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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Plant Sci.</journal-id>
<journal-title>Frontiers in Plant Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Plant Sci.</abbrev-journal-title>
<issn pub-type="epub">1664-462X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpls.2023.1117903</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Plant Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Direct and indirect effects of dominant plants on ecosystem multifunctionality</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Jingwei</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Ziyang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cui</surname>
<given-names>Hanwen</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1378249"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Song</surname>
<given-names>Hongxian</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Jiajia</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gao</surname>
<given-names>Haining</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Chen</surname>
<given-names>Shuyan</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1895388"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Kun</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1042912"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Zi</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Yajun</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Xiangtai</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Xiaoli</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2157254"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Meng</surname>
<given-names>Lihua</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>An</surname>
<given-names>Lizhe</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xiao</surname>
<given-names>Sa</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1724932"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Le Bagousse-Pinguet</surname>
<given-names>Yoann</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2131499"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>State Key Laboratory of Grassland and Agro-ecosystems, College of Ecology, Lanzhou University</institution>, <addr-line>Lanzhou, Gansu</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Ministry of Education Key Laboratory of Cell Activities and Stress Adaptations, School of Life Sciences, Lanzhou University</institution>, <addr-line>Lanzhou, Gansu</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>College of Life Science and Engineering, Hexi University</institution>, <addr-line>Zhangye, Gansu</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Aix Marseille Univ, Centre national de la recherche scientifique, Avignon Universit&#xe9;, Institut de Recherche pour le D&#xe9;veloppement, Institut M&#xe9;diterran&#xe9;en de Biodiversit&#xe9; et d&#x2019;&#xc9;cologie marine et continentale, Technop&#xf4;le Arbois-M&#xe9;diterran&#xe9;e</institution>, <addr-line>Aix-en-Provence</addr-line>, <country>France</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Huakun Zhou, Northwest Institute of Plateau Biology (CAS), China</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Guofang Liu, Institute of Botany (CAS), China; Jinlong Zhang, Kadoorie Farm and Botanic Garden, Hong Kong SAR, China</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Shuyan Chen, <email xlink:href="mailto:chenshy@lzu.edu.cn">chenshy@lzu.edu.cn</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>02</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1117903</elocation-id>
<history>
<date date-type="received">
<day>07</day>
<month>12</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>17</day>
<month>02</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Chen, Liu, Cui, Song, Wang, Gao, Chen, Liu, Yang, Wang, Wang, Yang, Meng, An, Xiao and Le Bagousse-Pinguet</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Chen, Liu, Cui, Song, Wang, Gao, Chen, Liu, Yang, Wang, Wang, Yang, Meng, An, Xiao and Le Bagousse-Pinguet</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Biodiversity is essential for the provision of multiple ecosystem functions simultaneously (ecosystem multifunctionality EMF). Yet, it remains unclear whether and how dominant plant species impact EMF. Here, we aimed at disentangling the direct from indirect above- and belowground pathways by which dominant plant species influence EMF. We evaluated the effects of two dominant plant species (<italic>Dasiphora fruticosa</italic>, and the toxic perennial plant <italic>Ligularia virgaurea</italic>) with expected positive and negative impacts on the abiotic environment (soil water content and pH), surrounding biological communities (plant and nematode richness, biomass, and abundance in the vicinity), and on the EMF of alpine meadows, respectively. We found that the two dominant plants enhanced EMF, with a positive effect of <italic>L. virgaurea</italic> on EMF greater than that of <italic>D</italic>. <italic>fruticosa</italic>. We also observed that dominant plants impacted on EMF through changes in soil water content and pH (indirect abiotic effects), but not through changes in biodiversity of surrounding plants and nematodes (indirect biotic pathway). Our study suggests that dominant plants may play an important role in promoting EMF, thus expanding the pervasive mass-ratio hypothesis originally framed for individual functions, and could mitigate the negative impacts of vegetation changes on EMF in the alpine meadows.</p>
</abstract>
<kwd-group>
<kwd>ecosystem multifunctionality</kwd>
<kwd>plant biodiversity</kwd>
<kwd>soil biodiversity</kwd>
<kwd>dominant plants</kwd>
<kwd>alpine meadow</kwd>
</kwd-group>
<contract-num rid="cn001">41830321, 31870412, 32071532</contract-num>
<contract-sponsor id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content>
</contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="0"/>
<equation-count count="2"/>
<ref-count count="104"/>
<page-count count="11"/>
<word-count count="5095"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Biodiversity is essential for the provision of multiple ecosystem functions simultaneously (ecosystem multifunctionality; EMF, <xref ref-type="bibr" rid="B45">Hector and Bagchi, 2007</xref>), such as litter decomposition, ecosystem production, food web stability, and climate regulation (<xref ref-type="bibr" rid="B89">Van Der Heijden et&#xa0;al., 2008</xref>; <xref ref-type="bibr" rid="B14">Bodelier, 2011</xref>; <xref ref-type="bibr" rid="B9">Bardgett and Van Der Putten, 2014</xref>; <xref ref-type="bibr" rid="B43">Handa et&#xa0;al., 2014</xref>). While multiple facets of biodiversity can impact on EMF (e.g. <xref ref-type="bibr" rid="B36">Flynn et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B86">Soliveres et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B58">Le Bagousse-Pinguet et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B56">Le Bagousse-Pinguet et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B103">Yuan et&#xa0;al., 2021</xref>), focusing solely on diversity <italic>stricto sensu</italic> ignores the overwhelming