Changes in the soil microbial communities of alpine steppe at Qinghai-Tibetan Plateau under different degradation levels

Science of the Total Environment 651 (2019) 2281–2291

Contents lists available at ScienceDirect

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

Changes in the soil microbial communities of alpine steppe at Qinghai-Tibetan Plateau under different degradation levels Heng Zhou ⁎, Degang Zhang ⁎, Zhehao Jiang, Peng Sun, Hailong Xiao, Yuxing Wu, Jiangang Chen College of Grassland Science, Gansu Agricultural University, Lanzhou, Gansu, People's Republic of China Key Laboratory of Grassland Ecosystem, Gansu Agricultural University, Lanzhou, Gansu, People's Republic of China Ministry of Education/Sino, U.S. Center for Grazing Land Ecosystem Sustainability, Lanzhou, Gansu, 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

• We investigated the soil microbial communities in degraded alpine steppe using 16S rRNA. • There were no differences in the αdiversity of the microbes in the degraded alpine steppe. • The microbial community structures significantly differed along with the alpine steppe degradation. • The microbial community diversity and abundance were mainly affected by the Gramineae coverage and soil total phosphorous. • The potential function profiling discovered human disease pathways involved in degraded alpine steppe.

a r t i c l e

i n f o

Article history: Received 2 June 2018 Received in revised form 14 August 2018 Accepted 26 September 2018 Available online 01 October 2018 Editor: Jose Julio Ortega-Calvo Keywords: Microbial diversity Microbial community Metabolic pathway Degradation steppe 16s rRNA Illumina MiSeq sequencing

a b s t r a c t The alpine steppe at Qinghai-Tibetan Plateau is an important area for conserving water source and grassland productivity; however, knowledge about the microbial community structure and function and the risk to human health due to alpine plant-soil ecosystems is limited. Thus, we used prediction methods, such as Tax4Fun, and performed a metagenome pre-study using 16S rRNA sequencing reads for a small scale survey of the microbial communities at degraded alpine steppes (i.e., non-degraded (ND), lightly degraded (LD), moderately degraded (MD), heavily degraded (HD), and extremely degraded (ED) steppes) by Illumina high-throughput sequencing technology. Although there were no significant differences in the microbial alpha diversity among the different degraded alpine steppes and the dominant phyla at the different degraded alpine steppes, including Actinobacteria, Proteobacterial, Acidobacteria and Chloroflexi, were similar, the beta-diversity significantly differed, indicating that alpine steppe degradation might result in variation in microbial community compositions. The linear discriminate analysis (LDA) effect size (LEfSe) analysis found twenty-one biomarkers, most of which belonged to Actinobacteria, suggesting that microbes with a special function (such as the decomposition soil organic matter) might survive in alpine steppes. In addition, the functional profiles of the bacterial populations revealed an association with many human diseases, including infectious diseases. In addition, the microbial communities were mainly correlated with the populations of Gramineae and soil total phosphorous. These results suggested that alpine steppe degradation could result in variations in the microbial community composition, structure and function at Qinghai-Tibetan Plateau. Further studies investigating the degraded alpine

⁎ Corresponding authors at: College of Grassland Science, Gansu Agricultural University, Lanzhou, Gansu, People's Republic of China. E-mail addresses: [email protected] (H. Zhou), [email protected] (D. Zhang).

https://doi.org/10.1016/j.scitotenv.2018.09.336 0048-9697/© 2018 Elsevier B.V. All rights reserved.

2282

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

steppe environment are needed to isolate these potential pathogenic microbes and help protect livestock using these alpine steppes. © 2018 Elsevier B.V. All rights reserved.

1. Introduction The grassland at Qinghai-Tibetan Plateau, which is mainly located in the headstream region of the Yellow, Yangtze, Lancang and Mekong Rivers, covers nearly 1.5 million km2 of the total plateau area and plays a key role as a water source conserving grassland productivity (C.T. Wang et al., 2007; Ren et al., 2012). However, for decades, accelerated frozen-thaw phenomena, climate change (Klein et al., 2004; Zhou et al., 2005; Shang et al., 2008; Feng et al., 2009), rodent destruction of sward and vegetation due to tunnel digging and plant root chewing, as well as human pressures, such as overgrazing, have led to decreased diversity, structure and function in the plant community and destroyed soil physical structures, such as soil porosity and permeability (Cerdà and Lavée, 1999; Angassa, 2015; Lin et al., 2015); furthermore, black soil land has formed, hampering the growing any plants (Shang et al., 2008) and eventually resulting in the degradation of this important ecosystem. Approximately one-third of the total alpine meadow and one-fifteenth of the alpine steppe experienced degradation or heavy degradation (Wang, 1997; Ma et al., 1999). These problems, in turn, caused serious environmental problems since land degradation and land use change over the last 30 years at the Plateau has resulted in the loss of 3.02 Pg carbon (Genxu et al., 2002). Soil is a complicated biosystem that provides water and nutrients required for plant productivity and is the habitat where biogeochemical cycles occur (Wang et al., 2016). The absence of plants can lead to desertification and lower the microbial numbers (Hirsch et al., 2016). In addition, soil microbial communities are highly important to the ecosystem due to their important role in regulating biogeochemical processes, such as carbon and nitrogen cycles (Balser and Firestone, 2005; Allison and Martiny, 2008). Different microbes perform different functions during the soil nutrient cycle, such as decomposing organic matters and litters (Strickland et al., 2009; Allison et al., 2010; Allison et al., 2013). A previous study found that some bacteria from degraded soil were harmful to plant growth (Olff et al., 2000) because the steppe degradation changed their metabolism. Biotic and abiotic factors (such as litter inputs, soil physical properties, and soil nutrient status) control microbial populations, community structure and activities (Wang et al., 2016). Thus, changes in the soil nutrient status and plant diversity due to alpine steppe degradation could result in variation in the microbial community structure and composition and present an indication of ecosystem variations. Therefore, studies investigating microbial community changes and their function in degradation are important for grassland management and health evaluations. However, research investigating the diversity of microbial communities at the degraded alpine steppe of Qinghai-Tibetan Plateau is scarce (Liu et al., 2015). Therefore, we use high-throughput sequencing technology to identify the different operational taxonomic units (OTUs) and analyse the differences and similarities in microbial community structures among different degraded steppe samples and use a LefSe analysis to perform a linear discriminant analysis of samples from different grouping conditions according to their taxonomic composition to determine the communities or species that exhibit significant differences in degradation (which is known as a “biomarker”) (Segata et al., 2011). However, the information available from 16S rRNA analyses is relatively limited, and further analysis is generally difficult to conduct. Recently, the potential KEGG Orthologue (KO) functional profiles of microbial communities were predicted by 16S rRNA gene sequences using the PICRUSt (Langille et al., 2013), FAPROTAX, BugBase and Tax4Fun approaches (Aßhauer et al., 2015). The PICRUSt approach was used to

