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\begin{document}

\title{The amount of soil microbial biomass carbon and nitrogen in global
dryland regions}



\author[1]{Yujue Wang}%
\author[1]{Shuchen Wang}%
\author[1]{Jing Wang}%
\author[2]{Lei Hou}%
\author[1]{Weiguo Sang}%
\author[1]{Chunwang Xiao}%
\affil[1]{Minzu University of China}%
\affil[2]{Beijing Pollution Source Releated Affair Management Center}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Abstract: Dryland regions cover 41\% of the Earth's land surface and
support the livelihood of half of the world's population. Soil microbes
play an important role in carbon (C) and nitrogen (N) cycling, therefore
affecting soil health. Soil microbial biomass C (SMBC) and N (SMBN) are
indicators of soil microbial activities. A better understanding of
patterns and drivers of SMBC and SMBN in global dryland regions can
provide important insights to enhance ecosystem functioning services.
Here, we compiled 109 observations of SMBC (0-30 cm) and 79 observations
of SMBN (0-30 cm) from 100 sites across global dryland regions with
aridity index less than 0.65. The results showed that the average amount
of SMBC and SMBN in dryland regions were 358.47 \selectlanguage{ngerman}± 25.45 mg kg-1 and
51.86 ± 4.59 mg kg-1. The amount of SMBC and SMBN did significantly vary
among different dryland types and ecosystem types. Meanwhile, the ratio
of SMBC to SMBN in global dryland regions was 8.73. Soil sand fraction
and pH had significant negative effect on the ratio of SMBC to SMBN. Our
research has initially explored the pattern and control factors of soil
microbial biomass in dryland, and provided basic research data for
dryland management in the future.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{The amount of soil microbial biomass carbon and nitrogen in
global dryland regions}

Yujue Wang\textsuperscript{1,2}, Shuchen Wang\textsuperscript{1,2}, Jing
Wang\textsuperscript{1,2}, Lei Hou\textsuperscript{3}, Weiguo
Sang\textsuperscript{1,2,*}, Chunwang Xiao\textsuperscript{1,2,*}

1. Key Laboratory of Ecology and Environment in Minority Areas (Minzu
University of China), National Ethnic Affairs Commission, Beijing,
100081, China

2. College of Life and Environmental Sciences, Minzu University of
China, Beijing, 100081, China

3. Beijing Pollution Source Releated Affair Management Center, Beijing
100089, China

* Corresponding author. E-mail:

Weiguo Sang, swg@muc.edu.cn

Chunwang Xiao, cwxiao@muc.edu.cn

\textbf{Abstract:} Dryland regions cover 41\% of the Earth's land
surface and support the livelihood of half of the world's population.
Soil microbes play an important role in carbon (C) and nitrogen (N)
cycling, therefore affecting soil health. Soil microbial biomass C
(SMBC) and N (SMBN) are indicators of soil microbial activities. A
better understanding of patterns and drivers of SMBC and SMBN in global
dryland regions can provide important insights to enhance ecosystem
functioning services. Here, we compiled 109 observations of SMBC (0-30
cm) and 79 observations of SMBN (0-30 cm) from 100 sites across global
dryland regions with aridity index less than 0.65. The results showed
that the average amount of SMBC and SMBN in dryland regions were 358.47
\selectlanguage{ngerman}± 25.45 mg kg\textsuperscript{-1} and 51.86 ± 4.59 mg
kg\textsuperscript{-1}. The amount of SMBC and SMBN did significantly
vary among different dryland types and ecosystem types. Meanwhile, the
ratio of SMBC to SMBN in global dryland regions was 8.73. Soil sand
fraction and pH had significant negative effect on the ratio of SMBC to
SMBN. Our research has initially explored the pattern and control
factors of soil microbial biomass in dryland, and provided basic
research data for dryland management in the future.

\textbf{Keywords:}

soil microbial biomass, ratio of soil microbial biomass carbon to soil
microbial biomass nitrogen, global dryland regions, aridity index,
carbon and nitrogen cycling