influence of species dominance on ecosystem functions (<xref ref-type="bibr" rid="B40">Grime, 1998</xref>; <xref ref-type="bibr" rid="B38">Garnier et&#xa0;al., 2004</xref>). According to the mass-ratio hypothesis (<xref ref-type="bibr" rid="B40">Grime, 1998</xref>), the effects of plant species on ecosystem functioning are proportional to plant biomass (<xref ref-type="bibr" rid="B40">Grime, 1998</xref>). However, this hypothesis was originally proposed for single functions (<xref ref-type="bibr" rid="B40">Grime, 1998</xref>; <xref ref-type="bibr" rid="B84">Smith and Knapp, 2003</xref>; <xref ref-type="bibr" rid="B38">Garnier et&#xa0;al., 2004</xref>), but yet remains less clear when focusing on EMF (<xref ref-type="bibr" rid="B58">Le Bagousse-Pinguet et&#xa0;al., 2019</xref>). Determining the key role of dominant species on EMF not only expands our fundamental understanding of the Biodiversity-EMF relationships, but could also help to prioritize relevant biodiversity attributes in conservation programs (<xref ref-type="bibr" rid="B7">Balvanera et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B18">Brum et&#xa0;al., 2017</xref>).</p>
<p>Dominant species can impact on ecosystem functioning through multiple pathways. On the one hand, dominant species and their traits can affect directly individual ecosystem functions, such as biomass production or nutrient cycling (e.g. <xref ref-type="bibr" rid="B38">Garnier et&#xa0;al., 2004</xref>). For instance, species assemblages dominated by recalcitrant species (i.e. the dominant species that exhibit high leaf lignin concentration) have been shown to decrease EMF, particularly the functions related to decomposition processes (<xref ref-type="bibr" rid="B6">Austin and Ballare, 2010</xref>; <xref ref-type="bibr" rid="B56">Le Bagousse-Pinguet et&#xa0;al., 2021</xref>). On the other hand, dominant species could also have indirect effects on ecosystem functioning, i.e. through the changes in local above- and belowground diversities. Dominant plant species can have negative effects on plant growth and establishment (e.g. due to asymmetric light competition, <xref ref-type="bibr" rid="B39">Grime, 1973</xref>), decreasing local plant biodiversity (<xref ref-type="bibr" rid="B47">Hejda et&#xa0;al., 2021</xref>), and ultimately ecosystem functioning (<xref ref-type="bibr" rid="B65">Livingstone et&#xa0;al., 2020</xref>). However, these species could also have positive effects (i.e. facilitation, <xref ref-type="bibr" rid="B13">Bertness and Callaway, 1994</xref>), and play as ecosystem engineers that enhance abiotic conditions (e.g. soil water content and pH) in their vicinity (<xref ref-type="bibr" rid="B21">Cavieres et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B33">Ellison, 2019</xref>). In these cases, dominant plants could promote plant (<xref ref-type="bibr" rid="B70">Michalet et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B59">Le Bagousse-Pinguet et&#xa0;al., 2014</xref>) and soil biodiversity and activity, including soil bacterial (<xref ref-type="bibr" rid="B50">Hortal et&#xa0;al., 2015</xref>), and fungal diversity and abundance (<xref ref-type="bibr" rid="B29">Delgado-Baquerizo et&#xa0;al., 2016b</xref>; <xref ref-type="bibr" rid="B56">Le Bagousse-Pinguet et&#xa0;al., 2021</xref>). Disentangling the direct (positive and negative) from indirect above- and belowground pathways by which dominant plant species influence EMF remains poorly explored, although such an integrative framework could contribute to the global understanding of the impact of biodiversity on ecosystem functioning.</p>
<p>Finally, the mediating role of soil nematodes in the influence of dominant plants on EMF has rarely been explored. Nematodes include a wide variety of trophic groups, such as herbivores, omnivores, predators, and microbial feeders, and are known to play important roles in soil food webs (<xref ref-type="bibr" rid="B34">Ferris, 2010</xref>). Furthermore, dominant plants can play a key role in regulating soil nematode abundances (<xref ref-type="bibr" rid="B27">De Deyn et&#xa0;al., 2004</xref>) or their richness through changes in understory plant and microbial diversity (<xref ref-type="bibr" rid="B95">Wang et&#xa0;al., 2019b</xref>). Altogether, soil nematodes represent key organisms influencing ecosystem functioning, e.g. by grazing on plant roots (<xref ref-type="bibr" rid="B102">Yeates et&#xa0;al., 2009</xref>) or by regulating microbial communities, litter decomposition, and nutrient cycling (<xref ref-type="bibr" rid="B102">Yeates et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B37">Garc&#xed;a-Palacios et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B101">Yeates and Coleman, 2021</xref>). Yet, exploring the impacts of soil nematodes on EMF and their role in the indirect pathways by which dominant plants influence EMF is essential for maintaining high levels of ecosystem function in the context of grassland vegetation change.</p>
<p>Previous studies have shown that soil pH and water content are important drivers of soil biological communities (<xref ref-type="bibr" rid="B35">Fierer and Jackson, 2006</xref>), and ecosystem functions or processes (e.g. soil organic carbon accumulation, nitrogen mineralization, and plant productivity) (<xref ref-type="bibr" rid="B22">Chen et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B53">Jing et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B62">Li et&#xa0;al., 2017</xref>). Therefore, here we investigated the role of dominant plant species on the abiotic environment (soil pH and water content), on biological communities (surrounding plants, nematodes, and microbial richness, abundances, and biomasses), and on the EMF of grassland ecosystems using nine soil functions related with biological productivity, nutrient cycling, and build-up of nutrient pools. We focused on alpine grasslands because they cover 86% of the Qinghai-Tibet Plateau (<xref ref-type="bibr" rid="B90">Wang and Cheng, 2001</xref>), are biodiversity hot-spots (<xref ref-type="bibr" rid="B12">Bengtsson et&#xa0;al., 2019</xref>), and support the provision of essential ecosystem services such as animal husbandry, forage production or carbon sequestration (<xref ref-type="bibr" rid="B25">Clough et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B75">Newbold et&#xa0;al., 2016</xref>). We used two common plant species with potentially negative and positive effects on biodiversity and EMF: <italic>Ligularia virgaurea</italic> Mattf. ex Rehder. &amp; Kobuski. (<italic>Ligularia</italic> Cass, <italic>Asteraceae</italic>) and <italic>Dasiphora fruticosa</italic> (L.) Rydb. (<italic>Dasiphora</italic>, <italic>Rosaceae</italic>). <italic>L. virgaurea</italic> is a perennial herb widely distributed in the alpine meadows of the Qinghai-Tibet Plateau, and it is a poisonous weed plant (<xref ref-type="bibr" rid="B96">Wang et&#xa0;al., 2008</xref>) that can be fatal for animals (<xref ref-type="bibr" rid="B67">Ma et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B83">Shi et&#xa0;al., 2011</xref>). <italic>D. fruticosa</italic> is a common shrub of the alpine meadows within an elevation range from 2700 to 4500&#xa0;m.a.s.l (<xref ref-type="bibr" rid="B93">Wang et&#xa0;al., 2017</xref>). <italic>D. fructicosa</italic> has been shown to promote the survival of surrounding graminoids (<xref ref-type="bibr" rid="B100">Xu et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B71">Michalet et&#xa0;al., 2014</xref>) or through indirect effects that promote nematode abundance by increasing grass biomass (<xref ref-type="bibr" rid="B94">Wang et&#xa0;al., 2018</xref>). However, these positive effects can be hidden by complex indirect interactions among plant functional groups (<xref ref-type="bibr" rid="B100">Xu et&#xa0;al., 2010</xref>; <xref ref-type="bibr" rid="B71">Michalet et&#xa0;al., 2014</xref>). We hypothesized that: (1) <italic>D. fruticosa</italic> would increase and <italic>L. virgaurea</italic> would reduce the EMF; (2) dominant plants would influence EMF through modifying the abiotic drivers, such as soil water content and pH; (3) dominant species would also indirectly affect EMF by influencing biodiversity, especially soil nematodes.