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  <front>
    <journal-meta>
<journal-id journal-id-type="publisher">SE</journal-id>
<journal-title-group>
<journal-title>Solid Earth</journal-title>
<abbrev-journal-title abbrev-type="publisher">SE</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Solid Earth</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1869-9529</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>

    <article-meta>
      <article-id pub-id-type="doi">10.5194/se-8-1119-2017</article-id><title-group><article-title>Land-use changes influence soil bacterial communities in a meadow grassland
in Northeast China</article-title>
      </title-group><?xmltex \runningtitle{Land-use changes influence soil bacterial communities}?><?xmltex \runningauthor{C. Cao et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Cao</surname><given-names>Chengyou</given-names></name>
          <email>caochengyou@mail.neu.edu.cn</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Zhang</surname><given-names>Ying</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Qian</surname><given-names>Wei</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Liang</surname><given-names>Caiping</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Congmin</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Tao</surname><given-names>Shuang</given-names></name>
          
        </contrib>
        <aff id="aff1"><institution>College of Life and Health Sciences, Northeastern University, Shenyang
110169, People's Republic China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Chengyou Cao (caochengyou@mail.neu.edu.cn)</corresp></author-notes><pub-date><day>26</day><month>October</month><year>2017</year></pub-date>
      
      <volume>8</volume>
      <issue>5</issue>
      <fpage>1119</fpage><lpage>1129</lpage>
      <history>
        <date date-type="received"><day>3</day><month>July</month><year>2017</year></date>
           <date date-type="rev-request"><day>19</day><month>July</month><year>2017</year></date>
           <date date-type="rev-recd"><day>12</day><month>September</month><year>2017</year></date>
           <date date-type="accepted"><day>24</day><month>September</month><year>2017</year></date>
      </history>
      <permissions>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions><self-uri xlink:href="https://se.copernicus.org/articles/.html">This article is available from https://se.copernicus.org/articles/.html</self-uri>
<self-uri xlink:href="https://se.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://se.copernicus.org/articles/.pdf</self-uri>


