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\begin{document}

\title{Effects of grazing intensity on richness and composition of rhizosphere
and non-rhizosphere microbial communities in a semiarid grassland}



\author[1]{Yang Yang}%
\author[2]{Zhang Hao}%
\author[3]{Liu Wei}%
\author[3]{Sun Jiamei}%
\author[2]{Mengli Zhao}%
\author[2]{Guodong Han}%
\author[4]{Qingmin Pan}%
\affil[1]{Chinese Academy of Sciences}%
\affil[2]{Inner Mongolia Agricultural University}%
\affil[3]{Institute of Botany Chinese Academy of Sciences}%
\affil[4]{Institute of Botany, Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 23, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
1.Overgrazing-induced grassland degradation has become a severe
ecological problem worldwide. The diversity and composition of soil
microbial communities are responsive to grazing disturbance. Yet, our
understanding is limited with respect to the effects of grazing
intensity on bacterial and fungal communities, especially in plant
rhizosphere. 2.Using a long-term (15 years) grazing experiment, we
evaluated the richness and composition of microbial communities in both
rhizosphere and non-rhizosphere regions, under light, moderate, and
heavy intensities of grazing, in a semiarid grassland. We also examined
the relative roles of grazing-induced changes in some abiotic and biotic
factors in affecting the richness and composition of microbial
communities. 3.Our results showed that the responses of soil bacteria to
grazing intensity differed greatly between rhizosphere and
non-rhizosphere, and so did soil fungi. Specifically, the bacterial
richness decreased markedly under moderate and heavy grazing in
rhizosphere soil, whereas little impact on the fungal richness was
observed. For microbial composition, with the increase in grazing
intensity, an increase in dissimilarity among bacterial communities was
observed, and this trend also held true for the fungal communities.
Hierarchical partitioning analyses indicated that the bacterial
composition in rhizosphere was primarily driven by root nitrogen and
soil nitrogen concentrations while that in non-rhizosphere by soil
available phosphorus. In addition, soil available phosphorus played an
important role in affecting the fungal composition in both rhizosphere
and non-rhizosphere regions. 4.Synthesis: This study provides direct
experimental evidence that the richness and composition of microbial
communities were severely altered by heavy grazing in a semiarid
grassland. Thus, to restore the grazing-induced, degraded grasslands, we
should pay more attention to the conservation of soil microbe in
addition to vegetation recovery.%
\end{abstract}%



\sloppy


\textbf{Effects of grazing intensity on richness and composition of
rhizosphere and non-rhizosphere microbial communities in a semiarid
grassland}

\textbf{Yang Yang \textsuperscript{a, b, c}, Hao Zhang
\textsuperscript{c}, Mengli Zhao \textsuperscript{c}, Guodong Han
\textsuperscript{c}, Qingmin Pan\textsuperscript{a, b*}, Wei Liu
\textsuperscript{a}, Jiamei Sun\textsuperscript{a},}

\textsuperscript{a}State Key Laboratory of Vegetation and Environmental
Change, Institute of Botany, the Chinese Academy of Sciences, Beijing
100093, China;

\textsuperscript{b} University of Chinese Academy of Sciences, Beijing
100049, China;

\textsuperscript{c} Inner Mongolia Agriculture University, College of
Grassland, Resources and Environment, Hohhot 010019, China

\textbf{Corresponding author:}

Prof. Qingmin Pan

State Key Laboratory of Vegetation and Environmental Change, Institute
of Botany, the Chinese Academy of Sciences, Beijing, 100093, China.