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study site</title>
<p>The experiment was conducted in the alpine grasslands of the eastern Qinghai-Tibet Plateau, i.e. at the Gansu Gannan Grassland Ecosystem National Observation and Research Station in Maqu (33&#xb0;40&#x2032;N, 101&#xb0;51&#x2032;E) at 3550&#xa0;m.a.s.l, Gansu, China. The mean annual temperature is 1.2&#xb0;C, with the lowest temperatures occurring in January (-10&#xb0;C) and the highest in July (11.7&#xb0;C). The mean annual precipitation reaches 564&#xa0;mm, mostly concentrated from May to September. Yaks have been grazing the alpine grassland in the study area since 1999, with a density of 1.6 head ha<sup>-1</sup> (<xref ref-type="bibr" rid="B51">Hu et&#xa0;al., 2015</xref>). Vegetation did not experience significant degradation over the least 20 years. The vegetation cover of the study site is dominated by the shrub <italic>Dasiphora fruticosa</italic> (Rosaceae) and <italic>Ligularia virgaurea</italic> (Asteraceae), but also includes other perennial plant species such as <italic>Carex atrofusca</italic> (Cyperaceae), <italic>Agrostis hugoniana</italic> (Poaceae), <italic>Euphorbia altotibetica</italic> (Euphorbiaceae) and <italic>Halenia elliptic</italic>a (Gentianaceae) (See <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref> for more information about each plant species).</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Experimental design</title>
<p>Our observational design was set up in early June 2016 (See <xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Figure&#xa0;1</bold>
</xref>). Fifteen blocks were set up within a homogenous and flat alpine grassland landscape, which is one of the representative landscape types of the Tibetan Plateau and the main typical habitat of the distribution of <italic>D. fruticosa</italic> and <italic>L. virgaurea</italic> (<xref ref-type="bibr" rid="B24">Chu et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B79">Qian et&#xa0;al., 2022</xref>). Two hundred individuals of <italic>D. fruticosa</italic> could be observed (25% of the total cover), and thousands of <italic>L. virgaurea</italic> (15% of the cover) in this landscape. Each block included 3 plots of 30&#xa0;cm &#xd7; 30&#xa0;cm: one grassland plot including the dominant shrub <italic>D. fruticosa</italic>, one including the dominant poisonous weed <italic>L. virgaurea</italic> and one grassland control without <italic>D. fruticosa</italic> or <italic>L. virgaurea</italic>. The canopy size of each shrub was around 50&#xa0;cm &#xd7; 70&#xa0;cm in our study, so to ensure that the sample plot was completely under the canopy, we set up a 30&#xa0;cm &#xd7; 30&#xa0;cm plot. The dominant plant represented the center of the plot. Altogether, our study included a total of 45 plots.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Sampling and measurement</title>
<p>In each plot, we collected a composite soil sample, resulting from three sub-samples (4&#xa0;cm diameter) randomly taken by a soil auger at 15&#xa0;cm depth. We mixed each composite soil sample and removed the gravel. We divided the composite soil samples into two replicates, and stored them at 4&#xb0;C: one replicate was used to extract nematodes, and the other to measure soil physicochemical properties.</p>
<p>We measured 9 soil variables that were uncorrelated with each other (See <xref ref-type="supplementary-material" rid="SF2">
<bold>Supplementary Figure&#xa0;2</bold>
</xref>), and together constitute good proxies for biological productivity, nutrient cycling, and nutrient pools establishment (<xref ref-type="bibr" rid="B15">Bowker et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B86">Soliveres et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B28">Delgado-Baquerizo et&#xa0;al., 2016a</xref>; <xref ref-type="bibr" rid="B91">Wang et&#xa0;al., 2019a</xref>): soil nitrate (NO<sub>3</sub>
<sup>-</sup>), soil ammonium (NH<sub>4</sub>
<sup>+</sup>), soil organic carbon (SOC), soil total phosphorus (TP), soil total nitrogen (TN), urease (URE), phosphatase (PHO), invertase (INV), protease (PRO).</p>
<p>We measured soil water content (SWC) by taking 5&#xa0;g fresh soil and placing them in an oven at 90&#xb0;C for 48&#xa0;h until constant weight. After removing plant residues and gravel, the remaining soil was air-dried (avoiding direct sunlight), and then sieved (aperture of 0.25&#xa0;mm). Soil pH was quantified in a 1:2.5 soil: deionized water slurry using a pH meter (PHSJ-3F, Shanghai INESA Scientific Instrument Co., Ltd, China). TP and TN were digested by concentrated H<sub>2</sub>SO<sub>4</sub>, followed by Mo-Sb antispetrophotography and semi micro-Kjeldahl (<xref ref-type="bibr" rid="B8">Bao, 2000</xref>) with an auto chemistry analyzer (SmartChem 200, AMS Alliance, Italy). SOC was determined following the wet oxidation method. Soil NH<sub>4</sub>
<sup>+</sup> and NO<sub>3</sub>
<sup>-</sup> were extracted using 2 M KCl (1:10 soil: solution ratio) and analyzed with an auto chemistry analyzer.</p>
<p>The activity of URE was measured by the reaction of urease enzyme ammonia with phenol-sodium hypochlorite in an alkaline medium (<xref ref-type="bibr" rid="B52">Huang et&#xa0;al., 2015</xref>). The activity of INV was measured by using the dinitrosalicylic acid method (<xref ref-type="bibr" rid="B5">Asare-Brown and Bullock, 1988</xref>). PHO activity was determined by using disodium diphenyl phosphate colorimetry (<xref ref-type="bibr" rid="B87">Tabatabai and Bremner, 1969</xref>). Ninhydrin colorimetry was used to determine PRO activity (<xref ref-type="bibr" rid="B97">Watanabe and Hayano, 1995</xref>).</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Biodiversity indices</title>
