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\begin{document}

\title{Grazing decreases net ecosystem carbon exchange by decreasing shrub and
semi-shrub biomass in a desert steppe}



\author[1]{xin Ju}%
\author[2]{Bingying Wang}%
\author[1]{Xiaojia Zhang}%
\author[1]{Qian Wu}%
\author[1]{Guodong Han}%
\affil[1]{Inner Mongolia Agricultural University}%
\affil[2]{ Forest and Grassland Protection and Development Center, Bairin Right Banner }%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Livestock grazing can strongly determine how grasslands function and
their role in carbon cycle. However, how ecosystem carbon exchange
responds to grazing and the underlying mechanisms remain unclear. We
measured ecosystem carbon fluxes to explore the changes in carbon
exchange and their driving mechanisms in a 16-year long term experiment
with different grazing intensities in a desert steppe grassland. We
found that grazing intensity influenced above- and belowground biomass
during the peak growing season, primarily by decreasing shrubs and
semi-shrubs and perennial forbs. Furthermore, alter patterns of net
ecosystem exchange primarily via their negative influence on the biomass
of shrub and semi-shrub. In addition, grazing-induced reduction
belowground biomass, as well as in total plant nitrogen and soil
ammonium nitrogen, can strongly influence ecosystem carbon exchange and
soil respiration. When nitrogen is lost from the soil due to grazing,
plants reallocate resources belowground to maintain growth and
development, thus promoting photosynthesis and respiration. Our study
indicates that soil available nitrogen and shrubs and semi-shrubs are
important factors in regulating ecosystem carbon exchange under grazing
disturbance in the desert steppe, which provide a basis for grazing
management.%
\end{abstract}%



\sloppy


\textbf{Running title:} Grazing effects on ecosystem carbon exchange

\textbf{Grazing decreases net ecosystem carbon exchange by decreasing
shrub and semi-shrub biomass in a desert steppe}

\emph{Xin Ju\textsuperscript{1}, Bing-ying Wang\textsuperscript{2},
Xiao-jia Zhang \textsuperscript{1}, Qian Wu\textsuperscript{1,*},
Guo-dong Han\textsuperscript{1, *}}

\emph{\textsuperscript{1} Key Laboratory of Grassland Resources of the
Ministry of Education, Key Laboratory of Forage Cultivation, Processing
and High Efficient Utilization of the Ministry of Agriculture and Rural
Affairs, Inner Mongolia Key Laboratory of Grassland Management and
Utilization, College of Grassland, Resources and Environment, Inner
Mongolia Agricultural University, Hohhot 010011, Inner Mongolia, China.}

\emph{\textsuperscript{2} Forest and Grassland Protection and
Development Center, Bairin Right Banner 025150, Inner Mongolia, China.}

* Correspondence: Guo-dong Han

E-mail: hanguodong@imau.edu.cn

Tel \& Fax: + 86 13087122590

Qian Wu

E-mail: qianwu@imau.edu.cn

Tel \& Fax: + 86 13848140272

\textbf{Abstract}

Livestock grazing can strongly determine how grasslands function and
their role in carbon cycle. However, how ecosystem carbon exchange
responds to grazing and the underlying mechanisms remain unclear. We
measured ecosystem carbon fluxes to explore the changes in carbon
exchange and their driving mechanisms in a 16-year long term experiment
with different grazing intensities in a desert steppe grassland. We
found that grazing intensity influenced above- and belowground biomass
during the peak growing season, primarily by decreasing shrubs and
semi-shrubs and perennial forbs. Furthermore, alter patterns of net
ecosystem exchange primarily via their negative influence on the biomass
of shrub and semi-shrub. In addition, grazing-induced reduction
belowground biomass, as well as in total plant nitrogen and soil
ammonium nitrogen, can strongly influence ecosystem carbon exchange and
soil respiration. When nitrogen is lost from the soil due to grazing,
plants reallocate resources belowground to maintain growth and
development, thus promoting photosynthesis and respiration. Our study
indicates that soil available nitrogen and shrubs and semi-shrubs are
important factors in regulating ecosystem carbon exchange under grazing
disturbance in the desert steppe, which provide a basis for grazing
management.

\emph{Keywords} Grazing intensity, Net ecosystem carbon exchange,
Ecosystem respiration, Soil respiration, \emph{Stipa breviflora} desert
steppe

\textbf{Introduction}

Grassland ecosystems cover a large proportion of the arid and semi-arid
regions of the world, playing an important role in the global carbon
cycle (Scurlock and Hall 1998; Schuman \emph{et al.} 2002; Zhou \emph{et
al.} 2019; Lei \emph{et al.} 2020). The degree to which terrestrial
ecosystems serve as net carbon sinks or sources depends on the balance
between the carbon fixed by plant photosynthesis and the carbon released
into the atmosphere by plant and soil respiration (Peng \emph{et al} .
2014; Li \emph{et al.} 2017b; Jin \emph{et al.} 2023). While numerous
evidence has addressed the degree to which forested systems serve as net
carbon sinks or sources (Martens \emph{et al.} 2004; Rebane \emph{et
al.} 2020), the status of grassland ecosystems as sources or sinks can
be highly variable (Dai \emph{et al.} 2014; Smith 2014; Chang \emph{et
al.} 2021). Grasslands can be an important carbon sink in some places or
times (Hafner \emph{et al.} 2012; Sha \emph{et al.} 2020), but in other
places and times, grassland carbon fluxes can be in equilibrium (neither
a source or sink) (Hao \emph{et al.} 2017), a net carbon source
(Kuzyakov and Gavrichkova 2010), or fluctuate between states
(Dai\emph{et al.} 2014; Zhang \emph{et al.} 2020).