Tel.: +86-10-6283-6977

Fax: +86-10-6283-6279

E-mail: \emph{pqm@ibcas.ac.cn}

\textbf{Abstract:}

\begin{enumerate}
\tightlist
\item
  Overgrazing-induced grassland degradation has become a severe
  ecological problem worldwide. The diversity and composition of soil
  microbial communities are responsive to grazing disturbance. Yet, our
  understanding is limited with respect to the effects of grazing
  intensity on bacterial and fungal communities, especially in plant
  rhizosphere.
\item
  Using a long-term (15 years) grazing experiment, we evaluated the
  richness and composition of microbial communities in both rhizosphere
  and non-rhizosphere regions, under light, moderate, and heavy
  intensities of grazing, in a semiarid grassland. We also examined the
  relative roles of grazing-induced changes in some abiotic and biotic
  factors in affecting the richness and composition of microbial
  communities.
\item
  Our results showed that the responses of soil bacteria to grazing
  intensity differed greatly between rhizosphere and non-rhizosphere,
  and so did soil fungi. Specifically, the bacterial richness decreased
  markedly under moderate and heavy grazing in rhizosphere soil, whereas
  little impact on the fungal richness was observed. For microbial
  composition, with the increase in grazing intensity, an increase in
  dissimilarity among bacterial communities was observed, and this trend
  also held true for the fungal communities. Hierarchical partitioning
  analyses indicated that the bacterial composition in rhizosphere was
  primarily driven by root nitrogen and soil nitrogen concentrations
  while that in non-rhizosphere by soil available phosphorus. In
  addition, soil available phosphorus played an important role in
  affecting the fungal composition in both rhizosphere and
  non-rhizosphere regions.
\item
  \emph{Synthesis} : This study provides direct experimental evidence
  that the richness and composition of microbial communities were
  severely altered by heavy grazing in a semiarid grassland. Thus, to
  restore the grazing-induced, degraded grasslands, we should pay more
  attention to the conservation of soil microbe in addition to
  vegetation recovery.
\end{enumerate}

\textbf{KEYWORDS:}bacterial diversity, bacterial composition, fungal
diversity, fungal composition, grassland degradation, grazing intensity,
non-rhizosphere, rhizosphere

\textbf{1 \textbar{} INTRODUCTION}

Grasslands cover about 40\% of Earth's land surface and are mostly used
for livestock grazing (Suttie, Reynolds, \& Batello, 2005). While light
and/or moderate grazing can improve the diversity and productivity of
grasslands, heavy grazing usually causes severe grassland degradation
(Kemp et al., 2013; McNaughton, 1979). By far, overgrazing-induced
grassland degradation has become a major ecological problem worldwide
(Dlamini, Chivenge, \& Chaplot, 2016; Eldridge et al., 2017; van de
Koppel, Rietkerk, \& Weissing, 1997). This is especially the case in
China, where more than 90\% of grasslands are in a degraded status (Kemp
et al., 2013).

Soil microbes play important roles in regulating grassland structure and
functioning (Bardgett \& van der Putten, 2014). However, soil microbial
communities are prone to be affected by heavy grazing (Hu et al., 2017;
Lame, Kennedy, \& Siciliano, 2011; Wang et al., 2021a; Zhang et al.,
2020). Grazing can influence the soil microbe through several
mechanisms. First, herbivory on plants can have dual effects on soil
microbial communities. On the one hand, removal of above-ground biomass
decreases the litter input into the soil, which reduces the supply of
carbon resource to soil microbe, and thus causes reduction in soil
microbial richness (Dwivedi et al., 2017). On the other hand, removal of
above-ground parts may also stimulate the allocation of organic
nutrients to belowground organs (Mueller et al., 2017), resulting in
enrichment of root exudates (Hamilton \& Frank, 2001), which in turn
affects the structure of microbial community (Hu et al., 2018). Second,
manure from livestock may affect soil pH values (Wang et al., 2021a;
Yang et al., 2018) and improve the available soil nitrogen (Han et al.,
2008). As a result, the relative abundance of copiotrophic taxa were
enhanced while that of oligotrophic taxa was reduced (Nie et al., 2018).
Finally, livestock trampling decreases soil moisture due to increased
soil compactness (Kobayashi, Hori, \& Nomoto, 1997), which in turn
affects the soil microbial properties (Chen et al., 2019). These studies
provide important insights into grazing impacts on soil microbial
communities, however, most studies did not distinguish the microbial
responses between non-rhizosphere and rhizosphere regions. Given that
plant rhizosphere is a micro-region that plant-soil-microbe interacts
directly (Curl \& Truelove, 1986), the influence of grazing on microbial
communities in this region is likely stronger than those in
non-rhizosphere.

Here we examined the impacts of grazing intensity on the richness and
composition of microbial communities in rhizosphere vs. non-rhizosphere
by a 15-year grazing intensity experiment in a semiarid grassland. We
focused on three specific questions: i) How do microbial communities
differ in richness and composition between rhizosphere and
non-rhizosphere? ii) How do bacterial and fungal communities respond to
grazing intensities? iii) What are the relative roles of abiotic and
biotic factors in affecting bacterial and fungal responses to grazing
intensity?