<p>We focused on plant species richness, biomass, and abundance as potential aboveground indirect biotic drivers, as they are known to have an impact on EMF (e.g. <xref ref-type="bibr" rid="B69">Maestre et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B86">Soliveres et&#xa0;al., 2016</xref>). In each plot, all herbaceous plants were thus identified at the species level to calculate the plant richness. We also counted the number of each individual plant species to evaluate the abundance of aboveground plant communities and then harvested per species to assess the aboveground biomasses. The plant material was oven-dried for 48&#xa0;h at 80 &#xb0; before weighing. Note that the Shannon index of plant diversity was also calculated. However, this index was further removed from subsequent analyses due to high correlation with plant richness (r = 0.8, <xref ref-type="supplementary-material" rid="SF3">
<bold>Supplementary Figure&#xa0;3</bold>
</xref>).</p>
<p>We also considered the richness, biomass, and abundance of nematodes as potential indirect soil biotic drivers of dominant plant species on EMF. We used the modified Baermann wet funnel technique to extract nematodes from 50&#xa0;g. of fresh soil (<xref ref-type="bibr" rid="B64">Liu et&#xa0;al., 2008</xref>). We identified all nematodes in each sample to genus/species level and converted nematode abundances to the number of individuals per kg. of dry soil (ind. kg<sup>-1</sup> dry soil). We also calculated the Shannon index of nematode diversity. However, this index was further removed from subsequent analyses due to high correlation with nematode richness (r = 0.9, <xref ref-type="supplementary-material" rid="SF3">
<bold>Supplementary Figure&#xa0;3</bold>
</xref>). We finally measured the maximum width and length of all nematodes observed. We used Andrassy&#x2019;s formula (<xref ref-type="bibr" rid="B3">Andr&#xe1;ssy, 1967</xref>) to estimate nematode biomass:</p>
<disp-formula>
<label>
<italic>eq. 1</italic>,</label>
<mml:math display="block" id="M1">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>Weight</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mtext>nematode</mml:mtext>
</mml:mrow>
</mml:msub>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mtext>&#x3bc;g</mml:mtext>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msup>
<mml:mtext>W</mml:mtext>
<mml:mn>2</mml:mn>
</mml:msup>
<mml:mtext>(&#x3bc;m)</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>1</mml:mn>
<mml:msup>
<mml:mrow>
<mml:mn>.6</mml:mn>
<mml:mo>&#xd7;</mml:mo>
<mml:mn>10</mml:mn>
</mml:mrow>
<mml:mn>6</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
<mml:mo>&#xd7;</mml:mo>
<mml:mtext>L&#xa0;(&#x3bc;m)</mml:mtext>
</mml:mrow>
</mml:math>
</disp-formula>
<p>where W is the maximal width of nematodes and the L is their length.</p>
<p>Finally, we considered microbial C and N biomasses as proxies of the C and N sources in the systems (<xref ref-type="bibr" rid="B81">Sanaullah et&#xa0;al., 2011</xref>). They were measured based on the Chloroform fumigation extraction method (<xref ref-type="bibr" rid="B16">Brookes et&#xa0;al., 1985</xref>). Then a microbial C:N biomass ratio was calculated and used as a surrogate of ecosystem productivity and soil fertility (<xref ref-type="bibr" rid="B81">Sanaullah et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B23">Cheng et&#xa0;al., 2020</xref>).</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Multifunctionality indices</title>
<p>We evaluated EMF using both the averaging (<xref ref-type="bibr" rid="B74">Mouillot et&#xa0;al., 2011</xref>) and the multiple threshold approaches (<xref ref-type="bibr" rid="B19">Byrnes et&#xa0;al., 2014</xref>), which allowed us to assess whether multiple functions are simultaneously performing at high levels, and to consider potential trade-offs between the functions assessed (<xref ref-type="bibr" rid="B58">Le Bagousse-Pinguet et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B56">Le Bagousse-Pinguet et&#xa0;al., 2021</xref>). The averaged EMF index (EMF<sub>A</sub>) was calculated using each of the 9 soil variables (SOC, TN, TP, NH<sub>4</sub>
<sup>+</sup>, NO<sub>3</sub>
<sup>-</sup>, URE, PRO, PHO, INV). We scaled the 9 variables to range from 0 to 1 with the formula:</p>
<disp-formula>
<label>
<italic>eq.2</italic>,</label>
<mml:math display="block" id="M2">
<mml:mrow>
<mml:mi>f</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>x</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mrow>
<mml:mi>m</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>n</mml:mi>
<mml:mo>&#xa0;</mml:mo>
</mml:mrow>
</mml:msub>
</mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mrow>
<mml:mi>m</mml:mi>
<mml:mi>a</mml:mi>
<mml:mi>x</mml:mi>
</mml:mrow>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mrow>
<mml:mi>m</mml:mi>
<mml:mi>i</mml:mi>
<mml:mi>n</mml:mi>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<p>where <italic>x</italic> is the value of the function with its maximum (<italic>x</italic>
<sub>max</sub>) and minimum (<italic>x</italic>
<sub>min</sub>) values measured, then we averaged the standardized variables to obtain the EMF<sub>A</sub> for each plot. We also computed EMF-threshold values of 25% (MF<sub>T25</sub>), 50% (MF<sub>T50</sub>), 75% (MF<sub>T75</sub>), according to <xref ref-type="bibr" rid="B19">Byrnes et&#xa0;al. (2014)</xref>.</p>
</sec>
<sec id="s2_6">
<label>2.6</label>
<title>Statistical analyses</title>
<p>Generalized linear mixed modeling (GLMM) was used to evaluate the effects of <italic>D. fruticosa</italic>, <italic>L. virgaurea</italic>, and the control on EMF, and on plant and nematode diversity and biomass and soil properties, with the treatments as fixed effects, and block as a random effect. The uniformity and dispersion of these models were checked and adjusted accordingly. The beta and poisson generalized linear models were used for proportional and counting data, respectively (<xref ref-type="bibr" rid="B30">Douma and Weedon, 2019</xref>). We also used &#x201c;compois&#x201d; or &#x201c;genpois&#x201d; distribution due to the presence of underdispersion (i.e. variance &lt; mean) in the data (<xref ref-type="bibr" rid="B104">Zuur et&#xa0;al., 2007</xref>). Tukey&#x2019;s HSD test was used for post-hoc analyses to determine significant differences between treatments.</p>
<p>Linear mixed effect models (LMM) were used to evaluate the impacts of abiotic and biotic drivers on EMF. We used the EMF indices as response variables, and soil (pH, SWC, and their quadratic term) and biotic attributes (dominant plants; plant richness, abundance, and biomass; nematode richness, abundance, and biomass; microbial biomass C:N) as predictors, and included the block as a random effect. Note that since some diversity index do not necessarily change linearly along environmentally strong gradients (e.g. soil pH and water content), we considered the quadratic term of soil pH and water content. Before regression analysis, the predictors highly correlated (r &gt; 0.7) and the variance inflation factor (VIF) value of more than 10, such as plant and nematode Shannon diversity, were removed from all analyses (See <xref ref-type="supplementary-material" rid="SF3">
<bold>Supplementary Figure&#xa0;3</bold>
</xref> and <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;2</bold>
</xref>).</p>