Livestock grazing is a major land-use category by which human activities
can influence the structure and function of grassland ecosystems,
profoundly altering the carbon cycle and stability of grassland
productivity (Zhang \emph{et al.} 2023). Grazing directly affects plant
productivity and respiration because livestock foraging removes leaves
while promoting compensatory growth, and their trampling and excretion
redistributes soil organic matter and alters soil respiration
(Cao\emph{et al.} 2004; Chen \emph{et al.} 2015; Barthelemy \emph{et
al.}2018; Veldhuis \emph{et al.} 2018); Grazing also alters soil
nitrogen content and other processes important to the carbon cycle, such
as litter decomposition and photosynthate distribution (Xia and Wan
2008). As a result, grazing can moderate the net ecosystem exchange of
grasslands and whether they serve as a net carbon sink or source. In
some cases, light to moderate levels of grazing can facilitate
grasslands being net carbon sinks (Derner \emph{et al.} 2006;
Sha\emph{et al.} 2020; Chang \emph{et al.} 2021), while high levels of
grazing can accelerate the release of carbon and switch the ecosystem
from a carbon sink to a carbon source (Liang \emph{et al.} 2017;
Tang\emph{et al.} 2018); In other cases, grazing appears to have little
influence on the carbon budget of grassland ecosystems (Fang \emph{et
al.} 2010; Pi\selectlanguage{ngerman}ñeiro \emph{et al.} 2010).

Desert steppe is particularly vulnerable to degradation due to livestock
grazing and to carbon sinks transitioning to carbon sources
(Zhang\emph{et al.} 2020). We assessed ecosystem carbon balances over a
sustained 10-year period and explored the influencing factors. We
concluded that precipitation patterns and grazing combine to cause
changes in the carbon sink function of grasslands (Jin \emph{et
al.}2023; Wang \emph{et al.} 2023), but that ecosystem carbon exchange
is disturbed by a combination of environmental (soil, climate) and
biological (grazing) factors. How environmental and biological factors
influence net ecosystem carbon exchange depends on the relationship
between carbon uptake via primary productivity and carbon release via
plant and soil respiration. Furthermore, there is considerable
variability and uncertainty regarding the factors influencing carbon
exchange in grassland ecosystems (Liu \emph{et al.} 2015; Sha \emph{et
al.} 2020). This is likely because the variability in grassland types is
mediated by climate, vegetation and soil (Helfter \emph{et al.} 2015;
Hussain \emph{et al.} 2015; Liang \emph{et al.} 2020), as well as by
grazing practices (Fang \emph{et al.} 2010; Dai \emph{et al.} 2014).

Thus, simply measuring net ecosystem exchange and aboveground biomass is
not enough to fully understand the influence of biotic and abiotic
factors on these rates (Li \emph{et al.} 2017a; Bajgain \emph{et
al.}2018). It is necessary to more fully identify how carbon exchange
and soil respiration are influenced by grazing and background
environmental factors, in particular. This information will not only
help us better understand the factors influencing the carbon dynamics of
these important ecosystems, but it will also help inform the formulation
of policies for the sustainable management and conservation of grassland
resources. In this study, we measured ecosystem carbon fluxes and their
associations in respond to a long-term (16-year) grazer manipulation
experiment in a desert steppe grassland in Inner Mongolia, China. We
specifically asked (1) how does grazing influence features of the plant
community and soil conditions and (2) how those effects influence the
parameters of net ecosystem carbon exchange, including gross ecosystem
productivity and respiration. On the basis of our previous research, we
further measured aboveground and belowground biomass, plant nutrients
(carbon and nitrogen content of plant communities) and soil nutrient
indexes to analyze the main drivers that influence the changes of
CO\textsubscript{2} fluxes in desert steppe ecosystems and their
responses to grazing disturbances, and to provide theoretical basis for
the adaptive management of desert steppe.

\textbf{Methods}

\textbf{Study Site}

Our study took place within a long-term grazing experiment located in
Siziwang Banner (41°46'43 ''N, 111°53'42'' E, elevation 1456 m) at the
comprehensive experiment and demonstration center of the Inner Mongolia
Academy of Agriculture and Animal Husbandry Sciences, China. The study
site is a typical desert steppe ecosystem dominated by \emph{Stipa
breviflora} Griseb., \emph{Artemisia frigida} Willd,
and\emph{Cleistogenes songorica} (Roshev.) Ohwi. Subordinate species
include \emph{Convolvulus ammannii} Desr., \emph{Kochia prostrata} (L.)
Schrad.\emph{, Caragana stenophylla} Pojark. and \emph{Caragana
microphylla} Lam.. The soil is primarily a sandy loam texture with low
nitrogen, phosphorus, and organic matter content, but high potassium.
Over the course of the experiment (2004 to 2020), the average annual
temperature was 3.4\selectlanguage{english} and the average annual precipitation was 221.7 mm
(the majority falling from June to August). We present the air
temperature and precipitation during the growing season in which we
collected data (2020) in Fig. S1.

\textbf{Experimental Design}

A grazing manipulation experiment was established in June 2004 in a
natural grassland (\textasciitilde{}50 ha) with relatively flat terrain
and relatively homogeneous vegetation and soil types. The plots were
divided into three experimental blocks which each received one of four
grazing treatments, control (no grazing), light grazing, medium grazing
and heavy grazing (thus, there were three replicates for each
treatment). Each experimental plot was 4.4 ha and constructed with iron
wire fencing material. The stocking rates in each treatment were 0
(control/ no grazing), 0.91 (light grazing), 1.82 (moderate grazing, MG)
and 2.71 (heavy grazing) sheep unit *
(hm\textsuperscript{2}A\textsuperscript{-1}) \textsuperscript{-1}. Each
grazing plot was grazed by adult sheep from June 1 to October 1 each
year. During the grazing season, the sheep were driven into the grazing
area at 6:00 every day and left to forage freely until their return to
the corral at 18:00.

