<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing with OASIS Tables v3.0 20080202//EN" "journalpub-oasis3.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table" dtd-version="3.0"><?xmltex \makeatother\@nolinetrue\makeatletter?>
  <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-7-631-2016</article-id><title-group><article-title>The effects of grazing on the spatial pattern of elm <?xmltex \hack{\break}?>(<italic>Ulmus pumila</italic> L.) in the sparse
woodland steppe <?xmltex \hack{\break}?>of Horqin Sandy Land in northeastern China</article-title>
      </title-group><?xmltex \runningtitle{The effects of grazing on the spatial pattern of elm}?><?xmltex \runningauthor{M.~Zhang et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Zhang</surname><given-names>M.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Wu</surname><given-names>J.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Tang</surname><given-names>Y.</given-names></name>
          <email>tangyi@lnu.edu.cn</email>
        </contrib>
        <aff id="aff1"><label>1</label><institution>School of Life Sciences, Liaoning University, Shenyang,
China</institution>
        </aff>
        <aff id="aff2"><label>*</label><institution>These authors contributed equally to this work.</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Y. Tang (tangyi@lnu.edu.cn)</corresp></author-notes><pub-date><day>19</day><month>April</month><year>2016</year></pub-date>
      
      <volume>7</volume>
      <issue>2</issue>
      <fpage>631</fpage><lpage>637</lpage>
      <history>
        <date date-type="received"><day>29</day><month>October</month><year>2015</year></date>
           <date date-type="rev-request"><day>27</day><month>November</month><year>2015</year></date>
           <date date-type="rev-recd"><day>29</day><month>March</month><year>2016</year></date>
           <date date-type="accepted"><day>4</day><month>April</month><year>2016</year></date>
      </history>
      <permissions>
<license license-type="open-access">
<license-p>This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link></license-p>
</license>
</permissions><self-uri xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016.html">This article is available from https://se.copernicus.org/articles/7/631/2016/se-7-631-2016.html</self-uri>
<self-uri xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016.pdf">The full text article is available as a PDF file from https://se.copernicus.org/articles/7/631/2016/se-7-631-2016.pdf</self-uri>


      <abstract>
    <p>The aim of this study was to explore the effects of grazing on the formation
of the spatial pattern of elm growth in a sparse woodland steppe. We used a
point pattern method to analyze the elm trees within different diameter at
breast height (DBH) classes in both grazed and fenced plots, which were
established in Horqin Sandy Land of northeastern China. The results showed
that, in the grazed plot, the distances where transformation between random
and clustered patterns occurred in class 1 (10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm) and class 2 (15 cm <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm) were 2.27 and
2.37 m, respectively. Meanwhile, in the fenced plot, the distances between
random and aggregated patterns that occurred in classes 1, 2 and 3
(DBH &gt; 20 cm) were 3.13, 3.13 and 7.85 m, respectively. In the
fenced plot, at distances larger than 67.72 m there was a negative
association between classes 1 and 2, which was also the case between
classes 2 and 3 and between classes 1 and 3 for distances greater than 104.09
and 128.54 m, respectively. Meanwhile, negative associations occurred only
at distances larger than 29.38 m in the grazed plot. These findings suggest
that grazing reduced the competition intensity between elm trees; and
therefore, grazing management could be an effective strategy used to regulate
the elm population in the degraded sandy land of northern China.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

      <?xmltex \hack{\newpage}?>
<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p>Vegetation and soil are key parts of the Earth system that sustain societies with services, goods and resources (Berendse et al., 2015;
Brevik et al., 2015). The grazing impact on soils and vegetation causes some ecosystems to be affected by land degradation processes (Cerdà
and Lavee, 1999; Mekuria et al., 2013; Tarhouni et al., 2015). Desertification, one of the most important types of land degradation, occurs
widely in the world, especially in China (Izzo et al., 2013; Jafari et al.,
2013; Salvati et al., 2013; Wang et al., 2013; Bisaro et al., 2014; Fleskens
and Stringer, 2014).</p>
      <p>In arid and semiarid land of northern China, grazing is considered as a key
driving force behind desertification, as it affects vegetation and soil
(Deregibus et al., 1994; Kraaij and Ward, 2006; Wang et al., 2015).
