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\begin{document}

\title{Particular microbial taxa rather than total microbial diversity best
predict the vertical profile variation in soil multifunctionality in
desert ecosystems}



\author[1]{Honglei Wang}%
\author[1]{Lianyan Bu}%
\author[1]{Fangqin Song}%
\author[1]{Jing Tian}%
\author[1]{Gehong Wei}%
\affil[1]{Northwest A\&F University}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\selectlanguage{english}
\begin{abstract}
In desert ecosystems, the desertification process is characterized by
increasing attenuation of plant productivity and deterioration of soil
habitats, leading to enhanced environmental stress gradients for soil
microbiomes. Despite the significance of microbial communities for
multifunctionality in terrestrial ecosystems, the feedback dynamics of
microbiomes and their contributions to maintaining subsurface soil
multifunctionality as desertification progresses have yet to be
evaluated. Here, we used three sites with different desertification
stages and investigated the variation trends of microbiomes in soil
profiles (0-100 cm) and their contributions to regulating
multifunctionality. We first confirmed that multifunctionality did not
exhibit a significant difference between superficial soils (0-20 cm) and
deep soils (20-100 cm) and slightly decreased as soil depth increased
throughout the entire profile. Desertification progression drove
distinct variation trends of microbiomes in vertical soil profiles. Soil
bacterial communities received on average more positive and progressive
feedback from desertification development than fungal and archaeal
communities, characterized by significant variation in bacterial alpha-
and beta-diversity and slight variation in fungal and archaeal alpha-
and beta-diversity. The most abundant phyla in the microbiomes did not
vary between the superficial and deep soils at any desertification
stage. Significant declines in microbial clades within Acidobacteria are
an important feature as desertification proceeds. Particular microbial
taxa rather than total microbial diversity best predict and explain the
vertical profile variation in soil multifunctionality in desert
ecosystems. Our results highlight the significance of microbial
community composition in subsurface soils for regulating
multifunctionality in desert ecosystems.%
\end{abstract}%



\sloppy


\section*{1. INTRODUCTION}

{\label{introduction}}

Desert ecosystems, the arid areas (approximately 45.6 million
km\textsuperscript{2}, covering at least 35\% of Earth's land surface)
most environmental sensitive to changes in climate and human
disturbances, are suffering from increasing land degradation and
biological diversity loss (Bastin et al., 2017; Schimel, 2010; Wang et
al., 2019a). In arid and semiarid regions, fragile steppe grassland
ecosystems are especially prone to desertification, which results in the
destruction of plant~communities, soil physical texture, and nutrient
losses. Restoring and rehabilitating desertification grassland has been
a serious issues in ecology (D'Odorico et al., 2019). Soil
microorganisms have a predominant potential to reveal and regulate the
changes in soil ecosystem functions and services, so characterizing the
microbial successional patterns and their interactions with plants and
soils is essential for increasing our understanding of the mechanisms of
ecological restoring and rehabilitating, improving our capacity to
predict the responses of ecosystems to human disturbance, and optimizing
the design of large-scale restoration projects.

Soil multifunctionality has been widely used to quantify and compare the
ability of various ecosystems to support overall functionality (Manning
et al., 2018). Numerous studies have widely emphasized the significance
of multifunctionality in superficial soils (top \textasciitilde{}20 cm)
due to their predominant ecological importance (Delgado-Baquerizo et
al., 2017; Delgado-Baquerizo et al., 2016; Wagg et al., 2014; Wagg et
al., 2019; Zheng et al., 2019), where the density, activity, and
diversity of microorganisms are higher than those in deep soils (i.e.,
below 20 cm) (Fierer et al., 2003). However, due to the large bulk of
soil profiles, deep soils may accumulate abundant soil nutrients,
leading to an enhanced ability to provide multifunctionality during
ecosystem development. For example, compared to superficial soils, deep
soils in typical forest and grassland ecosystems can store over
two-thirds the organic carbon and nearly equal amounts of phosphorus due
to the long-term input from plant roots and soil leaching (Balesdent et
al., 2018; Upton et al., 2020; Zhou et al., 2018). Indeed, increasing
evidence has confirmed that a large percentage of soil
multifunctionality is hidden in deep soils (Jiao et al., 2018; Upton et
al., 2020). Intriguingly, in desert ecosystems, the desertification
process can result in a decrease in the water-holding capacity of
superficial soils and a steady increase in the soil leaching capacity
(D'Odorico et al., 2013; D'Odorico et al., 2019; Neilson et al., 2017),
leading to the enhanced downward elemental (i.e., carbon (C), nitrogen
(N), and phosphorus (P)) transfer responsible for the persistent
accumulation of nutrients in deep soils. Thus, it is expected that deep
soil multifunctionality in desert ecosystems plays increasingly
important roles in regulating and buffering overall ecological service
functions.

