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  <front>
    <journal-meta><journal-id journal-id-type="publisher">SOIL</journal-id><journal-title-group>
    <journal-title>SOIL</journal-title>
    <abbrev-journal-title abbrev-type="publisher">SOIL</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">SOIL</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">2199-398X</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/soil-8-687-2022</article-id><title-group><article-title>Biotic factors dominantly determine soil inorganic carbon stock across
Tibetan alpine grasslands</article-title><alt-title>Biotic factors dominantly determine soil inorganic carbon stock</alt-title>
      </title-group><?xmltex \runningtitle{Biotic factors dominantly determine soil inorganic carbon stock}?><?xmltex \runningauthor{J. Pan et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Pan</surname><given-names>Junxiao</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0416-3336</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Wang</surname><given-names>Jinsong</given-names></name>
          <email>wangjinsong@igsnrr.ac.cn</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Tian</surname><given-names>Dashuan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Zhang</surname><given-names>Ruiyang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Li</surname><given-names>Yang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Song</surname><given-names>Lei</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Yang</surname><given-names>Jiaming</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wei</surname><given-names>Chunxue</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff2">
          <name><surname>Niu</surname><given-names>Shuli</given-names></name>
          <email>sniu@igsnrr.ac.cn</email>
        </contrib>
        <aff id="aff1"><label>1</label><institution>Key Laboratory of Ecosystem Network Observation and Modeling,
Institute of Geographic Sciences and Natural Resources Research, Chinese
Academy of Sciences, Beijing 100101, People's Republic of China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>College of Resources and Environment, University of Chinese Academy
of Sciences, <?xmltex \hack{\break}?> Beijing 100049, People's Republic of China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Jinsong Wang (wangjinsong@igsnrr.ac.cn) and Shuli Niu (sniu@igsnrr.ac.cn)</corresp></author-notes><pub-date><day>28</day><month>October</month><year>2022</year></pub-date>
      
      <volume>8</volume>
      <issue>2</issue>
      <fpage>687</fpage><lpage>698</lpage>
      <history>
        <date date-type="received"><day>29</day><month>June</month><year>2022</year></date>
           <date date-type="rev-request"><day>20</day><month>July</month><year>2022</year></date>
           <date date-type="rev-recd"><day>6</day><month>October</month><year>2022</year></date>
           <date date-type="accepted"><day>11</day><month>October</month><year>2022</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2022 Junxiao Pan et al.</copyright-statement>
        <copyright-year>2022</copyright-year>
      <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://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022.html">This article is available from https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022.html</self-uri><self-uri xlink:href="https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022.pdf">The full text article is available as a PDF file from https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e164">The soil inorganic carbon (SIC) pool is a major component of soil carbon (C) pools, and clarifying the predictors of SIC stock is urgent for decreasing soil C losses and maintaining soil health and ecosystem functions. However, the drivers and their relative effects on the SIC stock at different soil depths remain largely unexplored. Here, we conducted a large-scale sampling to investigate the effects and relative contributions of abiotic (climate and soil) and biotic (plant and microbe) drivers on the SIC stock between topsoils (0–10 cm) and subsoils (20–30 cm) across Tibetan alpine grasslands. Results showed that the SIC stock had no significant differences between the topsoil and subsoil. The SIC stock showed a significant increase with altitude, pH and sand proportion, but declined with mean annual precipitation (MAP), plant aboveground biomass (PAB), plant coverage (PC), root biomass (RB), available nitrogen (AN), microbial biomass carbon (MBC), and bacterial abundance (BA) and fungal gene abundance (FA). For both soil layers, biotic factors had larger effects on the SIC stock than abiotic factors did. However, the relative importance of these determinants varied with soil depth, with the effects of plant and microbial variables on SIC stock weakening with soil depth, whereas the importance of climatic and edaphic variables increased with soil depth. Specifically, BA, FA and PC played dominant roles in regulating SIC stock in the topsoil, while soil pH contributed largely to the variation of SIC stock in the subsoil. Our findings highlight differential drivers over SIC stock with soil depth, which should be considered in biogeochemical models for better simulating and predicting SIC dynamics and its feedbacks to environmental changes.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e176">Soils store approximately 1500 Pg of organic carbon (SOC) and 940 Pg of
inorganic carbon (SIC) to a depth of 1 m (Batjes, 1996; Jobbágy and Jackson, 2000), which are the largest carbon (C) pool in the terrestrial ecosystem and play a critical part in the global C cycling (Darwish et al., 2018; Lal, 2004; Prietzel et al., 2016). To alleviate the elevated levels of atmospheric carbon dioxide (CO<inline-formula><mml:math id="M1" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>), most previous studies concentrate on the SOC pool because it responds quickly to global climate change such as warming and nitrogen (N) deposition, and it is strongly linked with various ecosystem functions (Wang et al., 2002; Yang et al., 2012). Compared to the relatively short turnover time of SOC, SIC has a long residence time due to soil weathering (Monger et al., 2015; Zang et al., 2018), which is considered to be fairly stable and has less contribution to changes in terrestrial ecosystem C balance (Yang et al., 2012). Therefore, previous studies have paid little attention to SIC. However, recent studies suggest that SIC is also responsive to anthropogenic activities and global climate changes such as soil acidification, atmospheric N deposition, and global warming (Yang et al., 2010; Song et al., 2022), acting as a critical C source (Liu et al., 2020) or C sink (Gao et al., 2018; Liu et al., 2021). Thus, the preservation of SIC and its roles in climate mitigation should not be neglected, especially in arid and semiarid grasslands which store a large amount of SIC (Yang et al., 2012).</p>
      <p id="d1e188">The SIC stock and stability can be fundamentally altered by an array of abiotic and biotic processes (Raza et al., 2020). High precipitation can promote the weathering of soil silicate minerals and removal of base cations (Ca<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula>, Mg<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula>, K<inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>+</mml:mo></mml:msup></mml:math></inline-formula> and Na<inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>+</mml:mo></mml:msup></mml:math></inline-formula>) by leaching (Vicca et al., 2022). Soil acidification due to atmospheric nitrogen (N) and acid deposition and the nitrification of NH<inline-formula><mml:math id="M6" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">4</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> may greatly accelerate soil carbonate
dissolution and CO<inline-formula><mml:math id="M7" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> releases (Raza et al., 2020; Song et al., 2022).