\textbf{Introduction}

Drylands cover about 41\% of the Earth's land surface and support more
than 38\% of the total global population, representing a globally
important biome of great significance to human welfare (Millennium
Ecosystem Assessment, 2005). In the next decades, dryland regions may
face less frequent rainfall events and experience more intense drought
events due to climate change. As a consequence, a worldwide expansion of
drylands is expected for the near future (Gao and Giorgi, 2008; Dai et
al., 2013; Feng and Fu, 2013; Berg et al., 2016; Huang et al., 2016).
These changes may exacerbate water scarcity, land desertification, and
soil degradation, threatening the populations depending on drylands for
food production and the associated income. The United Nations
Environment Programme (UNEP) defines drylands according to aridity index
(AI), which is the ratio of mean annual precipitation to mean annual
potential evaporation, and drylands are lands with an AI of less than
0.65 (UNEP, 1992; UN Environment Management Group, 2011). To a certain
extent, AI can also indicate whether a certain area is rich in water
with a higher value of AI (Wan et al. 2021). In general, soil water
content is of particular importance as a driver of microbial community
since many microbial metabolism processes such as heterotrophic
respiration are affected by soil water (Moyano et al., 2012). Microbial
activity and the amount of soil microbial biomass generally increase
with soil water content to reach a maximum; but extremely high soil
water content can induce oxygen limitation and decrease soil microbial
activity (Moyano et al., 2012). Several studies indicated that soil
water content may vary with space and time, and the reason is generally
correlated closely with AI and air temperature (Delgado-Baquerizo et
al., 2013; Wang et al., 2014). Therefore, we may expect negative
correlations between the amount of soil microbial biomass and AI,
especially in dryland regions where water is often the main limiting
factor for soil living organisms (Xu et al., 2017).

A key composition of ecosystem is the soil microbes, which take part in
energy flow, nutrient cycling and ultimately ecosystem productivity
(Wardle et al., 1998). Soil microbial biomass carbon (SMBC) and nitrogen
(SMBN) are indicators of soil microbial activities. In fact, soil
microbial biomass (SMB) just accounts for a small fraction of soil
weight, and it has been suggested that the amount of SMBC in forest
soils is estimated to be less than 5\% of the total soil organic carbon
stock (Fierer et al., 2009). Nevertheless, SMB is undoubtedly the most
active nutrient pool in soils. Likewise, the ratio of SMBC to SMBN also
provides evidence and insights to understand soil nutrient cycle and
other biochemical characteristics of soil (Bargali et al., 2018; Li et
al., 2018). Moreover, the amount of SMBC and SMBN and the ratio of SMBC
to SMBN can be used as indicators of soil quality (Schloter et al.,
2003), as their high values was generally associated with energetic and
nutritious soils. It is attributed to the fact that soil microbes are
the drivers of these flows and cycles, because soil microbes have short
turnover time and are sensitive to environmental disturbance. A recent
study provides evidence that soil fungal communities showed significant
differences with the different amount of SMBC, and the amount of SMBC
was the prime factor in fungal community structure (Liu et al., 2019).
Additionally, some studies also suggest that soil condition was
correlated with the amount of SMBC (Zhou et al., 2015). Collectively,
the amount of SMB and ratio of SMBC to SMBN are robust indexes to
evaluate and understand the underground ecology.

Given different conditions between dryland regions and other terrestrial
regions, including differences in above ground productivity, edaphic
properties and nutrient cycling, general universal law in terrestrial
regions do not represent necessarily a relevant benchmark for the
fragile dryland regions. There have been some experiments and studies on
the driving factors on the amount of SMB, but the results of these
studies are often inconsistent or even completely opposite. The amount
of SMBC and SMBN is not only affected by the amount of dead primary
products and soil organic matter (Kaiser et al., 1992; Zak et al.,
2003), but also regulated by soil environmental conditions, such as soil
temperature, pH, oxygen, soil water content and nutrient availability
(Gallardo and Schlesinger, 1992; Zhou et al., 2015). However, the
relative contributions of these factors are inconsistent among the above
studies. Therefore, we still know little about the controlling factors
of SMB in dryland regions.

Since most drylands region are relatively infertile and fragile
(Reynolds et al., 2007), a better understanding of patterns and drivers
of SMBC and SMBN is essential for the sake of maintaining dryland
regions ecosystem functioning and services. We hypothesized that in the
dryland regions, (1) in global dryland regions, the higher the degree of
drought (the lower the aridity index), the lower the amount of SMBC and
SMBN, (2) the amount of SMBC and SMBN would be different among ecosystem
types, (3) the pattern of SMB in dryland regions would be affected by
climatic factors and soil factors.