\textbf{Measurement of aboveground biomass and belowground biomass}

We measured aboveground biomass of plants from June to September 2020.
In each month, we randomly selected three (1 m\textsuperscript{2})
quadrats (108 quadrats in total) near the other sampling locations in
each plot to record the community characteristics of plants. In each
quadrat, we clipped all aboveground biomass and separated them to
species. We then dried plants at 65 \selectlanguage{english} for 48 h and weighed them. We
categorized species into four functional groups (perennial grass, shrub
and semi-shrub, perennial forb, annual and biennial plants) based on
their life type (Table S1).

We measured belowground biomass in August 2020. To do so, we selected
six points near the other sampling locations and collected samples from
the 0-10 cm layer with a root auger (7 cm diameter). We took two samples
at each point and combined them for analyses. We picked roots from the
soil, washed them and dried and weighed them as above.

\textbf{Measurement of plant total nitrogen and carbon content}

We measured total carbon and total nitrogen content from three of the
aboveground sampling quadrats in each plot. After weighing, we ground
tissues using a ball mill and measured powder samples using an elemental
analyzer (Elementar Vario MACRO CUBE).

\textbf{Measurement of soil properties}

We determined several soil physical and chemical properties in August
2020 by collecting soil samples from the 0-10 cm layer. We selected six
points in each plot near the other sampling points and collected soil at
each point from 0-10 cm using a soil auger (3 cm diameter). At each
point, we collected two soil samples, combined them, and passed soil
through a 2 mm sieve for determining the physical and chemical
properties of the soil in the laboratory.

For each soil sample, we determined total carbon and total nitrogen
content in the soil using an elemental analyzer (Elementar Vario MACRO
CUBE); total phosphorus content using an ultraviolet spectrophotometer
(UV-1800, Mapada, Shanghai, China) with the sodium hydroxide fusion
method; organic carbon content using the potassium dichromate external
heating method; nitrate (NO\textsubscript{3}\textsuperscript{-}-N) and
ammonium (NH4\textsuperscript{+}-N) by extraction using KCl (2
mol*L\textsuperscript{-1}) with a flow analyzer; available phosphorus
content using the sodium bicarbonate molybdenum antimony
anti-colorimetric method; and microbial biomass carbon and microbial
biomass nitrogen using the chloroform fumigation extraction method.

\textbf{Measurement of ecosystem CO\textsubscript{2} exchange}

We measured net ecosystem CO\textsubscript{2} exchange and ecosystem
respiration monthly during the growing season (June to October) in 2020.
To do so, we used a Li-6400 portable photosynthetic (Li-COR, USA)
instrument with the static chamber method. We collected measurements
between 8:00 a.m. to 12:00 p.m (Niu \emph{et al.} 2008; Wu \emph{et
al.}2021). For measurements, we choose a clear, cloudless and windless
day as much as possible, at least three days after a rainfall. For
measurements we connected a leaf chamber (50 x 50 x
50cm\textsuperscript{3} transparent plexiglass box) to the portable
photosynthetic instrument and installed small fan in each diagonal
direction at the upper end of the glass box so that gas was fully mixed.
We place the glass box on one of three aluminum sink frames (50 x
50cm\textsuperscript{2}) placed randomly within each plot to ensure a
smooth and airtight seal. We repeated measurements on each of the three
frames.

At each sample point, we collected measurements for 120 s and
CO\textsubscript{2} concentration and water exchange flux values were
automatically recorded every 10 s. After these measurements, we
ventilated the leaf chamber to ensure it was filled with
convection-exchanged air, covered it with a black cloth to ensure no
light transmission and repeated the above procedure to determine
ecosystem respiration.

We measured soil respiration using an open circuit Li-8100 soil carbon
flux meter (Li-COR, Inc, Lmcoln, NE, USA) at the same time as the net
ecosystem exchange measurements. We measured soil respiration within
three PVC rings (10.5 cm in diameter and 8 cm in height) that were
randomly placed 2 cm above the ground surface in each plot. Prior to
measurements, we clipped plants inside the rings flush with the ground
and removed debris.

We calculated net ecosystem CO\textsubscript{2} exchange (NEE) and gross
ecosystem productivity (GEP) as follows: \selectlanguage{english}[?]C\selectlanguage{english}' \selectlanguage{english}[?]t = INDEX(LINEST(Y1: Y12,
A1: A12)\selectlanguage{english},1);\(NEE=\frac{10VP(1-\frac{W}{1000})}{RS(T+273.15)}\frac{\partial C\prime}{\partial t}\); NEE = GEP - ER.

Where ER is the measured ecosystem respiration. NEE, ER, GEP are in
\selectlanguage{greek}μ\selectlanguage{english}mol\selectlanguage{ngerman}·m\textsuperscript{-2}·s\textsuperscript{-1}, Y1-Y12 is the
CO\textsubscript{2} concentration value, A1-A1 is the measurement time,
V represents the volume of the box (cm\textsuperscript{3}), P is the
atmospheric pressure inside the box (kpa), W is the water pressure
inside the chamber (mmol·mol\textsuperscript{-1}), S is the bottom area
of the chamber (cm\textsuperscript{2}), T is the temperature of the gas
inside the chamber (\selectlanguage{english}), and R=8.314 J-mol-1-K-1 (constant). We used
values of ecosystem CO\textsubscript{2} exchange and soil respiration
during the growing season (June-October) to calculated the values of
NEE, GEP, ER, SR for each treatment.

\textbf{Measurement of air temperature and precipitation}

We collected meteorological data in 2020 using a small weather station
(Gro Weather software version 1.2, Davis instruments corporation, USA).
The station recorded temperature and precipitation data automatically at
1 h intervals, which we downloaded and collated at regular intervals.