Plant growth can deplete soil carbonates by releasing proton and organic
acids from root rhizosphere (Goulding, 2016; Kuzyakov and Razavi, 2019), and biological N<inline-formula><mml:math id="M8" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> fixation by some legumes are likely to cause SIC losses (Tang et al., 1999). Furthermore, plant autotrophic and microbial
heterotrophic respiration often facilitate carbonate dissolution by enhancing CO<inline-formula><mml:math id="M9" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> partial pressures (An et al., 2019; Liu et al., 2021). Nevertheless, how these abiotic and biotic factors affect SIC stock and what the relative importance is of these confounding drivers remain largely uncertain.</p>
      <p id="d1e273">Previous studies on SIC stock have mostly focused on the topsoil within 10 cm soil depth (Yost and Hartemink, 2020), which are relatively different
from the subsoil (i.e., soils residing <inline-formula><mml:math id="M10" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">20</mml:mn></mml:mrow></mml:math></inline-formula> cm below ground) in
the aspect of biochemical processes, plant roots, soil properties, and
microbial communities (Rumpel et al., 2012; Zhou et al., 2021), while the
patterns of SIC stock in the subsoil on a large scale remain elusive. The
predictors of SIC stock in the subsoil may differ from those in the topsoil
due to distinct soil microenvironments, soil physicochemical properties,
root exudates, and microbial abundance and functions (Jia et al., 2017). For
instance, the topsoil has larger root biomass (RB) and higher microbial activity than the subsoil, but the subsoil tends to preserve soil parent material because of the weakened weathering by the isolation of heat and energy from the surface soil (Crowther et al., 2016). Thus, the abiotic and biotic variables may exhibit different effects on SIC stock in the subsoil compared to the topsoil due to the various importance of these variables.</p>
      <p id="d1e286">The Tibetan Plateau has the largest alpine grassland on the Eurasian
continent, which is a vital component of global terrestrial ecosystems,
providing an ideal platform to explore SIC stock and its determinants (Wang
et al., 2002; Yang et al., 2010). During the past several decades, the plateau has experienced significant warming (Wang et al., 2008) and pronounced atmospheric N deposition (Liu et al., 2013; Yu et al., 2019). This continuous warming and N deposition have resulted in a significant increase in plant growth and soil acidification (Ding et al., 2017; Yang et al., 2012), which could likely induce potential CO<inline-formula><mml:math id="M11" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> releases from soil carbonates by biogeochemical processes (Raza et al., 2020). However, a general understanding of SIC stock with soil depth across Tibetan alpine grasslands remains unexplored.</p>
      <p id="d1e299">Here, we researched the relative importance of climatic, edaphic, plant and
microbial variables to SIC stock at different soil layers along an
approximately 3000 km transect of alpine grasslands on the Tibetan Plateau,
spanning a broad range of climatic and geographical conditions. Specifically, two key questions are addressed in this study: (1) What are
the differences of SIC stock between the topsoil and subsoil? (2) What is
the relative importance of climatic, edaphic, plant and microbial variables
to the variation of SIC stock along with soil depth?</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Materials and methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Study area and field sampling</title>
      <p id="d1e317">From 30 July to 28 August 2020, we conducted large-scale systematic field
surveys and samplings in Tibetan alpine grasslands. The total 25 sampling
sites covered approximately 3000 km and included three grassland types
(i.e., 11 alpine meadow, 8 alpine steppe, and 6 alpine desert sites). The
distance between nearby sampling sites was about 120 km. The study sites
cover a broad geographic and climatic range, with longitude and latitude
ranging from 79<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>49<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>39<inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> to 102<inline-formula><mml:math id="M15" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>25<inline-formula><mml:math id="M16" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>31<inline-formula><mml:math id="M17" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> E and
31<inline-formula><mml:math id="M18" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>06<inline-formula><mml:math id="M19" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>37<inline-formula><mml:math id="M20" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> to 32<inline-formula><mml:math id="M21" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>43<inline-formula><mml:math id="M22" display="inline"><mml:msup><mml:mi/><mml:mo>′</mml:mo></mml:msup></mml:math></inline-formula>09<inline-formula><mml:math id="M23" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>′</mml:mo><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> N, respectively, and the
altitude ranging from 3500 to 5016 m. These sites covered a broad
precipitation gradient varying between 72 and 706 mm. The mean annual
temperature (MAT) ranged from <inline-formula><mml:math id="M24" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3.9</mml:mn></mml:mrow></mml:math></inline-formula> to 5.8 <inline-formula><mml:math id="M25" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C. The plant communities were dominated by <italic>Kobresia tibetica Maxim</italic>, <italic>Stipa caucasica</italic>, <italic>Kobresia pygmaea</italic>, <italic>Stipa purpurea</italic>, and <italic>Leontopodium pusillum</italic>. Soils were <italic>Cambisol</italic> and some were
loess-derived <italic>Luvisol</italic>. The site location, grassland type, climatic, and plant parameters are detailed in Table S1 in the Supplement.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Climatic data</title>
      <p id="d1e491">The climatic data were derived from the Loess Plateau Scientific Data
Center (LPSDC, <uri>http://loess.geodata.cn/</uri>, last access: 25 October 2022) (Peng et al., 2019). The Kriging
interpolation was conducted to obtain spatial distributions of 30-year (1987–2017) MAT and mean annual precipitation (MAP) at each sampling site by a geographic coordinate system.</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Soil properties</title>
      <p id="d1e505">At each site, we selected four 1 m <inline-formula><mml:math id="M26" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 1 m plots for soil and plant
samplings and the distance between nearby sampling plots was 25 m. In each
plot, a 7.5 cm diameter soil drill was used to take five soil cores at fixed
soil depths (0–10, 10–20, and 20–30 cm), and a 2 mm mesh was used to
remove stones. Based on our field observation, the soil depth is relatively
shallow (less than 40 cm) for alpine grasslands, especially for the alpine
desert. Moreover, most of the belowground roots in alpine grasslands distribute on the surface of 10 cm and decrease sharply below 20 cm. Thus,
we defined the topsoil and subsoil as 0–10 and 20–30 cm soils, respectively. After mixing, 100 g of fresh soils from each plot were
collected and stored in a <inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M28" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C portable icebox, then returned to
the laboratory and stored at <inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">20</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M30" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for microbial properties. The