\textbf{2 \textbar{} MATERIALS AND METHODS}

\textbf{2.1 \textbar{} Study site}

This study was conducted in a semiarid grassland on the Inner Mongolia
Plateau (41\selectlanguage{ngerman}°46\selectlanguage{english}'43.6\selectlanguage{english}" N, 111deg53\selectlanguage{english}'41.7\selectlanguage{english}" E; elevation 1456 m). This area has
a temperate continental grassland climate, with annual mean temperature
and precipitation of 3.6 degC and 214 mm, respectively (Wang et al.,
2014). About 80\% of precipitation occurs during the growing season (May
to September). The vegetation in the experiment site was dominated
by\emph{Stipa breviflora} and \emph{Cleistogenes songorica} . The soil
is a Kastanozem soil (FAO soil classification) with a sandy loam
texture. This area is traditionally used as pastures for free grazing
and 90 \% of them have been degraded due to overgrazing (Kemp et al.,
2013).

\textbf{2.2 \textbar{} Experimental design}

A long-term grazing intensity experiment was established in 2004. We had
four grazing intensities: control (zero sheep-unit
ha\textsuperscript{-1} month\textsuperscript{-1}, G0), light (0.15
sheep-unit ha\textsuperscript{-1} month\textsuperscript{-1}, G0.15),
moderate (0.30 sheep-unit
ha\textsuperscript{-1}month\textsuperscript{-1}, G0.30), and heavy
grazing (0.45 sheep-unit ha\textsuperscript{-1}
month\textsuperscript{-1}, G0.45) (Zhang et al., 2021). Each treatment
had three replicates. Under a complete randomized block design, 12
plots, each with an area of 4.4 ha, were separated by fences. Sheep with
similar size were selected in advance and the grazing period lasted from
June 1 to November 30 every year.

\textbf{2.3 \textbar{} Field sampling and laboratory analysis}

We randomly harvested 30 bunches of \emph{C. songorica} in each plot in
late August of 2018, corresponding to peak biomass time. Each bunch of
plants with soil were dug out to a depth of 15 cm, with approximately
90\% of the roots harvested. The non-rhizosphere soil was defined as
those shaken off from the roots while the rhizosphere soil as those that
adhere to the roots tightly and cannot be shaken off (Chaudhary et al.,
2015). Plants of \emph{C. songorica} were separated into shoots and
roots, and oven-dried at 65degC for 72 h and weighed. Soil samples from
the same plot were mixed to form a composite sample, thus, we obtained
12 rhizosphere and 12 non-rhizosphere composite soil samples (4
treatments x 3 replications).

Each composite soil sample was equally separated into three portions:
one air-dried, one kept fresh at 4degC, and one stored at -80 degC. The
fresh sample was used to determine the soil moisture and inorganic
nitrogen concentration. Concentrations of ammonium nitrogen and nitrate
nitrogen were measured using a continuous flow analyzer (Auto Analyzer
III, Bran+Luebbe, Germany), and the inorganic nitrogen was their sum.
The air-dried samples were used to determine the concentrations of total
carbon, total nitrogen, total phosphorus, available phosphorus, and the
pH values. Concentrations of total carbon and total nitrogen in
soil/root samples were determined by an element analyzer (Elementar
Vario Micro Cube, Germany), whereas soil total phosphorus and available
phosphorus by a spectrophotometer. Soil pH was measured by a pH meter.
The frozen samples were used to extract DNA for evaluating the richness
and composition of microbial communities.

\textbf{2.4 \textbar{} DNA extraction, PCR amplification, and
sequencing}

For each sample, DNA was extracted from 0.25 g thawed soil by a DNeasy(r)
PowerSoil(r) DNA Isolation Kit (Qiagen, Germany). The quality of isolated
DNA was determined by electrophoresis on 1\% agarose gel. The purity was
examined using NanoDrop. In total, 12 rhizosphere and 12 non-rhizosphere
DNA samples were prepared, respectively.

The 16S rRNA gene was amplified to estimate the relative abundance of
soil bacteria while internal transcribed spacer (ITS) region for the
fungi. The paired primers, 341F and 806R, were used to target the
hypervariable V3+V4 region of the 16S rRNA gene; while the paired
primers, ITS5-1737F and ITS2-2043R, for the ITS1 regions of fungi. The
primers in each sample were tagged with unique barcodes. All PCRs were
carried out in 30-\selectlanguage{greek}µ\selectlanguage{english}L reactions with10 ng of template DNA, 0.2 \selectlanguage{greek}µ\selectlanguage{english}M of
forward and reverse primers, and 15 \selectlanguage{greek}µ\selectlanguage{english}L of Phusion\selectlanguage{ngerman}® High-Fidelity PCR
Master Mix (New England Biolabs). For thermal cycles, the initial
denaturation was at 98 °C for 1 min, followed by 30 cycles of
denaturation at 98 °C for 10 s. Then annealing was at 55 °C for 30 s,
and elongation at 72 °C for 30 s. Finally, an extension was at 72 °C for
5 min.