<p>All response variables and predictors were Z-scored (standardized deviated) prior to analyses to account for parameter estimates within a comparable scale. To assess the relative effect of each predictor on EMF, we used a method similar to the variance decomposition. In short, the method can be simply calculated the ratio between the standardized regression coefficients of predictors and the sum of all standardized regression coefficients in the models (<xref ref-type="bibr" rid="B41">Gross et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B103">Yuan et&#xa0;al., 2021</xref>). We also repeated these analyses without random effect to ensure the robustness of our results (See <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;3</bold>
</xref>).</p>
<p>For each EMF index, a model selection procedure was used to select the most parsimonious set of predictors (<xref ref-type="bibr" rid="B55">Le Bagousse-Pinguet et&#xa0;al., 2017</xref>). We first generated all possible combinations of predictors, and then selected the set of best-supported models within a &#x394;AICc of 2 (See <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;4</bold>
</xref>). Before analysis, we scaled all predictors using the Z-scored (standardized deviated) method (<xref ref-type="bibr" rid="B55">Le Bagousse-Pinguet et&#xa0;al., 2017</xref>).</p>
<p>Finally, piecewise structural equation modeling (pSEM) was used to test for the direct and indirect effects of <italic>L. virgaurea</italic> and <italic>D. fruticosa</italic> on EMF through changes in abiotic and biotic attributes. We set up an a-priori models, while only considering dominant plants, and the significant biotic and abiotic indirect drivers found with the linear modeling procedure (<xref ref-type="supplementary-material" rid="SF5">
<bold>Supplementary Figure&#xa0;5</bold>
</xref>). We thus considered the dominant plant type <italic>D. fruticosa</italic> or <italic>L. virgaurea)</italic> as predictors, SWC and pH as indirect abiotic drivers, and plant and nematode richness as indirect biotic drivers of EMF. The model fits of pSEM were assessed using Shipley&#x2019;s test of d-separation through Fisher&#x2019;s <italic>C</italic> statistic (<xref ref-type="bibr" rid="B60">Lefcheck, 2016</xref>).</p>
<p>Our data analyses were conducted in R software, ver. 4.0.3 (<xref ref-type="bibr" rid="B80">R core Team, 2020</xref>). The calculation of EMF indices was conducted using the <italic>getStdAndMeanFunctions</italic> function in the &#x2018;multifunc&#x2019; package (<xref ref-type="bibr" rid="B19">Byrnes et&#xa0;al., 2014</xref>). Shannon diversity was calculated using the <italic>diversity</italic> function in the &#x2018;vegan&#x2019; package (<xref ref-type="bibr" rid="B78">Oksanen et&#xa0;al., 2019</xref>). The GLMMs were performed using the <italic>glmmTMB</italic> function with a genpois link (i.e. count data for the underdispersion), or with a beta link (i.e. proportional data) in the &#x2018;glmmTMB&#x2019; package (<xref ref-type="bibr" rid="B17">Brooks et&#xa0;al., 2017</xref>) and the <italic>lmer</italic> (i.e. normal data) and <italic>glmer</italic> function with a poisson link (i.e. count data for no dispersion) in the &#x2018;lme4&#x2019; package (<xref ref-type="bibr" rid="B11">Bates et&#xa0;al., 2015</xref>). The model diagnosis of linear mixed models was conducted using the <italic>testUniformity</italic> function in the &#x2018;DHARMa&#x2019; package (<xref ref-type="bibr" rid="B44">Hartig, 2020</xref>), and the <italic>testDispersion</italic> function in the &#x2018;DHARMa&#x2019; package (<xref ref-type="bibr" rid="B44">Hartig, 2020</xref>) for the dispersion test. The <italic>dredge</italic> function in the &#x2018;MuMIn&#x2019; package (<xref ref-type="bibr" rid="B10">Barto&#x144;, 2020</xref>) for the model selection procedure. The marginal means (EMMS) of GLMMs and LMMs were estimated using the <italic>emmeans</italic> function in package &#x2018;emmeans&#x2019; (<xref ref-type="bibr" rid="B61">Lenth et&#xa0;al., 2020</xref>). The pSEMs were conducted using the <italic>psem</italic> function in the R package &#x2018;piecewiseSEM&#x2019; (<xref ref-type="bibr" rid="B60">Lefcheck, 2016</xref>). Package &#x2018;ggplot2&#x2019; (<xref ref-type="bibr" rid="B99">Wickham, 2016</xref>) was used to plot figures.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<p>EMF indices strongly varied in response to dominant plants (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). The allopathic <italic>L. virgaurea</italic> significantly increased all EMF indices compared to the control plots (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1A&#x2013;D</bold>
</xref>). This was particularly true for individual functions such as SOC, TN, NH4<sup>+</sup>, and for most of the enzymes considered (See <xref ref-type="supplementary-material" rid="SF4">
<bold>Supplementary Figures&#xa0;4A, C, E&#x2013;I</bold>
</xref>). The facilitative species <italic>D. fruticosa</italic> mostly led to intermediate values of EMF indices (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1A, C, D</bold>
</xref>), although a significant increase in EMF was observed when the ecosystem was performing low (MF<sub>T25</sub>: <xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1B</bold>
</xref>). This pattern mostly occurred because the effect of <italic>D. fruticosa</italic> on individual ecosystem functions were not consistent, and could be either positive (SOC, TN, NH4<sup>+</sup>, INV) (See <xref ref-type="supplementary-material" rid="SF4">
<bold>Supplementary Figures&#xa0;4A, C, E, F</bold>
</xref>) or neutral (TP, NO<sub>3</sub>
<sup>-</sup>, URE, PHO) (See <xref ref-type="supplementary-material" rid="SF4">
<bold>Supplementary Figures&#xa0;4B, D, H, I</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>The effects of dominant plants on EMF<sub>A</sub> <bold>(A)</bold>, MF<sub>T25</sub> <bold>(B)</bold>, MF<sub>T50</sub> <bold>(C)</bold>, MF<sub>T75</sub> <bold>(D)</bold>, plant richness <bold>(E)</bold>, plant biomass <bold>(F)</bold>, plant abundance <bold>(G)</bold>, nematode richness <bold>(H)</bold>, nematode biomass <bold>(I)</bold>, nematode abundance <bold>(J)</bold>, Microbial biomass C:N ratio <bold>(K)</bold>, soil pH <bold>(L)</bold>, soil water content <bold>(M)</bold>. Different lowercase letters within panels indicate significant (<italic>p</italic>-value &lt; 0.1) differences between treatment means, after using Tukey&#x2019;s method to correct for multiple comparisons. Error bars represent means &#xb1; SE (NS, <italic>p</italic>&gt; 0.05; *<italic>p &lt;</italic>0.05; **<italic>p &lt;</italic>0.01; ***<italic>p &lt;</italic>0.001).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-14-1117903-g001.tif"/>
</fig>
<p>We found that dominant plants had contrasted effects on the indirect biotic and abiotic drivers considered (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1E&#x2013;M</bold>
</xref>). <italic>D. fruticosa</italic> significantly decreased plant biomass and abundance (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1F, G</bold>
</xref>). <italic>L.virgaurea</italic> had a negative effect on plant abundance (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1G</bold>
</xref>), a positive effect on nematode abundance (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1J</bold>
</xref>), and a neutral effect on plant biomass (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1F</bold>