Therefore, exploring the effects of grazing on vegetation, especially
original vegetation, is helpful for understanding the desertification
process.</p>
      <p>Elm (<italic>Ulmus pumila</italic> L.) trees are the main constituents in forming
sparse woodland steppes, which is considered the original vegetation type and
is the focus of the vegetation restoration efforts in Horqin Sandy Land, one
of the largest sandy lands in China (Yu and Chen, 2007; Tang et al., 2014).
Sparse elm steppes play an important role in promoting vegetation restoration
(Jiang et al., 2003), reducing wind erosion (Jin et al., 2009) and
increasing carbon storage (Zeng et al., 2009).</p>
      <p>The effect of grazing on elm trees has raised great attention recently
(Dulamsuren et al., 2009). Previous reports have documented an increase in
seed production in grazed pastureland (Tang et al., 2014) and a reduction in
elm population density (Li et al., 2003). Few studies have focused on the
spatial pattern of elm distribution in these areas. Plant spatial patterns
provide fundamental information for understanding the regeneration processes of
species and for inferring mechanisms resulting in species' coexistence in
communities (Leps and Kindlmann, 1987; Ward et al., 1996). For instance,
Hubbell (1979) studied the trees' dispersal in a deciduous forest to explain
the high species diversity in tropical forests. Janzen (1970) predicts that
the spatial pattern of seedlings' recruitment is influenced by a density- or
distance-dependent factor, such as host-specific predators or pathogens.
Therefore, exploring the effects of grazing on the spatial pattern of elm in a sparse
woodland steppe is helpful for explaining the formation of spatial structure
in a sparse elm steppe and is also important for protecting biodiversity
and promoting vegetation restoration.</p>
      <p>To our knowledge, few studies have been carried out on the spatial pattern of
elm distribution. This is primarily due to the large-scale experimental region
required to explore sparse space structure. In the 1980s, a 13 ha area was
considered a large-scale experiment (Hubbell, 1979); yet, in recent years
20–25 ha is now the norm (Li et al., 2009). The increasing standard of area
in large-scale experiments reflects the growing appreciation of spatial
scale dependence. Spatial scale dependence reflects how the plant spatial
pattern differs for each measured scale (Picard et al., 2009). In sparse
forests, the average distance between elm trees is larger than what is found
for plants in a rainforest. Thus, a large-scale experimental region is
necessary to explore effects of grazing on spatial pattern of elm in sparse
woodland steppes. This study provided experimental evidence of grazing
effects on the spatial pattern of sparse elm steppes in a large-scale region.
The main aims of this study were (1) to study the effects of grazing on
the spatial pattern of elm in sparse woodland steppes, (2) to investigate the
change in spatial patterns at a large scale and (3) to identify the
associations of spatial pattern between different scaled groups. Our findings
could contribute to the understanding of the formation of the spatial pattern of
elm population in a sparse woodland steppe.</p>
</sec>
<sec id="Ch1.S2">
  <title>Materials and methods</title>
<sec id="Ch1.S2.SS1">
  <title>Study area</title>
      <p>The study was conducted in the Wulanaodu region in Horqin Sandy Land in
northeastern China (119<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>39<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>–120<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>02<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula> E,
42<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>29<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>–43<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>06<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula> N; 480 m a.s.l.). In the region, the
seasonal grazing regime, which prohibits grazing from May to September, has
prevailed in recent years. The study area belongs to a semiarid climate. Mean
minimum temperature is <inline-formula><mml:math display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn>14.0</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in the coldest month (January) and
23.0 <inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in the warmest month (July). The mean annual precipitation
is 340 mm, 70 % of which falls between June and August (Li et al., 2006;
Yan and Liu, 2010). Annual mean wind velocity is 4.4 m s<inline-formula><mml:math 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> and the
number of gale days (&gt; 16 m s<inline-formula><mml:math 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> is 21–80. The windy
season is from March to May, and the growing season begins in late April and
ends in late September. The prevalent wind direction is northwest, and the
second prevalent wind direction is southwest. The landscape of the study area