The PCR products of each sample and 1X loading buffer (with SYBR green)
were mixed with equal volumes and then electrophoresed on a 2\% agarose
gel to examine the successful amplification of target DNA fragments. The
PCR products for each sample were quantified, and mixed with 80ng of DNA
in equal proportions. The resultant mixed PCR products were
electrophoresed by 2\% agarose gel, and the fragments of DNA ranging
from 400 - 450bp were excised and then purified with a
GeneJET\textsuperscript{TM} Gel Extraction Kit (Thermo Scientific).

Following the manufacturer's recommendation, Ion Plus Fragment Library
Kit 48 rxns (Thermo Scientific) was used for sequencing libraries
preparation. Qubit@ 2.0 fluorometer (Thermo Scientific) and Agilent
Bioanalyser 2100 system (Agilent, USA) were used to evaluate the library
quality. The libraries were sequenced on IonS5\textsuperscript{TM}XL
platform, generating 400-bp/600-bp single-end reads.

\textbf{2.5 \textbar{} Bioinformatics analyses}

Based on the unique barcode of each sample, single-end reads were
identified and truncated by shearing off the barcode and primer
sequences. The Cutadapt (Martin, 2011) quality controlled process was
used to filter the raw reads under specific filtering conditions, we
obtained the high-quality clean reads. The reads were compare with the
reference database (Silva and Unite database) (Kõljalg et al., 2013;
Quast et al., 2013) using UCHIME (Edgar, et al., 2011) algorithm to
detect chimera sequences, and removed them. Then final clean reads were
generated. We did sequence analysis using Uparse software (Uparse
v7.0.1001) (Edgar, 2013). Sequences with a similarity higher than 97\%
were assigned to the same operational taxonomic unit (OTU).
Representative sequence for each OTU was screened for further
annotation. The taxonomic information of bacterial representative
sequences was annotated using the Silva132 SSUrRNA database (Quast et
al., 2013) based on Mothur algorithm. For fungal representative
sequences, we annotated taxonomic information using the Unite (v7.2)
database (Kõljalg et al., 2013) based Blast algorithm which was
calculated by QIIME software (Version 1.9.1).

In total, we obtained 947,987 and 958,264 bacterial high-quality
sequences from rhizosphere and non-rhizosphere samples, respectively.
These reads were clustered into 23,160 and 37,278 OTU. For fungi, we
obtained 961,649 and 928,290 high-quality sequences from rhizosphere and
non-rhizosphere samples, respectively, which were clustered into 11,695
and 17,424 OTU (Table S1).

\textbf{2.6 \textbar{} Statistical analysis}

All statistical analyses were conducted in R 4.0.2 (R Core Team, 2020).
We used microbial OTU richness as an index of microbial diversity. A
t-test was used to evaluate the differences in microbial richness
between rhizosphere and non-rhizosphere. Differences in microbial
community composition between the two regions were evaluated using
permutational multivariate analysis of variance (PERMANOVA) with the
function ``adonis'' in the package ``vegan'' with 999 permutations
(Oksanen et al., 2020), and visualized by the principal coordinate
analysis (PCoA). One-way ANOVA followed by Tukey-Test was used to assess
the effects of grazing intensity on microbial richness, composition
(i.e., the relative abundance of main microbial taxa), biotic variables
(shoot biomass, root biomass, root total carbon, and root total nitrogen
of \emph{C. songorica} ), and abiotic variables (concentrations of total
carbon, nitrogen, and phosphorus, concentrations of available nitrogen
and phosphorus, soil pH and soil moisture). Prior to analyses,
Shapiro-Wilks was used for normal distribution evaluation, while
Bartlett's test for homogeneity of the data. The dissimilarity in
microbial community composition was determined by the Bray-Curtis
distance among treatments, performed with the ``vegdist'' function in
package ``vegan'' (Oksanen et al., 2020).

A Mantel test was used to evaluate how changes in microbial community
diversity or composition are related to biotic and abiotic variables by
the ``mantel test'' function in the package ``vegan'', and visualized by
the package ``linkET'' (Huang et al., 2021). The multicollinearity of
variables was assessed based on variance inflation factors (VIF), and
those with a VIF value less than 10 were regarded as variables with low
collinearity. The relative importance of variables in affecting
microbial richness and composition was evaluated with the package of
``rdacca.hp'' (Lai et al., 2021).