\textbf{1 Materials and methods}

\textbf{1.1 Data collection}

Data in our study was collected from Web of Science and Science Direct.
We compiled the dataset by searching the literature published from
January 2000 to March 2021 with the following key words: ``dryland'' OR
``arid'' OR ``semi-arid'' AND ``microbial biomass''. This resulted in a
total of 2755 published references, after a duplicated filtering
literature retrieved from both database with EndNote references manager
software (EndNote X9). We performed a filtering through the abstract and
title of each reference, then through the full text to decide whether
the article matched our selection criteria. Data was chosen based on the
following criterion: (1) the article contained at least the amount of
SMBC or SMBN; (2) the amount of SMBC or SMBN was measured by the
fumigation extraction method, which is the most widely applied and
standardized method for the estimation of SMBC and SMBN (Vance et al.,
1987). We use only one method and ignore those based on dilution plate
court, microware irradiation, microscopy, phospholipid fatty acid,
quantitative PCR or substrate-induced respiration to avoid
methodological discrepancies; meanwhile fumigation extraction method was
most frequent method we found; (3) experiments were taken under natural
conditions (laboratory or greenhouse experiments were excluded), when
manipulation experiments were reported, only data from the ``control''
plots was used; (4) measurements were conducted in dryland ecosystems
(AI \textless{} 0.65).

Altogether, we found 86 relevant published papers, including 109
observations of SMBC and 79 observations of SMBN from 100 study sites
across global dryland regions (\textbf{Fig. 1} ). A list of articles
including climatic and soil information was presented in Appendix. All
original data were extracted from the text, tables, figures and
appendices of the publications. If data was present graphically, we used
the Web Plot Digitizer 4.2 software to digitize the numerical data.

\textbf{1.2 Data preparation}

Given that the soil was collected from different depth in the original
studies, we treated soil depth as a continuous variable when performing
statistical analysis and processed according to the following rules. At
the same location (same latitude and longitude), if the amount of SMB
came from different depths (such as 0-10, 10-20, 20-30 cm), we averaged
them and the result was used as represent 0-30cm soil layer data. In one
location, data of soil samples from the same soil layer would be
averaged. If the soil depth was less than 30cm, we assumed that the
measurements represent the top 0-30 cm soil profile.

For each selected study site, we collected the geographic location,
ecosystem type, parameters of soil (soil organic carbon\selectlanguage{english}(SOC\selectlanguage{english}), soil pH,
and soil sand fraction), SMBC and/or SMBN, if reported. Other data, such
as AI, were collected in open access databases based on their geographic
location (http://www.csi.cgiar.org). To explore the pattern, we
classified AI into different categories. According to FAO guidelines,
sites with AI \textless{} 0.65 were considered as drylands and further
divided into drought subtypes of hyper-arid class (AI \textless{} 0.05),
arid class (0.05 \selectlanguage{english}[?] AI \textless{} 0.2), semi-arid class (0.2 \selectlanguage{english}[?] AI
\textless{} 0.5), and dry sub-humid class (0.5 \selectlanguage{english}[?] AI \textless{} 0.65)
(UNEP, 1992). In our study, the values of AI ranged from 0.0868 to
0.6477. Ecosystem types are mainly divided into four categories: desert,
grassland, forest, and cropland. The classification standard is to look
for keywords in the method of the references. If those data were not
given in the references, we used the Harmonized World Soil Database V1.2
to extract such information based on the coordinates.

\textbf{1.3 Statistical analysis}

The original data of SMBC and SMBN was ln-transformed to improve the
normality of the dataset. Violin plot and column chart was used to
report the mean and standard error graphically. One-way analysis of
variance (ANOVA) followed by Duncan's post hoc tests were used to detect
differences in the amount of SMBC, SMBN, the ratio of SMBC to SMBN among
the three drought classes and four ecosystem types (a level of
\emph{p}\textless{} 0.05 was accepted as significant). Scatter plots and
linear fitting are used to report the relationship and trend between two
parameters. Pearson correlation coefficient was used to verify the
correlation between two factors (significant level: * means 0.05, **
means 0.01). Besides, Pearson correlation coefficient test, univariate
analysis of variance and one-way analysis of variance test were used IBM
SPSS Statistics 23 software. Origin 9.0 was used to generate graphs and
Arc GIS 10.7 was used to generate the map of sample sites.

\textbf{2. Results}

\textbf{2.1 SMB among different drought classes}

In our study, the average contents of SMBC and SMBN in global dryland
regions were 358.47 +- 25.45 mg kg\textsuperscript{-1} and 51.86 +- 4.59
mg kg\textsuperscript{-1}, respectively. At drought level, the amount of
SMBC and SMBN did vary among different drought classes, which both
increased from arid to semi-arid to dry sub-humid drought classes
(\textbf{Fig. 2} ). The amount of SMB in arid drought class was
significantly less than the other two classes, but there was not a
significantly difference between semi-arid drought and dry sub-humid
drought classes. The relationship between SMBC and SMBN is a crucial
parameter to determine soil stoichiometry balance, which is closely
related with the microbes' use efficiency. Our study showed that the
relationship between the amount of SMBC and SMBN was significantly
positive (\textbf{Fig.3} ).