rest of the soil samples, about 700 g, were also sent back to the laboratory and air-dried for measurements of other soil properties including soil pH, available nitrogen (AN), and mechanical composition. A 40 cm <inline-formula><mml:math id="M31" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 40 cm <inline-formula><mml:math id="M32" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 40 cm (length <inline-formula><mml:math id="M33" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> width <inline-formula><mml:math id="M34" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> depth) pit was dug for measuring soil bulk density (BD) by using a constant volume soil-sampling drill (100 cm<inline-formula><mml:math id="M35" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msup></mml:math></inline-formula>), and the undisturbed soil was preserved in aluminum specimen boxes, returned to the laboratory and oven-dried for 48 h at 105 <inline-formula><mml:math id="M36" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and weighed. The oven-dried soil (20 g) was screened for gravel by sifting through a 2 mm mesh sieve and materials larger than 2 mm were collected and weighed to determine the percentage of gravels. Soil pH (1 : 25 soil : H<inline-formula><mml:math id="M37" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O) was measured using a soil pH meter, and AN was determined by the alkaline-hydrolysis diffusion method. A laser particle analyzer (Mastersizer 2000, Malvern Panalytical, UK) was applied to measure soil mechanical compositions, including the proportion of clay (<inline-formula><mml:math id="M38" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M39" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m), silt (2–50 <inline-formula><mml:math id="M40" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m), and sand (<inline-formula><mml:math id="M41" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M42" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m). The SIC was determined by using an inorganic C elemental analyzer (multi EA<sup>®</sup> 4000; Analytic Jena, Germany). The multi EA 4000 C elemental analyzer was equipped with the automatic total inorganic carbon (TIC) solids module and calibrated before the analysis. The sample boat was acidified automatically with 40 % H<inline-formula><mml:math id="M43" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula>PO<inline-formula><mml:math id="M44" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> in the reactor of the TIC module. The CO<inline-formula><mml:math id="M45" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> from the carbonate was released, the measuring gas was dried and cleaned and the carbon content was measured by means of the wide-range nondispersive infrared sensor (NDIR) detector. Before being analyzed directly, all soil samples were ground into solid fine powders with a mortar, and for the determination of TIC, a standard, prepared by the dilution of the solids CaCO<inline-formula><mml:math id="M46" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> with SiO<inline-formula><mml:math id="M47" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> (0.2 % C), was used, with a weighting range of 7–200 mg, to cover a wide concentration range.</p>
</sec>
<sec id="Ch1.S2.SS4">
  <label>2.4</label><title>Plant properties</title>
      <p id="d1e711">In each plot, we estimated plant coverage (PC) by the projection method,
namely the proportion of vegetation projection to the area of the sampling
plot. In addition, plant aboveground biomass (PAB) was clipped to ground level and collected, belowground roots were sampled by three soil samples in each plot which were mixed by two soil cores with a 7.5 cm diameter drill and collected from soil by rinsing them in water. Finally, they were oven-dried at 60 <inline-formula><mml:math id="M48" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and weighed to determine PAB and RB, respectively.</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S2.SS5">
  <label>2.5</label><title>Microbial attributes</title>
      <p id="d1e733">Soil microbial biomass carbon (MBC) was measured by using a chloroform
fumigation–extraction procedure (Brookes et al., 1985). Briefly, 10 g of
unfumigated and chloroform-fumigated fresh soil samples were extracted by
using 0.5 M K<inline-formula><mml:math id="M49" 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="M50" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> after 24 h of incubation, respectively. Then,
the extracts were analyzed by using a total organic carbon (TOC) analyzer (multi N/C<sup>®</sup> 3100; Analytic Jena, Germany). The MBC was determined by the differences in C concentrations between unfumigated and chloroform-fumigated samples, and the correction factor (i.e., KC <inline-formula><mml:math id="M51" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.45) was used to convert microbial C to MBC (Joergensen, 1996).</p>
      <p id="d1e764">Real-time polymerase chain reaction (qPCR) was used to quantify bacterial abundance (BA) and fungal gene abundance (FA) by the absolute quantification method based on the gene copy number (Tatti et al., 2016). Each reaction was carried out 3 times with a mixture of a total 20 <inline-formula><mml:math id="M52" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L volume, including 2 <inline-formula><mml:math id="M53" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L of DNA template, 10 <inline-formula><mml:math id="M54" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L of 2 <inline-formula><mml:math id="M55" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> ChamQ SYBR Color qPCR Master Mix, and 0.4 <inline-formula><mml:math id="M56" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>L (5 <inline-formula><mml:math id="M57" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>M concentration) each of forward and reverse primer, specific for each gene; and the qPCR conditions were 95 <inline-formula><mml:math id="M58" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 5 min, then 40 cycles for the 18S rRNA (ribosomal ribonucleic acid) gene and 16S rRNA gene. Each cycle involved melting at 95 <inline-formula><mml:math id="M59" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 30 s, annealing at
55 <inline-formula><mml:math id="M60" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 30 s, an extension of 72 <inline-formula><mml:math id="M61" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for 40 s, and
finally 10 <inline-formula><mml:math id="M62" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C until terminated. The primer pair SSU0817/1196 and
Eub338/Eub806 were used for amplifying fungi and bacteria in qPCR
amplification, respectively. Finally, the DNA concentration was determined
by using a QuantiFluor™-ST fluorescent quantitative system
(Promega, Fitchburg, WI, USA). The abbreviations of all variables were
detailed in Table S2.</p>
</sec>
<sec id="Ch1.S2.SS6">
  <label>2.6</label><title>Statistical analyses</title>
      <p id="d1e868">The total SIC density (C stock per land area) in each soil-depth layer was
calculated using Eq. (1) (Pan et al., 2019):