\textbf{Measurement of soil temperature and moisture}

In parallel with net ecosystem exchange measurements, we measured soil
temperature at 10 cm depth in the leaf chamber with two TP3001
electronic thermometers. At the same time, we collected 10 cm soil
samples using a 2.5 cm diameter x 10 cm high soil auger, which we
collected in an aluminum box, weighed and recorded the wet mass, and
then dried at 105 \selectlanguage{english} for 24 h to weigh the dry mass and then calculate
the mass water content.

\textbf{Data Analysis}

After ensuring data met normality and homogeneity of variance
assumptions using the Shapiro-Wilk test, we evaluated the influence of
grazing treatment on aboveground and belowground biomass, plant nitrogen
and carbon content, plant functional groups and several soil chemical
variables, as well as the ecosystem CO\textsubscript{2} exchange and
soil respiration. To do so, we used repeated measures ANOVA to test the
effects of grazing intensity and sampling month on the aboveground
biomass, plant functional group biomass, ecosystem
CO\textsubscript{2}exchange and soil respiration. We used one-way ANOVA
followed by a Duncan test for pairwise comparison to test the effects of
grazing intensity on the belowground biomass, plant total carbon, plant
total nitrogen and soil nutrient content. A \emph{P} \textless{} 0.05
indicated signi\selectlanguage{english}ficance in the treatment e\selectlanguage{english}ffects.

We correlated several abiotic factors with ecosystem carbon exchange,
including temperature, precipitation, soil temperature, and soil
moisture in each treatment using regression analysis.

To investigate the influence of soil and plant factors on ecosystem
carbon exchange, we used redundancy analysis to rank the impact of the
factors on carbon exchange. Furthermore, we used a generalized linear
model (GLM) and structural equation model (SEM) to determine the effects
of plant and soil factors on ecosystem CO\textsubscript{2} exchange and
soil respiration. To do so, we first calculated the contribution of the
plant and soil factors to the ecosystem CO\textsubscript{2} exchange and
soil respiration using the GLM, and then we removed insignificant
pathways and simplified the SEM model based on the GLM results. We
obtained path coe\selectlanguage{english}fficients using a maximum likelihood estimation
technique.

We performed ANOVA, repeated measures ANOVA and the GLM analyses in
version R 4.0.3. The SEM analyses were performed using the ``piecewise
SEM'' package (Lefcheck, 2016) in R version 4.0.3. We performed
regression and redundancy analyses in Origin 2023 software.

\textbf{Results}

\textbf{Grazing Intensity Effects on the Plant Functional Group
Productivity and Plant Community Carbon and Nitrogen Content}

We found that both aboveground (Fig. 1a) and belowground (Fig. 1b)
biomass was influenced by the grazing treatment. All grazing treatments
had lower aboveground and belowground biomass than the control treatment
with no grazing. Aboveground biomass was lowest in the heavy grazing
treatment while there were no differences between the two lower grazing
intensity treatments (Fig. 1a), belowground biomass was incrementally
lower with each increased grazing intensity treatment (Fig. 1b). When we
analyzed differences in nutrient content, we found that the total carbon
content of the plant community was lowest in the heavy grazing treatment
(Fig. 1c), while the total nitrogen content of the plant community was
lowest in the moderate grazing treatment (Fig. 1d). Aboveground biomass
differed significantly between months, though the difference was not
significant for the interaction between month and grazing intensity
(Table 1). When we divided plants into functional groups (Fig. 2b-e), we
found that most groups strongly declined with increasing grazing,
particularly shrubs and semi-shrubs (Fig. 2c), as well as perennial
forbs (Fig. 2d). Perennial grasses, in fact, had greater biomass in the
moderate grazing treatment (Fig. 2b) We performed repeated measures
ANOVAs for different plant functional groups and found that most
functional groups differed significantly by month, grazing intensity,
and the interaction between month and grazing intensity. This was
especially true for the perennial forbs, shrubs and semi-shrubs. Annuals
and biennials did not differ significantly by the interaction between
month and grazing intensity, and perennial grasses did not significant
significantly by month, grazing intensity or the interaction between
month and grazing intensity (Table 1, Fig. 2b-e).

\textbf{Grazing Intensity Effects on Soil Nutrients}

Of the soil chemical variables, we found no differences in total carbon
(Fig. 3a), total phosphorus (fig. 3c), organic carbon (Fig. 3d), and
microbial biomass carbon (Fig. 3g) among grazing intensity treatments.
However, we found that total nitrogen (Fig. 3b), ammonium nitrogen (Fig.
3e), microbial biomass nitrogen (Fig. 3i), and available phosphorus
(Fig. 3g) content tended to be lower in the highest grazing intensity
treatments.

\textbf{Differences in ecosystem CO\textsubscript{2} exchange under
different grazing intensities}

During the 2020 growing season, we found that NEE, ER, GEP, and SR
showed strong seasonal dynamics consistent with the monthly variation in
precipitation (Fig. S2). According to the results of repeated measures
ANOVA, NEE, ER, GEP, and SR varied significantly between months. NEE and
GEP also varied significantly between grazing intensities and the
interaction between month and grazing intensity, but ER and SR did not
differ significantly between grazing intensities or the interaction
between month and grazing intensity (Table 2). During July, NEE was
positive indicating release as a carbon source (Fig. 4a); it was
negative, indicating a carbon sink for the rest of the growing season.
Both NEE (Fig. 4a) and GEP (Fig. 4c) were lowest in August, while ER
(Fig. 4b) and SR (Fig. 4d) were highest in August. When we compared
grazing treatments, we found that the rates of NEE (Fig. 4a), ER (Fig.
4b), GEP (Fig. 4c) and SR (Fig. 4d) were all significantly lower than
the control plots, with the heavy grazing treatment often having lowest
(or highest) values.

