas similar microbial community composition was observed among different plantation stages (Table 3 and Table S4). This result may indicate that CP plantation along a 20-year chronosequence in the studied areas may not be sufficient to significantly impact on soil microbial community composition. qPCR results showed that the soil bacterial abundance significantly changed after conversion of cropland to CP plantation. The highest abundance was detected in the 20-year CP, indicating that long-term CP plantation provides a better habitat for certain soil bacterial communities due to the cessation of tillage and application of pesticides. The Actinobacteria, Acidobacteria, and Proteobacteria were the most dominant phyla regardless of CP plantation stage. Many other studies found

that Proteobacteria were normally recognized as the most abundant phylum in the GGP region (Zhang et al., 2016a; Zeng et al., 2017). By contrary, we found that the phylum Actinobacteria were the most abundant phylum (accounting for approximately 40% of the total bacterial sequences) and significantly increased after cropland to CP conversion. Members of phylum Actinobacteria are generally recognized as copiotrophic organisms, abundant in higher total C and N soils (Fierer et al., 2012; Tripathi et al., 2016). The findings demonstrated that conversion of cropland to CP facilitated the growth of copiotrophic taxa with improving soil C and N status. Therefore, investigation of the belowground microbial community can provide an effective way of assessing ecological recovery of restoration projects. The DESeq2 analysis allowed us to explore differential enriched OTUs after cropland to CP conversion. Our results further confirmed that members from Actinobacteria (33% enriched OTUs) tended to increase their relative abundance in responding to land-use conversion. At the family level, these enriched OTUs within Actinobacteria mainly belonged to the Gaiellaceae and Solirubrobacteraceae families. Specifically, members of Solirubrobacteraceae were enriched in each CP plantation stage, which may reflect that these microorganisms can be applied as sensitive responders to a land-use conversion event. Besides Solirubrobacteraceae, members of the family Acetobacteraceae were also detected enriched in all the CP plantations (Fig. 6) These groups

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are typically found in acidic environment (Mapelli et al., 2011), which also have been described as nitrogen-fixing bacteria able to promote plant growth by a variety of mechanisms (Reis and Teixeira, 2015). In cropland soils, Micromonosporaceae significantly enriched in the cropland. Micromonosporaceae are associated with secondary metabolite production, and some species have the ability to solubilize of rock phosphate (Hamdali et al., 2008). This family plays an important role in contributing soil phosphorus cycling in different agricultural soils (Hamdali et al., 2008; Wang et al., 2016b). Overall, given the rapid yet differential response to restoration, our results document that investigation of the belowground microbial community can provide an effective way of assessing ecological recovery of restoration projects. 5. Conclusions Conversion of cropland to Chinese prickly ash orchard not only enhanced soil nutrient contents but also changed soil microbial community composition. However, the soil microbial biomass and soil bacterial diversity remain unchanged within a 20-year land-use conversion chronosequence. In particular, various members of Actinobacteria (e.g., Solirubrobacteraceae) tended to significantly increase in their relative abundance in responding to land-use conversion. Further studies of the karst region can focus on the indicator taxa within Actinobacteria. Together, our results document that investigation of the belowground microbial community can provide an effective way of assessing ecological recovery of restoration projects. Acknowledgments This work was supported by the National Natural Science Foundation of China (41601249, 41661045, and 41461072), Soil Quality Regulation and Restoration Governance Science and Technology Innovation Talent Team for Karst Mountain region of Guizhou Province (Qian Science [2018] No.5609). We are indebted to the editors and anonymous reviewers for their constructive comments and invaluable suggestions on this manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.05.246. References Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol 26, 32–46. Bardgett, R.D., Freeman, C., Ostle, N.J., 2008. Microbial contributions to climate change through carbon cycle feedbacks. ISME J 2, 805–814. Biddle, J.F., Fitz-Gibbon, S., Schuster, S.C., Brenchley, J.E., House, C.H., 2008. Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. Proc. Natl. Acad. Sci. U. S. A. 105, 10583–10588. Bissett, A., Richardson, A.E., Baker, G., Thrall, P.H., 2011. Long-term land use effects on soil microbial community structure and function. Appl. Soil Ecol. 51, 66–78. Bossio, D., Girvan, M.S., Verchot, L., Bullimore, J., Borelli, T., Albrecht, A., Scow, K., Ball, A.S., Pretty, J., Osborn, A.M., 2005. Soil microbial community response to land use change in an agricultural landscape of western Kenya. Microb. Ecol. 49, 50–62. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Methods 7, 335–336. Castro, H.F., Classen, A.T., Austin, E.E., Norby, R.J., Schadt, C.W., 2010. Soil microbial community responses to multiple experimental climate change drivers. Appl. Environ. Microbiol. 76, 999–1007. Cheng, J.Z., Lee, X.Q., Theng, B.K.G., Zhang, L.K., Fang, B., Li, F.S., 2015. Biomass accumulation and carbon sequestration in an age-sequence of Zanthoxylum bungeanum plantations under the grain for green program in karst regions, Guizhou province. Agric. For. Meteorol. 203, 88–95. Deng, Q., Cheng, X., Hui, D., Zhang, Q., Li, M., Zhang, Q., 2016. Soil microbial community and its interaction with soil carbon and nitrogen dynamics following afforestation in central China. Sci. Total Environ. 541, 230–237. Donnison, L.M., Griffith, G.S., Hedger, J., Hobbs, P.J., Bardgett, R.D., 2000. Management influences on soil microbial communities and their function in botanically diverse haymeadows of northern England and Wales. Soil Biol. Biochem. 32, 253–263.

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