</xref>) compared to the control plots. In contrast, dominant plants had no impact on the richness of understory plant and nematode (<xref ref-type="fig" rid="f1">
<bold>Figures&#xa0;1E, H</bold>
</xref>), nematode biomass (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1I</bold>
</xref>), and microbial biomass C:N ratio (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1K</bold>
</xref>). Finally, dominant plants had significant effects on the abiotic attributes considered, particularly by significantly decreasing the soil pH (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1L</bold>
</xref>). <italic>D. fruticosa</italic> furthermore had a significant negative effect on SWC, while SWC under <italic>L. virgaurea</italic> remained as high as in the control plots (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1M</bold>
</xref>).</p>
<p>The multiple linear regression models explained a fair amount of variation in EMF, i.e. ~68%, 35%, 37%, and 43% of variations in EMF<sub>A</sub>, MF<sub>T25</sub>, MF<sub>T50,</sub> and MF<sub>T75</sub> respectively (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). The dominant plants <italic>D. fruticosa</italic> and <italic>L. virgaurea</italic> together explained on average ~11% of the variation in EMF (5%~20%). In comparison, the abiotic attributes considered explained ~20% of the variations in EMF (2~38%), and the cumulative above- and belowground attributes accounted for ~9% (3%~16%) and 7% (3%~11%) of the variations in EMF respectively.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>The effects of biotic and abiotic factors on EMF<sub>A</sub> <bold>(A)</bold>, MF<sub>T25</sub> <bold>(B)</bold>, MF<sub>T50</sub> <bold>(C)</bold>, MF<sub>T75</sub> <bold>(D)</bold>. Standardized regression coefficients of model predictors, associated 95% confidence intervals and relative importance of each factor, expressed as the percentage of explained variance. The R<sup>2</sup> of the averaged model and the <italic>p</italic>-value of each predictor are given as: *<italic>p</italic> &lt; 0.05; **<italic>p</italic> &lt; 0.01; ***<italic>p</italic> &lt; 0.001. lig, <italic>L. virgaurea</italic>; das, <italic>D. fruticosa</italic>; SWC, soil water content; pH, soil pH; neA, nematode abundance; neR, nematode richness; neB, nematode biomass; MB, microbial biomass; C, N ratio; plantA, plant abundance; plantR, plant richness; plantB, plant biomass.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-14-1117903-g002.tif"/>
</fig>
<p>
<italic>L. virgaurea</italic>, SWC, soil pH, and plant richness were the main predictors of EMF (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2A</bold>
</xref>). However, these effects were highly dependent on the level of performance of the system. <italic>D. fruticosa</italic> and <italic>L. virgaurea</italic> positively affected EMF at the low level of ecosystem performance (MF<sub>T25</sub>). Plant richness had a negative effect on EMF, specifically at the higher level of EMF (MF<sub>T75</sub>). Increasing SWC enhanced EMF, specifically at the higher level of performance (MF<sub>T75</sub>), and soil pH also reduced EMF in the case of higher levels of ecosystem performance (MF<sub>T50</sub> and MF<sub>T75</sub>).</p>
<p>The pSEMs explained 52%, 39%, 49%, and 35% of the variations in EMF<sub>A</sub>, MF<sub>T25</sub>, MT<sub>T50</sub>, and MF<sub>T75</sub> respectively (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). From the pSEMs results, we can find that the impacts of <italic>D. fruticosa</italic> and <italic>L. virgaurea</italic> on EMF were both a promotion effect, irrespective of the EMF threshold considered, and the promotion effect of <italic>L. virgaurea</italic> was greater than that of <italic>D. fruticosa</italic> (except MF<sub>T25</sub>). The pSEMs showed that <italic>L. virgaurea</italic> can directly enhance EMF<sub>A</sub> and MF<sub>T25</sub>, and <italic>D. fruticosa</italic> significantly improved MF<sub>T25</sub> directly. Meanwhile, both <italic>D. fruticosa</italic> and <italic>L. virgaurea</italic> indirectly affected the EMF through the abiotic pathway. The pSEMs also showed indirect effects of <italic>L. virgaurea</italic> on EMF<sub>A</sub> and MF<sub>T50</sub>
<italic>via</italic> soil pH (<xref ref-type="fig" rid="f3">
<bold>Figures&#xa0;3A, E</bold>
</xref>), while <italic>D. fruticosa</italic> influenced EMF<sub>A</sub> indirectly through soil pH and SWC (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). We also considered other biological pathways, such as nematode and plant abundance (See <xref ref-type="supplementary-material" rid="SF6">
<bold>Supplementary Figure&#xa0;6</bold>
</xref>), nematode, plant, and microbial biomass (See <xref ref-type="supplementary-material" rid="SF7">
<bold>Supplementary Figure&#xa0;7</bold>
</xref>). The results also showed that the dominant plants affected EMF mainly through the direct path and abiotic indirect path, and the promotion effect of <italic>L. virgaurea</italic> on EMF was higher than that of <italic>D. fruticosa</italic>.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Structural equation model assessing the direct and indirect effects of dominant plants on EMF<sub>A</sub> <bold>(A)</bold>, MF<sub>T25</sub> <bold>(C)</bold>, MF<sub>T50</sub> <bold>(E)</bold> and MF<sub>T75</sub> <bold>(G)</bold>. Numbers adjacent to arrows are indicative of the effect size of the relationship. Only significant and marginally significant pathways were shown. Significance levels are as follows: &#x2022;, <italic>p</italic> &lt; 0.10; *<italic>p</italic> &lt; 0.05; **<italic>p</italic> &lt; 0.01; ***<italic>p</italic> &lt; 0.001. The right panel of the figure showed that total, direct and indirect standardized effects of the different drivers of EMFA <bold>(B)</bold>, MF<sub>T25</sub> <bold>(D)</bold>, MF<sub>T50</sub> <bold>(F)</bold> and MF<sub>T75</sub> <bold>(H)</bold>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpls-14-1117903-g003.tif"/>
</fig>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Here we aimed at disentangling the direct from indirect abiotic and biotic (above- and belowground diversities) pathways by which dominant plant species influence EMF. Our results indicated that the two dominant plants overall promoted the EMF of alpine grasslands. Our result brings new evidence on the importance of dominant plants for EMF and thus expands to multiple ecosystem functions simultaneously the mass-ratio hypothesis (<xref ref-type="bibr" rid="B40">Grime, 1998</xref>), originally framed for individual functions (<xref ref-type="bibr" rid="B84">Smith and Knapp, 2003</xref>; <xref ref-type="bibr" rid="B38">Garnier et&#xa0;al., 2004</xref>).</p>