            <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M63" display="block"><mml:mtable rowspacing="0.2ex" class="split" displaystyle="true" columnalign="right left"><mml:mtr><mml:mtd/><mml:mtd><mml:mrow><mml:mi mathvariant="normal">SIC</mml:mi><mml:mspace linebreak="nobreak" width="0.25em"/><mml:mi mathvariant="normal">density</mml:mi><mml:mspace linebreak="nobreak" width="0.25em"/><mml:mo>(</mml:mo><mml:mrow class="unit"><mml:mi mathvariant="normal">g</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mi mathvariant="normal">C</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">m</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mi mathvariant="normal">SIC</mml:mi><mml:mspace width="0.25em" linebreak="nobreak"/><mml:mo>(</mml:mo><mml:mrow class="unit"><mml:mi mathvariant="normal">g</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">C</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">kg</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd/><mml:mtd><mml:mrow><mml:mspace width="1em" linebreak="nobreak"/><mml:mo>×</mml:mo><mml:mi mathvariant="normal">BD</mml:mi><mml:mspace linebreak="nobreak" width="0.25em"/><mml:mo>(</mml:mo><mml:mrow class="unit"><mml:mi mathvariant="normal">g</mml:mi><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msup><mml:mi mathvariant="normal">cm</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo><mml:mo>×</mml:mo><mml:mi>d</mml:mi><mml:mspace width="0.25em" linebreak="nobreak"/><mml:mo>(</mml:mo><mml:mrow class="unit"><mml:mi mathvariant="normal">cm</mml:mi></mml:mrow><mml:mo>)</mml:mo><mml:mo>×</mml:mo><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>-</mml:mo><mml:mi>g</mml:mi><mml:mo>)</mml:mo><mml:mo>/</mml:mo><mml:mn mathvariant="normal">100</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
          where SIC is the soil inorganic C content, <inline-formula><mml:math id="M64" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula> is the depth of the soil layer (0.1 m), BD is bulk density, and <inline-formula><mml:math id="M65" display="inline"><mml:mi>g</mml:mi></mml:math></inline-formula> is the percentage of gravel fraction
(<inline-formula><mml:math id="M66" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> mm).</p>
      <p id="d1e1012">First, the differences of SIC stock and corresponding abiotic and biotic
variables between the topsoil and subsoil were examined by <inline-formula><mml:math id="M67" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula>-test. Second,
SIC density and various abiotic and biotic variables were log-transformed
and standardized (<inline-formula><mml:math id="M68" display="inline"><mml:mi>Z</mml:mi></mml:math></inline-formula>-score normalization) to perform the assumption of
normality and homogeneity by the Shapiro–Wilk test and Levene's test, respectively (Pan et al., 2021). Then, the linear regressions were used to test SIC density with different variables for both the topsoil and subsoil across sites. Also, the Pearson correlation coefficients between SIC density and each variable are analyzed in Table S3.</p>
      <p id="d1e1029">Third, a linear model (lm) was employed to examine SIC density with abiotic and biotic variables by using the maximum likelihood estimation with the lm
package. The relative effect of all parameter estimates was calculated
to evaluate the relative importance of drivers in predicting SIC density. Each predictor variable was simultaneously tested in the model, which was comparable for the contribution of different types of predictor factors to SIC density. The absolute values of standardized regression coefficients of the explanatory variables accounting for the percentage of the sum of all standardized regression coefficients were used to express the importance of predictors (Gross et al., 2017; Le Provost et al., 2020). Also, SIC density and abiotic and biotic variables were standardized before analyses using the <inline-formula><mml:math id="M69" display="inline"><mml:mi>Z</mml:mi></mml:math></inline-formula>-score to interpret variable estimates on a comparable scale (Gross et
al., 2017):
            <disp-formula id="Ch1.E2" content-type="numbered"><label>2</label><mml:math id="M70" display="block"><mml:mtable rowspacing="0.2ex" class="split" displaystyle="true" columnalign="right left"><mml:mtr><mml:mtd><mml:mrow><mml:mi mathvariant="normal">log</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mo>(</mml:mo><mml:mi mathvariant="normal">SIC</mml:mi><mml:mspace width="0.25em" linebreak="nobreak"/><mml:mi mathvariant="normal">density</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi mathvariant="italic">β</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant="italic">β</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mi>log⁡</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant="italic">β</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:msub><mml:mi>log⁡</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd/><mml:mtd><mml:mrow><mml:mo>+</mml:mo><mml:mi mathvariant="normal">…</mml:mi><mml:msub><mml:mi mathvariant="italic">β</mml:mi><mml:mn mathvariant="normal">12</mml:mn></mml:msub><mml:mi>log⁡</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mn mathvariant="normal">12</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
          where <inline-formula><mml:math id="M71" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">β</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M72" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">β</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M73" display="inline"><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>, 2, 3…12) are
intercept and coefficients, respectively. To explore the predictors of SIC
density in different soil depths across all sites, the absolute values of
slopes of the variables were extracted and plotted. Then, to quantify their
relative contribution to SIC density, 12 predictor variables were
categorized into 4 groups, including climatic (MAP, MAT and altitude),
edaphic (pH, AN and sand proportion), plant (PB, PC and RB), and microbial
(MBC, BA and FA) factors. The detailed categorization of explanatory
variables is listed in Table S2.</p>
      <p id="d1e1151">Furthermore, the relative importance of abiotic (climatic and edaphic) and
biotic (plant and microbial) variables in predicting SIC density was
quantified by performing variation partitioning analyses (VPAs; Borcard et
al., 1992) and using the “vegan” package in R 4.1.3 which was used to
divide the variation of SIC density among two types of explanatory variables
for their individual and joint effects. In this analysis, the common and
unique contribution of sets of explanatory variables (two sets including
abiotic and biotic variables) in SIC density is determined. Additionally, the
residuals were determined by a fraction of variations in response variables,
which could not be explained by any of the explanatory variables. The VPA
method allows us to explore the variation clearly by the percentage of
explanatory variables, which are easy to interpret and can be discussed in
the context of SIC density.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>SIC density and influencing variables in different soil depths</title>
      <p id="d1e1170">The SIC density and SIC content had no significant differences between the
topsoil and subsoil, but the BD in the subsoil was much higher compared with the topsoil. Specifically, SIC density in the topsoil and subsoil ranged from 1.8 to 3271 and 5.4 to 3214 g C m<inline-formula><mml:math id="M74" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> across 25 sampling sites, with an average of <inline-formula><mml:math id="M75" display="inline"><mml:mrow><mml:mn mathvariant="normal">802</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">220</mml:mn></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M76" display="inline"><mml:mrow><mml:mn mathvariant="normal">814</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">236</mml:mn></mml:mrow></mml:math></inline-formula> g C m<inline-formula><mml:math id="M77" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, respectively (Fig. 1). No significant changes in SIC density with soil depth were observed in both the alpine steppe and alpine desert (<inline-formula><mml:math id="M78" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.113</mml:mn></mml:mrow></mml:math></inline-formula> and