predict metagenomes with a higher frequency of citations. PICRUSt infers unknown gene content by an extended ancestral state reconstruction algorithm and gene function profiles of other unknown species in the Greengenes database to construct whole genealogical function prediction profiles of archaea and bacteria (Langille et al., 2013). Finally, the sequenced bacterial flora composition was “mapped” to the database to predict the metabolic function of bacterial flora. Compared with the metagenome data, the 16S rRNA data contain a large proportion of unknown phyla in measured microbial communities, which could be problematic in predicting function profiles. Meanwhile, the Tax4Fun (Aßhauer et al., 2015) approach could use minimum similarity 16S rRNA gene sequence data to perform a functional annotation by the nearest identification method and map the reads to SILVA. The results obtained by Tax4Fun have been reported to outperform those obtained by PICRUSt tool. Thus, the aims of this study were to 1) reveal the effect of steppe degradation on microbial diversity and composition and determine the differences and similarities in the microbial community structures in different degraded steppes, 2) identify microbes that could serve as biomarkers of degraded alpine steppes, and 3) determine the potential bacterial functional profiles in degraded alpine steppes. 2. Materials and methods 2.1. Study area We carried out this study in 2017 in Maduo County, Guoluo Prefecture, Qinghai Province, China (96°56′6″–99°20′13.2″N, 34°0′ 43.2″–35°37′19.2″E; 4223 m a.s.l); this study was performed at Sanjiangyuan Natural Reserve near Eling Lake. The moderately degraded steppe was approximately 7 km; the other degraded steppes were 55 km east of Maduo, and 340 km southwest of Xining (Fig. S1). The annual mean temperature and precipitation were −4 °C and 303.9 mm, respectively. Most rainfall occurred during the summer. The soil type was alpine steppe soil (Cambisol in the FAO/UNESCO taxonomy) (G. Wang et al., 2007). Based on a method described by Li et al. (2016), we selected five degraded steppes, including non-degraded (ND), lightly degraded (LD), moderately degraded (MD), heavily degraded (HD) and extremely degraded (ED) alpine steppes. The dominant plant species were Stipa purpurea and Kobresia myosuroides in the ND steppe, Stipa purpurea in the LD and MD steppes, Polygonum sibiricum in the HD steppe, and Saussurea arenaria in the ED steppe (Table 1). The plant community features and soil physical and chemical characteristics are presented in Tables 2 and 3. 2.2. Plant measurements and soil sampling Each alpine steppe degradation level was randomly applied to three replicates (each 200 × 200 m2) of 5 randomly selected quadrats (50 cm × 50 cm), and the distance between each quadrat was N50 m, which surpassed the space pertinence of the microbial variables; thus, each quadrat was independent from the others. The plant coverage, species, and height in each quadrat were recorded. In addition, the plant species richness (the number of species in a community or habitat) and Shannon-Wiener index were calculated (Klein et al., 2007). After the plant community measurement, three soil samples from each quadrat (upper 10 cm) were randomly collected with soil augur (10 cm diameter) on August 6, 2017; mixed, homogenized, and sieved

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291 Table 1 Description of basic situation of non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and extremely degraded (ED) alpine steppe. Degradation level

Altitude Species

Importance value

ND

4375 m

24.1 21.7

LD

4275 m

MD

4246 m

HD

4369 m

ED

4375 m

a

Stipa purpurea Griseb. Kobresia humilis (C. A. Mey. ex Trautv.) Sergiev Poa annua L. Leontopodium leontopodioides Youngia japonica Potentilla multicaulis Bge. Stipa purpurea Griseb. Potentilla multicaulis Bge. Poa annua L. Leontopodium leontopodioides Potentilla bifurca L. Astragalus membranaceus (Fisch.) Bunge. Stipa purpurea Griseb. Leontopodium pusillum (Beauv.) Hand.-Mazz. Potentilla bifurca L. Heracleum hemsleyanum Diels Thalictrum aquilegifolium Linn. var. sibiricum Regel et Tiling Artemisia Linn. Sensu stricto, excl. Sect. Seriphidium Bess.a Leymus secalinus (Georgi) Tzvel. Polygonum sibiricum Laxm. Ajania tenuifolia (Jacq.) Tzvel. Aster alpinus L. Poa annua L. Saussurea arenaria Euphorbia pekinensis Rupr. Saussurea japonica (Thunb.) DC. Cruciferaea Potentilla bifurca L.

14.8 5.9 3.3 3.1 23.4 12 11.5 11.2 8.6 6.2 24.7 13.8 9.6 8.3 5.1 3.4 2.4 23.2 9.1 8.1 6.2 47.4 11.9 9 4.8 2.2

2283

determined by an automated flow injection analyzer (FS3100, O.I. Corporation/Xylem Inc., USA) after the extraction of the soil samples with 2 M KCl. The soil total carbon content (TC) was measured by a TOC-analyzer (multi N/C 2100S/1, Analytik Jena AG, Germany). The total phosphorus concentration was measured colourimetrically after wet digestion with H2SO4 and HClO4 (UV2800A ultraviolet spectrophotometer, UNIC Inc., China) (Fu et al., 2004). The total potassium (TK) concentration was measured by the flame emission technique with an FP6431 flame photometer (YD LTD. Shanghai, China). The soil pH value was measured by using 5 g soil and 10 mL deionized water by a pH metre (ST2100,OHRUS, Co. Ltd., Jiangsu, China) (Long et al., 2016; Cao et al., 2017a). The soil compaction was measured by a soil compaction metre (SC 900, Spectrum@ Technologies, Inc. USA). 2.4. DNA extraction and PCR amplification In total, 5 g soil were used to extract the microbial DNA by a FastDNA®SPIN Kit for Soil (MP Biomedicals, CA, USA) according to the manufacturer's instructions. The V3-V4 region of the bacterial 16S rRNA gene was amplified with the primers 338F and 806R (Chu et al., 2015). The 20 μL PCR mixture was prepared in triplicate with 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase and 10 ng of template DNA (China). The following programmes were used to perform the PCR analysis: 3 min of denaturation at 95 °C, 27 cycles of 30 s at 95 °C, 30 s of annealing at 55 °C, 45 s of elongation at 72 °C, and a final extension at 72 °C for 10 min. The PCR products were extracted on a 2% agarose gel, further purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA) according to the manufacturer's protocol.

Means unknown species.

2.5. Illumina MiSeq sequencing (b2 mm) to remove the roots and other plant materials; and stored in an ice box. Thus, 3 composite soil samples (regarded as sub-samples) of each of the 5 different degraded steppe alpines were obtained (for a total of 15 sub-samples). The samples were brought to the laboratory immediately after collection and divided into two equal parts. One part was stored at 4 °C for the measurement of soil physicochemical properties, and the other part was stored at −80 °C for the molecular analyses. The aboveground plant biomasses were mowed, dried in an oven at 60 °C for 48 h and weighted. 2.3. Soil physicochemical properties The gravimetric method was used to measure the soil moisture (Wang et al., 2016). The cutting-ring method was used to measure the soil bulk density (BD), which was calculated with the following formula: BD (g·cm−3) = dry soil weight (g) / soil volume (cm3). The soil total nitrogen (TN), ammonium and nitrate concentrations were

The purified amplicons were pooled equally and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). 2.6. Processing of sequencing data We used Trimmomatic and FLASH to quality-filter and merge the raw fastq files of 15 alpine steppe soil samples, and the reads were truncated to obtain an average quality score b20, and a sliding window over 50 bp and over 3 continuous bases. Then, we used UPARSE (version 7.0 http://drive5.com/uparse/) to cluster the optimized sequences with 97% similarity into operational taxonomic units (OTUs) (Caporaso et al., 2012). Each 16S rRNA gene sequence was aligned against SILVA (Release128 http://www.arb-silva.de) at a confidence threshold of 70% to obtain the taxonomy according to the RDP Classifier algorithm (Release 11.1 http://rdp.cme.msu.edu/).