\textbf{3 \textbar{} Results}

\textbf{3.1 \textbar{}Microbial richness and composition in rhizosphere
vs. non-rhizosphere}

Microbial communities differed markedly between rhizosphere and
non-rhizosphere regions in OTU richness (Figure 1). The bacterial
richness was significantly lower in rhizosphere than in non-rhizosphere
(Figure 1a), and so was the fungal richness (Figure 1b).

The bacterial community composition differed greatly between the two
regions (Figure 1c). This was more evident in the relative abundances of
three dominant bacterial phyla, \emph{Proteobacteria}
,\emph{Acidobacteria,} and \emph{Actinobacteria} . For example, the
relative abundance of \emph{Proteobacteria} was 74.4\% in rhizosphere,
but decreased to 23.6\% in non-rhizosphere (Figure 1e, Table S2). In
contrast, the relative abundances of \emph{Acidobacteria}and
\emph{Actinobacteria} were 6.4 \% and 5.9 \% in rhizosphere, but
increased to 21.2\% and 26.7 \% in non-rhizosphere, respectively (Figure
1e, Table S2). The fungal community composition also differed between
the two regions (Figure 1 d). For example, the relative abundance
of\emph{Ascomycota} was 48.8\% in rhizosphere but decreased to 38.6\% in
non-rhizosphere (Figure 1f, Table S2).

\textbf{3.2 \textbar{} Effects of grazing intensity on microbial
richness and composition}

Grazing intensity had significant impacts on rhizosphere bacterial
richness but not on that in non-rhizosphere (Figure 2a, b). However,
grazing intensity had little impact on fungal richness in both regions
(Figure 2c, d).

For microbial composition, the dissimilarity in bacterial community
exhibited an increasing trend with the increase in grazing intensity in
both regions (Figure 3a). In rhizosphere, the relative abundance
of\emph{Proteobacteria} increased significantly under moderate and heavy
grazing while that of \emph{Acidobacteria} decreased (Figure 3b; Table
S3). In non-rhizosphere, the relative abundances of\emph{Proteobacteria}
and \emph{Acidobacteria} declined with the heavy grazing while that
of\emph{Actinobacteria}increased (Figure 3c; Table S3). The
dissimilarity in fungal community also displayed an increasing trend
with the increase in grazing intensity in both regions (Figure 3d).
Specifically, the dominant taxa,\emph{Ascomycota} , displayed a marked
increase in relative abundance under moderate and heavy grazing (Figure
3e, f; Table S3).

\textbf{3.3 \textbar{} Biotic and abiotic drivers for microbial
richness}

In general, all examined biotic and abiotic variables, except for soil
total phosphorus and soil pH, significantly affected soil bacterial
richness in rhizosphere, whereas that in non-rhizosphere was not
affected by these variables (Figure 4). The fungal richness in both
regions was not affected by examined biotic and abiotic variables
(Figure 4).

\textbf{3.4 \textbar{} Biotic and abiotic drivers for microbial
community composition}

In general, all examined biotic and abiotic variables, except for soil
total phosphorus and soil pH, affected the composition of bacterial or
fungal communities in both regions (Figure 5a). Moreover, among six
variables with low collinearity, the rhizosphere bacterial composition
was mainly affected by root total nitrogen and soil total nitrogen,
whereas that in non-rhizosphere by soil available phosphorus (Figure 5b,
c). In addition, soil available phosphorus was consistently the primary
variable affecting the fungal composition in both rhizosphere and
non-rhizosphere (Figure 5d, e).

\textbf{4 \textbar{} Discussion}

\textbf{4.1 \textbar{} Differences in microbial richness and composition
between rhizosphere and non-rhizosphere}

Our results demonstrated that the microbial communities differed
markedly between rhizosphere and non-rhizosphere in richness and
composition in this grassland ecosystem. The rhizosphere microbial
richness was lower than that in non-rhizosphere (Figure 1), which is in
line with previous findings that the microbial diversity decreased with
decreasing distance to plant roots (Donn et al., 2015; Fan et al.,
2017). Such a difference can be explained by a mechanism that
rhizosphere microbiomes are derived from non-rhizosphere microbiomes
under selection effect of host plants (Hartmann et al., 2009; Nan et
al., 2020).