      <abstract>
    <p>The conversion of
natural grassland into agricultural fields is an intensive anthropogenic
perturbation commonly occurring in semiarid regions, and this perturbation
strongly affects soil microbiota. In this study, the influences of land-use
conversion on the soil properties and bacterial communities in the Horqin
Grasslands in Northeast China were assessed. This study aimed to investigate
(1) how the abundances of soil bacteria changed across land-use types,
(2) how the structure of the soil bacterial community was altered in each
land-use type, and (3) how these variations were correlated with soil
physical and chemical properties. Variations in the diversities and
compositions of bacterial communities and the relative abundances of dominant
taxa were detected in four distinct land-use systems, namely, natural meadow
grassland, paddy field, upland field, and poplar plantation, through the
high-throughput Illumina MiSeq sequencing technique. The results indicated
that land-use changes primarily affected the soil physical and chemical
properties and bacterial community structure. Soil properties, namely,
organic matter, pH, total N, total P, available N and P, and microbial
biomass C, N, and P, influenced the bacterial community structure. The
dominant phyla and genera were almost the same among the land-use types, but
their relative abundances were significantly different. The effects of
land-use changes on the structure of soil bacterial communities were more
quantitative than qualitative.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p>Land-use conversion, habitat destruction, climate change, and other
intensive anthropogenic activities strongly alter natural ecosystems (Pabst
et al., 2013). Over 38 % of all natural landscapes worldwide have
been converted to managed systems (Holland et al., 2016). Thus, ecological
processes in the conversions should be elucidated to predict the development
and sustainability of ecosystem services. Arid and semiarid areas account
for 30 % of the total land area worldwide (Ran et al., 2014). These areas
are also known for their ecological fragility and resource specificity. The
conversion of natural grassland into agricultural fields is a dominant human
perturbation commonly occurring in semiarid areas. In the western part of
Northeast China, large grassland areas have been converted to upland fields
or paddy fields because of high income from farming. This conversion can
cause progressive and cumulative soil disturbances and is thus considered a
key factor that affects land desertification development, reduces
biodiversity, and alters ecological processes in terrestrial ecosystems
(Sala et al., 2000). Intensive soil perturbations can also change intrinsic
soil properties by promoting soil nutrient cycles and modifying soil
physical, chemical, and microbiological properties and the structures of
dwelling microbial communities in local ecosystems (Lauber et al., 2008; Lin
et al., 2011, 2013; Kuramae et al., 2012). Therefore,
land-use or land-cover change is an important factor affecting soil quality.
Soil microbial diversity and community structure function as sensitive
indicators of soil health and quality and hence rapidly respond to land-use
conversions (He et al., 2008).</p>
      <p>The effects of land-use conversion on soil physical, chemical, and
microbiological properties have been comprehensively investigated. Land-use
conversion can elicit significant and long-term effects on the moisture,
texture, aeration, pH, nutrient status, microbial biomass, and enzymatic
activities of soil largely because of the changes in plant community
composition and different management practices across land-use types (Murty
et al., 2002; Nishimura et al., 2008; Rahman et al., 2008; Wallenius et al.,
2011). However, the effects of land-use conversion on soil biology have not
yet been sufficiently assessed. Although land-use conversion has been
confirmed to affect the structure of soil microbial communities
significantly (Steenwerth et al., 2002; Johnson et al., 2003; Lauber et al.,
2008), changes in the abundance of soil-specific taxonomic groups modified
during land-use conversion and alterations in plant cover and soil
properties remain poorly understood. Soil microbial communities are
structured by climate, geology, land use, and soil physical and chemical
factors, such as soil organic matter, porosity, and pH (Thomson et al.,
2010, 2015; Griffiths et al., 2011; Hartmann et al., 2012).
Land-use conversion may lead to unfavorable modification of several
environmental variables, which can indirectly or directly affect soil
microbial diversity. Therefore, the consequences of changes in soil
microbial diversity and community structure during land-use conversion
should be investigated to provide relevant information on microbial
responses to changes in the soil environment and interactions between soil
microbial communities and soil physical and chemical properties.</p>
      <p>Culture-independent techniques, such as phospholipid fatty acid biomarkers,
polymerase chain reaction–denaturing gradient gel electrophoresis, clone
libraries, fluorescence in situ hybridization, enterobacterial repetitive
intergenic consensus, and terminal restriction fragment length polymorphism,
have been widely employed to examine soil microbial community changes as a
consequence of land-cover change, soil pollution, and fertilization (Baath et
al., 2003; He et al., 2007; Berg et al., 2012; Suleiman et al., 2013; Thomson
et al., 2015). However, these techniques yield unsatisfactory performances in
terms of the accurate detection of the changes in ecologically important soil
microbial groups under different soil environmental conditions because of
resolution limits, fingerprint complexity, and highly abundant indigenous
microbes in soil (Bruce et al., 2000; Quince et al., 2009). Conversely,
DNA-characterization-based high-throughput methodologies, such as sequencing
and subsequent sequence alignment, can directly detect microbial community
composition and variations in species with low richness (Shi et al., 2014;
Zhao et al., 2014). These methodologies have also been widely adopted to
assess the genetic structure and diversity of microbial communities in
various environments (Quince et al., 2009; Golebiewski et al., 2014; Liu et
al., 2014; Portune et al., 2014; Ren et al., 2014; Singh et al., 2014;
Thomson et al., 2015).</p>
      <p>Considering the increasing number of reclamation activities in native meadow
grasslands in Northeast China, we applied Illumina MiSeq sequencing to
investigate the changes in the diversities, compositions, and relative
abundances of the dominant taxa of soil bacterial communities in native
meadow grassland and adjacent upland fields, paddy fields, and woodland
habitats. We then classified the microbial species and compared their
structures among different land-use types. We also analyzed the correlation
of specific microbial phyla with soil physical and chemical properties.
Thus, we could obtain relevant data to enhance our understanding of the
effects of the use change of native grassland on soil microbiota and provide
useful information for the sustainable use of ecosystem services in fragile
grasslands. We also statistically analyzed our data to evaluate the
differences in the structures of soil bacterial communities among different
land-use types and to determine their correlations with soil physical and
chemical properties. We hypothesized that soil bacterial communities respond
to land-use conversion, and these responses are related to variations in
soil properties induced by different management measures during changes. This study
aimed (1) to determine the changes in the abundances of soil bacteria across
land-use types, (2) to identify the alterations in the structure of soil
microbial communities in each land-use type, and (3) to observe the
responses of specific microbial groups to land-use changes and the
correlation of these variations with soil physical and chemical properties.</p>
</sec>
<sec id="Ch1.S2">
  <title>Materials and methods</title>
<sec id="Ch1.S2.SS1">
  <title>Study location and site description</title>
      <p>This study was conducted at the Wulanaodu Experimental Station of
Desertification (43<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>02<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula> N, 119<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>39<inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula> E) under the
Institute of Applied Ecology, Chinese Academy of Sciences. The station is
located in the western part of the Horqin Grasslands in Northeast China. The
Wulanaodu region is located in the temperate zone and thus has a continental
semiarid monsoon climate with mean annual precipitation of approximately
284.4 mm. In addition, 70–80 % of the precipitation occurs from May
to September. The annual average temperature and wind velocity in the area
are 6.3 <inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and 4.4 m s<inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, respectively. The soils
are classified as alkali meadow soil. The landscape is characterized by a
mosaic of gently undulating sand dunes and interdune meadow grassland (Zhang
et al., 2016). The original vegetation belongs to the Mongolian flora, which
mainly includes <italic>Aneurolepidium chinense</italic> Kitag.,
<italic>Chenopodium acuminatum</italic> Willd., <italic>Bassia dasyphylla</italic> O. Kuntze,
<italic>Lespedeza davurica</italic> Schindl., <italic>Thermopsis lanceolata</italic> R. Br.,
<italic>Achnatherum cristatum</italic> Gaertner, <italic>Arundinella hirta</italic> C. Tanaka,
<italic>Caragana microphylla</italic> Lam., <italic>Salix mongolica</italic> Siuzev,
<italic>Spodiopogon sibiricus</italic> Trin., and <italic> Artemisia frigida</italic> Willd. (Cao et al.,
2008). Most of the herbaceous plant species in this region are distributed
in the meadow grassland. Furthermore, a large native meadow grassland lies
in the Wulanaodu region and supports the production of local animal
husbandry. The meadow grassland is typically used as clipping pastures,
which are enclosed during the growing season (from April to late September)
and openly grazed after the grasses are harvested (Zhang et al., 2015). In
recent decades, the grasslands have been seriously degraded because of
climate change, overgrazing, and the aridification of the habitat (Cao et al.,
2006); a vast area of the degraded meadow grasslands was converted to
farmland. At present, a mosaic of grassland, paddy fields, upland fields, and
woodland can be observed in the Wulanaodu region, and these ecosystems can
be used to study the response of soil microbial community structures to
land-use conversion.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Experimental design and soil sampling</title>
      <p>The soils were sampled in May 2016. An adjacent native meadow grassland,
a 26-year paddy field (<italic>Oryza sativa</italic> L.), a 50-year upland field
(<italic>Zea mays</italic> L.), and a 34-year poplar
plantation (designated as NMG, PF, UF, and PP, respectively) were selected
as experimental sites. PF, UF, and PP were all converted from NMG, and thus
the soil types were the same. NMG was openly grazed except for the growing
season. At PF and UF sites, urea, (NH<inline-formula><mml:math id="M7" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub><mml:msub><mml:mo>)</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>HPO<inline-formula><mml:math id="M8" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula>, and KCl were
annually applied during maize seeding, rice transplanting, and crop growth.
The averages of height and diameter at the breast height of the trees were 18.5 m
and 20.6 cm at the PP site, respectively. In each site, three 30 m <inline-formula><mml:math id="M9" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 30 m
plots were installed for sampling, and each plot was 300 m away from the
other plots. Approximately 0–10 cm soil samples were collected. All the
samples were sieved in the field using a 2 mm screen. Half of each sample
was immediately frozen at <inline-formula><mml:math id="M10" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>80 <inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for DNA extraction, while the
other half of the sample was used for the analysis of the chemical and physical
properties.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <title>Soil physical and chemical properties and microbial biomass</title>
      <p>Soil water content was determined gravimetrically by drying the soil at 105 <inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
for 24 h. In the laboratory, the air-dried soil was ground to
pass through a 2.0 mm mesh and was then analyzed for electrical conductivity
(EC; 1 : 5 soil to water ratio), pH (1 : 2.5 soil to water ratio), total K, and
available N, P, and K. The soil organic matter (SOM), total N, and total P
of the samples ground to pass through a 0.25 mm mesh were analyzed.
Subsequently, the amount of organic matter, total N, total P, available N,
and available P were analyzed through the
K<inline-formula><mml:math id="M13" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>Cr<inline-formula><mml:math id="M14" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O<inline-formula><mml:math id="M15" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">7</mml:mn></mml:msub></mml:math></inline-formula>-H<inline-formula><mml:math id="M16" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>SO<inline-formula><mml:math id="M17" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> oxidation method, semimicro-Kjedahl
digestion method, acid-digestion molybdate colorimetric method, alkaline
diffusion method, and molybdate ascorbic acid method (in 0.5 M NaHCO<inline-formula><mml:math id="M18" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>,
respectively. All the analyses were performed according to the procedures
described in ISSCAS (1978). The C and N in the soil microbial biomass were
estimated through the chloroform fumigation-incubation method (Jenkinson and
Powlson, 1976). The P in the microbial biomass was analyzed according to the
methods described by Brookes et al. (1982).</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S2.SS4">
  <title>DNA extraction, PCR amplification, and 16S rDNA sequencing</title>
      <p>Microbial DNA was extracted from 0.3 g of fresh soil sample using a soil
DNA extraction kit (Tiangen Biotech, China) according to the manufacturer
instructions. The 16S rRNA genes were amplified using the bar code primers
341F and 805R (Hugerth et al., 2014). The 50 <inline-formula><mml:math id="M19" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>L PCR mixture contained 5 <inline-formula><mml:math id="M20" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>L
of 10<inline-formula><mml:math id="M21" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> reaction buffer, 10 ng of template DNA, 0.5 <inline-formula><mml:math id="M22" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>L