Table 2 Descriptive statistics for plant communities feature of non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and extremely degraded (ED) alpine steppe. Values are mean ± standard error.

Species richness Coverage (%) Shannon-Wiener index Gramineae (%) Cyperaceae (%) Other Grasses (%) AGB (g/m2)

ND

LD

MD

HD

ED

7.4 ± 0.55a 94.0 ± 2.55a 0.65 ± 0.10a 37.11 ± 6.82a 41.33 ± 8.77a 21.55 ± 5.75c 33.00 ± 4.17ab

8.0 ± 1.41a 76.0 ± 7.31b 0.70 ± 0.03a 51.41 ± 8.58a 4.94 ± 5.32bc 43.65 ± 12.24b 40.10 ± 7.55a

8.2 ± 3.77a 70.0 ± 8.80b 0.65 ± 0.19a 50.36 ± 14.03a 9.16 ± 7.49b 40.48 ± 15.06b 29.44 ± 8.73bc

5.6 ± 0.55a 42.4 ± 19.53c 0.51 ± 0.15a 12.71 ± 18.29b 0.77 ± 1.72c 86.52 ± 18.41a 22.58 ± 6.78cd

3.0 ± 1.58b 24.6 ± 6.19d 0.26 ± 0.22b 0.00 ± 0.00b 0.00 ± 0.00c 100 ± 0.00a 19.61 ± 6.10d

Note: Abbreviation: AGB (Aboveground biomass). In a list, if a minuscule alphabet is same, the divergence is not significant; on the contrary, divergence is significant at 0.05 levels.

2284

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

Table 3 Summaries of soil properties of non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded alpine steppe (ED). Values are mean ± standard error.

pH WC (%) BD (g/cm3) TOC (g/kg) TN (g/kg) TP (g/kg) TK (g/kg) NH+ 4 -N (mg/kg) NO− 3 -N (mg/kg) SC (KPa)

ND

LD

MD

HD

ED

7.45 ± 0.15ab 12.27 ± 0.41b 0.93 ± 0.07d 15.88 ± 3.33ab 0.81 ± 0.08a 0.54 ± 0.05a 9.03 ± 0.29a 51.47 ± 1.57b 22.10 ± 8.15a 2390.80 ± 211.87a

7.23 ± 0.32b 11.01 ± 0.44b 1.18 ± 0.07c 19.18 ± 6.92a 0.52 ± 0.13bc 0.56 ± 0.03a 8.91 ± 0.24a 94.78 ± 6.22a 17.09 ± 4.91b 1644.47 ± 100.29b

7.79 ± 0.36a 6.10 ± 1.01d 1.42 ± 0.07b 14.30 ± 2.60ab 0.29 ± 0.04d 0.46 ± 0.02b 6.41 ± 0.68b 46.42 ± 16.87b 14.14 ± 2.50b 1534.67 ± 116.98b

7.48 ± 0.22ab 14.26 ± 1.91a 1.32 ± 0.07b 9.07 ± 1.49b 0.60 ± 0.06b 0.52 ± 0.03a 9.36 ± 0.11a 47.63 ± 5.58b 15.77 ± 2.83b 1436.33 ± 88.75b

7.52 ± 0.27ab 8.66 ± 0.46c 1.65 ± 0.06a 8.93 ± 1.27b 0.43 ± 0.01c 0.51 ± 0.00ab 8.81 ± 0.18a 43.54 ± 13.44b 14.90 ± 2.81b 959.20 ± 69.03c

Note: Abbreviations: WC (water content), BD (bulk density), TOC (total organic carbon), TN (total nitrogen), TP (total phosphorous), TK (total potassium), NH+ 4 -N (ammonia nitrogen), NO− 3 -N (nitrate nitrogen), SC (soil compactness). In a list, if a minuscule alphabet is same, the divergence is not significant; on the contrary, divergence is significant at 0.05 levels.

2.7. Statistic analysis The rarefaction curves and alpha-diversity, including Shannon, chao and Good's coverage, were calculated by Mothur (version v.1.30.1 http://www.mothur.org/wiki). The rarefaction curve is mainly constructed based on the alpha diversity index of microorganisms at different sequencing depths and reflects the microbial diversity in the samples at different sequencing quantities. This curve can be used to show whether the amount of sequencing data in the samples is reasonable. Rarefaction curves can be generated by randomly sampling the number of sequences and their corresponding species (e.g., OTU) or their diversity index. For example, sobs is an index that reflects the number of species actually observed. If the curve is flat, the amount of sequencing data is reasonable, and additional data will not produce many new species. The alpha-diversity reflects the diversity of species in a community or habitat and mainly focuses on the number of species in a local homogeneous habitat. Therefore, alpha-diversity is also called within-habitat diversity and measures species diversity in a community. Chao reflects the community richness, good's coverage reflects the community coverage, and Shannon reflects community evenness. PCoA (Principal co-ordinates analysis), which is a Principal coordinates analysis, is a non-binding data dimension reduction analysis method; this method can be used to research the community composition or similarities or differences among samples by first sorting a series of eigenvalues and eigenvectors and then selecting the top most characteristic values and performances in the coordinate system to determine the underlying principal component affecting the sample community composition differences. Considering the collinearity of factors across large spatial distances, we conducted a variance inflation factor (VIF) in SPSS 19.0 to determine the collinearity among different factors as follows: VIF = 1 / (1 − Ri2), where Ri2 represents the variance ratio of an independent variable to other independent variables in the model and is used to measure the collinearity relationship between an independent variable and other independent variables. The higher the VIF value, the more serious the multicollinearity relationship between independent variables. In addition, environmental factors exhibiting P N 0.05 or VIF N 10 were removed from the subsequent analysis. The VIF values of the AGB (aboveground biomass), COV (coverage), SC (soil compactness), TK (total potassium), and BD (bulk density) were higher than 10 and removed. A redundancy analysis (RDA) was performed using R 3.5.1 (https:// cran.r-project.org/web/packages/rda/) to observe the relationship between the sample distribution and environmental factors. The selection principle of the RDA or CCA model is as follows: first, the species sample data (97% similar sample OTU table) are used for the DCA analysis, and then, the length of the first axis of the gradient in the analysis results is shown. If the length is N4.0, the length of CCA should be selected. In this study, the value was 1.868; thus, RDA was chosen. A subset of