\textbf{2.2 SMB among different ecosystem types}

In order to conduct vegetation-level analysis, we aggregated the
original data into four ecosystem types: desert, grassland, forest and
cropland. On the whole, the drought degree of the desert ecosystem was
significantly higher, because its aridity index was significantly lower
than that of the other three ecosystems (\textbf{Fig. 4} ). At ecosystem
level, the amount of SMB and the ratio of SMBC to SMBN varied among
different ecosystem types. The amount of SMBC in desert was
significantly lower than those of other ecosystem types, and the
remaining three ecosystem types were not significantly different from
each other (\textbf{Fig. 5} ). The amount of SMBN had same order as
SMBC; desert was also the lowest one, but there was a significant
difference between grassland and cropland (\textbf{Fig. 5} ). The ratio
of SMBC to SMBN showed a completely different pattern between cropland
and the rest three types. Cropland ecosystem had the highest ratio of
SMBC to SMBN (\textbf{Fig. 5} ).

\textbf{2.3} \textbf{Factors affecting the SMB}

Based on pearson correlation analysis, the amount and ratios of SMB were
controlled by a variety of factors (\textbf{Table 1} ). Mean annual
temperature, soil bulk density and soil C/N had positive relationship
with the amount of SMBC. Opposite relationships were observed for SMBN.
There was no significant difference between environmental factors and
the amount of SMB. The ratio of SMBC to SMBN had a strong and negative
correlation with soil properties as pH (\emph{p} \textless{} 0.05) and
sand fraction (\emph{p} \textless{} 0.05); whereas, soil bulk density
and soil C/N did not differ. Soil bulk density and soil C/N increased
with aridity index, while mean annual temperature, soil sand fraction
and soil pH decreased with it.

\textbf{3. Discussion}

\textbf{3.1 SMB under low vegetation cover level in dryland regions}

Our study summarized that in the dryland regions, the amount of SMBC and
SMBN were 358.47 +- 25.45 mg kg\textsuperscript{-1} and 51.86 +- 4.59 mg
kg\textsuperscript{-1} in the 0-30 cm soil profile, which were almost
half of the previous terrestrial ecosystems study (SMBC 680.4 mg
kg\textsuperscript{-1}and SMBN 105 mg kg\textsuperscript{-1}, Xu et al.
2013). It has been speculated that the discrepancy might be due to the
study regions. Dryland regions are part of land and just cover about
41\% of Earth's land surface, which include four main ecosystem types:
grassland, desert, cropland and forest. The total area of these four
ecosystem types accounts for at least 65\% of the dryland regions area;
meanwhile, forests which with high vegetation cover only account for
18\% of the dryland regions area. (Millennium Ecosystem Assessment,
2005). Desert and grassland have lower vegetation cover rate and plant
diversity compared to tropical forest (Schuldt et al., 2019; Conti et
al., 2013; Cook-Patton et al., 2011). Vegetation cover and plant
diversity influence the plant quantity directly and the quality of plant
litter and root exudates which in turn indirectly affect the soil
condition (Drenovsky et al., 2010; Xiao et al., 2017); meanwhile, the
microbial processes of carbon and nitrogen cycles were affected by soil
condition due to the differences in quality and quantity of nutrient
(Bargali et al., 1993; Kara et al., 2008).

Consistent with our hypothesis, the amount of SMBC and SMBN were
significantly different among ecosystem types in dryland regions
(\textbf{Fig. 5} ). Different ecosystems develop unique patterns of
adaptation, especially under extreme conditions such as drought. Desert
which was the main ecosystem type in drylands region is with sparse
vegetation cover, in turn the soil in desert was lacked of ample root
networks and plant litter to provide enough nutrient to soil microbes
(Yang et al., 2014; Luna et al., 2016; Gang et al., 2012). The reason
for low amount of SMBC and SMBN in the desert was likely a deficiency
available nutrient content by low degree of aboveground vegetation
cover. To some extent, the greater amount of organic matter accumulates
may indirectly affect soil microbial activity and abundance, then soil
becomes more suitable for microbial growth (Shrestha et al., 2008).