is characterized by a mosaic of active sand dunes, stabilized dunes and dune
slacks (Cao et al., 2011). Soil of the sand dunes and the dune slacks was
aeolian sandy soil and meadow soil, respectively (Liu et al., 2012).</p>
      <p>Two permanent plots, one of 44.2 ha (650 m <inline-formula><mml:math display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 680 m) that was
fenced in 1998, and another of 10.5 ha (300 m <inline-formula><mml:math display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 350 m) that was
grazed, were included in the test area. The slopes in the two regions were
3–5<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> on average. The fenced plot was almost the same as the grazed
plot in terms of soil physical and chemical characteristics and
microtopography before it was fenced. For example, pH, soil bulk density,
total N, and total P were 7.75–7.87, 1.55–1.58 g m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, 0.045–0.051
and 0.29–0.54 g kg<inline-formula><mml:math 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 in the fenced plot (Han, 2012). In
the grazed plot, pH, soil bulk density, total N, and total P were 7.7,
1.65–1.72 g m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, 0.046–0.080 and 0.26–0.30 g kg<inline-formula><mml:math 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 (Cao, 1990). The species included elm and the following other
species: <italic>Caragana microphylla</italic> Lam., <italic>Setaria viridis</italic> (Linn.)
Beauv. subsp. viridis, <italic>Bassia dasyphylla</italic> (Fisch. et Mey.) O. Kuntze,
<italic>Chenopodium acuminatum</italic> Willd., <italic>Chenopodium glaucum</italic> Linn.,
<italic>Chenopodium aristatum</italic> Linn., <italic>Lespedeza davurica</italic> (Laxm.)
Schindl., and <italic>Pennisetum centrasiaticum</italic> Tzvel.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p>The location of the study area and relative coordinates of elm trees in
grazed plots <bold>(a)</bold> and fenced plots <bold>(b)</bold>.</p></caption>
          <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016-f01.png"/>

        </fig>

</sec>
<sec id="Ch1.S2.SS2">
  <title>Data collection and analysis</title>
      <p>Within the two plots mentioned above, the diameter at breast height (DBH) was
measured and the number of trees with DBH <inline-formula><mml:math display="inline"><mml:mo>≥</mml:mo></mml:math></inline-formula> 10 cm was recorded. As
superior competitive ability requires a relatively large plant body size, we
thought that 10 cm DBH is enough to avoid the potential effects of
inter-specific competition between elm and other species in the study area.
According to the DBH, elm trees were divided into three groups: class 1
(10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm), class 2 (15 cm <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm) and class 3 (DBH &gt; 20 cm) (Li et al., 2011).</p>
      <p>To estimate the spatial pattern of elm trees, coordinates of trees with
DBH <inline-formula><mml:math display="inline"><mml:mo>≥</mml:mo></mml:math></inline-formula> 10 cm were recorded using GPS (Global Position System; the
accuracy is 0.5 m at a maximum). The GPS coordinates were transformed into
a relative <inline-formula><mml:math display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math display="inline"><mml:mi>y</mml:mi></mml:math></inline-formula> axis (Fig. 1). Point patterns of trees were analyzed
using Ripley's <inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>r</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> function (Gray and He, 2009). <inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>r</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula> for completely
spatial randomness (CSR), <inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>r</mml:mi><mml:mo>)</mml:mo><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="italic">&gt;</mml:mi><mml:mn> 0</mml:mn></mml:mrow></mml:math></inline-formula> for clustered pattern,
while <inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>r</mml:mi><mml:mo>)</mml:mo><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn> 0</mml:mn></mml:mrow></mml:math></inline-formula> for uniform pattern. To assess the significance
level of non-CSR, Monte Carlo simulation (<inline-formula><mml:math display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>99</mml:mn></mml:mrow></mml:math></inline-formula>) was used.</p>
      <p>The second-order bivariate estimator of the Ripley's <inline-formula><mml:math display="inline"><mml:mi>L</mml:mi></mml:math></inline-formula> function was used
for detecting associations between the three classes mentioned above. The
<inline-formula><mml:math display="inline"><mml:mi>L</mml:mi></mml:math></inline-formula> functions and Monte Carlo simulations were calculated using the
spatstat package in the R statistical programming software (Baddeley et
al., 2005).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><caption><p>The proportion of elm tree classes in grazed and fenced plots.