\textbf{plant factors and soil factors on ecosystem CO\textsubscript{2}
exchange}

We used RDA model to examine the relationship between the explanatory
variable (plant and soil factors, blue lines with arrows) and response
variable (ecosystem carbon exchange and soil respiration, red lines with
arrows) in Fig. 6. We found that plant factors (e.g., above and below
ground biomass, plant carbon and nitrogen nutrients) explained 98.10\%
of the variance of ecosystem CO\textsubscript{2} exchange and soil
respiration (Axis 1 explained 71.49 \% of the total variance while Axis
2 explained 26.61\%; Fig. 6a). Soil factors (e.g., Soil nutrient index)
explained 98.20 \% of the variance of ecosystem
CO\textsubscript{2}exchange and soil respiration (Axis 1 explained 73.50
\% of the total variance while Axis 2 explained 24.70 \%; Fig. 6b). For
plant and soil factors, SS (R\textsuperscript{2} = 0.36) contributed the
highest degree of variance to NEE, and next highest was AGB
(R\textsuperscript{2}= 0.21, Fig. 5c); AGB (R\textsuperscript{2} = 0.28)
contributed the highest degree of variance to GEP, and next highest was
SS (R\textsuperscript{2} = 0.22, Fig. 5E); BGB (R\textsuperscript{2} =
0.25, R\textsuperscript{2} = 0.23) contributed the highest degree of
variance to ER and SR (Fig. 5d, Fig. 5f);

Based on the results of the redundancy and GLM analyses, we developed
structural equation models to better explain the driving mechanisms of
ecosystem carbon exchange and soil respiration. Our SEM analysis showed
that grazing had a direct negative effect on NEE and GEP. Specifically,
grazing reduced NEE and GEP by reducing aboveground biomass, especially
through the indirect reduction of NEE due to lower shrub and semi-shrub
biomass (Fig. 6a and c). However, the lower soil nutrient content in the
grazing treatment was not associated with NEE and GEP (Fig. 6e and g).
In contrast, grazing and aboveground biomass did not directly affect ER
and SR (Fig. 6b and d), but they did directly affect belowground biomass
and indirectly reduce belowground biomass by decreasing ammonium N. This
came to affect the rate of SR as well (Fig. 7f and h).

\textbf{Discussion}

Grazing by livestock influences the productivity and stability of
grassland ecosystems, which in turn generates feedback mechanisms on the
carbon cycle. However, the factors underlying the complex changes of
vegetation and soil as a result of grazing and their impact on the
processes underlying carbon cycling remain poorly understood
(Yuan\emph{et al.} 2011; Hussain \emph{et al.} 2015; Oram \emph{et al.}
2023; Zaret \emph{et al.} 2023). Our results from a 16-year long grazing
intensity manipulation study in a desert steppe grassland show that
grazers alter patterns of net ecosystem exchange primarily via their
negative influence on the biomass of shrub and semi-shrub, which play a
prominent role in ecosystem functions.

\textbf{Effect of grazing intensity on net ecosystem
CO\textsubscript{2}exchange}

Grazing can shift the balance between vegetation being a carbon source
or sink in steppe grasslands (de la Motte \emph{et al.} 2018;
Ondier\emph{et al.} 2021). Our finding that NEE, ER, and GEP decreased
with increasing grazing intensity is consistent with previous results
from desert steppe (Jin \emph{et al.} 2023; Wang \emph{et al.} 2023).
Our finding that heavy grazing had a stronger inhibitory effect on ER
than GEP is consistent with the results of Peng et al. (2007). This may
have resulted because livestock feeding reduces the aboveground biomass
which cannot be compensated by regrowth (Zhang \emph{et al.} 2018;
Zhang\emph{et al.} 2023), such that the effective amount of leaf area
available for both photosynthesis and respiration is reduced so that the
net CO\textsubscript{2} exchange rate decreases (Oba \emph{et al.} 2000;
Shi \emph{et al.} 2022).

Although our finding of the positive correlation between productivity
and NEE is consistent with many previous studies (Danielewska \emph{et
al.} 2015; Xu \emph{et al.} 2022), we also found a positive correlation
between aboveground biomass and NEE, primarily driven by shrubs and
semi-shrubs and perennial forbs, which is consistent with previous work
(Zhang \emph{et al.} 2023). Our finding that shrubs and semi-shrubs and
perennial forbs were strongly influenced by grazing, while grasses were
less so is consistent with the idea that shrubs and semi-shrubs and
perennial forbs are more palatable and have higher nutritional value
than grasses, mainly \emph{stipa breviflora} , which are not preferred
by livestock. Shrub roots can reach up to 70 cm deep into the soil
layer, allowing them to better utilize deeper water and nutrients to
maintain a high carbon fixation capacity and a high net carbon uptake
capacity (Niu\emph{et al.} 2023). which can explain why their loss
dramatically influenced NEE.

Our finding that plant N content is negatively correlated with net
ecosystem CO\textsubscript{2} exchange is inconsistent with previous
findings that loss of leaf N attenuates ecosystem carbon cycling (Wang
et al. 2014 in Chinese)(Gong \emph{et al.} 2021), This may be because
altered plants allocated more N to non-photosynthetic proteins to
increase their compensatory growth in response to grazing, but with
reduced photosynthetic capacity (Onoda \emph{et al.} 2004), resulting in
a decrease in net CO\textsubscript{2} exchange rate (Zhang \emph{et
al.}2006). A study by Wu et al. (2021) showed that N addition in desert
steppe increased the net CO\textsubscript{2} exchange rate, while You et
al. (2016) showed that high levels of N addition inhibited NEE, but
moderate levels promoted NEE. This suggests that the changes of nitrogen
absorbed and used by plants are complex and require further
investigation (Schimel \emph{et al.} 2001).