According to the plant-soil-microbes theory supported by many studies,
the response among the three parts is significant and sensitive. The
roots-dominated soil usually shows a greater chance of nutrient supply
and cycling, thus enhances the contents of SMBC and SMBN around the root
zone especially in desert soil (Jia et al., 2017). Jia et al. (2017)
found after plant restoration in an arid desert of northern China, the
vegetation cover rate increased and the greater amount of plant litter
enters the soil, which in turn governs the soil organic matter and
nutrients, consequently the content of SMBC significantly increased. In
the grassland ecosystem, grazing was one of the major and special
factors to affect the ecosystem nutrient cycles. There was no doubt that
microbes and animals, such as sheep, competed for ecosystem nutrient.
When grazing was reduced, higher organic matter inputs to soil from root
exudates and plant litter which are more conducive to microbial growth,
and then the contents of SMBC and SMBN increased (Wu et al., 2014). On
the contrary, some researchers found no evidence of a difference between
SMB and land cover, soil quality was important instead (Moghimian et
al., 2017).

\textbf{3.2 stoichiometry of soil and soil microbial biomass}

The stoichiometry of SMB from open-access papers and our study were
together summarized in the table (\textbf{Table 2} ). We indicated that
the soil C/N and SMBC/SMBN in dryland regions was 10.69 and 8.73, which
were different compared to other publications. Firstly, the data only
included research from drylands region (aridity index \textless{} 0.65)
in our study, concluding grasslands and deserts mainly (accounted for
75.4\%); while the data in other studies like Xu et al. (2013) and
Cleveland et al. (2007) were extracted from global region, including all
ecosystem types. Differences in vegetation cover and plant diversity are
the main reasons. Secondly, the sampling depth used in our study was
different from that of the studies at global scale (i.e., 0-30 cm vs.
0-10 cm). There were a lot of studies that show the microbes were
distributed differently in the vertical direction of soil, and the
content in the top layer soil was higher than that of the deep soil (Jia
et al. 2017; Xu et al., 2013).

The ratio of SMBC to SMBN was an indicator of soil stoichiometry balance
and was close related with microbial C use efficiency (CUE) and N use
efficiency (NUE), which was important in predicting efficiency of
nutrient cycling (Mooshammer et al., 2014). The definition of CUE or NUE
is a ration of nutrient allocated to growth over nutrient taken up by
microbial community composition (Manzoni et al., 2012; Sinsabaugh et
al., 2013). High ratio of soil C/N meant soil is rich in available C (N
deficiency), microbes would have high NUE and low CUE, finally lead a
low degree of SMBC/SMBN. In our study, the ratio of soil C/N was 10.69,
which waslower than other previous results, indicated that in dryland
regions, the soil was at a high substrate N sufficient condition. Thus,
it was not surprising that microbes would be adjusted to high CUE, then
lead to a high SMBC/SMBN. This inference was consistent with our
research result, the ratio of SMBC/SMBN in our study was 8.73, which was
higher than other results.

Providing additional evidence that the SMBC/SMBN was higher in dryland
regions, we also found a previous study shown that soil with high water
content usually had more available C than available N. Actually, it
would result in lower SMBC/SMBN. The study reinforced our results.
Dryland regions often were characterized by water deficiency, which is a
plausible explanation for SMBC/SMBN in dryland regions was lower than
global average. To the best of our knowledge, the mechanisms behind this
relationship are not clear, and this might be also related to
differences in microbial communities at different soil depths.

\textbf{3.3 Factors affecting SMB}

Accurately predicting SMBC and SMBN response to drought requires more
comprehensive insights into the influence of edaphic properties and
environmental conditions. In our study, we found the amount of SMBC and
SMBN varied significantly among drought types (\textbf{Fig. 2} ). Recent
study suggested that increasing drought and global change have
pronounced negative effects on the SMB in terrestrial ecosystems (Wan et
al., 2021), which was in line with our results. Thakur et al. (2015)
found that soil microbes were sensitive to soil water content, even in a
short-term drought, microbial physiology and plant diversity would
change a lot. Similarly, drought's legacy effect to shrubland lasting
for two years after the drought ended has been observed by Hinojosa et
al. (2019) in a post-fire experiment, drought mainly did have an
important impact on soil processes. The amount of SMBC increased but
SMBN decreased with drought, and in total the drought decreased the
amount of SMB (Hinojosa et al., 2019).