of 10 mM dNTPs, 0.5 <inline-formula><mml:math id="M23" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>L of 50 <inline-formula><mml:math id="M24" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>M each primer, and 0.5 <inline-formula><mml:math id="M25" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>L of 5 U <inline-formula><mml:math id="M26" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>L<inline-formula><mml:math id="M27" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>
Platinum <italic>Taq</italic> polymerase. PCR was performed using
the following program: 94 <inline-formula><mml:math id="M28" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 3 min; 5 cycles of 94 <inline-formula><mml:math id="M29" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
for 30 s, 45 <inline-formula><mml:math id="M30" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 20 s, 72 <inline-formula><mml:math id="M31" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 30 s; 20 cycles of
94 <inline-formula><mml:math id="M32" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 20 s, 55 <inline-formula><mml:math id="M33" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 20 s, 72 <inline-formula><mml:math id="M34" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
for 30 s; and a final extension at 72 <inline-formula><mml:math id="M35" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 10 min.
The PCR products were purified using the agarose gel extraction kit
(Tiangen Biotech, China) according to the manufacturer instructions and
then quantified using a Qubit 2.0 fluorometer. The purified PCR products of
all the samples were mixed in equal mole amounts and sequenced on an
Illumina MiSeq platform (Genewiz Biotechnology Co.,
Ltd, Suzhou, China) according to the standard protocols.</p>
</sec>
<sec id="Ch1.S2.SS5">
  <title>Processing and analyzing of sequencing data</title>
      <p>Raw sequence files were analyzed and quality filtered using the Quantitative
Insights Into Microbial Ecology (QIIME version 1.17; <uri>http://qiime.org</uri>).
Reads shorter than 200 bp and average quality scores &lt; 25 were
discarded. The chimeric sequences were then identified and removed using the
UCHIME software (<uri>http://drive5.com/uchime</uri>). The operational taxonomic
units (OTUs) with 97 % similarity cutoff were clustered using UPARSE
(version 7.1; <uri>http://drive5.com/uparse</uri>). The representative sequences
of each OTU were taxonomically classified using the ribosomal database
project (RDP) naive Bayesian 16S rRNA classifier (Wang et al., 2007), which
assigns the complete taxonomic information from domain to species to each
sequence in the database with 80 % taxonomy confidence and an <inline-formula><mml:math id="M36" display="inline"><mml:mi>e</mml:mi></mml:math></inline-formula> value of
0.001. OTU richness analysis was conducted using Mothur software
(<uri>https://www.mothur.org</uri>; version 1.21.1), and the alpha diversity
indices, including the Shannon–Wiener index (SW), Chao's species richness
estimator (Chao), and the abundance-based coverage estimator (ACE), were
calculated (Schloss et al., 2009). Heat map analysis was performed using MeV
version 4.2. The Bray–Curtis dissimilarity measure was performed to
calculate the between-sample similarity matrix, and principal coordinate
analysis (PCoA) was performed to compare and visualize the similarities among
the soil samples. All raw sequences were submitted to the NCBI Sequence Read
Archive under the accession number SRR5083094- SRR5083105.</p>
      <p>ANOVA and multiple comparisons were performed to determine the differences
among the land-use types. All the statistical analyses were performed using
the SPSS software package (version 13.0; SPSS Co., Ltd, Chicago, USA). A
difference at <inline-formula><mml:math id="M37" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> &lt; 0.05 was considered to be statistically
significant.</p>
</sec>
<sec id="Ch1.S2.SS6">
  <title>Canonical correspondence analysis</title>
      <p>Canonical correspondence analysis (CCA) was performed using CANOCO 4.5
(Biometris, Wageningen, the Netherlands) to determine the soil
chemical properties that have the most significant effect on the composition of
the soil microbial community. The correlations of the soil parameters were
examined using Monte Carlo permutation, and the figures were generated by
CanoDraw 4.0 (Biometris Plant Research International, Wageningen, the
Netherlands).</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results</title>
<sec id="Ch1.S3.SS1">
  <title>Soil physical and chemical parameters</title>
      <p>The physical and chemical properties and microbial biomass (C, N, and P) of
the soil varied according to land use. Significant differences in the soil
moisture, pH, EC, SOM, total N, available P, and microbial C, N, and P among
the land-use types were observed (Table 1; <inline-formula><mml:math id="M38" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> &lt; 0.05). Meanwhile, no
significant difference in soil total P and available N among the samples was
observed. The conversions from native meadow grassland to UF, PF, or PP
significantly decreased the soil pH and increased the EC and available P.
The SOM, total N, and microbial C were lower in UF and PF, but higher in PP
than those in NMG, respectively: PP &gt; NMG &gt; UF &gt; PF.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><caption><p>Soil pH, EC, nutrients, and microbial biomass of   samples (average of
three replicates <inline-formula><mml:math id="M39" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula>SD).</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Items</oasis:entry>  
         <oasis:entry colname="col2">NMG</oasis:entry>  
         <oasis:entry colname="col3">UF</oasis:entry>  
         <oasis:entry colname="col4">PF</oasis:entry>  
         <oasis:entry colname="col5">PP</oasis:entry>  
         <oasis:entry colname="col6"><inline-formula><mml:math id="M41" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col7"><inline-formula><mml:math id="M42" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula></oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">Soil moisture (%)</oasis:entry>  
         <oasis:entry colname="col2">13.11 <inline-formula><mml:math id="M43" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2.490a</oasis:entry>  
         <oasis:entry colname="col3">13.25 <inline-formula><mml:math id="M44" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2.632a</oasis:entry>  
         <oasis:entry colname="col4">28.26 <inline-formula><mml:math id="M45" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3.262c</oasis:entry>  
         <oasis:entry colname="col5">20.97 <inline-formula><mml:math id="M46" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 1.631b</oasis:entry>  
         <oasis:entry colname="col6">23.803</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">pH</oasis:entry>  
         <oasis:entry colname="col2">8.67 <inline-formula><mml:math id="M47" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.200b</oasis:entry>  
         <oasis:entry colname="col3">7.95 <inline-formula><mml:math id="M48" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.128a</oasis:entry>  
         <oasis:entry colname="col4">7.85 <inline-formula><mml:math id="M49" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.290a</oasis:entry>  
         <oasis:entry colname="col5">7.78 <inline-formula><mml:math id="M50" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.020a</oasis:entry>  
         <oasis:entry colname="col6">14.262</oasis:entry>  
         <oasis:entry colname="col7">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Electrical conductivity (<inline-formula><mml:math id="M51" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>S cm<inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">86.90 <inline-formula><mml:math id="M53" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 6.329a</oasis:entry>  
         <oasis:entry colname="col3">196.0 <inline-formula><mml:math id="M54" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 53.88b</oasis:entry>  
         <oasis:entry colname="col4">111.2 <inline-formula><mml:math id="M55" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 33.11a</oasis:entry>  
         <oasis:entry colname="col5">306.0 <inline-formula><mml:math id="M56" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 14.91c</oasis:entry>  
         <oasis:entry colname="col6">27.631</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Organic matter (g kg<inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">4.186 <inline-formula><mml:math id="M58" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.064b</oasis:entry>  
         <oasis:entry colname="col3">3.824 <inline-formula><mml:math id="M59" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.588b</oasis:entry>  
         <oasis:entry colname="col4">1.589 <inline-formula><mml:math id="M60" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.130a</oasis:entry>  
         <oasis:entry colname="col5">4.579 <inline-formula><mml:math id="M61" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.097c</oasis:entry>  
         <oasis:entry colname="col6">57.251</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Total N (g kg<inline-formula><mml:math id="M62" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">0.103 <inline-formula><mml:math id="M63" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.005c</oasis:entry>  
         <oasis:entry colname="col3">0.084 <inline-formula><mml:math id="M64" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.006b</oasis:entry>  
         <oasis:entry colname="col4">0.060 <inline-formula><mml:math id="M65" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.003a</oasis:entry>  
         <oasis:entry colname="col5">0.131 <inline-formula><mml:math id="M66" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.003d</oasis:entry>  
         <oasis:entry colname="col6">136.790</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Total P (g kg<inline-formula><mml:math id="M67" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">0.061 <inline-formula><mml:math id="M68" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.007a</oasis:entry>  
         <oasis:entry colname="col3">0.073 <inline-formula><mml:math id="M69" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.033a</oasis:entry>  
         <oasis:entry colname="col4">0.031 <inline-formula><mml:math id="M70" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.004a</oasis:entry>  
         <oasis:entry colname="col5">0.053 <inline-formula><mml:math id="M71" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.006a</oasis:entry>  
         <oasis:entry colname="col6">3.109</oasis:entry>  
         <oasis:entry colname="col7">0.089</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Available N (mg g<inline-formula><mml:math id="M72" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">2.240 <inline-formula><mml:math id="M73" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.285a</oasis:entry>  
         <oasis:entry colname="col3">1.866 <inline-formula><mml:math id="M74" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 1.090a</oasis:entry>  
         <oasis:entry colname="col4">3.219 <inline-formula><mml:math id="M75" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.056a</oasis:entry>  
         <oasis:entry colname="col5">1.438 <inline-formula><mml:math id="M76" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.871a</oasis:entry>  
         <oasis:entry colname="col6">3.411</oasis:entry>  
         <oasis:entry colname="col7">0.073</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Available P (mg g<inline-formula><mml:math id="M77" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">5.252 <inline-formula><mml:math id="M78" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.394a</oasis:entry>  
         <oasis:entry colname="col3">28.34 <inline-formula><mml:math id="M79" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 11.873b</oasis:entry>  
         <oasis:entry colname="col4">12.20 <inline-formula><mml:math id="M80" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3.034a</oasis:entry>  
         <oasis:entry colname="col5">7.457 <inline-formula><mml:math id="M81" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 1.643a</oasis:entry>  
         <oasis:entry colname="col6">8.532</oasis:entry>  
         <oasis:entry colname="col7">0.007</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Microbial biomass C (mg kg<inline-formula><mml:math id="M82" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">317.1 <inline-formula><mml:math id="M83" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 29.638b</oasis:entry>  
         <oasis:entry colname="col3">96.84 <inline-formula><mml:math id="M84" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3.646a</oasis:entry>  
         <oasis:entry colname="col4">76.32 <inline-formula><mml:math id="M85" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2.279a</oasis:entry>  
         <oasis:entry colname="col5">466.6 <inline-formula><mml:math id="M86" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 34.71c</oasis:entry>  
         <oasis:entry colname="col6">199.019</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Microbial biomass N (mg kg<inline-formula><mml:math id="M87" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">44.29 <inline-formula><mml:math id="M88" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3.711b</oasis:entry>  
         <oasis:entry colname="col3">17.28 <inline-formula><mml:math id="M89" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3.014a</oasis:entry>  
         <oasis:entry colname="col4">83.40 <inline-formula><mml:math id="M90" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 9.679c</oasis:entry>  
         <oasis:entry colname="col5">38.16 <inline-formula><mml:math id="M91" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2.776b</oasis:entry>  
         <oasis:entry colname="col6">73.646</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Microbial biomass P (mg kg<inline-formula><mml:math id="M92" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">55.43 <inline-formula><mml:math id="M93" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.685c</oasis:entry>  
         <oasis:entry colname="col3">60.75 <inline-formula><mml:math id="M94" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 1.656d</oasis:entry>  
         <oasis:entry colname="col4">19.23 <inline-formula><mml:math id="M95" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.698a</oasis:entry>  
         <oasis:entry colname="col5">26.68 <inline-formula><mml:math id="M96" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2.524b</oasis:entry>  
         <oasis:entry colname="col6">507.274</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p>NMG: native meadow grassland; UF: 50-year upland field; PF: 26-year paddy
field; PP: 34-year poplar plantation.
Means in the row followed by a different letter are significantly different
(<inline-formula><mml:math id="M40" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> &lt; 0.05).</p></table-wrap-foot></table-wrap>