Soil microbial diversity and community composition drive and determine
soil multifunctionality in various ecosystems (Delgado-Baquerizo et al.,
2016; Wagg et al., 2014; Zheng et al., 2019). Different influences of
biodiversity and community composition on soil multifunctionality have
been investigated (Delgado-Baquerizo et al., 2017; Wagg et al., 2014).
For example, the bacterial and fungal diversity in superficial soils
(top \textasciitilde{}20 cm) positively influenced multifunctionality
(Wagg et al., 2019), especially in arid ecosystems (Delgado-Baquerizo et
al., 2016), while particular taxa of bacteria and fungi but not their
total richness and abundance determined the resistance of superficial
soil multifunctionality in arid ecosystems (Delgado-Baquerizo et al.,
2017). Furthermore, although such vast soil microbes in superficial
soils may play a central role in driving nutrient turnover with
supportive effects on soil multifunctionality, microbial diversity and
particular microbial attributes in deep soils share indispensable
ecological drivers (Delgado-Baquerizo et al., 2019; Jiao et al., 2018).
Indeed, deep soil contains a significant portion (35\% \textasciitilde{}
50\%) of total microbial diversity and biomass, suggesting its
non-negligible ecological functions (Eilers et al., 2012). Increasing
evidence, based mostly on results from ecosystems with typical
development (reforested ecosystems) and grassland ecosystems with high
rainfall, suggests that particular microbial attributes (i.e.,
individual species, particular communities and their cooperation) rather
than alpha-diversity levels (i.e., total abundance, species richness,
and the Shannon index) may play more important roles in driving deep
soil nutrient (i.e., C, N, and P) cycling with direct feedback effects
on soil multifunctionality (Jiao et al., 2018; Upton et al., 2020), as
particular microbial attributes compose significant regulators of
microbial growth and interactions and predominant functional attributes
(e.g., soil C cycling or N mineralization) (Delgado-Baquerizo et al.,
2017). In contrast to the above ecosystems with typical development and
less drought stress, ecosystems undergoing desertification display
enhanced environmental stress gradients (i.e., decreasing plant
productivity and soil nutrients and increasing soil leaching), while the
dynamics of soil microbiomes and their contributions to regulating deep
soil multifunctionality remain largely unexplored, particularly the
contributions of particular microbial attributes.

Herein, we assess the importance of vertical soil microbiomes and their
contributions to multifunctionality during the desertification process
in desert ecosystems. We used three sites in different typical
desertification stages (potential, moderate, and severe desertification)
that represent the process of desertification. The multifunctionality
and diversity of soil bacteria, fungi, and archaea were investigated at
soil depths of 0-100 cm. We tested the hypotheses that (1)
multifunctionality in deep soils (20-100 cm) would, at least partially,
be equally~as important~as that of superficial soils (0-20 cm) in the
desert ecosystem and (2) the combined effects of the particular species
richness of soil microbiomes would better predict multifunctionality in
desert ecosystems, especially in deeper soil profiles.

\section*{2. METHODS}

{\label{methods}}

\subsection*{2.1 Study site}

{\label{study-site}}

This study was conducted in the Mu Us Desert, south of the Ordos Plateau
of China, with an area of approximately 4 \selectlanguage{ngerman}×
10\textsuperscript{4}km\textsuperscript{2} (N38°55', E 109°53'). The
average altitude is between 1100 and 1300 m. The climate is semi-arid
(rainfall: 440 mm/year), and the mean annual temperature is 7.7°C. The
soil is mainly composed of sand and loessal soil. The vegetation is
mainly composed of xerophytes such as \emph{Artemisia~desertorum} ,
\emph{Hedysarum laeve} ,\emph{Psammochloa~villosa} , and \emph{Hedysarum
scoparium} .