In our study, pH insignificantly affected the amount of SMBC and SMBN
but had a significantly negative correlation with the ratio of SMBC to
SMBN (Table 1). The soil pH has been regarded as a crucial factor for
the growth of soil microbial communities. A study found that bacteria
grow better in slightly alkaline soil, while most fungi prefer acidic
soil (Ma et al., 2016). The mainstream view is that compared to
bacteria, fungi characterized by higher growth efficiency and slower
degradation rate. Thus, the soil rich in fungi would accumulate higher
contents of SMBC and SMBC than which rich in bacteria (Six et al.,
2006). Several publications confirmed that soil pH was the main factor
regulating the microbial community structure and biomass content (Liu et
al., 2014; Cao et al., 2017). Moreover, Cao et al., 2017 showed pH was a
significant driver to regulate the size and abundance of the microbial
community and Mendes confirmed that pH had a strong relationship with
the bacterial community structure in south-eastern region of the Amazon
(Mendes et al., 2015).

Temperature played an important role in soil microbes carbon and
nitrogen cycling. Moreover, opposite responses along temperature
gradient were observed in our results, temperature had a positive
correlation with the amount of SMBC but was negative with SMBN
(Table.1). The results were not totally consistent with a prior study
that SMBC and SMBN showed positive response (3.61\% and 5.85\%
respectively) to warming (Xu et al., 2017). Water loss due to rising
temperature might inhibit microbial growth, which eventually decrease
the contents of SMBC and SMBN, especially in water-limited ecosystem
(i.e., grassland). Therefore, the influence of temperature on microbes
has both direct and indirect effects, and the final change of microbial
biomass was the result of multiple processes. In general, it was
difficult to summarize a universally suitable law about the microbes'
adaptation to temperature. The main reasons for this finding were,
firstly, the increase of temperature would enhance the activity of soil
microbes, which would promote the development of more organisms, then
increase the soil microbial biomass of the community. However,
increasing temperature can contribute to greater decomposition of SOC,
and cause the loss of soil carbon pool. SOC was the main nutrient source
for soil microbes, and the loss of SOC directly leads to the decrease of
nutrient source for microbes. Altogether, these finding emphasized the
importance of environmental factors in regulating the microbes.

\textbf{4. Conclusion}

We used the dataset to determine the amount and pattern of SMBC and SMBN
in global dryland regions. Here, we compiled 109 observations of SMBC
(0-30 cm) and 79 observations of SMBN (0-30 cm) from 100 sites across
global dryland regions with aridity index less than 0.65. The results
showed that the average amount of SMBC and SMBN in global drylands were
358.47 +- 25.45 mg kg\textsuperscript{-1} and 51.86 +- 4.59 mg
kg\textsuperscript{-1}, respectively. The amount of SMBC and SMBN did
significantly vary among different drought types and ecosystem types. A
significant positive correlation was showed between the amount of SMBC
and SMBN regardless of the level of drought. Meanwhile, the ratio of
SMBC to SMBN in global drylands region was 8.73, which is lower than the
global value (7.6). Soil sand fraction and pH had significant negative
effect on the ratio of SMBC to SMBN. To further improve our
understanding on how dryland regions might respond to climate changes,
long-term ecosystem-scale studies on soils are urgently required.
Although there are a lot number of uncertainties, this study applies a
new attempt to focus on the fragile dryland regions.

\textbf{Acknowledgments}

This study was financially supported by the National Natural Science
Foundation of China (Number: 31770501). We are grateful to Dr. Yong
Zhou, Dr. Lizhi Jia and Dr. Bertrand Guenet for their constructive
comments of the manuscript.

\textbf{Data Accessibility}

literature sources, geographic information of sample sites, aridity
index, soil properties, climate data, the amount of soil microbial
biomass carbon and nitrogen, etc: Dryad

https://doi.org/10.5061/dryad.59zw3r292

\textbf{References}

Bargali K, Manral V, Padalia K, et al., 2018. Effect of vegetation type
and season on microbial biomass carbon in Central Himalayan forest
soils, India. Catena, 171: 125-135.

Bargali S S, Singh S P, Singh R P., 1993. Patterns of weight loss and
nutrient release from decomposing leaf litter in an age series of
eucalypt plantations. Soil. Biol. Biochem, 25(12): 1731-1738.

Cao C, Zhang Y, Qian W, et al., 2017. Land-use changes influence soil
bacterial communities in a meadow grassland in Northeast China. Solid
Earth, 8(5): 1119-1129.

Cleveland, C.C., Liptzin, D., 2007. C:N:P stoichiometry in soil, is
there a `` Redfield ratio '' for the microbial biomass?.
Biogeochemistry, 85:235--252.

Conti G, Diaz S, 2013. Plant functional diversity and carbon storage--an
empirical test in semi\selectlanguage{english}-arid forest ecosystems. J. Ecol, 101(1): 18-28.