</sec>
<sec id="Ch1.S3.SS2">
  <title>Sequencing results and diversity indices</title>
      <p>A total of 646 127 valid sequences and 87 428 OTUs were obtained from the
12 samples through MiSeq sequencing analysis. Each sample contained 4.0441 to
6.6811 reads and had different phylogenetic OTUs ranging from 6.114 to
8.744 using a 3 % nucleotide cutoff (Table 2). In addition, the indices
of the alpha diversity, including Chao, SW, coverage, and ACE, were also
calculated to estimate the species richness and biodiversity in the 12
samples. Significant differences in the mean values of ACE, Chao, and SW
index among the land-use types were observed (Table 2; <inline-formula><mml:math id="M97" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> &lt; 0.05). The average values of the ACE and Chao in PF were
significantly lower than in the NMG, UF, and PP, while its value on the SW
index was higher than those in NMG and PP. The coverage index, ranged from
0.93 to 0.96, indicated that significant differences among the samples were
absent. PCoA was performed according to the Bray–Curtis distance matrix
(Fig. 1). The PCoA plot showed that the microbial communities from the NMG
and PP grouped together, and UF1-UF3 and PF1-PF3 separately formed other
groups. These findings indicated that land-use type can alter the soil
microbial communities, but it is not the only determinant.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2" specific-use="star"><caption><p>MiSeq sequencing results and diversity estimates for each sampling
site.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="8">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="left"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:colspec colnum="8" colname="col8" align="right"/>
     <oasis:thead>
       <oasis:row>  
         <oasis:entry colname="col1">Sample</oasis:entry>  
         <oasis:entry rowsep="1" namest="col2" nameend="col3" align="center">Sequencing results </oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry rowsep="1" namest="col5" nameend="col6" align="center">Diversity estimates </oasis:entry>  
         <oasis:entry namest="col7" nameend="col8" align="center"/>
       </oasis:row>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1"/>  
         <oasis:entry colname="col2">Total sequences</oasis:entry>  
         <oasis:entry colname="col3">Total OTUs</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">ACE</oasis:entry>  
         <oasis:entry colname="col6">Chao</oasis:entry>  
         <oasis:entry colname="col7">SW</oasis:entry>  
         <oasis:entry colname="col8">Coverage</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">NMG1</oasis:entry>  
         <oasis:entry colname="col2">62 032</oasis:entry>  
         <oasis:entry colname="col3">7298</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6600</oasis:entry>  
         <oasis:entry colname="col6">6345</oasis:entry>  
         <oasis:entry colname="col7">10.15</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">NMG2</oasis:entry>  
         <oasis:entry colname="col2">59 445</oasis:entry>  
         <oasis:entry colname="col3">7735</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6695</oasis:entry>  
         <oasis:entry colname="col6">6491</oasis:entry>  
         <oasis:entry colname="col7">10.39</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">NMG3</oasis:entry>  
         <oasis:entry colname="col2">52 383</oasis:entry>  
         <oasis:entry colname="col3">7691</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">7259</oasis:entry>  
         <oasis:entry colname="col6">6988</oasis:entry>  
         <oasis:entry colname="col7">10.62</oasis:entry>  
         <oasis:entry colname="col8">0.93</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Mean value</oasis:entry>  
         <oasis:entry colname="col2">57 953</oasis:entry>  
         <oasis:entry colname="col3">7575</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6851b</oasis:entry>  
         <oasis:entry colname="col6">6608b</oasis:entry>  
         <oasis:entry colname="col7">10.39a</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">UF1</oasis:entry>  
         <oasis:entry colname="col2">40 255</oasis:entry>  
         <oasis:entry colname="col3">6114</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6499</oasis:entry>  
         <oasis:entry colname="col6">6286</oasis:entry>  
         <oasis:entry colname="col7">10.49</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">UF2</oasis:entry>  
         <oasis:entry colname="col2">53 850</oasis:entry>  
         <oasis:entry colname="col3">6825</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6438</oasis:entry>  
         <oasis:entry colname="col6">6236</oasis:entry>  
         <oasis:entry colname="col7">10.46</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">UF3</oasis:entry>  
         <oasis:entry colname="col2">58 719</oasis:entry>  
         <oasis:entry colname="col3">7401</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6726</oasis:entry>  
         <oasis:entry colname="col6">6555</oasis:entry>  
         <oasis:entry colname="col7">10.55</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Mean value</oasis:entry>  
         <oasis:entry colname="col2">50 941</oasis:entry>  
         <oasis:entry colname="col3">6780</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6554b</oasis:entry>  
         <oasis:entry colname="col6">6359b</oasis:entry>  
         <oasis:entry colname="col7">10.50ab</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">PF1</oasis:entry>  
         <oasis:entry colname="col2">66 811</oasis:entry>  
         <oasis:entry colname="col3">8744</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6137</oasis:entry>  
         <oasis:entry colname="col6">6153</oasis:entry>  
         <oasis:entry colname="col7">10.81</oasis:entry>  
         <oasis:entry colname="col8">0.95</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">PF2</oasis:entry>  
         <oasis:entry colname="col2">46 571</oasis:entry>  
         <oasis:entry colname="col3">7279</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">5999</oasis:entry>  
         <oasis:entry colname="col6">5958</oasis:entry>  
         <oasis:entry colname="col7">10.71</oasis:entry>  
         <oasis:entry colname="col8">0.95</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">PF3</oasis:entry>  
         <oasis:entry colname="col2">45 213</oasis:entry>  
         <oasis:entry colname="col3">6776</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">5342</oasis:entry>  
         <oasis:entry colname="col6">5414</oasis:entry>  
         <oasis:entry colname="col7">10.77</oasis:entry>  
         <oasis:entry colname="col8">0.96</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Mean value</oasis:entry>  
         <oasis:entry colname="col2">52 865</oasis:entry>  
         <oasis:entry colname="col3">7600</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">5826a</oasis:entry>  
         <oasis:entry colname="col6">5842a</oasis:entry>  
         <oasis:entry colname="col7">10.76b</oasis:entry>  
         <oasis:entry colname="col8">0.95</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">PP1</oasis:entry>  
         <oasis:entry colname="col2">57 894</oasis:entry>  
         <oasis:entry colname="col3">7579</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6515</oasis:entry>  
         <oasis:entry colname="col6">6442</oasis:entry>  
         <oasis:entry colname="col7">10.57</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">PP2</oasis:entry>  
         <oasis:entry colname="col2">62 513</oasis:entry>  
         <oasis:entry colname="col3">7731</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6771</oasis:entry>  
         <oasis:entry colname="col6">6561</oasis:entry>  
         <oasis:entry colname="col7">10.31</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">PP3</oasis:entry>  
         <oasis:entry colname="col2">40 441</oasis:entry>  
         <oasis:entry colname="col3">6255</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6860</oasis:entry>  
         <oasis:entry colname="col6">6605</oasis:entry>  
         <oasis:entry colname="col7">10.36</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">Mean value</oasis:entry>  
         <oasis:entry colname="col2">53 616</oasis:entry>  
         <oasis:entry colname="col3">7188</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6715b</oasis:entry>  
         <oasis:entry colname="col6">6536b</oasis:entry>  
         <oasis:entry colname="col7">10.41a</oasis:entry>  
         <oasis:entry colname="col8">0.94</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M99" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">–</oasis:entry>  
         <oasis:entry colname="col3">–</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">6.912</oasis:entry>  
         <oasis:entry colname="col6">4.836</oasis:entry>  
         <oasis:entry colname="col7">4.497</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M100" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">–</oasis:entry>  
         <oasis:entry colname="col3">–</oasis:entry>  
         <oasis:entry colname="col4"/>  
         <oasis:entry colname="col5">0.013</oasis:entry>  
         <oasis:entry colname="col6">0.033</oasis:entry>  
         <oasis:entry colname="col7">0.040</oasis:entry>  
         <oasis:entry colname="col8">–</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p>ACE: abundance-based coverage estimator, Chao: Chao's species richness
estimator, SW: Shannon–Wiener index.
Species level, 97 % similarity threshold used to define the OTUs.
NMG: native meadow grassland; UF: 50-year upland field; PF: 26-year paddy
field; PP: 34-year poplar plantation.
Means in the column followed by a different letter are significantly different
(<inline-formula><mml:math id="M98" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> &lt; 0.05).</p></table-wrap-foot></table-wrap>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1"><caption><p>Ordination plot showing the grouping of soil bacterial communities
according to PCoA based on Bray–Curtis distance matrix. NMG: native meadow
grassland; UF: 50-year upland field; PF: 26-year paddy field; PP: 34-year
poplar plantation.</p></caption>
          <?xmltex \igopts{width=227.622047pt}?><graphic xlink:href="https://se.copernicus.org/articles/8/1119/2017/se-8-1119-2017-f01.png"/>