<inline-formula><mml:math id="M79" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.068</mml:mn></mml:mrow></mml:math></inline-formula>, respectively; Fig. 1) sites, but SIC density was higher in the subsoil than that in the topsoil in the alpine meadow (<inline-formula><mml:math id="M80" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.002</mml:mn></mml:mrow></mml:math></inline-formula>, Fig. 1).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e1260">Soil inorganic C (SIC) content, bulk density (BD), and SIC density (SICD) in the topsoil and subsoil. The horizontal solid and hollow lines inside each box represent medians and mean values, respectively. Significant differences between the topsoil and subsoil were inspected according to Tukey's test.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022-f01.png"/>

        </fig>

      <p id="d1e1269">Meanwhile, the majority of abiotic and biotic drivers had significant
differences between the topsoil and subsoil (Table 1). The RB, AN, MBC, BA, and FA in the topsoil were significantly larger than those in the subsoil (all <inline-formula><mml:math id="M81" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.001</mml:mn></mml:mrow></mml:math></inline-formula>). In contrast, pH was significantly lower in the topsoil
than in the subsoil (<inline-formula><mml:math id="M82" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.001</mml:mn></mml:mrow></mml:math></inline-formula>, Table 1). However, the sand proportion
between the two soil depths had no significant differences (Table 1).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e1300">Edaphic, plant and microbial properties between the topsoil and
subsoil for 25 sampling sites.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="4">
     <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:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Parameters</oasis:entry>
         <oasis:entry colname="col2">Topsoil</oasis:entry>
         <oasis:entry colname="col3">Subsoil</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M85" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> value</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">RB (g m<inline-formula><mml:math id="M86" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col2">1670 <inline-formula><mml:math id="M87" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 359</oasis:entry>
         <oasis:entry colname="col3">95.2 <inline-formula><mml:math id="M88" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 15.3</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M89" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">pH</oasis:entry>
         <oasis:entry colname="col2">7.66 <inline-formula><mml:math id="M90" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.28</oasis:entry>
         <oasis:entry colname="col3">7.85 <inline-formula><mml:math id="M91" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.26</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M92" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">AN (mg kg<inline-formula><mml:math id="M93" 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">217 <inline-formula><mml:math id="M94" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 43.7</oasis:entry>
         <oasis:entry colname="col3">131 <inline-formula><mml:math id="M95" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 22.0</oasis:entry>
         <oasis:entry colname="col4">0.004</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">SP (%)</oasis:entry>
         <oasis:entry colname="col2">47.1 <inline-formula><mml:math id="M96" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 4.33</oasis:entry>
         <oasis:entry colname="col3">45.6 <inline-formula><mml:math id="M97" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 4.87</oasis:entry>
         <oasis:entry colname="col4">0.698</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">MBC (mg kg<inline-formula><mml:math id="M98" 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">385 <inline-formula><mml:math id="M99" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 73.8</oasis:entry>
         <oasis:entry colname="col3">101 <inline-formula><mml:math id="M100" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 9.7</oasis:entry>
         <oasis:entry colname="col4">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">BA (10<inline-formula><mml:math id="M101" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">9</mml:mn></mml:msup></mml:math></inline-formula> gene copies g<inline-formula><mml:math id="M102" 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> soil)</oasis:entry>
         <oasis:entry colname="col2">27.2 <inline-formula><mml:math id="M103" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 5.68</oasis:entry>
         <oasis:entry colname="col3">12.6 <inline-formula><mml:math id="M104" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 2.86</oasis:entry>
         <oasis:entry colname="col4">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">FA (10<inline-formula><mml:math id="M105" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">7</mml:mn></mml:msup></mml:math></inline-formula> gene copies g<inline-formula><mml:math id="M106" 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> soil)</oasis:entry>
         <oasis:entry colname="col2">14.2 <inline-formula><mml:math id="M107" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 3.25</oasis:entry>
         <oasis:entry colname="col3">3.62 <inline-formula><mml:math id="M108" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> 0.84</oasis:entry>
         <oasis:entry colname="col4">0.001</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p id="d1e1303">RB: root biomass; AN: soil available nitrogen; SP: sand proportion; MBC:
microbial biomass carbon; BA: soil bacterial abundance; FA: soil fungal
abundance. Values are means <inline-formula><mml:math id="M83" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> standard error (SE); <inline-formula><mml:math id="M84" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula> values represent
significant differences between the topsoil and subsoil according to Tukey's
test.</p></table-wrap-foot></table-wrap>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Associations of SIC density with abiotic and biotic variables</title>
      <p id="d1e1667">The SIC density was closely related to multiple abiotic and biotic variables
(Table S3, Figs. 2 and 3 for topsoil and subsoil, respectively). For both
the topsoil and subsoil, the SIC density showed a significant increasing trend with altitude, pH, and sand proportion, but declined with MAP, PAB, PC, RB, AN, BA and FA (all <inline-formula><mml:math id="M109" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>). The SIC density showed a correlation
with MBC in the topsoil (<inline-formula><mml:math id="M110" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>, Fig. 2) but not in the subsoil
(Fig. 3). Meanwhile, the SIC density in both soil depths did not significantly correlate with MAT (Figs. 2 and 3). In addition, the absolute value of slope for the regression equation for the most explanatory variables
(except for AN, MAT and MBC) in the topsoil was larger than that of the
subsoil, especially for RB and sand proportion (Figs. 2 and 3).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e1696">SIC density in relation to climatic, edaphic, plant and microbial
factors in the topsoil. The solid lines are fitted by ordinary least-squares
regressions, and the shadow areas correspond to 95 % confidence intervals. AM: alpine meadow; AS: alpine steppe; AD: alpine desert; MAP: mean annual precipitation; PAB: plant aboveground biomass; PC: plant coverage. The
abbreviations for other variables are shown in Table 1. <inline-formula><mml:math id="M111" display="inline"><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M112" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>;