environmental factors was screened by the bioenv function to determine the maximum Pearson correlation coefficient between the environmental factors and the sample community distribution; then, RDA was conducted, and the significance of all physicochemical factors was tested with Monte Carlo permutations (permu = 999). Mantel and partial mantel tests were conducted using QIIME 1.9.1 (http://qiime.org/index.html). We used mantel and partial mantel tests to determine the correlation between the environmental factors (such as the soil properties and vegetation characteristics) and microbial communities by the weighted UniFrac distance matrix in QIIME 1.9.1 (ref. 65) using the script compare_distance_matrices.py. Potential microbial biomarkers were obtained by the linear discriminate analysis (LDA) effect size (LEfSe) method (http://huttenhower. sph.harvard.edu/lefse/) (Segata et al., 2011). Briefly, first, the nonfactorial parametric Kruskal Wallis (KW) sum-rank test was used to determine significant differences in the abundance, and groups with a significant abundance difference were identified. Finally, LEfSe was performed using a linear discriminant analysis (LDA) to measure the effect of the abundance of each component (species) on the difference effect and determine the communities or species that have significant differences in degradation (i.e., “biomarkers”). In addition, to better understand the potential functional contributions of the observed microbes in the degraded alpine steppes, we used 16S rRNA gene sequences to calculate the metabolic cycles and pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) by the Tax4fun package in R 3.5.1 (Aßhauer et al., 2015). This technique maps partial 16S rRNA gene sequences to SILVA (SILVA SSU Ref NR database release 125 and KEGG database release 87.0) reference phylogeny using QIIME 1.9.1. to predict potential function profiles. Following the functional assignment, the KEGG pathways were summarized in R by the Tax4Fun package. In addition, the subsystem level 3 of KEGG Orthologues was downloaded for the analysis. The degradation effects on the plant characteristics and soil physicochemical properties were examined by a one-way analysis of variance and multiple comparisons were performed by Duncan's new multiple range method. All statistical analyses were conducted by SPSS version 19 (SPSS, Inc., Chicago, IL, USA). 3. Results 3.1. Variations in plant and soil characteristics due to alpine steppe degradation 3.1.1. Variations in plant characteristics due to degraded alpine steppes The plant characteristics differed among the degraded alpine steppes. The plant species richness was significantly lower in ED, but no significant differences were found among ND, LD, MD and HD. ED decreased the plant species richness by 59.5%, 62.5%, 63.4%, and 46.4% compared with that in ND, LD, MD, and HD. Significant differences in

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

the Shannon-Wiener index were found only between LD and MD (P b 0.05), and no significant differences were found in the other indexes (P N 0.05). In addition, the Shannon-Wiener index in ED decreased by 60%, 62.9%, 60%, and 49% compared with that in ND, LD, MD, and HD (Table 2). In terms of plant coverage, there was no significant difference between LD and MD (P N 0.05), and the coverage was significantly higher in ND (P b 0.05) and significantly lower in ED by 73.8, 67.6, 64.9%, and 42.0% compared with that in ND, LD, MD and HD, respectively (P b 0.05, Table 2). The above ground biomass decreased as the degradation level increased, and the largest biomass was found in LD; the biomass in ED was decreased by 40.58%, 51.1%, 33.39% and 13.15% (P b 0.05, Table 2) compared with that in ND, LD, MD and HD. The plant height did not significantly differ as the degradation level increased. As the degradation level increased, the coverage of Cyperaceae and Gramineae significantly decreased, but the coverage of the other grasses increased (P b 0.05, Table 2). 3.1.2. Variations in the soil characteristics due to alpine steppe degradation The soil physical and chemical properties significantly differed among the degraded steppes. The contents of TOC, TN, NH4+-N, NO3−-N, and SC significantly decreased as the degradation level increased (P b 0.05), while the pH and BD valued significantly increased as the degradation level increased (P b 0.05, Table 3). TOC was decreased by 17.21%, 25.44%, 52.71% and 53.44% in ND, MD, HD and ED compared with that in LD (Table 3). LD, MD, HD and ED exhibited decreased TN by 35.66%, 63.93%, 26.64% and 46.72% compared with that in ND (Table 3). NH4+-N was higher in LD and decreased by 45.93%, 51.28%, 50.00% and 54.34% in ND, MD, HD and ED compared with that in LD (Table 3). NO3−-N in LD, MD, HD and ED was decreased by 22.66%, 36.03%, 28.67% and 32.58% compared with that in ND (Table 3). SC decreased by 31.21%, 35.81%, 39.92% and 59.88% in LD, MD, HD and ED compared with that in ND, respectively (Table 3). In addition, the TP content was significantly higher in MD (P b 0.05), and no significant differences were found

2285

among ND, LD, MD and ED (P N 0.05). TP decreased by 14.72%, 17.94%, 11.45% and 9.92% in ND, LD, MD and ED compared with that in MD (Table 3). The soil pH values in ND, LD, MD, HD and ED were 7.45, 7.23, 7.79, 7.48 and 7.52, respectively. There was no significant difference in the pH valued among ND, HD and ED, but significant differences were observed between LD and MD. BD increased by 20.85%, 34.19%, 29.22% and 43.35% in LD, MD, HD and ED compared with that in ND. WC was significantly higher in HD, and no significant difference between ND and LD was observed. 3.2. Sequence data analysis In total, 824,101 quality sequences were obtained from 15 samples, and the fragment length ranged from 266 to 557 bp. These 15 soil samples contained 25,883 to 512,498 reads, and the smallest number (25883) was chosen to compare all degraded alpine steppe soil samples at the same sequencing level. In total, 2730 OTUs were observed across all samples, ranging from 1078 to 1624 OTUs in each soil samples. The lowest OTU number was observed in LD, while the highest OTU number was observed in MD. Among the OTUs, 1.06 (n = 29), 0.95 (n = 26), 1.65 (n = 45), 5.27 (n = 144) and 1.61% (n = 44) of OTUs were exclusively detected in the ND, LD, MD, HD and ED soil samples, respectively, and most OTUs (N = 1190, 43.59%) were observed in the degraded soil samples and non-degraded soil samples. As the depth of classification increased, the mean percentage of uncategorized sequences increased, ranging from 0.17% (phylum level) to 8.33% (genus level) (Fig. S2), illustrating that there were more original sequences in the degraded alpine steppes. 3.3. Diversity of the microbial community There were no significant (P N 0.05) differences in the alpha diversity indexes (observed OTUs, Chao richness index, and Shannon's diversity index) among the degraded steppes (Fig. 1a–d). The calculated

Fig. 1. Alpha diversity of non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe. (a) Observed OTUs, (b) Chao richness, (c) Shannon's diversity and (d) sequencing coverage.

2286

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

Fig. 2. The analysis of similarities (AMOSIM) and the principal coordinate analysis (PCoA) of all bacteria community composition for non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe.

sampling coverage among the five degraded alpine steppes showed no significant difference (Fig. 1d). The coverage values of all degraded alpine steppe soil samples were over 0.98 (Fig. 1d), and the rarefaction curves were saturated (Fig. 1a), indicating that the sequencing depth was adequate to cover most microorganisms and even included some rare species. The analysis of similarities (ANOSIM) (r = 0.90, P b 0.01) (Fig. 2a) showed that the samples from the different degraded alpine steppes significantly differed from each other. The principal coordinate analysis (PCoA) (Fig. 2b) showed that the bacterial communities in ND and LD clustered closely and were grouped separately from those in MD, HD

and ED; coordinate axes 1, 2, and 3 (PC1, PC2, and PC3) can explain 34.43%, 21.75% and 15.41% of the variation. 3.4. Microbial community composition The microbial composition at the phylum level (relative abundance N0.1% in the 15 samples) is shown in Fig. 3(a). Actinobacteria was the most abundant across all samples, and the average abundance was 39.90%, 38.85%, 35.56%, 32.20% and 34.29% in ND, LD, MD, HD and ED, respectively. The second most dominant phylum was Proteobacterial with an average abundance of 39.90%, 38.85%, 35.56%, 32.20% and

Fig. 3. Microbial community composition of non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe. (a) Bacterial phyla (N0.1%), (b) classes (N0.1%), (c) classified genera (top 50) and (e) unclassified clades.