        </fig>

<?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S3.SS3">
  <title>Taxonomic profiles</title>
      <p>The representative sequences of each OTU were assigned using the RDP
classifier at the phylum, family, genus, and species level. The OTUs were
classified into 46 different phyla, 416 orders, 776 families, 1289 genera, or
1801 species. Out of the 46 bacterial phyla, 13 dominant phyla with a relative
abundance of &gt; 1 % were detected, and they include
Proteobacteria, Acidobacteria, Actinobacteria,
Bacteroidetes, Chloroflexi, Gemmatimonadetes,
Firmicutes, Planctomycetes, Verrucomicrobia,
Nitrospirae, Thaumarchaeota, Chlorobi, and
candidate_division_TM7 in all the samples, which accounted for 92.48 to
97.68 % of the total OTUs in the different samples. The average relative
abundances of the top 30 phyla of the samples are shown in Fig. 2. The
relative abundance of Proteobacteria ranged from 29.13 % in PP
to 30.50 % in MS and was considered to be the dominant group in all the
samples. Acidobacteria, Actinobacteria,
Bacteroidetes, and Chloroflexi had higher relative
abundances as well, and they ranged from 10.41 to 31.79, 8.46 to 16.14, 7.24
to 12.56, and 5.37 to 16.63 %, respectively. Four subdivisions of
Proteobacteria (<inline-formula><mml:math id="M101" display="inline"><mml:mi mathvariant="italic">α</mml:mi></mml:math></inline-formula>, <inline-formula><mml:math id="M102" display="inline"><mml:mi mathvariant="italic">β</mml:mi></mml:math></inline-formula>, <inline-formula><mml:math id="M103" display="inline"><mml:mi mathvariant="italic">γ</mml:mi></mml:math></inline-formula>, and <inline-formula><mml:math id="M104" display="inline"><mml:mi mathvariant="italic">δ</mml:mi></mml:math></inline-formula>-Proteobacteria) were identified in all the libraries. Significant
differences in the relative abundance of each subdivision among the land-use
types were found (Fig. 3, <inline-formula><mml:math id="M105" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> &lt; 0.05), indicating the variations in
the Proteobacteria phylum after land-use conversion. The <inline-formula><mml:math id="M106" display="inline"><mml:mi mathvariant="italic">α</mml:mi></mml:math></inline-formula>-Proteobacteria had the highest relative abundance among the four
subdivisions in all the samples and accounted for 11.14–13.82 % of
the total reads in the NMG and PP and 6.45–9.78 % of the total reads in
the PF and UF samples (Fig. 3).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><caption><p>Top taxonomic distribution of soil samples at phylum level.
NMG: native meadow grassland; UF: 50-year upland field; PF: 26-year paddy
field; PP: 34-year poplar plantation.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://se.copernicus.org/articles/8/1119/2017/se-8-1119-2017-f02.png"/>