Based on standardized vegetation coverage criteria (Guo-dong, 2004),
sites at three different desertification stages were selected as
sampling sites using a Global Positioning System (GPS) unit, including
potential desertification (PD), moderate desertification (MD), and
severe desertification (SD) (Table S1). In July 2018, we built ten
replicate plots (20 m × 20 m) for PD and MD due to the high variability
of vegetation cover and edatope, respectively. We built five replicate
plots (20 m × 20 m) for the SD stage due to the low and indistinctive
vegetation cover and edatope. These 25 plots were randomly selected at
each site, with an interval of at least 200 m. Nine subsamples were
obtained by \emph{S} -type sampling and mixed into one sample from each
soil layer (0 - 10, 10 - 20, 20 - 30, 30 - 60, and 60 - 100 cm) using a
soil auger with a diameter of 5 cm. Each soil sample (125 samples in
total) was fully mixed and divided into two parts. One part was stored
at -80°C until DNA extraction, and the other part was air dried for
physicochemical analysis.

\subsection*{2.2 Edaphic variables and functional
genes}

{\label{edaphic-variables-and-functional-genes}}

Soil physicochemical analysis of organic carbon (OC), total nitrogen
(TN), nitrate (NO\textsubscript{3}\selectlanguage{english}--N), ammonium
(NH\textsubscript{4}\textsuperscript{+}-N), total phosphorus (TP),
available phosphorus (AP), pH, electrical conductivity (EC), bulk
density (BD) and soil moisture was conducted by using standard methods
described elsewhere (Bao, 2000; Lozano et al., 2014; Wang et al., 2017).
Microbial DNA was extracted from 0.6\textasciitilde{}1.0 g of each
desert soil (125 samples) by using the MP Fast DNA Spin Kit for Soil (MP
Biomedicals, Santa Ana, CA, USA) according to the manufacturer's
protocol. Total Ca\textsuperscript{2+} was measured by flame atomic
absorption spectrophotometry (AAS), and Zn\textsuperscript{2+} was
measured sequentially by graphite furnace atomic AAS on a Hitachi Z\selectlanguage{english}-2000
(Hitachi, Tokyo, Japan).

The abundance of functional genes associated with key microbial
processes was quantified to assess multifunctionality. The abundances of
functional genes associated with the C fixation process
(\emph{CBBL}gene), N fixation process (\emph{nifH} gene), P dissolution
process (\emph{phoD} gene), N mineralization process (\emph{apr} and
\emph{chiA}gene\emph{s} ), NH\textsubscript{4}\textsuperscript{+}-N
transformation process (\emph{AOA} and \emph{AOB} genes),
NH\textsubscript{4}\textsuperscript{+}-N loss process
(\emph{anammox}gene), and NO\textsubscript{3}\selectlanguage{english}\textsuperscript{-}-N loss
process (denitrification, including \emph{nirK} , \emph{nirS} ,
\emph{qnorB} , and\emph{nosZ} genes) were quantified by a real-time PCR
system (QuantStudio 6 Flex, Thermo Fisher, Singapore City, Singapore).
These genes were described elsewhere (Levy-Booth et al., 2014; Li et
al., 2018; Wang et al., 2017). Detailed information on the gene primers
is given in Table S2. Reaction mixtures (20 \selectlanguage{greek}μ\selectlanguage{english}L) contained 10 \selectlanguage{greek}μ\selectlanguage{english}L of SYBR\selectlanguage{ngerman}®
Premix ExTaq\selectlanguage{english} (Dining), 0.5 \selectlanguage{greek}μ\selectlanguage{english}L of DNA, 0.5 \selectlanguage{greek}μ\selectlanguage{english}L of primers (forward and
reverse) (20 \selectlanguage{greek}μ\selectlanguage{english}M), and 8.5 \selectlanguage{greek}μ\selectlanguage{english}L of sterile distilled water. At the same
time, we tested the amplification specificity by constructing a
dissolution curve.