Class 1 consists of elm trees with 10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm; class 2
elm trees with 15 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm; class 3 elm trees with
DBH &gt; 20 cm.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016-f02.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results</title>
<sec id="Ch1.S3.SS1">
  <title>Density of elm trees in grazed and fenced plots</title>
      <p>Within the grazed plot, the density of elm trees (DBH &gt; 10 cm)
was 8.95 individual ha<inline-formula><mml:math 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>. The proportion of trees in classes 1 and 2
was 58.5 and 41.5 %, respectively. The elm trees in class 3 were not
found in the grazed plot (Fig. 2). Meanwhile, within the fenced plot, the
density of elm trees (DBH &gt; 10 cm) was
4.37 individual ha<inline-formula><mml:math 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>. The proportion of trees in classes 1, 2 and 3
was 20.2, 55.4 and 24.4 %, respectively (Fig. 2).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><caption><p>Ripley's <inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>r</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> functions and Monte Carlo intervals for elm trees in
different classes. Observed patterns (red color, straight line) fell above,
below and within 95 % Monte Carlo intervals (green color, dashed line).
The spatial pattern of all elm trees with DBH <inline-formula><mml:math display="inline"><mml:mo>≥</mml:mo></mml:math></inline-formula> 10 cm in the grazed
plot was shown in <bold>(a)</bold>. The spatial patterns of elm with two classes
in the grazed plot were shown in <bold>(b)</bold> 10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm and <bold>(c)</bold> 15 cm <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016-f03.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <title>Spatial patterns of elm trees in grazed and fenced plots</title>
      <p>Within the grazed plot, the random pattern of elm distribution transformed
into a clustered pattern at a distance of 1.87 m (Fig. 3a). For elm trees in
class 1, the transformation of a random to a clustered pattern occurred at a
distance of 2.27 m (Fig. 3b). When the distance was over 3.27 m, a random
spatial pattern appeared for trees in class 2, and then an aggregated pattern
was present (Fig. 3c).</p>
      <p>The random pattern transformed into the clustered pattern at the distance of
1.58 m in the fenced plot (Fig. 4a). The intensity of aggregation was
positively correlated to an increase in distance. For elm trees in class 1,
the transformation of the random pattern to the clustered pattern appeared at
the distance of 3.13 m (Fig. 4b). If the distance was over 3.13 m, a random
pattern appeared for trees in class 2 and then the clustered pattern occurred
(Fig. 4c). For trees in class 3 (DBH &gt; 20 cm), the random
pattern appeared over a distance of 7.85 m and the clustered pattern
occurred within greater distances (Fig. 4d).</p>
</sec>
<sec id="Ch1.S3.SS3">
  <title>Associations between elm groups in grazed and fenced plots</title>
      <p>Within the grazed plot, trees designated in classes 1 and 2 had distribution
patterns that were positively associated within a range from 4.54 to 6.11 m
but had a negative association when the distance was larger than 29.38 m.
The association correlation did not show a significant effect with a distance
of zero (Fig. 5a).</p>
      <p>The negative and positive associations appeared alternately within the fenced
plot. For elm trees in classes 1 and 2, a positive association was evident
between them within a range of 3.78 to 35.12 m, and a negative association
observed when in a range of a distance larger than 67.72 m (Fig. 5b). The
association of elm trees in classes 1 and 3 was positive at a distance of
8.16–20.07 m, and negative at distance larger than 128.54 m (Fig. 5c).
Meanwhile, the association of elm trees in classes 2 and 3 was negative at
larger distances greater than 104.09 m (Fig. 5d).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><caption><p>Ripley's <inline-formula><mml:math display="inline"><mml:mrow><mml:mi>L</mml:mi><mml:mo>(</mml:mo><mml:mi>r</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> functions and Monte Carlo intervals for elm trees in
different classes. Observed patterns (red color, straight line) fell above,
below and within 95 % Monte Carlo intervals (green color, dashed line).