Although NEE decreased in response to increasing grazing intensity, it
is of interest that there was no significant difference in NEE rates
between the LG and MG treatments in our study (Fig. 4a, bars), because
although short-flowered needlegrass was a well-established species and
widely distributed in our experimental sample plots, livestock did not
prefer it, resulting in no significant difference in vegetation stock
and cover between the LG and MGC treatments and the non-grazed areas, so
their net CO\textsubscript{2} exchange rates were not significantly
different from those of ck. The net CO\textsubscript{2} exchange rate
was not significantly different from that of CK (\emph{p} \textgreater{}
0.05, Fig. S1a).

Although NEE decreased in response to increasing grazing intensity, we
found no difference in NEE rates between the light and moderate grazing
treatments. This was likely because less preferred grasses dominated
both treatments.

\textbf{Effect of grazing intensity on soil resperation}

Desert steppe is sparsely vegetated, so soil respiration is likewise an
important determinant of carbon balance in the ecosystem. The rate of SR
decreased with grazing intensity (Fig. 4d, bars), and belowground
biomass (Fig. S1b), ammonium N (Fig. S2e), and available P (Fig. S2g)
also significantly decreased (\emph{p} \textless{} 0.05), but the effect
of different grazing intensities on soil organic carbon was not
significant (\emph{p} \textgreater{} 0.05, Fig. S2d). In this study,
belowground biomass, available P, and soil organic carbon were all
significantly correlated with SR based on redundancy analysis. We
further constructed structural equations and the results showed that
grazing did not directly affect SR, but indirectly reduced the rate of
SR by decreasing belowground biomass and ammonium N (Fig. 7b).

Belowground biomass is highly correlated with soil respiration
(Pregitzer \emph{et al.} 2008; Wu \emph{et al.} 2016; Diao \emph{et
al.}2022). Higher CO\textsubscript{2} fluxes may be caused by higher
root biomass, which can promote soil respiration by releasing more
secretions at the inter-root level and providing a favorable environment
for soil microbial respiration (Wu \emph{et al.} 2016). In contrast,
heavy grazing reduced above- and belowground biomass, thus reducing the
amount of root growth, soil microbial load and soil enzyme activity,
which likely led to the inhibition of microbial respiration and
ultimately reduced soil respiration rate (Li \emph{et al.} 2013).

In addition, based on the GLM and SEM analyses, we also found that soil
ammonium N content correlated with respiration (Fig. 5d and f, Fig. 6f
and h), which is consistent with the result that nitrogen addition can
stimulate soil respiration in nutrient-poor soil (Smith, 2005). The
affinity of dissolved oxygen and aeration tissue for
NH\textsubscript{4}\textsuperscript{+} and
NO\textsubscript{3}\textsuperscript{-} in root respiration mainly
depends on NH\textsubscript{4}\textsuperscript{+}, which is enhanced
when NH\textsubscript{4}\textsuperscript{+} is absorbed. The enhancement
of glutamate dehydrogenase regulation and amino acid metabolic reactions
increases root N use efficiency and promotes root growth (Knapp \emph{et
al.} 2017). Thus, the change in soil ammonium N content is one of the
main factors influencing soil respiration (Onoda \emph{et al.} 2004;
LeBauer and Treseder, 2008; Gong \emph{et al.} 2021).

\textbf{Effects of climate variables on ecosystem carbon exchange and
soil respiration}

As expected, we found that NEE, ER and GEP were all influenced by
precipitation which was highest in July and August (Fig. S1, Fig. S2b).
This is likely primarily a result of the influence of aboveground
biomass and its influence on productivity and ecosystem carbon exchange,
which is strongly influenced by variation in rainfall (Jobbagy \emph{et
al.} 2002). We also showed that soil moisture was positively correlated
with ER and SR (Fig. S2d), which likely promoted the growth of plant
roots to enhance microbial activity and promote organic matter
decomposition, leading to an increase soil respiration (Helfter \emph{et
al.} 2015; Peng \emph{et al.} 2015). This is consistent with previous
studies in desert steppe (Jin \emph{et al.}2023; Wang \emph{et al.}
2023).

Likewise, variation in temperature influences ecosystem carbon exchange
mainly by affecting GEP and ER (Luo \emph{et al.} 2001;
Ganjurjav\emph{et al.} 2018; Li \emph{et al.} 2019). However, consistent
with our results showing a minimal influence of temperature on ecosystem
carbon exchange in a desert steppe (Fig. S2a), WU et al. (2021) found
similar results in a 12-year study. We did, however, find that variation
in soil temperature contributed to ER (Fig. S2c), which was consistent
with results from a previous analysis.

\textbf{Conclusions}

In this study, we assessed the impact of different levels of grazing
intensity, as well as the associated direct and indirect effects, on
ecosystem carbon exchange and soil respiration. Over the course of the
growing season, we found that the desert steppe remained in a state of
carbon uptake (carbon sink) under the conditions of 16 years of
continuous grazing. Our study shows that alter patterns of net ecosystem
exchange primarily via their negative influence on the biomass of shrubs
and semi-shrubs. In addition, grazing-induced reduction belowground
biomass, as well as in total plant nitrogen and soil ammonium nitrogen,
can strongly influence ecosystem carbon exchange and soil respiration.
When nitrogen is lost from the soil due to grazing, plants reallocate
resources belowground to maintain growth and development, thus promoting
photosynthesis and respiration.

\textbf{Author contributions}

\textbf{Xin Ju} : Data curation (equal); formal analysis (lead);
investigation (lead); methodology (lead); resources (equal); software
(lead); visualization (lead); writing-original draft (lead);
writing-review and editing (lead). \textbf{Bingying Wang}
and\textbf{Xiaojia Zhang} : Data curation (Equal). \textbf{Qian Wu} :
Conceptualization (lead); funding acquisition (lead); project
administration (lead); supervision (lead); validation (lead);
Writing-review \& editing (Lead). \textbf{Guodong Han} :
Conceptualization (lead); funding acquisition (lead); project
administration (lead); supervision (lead); validation (lead).