<p>The positive effect of dominant plants on EMF occurred irrespective of their expected (positive and negative) effects. Two main reasons may explain this pattern. First, dominant plants can alter the spatial allocation of resources (e.g. fertilizer island effects) (<xref ref-type="bibr" rid="B68">Maestre and Puche, 2009</xref>; <xref ref-type="bibr" rid="B32">Eldridge et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B77">Ochoa-Hueso et&#xa0;al., 2018</xref>). For instance, <xref ref-type="bibr" rid="B85">Soliveres and Eldridge (2014)</xref> found that increasing shrub encroachment did not impede ecosystem functions, but instead had positive effects on plant and soil properties. Second, grazing may have played an important role in this process (<xref ref-type="bibr" rid="B26">Daryanto et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B85">Soliveres and Eldridge, 2014</xref>). Upon herbivore trampling effects on soil compaction and water redistribution that could reduce nematode diversity and ecosystem functioning (<xref ref-type="bibr" rid="B20">Castellano and Valone, 2007</xref>; <xref ref-type="bibr" rid="B2">Allington and Valone, 2010</xref>; <xref ref-type="bibr" rid="B4">Andriuzzi and Wall, 2017</xref>), the unexpected facilitative effect of <italic>L. virgaurea</italic> on EMF may arise from mechanisms of associational avoidance (see <xref ref-type="bibr" rid="B73">Milchunas and Noy-Meir, 2002</xref> for a review). On the other hand, and according to the attractant decoy hypothesis (<xref ref-type="bibr" rid="B73">Milchunas and Noy-Meir, 2002</xref>), herbivores preferentially consume palatable plants, such as grasses, thereby altering the aboveground plant community composition of grasslands (control) and affecting nutrient input and decomposition (<xref ref-type="bibr" rid="B1">Abule et&#xa0;al., 2005</xref>; see <xref ref-type="bibr" rid="B48">Hempson et&#xa0;al., 2015</xref> for a review). We acknowledge that our approach does not allow us to fully conclude on the mechanisms explaining the observed patterns, but this overall positive effect clearly stimulates the need for further research on the impact of dominants on EMF.</p>
<p>Contrary to the first hypothesis, we did not observe any positive effects of <italic>D. fruticosa</italic> on EMF indices (except MF<sub>T25</sub>), although it is often regarded as a nurse species (<xref ref-type="bibr" rid="B72">Michalet et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B93">Wang et&#xa0;al., 2017</xref>). Our result may relate to the environmental conditions under which our experimental design was performed. Species interactions can shift from competitive to facilitative interactions from low to moderate stress environmental conditions (<xref ref-type="bibr" rid="B13">Bertness and Callaway, 1994</xref>). Facilitation then diminishes from moderate to highly stressed conditions (<xref ref-type="bibr" rid="B70">Michalet et&#xa0;al., 2006</xref>; <xref ref-type="bibr" rid="B71">Michalet et&#xa0;al., 2014</xref>). In this context, and depending on the environmental conditions, the dominant plant <italic>D. fruticosa</italic> could have various effects on local species diversity (<xref ref-type="bibr" rid="B57">Le Bagousse-Pinguet et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B63">Liancourt et&#xa0;al., 2017</xref>).</p>
<p>The positive effects of <italic>L. virgaurea</italic> on EMF were greater than that of <italic>D. fruticosa</italic>, to significantly increase each component of EMF (e.g. soil organic carbon, total nitrogen, ammonium, and for most of the enzymes considered). <italic>L. virgaurea</italic> has been found to increase total soil organic carbon concentrations, soil organic C:N ratio, and enzymatic activity (<xref ref-type="bibr" rid="B83">Shi et&#xa0;al., 2011</xref>), or root secretions to promote multiple bacterial groups (<xref ref-type="bibr" rid="B92">Wang et&#xa0;al., 2022</xref>). Also, leaching from allelopathic plants such as <italic>L. virgaurea</italic> can also improve soil conditions in their vicinity (<xref ref-type="bibr" rid="B49">Hierro and Callaway, 2003</xref>). However, our results do not call for their expansion as a potential standardization of management. Previous studies have also found that the expansion of toxic weeds could reduce grassland areas (<xref ref-type="bibr" rid="B88">Van Auken, 2009</xref>), thus limiting the access to palatable food sources for herbivores (<xref ref-type="bibr" rid="B67">Ma et&#xa0;al., 2006</xref>). Therefore, and while beneficial to soil functions, this species may have negative effects on key functions on which human society depends. These antagonistic effects warn for the need of comprehensive efforts when formulating management policies to deal with the expansion of toxic weeds rather than a &#x2018;one size fits all&#x2019; management approach.</p>
<p>Our results also showed that dominant plants mostly impacted on EMF through changes in soil pH and water content. Soil pH is known to be an important driver impacting on microbial communities (<xref ref-type="bibr" rid="B54">Lauber et&#xa0;al., 2009</xref>) and EMF (<xref ref-type="bibr" rid="B29">Delgado-Baquerizo et&#xa0;al., 2016b</xref>; <xref ref-type="bibr" rid="B66">Luo et&#xa0;al., 2018</xref>). Furthermore, soil pH was highly related to soil organic carbon, and soil acidification could contribute&#xa0;to soil organic carbon accumulation, thereby improving the ability of grassland ecosystems to maintain multiple functions (<xref ref-type="bibr" rid="B53">Jing et&#xa0;al., 2015</xref>). Soil water content had an indirect positive effect on ecosystem functions, which may relate to the increase of water holding capacity, further promoting nutrient cycling and grassland productivity (<xref ref-type="bibr" rid="B42">Guo et&#xa0;al., 2012</xref>).</p>
<p>Contrary to expectation, we found negligible indirect biotic effects, indicating that dominant plants weakly affected the EMF of alpine grasslands through changes in the richness of plants and key soil organisms such as nematodes. Furthermore, the effects of plant richness were only observed at high levels of ecosystem performance. Our results align with the view of considering other facets of biodiversity such as functional and phylogenetic diversity facets, which have been found to contribute more to EMF than taxonomic richness only (<xref ref-type="bibr" rid="B36">Flynn et&#xa0;al., 2011</xref>; <xref ref-type="bibr" rid="B41">Gross et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B58">Le Bagousse-Pinguet et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B98">Wen et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B56">Le Bagousse-Pinguet et&#xa0;al., 2021</xref>). Also, the absence of effect of belowground biodiversity may arise from the consideration of nematodes only as an indicator of belowground biodiversity, while multiple trophic levels are needed to promote EMF (<xref ref-type="bibr" rid="B53">Jing et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B82">Schuldt et&#xa0;al., 2018</xref>). The reason may be that the soil functions (e.g. soil enzymes) we selected may be more related to abiotic factors (e.g. soil pH and water content) than to nematode diversity. Studies on the relationship between soil species richness and ecosystem function also show that for nutrient cycling, it depends to some extent on species traits rather than species richness (<xref ref-type="bibr" rid="B46">Heemsbergen et&#xa0;al., 2004</xref>; <xref ref-type="bibr" rid="B76">Nielsen et&#xa0;al., 2011</xref>). In addition, there may be trade-offs in the relationship between nematodes of different feeding types and EMF. For example, <xref ref-type="bibr" rid="B31">Du et&#xa0;al. (2022)</xref> found that bacterial feeders were positively correlated with EMF, while fungal feeders and omnivorous feeders were negatively correlated with EMF.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusions</title>