        </fig>

<?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S3.SS4">
  <title>Core genera</title>
      <p>The dominant bacterial phyla in the soil samples were compared with one
another at the genus levels to further reveal the changes in the soil
microbial communities among the land-use types. In total, 1289 genera were
identified in the 12 samples. The highest and lowest number of genera were
found in the PF (1289) and PP (890) samples. Most of genera
were commonly distributed in all the samples; however, the relative
abundances of the most dominant genera were significantly different among the
samples, and thus they reflected differences among the structures of the
soil microbial community (Table 3). The dominant genera were selected and
then used to draw a histogram based on their relative abundances (Fig. 4)
to depict the distribution patterns of the bacterial genera among the
land-use types. The dominant genera among land-use types had similar
distribution patterns, as indicated in Fig. 4. This finding suggested
that the land-use conversion of NGM has no distinct effect on the dominant
genus compositions of the soil bacterial community. Among these enriched
genera, Acidobacteria bacterium, <italic>Blastocatella</italic>, <italic>Sphingomonas</italic>, and
<italic>Bryobacter</italic> were dominant in all the samples. However, the conversion
from NMG to UF, PF, and PP significantly increased the relative abundances
of some specific genera (&gt; 1 %), which then became the new
dominant taxa: <italic>Marmoricola</italic>, <italic>Ferruginibacter</italic>, and
<italic>Anaerolinea</italic> in PF, <italic>Arthrobacter</italic>, <italic>Nocardioides</italic>,
<italic>Massilia</italic>, <italic>Adhaeribacter</italic>, <italic>Flavisolibacter</italic>, and <italic>Rubrobacter</italic> in PF, and
<italic>Chryseolinea</italic> in PP, which induced the structural shift of the soil microbial community
at the genus level.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><caption><p>Subdivision distribution of Proteobacteria phylum.
NMG: native meadow grassland; UF: 50-year upland field; PF: 26-year paddy
field; PP: 34-year poplar plantation.</p></caption>
          <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://se.copernicus.org/articles/8/1119/2017/se-8-1119-2017-f03.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><caption><p>Top taxonomy heat map of soil samples at genus level.
NMG: native meadow grassland; UF: 50-year upland field; PF: 26-year paddy
field; PP: 34-year poplar plantation.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://se.copernicus.org/articles/8/1119/2017/se-8-1119-2017-f04.png"/>

        </fig>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T3" specific-use="star"><caption><p>Results of one-way ANOVA for the relative abundance of phyla and
genera in different land-use types.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Order</oasis:entry>  
         <oasis:entry colname="col2">Phylum</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M107" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math id="M108" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col5">Genus</oasis:entry>  
         <oasis:entry colname="col6"><inline-formula><mml:math id="M109" display="inline"><mml:mi>F</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col7"><inline-formula><mml:math id="M110" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula></oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">1</oasis:entry>  
         <oasis:entry colname="col2">Proteobacteria</oasis:entry>  
         <oasis:entry colname="col3">7.58</oasis:entry>  
         <oasis:entry colname="col4">0.010</oasis:entry>  
         <oasis:entry colname="col5">Uncultured bacterium</oasis:entry>  
         <oasis:entry colname="col6">21.03</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">2</oasis:entry>  
         <oasis:entry colname="col2">Acidobacteria</oasis:entry>  
         <oasis:entry colname="col3">24.47</oasis:entry>  
         <oasis:entry colname="col4">&lt; 0.001</oasis:entry>  
         <oasis:entry colname="col5">Acidobacteria bacterium</oasis:entry>  
         <oasis:entry colname="col6">13.60</oasis:entry>  
         <oasis:entry colname="col7">0.002</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">3</oasis:entry>  
         <oasis:entry colname="col2">Actinobacteria</oasis:entry>  
         <oasis:entry colname="col3">14.55</oasis:entry>  
         <oasis:entry colname="col4">0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Blastocatella</italic></oasis:entry>  
         <oasis:entry colname="col6">14.98</oasis:entry>  
         <oasis:entry colname="col7">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">4</oasis:entry>  
         <oasis:entry colname="col2">Bacteroidetes</oasis:entry>  
         <oasis:entry colname="col3">4.817</oasis:entry>  
         <oasis:entry colname="col4">0.034</oasis:entry>  
         <oasis:entry colname="col5"><italic>Sphingomonas</italic></oasis:entry>  
         <oasis:entry colname="col6">2.843</oasis:entry>  
         <oasis:entry colname="col7">0.105</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">5</oasis:entry>  
         <oasis:entry colname="col2">Chloroflexi</oasis:entry>  
         <oasis:entry colname="col3">21.33</oasis:entry>  
         <oasis:entry colname="col4">&lt; 0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Bryobacter</italic></oasis:entry>  
         <oasis:entry colname="col6">2.338</oasis:entry>  
         <oasis:entry colname="col7">0.150</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">6</oasis:entry>  
         <oasis:entry colname="col2">Gemmatimonadetes</oasis:entry>  
         <oasis:entry colname="col3">21.38</oasis:entry>  
         <oasis:entry colname="col4">&lt; 0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Arthrobacter</italic></oasis:entry>  
         <oasis:entry colname="col6">9.194</oasis:entry>  
         <oasis:entry colname="col7">0.006</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">7</oasis:entry>  
         <oasis:entry colname="col2">Firmicutes</oasis:entry>  
         <oasis:entry colname="col3">2.219</oasis:entry>  
         <oasis:entry colname="col4">0.163</oasis:entry>  
         <oasis:entry colname="col5"><italic>Massilia</italic></oasis:entry>  
         <oasis:entry colname="col6">11.92</oasis:entry>  
         <oasis:entry colname="col7">0.003</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">8</oasis:entry>  
         <oasis:entry colname="col2">Planctomycetes</oasis:entry>  
         <oasis:entry colname="col3">19.09</oasis:entry>  
         <oasis:entry colname="col4">0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Nocardioides</italic></oasis:entry>  
         <oasis:entry colname="col6">3.937</oasis:entry>  
         <oasis:entry colname="col7">0.054</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">9</oasis:entry>  
         <oasis:entry colname="col2">Verrucomicrobia</oasis:entry>  
         <oasis:entry colname="col3">1.330</oasis:entry>  
         <oasis:entry colname="col4">0.331</oasis:entry>  
         <oasis:entry colname="col5"><italic>Adhaeribacter</italic></oasis:entry>  
         <oasis:entry colname="col6">7.012</oasis:entry>  
         <oasis:entry colname="col7">0.013</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">10</oasis:entry>  
         <oasis:entry colname="col2">Thaumarchaeota</oasis:entry>  
         <oasis:entry colname="col3">6.851</oasis:entry>  
         <oasis:entry colname="col4">0.013</oasis:entry>  
         <oasis:entry colname="col5"><italic>Flavisolibacter</italic></oasis:entry>  
         <oasis:entry colname="col6">7.824</oasis:entry>  
         <oasis:entry colname="col7">0.009</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">11</oasis:entry>  
         <oasis:entry colname="col2">Nitrospirae</oasis:entry>  
         <oasis:entry colname="col3">0.288</oasis:entry>  
         <oasis:entry colname="col4">0.833</oasis:entry>  
         <oasis:entry colname="col5"><italic>Chryseolinea</italic></oasis:entry>  
         <oasis:entry colname="col6">30.80</oasis:entry>  
         <oasis:entry colname="col7">&lt; 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">12</oasis:entry>  
         <oasis:entry colname="col2">Chlorobi</oasis:entry>  
         <oasis:entry colname="col3">16.60</oasis:entry>  
         <oasis:entry colname="col4">0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Rubrobacter</italic></oasis:entry>  
         <oasis:entry colname="col6">4.162</oasis:entry>  
         <oasis:entry colname="col7">0.047</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">13</oasis:entry>  
         <oasis:entry colname="col2">Candidate_division_TM7</oasis:entry>  
         <oasis:entry colname="col3">18.91</oasis:entry>  
         <oasis:entry colname="col4">0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Pontibacter</italic></oasis:entry>  
         <oasis:entry colname="col6">8.728</oasis:entry>  
         <oasis:entry colname="col7">0.007</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">14</oasis:entry>  
         <oasis:entry colname="col2">Armatimonadetes</oasis:entry>  
         <oasis:entry colname="col3">30.36</oasis:entry>  
         <oasis:entry colname="col4">&lt; 0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Bacillus</italic></oasis:entry>  
         <oasis:entry colname="col6">1.616</oasis:entry>  
         <oasis:entry colname="col7">0.261</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">15</oasis:entry>  
         <oasis:entry colname="col2">Candidate_division_WS3</oasis:entry>  
         <oasis:entry colname="col3">0.890</oasis:entry>  
         <oasis:entry colname="col4">0.486</oasis:entry>  
         <oasis:entry colname="col5"><italic>Nitrospira</italic></oasis:entry>  
         <oasis:entry colname="col6">5.724</oasis:entry>  
         <oasis:entry colname="col7">0.022</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">16</oasis:entry>  
         <oasis:entry colname="col2">Cyanobacteria</oasis:entry>  
         <oasis:entry colname="col3">6.239</oasis:entry>  
         <oasis:entry colname="col4">0.017</oasis:entry>  
         <oasis:entry colname="col5"><italic>Haliangium</italic></oasis:entry>  
         <oasis:entry colname="col6">1.305</oasis:entry>  
         <oasis:entry colname="col7">0.338</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">17</oasis:entry>  
         <oasis:entry colname="col2">Euryarchaeota</oasis:entry>  
         <oasis:entry colname="col3">285.6</oasis:entry>  
         <oasis:entry colname="col4">&lt; 0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Gaiella</italic></oasis:entry>  
         <oasis:entry colname="col6">9.340</oasis:entry>  
         <oasis:entry colname="col7">0.005</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">18</oasis:entry>  
         <oasis:entry colname="col2">Elusimicrobia</oasis:entry>  
         <oasis:entry colname="col3">2.645</oasis:entry>  
         <oasis:entry colname="col4">0.121</oasis:entry>  
         <oasis:entry colname="col5"><italic>Marmoricola</italic></oasis:entry>  
         <oasis:entry colname="col6">2.819</oasis:entry>  
         <oasis:entry colname="col7">0.107</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">19</oasis:entry>  
         <oasis:entry colname="col2">JL-ETNP-Z39</oasis:entry>  
         <oasis:entry colname="col3">3.748</oasis:entry>  
         <oasis:entry colname="col4">0.060</oasis:entry>  
         <oasis:entry colname="col5"><italic>Arenimonas</italic></oasis:entry>  
         <oasis:entry colname="col6">8.997</oasis:entry>  
         <oasis:entry colname="col7">0.006</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">20</oasis:entry>  
         <oasis:entry colname="col2">Candidate_division_OP11</oasis:entry>  
         <oasis:entry colname="col3">364.2</oasis:entry>  
         <oasis:entry colname="col4">&lt; 0.001</oasis:entry>  
         <oasis:entry colname="col5"><italic>Streptomyces</italic></oasis:entry>  
         <oasis:entry colname="col6">7.261</oasis:entry>  
         <oasis:entry colname="col7">0.011</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p>Phyla and genera are arranged according to relative abundances.</p></table-wrap-foot></table-wrap>