\textbf{Data availability statement}

The data that support the findings of this study are available in the
Supporting Information of this article.

\textbf{Acknowledgments}

We thank Ting-Ting Shen, Yu-Xin Wang, Feng-Miao Zhao for continuously
collecting data in the field and for the laboratory analysis during the
study period. We thank the Key Laboratory of Grassland Resources of the
Ministry of Education (Inner Mongolia Agricultural University) for their
support, and also thank anonymous referees for their constructive
comments that significantly. This study was financially supported by
High-Level~Talent~Research~Project of Inner Mongolia Agricultural
University (NDYB2020-5), National Natural Science Foundation of
China~(32192463).

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\textbf{Table 1:} Repeated-measures ANOVA for above-ground biomass and
biomass of plant functional groups.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
plant biomass & Month & Month & Grazing intensity & Grazing intensity &
Month \selectlanguage{ngerman}× Grazing intensity & Month \selectlanguage{ngerman}× Grazing intensity\tabularnewline
\midrule
\endhead
& \emph{F} value & \emph{P} value & \emph{F} value & \emph{P} value &
\emph{F} value & \emph{P} value\tabularnewline
AGB(g\selectlanguage{ngerman}·m\textsuperscript{-2}) & 6.59 & 0.002 & 10.91 & 0.003 & 0.63 &
0.75\tabularnewline
PG(g\selectlanguage{ngerman}·m\textsuperscript{-2}) & 1.78 & 0.18 & 1.77 & 0.24 & 0.53 &
0.83\tabularnewline
SS(g\selectlanguage{ngerman}·m\textsuperscript{-2}) & 4.22 & 0.02 & 10.62 & 0.004 & 2.22 &
0.05\tabularnewline
PF(g\selectlanguage{ngerman}·m\textsuperscript{-2}) & 9.74 & <0.001 & 8.28 & 0.008 & 3.96 &
0.003\tabularnewline
AB(g\selectlanguage{ngerman}·m\textsuperscript{-2}) & 19.62 & <0.001 & 4.66 & 0.025 & 0.97 &
0.49\tabularnewline
\bottomrule
\end{longtable}

The \emph{F} values are presented together with their levels of
significance. AGB, PG, SS, PF and AB represent aboveground biomass,
perennial grass biomass, shrub and semi-shrub biomass, perennial forb
biomass and annual and biennial plant biomass.

\textbf{Table 2:} Repeated-measures ANOVA for ecosystem carbon fluxes
and soil respiration.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
ecosystem fluxes & Month & Month & Grazing intensity & Grazing intensity
& Month \selectlanguage{ngerman}× Grazing intensity & Month \selectlanguage{ngerman}× Grazing intensity\tabularnewline
\midrule
\endhead
& \emph{F} value & \emph{P} value & \emph{F} value & \emph{P} value &
\emph{F} value & \emph{P} value\tabularnewline
NEE(\selectlanguage{greek}μ\selectlanguage{english}mol\selectlanguage{ngerman}·m-2·s-1) & 1280.10 & <0.001 & 32.56 & 0.004 & 7.59 &
<0.001\tabularnewline
ER(\selectlanguage{greek}μ\selectlanguage{english}mol\selectlanguage{ngerman}·m-2·s-1) & 190.52 & <0.001 & 2.28 & 0.16 & 3.64 &
0.06\tabularnewline
GEP(\selectlanguage{greek}μ\selectlanguage{english}mol\selectlanguage{ngerman}·m-2·s-1) & 1082.33 & <0.001 & 40.77 & <0.001 & 8.02 &
<0.001\tabularnewline
SR(\selectlanguage{greek}μ\selectlanguage{english}mol\selectlanguage{ngerman}·m-2·s-1) & 48.76 & <0.001 & 1.98 & 0.2 & 1.42 &
0.21\tabularnewline
\bottomrule
\end{longtable}

The \emph{F} values are presented together with their levels of
significance. NEE, ER, GEP and SR represent net exchange of ecosystem
CO\textsubscript{2}, ecosystem respiration, gross ecosystem productivity
and soil respiration.

\textbf{Figure legends}

\textbf{Figure 1:} The effects of grazing intensity on plant aboveground
biomass, belowground biomass, plant community carbon and nitrogen
content. Different lowercase letters indicate significant differences
between means at \emph{p} \textless{} 0.05. Error bards are \selectlanguage{ngerman}± SE, Codes
of different treatments are as follows: CK, control/no grazing; LG,
light grazing; MG, moderate grazing; HG, heavy grazing.

\textbf{Figure 2:} The effects of grazing intensity and month on plant
aboveground biomass and biomass of plant functional groups. Each panel
represents a different grouping of plant biomass. Different lowercase
letters indicate significant differences between means at
\emph{p}\textless{} 0.05. Error bards are ± SE, and the lines in panels
b-e show the biomass of each plant functional group during the 2020
growing season. Codes of different treatments are as follows: CK,
control/no grazing; LG, light grazing; MG, moderate grazing; HG, heavy
grazing.

\textbf{Figure 3:} The effects of grazing intensity on soil nutrients.
Different lowercase letters indicate significant differences between
means at \emph{p} \textless{} 0.05. Error bars are ± SE\textbf{.} Codes
of different treatments are as follows: CK, control / no grazing; LG,
light grazing; MG, moderate grazing; HG, heavy grazing.