The microbial composition also differed greatly between the two regions
(Figure 1c,d). This was especially evident for the relative abundances
of dominant microbial taxa. The relative abundance
of\emph{Proteobacteria} , a dominant bacterial phylum, was 23.6\% in
non-rhizosphere, but increased to 74.4 \% in rhizosphere (Figure 1e;
Table S2), likely due to its strategy in nutrient
use.\emph{Proteobacteria} is a representative copiotrophic bacteria
phylum (Nie et al., 2018; Zhang et al., 2018; Zhang et al., 2020).
Increased availability of nutrient in rhizosphere due to the acid
activation by root exudates (Cesco et al., 2010) fosters its growth,
because more nutrients facilitate the copiotrophic bacteria but restrain
the oligotrophic bacteria according to the oligotroph-copiotroph
hypothesis (Fierer, Bradford, \& Jackson, 2007; Leff et al., 2015).
Similarly,\emph{Ascomycota} is a dominant fungal phylum, and its
relative abundance increased from 38.6\% in non-rhizosphere to 48.8\% in
rhizosphere (Figure 1f; Table S2). Such a difference in relative
abundance of \emph{Ascomycota} between two regions has also been
observed in an alpine grassland (Jiang et al., 2021). Collectively,
these results suggest that the plant rhizosphere microenvironment may
facilitate the flourishing of certain bacterial and fungal taxa.

\textbf{4.2 \textbar{} Responses of bacterial and fungal communities to
grazing intensity}

An intriguing result from our experiment was that the rhizosphere
bacterial richness was more sensitive to grazing intensity than that in
non-rhizosphere (Zhang et al., 2019). As indicated in Figure 2, the
rhizosphere bacterial richness sharply declined under heavy grazing
intensity, whereas that in non-rhizosphere remained unchanged (Zhang et
al., 2020). Two mechanisms may underlie grazing-induced decline in
rhizosphere bacterial richness. First, herbivory removes aboveground
organs of a plant, resulting in a reduction in assimilated carbon
allocated to belowground organs, and thus a decline in the quantity of
carbon sources available for bacteria in plant rhizosphere (Aldezabal et
al., 2015; Mueller et al., 2017; Byrnes et al., 2018), which in turn
reduces rhizosphere bacterial richness. In this study, the rhizosphere
bacterial richness was significantly affected by root carbon (Figure 4),
providing support for this mechanism. Second, plant diversity is usually
coupled with microbial diversity in a community (De Deyn \& van der
Putten, 2005). Thus, grazing-induced reduction in plant diversity may
decrease the diversity of carbon sources for bacterial taxa (Dwivedi et
al., 2017), as a consequence, some bacterial taxa cannot grow well or
disappear and the rhizosphere bacterial diversity declines.

In contrast to the response of rhizosphere bacterial richness, the
fungal richness was not significantly affected by grazing intensity in
both regions (Figure 2c,d), which is consistent with results from
grazing experiments (Zhang \& Fu, 2020; Yang et al., 2021). The weak
response of fungal richness may be due to the fact that fungi were more
tolerant to environmental stresses than bacteria (Rousk \& Bååth, 2011).
For example, fungi often exhibit more resistance to soil drought or
acidity than bacteria (Rousk \& Bååth, 2011). In this study, the fungal
richness was not significantly affected by examined biotic and abiotic
factors (Figure 4), implying that grazing-induced changes in these
variables did not exert significant impact on fungal richness. Overall,
our results suggest that grazing in this grassland has little impact on
soil fungal richness.

For bacterial composition, previous studies have almost exclusively
concentrated on non-rhizosphere bacteria (Hu et al., 2017; Zhang et al.,
2020; Wang et al., 2021a). Our results suggest that grazing intensity
has stronger impacts on bacterial composition in rhizosphere than that
in non-rhizosphere. For example, heavy grazing resulted in a 6.55-fold
increase in community dissimilarity in rhizosphere while a 1.89-fold
increase in non-rhizosphere (Figure 3a). An impressive result from this
experiment was that, with the increase of grazing intensity, the
relative abundance of a dominant bacterial phylum,\emph{Proteobacteria}
, decreased in non-rhizosphere but increased in rhizosphere (Figure 3b,
c). Such opposite responses to grazing intensity were likely due to its
copiotrophic strategy in nutrient use (Leff et al., 2015). Increased
grazing intensity led to a relatively higher plant root nutrient (Tables
S4, S5), which facilitates the growth of copiotrophic bacteria\emph{.}
This is supported by our results that the relative abundance of
\emph{Proteobacteria} in rhizosphere was positively related to plant
root nitrogen (Figure S1a). By contrast, grazing-induced decrease in the
relative abundance of\emph{Proteobacteria} in non-rhizosphere was
related to the reduction in soil total nitrogen (Figure S1b; Tables S4,
S5).