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

2287

Fig. 4. LEfSe analysis of microbial abundance in non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe. (a) Is the LEfSe results on degraded alpine steppe microbial communities. (b) Is the histogram of LDA scores computed for differentially abundant microbe among degraded alpine steppe identified with a threshold value of 4.0. (c) Is comparisons of microbial abundances with significant different from phyla to genus level detected by LEfSe analysis.

34.29% in ND, LD, MD, HD and ED, respectively. Following Proteobacterial, the phyla were dominant by Acidobacteria and Chloroflexi. The other dominant phyla were Gemmatimonadetes (2.428%–3.66%), Bacteroidetes (3.724%–2.274%), Nitrospirae (1.297%–0.365%), Verrucomicrobia (1.236%–0.165%), Planctomycetes (1.52%–0.604%), and Saccharibacteria (1.03–0.102%). In addition to these 10 dominant phyla, the remaining 20 phyla with a relative abundance b0.1% in any sample are presented in “other phyla”, including Firmicutes Tectomicrobia, Cyanobacteria, etc. The average proportion of “other phyla” accounted for 3.14–4.18%. The microbial community at the class level is shown in Fig. 3(b). A representative of each of the 22 shared classes was screened (average abundance N0.1%) in all 15 samples. The most abundant classes were Actinobacteria and Acidobacteria, which accounted for 32.32%–39.85% and 11.51%–21.81% in the different degraded alpine steppes, respectively. In addition, 13 of the 22 shared clades were members of Chioroflexia,

Proteobacteria and Bacteroidetes. Within Chioroflexia, Gemmatimonadetes represented 2.43%–3.67%, followed by KD4-96, Anaerolineae, TK10, Chloroflexia, Gitt-GS-136 and Ktedonobacteria. Proteobacteria was mainly composed of four classes, i.e., Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria and Gammaproteobacteria. Alphaproteobacteria accounted for 9.45%–19.11%, while the average abundances of Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria were 2.51%–3.39%, 2.17%–2.72%, and 1.61%–2.53%, respectively. Moreover, the average abundance of the Bacteroidetes was 0.94%–2.08% (Sphingobacteriia) and 0.29%–1.71% (Cytophagia). In addition to the abovementioned classes, the other dominant shared classes were Spartobacteria, Planctomycetacia, Bacilli, Tectomicrobia_Incertae_Sedis and norank_p__Saccharibacteria. For a more detailed analysis, a hierarchically clustered heat map based on the Bray-Curtis similarity index was generated to exhibit the

2288

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

hierarchical relationships among the top 50 classified genera among the 15 degraded alpine steppe samples (Fig. 3c). The results showed that the samples in MD, HD and ED were the most relevant. A close relationship was found between ND-2 and LD-3 and among ND-3, LD-1, LD-2 and ND-1, while these two clusters showed distant relationship. Similar results were also found in the PCoA analysis (Fig. 2b). The heat map showed that the most often detected bacterial genera and the distribution characteristics of the higher abundance genera were similar across the different degraded alpine steppes. The most representative bacterial genera were norank-c-Acidobacteria with average values of 6.52%, 5.99%, 7.46%, 5.65%, and 12.60% in ND, LD, MD, HD and ED, respectively (Fig. 3c). In addition, 44 of the top 50 genera, including members of Actinoplanes, Bryobacter, Gaiella, Microlunatus, Nitrospira, Pedomicrobium, Roseiflexus, Solirubrobacter, and Variibacter, were shared by all soil samples. The linear discriminant analysis (LDA) effect size (LEfSe) method was used to detect groups or species causing significant differences among the degradation levels. As shown in Fig. 4(a), 23 bacteria clades exhibited statistically significant differences among the different degraded alpine steppes with an LDA threshold of 4.0 (Fig. 4b). Most differential bacteria were significantly enriched as the degradation level increased, while only 3 clades were abundant in ND (Fig. 4c). Specifically, Solirubrobacterales (order), unclassified_o__Solirubrobacterales (family) and unclassified_o__Solirubrobacterales (genus) were enriched in ND. Pseudonocardiales (order) and Pseudonocardiaceae (family) were enriched in LD. Micromonosporales (order), Micromonosporaceae (family), and Methylobacteriaceae (family) were enriched in MD. Bacteroidetes (phyla), Euzebyales (order), Rhodobacterales (order), Rhodospirillaceae (family), MSB_1E8 (family), OM1_clade (family), Euzebyaceae (family), norank_f__OM1_clade (genus), and unclassified_f__Acidimicrobiaccae were enriched in HD. Moreover, Gaiellales (order), norank_o__Gaiellales (family), norank_c__Actinobacteria (order), norank_c__Actinobacteria (family), norank_c__Actinobacteria (genus) and norank_o__Gaiellales were enriched in the ED grassland samples (Fig. 4c). 3.5. Metabolic pathway in degraded alpine steppes The functional contributions of the bacteria in the degraded steppe alpine soil samples were predicted based on OTUs using the Tax4Fun

package in R. The results revealed a total of 280 groups at the level 3 KEGG orthologues (Table S3). The most abundant functional classes were ABC transporter, two-component system, purine metabolism, aminoacyl-tRNA biosynthesis, starch and sucrose metabolism, and yrimidine metabolism (Table S3). In addition, 6469 KEGG orthologues (KO) were found across all samples mainly belonging to metabolism, genetic information processing, environmental information processing, cellular processes and even human diseases. The top abundant functional pathways are presented in Fig. 5. Of all the different functional pathways identified, energy metabolism, metabolism of other amino acids, signal transduction, lipid metabolism, metabolism of terpenoids and polyketides, signalling molecules and interaction, and amino acid metabolism were over-represented compared to the other pathways. In addition, of the 19 functional pathways (relative abundance N1%), 13 functional pathways showed significant differences among the different degraded alpine steppe samples. Of the 7 over-represented pathways, energy metabolism and metabolism were significant higher in HD, and signal transduction was higher in ND. In addition, we found bacteria related to human diseases, including infectious diseases as follows: these bacteria were significantly higher in MD and HD. The details of the functional classification of the top 280 KEGG items are shown in Table S5. Due to the importance of the grassland quality to livestock and human health, we targeted the human disease functional classes. The results showed that the alpine steppe samples contained many human disease functional classes. The results also showed that the bacterial populations in the alpine steppe samples mainly included bacterial invasion of epithelial cells, legionellosis, pertussis, salmonella infection, tuberculosis, beta-lactam resistance, and pathways in infectious diseases: Viral (Table S4). In addition, all human disease categories were significantly higher in the degraded alpine steppe samples. 3.6. Relationships between shifts in microbial community composition and vegetation and environment variables A redundancy analysis (RDA) was applied to reveal the effect of environment factors on the dominant microbial community (Fig. 6). The environmental and vegetation factors were acutely related to these five degraded alpine steppes, except for pH and water content, which showed obtuse relationships. In addition, at the phyla level, total P

Fig. 5. The predicted KEGG categories abundance and percentage of non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe.