\textbf{Figure 4:} Monthly dynamics of ecosystem fluxes\textbf{.} Panels
show the mean value (±SE) of net exchange of ecosystem
CO\textsubscript{2} (a, NEE), ecosystem respiration (b, ER), gross
ecosystem productivity (c, GEP) and soil respiration (d, SR) in the
growing season (June-October) of 2020. The inset reflects the
differences between treatments in the 2020 growing season, where
positive and negative values represent net carbon release and uptake by
the ecosystem and do not indicate the magnitude of the values. Different
lowercase letters indicate significant differences between treatments
(\emph{p} \textless{} 0.05), Codes of different treatments are the same
as in Figure 3.

\textbf{Figure 5:} Biplot of ecosystem carbon exchange (NEE, ER, GEP,
SR) from redundancy analysis (RDA) for plant factors (a) and soil
factors (b). GLM analysis was used to study the contribution of the
plant and soil factors to the net exchange of ecosystem
CO\textsubscript{2} (c, NEE), ecosystem respiration (d, ER), gross
ecosystem productivity (e, GEP) and soil respiration (f, SR). a-b,
Ecosystem carbon exchange is represented as red lines with arrows; plant
factors (a) and soil factors (b) are represented as blue lines with
arrows. The length of the line indicates the magnitude of the
correlation between the explanatory variable and ecosystem carbon
exchange. The angle between the lines indicates the correlation between
the variables, and the angle between the red and blue arrows is less
than 90° for positive correlations. Codes of different plant factors (a)
are as follows: AGB, aboveground biomass; BGB, belowground biomass; PG,
perennial grass biomass; AB, annual and biennial plant biomass; PF,
perennial forb biomass; SS, shrub and semi-shrub biomass; PTC, plant
total carbon; PTN, plant total nitrogen; C/N, the ratio of total plant
carbon content to total plant nitrogen content. Codes of different soil
factors (b) are as follows: TC, total carbon; TN, total nitrogen; TP,
total phosphorus; SOC, organic carbon; AN, ammonium nitrogen; NN,
nitrate nitrogen; AP, available phosphorus; MBC, microbial biomass
carbon; MBN, microbial biomass nitrogen. c-f, Importance of individual
environmental variables across models for ecosystem carbon exchange is
shown for each indicator as variable importance weighted by \% of
R\textsuperscript{2}.

\textbf{Figure 6:} Structural equation models (SEM) examining the
standard total effects of plant and soil factors on Ecosystem carbon
exchange under different grazing intensities. Boxes stand for measured
variables in the model. Standardized path coefficients are given. Solid
black lines represent positive paths (\emph{p} \textless{} 0.05), solid
red lines represent negative paths (\emph{p} \textless{} 0.05), and
dotted black arrows represent non-significant paths
(\emph{p}\textgreater{} 0.05).

\textbf{Supplementary Material}

\textbf{Table S1:} Basic information on plant species and plant
functional groups during 2020 at the study site.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Latin name of specie}s & \textbf{Plant functional
groups}\tabularnewline
\midrule
\endhead
\emph{Stipa breviflora} Griseb. & perennial grass\tabularnewline
\emph{Stipa krylovii} Roshev. & perennial grass\tabularnewline
\emph{Cleistogenes songorica} (Roshev.) Ohwi. & perennial
grass\tabularnewline
\emph{Leymus chinensis} (Trin.) Tzvel. & perennial grass\tabularnewline
\emph{Cleistogenes squarrosa} (Trin.) Keng. & perennial
grass\tabularnewline
\emph{Agropyron~cristatum~}(L.) Gaertn. & perennial grass\tabularnewline
\emph{Convolvulus ammannii} Desr. & perennial forb\tabularnewline
\emph{Allium tenuissimum} L. & perennial forb\tabularnewline
\emph{Allium mongolicum} Regel. & perennial forb\tabularnewline
\emph{Astragalus galactites} Pall. & perennial forb\tabularnewline
\emph{Lagochilus ilicifolius} Bunge ex Benth. & perennial
forb\tabularnewline
\emph{Carex pediformis} C. A. Mey\emph{.} & perennial
forb\tabularnewline
\emph{Aster~altaicus~}Willd. & perennial forb\tabularnewline
\emph{Cymbaria~daurica}~L. & perennial forb\tabularnewline
\emph{Allium~ramosum~}L. & perennial forb\tabularnewline
\emph{Iris~tenuifolia}~Pall. & perennial forb\tabularnewline
\emph{Sibbaldianthe~bifurca}~(L.) Kurtto \& T. Erikss. & perennial
forb\tabularnewline
\emph{Artemisia frigida} Willd. & subshrub\tabularnewline
\emph{Caragana microphylla} Lam. & shrub\tabularnewline
\emph{Caragana stenophylla} Pojark. & shrub\tabularnewline
\emph{Bassia~prostrata~}(L.) Beck. & subshrub\tabularnewline
\emph{Kali~collinum}~(Pall.) Akhani \& Roalson. & annual and biennial
plant\tabularnewline
\emph{Neopallasia pectinata} (Pall.) Poljak. & annual and biennial
plant\tabularnewline
\emph{Artemisia scoparia} Waldst. et Kit. & annual and biennials
plant\tabularnewline
\emph{Chenopodium glaucum} L. & annual and biennial plant\tabularnewline
\emph{Teloxys aristata} (L.) Moq. & annual and biennial
plant\tabularnewline
\emph{Euphorbia~humifusa~}Willd. & annual and biennial
plant\tabularnewline
\emph{Lappula~myosotis~}Moench. & annual and biennial
plant\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure S1:} Daily mean air temperature (lines) and daily
precipitation (bars) in the growing seasons in 2020.

\textbf{Figure S2:} Soil temperature (a) and soil moisture (b) at 10 cm
soil depth under different grazing intensity in growing seasons (June -
October 2020)

\textbf{Figure S3:} Correlations between air temperature (a),
precipitation (b), soil temperature (c), soil moisture (d) and ecosystem
CO\textsubscript{2} fluxes (NEE, ER, GEP, SR) in the growing season of
2020.\selectlanguage{english}
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