Amplicon libraries were constructed according to the dual indexing
strategy. The V4-V5 hypervariable regions were targeted using primer
pair 515F (GTGCCAGCMGCCGCGGTAA)/907R (CCGTCAATTCCTTTGAGTTT) for the
bacterial 16S rRNA gene and primer pair Arch519F (CAGCCGCCGCGGT
AA)/Arch915R (GTGCTCCCCCGCCAATTCCT) for the archaeal 6S rRNA gene. The
fungal ITS1 gene was amplified by the primer pair ITS5-1737F
(GGAAGTAAAAGTCGTAACAAGG)/ITS2-2043R (GCTGCGTTCTTCATCGATGC). All the
samples were sequenced on the Illumina MiSeq (300-bp paired-end reads)
platform (Illumina Inc., San Diego, USA) at Personal Biotechnology Co.,
Ltd. (Shanghai, China). As a~standard~data~analysis

method, the acquired sequences were filtered for quality, and any
chimeric sequences were removed with the Quantitative Insights into
Microbial Ecology (QIIME, v 1.8.0, http://qiime.org/) tool. These
original sequences were filtered for quality and assigned to operational
taxonomic units (OTUs) (DeSantis et al., 2006). The OTUs with fewer than
two sequences were first removed, and then the OTUs were classified
within the SILVA database (release 132, http://www.arb-silva.de) for
bacteria and archaea and the UNITE database (release 7.2,
http://unite.ut.ee/index.php) for fungi. The soil microbiome dataset has
been deposited in the NCBI Sequence Read Archive under accession number
PRJNA541211.

\subsection*{2.3 Assessing
multifunctionality}

{\label{assessing-multifunctionality}}

Multifunctionality has been broadly defined as the simultaneous
provisioning of multiple functions and services (Manning et al., 2018).
These processes involve, among others, soil nutrient turnover (i.e., OC
and TN contents, N and P availability, and N mineralization and loss)
and primary production (i.e., net primary productivity) (Manning et al.,
2018; Wagg et al., 2019). Compared to superficial soils, deep soils are
mainly anaerobic and low-temperature environments. Although maximal
nutrient turnover rates can be measured using indoor culture
experiments, it may be largely inappropriate to adopt indoor culture
experiments to measure nutrient turnover rates in deep soils. The
abundance of functional genes has been identified as the predominant
variable to predict potential nutrient turnover rates (Attard et al.,
2011; Graham et al., 2014; Petersen et al., 2012; Wang et al., 2017).
Thus, to summarize the changes in the overall functioning of deep soils,
the abundance of functional genes affiliated with the nutrient turnover
process was used to indirectly characterize their potential functioning
capacity (de Vries et al., 2018; Graham et al., 2014; Li et al., 2018;
Zhi et al., 2015). In desert ecosystems, the desertification process is
characterized by the long-term deterioration of soil multifunctionality
and appearance of environmental stress gradients for soil microorganisms
(Fierer, 2017; Ravi et al., 2010). Thus, functional gene abundance is
likely to be more accurate than other metrics as a long-term proxy and
predictor of the in-site functioning capacity in deep soils (Li et al.,
2018; Wang et al., 2019b). Based on well-known methods, the abundance of
functional genes associated with functional processes (i.e., C, N and P
transformation) was organized into a multifunctionality index to obtain
and summarize a multidimensional multifunctionality index in deep soils.
In total, we quantified multifunctionality using related variables as
follows: C functions (\emph{CBBL} ), N functions (TN,
NH\textsubscript{4}\textsuperscript{+}-N,
NO\textsubscript{3}\textsuperscript{-}-N, \emph{nifH} , \emph{apr}
,\emph{chiA,} \emph{AOA} , \emph{AOB,} \emph{anammox} , \emph{nirK}
,\emph{nirS} , \emph{qnorB} , and \emph{nosZ} ), P functions (TP, AP,
and\emph{phoD} ), Ca\textsuperscript{2+} and Zn\textsuperscript{2+}.