Cook-Patton S C, McArt S H, Parachnowitsch A L, et al., 2011. A direct
comparison of the consequences of plant genotypic and species diversity
on communities and ecosystem function. Ecology, 92(4): 915-923.

Dai, A.G., 2013. Increasing drought under global warming in observations
and models. Nat. Clim. Change, 3(1):52-58.

Delgado-Baquerizo, M., Maestre, F.T., et al., 2013. Decoupling of soil
nutrient cycles as a function of aridity in global drylands. Nature,
502:672-676.

Drenovsky R E, Steenwerth K L, Jackson L E, et al., 2010. Land use and
climatic factors structure regional patterns in soil microbial
communities. Global. Ecol. Biogeogr, 19(1): 27-39.

Feng S, Fu Q., 2013. Expansion of global drylands under a warming
climate. Atmos. Chem. Phys, 13(10): 081-10.

Fierer, N., Strickland, M. S., et al. (2009). Global patterns in
belowground communities. Ecol. Lett, 12:1238--1249.

Gallardo, A., Schlesinger, W.H., 1992. Carbon and Nitrogen Limitations
of Soil Microbial Biomass in Desert Ecosystems. Biogeochemistry,
18:1-17.

Gang H, Xue-yong Z, Yu-qiang L, et al., 2012. Restoration of shrub
communities elevates organic carbon in arid soils of northwestern China.
Soil. Biol. Biochem, 47: 123-132.

Gao, X.J., Giorgi, F., 2008. Increased aridity in the Mediterranean
region under greenhouse gas forcing estimated from high resolution
simulations with a regional climate model. Global Planet. Change,
62:195-209.

Hinojosa, Maria Belen, Laudicina V A , Parra A , et al, 2019. Drought
and its legacy modulate the post\selectlanguage{english}-fire recovery of soil functionality and
microbial community structure in a Mediterranean shrubland. Global
Change Biol, 25(4):1409-1427.

Huang J, Yu H, Guan X, et al., 2016. Accelerated dryland expansion under
climate change. Nat. Clim. Change, 6(2): 166.

Jia X, Li Y, Wu B, et al., 2017. Effects of plant restoration on soil
microbial biomass in an arid desert in northern China. J. Arid. Environ,
144: 192-200.

Kaiser E A, Mueller T, Joergensen R G, et al., 1992. Evaluation of
methods to estimate the soil microbial biomass and the relationship with
soil texture and organic matter. Soil Biol. Biochem, 24(7): 675-683.

Kara O, Bolat \selectlanguage{polish}İ., \selectlanguage{english}2008. The effect of different land uses on soil
microbial biomass carbon and nitrogen in Bart\selectlanguage{polish}ı\selectlanguage{english}n Province. Turk. J.
Agric. for, 32(4): 281-288.

Li Y, Chang S X, Tian L, et al., 2018. Conservation agriculture
practices increase soil microbial biomass carbon and nitrogen in
agricultural soils: a global meta-analysis. Soil Biol. Biochem, 121:
50-58.

Liu D, Wang H, An S, et al., 2019. Geographic distance and soil
microbial biomass carbon drive biogeographical distribution of fungal
communities in Chinese Loess Plateau soils. Sci. Total. Environ., 660:
1058-1069.

Liu J, Hua Z S, Chen L X, et al., 2014. Correlating microbial diversity
patterns with geochemistry in an extreme and heterogeneous environment
of mine tailings. Appl. Environ. Microbiol. 80(12): 3677-3686.

Luna L, Pastorelli R, Bastida F, et al., 2016. The combination of quarry
restoration strategies in semiarid climate induces different responses
in biochemical and microbiological soil properties. Appl. Soil Ecol.,
107: 33-47.

Manzoni, S., Taylor, P., Richter, A., Porporato, A., Agren, G.I., 2012.
Environmental and stoichiometric controls on microbial carbon-use
efficiency in soils. New Phytol. 196, 79-91.

Ma H K, Bai G Y, Sun Y, et al, 2016. Opposing effects of nitrogen and
water addition on soil bacterial and fungal communities in the Inner
Mongolia steppe: A field experiment. Appl. Soil Ecol., 108:128-135.

Mendes L W, de Lima Brossi M J, Kuramae E E, et al., 2015. Land-use
system shapes soil bacterial communities in Southeastern Amazon region.
Appl. Soil Ecol., 95: 151-160.

Millennium Ecosystem Assessment. 2005. Dryland systems. Chapter 2 in:
Millennium ecosystem assessment. Washington, DC, World Resources
Institute.