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

2289

induced by the decreased litter input to the soil (Wu et al., 2014). In this study, the aboveground vegetation characteristics (coverage, AGB, vegetation composition and vegetation diversity indexes) and soil properties (soil nutrients, soil compactness, pH, and water content) significantly differed, which is consistent with previous studies (Zuo et al., 2008; Harris, 2010; Ren et al., 2012). These differences could contribute to the observed differences in the microbial community structure and composition. Our results suggested that microbial populations were less affected in the process of alpine steppe degradation and that the population of some microbes increased to replace the original microbes lost during alpine steppe degradation, thus altering the structure of the bacterial community. 4.2. Comparisons of microbial communities among alpine steppes with different degrees of degradation Fig. 6. The redundancy analysis (RDA) of the microbial community with vegetation and environment properties in non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe. Arrows indicate the direction and magnitude of vegetation and environmental properties associated with microbial community structures.

and pH were significant correlated with the microbial communities. At the genus level, Gramineae (%), NO3−-N and TOC were significantly correlated with the microbial communities. In addition, TP, Gramineae (%), and cyperaceae (%) in the Mantel test and Gramineae (%) in the partial Mantel test showed significant relationships with the microbial community composition across the different degraded alpine steppes (all P b 0.05, Table 4). 4. Discussion 4.1. Dynamics of diversity and structures of microbial communities in progressively degraded alpine steppes In our study, the bacterial community structures significantly differed as the alpine steppe degradation increased. This result is supported by a study that reported that microbial communities significantly differ along alpine steppe degradation at Qinghai-Tibetan Plateau (Li et al., 2016). These differences are attributed to the difference in the soil properties and vegetation characteristics among the different degraded grassland due to grassland degradation facilitated by human activities, such as overgrazing, resulting in plant and soil degradation, mainly due to physical injury to plant growth and the structure and nutrient status of the soil, which can further accelerate degradation. The disappearance of aboveground plants can limit the development and population of some soil microbial community (Zhang et al., 2014) due to a decrease in microbial substrate availability

Table 4 Mantel test and partial mantel test analysis in non-degraded (ND), light degraded (LD), moderately degraded (MD), high degraded (HD) and severely degraded (ED) alpine steppe. Mantel test

TP TOC pH NH+ 4 -N NO− 3 -N WC Gramineae (%) Cyperaceae (%)

Partial mantel test

r

P

r

P

0.247 0.096 −0.075 0.129 0.038 0.229 0.298 0.334

0.042 0.481 0.615 0.334 0.768 0.054 0.028 0.007

0.159 0.085 −0.004 0.205 0.127 0.228 0.227 −0.056

0.124 0.242 0.524 0.078 0.146 0.054 0.036 0.667

Note: Abbreviations: TN (total nitrogen), TP (total phosphorous), TK (total potassium), − TOC (total organic carbon), NH+ 4 -N (ammonia nitrogen), NO3 -N (nitrate nitrogen), SC (soil compactness), BD (bulk density), WC (water content), AGB (Aboveground biomass). The bold means the divergence is significant at 0.05 levels.

The bacterial communities at the Tibetan Plateau were dominated by Actinobacteria in this study. This finding contradicts previous findings from research conducted in a similar study region that showed that Proteobacterial was the predominant bacterial phylum (Chen et al., 2017). Actinobacteria species are considered to have strong metabolic capacity at low temperatures and a DNA repair mechanism (Johnson et al., 2007; Yergeau et al., 2010). Goordial et al. (2016) reported that Actinobacteria was among the dominant phyla in the Dry Valleys. Yergeau et al. (2010) analysed permafrost bacterial communities from Canadian high Arctic using microarray analyses and reported that Actinobacteria was the dominant phylum. In addition, the other abundant phyla included Proteobacterial, Acidobacteria and Chloroflexi. Proteobacterial may play a key role in phylogenetic, ecological and pathogenic values and participate in energy metabolism, such as the oxidation of organic and inorganic compounds and obtaining energy from light (Bryant and Frigaard, 2006; Mukhopadhya et al., 2012). Acidobacteria and some Chloroflexi play important roles in organic matter decomposition and nutrient cycles (Eichorst et al., 2018; Fang et al., 2018). This result suggested that microbes that perform the function of nutrient degradation and possess higher metabolic activity might survive well in degraded alpine steppes. However, no significant differences were found among these phyla at the degraded alpine steppes. This phenomenon seemed to confirm that Actinobacteria, Proteobacterial and Acidobacteria were well adapted to the habitat conditions (such as soil properties and vegetation variation) in the degraded alpine steppes at Qinghai-Tibetan Plateau. Similar to the phylum variation, Actinobacteria and Acidobacteria were the most abundant at the class level and higher in ND and ED, respectively. The following dominant class was alpha-proteobacteria, which was higher in HD. The different proportions of dominant classes in the degraded alpine steppes indicate a shift in the bacterial community structure during alpine steppe degradation. At the genus level, the populations and composition of the dominate genera were similar, and 44 of the top 50 genera were shared by all degraded alpine steppe soil samples, suggesting that degraded alpine steppes have a stable microbial community composition structure (Zhang et al., 2014). However, the relative abundance of the shared genera differed across the different degraded alpine steppes, indicating that microbes with special functions could survive in different degraded alpine steppes; thus, the alpine steppe degradation resulted in a change in the microbial community composition and structure, which can be supported by the results of the PCoA analysis. These results may reflect the ecological coherence at degraded alpine steppes. Furthermore, the LDA results showed that most biomarker bacteria belongs to Actinobacteria, and their relative abundance increased as the degradation level increased, possibly due to their ability to decompose organic matters and xenobiotic compounds, such as the soil nutrient contents; the vegetation diversity and biomass were lower in ED in this study. Cao et al. (2017a) suggested that plant shrubs or semi-shrubs could restore the soil microbial community in degraded