Our results also demonstrated strong impacts of grazing intensity on
fungal composition in this grassland. As indicated by the increased
dissimilarity of fungal community with grazing intensities (Figure 3d),
the fungal composition was dramatically altered by heavy grazing in both
regions. A significant increase in relative abundance was observed for a
dominant taxon, \emph{Ascomycota} , under heavy grazing intensity
(Figure 3e,f). This phenomenon could be explained by its oligotrophic
strategy, which facilitates its growth in relatively low carbon
environment (Sterkenburg et al., 2015). As indicated in this experiment,
heavy grazing resulted in a great reduction in carbon concentrations in
plant roots and soils (Tables S4, S5), and the relative abundance
of\emph{Ascomycota} was negatively related to these carbon
concentrations (Figure S2a, b).

\textbf{4.3 \textbar{} The roles of biotic and abiotic factors in
affecting microbial responses to grazing intensity}

In this experiment, grazing-induced decrease in rhizosphere bacterial
richness was related to changes in 9 of 11 variables (Figure 4),
consistent with a previous study in alpine meadow finding that soil
microbial richness was affected by multiple biotic and abiotic factors
(Zhang \& Fu, 2020). Moreover, we found that all examined biotic
variables were closely related to rhizosphere bacterial richness (Figure
4), highlighting the impacts of plant selection effect on rhizosphere
bacterial richness (Hartmann et al., 2009; Nan et al., 2020). However,
the examined variables had little effect on fungal richness in both
regions (Figure 4), consistent with previous results (Yang et a., 2018).
This may be due to the fact that fungal are more resistant to
environmental variability relative to bacteria (Rousk \& Bååth, 2011).

Previous studies have highlighted the role of soil pH in affecting
microbial richness (Qu et al., 2016; Yang et al., 2018; Zhang \& Fu,
2020). However, in this study, the changes in bacterial and fungal
richness were not affected by soil pH in both regions. The weak effect
of soil pH in our study could be explained by the results that the soil
pH in this experiment was not significantly altered by grazing intensity
(Table S4, S5).

Our results indicated that the bacterial and fungal community
compositions in both regions were co-affected by several biotic and
abiotic variables (Figure 5a). For bacteria, the rhizosphere community
composition was primarily affected by plant root biomass and root carbon
concentration, highlighting the importance of plant-related resources in
affecting rhizosphere bacterial communities (Costa et al., 2006; Berg \&
Smalla, 2009). In contrast, the bacterial community composition in
non-rhizosphere was primarily affected by soil available phosphorus,
highlighting the role of abiotic factors in affecting non-rhizosphere
bacterial composition (Tian et al., 2017; Schöps et al., 2018).
Moreover, soil available phosphorus was also the primary factor
affecting the fungal composition in both regions (Figure 5d,e). The
important role of soil available phosphorus in affecting microbial
composition may be due to the fact that the grasslands in northern China
have a relatively lower soil phosphorus than other grasslands (Han et
al., 2005). For example, the soil total phosphorus concentration (515 mg
kg\textsuperscript{-1}) in this experiment site (Table S5) was much
lower than the mean value (699 mg kg\textsuperscript{-1}) in grasslands
of Northern America (US Geological Survey, 2001). Also, soils in this
region are rich in Ca\textsuperscript{2+} and Mg\textsuperscript{2+},
and phosphorus is usually bounded to these metal ions, further
constraining the available phosphorus for soil fungi (Wang et al.,
2021b). Importantly, the lack of soil available phosphorus was
exacerbated by heavy grazing in this experiment (Table S4, S5). This is
likely due to the grazing-induced strong wind erosion that the
nutrient-rich, top-layer soils were removed from the grassland (Giese et
al., 2013). Given that some arbuscular mycorrhizal fungi can help plants
efficiently exploit and absorb soil phosphorus (van der Heijden et al.,
1998), the strong relationship between soil available phosphorus and
fungal community composition also suggests that some fungal taxa may
play important roles in mediating the availability of soil phosphorus in
this grassland. Overall, our study provides experimental evidence that
grazing intensity caused changes in abiotic and biotic factors, and
these changes in turn altered the soil microbial composition.

\textbf{5 \textbar{} Conclusion}

Our results demonstrated that rhizosphere bacterial richness was greatly
reduced by heavy grazing. At the same time, the bacterial and fungal
compositions were greatly altered in both rhizosphere and
non-rhizosphere regions. These findings highlight that overgrazing not
only causes severe vegetation degradation (Wang et al., 2014; Zhang et
al., 2021) but also great decrease in diversity and strong alteration in
composition of soil microbial communities, which may greatly demote the
functioning of the whole ecosystem (Bardgett \& van der Putten, 2014).
Given that soil microbes play an important role in the restoration of
degraded ecosystems (Averill et al., 2022; Coban, De Deyn, \& van der
Ploeg, 2022), we suggest that, along with vegetation conservation,
motoring and restoring microbial communities are important for the
restoration of extensive degraded grasslands in China.