The spatial pattern of all elm trees with DBH <inline-formula><mml:math display="inline"><mml:mo>≥</mml:mo></mml:math></inline-formula> 10 cm in the fenced
plot was shown in <bold>(a)</bold>. The spatial patterns of elm with three
classes in the fenced plot were shown in <bold>(b)</bold> 10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm, <bold>(c)</bold> 15 cm &lt; DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm
and <bold>(d)</bold> DBH &gt; 20 cm.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016-f04.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><caption><p>Associations between different classes. Observed patterns (red
color, straight line) fallen below and above 95 % Monte Carlo intervals
(green color, dashed line) stood for the significant difference. The
associations between class 1 (10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm) and
class 2 (15 cm <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm) in the grazed plot was shown
in <bold>(a)</bold>. The associations between class 1 (10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm) and class 2 (15 cm <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm) in the
fenced plot was shown in <bold>(b)</bold>. The associations between class 1
(10 cm <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 15 cm) and class 3 (DBH &gt; 20 cm)
was shown in <bold>(c)</bold>. And the associations between class 2
(15 cm <inline-formula><mml:math display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> DBH <inline-formula><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math></inline-formula> 20 cm) and class 3 (DBH &gt; 20 cm)
was shown in <bold>(d)</bold>.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://se.copernicus.org/articles/7/631/2016/se-7-631-2016-f05.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion</title>
      <p>According to our results, the transformation between random and clustered
patterns that were observed in the grazed plot between classes 1 and 2
occurred over a distance of 2.27 and 2.37 m, respectively. Meanwhile, in the
fenced plot, the transformation between these patterns for in classes 1, 2
and 3 occurred over distances of 3.13, 3.13 and 7.85 m, respectively. This
suggested that the distance between random and clustered patterns was larger
in the fenced plot than that in the grazed plot.</p>
      <p>The initial spatial pattern of elm population is formed during the seed rain
stage, and results in a clustered pattern (Yang et al., 2012, 2013). In
conjunction with the growth of the plant population, the spatial pattern of
all trees (not just elm trees) shows a shift from a clustered pattern to one
of uniform distribution (Weiner et al., 2001; Stoll and Bergius, 2005; Picard
et al., 2009). Previously, Barbour (1973) proposed that, statistically, the
most likely dispersal of shrubs is uniform in pattern, which is largely
determined by the competition for resources (Toft and Fraizer, 2003).
However, an important consideration is the fact that the transformation from
a clustered to a uniform pattern is a process, and midway during this
process, the spatial pattern appears random. For example, Barot et al. (1999)
found that the spatial pattern of adult savanna palm trees had a random
pattern or loosely clustered pattern, compared with that of the clustered
pattern of young trees. If competition led to the change in spatial pattern,
i.e., transformation from a clustered pattern to a uniform pattern, then the
larger distance between the clustered and uniform pattern suggests greater
competition intensity. Thus, grazing may reduce the competition intensity in
the sparse elm steppe, given the shorter distance between clustered and
uniform patterns noted here.</p>
      <p>Our results suggest that grazing regulates the spatial pattern of the sparse
woodland steppe, and by doing so, represents one of the underlying mechanisms
that govern the formation of plant spatial pattern. Here, the distance
between the clustered and uniform patterns within grazed and fenced plots was
1.87 and 1.58 m, respectively. This was consistent with work by Lin et
al. (2010), who found that grazing altered the fine-scale
(&lt; 2 m) processes in a desert steppe in Inner Mongolia, China.