2290

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

sandy grassland. In addition, a biomarker genus, i.e., Bacteroidete, is involved in C and N metabolism, such as the degradation of organic matter and nitrite oxidation (Han et al., 2017; Schauberger and Clemens, 2017; Eichorst et al., 2018; Fang et al., 2018); these results indicated that microbes related to matter degradation can survive better in degraded alpine steppes. 4.3. Altered metabolic category in the process of alpine steppe degradation As the degradation degree increases, grassland experiences larger environmental fluctuations with lower vegetation coverage, grass species and poor soil nutrient status. In addition, microbes might be involved in multiple strategies, such as entering a dormant state or producing specialized proteins to survive in the degraded alpine steppe environment. Without metagenomic data, the functional capacities of microbiomes cannot be effectively analysed. Tax4Fun analyses are useful in supplementing 16S rRNA analyses as an inexpensive alternative and outperform PICRUSt analyses (Aßhauer et al., 2015). In this study, the microbes were involved in diverse pathways (Fig. 5 and Tables S4, S5); most bacteria were involved in metabolic pathways, and some bacteria were involved in human diseases. Sandifer et al. (2015) reviewed several reports showing that environmental bacterial profiles are associated with human diseases. Although evidence that human health is correlated with biodiversity is lacking, some studies have indicated that exposure to microbial biodiversity can improve health, specifically by reducing certain diseases. In this study, most bacteria involved in human diseases increased as the alpine steppe degradation increased (Table S). These bacteria were related to human diseases, including cancers, immune diseases, neurodegenerative, endocrine, metabolic and infectious diseases, and substance dependence, of which the infectious diseases: bacterial was the most abundant. For example, Shigellosis, which is the cause of dysentery, one of the four great epidemic diseases worldwide, and the Shigellosis infections occur globally in all people without an age limit (Kotloff et al., 2018). The alpine steppes are the main livestock grazing areas, reflecting a direct relationship to the economic development of this area, while this behaviour has caused severe environment problems resulted in the loss of biodiversity. Thus, people who live in this area could suffer negative health effects using livestock without a prior examination and treatment. Most infections are reportedly caused by the consumption of contaminated meat (Tauxe, 1991). Approximately 1.4 million people suffer from Salmonella infections, and an estimated 600 associated deaths occur each year in the United States (Mermin et al., 2004). Thus, further investigations of human diseases are needed to ensure the health of the alpine steppe ecosystem, livestock communities and humans. Although we attempted to provide a profound awareness of the possible implication of microbes in alpine steppe degradation, there are some restrictions to be explored in future studies. First, it was difficult for us to observe the changes among the alpine steppe at different degradation stages probably due to the lack of a long-term monitoring site and the inability to perform wide-area sampling as we used 15 soil samples in our study obtained from different locations. Second, the pathway analysis in our study was conducted on the bases of the predicted genome of the 16S RNA sequence. Although genome approach (Tax4Fun) prediction is rarely utilized in 16S rRNA studies (Wood et al., 2016), the genetic sequencing in the degraded alpine steppes may uncover a relatively high exact microbial community composition and function. Third, degraded alpine steppe soil samples can be easily acquired, but the soil microbes cannot entirely reveal the structures of the degraded alpine steppe soil microbes because the soil in our study was topsoil (0–10 cm). Fourth, the results of our study could be affected by uncontrollable factors, such as climate, animal activities, and even grazing. Finally, it is crucial to consider that a microbe analysis must involve studies investigating pathogen bacteria and fungi in addition to considering the vegetation and soil properties, especially given the areal variation in degraded alpine steppes.

4.4. Relationship among microbial community, vegetation characteristics and soil environment factors One of the purposes of this research was to detect whether the species diversity and structure of the soil microbial community were correlated with the vegetation and soil properties. A previous study showed that the composition of microbes changed as the vegetation type changed due to the influence of plant growth on the soil structure, soil nutrient status, microclimate altered the living environment of microbes (Jangid et al., 2011). Cao et al. (2017b) and Chen et al. (2017) also found this result. According to the PCoA analysis, the structures of the degraded alpine steppes differed. The RDA analysis showed that the vegetation characteristics (Gramineae %, and cyperaceous %) and soil properties (N, P, K, and water content %) were the major drivers of microbial community diversity. In addition, the mantel test and partial mental test showed that the vegetation characteristics (Gramineae %) and soil properties (TP) were the major drivers of microbial community diversity. Comparing the two results suggested that the soil microbial community in the degraded alpine steppes at Qinghai-Tibetan Plateau were mainly limited by Gramineae (%) and TP, indicating that the disappearance of aboveground vegetation could limit the growth and population of some soil microbial communities (Zhang et al., 2014) due to a decrease in microbial substrate availability induced by decreased litter input to the soil (Wu et al., 2014). While Cao et al. (2017a) showed that the soil microbial community structure was not strongly influenced by vegetation, some studies have reported that the limiting factor of microbial growth was the TP content (Liu et al., 2012). TP was rather limited at the plateau (He et al., 2016) and thus affected the microbial communities. Such results might be due to the different study sites, sampling times, and study areas. Thus, the structures of the soil microbial communities under different degradation levels were expected differ, due to the plant community composition and worsened soil nutrient condition (such as short of P), which were the primary drivers of the formation and evolution of the soil microbial communities. 5. Conclusions This study revealed the distribution patterns and potential function profiles of microbial communities in degraded alpine steppes at Qinghai-Tibetan Plateau and improved our understanding of the microbial diversity of alpine steppes. The microbial ShannonWiener index significantly differed between LD and MD, and the microbial community structures significantly differed along with the alpine steppe degradation. The dominant phyla in the degraded alpine steppes were similar and mainly included Actinobacteria, Proteobacterial, Acidobacteria and Chloroflexi. Twenty-one biomarkers were found in the degraded alpine steppes through a linear discriminate analysis (LDA) effect size (LEfSe) analysis. The Tax4Fun prediction analysis showed that carbohydrate metabolism and amino acid metabolism were the main KEGG (level 2) categories, and several of these pathways were involved in human diseases, such as infectious diseases. By combining different methods, the microbial communities were mainly related to the coverage of Gramineae and TP. Further studies investigating fungi in QinghaiTibetan alpine steppe soils are necessary to reveal the detailed variation in soil microbes along alpine steppe degradation, evaluate the sustainability of grassland ecosystem and gain a more complete understanding of alpine steppe microbial ecology. Acknowledgements We thank Zhongnan Nie of the Department of Economic Development, Jobs, Transport and Resources, Hamilton, Victoria, Australia, for the paper review. In addition, we thank all fellows who contributed to this work.