<inline-formula><mml:math id="M113" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>*</mml:mo><mml:mo>*</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M114" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.01</mml:mn></mml:mrow></mml:math></inline-formula>; <inline-formula><mml:math id="M115" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>*</mml:mo><mml:mo>*</mml:mo><mml:mo>*</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M116" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.001</mml:mn></mml:mrow></mml:math></inline-formula>.</p></caption>
          <?xmltex \igopts{width=426.791339pt}?><graphic xlink:href="https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022-f02.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e1779">SIC density in relation to climatic, edaphic, plant and microbial
factors in the subsoil. The solid lines are fitted by ordinary least-squares
regressions, and the shadow areas correspond to 95 % confidence intervals. AM: alpine meadow; AS: alpine steppe; AD: alpine desert.</p></caption>
          <?xmltex \igopts{width=426.791339pt}?><graphic xlink:href="https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022-f03.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Determinants of SIC density in different soil depths</title>
      <p id="d1e1796">The linear model and VPA collectively showed that the predominant predictors of SIC density differed with soil depth (Figs. 4 and 5). Specifically, for the topsoil, the linear model revealed that microbial and plant variables largely explained the variations in the SIC density, followed by edaphic variables, and climate contributed the least (Fig. 4). Among these variables, PC, BA and FA exhibited larger effects on the SIC density compared with other predictor factors (Fig. 4). Moreover, the VPA illustrated that biotic factors explained the majority variation of SIC density compared with abiotic factors (Fig. 5). For the subsoil, the linear model showed that edaphic variables largely explained the variation in SIC density, followed by microbial and plant variables, and climate contributed the least (Fig. 4). Among these variables, the soil pH had larger contributions to the variation of SIC density than others (Fig. 4). Meanwhile, the VPA confirmed that the predictions of biotic factors on SIC density were better than those of abiotic factors in the subsoil (Fig. 5).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e1801">Relative effects of multiple drivers of SIC density in the <bold>(a)</bold> topsoil and <bold>(b)</bold> subsoil. Climatic variables include MAP, MAT and altitude; edaphic variables include pH, AN and sand proportion; plant variables include PB, PC and RB; microbial variables include MBC, BA and FA.</p></caption>
          <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022-f04.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e1818">Variation partitioning analyses (VPAs) reveal the relative
contribution of abiotic and biotic variables to SIC density in the <bold>(a)</bold> topsoil (61.2 % vs. 84.4 %) and <bold>(b)</bold> subsoil (73.4 % vs. 86.1 %), respectively. Results in three fractions: the unique effect of abiotic factors (X1), the unique effect of biotic factors (X2), and common interception of abiotic and biotic factors (X3).</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://soil.copernicus.org/articles/8/687/2022/soil-8-687-2022-f05.png"/>

        </fig>

<?xmltex \hack{\newpage}?>
</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Discussion</title>
      <p id="d1e1844">To the best of our knowledge, this study was the first to study large-scale
evidence of the relative contribution of abiotic and biotic drivers to the
variation of SIC stock at different soil depths, which has considerable
implications for grasping the importance of SIC in the ecosystem C cycling.
Due to considerably stable characteristics and the long turnover time (Mi et
al., 2008; Yang et al., 2010; Zamanian et al., 2018), SIC stock is
traditionally considered to be dominated by abiotic factors including soil
moisture, soil pH, CO<inline-formula><mml:math id="M117" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> partial pressure, and Ca<inline-formula><mml:math id="M118" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> concentrations
according to the equilibrium of carbonate precipitation–dissolution
reactions (CaCO<inline-formula><mml:math id="M119" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M120" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> H<inline-formula><mml:math id="M121" 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="M122" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> CO<inline-formula><mml:math id="M123" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M124" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> Ca<inline-formula><mml:math id="M125" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M126" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> 2HCO<inline-formula><mml:math id="M127" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> and Ca<inline-formula><mml:math id="M128" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M129" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> 2HCO<inline-formula><mml:math id="M130" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M131" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> CaCO<inline-formula><mml:math id="M132" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M133" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> H<inline-formula><mml:math id="M134" 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="M135" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> CO<inline-formula><mml:math id="M136" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>) and mineral carbonation (MgSiO<inline-formula><mml:math id="M137" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M138" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> 2CO<inline-formula><mml:math id="M139" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M140" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> 2MgCO<inline-formula><mml:math id="M141" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M142" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> SiO<inline-formula><mml:math id="M143" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> and CaMgSi<inline-formula><mml:math id="M144" 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="M145" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">6</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M146" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> CO<inline-formula><mml:math id="M147" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M148" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> H<inline-formula><mml:math id="M149" 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="M150" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> Ca<inline-formula><mml:math id="M151" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>Mg<inline-formula><mml:math id="M152" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">5</mml:mn></mml:msub></mml:math></inline-formula>Si<inline-formula><mml:math id="M153" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">8</mml:mn></mml:msub></mml:math></inline-formula>O<inline-formula><mml:math id="M154" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">22</mml:mn></mml:msub></mml:math></inline-formula>(OH)<inline-formula><mml:math id="M155" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M156" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> CaCO<inline-formula><mml:math id="M157" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M158" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> SiO<inline-formula><mml:math id="M159" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>)
(Mi et al., 2008; Rey, 2015; Yang et al., 2012; Yang and Yang, 2020). These
abiotic factors were proved to have large impacts on the dissolution and
deposition processes of inorganic C and ultimately determined the