However, in our study the increase in distance between the random and
clustered pattern occurred in an orderly way, rather than the clustered patterns
appearing on a fine scale and random patterns occurring on a relatively large
scale as reported previously (Niu et al., 2008; Ren and Zhao, 2013).</p>
      <p>In the fenced plot, negative associations appeared between classes 1 and 2 in
distances larger than 67.72 m, between classes 2 and 3 in distances larger
than 104.09 m and between classes 1 and 3 in distances larger than
128.54 m. Thus, as the diameter increases, negative associations of elm
trees groups tend to appear in larger distances. Individuals with large sizes
need more resources to live and the competition intensity between them may be
more severe than that between individuals with smaller sizes (Weiner et al.,
2001). Therefore, competition intensity is related to the distance where
negative association appears and when negative associations appear in the
larger distances, which results in more competition intensity. In the grazed
plot, the negative association occurred in a distance larger than 29.38 m,
which is far less than that in the fenced plot. Thus, the competition
intensity was reduced in the grazed plot, suggesting that grazing may be a
useful factor to regulate population structure and promote vegetation
restoration in Horqin Sandy Land. In Horqin Sandy Land, a lack of resources,
especially water supply, limits the performance of individuals and hampers
the recruitment in populations (Chesson et al., 2004). Importantly, these
findings offer an alternative to the prevailing view that suggests that
enclosure management is an effective measure to promote vegetation
restoration (Jiang et al., 2003).</p>
      <p>The co-occurrence of intra-specific and inter-specific competition is
common in nature (Packer and Clay, 2000; Condit et al., 2002) and it is
difficult to separate the effects of these on spatial distribution. The
inter-specific competition is not considered here, which is mainly due to
asymmetric competition between elm trees and other plants in the sparse elm
steppe. In the sparse woodland steppe, the size of elm trees is far larger
than shrubs and herbs, and allows them to have a competitive advantage
over smaller plants (Frecketon and Watkinson, 2001). Thus, we believed that
the effects of intra-specific competition played a more important role than
inter-specific competition in regulating the spatial pattern of elm trees
(Schwinning and Weiner, 1998). The communities with single dominant species,
as observed in this study, are more suitable for detecting specific
competition.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusions</title>
      <p>Our study suggests that grazing regulates the spatial pattern of elm trees
in a sandy land, and this is likely due to the reduction of intra-specific
competition between elm trees. Given this, we believe that grazing
management would favor the optimization of the spatial pattern in elm
populations through its role in reducing intra-specific competition. In
particular, grazing management would be an effective strategy to regulate
growth distribution of the elm population in the degraded sandy land of
northern China.</p>
</sec>

      
      </body>
    <back><ack><title>Acknowledgements</title><p>This work was supported by the National Basic Research Program of China
(no. 2013CB429905), National Nature Science Foundation of China
(no. 41201052) and Program for Liaoning Excellent Talents in University
(no. LJQ2013003). We would also like to thank Angela Scott at the McMaster
University for her assistance with English language and grammatical editing
of the manuscript.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?> Edited by: P. Pereira</p></ack><?xmltex \hack{\newpage}?><?xmltex \hack{\newpage}?><ref-list>
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    <!--<article-title-html>The effects of grazing on the spatial pattern of elm (<i>Ulmus pumila</i> L.) in the sparse
woodland steppe of Horqin Sandy Land in northeastern China</article-title-html>
<abstract-html><p class="p">The aim of this study was to explore the effects of grazing on the formation
of the spatial pattern of elm growth in a sparse woodland steppe. We used a
point pattern method to analyze the elm trees within different diameter at
breast height (DBH) classes in both grazed and fenced plots, which were
established in Horqin Sandy Land of northeastern China. The results showed
that, in the grazed plot, the distances where transformation between random
and clustered patterns occurred in class 1 (10 cm  ≤  DBH  ≤  15 cm) and class 2 (15 cm  &lt;  DBH  ≤  20 cm) were 2.27 and
2.37 m, respectively. Meanwhile, in the fenced plot, the distances between
random and aggregated patterns that occurred in classes 1, 2 and 3
(DBH &gt; 20 cm) were 3.13, 3.13 and 7.85 m, respectively. In the
fenced plot, at distances larger than 67.72 m there was a negative
association between classes 1 and 2, which was also the case between
classes 2 and 3 and between classes 1 and 3 for distances greater than 104.09
and 128.54 m, respectively. Meanwhile, negative associations occurred only
at distances larger than 29.38 m in the grazed plot. These findings suggest
that grazing reduced the competition intensity between elm trees; and
therefore, grazing management could be an effective strategy used to regulate
the elm population in the degraded sandy land of northern China.</p></abstract-html>
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