Moghimian N, Hosseini S M, Kooch Y, et al., 2017. Impacts of changes in
land use/cover on soil microbial and enzyme activities. Catena, 157:
407-414.

Mooshammer, Wanek, Hammerle, et al., 2014 Adjustment of microbial
nitrogen use efficiency to carbon:nitrogen imbalances regulates soil
nitrogen cycling. Nat. commun., 5.

Moyano F E, Vasilyeva N A, Bouckaert L, et al., 2012. The moisture
response of soil heterotrophic respiration: interaction with soil
properties. Biogeosciences, 9(3): 1173-1182.

Schloter, M., Dilly, O., Munch, J.C., 2003. Indicators for evaluating
soil quality. Agr. Ecosyst. Environ., 98:255-262.

Ran X, Shen Y, Marschner P. 2017.Soil water content during and after
plant growth influence nutrient availability and microbial biomass. J.
Soil Sci. Plant Nut., 17(3):702-715.

Reynolds, J.F., Stafford Smith, D.M., et al, 2007. Global
desertification: Building a science for dryland development. Science.
316:847-851.

Schuldt A, Ebeling A, Kunz M, et al., 2019. Multiple plant diversity
components drive consumer communities across ecosystems. Nat commun,
10(1): 1460.

Shrestha, G., Stahl, P.D., 2008. Carbon accumulation and storage in
semi-arid sagebrush steppe: effects of long-term grazing exclusion.
Agric. Ecosyst. Environ. 125:173--181.

Sinsabaugh, R.L., 2010. Phenol oxidase, peroxidase and organic matter
dynamics of soil. Soil Biol. Biochem., 42:391-404

Six J, Frey S D, Thiet R K, et al., 2006. Bacterial and fungal
contributions to carbon sequestration in agroecosystems. Soil Sci. Soc.
Am. J., 70(2): 555-569.

Thakur M P, Milcu A, Manning P, et al., 2015. Plant diversity drives
soil microbial biomass carbon in grasslands irrespective of global
environmental change factors. Global Change Biol., 21(11): 4076-4085.

UN Environment Management Group. 2011. Global drylands: a UN system-wide
response. United Nations.

UNEP. 1992. World atlas of desertification. London, UK, and Baltimore,
USA, United Nations Environment Programme (UNEP).

Vance E D, Brookes P C, Jenkinson D S., 1987. An extraction method for
measuring soil microbial biomass C. Soil Biol. Biochem., 19(6): 703-707.

Wang C, Wang X, Liu D, et al., 2014. Aridity threshold in controlling
ecosystem nitrogen cycling in arid and semi-arid grasslands. Nat commun,
5: 4799.

Wan X, Chen X, Huang Z, et al., 2021. Global soil microbial biomass
decreases with aridity and land-use intensification. Global Ecol.
Biogeogr.,30(5):1056-1069.

Wardle D A., 1998. Controls of temporal variability of the soil
microbial biomass: a global-scale synthesis. Soil Biol. Biochem.,
30(13): 1627-1637.

Wu X, Li Z, Fu B, et al., 2014. Restoration of ecosystem carbon and
nitrogen storage and microbial biomass after grazing exclusion in
semi-arid grasslands of Inner Mongolia. Ecol. Eng., 73: 395-403.

Xiao C, Zhou Y, Su J, et al., 2017. Effects of plant functional group
loss on soil microbial community and litter decomposition in a steppe
vegetation. Front. Plant Sci., 8: 2040.

Xu W, Yuan W., 2017. Responses of microbial biomass carbon and nitrogen
to experimental warming: A meta-analysis. Soil Biol. Biochem., 115:
265-274.

Xu X, Thornton P E, Post W M., 2013. A global analysis of soil microbial
biomass carbon, nitrogen and phosphorus in terrestrial ecosystems.
Global Ecol. Biogeogr., 22(6): 737-749.

Yang H, Li X, Wang Z, et al. 2014. Carbon sequestration capacity of
shifting sand dune after establishing new vegetation in the Tengger
Desert, northern China. Sci. Total. Environ., 478: 1-11.

Zak D R, Holmes W E, White D C, et al., 2003. Plant diversity, soil
microbial communities, and ecosystem function: are there any links?.
Ecology, 84(8): 2042-2050.

Zhou Y, Clark M, Su J, et al., 2015. Litter decomposition and soil
microbial community composition in three Korean pine (Pinus koraiensis)
forests along an altitudinal gradient. Plant. soil, 386(1-2): 171-183.

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