reservation and distribution of SIC (Rey, 2015; Rowley et al., 2018).</p>
      <p id="d1e2224">However, many biological processes and factors were not quantitatively
considered in previous studies. In this study, based on the approach of
large-scale field samplings across Tibetan alpine grasslands, we estimated
the predominant drivers of SIC stock in the topsoil and subsoil. Our results
found the predominant roles of microbial and plant factors in determining
SIC stock in both topsoil and subsoil. More importantly, the effects of
biotic factors on SIC stock weakened with soil depth (Fig. 4). These results
were different from those demonstrating the critical influence of abiotic
processes on SIC stock (Mi et al., 2008; Yang et al., 2010).</p>
      <p id="d1e2227">We found that SIC density showed a declining trend with the increasing plant
aboveground biomass, plant coverage, and root biomass (Figs. 2 and 3). Plant factors could contribute to the decline of SIC stock by three pathways including uptakes of exchangeable cations, plant organic matter inputs, and rhizosphere processes. First, a large decline in soil base cations is likely to be induced by plant uptake with increasing plant biomass; and the losses of soil exchangeable base cations can cause the transformation of SIC to CO<inline-formula><mml:math id="M160" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>, which is ultimately released into the atmosphere (Huang et al., 2015). Second, increasing plant residue inputs can enhance carbonic and organic acid production into soil water solutions via microbial decomposition, which reduces the availability of soil base cations through cation exchange in the soil (Sartori et al., 2007) and increases the
dissolution and leaching of carbonates, resulting in a decrease in the SIC.
Third, the plant rhizosphere effect on releasing CO<inline-formula><mml:math id="M161" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> from carbonates
should not be ignored, especially in alkaline soils. By releasing organic
acids and protons as well as CO<inline-formula><mml:math id="M162" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>, plant roots can reduce soil pH and
increase CO<inline-formula><mml:math id="M163" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> in the rhizosphere (Lenzewski et al., 2018), both of which
dissolve carbonates by neutralization (Harley and Gilkes, 2000). In
addition, organic compounds from plant root exudates, such as malate or
citrate, can stimulate mineral weathering by dissolving silicate minerals
(Dontsova et al., 2020).</p>
      <p id="d1e2266">Furthermore, the topsoil has a larger quantity and higher quality of plant
residues than the subsoil, which indicates a greater potential for carbonate
dissolution by biological processes for the surface soil (Liu et al., 2020).
The large root biomass in the topsoil can increase the uptake of base cations and result in increasing proton and organic acids in root exudates
(Li et al., 2007), thus reducing the soil carbonate content for maintaining
the charge balance. In addition, the larger plant roots exuded more organic
compounds in the topsoil that can stimulate parent mineral weathering and
dissolve silicate minerals by chelating reaction products (Doetterl et al.,
2018; Dontsova et al., 2020).</p>
      <p id="d1e2270">Also, the SIC density in both soil depths appears to have an increasing or
decreasing trend from the alpine meadow to the alpine steppe and alpine desert (Figs. 2 and 3) sites. In the present study, for example, the alpine meadow has larger plant productivity than the alpine steppe, which implies that more plant above- and belowground residues are deposited in alpine meadow soils compared to alpine steppe soils. Therefore, from the perspective of the whole ecosystem, the grassland type would be a better predictor for the quantity and distribution of SIC density.</p>
      <p id="d1e2273">Previous studies reported that microbial properties may not be important in
mediating SIC accumulation (Liu et al., 2021; Wang et al., 2015). However, our results found that microbial factors including microbial biomass and
bacterial and fungal gene abundance showed significant associations with SIC
stock (Figs. 2 and 3), which could be due to microbes driving the carbonate
dissolution processes, including microbial respiration, organic matter
mineralization, and releases of proton and organic acids by microbial
metabolic activity. First, the increase in microbial respiration can improve
CO<inline-formula><mml:math id="M164" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> production and enhance the partial pressure of CO<inline-formula><mml:math id="M165" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>, leading to
a decline in pH and further dissolution of carbonates (Chang et al., 2012).
In addition, soil organic matter mineralization and litter decomposition by
microbes can induce the dissolution of CO<inline-formula><mml:math id="M166" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> and the release of organic
acids (Goulding, 2016; Kuzyakov and Razavi, 2019), both of which decrease
the SIC stock. Meanwhile, chelates and enzymes excreted by microbes may
contribute to enhancing mineral dissolution rates and organic matter
decomposition (Xiao et al., 2015; Zaharescu et al., 2020).</p>
      <p id="d1e2303">We also revealed that bacterial and fungal gene abundance were significantly
correlated with SIC stock (Figs. 2 and 3), which was likely to account for
decreasing soil pH in the involvement of microbial biological reactions. For
instance, nitrifying bacteria can oxidize ammonium to nitrate
(NH<inline-formula><mml:math id="M167" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">4</mml:mn><mml:mo>+</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M168" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> OH<inline-formula><mml:math id="M169" display="inline"><mml:msup><mml:mi/><mml:mo>-</mml:mo></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M170" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> 2O<inline-formula><mml:math id="M171" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> <inline-formula><mml:math id="M172" display="inline"><mml:mo>→</mml:mo></mml:math></inline-formula> NO<inline-formula><mml:math id="M173" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M174" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> 2H<inline-formula><mml:math id="M175" 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="M176" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> H<inline-formula><mml:math id="M177" display="inline"><mml:msup><mml:mi/><mml:mo>+</mml:mo></mml:msup></mml:math></inline-formula>), and the increase in acidity is finally neutralized through accelerating carbonate dissolution (Zamanian et al., 2016). Also, some nitrogen-fixing bacteria that lived in symbiosis with leguminous plants can acidify the soil by excreting protons during N<inline-formula><mml:math id="M178" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> fixation (Vicca et al., 2022). Furthermore, fungi are likely to accelerate carbonate neutralization by exuding protons and organic acids (van Hees et al., 2006; Wild et al., 2021).</p>
      <p id="d1e2412">Microbial factors could also be better predictors for SIC stock in the
topsoil than in the subsoil. The large plant residues incorporated into the
topsoil provided substantial amounts of organic matter for microbial living
and decomposition (Oelkers et al., 2015; Ven et al., 2020), which can
stimulate microbial abundance and activities and promote microbial
extracellular enzymes. These extracellular excretions play a fundamental
role in microbial respiration and CO<inline-formula><mml:math id="M179" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> production, both of which
stimulate silicate weathering and carbonate dissolution (Vicca et al., 2022).