<p>Our findings provided evidence that contrasting dominant plants such as <italic>D. fruticosa</italic> and <italic>L. virgaurea</italic> can increase ecosystem multifunctionality in the alpine meadows, although our &#x201c;one-shot&#x201d; study should be complemented by longer-term and dynamical approaches. Finally, our results also showed that these effects not only arise from direct, but also indirect abiotic pathways through changes in soil conditions. Altogether, our study suggested that dominant plants may play a key role in promoting multiple ecosystem functions simultaneously, and could mitigate the negative impacts of vegetation changes on EMF in the alpine meadows.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>JC, SC, HG, KL, LA, and SX contributed to conception and design of the study. ZL, HC, HS, JW, ZY, YW, XW, XY, and LM conducted the field experiment and organized the database. JC performed all the statistical analyses. JC and YB-P wrote the manuscript. All authors contributed to the article and approved the submitted version.</p>
</sec>
</body>
<back>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The work was supported by the project of the National Natural Science Foundation of China (41830321, 31870412, 32071532), the &#x201c;111 Project&#x201d; (BP0719040), the Natural Science Foundation of Gansu Province (22JR5RG564), and the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0302).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We thank the Gansu Gannan Grassland Ecosystem National Observation and Research Station (Maqu Sub-station) for allowing us to use their site.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpls.2023.1117903/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpls.2023.1117903/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Image_1.jpeg" id="SF1" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;1</label>
<caption>
<p>The schematic diagram of experimental design.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_2.jpeg" id="SF2" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;2</label>
<caption>
<p>Pearson&#x2019;s correlation coefficients between each individual function and their relationships with EMF indices. Red to black color indicates negative to positive correlations. The number represent correlation coefficient. TP: soil total phosphatase; TN: soil total nitrogen; SOC: soil organic carbon; NO<sub>3</sub>
<sup>-</sup>: soil nitrate; NH<sub>4</sub>
<sup>+</sup>: soil ammonium; INV: invertase; PRO: protease; URE: urease; PHO: phosphatase; EMF<sub>A</sub>: averaged multifunctionality index; MF<sub>T25</sub>: 25% threshold-based multifunctionality index; MF<sub>T50</sub>: 50% threshold-based multifunctionality index; MF<sub>T75</sub>: 75% threshold-based multifunctionality index.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_3.jpeg" id="SF3" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;3</label>
<caption>
<p>Pearson&#x2019;s correlation coefficients between pairs of abiotic and biotic factors. Red to black color indicates negative to positive correlations. The number represent correlation coefficient. lig: <italic>L. virgaurea</italic>; das: <italic>D. fruticosa</italic>; SWC: soil water content; pH: soil pH; plantA: plant abundance; plantR: plant richness; plantB: plant biomass; plantS: plant Shannon diversity. neA: nematode abundance; neR: nematode richness; neB: nematode biomass; neS: nematode Shannon diversity; MB:microbial biomass C:N ratio.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_4.jpeg" id="SF4" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;4</label>
<caption>
<p>The effects of dominant plants on each of ecosystem functions. Different lowercase letters within panels indicate significant (NS: <italic>p</italic>&gt; 0.05; *: <italic>p &lt;</italic>0.05; **: <italic>p &lt;</italic>0.01; ***: <italic>p &lt;</italic>0.001) differences between treatment means, after using Tukey&#x2019;s method to correct for multiple comparisons. Error bars represent means &#xb1; SE. For abbreviations, see <xref ref-type="supplementary-material" rid="SF2">
<bold>Supplementary Figure&#xa0;2</bold>
</xref>.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_5.jpeg" id="SF5" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;5</label>
<caption>
<p>An a-priori conceptual model of piecewise structural equation modeling.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_6.jpeg" id="SF6" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;6</label>
<caption>
<p>Structural equation model assessing the direct and indirect effects of dominant plants on averaged ecosystem multifunctionality <bold>(A)</bold>, 25% threshold-based <bold>(C)</bold>, 50% threshold-based <bold>(E)</bold> and 75% threshold-based <bold>(G)</bold> ecosystem multifunctionality <italic>via</italic> abiotic factors (soil water content and pH) and biotic factors (nematode and plant abundance). Numbers adjacent to arrows are indicative of the effect size of the relationship. Only significant and marginally significant pathways were shown. Significance levels are as follows: &#x2022;, <italic>p</italic> &lt; 0.10; *, <italic>p</italic> &lt; 0.05; **, <italic>p</italic> &lt; 0.01; ***, <italic>p</italic> &lt; 0.001. The right panel of the figure showed that total, direct and indirect standardized effects of the different drivers of EMF.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_7.jpeg" id="SF7" mimetype="image/jpeg">
<label>Supplementary Figure&#xa0;7</label>
<caption>
<p>Structural equation model assessing the direct and indirect effects of dominant plants on averaged ecosystem multifunctionality <bold>(A)</bold>, 25% threshold-based <bold>(C)</bold>, 50% threshold-based <bold>(E)</bold> and 75% threshold-based <bold>(G)</bold> ecosystem multifunctionality <italic>via</italic> abiotic factors (soil water content and pH) and biotic factors (nematode, plant and microbial biomass). Numbers adjacent to arrows are indicative of the effect size of the relationship. Only significant and marginally significant pathways were shown. Significance levels are as follows: &#x2022;, <italic>p</italic> &lt; 0.10; *, <italic>p</italic> &lt; 0.05; **, <italic>p</italic> &lt; 0.01; ***, <italic>p</italic> &lt; 0.001. The right panel of the figure showed that total, direct and indirect standardized effects of the different drivers of EMF.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
<supplementary-material xlink:href="Table_2.docx" id="SM2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
<supplementary-material xlink:href="Table_3.docx" id="SM3" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
<supplementary-material xlink:href="Table_4.docx" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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