</sec>
<sec id="Ch1.S3.SS5">
  <title>Relationship between microbial community composition and soil
environment factors</title>
      <p>In this study, CCA was conducted to evaluate the relationship between the
compositions of the dominant phyla or genera and selected soil properties
(soil moisture, electrical conductivity, pH, organic matter, total N and P,
available N and P, microbial biomass C, N, and P). The results are
summarized in Fig. 5. The CCA plots based on the dominant phyla and genera
were nearly identical. The overall structures of the dominant phyla or
genera in different land-use samples were significantly linked to the
selected soil properties. Through CCA ordination, all the samples were
classified into three groups. Particularly, NMG and PP samples were grouped
into a distinct cluster, while UF and PF formed the other two clusters. The
main factors that grouped the NMG and PP samples were pH, organic matter,
total N, and microbial biomass C. Meanwhile, cluster UF appeared to be
mostly influenced by total P, available P, microbial biomass P, and
electrical conductivity. Cluster PF in turn was significantly affected by
soil moisture, available N, and microbial biomass N (Fig. 5).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><caption><p>Canonical correspondence analysis (CCA) of soil bacterial community
structures and environmental parameters. Arrows indicate the direction and
magnitude of environmental parameters associated with bacterial community
structures. Abbreviations: Av. P: available P; SOM: soil organic matter; TP:
total P; TN: total N; EC: electrical conductivity; MBC: microbial biomass C;
MBN: microbial biomss N; MBP: microbial biomass P; NMG: native meadow
grassland; UF: 50-year upland field; PF: 26-year paddy field; PP: 34-year
poplar plantation.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://se.copernicus.org/articles/8/1119/2017/se-8-1119-2017-f05.png"/>