Meanwhile, the higher CO<inline-formula><mml:math id="M180" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> flux and CO<inline-formula><mml:math id="M181" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> partial pressure resulting
from the biological activities of roots and soil microorganisms in the
topsoil could enhance carbonate dissolution and formations of pedogenic
inorganic C (Chang et al., 2012; Zamanian et al., 2016).</p>
      <p id="d1e2442">Different from plant and microbial factors, the prediction of edaphic
factors on SIC stock strengthened with soil depth, with soil pH being the
most important predictor among edaphic variables (Fig. 4). The buffering
capacity in soil solutions determines the equilibrium of ion inputs and
outputs by soil pH (Huang et al., 2015). In this study, soil pH in the
subsoil (7.85) was much higher than that in the topsoil (7.66) (Table 1).
The higher pH could buffer the replacement of the exchangeable cations with
protons (Frank and Stuanes, 2003) and increase the preservation of base
cations (Gandois et al., 2011). Given that base cations and carbonates
provide the major buffering capacity in the alkaline soil (Yang et al.,
2012), the topsoil could be subject to a larger loss of base cations and SIC
due to the lower soil pH compared to the subsoil.</p>
      <p id="d1e2445">Taken together, our results revealed that SIC stock was closely linked with
biotic factors, which highlights the roles of biological processes in
predicting SIC dynamics (Hong et al., 2019). These results imply that the
widespread enhancement of vegetation productivity under global environmental
changes (e.g., warming and rewetting) (Ding et al., 2017; Wang et al., 2008)
may aggravate the depletion of SIC stock (Raza et al., 2020). Meanwhile,
previous studies have urged the need for incorporating microbial processes
and indicators into Earth system models (ESMs) to reduce the uncertainty in
predicting soil C dynamics, especially SOC decomposition (Allison et al.,
2010; Moorhead and Sinsabaugh, 2006; Todd-Brown et al., 2013). However, our
findings highlighted the vital role of microbial factors in regulating soil
C balance from inorganic C preservation. Thus, incorporating microbial
processes into the models can aid the understanding of overall soil C
responses, because SOC and SIC are formed, protected, and lost in different
ways.</p>
      <p id="d1e2449">More importantly, the predictions of biotic factors on SIC stock weakened
with soil depth, which implies that SIC may be susceptive to environmental
changes in the topsoil which is the hotspot of root and microbial
activities. Even though biotic factors in the subsoil played a less important role in predicting SIC stock compared with the topsoil, an increase in rooting depth is expected in response to climate warming and land-use change (Liu et al., 2018), which is likely to cause SIC losses in the deep soil by root growth. Therefore, it is a necessity to further explore the effects of biotic factors on SIC stock in the deep soil in the context of global changes. Although most of the variations in SIC density were explained by our
measured explanatory variables, some other potential variables may also
predict SIC density (Fig. 5). Then, understanding the effects of other
potential abiotic and biotic factors on SIC density with soil depth is
urgently needed when predicting the response and feedback of SIC to climate
change in the future. Overall, the contribution of SIC to CO<inline-formula><mml:math id="M182" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> is not
ignored and SIC maintenance has a considerable effect on soil C losses and
is important to maintain the health and ecosystem functions (Raza et al.,
2020; Zamanian et al., 2018). Our study provides robust evidence that biotic
factors are correlated with SIC stock in the Tibetan plateau and that
topsoils and subsoils should be considered separately when modeling SIC
dynamics and its feedbacks on climate change (Yang et al., 2012; Zamanian
and Kuzyakov, 2019).</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusions</title>
      <p id="d1e2470">Our findings showed that SIC stock had no significant differences between
the topsoil and subsoil in the Tibetan grasslands; the climatic, edaphic,
plant and microbial variables jointly predicted SIC stock in the Tibetan
grasslands; and the biotic factors had a larger contribution than abiotic
factors to the variation of SIC stock. Furthermore, the relative importance
of explanatory variables to the variation of SIC stock varied with soil
depth, the predictions of microbial and plant variables on SIC stock
weakened with soil depth, while the predictions of edaphic variables
strengthened with soil depth. Our results revealed that biotic factors
should be considered seriously for predicting SIC stock due to their
regulating roles in biological processes. The contrasting responses and
drivers of SIC stock between the topsoil and subsoil highlight differential
mechanisms underlying SIC preservation with soil depth, which is crucial to
understanding and predicting SIC dynamics and its feedbacks to environmental
changes.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e2477">The data that support the findings of this study are available from the corresponding author upon reasonable request.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e2480">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/soil-8-687-2022-supplement" xlink:title="zip">https://doi.org/10.5194/soil-8-687-2022-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e2489">JP, JW and SN designed the study. JP, JW, DT, RZ, YL, LS, JY, CW and SN were involved in drafting and revising the manuscript. All authors read and approved the final manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e2495">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e2501">Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><?xmltex \hack{\newpage}?><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e2508">This study was financially supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (grant no. 2019QZKK0302), the National Natural Science Foundation of China (grant nos. 31988102 and 32101390), and the China National Postdoctoral Program for Innovative Talents (grant no. BX20200330).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e2514">This paper was edited by Marta Dondini and reviewed by Enrico Balugani and one anonymous referee.</p>
  </notes><ref-list>
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