\textbf{AUTHOR CONTRIBUTIONS}

Qingmin Pan, Hao Zhang, and Mengli Zhao conceived the ideas and designed
the study. Mengli Zhao and Guodong Han designed the grazing experiment.
Yang Yang and Hao Zhang conducted the field sampling and laboratory
analysis. Yang Yang, Wei Liu, and Jiamei Sun analyzed the data. Yang
Yang, Hao Zhang, and Qingmin Pan led writing of this manuscript. All
authors contributed to the draft and revision and gave the final
approval for publication.

\textbf{ACKNOWLEDGEMENTS}

This work was supported by the National Natural Science Foundation of
China (grant no. 31861143001; 32230069) and the Strategic Priority
Research Program of Chinese Academy of Sciences (Grant No. XDA26020101).
The authors are grateful to all students for their help on the field
work and lab analyses.

\textbf{CONFLICT OF INTERSET}

Authors declare that they have no competing interests.

\textbf{DATA ACCESSIBILITY STATEMENT}

We are willing to upload our experimental data to Dryad Digital
Repository, if our manuscript are accepted.

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\textbf{Supplementary information} includes Tables S1-S5 and Figs.
S1-S2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{FIGURE 1 Comparisons in microbial richness and composition
between rhizosphere (Rs) and non-rhizosphere (Non-Rs) regions} . Notes:
Bacterial \textbf{(a)} and fungal \textbf{(b)} richness; Difference in
bacterial \textbf{(c)} and fungal \textbf{(d)} community composition;
Relative abundance of main bacterial \textbf{(e) and} fungal\textbf{(f)}
taxa. The taxa with a relative abundance \textless{} 1\% were assigned
into ``Others''. *, **, and *** indicated significant difference between
two regions by t-test at \emph{p} = 0.05, 0.01, and 0.001, respectively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 2 Effects of grazing intensity on microbial richness in
rhizosphere and non-rhizosphere.} Notes: rhizosphere \textbf{(a)} and
non-rhizosphere \textbf{(b)} bacterial richness; Rhizosphere\textbf{(c)}
and non-rhizosphere \textbf{(d)} fungal richness. G0, G0.15, G0.30, and
G0.45 refer to control, light, moderate, and heavy grazing,
respectively. Different letters indicate significant difference among
treatments at \emph{p} = 0.05, while ``n.s.'' indicated no significant
difference among treatments\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 3 Effects of grazing intensity on dissimilarity of
microbial community composition and on relative abundance of microbial
taxa in rhizosphere (Rs) and non-rhizosphere (Non-Rs) regions} . Notes:
Bacterial \textbf{(a)} and fungal \textbf{(d)} community dissimilarity;
Relative abundance of bacterial phyla in rhizosphere \textbf{(b)} and
non-rhizosphere \textbf{(c)} ; Relative abundance of fungal phyla in
rhizosphere \textbf{(e)} and non-rhizosphere\textbf{(f)} . Different
letters indicate significant differences among treatments. G0, G0.15,
G0.30, and G0.45 are the same as in Figure 2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{FIGURE 4 Impacts of abiotic and biotic factors on bacterial and
fungal richness in rhizosphere (Rs) and non-rhizosphere (Non-Rs)} . SBI,
shoot biomass; RBI, root biomass; RTC, root total carbon; RTN, root
total nitrogen; STC, soil total carbon; STN, soil total nitrogen; STP,
soil total phosphorus; SAN: soil available nitrogen; SAP: soil available
phosphorus; SM: soil moisture. Overall \emph{R}
\textsuperscript{2}indicates the total explained fraction by the six
variables.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{FIGURE 5 Impacts of abiotic and biotic factors on microbial
compositions in rhizosphere (Rs) and non-rhizosphere (Non-Rs)} . Notes:
Mantel results on the impacts of examined variables on bacterial and
fungal compositions \textbf{(a)} . Relative roles of six variables with
low-collinearity on rhizosphere \textbf{(b)} and non-rhizosphere
bacterial compositions \textbf{(c)} . Relative roles of six variables
with low-collinearity on rhizosphere \textbf{(d)} and non-rhizosphere
fungal compositions \textbf{(e)} . SBI, RBI, RTC, RTN, STC, STN, STP,
SAN, SAP, and SM are the same as in Figure 4. Overall\emph{R}
\textsuperscript{2} indicates the total explained fraction by the six
variables.

\selectlanguage{english}
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