        </fig>

<?xmltex \hack{\newpage}?>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion</title>
      <p>The purpose of the present study was to evaluate the effects of land-use
conversion on the composition and structure of soil bacterial
communities and explore the relationship between the physical and chemical
properties of the soil and specific phyla in the Horqin region. In this
regard, UF, PP, and PF were formerly NMG, which is one of the dominant
land-use types in the Horqin Sandy Land. Consistent with studies in other
areas (Hartman et al., 2008; Wakelin et al., 2008), our results showed
significant differences among the structures of the soil bacterial
communities in the land-use types. These differences were related to
changes in the physical and chemical properties of the soil (especially in
pH and nutrient contents) and thus suggested that the soil properties were
significant factors that shape the bacterial community structure. During the
conversion from NMG to UF, PF, and PP, the vegetation cover and soil
properties were changed according to how the land conversion was managed.
Variations in the environment due to land-use conversion have a direct
influence on the microbial community inhabiting the soil (Zhang et al.,
2014; Mendes et al., 2015). In the Horqin region, the land-use conversion of
NMGs usually involves plowing and harrowing the soil and the cultivation of
crops or planting trees. This process dramatically altered the soil
physical, chemical, and microbiological properties by destroying the
vegetation and increasing the rates of organic matter decomposition (Su et
al., 2004). Previous studies reported that changes in the soil
properties are attributed to cultivation and fertilization of the soil (He
et al., 2007; Wang et al., 2012; Shen et al., 2010). Furthermore, the
changes in the soils may involve shifts in nutrient contents and soil
quality, as the natural grassland or forest plantation areas are expected to
accumulate litter deposits, which are relatively more recalcitrant than the
crop debris deposited in agricultural areas (Lauber et al., 2008). In an
agricultural system, a low input of organic matter, variations in
temperature and precipitation, and the management of soils were reported to be
related to soil properties and the abundance of organisms (Lauber et al., 2013;
Hartmann et al., 2015).</p>
      <p>PCoA and CCA showed differences among the structures of the
soil microbial communities during the land-use conversion of NMG. CCA
was performed on the soil environmental factors and relative abundances
of the dominant taxa and indicated that the changes in the environment due to
land-use conversion have direct and significant impacts on the community of
microorganisms inhabiting the soil. Soil P contents, such as total P,
available P, and microbial biomass P, were the most important factors for the
microbial community structure in the UF sites. By contrast, the formation of
the soil microbial community structures in the PF sites was significantly
related to soil moisture, available N, and microbial biomass N
(Fig. 5). However, NMG and PP had similar microbial community structures,
which were mainly affected by pH, organic matter, total N, and microbial
biomass C. Our results demonstrated that pH was an important factor that
shaped the structure of the soil microbial community. Some studies showed
that soil pH is generally related to the compositions and structures of
the soil microbial communities across a geographic scale (Högberg et
al., 2007; Jesus et al., 2009; Fierer and Jackson, 2006). Mendes et al. (2015)
studied the effects of land-use systems on soil bacterial communities
in the southeastern region of the Amazon, and they confirmed that pH is
correlated with the structure formation of a bacterial community during
land-use alternations. Wakelin et al. (2008) also found that pH influences
the structural composition and functional capacity of the soil bacterial
communities in Australian agricultural soils. The potential causes of this
correlation are the following: (1) integration of pH with other soil
physical, chemical, and microbiological properties and (2) a slight variation
in the soil pH that could expose microbes to stress, or in other words, the sensitivity of
the microbial cell to environmental change (Fierer and Jackson, 2006). Some
studies indicated that several parameters other than pH and elements (e.g.,
Mg, Al, and Cd) are also correlated with specific bacterial groups and
structural alternation of microbial communities (Pan et al., 2014; Jesus
et al., 2009; Mendes et al., 2015).</p>
      <p>The vegetation cover might affect the diversity and structure of the soil
microbial community because the differences in plant community compositions
can contribute to changes in litter quality and quantity, which then alter
the content and cycling processes of soil nutrients (Miki et al., 2010). In
this study, the four sites differed in terms of vegetation cover. Therefore,
differences among the soil properties and structures of the microbial
communities in different land-use types can be expected. In the agricultural
sites of our study (UF and PF), the cultivation of rice and corn was
performed through conventional management, while in NMG and PP, the
cultivation was performed through no-tillage management. The long-term
cultivation might affect the whole soil community. Souza et al. (2013)
showed that the major differences among the soil biodiversity and microbial
community structures were associated with tillage management. Differences
among the soil structures induced by different tillage systems and land-use
conversion influence the structures of microbial communities (Sessitsch et
al., 2001; Peixoto et al., 2006). Considering that the four land-use types
have the same soil type and the sampling sites were adjacent to each other,
we can assume that land-use conversion is a principal driving force that
alters the soil properties and microbial community structures. Jangid et
al. (2011) investigated the rRNA gene diversity of soil bacteria at two
successional gradients with different vegetation, and they found that
bacterial diversity remained unchanged in both gradients; their results
confirmed that land-use history was the main determinant of the species
composition in soil microbial communities rather than soil properties
and vegetation. Our results showed that NMG, UF, PF, and PP have nearly the
same dominant phyla, genera, and species, although the relative abundances
of the taxa were different from one another, which also confirms that
the species composition of soil microbial communities was not distinctly affected
by land-use change and duration. Similarly, Suleiman et al. (2013)
reported that land-use history might affect the present-day structure of the
soil microbial community because many highly resilient and resistant
microbial populations unrelated to soil type, soil properties, or land use
are widely present in the microbial community. Our results showed that the
land-use types have nearly the same dominant phyla, genera, and species,
although the relative abundances of the taxa were different from one
another. For this reason, the influences of land-use conversion were
suggested to have more quantitative effects than qualitative effects. In
other words, the structure of the soil microbial community may be affected
by changes in the relative abundances of specific groups. The
structure may change in response to land-use conversion, probably because
the abundance of specific bacterial taxa sensitive to soil environment
variation are extremely low and thus difficult to detect.</p>
      <p>The observed phyla are typically present in most soils. Their relative
abundances varied among the four land-use types. Hence, land use possibly
affected their abundances. Proteobacteria and Acidobacteria were the most abundant phyla in all of the
samples. Proteobacteria are commonly described as abundant free-living
bacteria in many habitats (Zhou et al., 2004; Drees et al., 2006; Yang et
al., 2015). The Acidobacterial community is correlated with soil properties (e.g., pH, P, K,
Ca, Mg, and Al contents), and its abundance can be altered by soil type and
land-use change (Navarrete et al., 2013; Mendes et al., 2015). Our study
showed a decrease in the relative abundance of the Acidobacteria group during the conversion
of NMG to UF, PF, and PP. At the genus level, the relative abundance of the
dominant genera (Acidobacteria bacterium, <italic>Blastocatella</italic>, and <italic>Sphingomonas</italic>)
also decreased during this process. This effect
indicated that bacterial community structures were altered during land-use
conversion.</p>
      <p>Our results confirm that land-use conversion directly alters soil properties
and microbial community structures in soil. These changes may produce
potential effects on the ecological functions of meadow grassland
ecosystems. However, the effect of land-use conversion on the functional
profile of soil microbes remains unknown. Therefore, further studies on
microbial functional profiles in Horqin soils should be performed to
understand the function of soil microbes and to evaluate the sustainability
of land-use systems.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusions</title>
      <p><list list-type="order">
          <list-item>

      <p>In this study, land-use conversion in the NMG of the Horqin Sandy Land
elicits a distinct effect on the physical and chemical parameters of soil
and the structure of soil bacterial communities. Differences among the
structures of soil microbial communities are caused by land-use change and
variability in land management practices, which directly influence soil
physical, chemical, and microbiological properties.</p>
          </list-item>
          <list-item>

      <p>Soil properties, including organic matter, pH, total N, total P,
available N and P, and microbial biomass C, N, and P, directly affect
microbial community structures. The effects of land-use change on the
structure of the soil microbial community are mostly quantitative rather than
qualitative. For example, their structure is affected by changing the
relative abundances of specific groups, and their structure becomes
altered in response to land-use conversion.</p>
          </list-item>
        </list></p><?xmltex \hack{\newpage}?>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p>High-throughput Illumina MiSeq sequencing of microbial 16S
rDNA was performed by Genewiz Biotechnology Co., Ltd, Suzhou, China. All data
used in this study to generate the figures are freely available upon request
by contacting the corresponding author at
caochengyou@mail.neu.edu.cn.</p>
  </notes><notes notes-type="competinginterests">

      <p>The authors declare that they have no conflict of
interest.</p>
  </notes><ack><title>Acknowledgements</title><p>This work was supported by funds from the National Key Research and
Development Program of China (grant no. 2016YFC0500803) and the National
Natural Science foundation of China (41371505). The authors would like to
express their gratitude to the members of the Wulanaodu Station of
Desertification Research under the Chinese Academy of Sciences for their
technical assistance.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: Marc Oliva <?xmltex \hack{\newline}?>
Reviewed by: two anonymous referees</p></ack><ref-list>
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<abstract-html><p class="p">The conversion of
natural grassland into agricultural fields is an intensive anthropogenic
perturbation commonly occurring in semiarid regions, and this perturbation
strongly affects soil microbiota. In this study, the influences of land-use
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(1) how the abundances of soil bacteria changed across land-use types,
(2) how the structure of the soil bacterial community was altered in each
land-use type, and (3) how these variations were correlated with soil
physical and chemical properties. Variations in the diversities and
compositions of bacterial communities and the relative abundances of dominant
taxa were detected in four distinct land-use systems, namely, natural meadow
grassland, paddy field, upland field, and poplar plantation, through the
high-throughput Illumina MiSeq sequencing technique. The results indicated
that land-use changes primarily affected the soil physical and chemical
properties and bacterial community structure. Soil properties, namely,
organic matter, pH, total N, total P, available N and P, and microbial
biomass C, N, and P, influenced the bacterial community structure. The
dominant phyla and genera were almost the same among the land-use types, but
their relative abundances were significantly different. The effects of
land-use changes on the structure of soil bacterial communities were more
quantitative than qualitative.</p></abstract-html>
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