H. Zhou et al. / Science of the Total Environment 651 (2019) 2281–2291

Funding This work was supported by the National Key Research and Development Program (2016YFC0501902) of Science and Technology Ministry of China. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.09.336. References Allison, S.D., Martiny, J.B., 2008. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. U. S. A. 105, 11512–11519. Allison, S.D., et al., 2010. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340. Allison, S.D., et al., 2013. Microbial abundance and composition influence litter decomposition response to environmental change. Ecology 94, 714–725. Angassa, A., 2015. Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in southern ethiopia. Land Degrad. Dev. 25, 438–451. Aßhauer, K.P., et al., 2015. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data: fig. 1. Bioinformatics 31, 2882–2884. Balser, T.C., Firestone, M.K., 2005. Linking microbial community composition and soil processes in a California annual grassland and mixed-conifer forest. Biogeochemistry 73, 395–415. Bryant, D.A., Frigaard, N.-U., 2006. Prokaryotic photosynthesis and phototrophy illuminated. Trends Microbiol. 14, 488–496. Cao, C., et al., 2017a. Soil bacterial community responses to revegetation of moving sand dune in semi-arid grassland. Appl. Microbiol. Biotechnol. 101, 6217–6228. Cao, C., et al., 2017b. Land-use changes influence soil bacterial communities in a meadow grassland in Northeast China. Solid Earth 8, 1119–1129. Caporaso, J.G., et al., 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624. Cerdà, Lavée, 1999. The effect of grazing on soil and water losses under arid and mediterranean climates. Implications for desertification. Pirineos 53–154, 159–174. Chen, Y.L., et al., 2017. Distinct microbial communities in the active and permafrost layers on the Tibetan Plateau. Mol. Ecol. 26, 6608–6620. Chu, Z.-r., et al., 2015. Microbial characterization of aggregates within a one-stage nitritation–anammox system using high-throughput amplicon sequencing. Chem. Eng. J. 262, 41–48. Eichorst, S.A., Trojan, D., Roux, S., Herbold, C., Rattei, T., Woebken, D., 2018. Genomic insights into the Acidobacteria reveal strategies for their success in terrestrial environments. Environ. Microbiol. 20, 1041–1063. Fang, D., et al., 2018. Microbial community structures and functions of wastewater treatment systems in plateau and cold regions. Bioresour. Technol. 249, 684–693. Feng, R., et al., 2009. Establishment of Elymus natans improves soil quality of a heavily degraded alpine meadow in Qinghai-Tibetan Plateau, China. Plant Soil 327, 403–411. Fu, B.-J., et al., 2004. Soil quality regime in relation to land cover and slope position across a highly modified slope landscape. Ecol. Res. 19, 111–118. Genxu, W., et al., 2002. Soil organic carbon pool of grassland soils on the Qinghai-Tibetan Plateau and its global implication (EI). Sci. Total Environ. 291, 207–217. Goordial, J., et al., 2016. Comparative activity and functional ecology of permafrost soils and lithic niches in a hyper-arid polar desert. Environ. Microbiol. 19. Han, S., et al., 2017. Nitrospira are more sensitive than Nitrobacter to land management in acid, fertilized soils of a rapeseed-rice rotation field trial. Sci. Total Environ. 599–600, 135–144. Harris, R.B., 2010. Rangeland degradation on the Qinghai-Tibetan plateau: a review of the evidence of its magnitude and causes. J. Arid Environ. 74, 1–12. He, D., et al., 2016. Composition of the soil fungal community is more sensitive to phosphorus than nitrogen addition in the alpine meadow on the Qinghai-Tibetan Plateau. Biol. Fertil. Soils 52, 1059–1072. Hirsch, P.R., et al., 2016. Soil resilience and recovery: rapid community responses to management changes. Plant Soil 412, 283–297. Jangid, K., et al., 2011. Land-use history has a stronger impact on soil microbial community composition than aboveground vegetation and soil properties. Soil Biol. Biochem. 43, 2184–2193.

2291

Johnson, S.S., et al., 2007. Ancient bacteria show evidence of DNA repair. Proc. Natl. Acad. Sci. U. S. A. 104, 14401–14405. Klein, J.A., et al., 2004. Experimental warming causes large and rapid species loss, dampened by simulated grazing, on the Tibetan Plateau. Ecol. Lett. 7, 1170–1179. Klein, J.A., et al., 2007. Experimental warming, not grazing, decreases rangeland quality on the Tibetan Plateau. Ecol. Appl. 17, 541–557. Kotloff, K.L., et al., 2018. Shigellosis. Lancet 391, 801–812. Langille, M.G., et al., 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821. Li, Y., et al., 2016. Changes of soil microbial community under different degraded gradients of alpine meadow. Agric. Ecosyst. Environ. 222, 213–222. Lin, L., et al., 2015. Predicting parameters of degradation succession processes of Tibetan Kobresia grasslands. Solid Earth 6, 1237–1246. Liu, L., et al., 2012. Effects of phosphorus addition on soil microbial biomass and community composition in three forest types in tropical China. Soil Biol. Biochem. 44, 31–38. Liu, J., et al., 2015. Effects of pasture management on soil fertility and microbial communities in the semi-arid grasslands of Inner Mongolia. J. Soils Sediments 16, 235–242. Long, X.-E., et al., 2016. Contrasting response of two grassland soils to N addition and moisture levels: N2O emission and functional gene abundance. J. Soils Sediments 17, 384–392. Ma, Y.S., Lang, B.N., Wang, Q.J., 1999. Review and prospect of the study on “black soil type” deteriorated grassland. Pratacultur. Sci. 16, 25–28. Mermin, J., et al., 2004. Reptiles, amphibians, and human Salmonella infection: a population-based, case-control study. Clin. Infect. Dis. 38, S253–S261. Mukhopadhya, I., et al., 2012. IBD-what role do Proteobacteria play? Nat. Rev. Gastroenterol. Hepatol. 9, 219–230. Olff, H., et al., 2000. Small-scale shifting mosaics of two dominant grassland species: the possible role of soil-borne pathogens. Oecologia 125, 45–54. Ren, G., et al., 2012. The relationship of vegetation and soil differentiation during the formation of black-soil-type degraded meadows in the headwater of the QinghaiTibetan plateau, China. Environ. Earth Sci. 69, 235–245. Sandifer, P.A., Sutton-Grier, A.E., Ward, B.P., 2015. Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: opportunities to enhance health and biodiversity conservation. Ecosyst. Serv. 12, 1–15. Schauberger, Clemens, 2017. The Environmental Detection of Comammox Nitrospira. Segata, N., et al., 2011. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60. Shang, Z.H., et al., 2008. Effect of fencing, artificial seeding and abandonment on vegetation composition and dynamics of ‘black soil land’ in the headwaters of the Yangtze and the Yellow Rivers of the Qinghai-Tibetan Plateau. Land Degrad. Dev. 19, 554–563. Strickland, M.S., et al., 2009. Testing the functional significance of microbial community composition. Ecology 90, 441. Tauxe, R.V., 1991. Salmonella: a postmodern pathogen. J. Food Prot. 54, 563–568. Wang, Q.J., 1997. The study of grassland resource, ecological environment and sustainable development on Qinghai-Tibet Plateau. Qinghai Prataculture 3, 1–11. Wang, C.T., et al., 2007. Effects of altitude on plant-species diversity and productivity in an alpine meadow, Qinghai–Tibetan plateau. Aust. J. Bot. 55. Wang, G., et al., 2007. Influences of alpine ecosystem responses to climatic change on soil properties on the Qinghai–Tibet Plateau, China. Catena 70, 506–514. Wang, C., et al., 2016. Fire alters vegetation and soil microbial community in alpine meadow. Land Degrad. Dev. 27, 1379–1390. Wood, J.L., et al., 2016. Microorganisms in heavy metal bioremediation: strategies for applying microbial-community engineering to remediate soils. AIMS Bioeng. 3, 211–229. Wu, G.-L., et al., 2014. Above- and belowground response along degradation gradient in an alpine grassland of the Qinghai-Tibetan Plateau. CLEAN - Soil, Air, Water 42, 319–323. Yergeau, E., et al., 2010. The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses. ISME J. 4, 1206–1214. Zhang, Y., et al., 2014. Diversity of nitrogen-fixing, ammonia-oxidizing, and denitrifying bacteria in biological soil crusts of a revegetation area in Horqin Sandy Land, Northeast China. Ecol. Eng. 71, 71–79. Zhou, H., et al., 2005. Alpine grassland degradation and its control in the source region of the Yangtze and Yellow Rivers, China. Grassl. Sci. 51, 191–203. Zuo, X., et al., 2008. Vegetation pattern variation, soil degradation and their relationship along a grassland desertification gradient in Horqin Sandy Land, northern China. Environ. Geol. 58, 1227–1237.