There are several approaches to quantify multifunctionality (i.e.,
single functions, averaging, and the threshold approach) (Manning et
al., 2018). Because each approach possesses different strengths and
weaknesses, we adopted two distinct approaches (single functions and the
averaging approach) to quantify and assess multifunctionality. We
normalized and standardized each nutrient cycling variable and
functional gene abundance using Z-score transformation (Manning et al.,
2018). These standardized ecosystem functions were then averaged to
obtain an overall multifunctionality index and single-function index
(i.e., C process, N process, and P process). This index is broadly
adopted in multifunctionality research and provides a straightforward
measure of the ability of different communities to sustain
multifunctionality (Manning et al., 2018; Wagg et al., 2019). In
addition, functional gene abundances for
NH\textsubscript{4}\textsuperscript{+}-N and
NO\textsubscript{3}\textsuperscript{-}-N loss were inverted, as higher
abundances of these genes are undesired functions (i.e., a lower
abundance of N loss indicates high N retention and better ecosystem
functioning, while a higher abundance indicates dysfunction and higher N
loss).

\subsection*{2.4 Data analysis}

{\label{data-analysis}}

Z-score values of ecosystem functions and related variables were
calculated using SPSS (IBM SPSS Statistics 25.0). The most abundant
phyla and alpha-diversity indexes (sobs, ACE, Shannon, and phylogenetic
diversity) of soil bacteria, archaea, and fungi were calculated in the R
environment (v3.5.3; http://www.r-project.org/) using MOTHUR R package
(https://mothur.org/wiki/calculators/), and their variations with
increasing soil depth were determined using linear least-squares
regression models (OriginLab Corporation, One Roundhouse Plaza, Suite
303, Northampton, MA 01060, United States). The beta-diversity of the
bacteria, archaea, and fungi was quantified by using two axes from a
nonmetric multidimensional scaling (NMDS) analysis of Bray-Curtis
dissimilarities within the OTU community matrix (Daum \& Schenk, 1997).
Similarity values among the sites were examined via the ANOSIM and
ADONIS test (\emph{P} \textless{} 0.001). The vertical spatial decay
relationships were calculated as the linear least-squares regression
relationships between soil depth and microbial community similarity
(Jiao et al., 2018).

Classification random forest (RF) analysis was adopted to identify the
importance of each predictor of soil multifunctionality (Wagg et al.,
2014). In these RF models, soil variables (i.e., depth, pH, EC, and BD)
and the most abundant phyla and alpha-diversity indexes of bacteria,
archaea, and fungi served as predictors of multifunctionality. These
analyses were conducted using the randomForest package in the R
environment (Delgado-Baquerizo et al., 2016). The significance of the
importance of each predictor of multifunctionality was identified by
using the rfPermute package for R
(https://cran.r-project.org/web/packages/rfPermute/index.html).
Structural equation modelling (SEM) was performed to determine the
direct and indirect effects of soil variables (i.e., depth, pH, EC, and
BD) and the most abundant phyla and alpha-diversity of bacteria,
archaea, and fungi on multifunctionality. We used the chi-square
(\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}) test and root MSE of approximation (RMSEA) to
assess SEM fit. The model has a good fit when
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}/\emph{df} (degrees of freedom) is low ([?] 2)
and\emph{P} is high (\textgreater{} 0.05) and when RMSEA is low ([?] 0.05)
and \emph{P} is high (\textgreater{} 0.05) (Schermelleh-Engel et al.,
2003). Furthermore, we evaluated the fit of the models using the
Bollen-Stine bootstrap test. Each SEM was fit using AMOS 20.0
(International Business Machines Corporation, Armonk, NY, United
States).

\section*{3. RESULTS}

{\label{results}}

\subsection*{3.1 Variation in
multifunctionality}

{\label{variation-in-multifunctionality}}

We first explored the vertical variation in multifunctionality in desert
ecosystems. Overall, the multifunctionality index did not exhibit a
significant difference between whole superficial soils (0-20 cm) and
deep soils (20-100 cm) (\emph{p} \textgreater{} 0.05) and slightly
decreased as soil depth increased through the soil profiles (Fig. 1 a,
b). We also found that the multifunctionality index significantly
decreased along the desertification gradient (Fig. 1 c) but did not show
different changes with depth between the superficial and deep soils
(Fig. 1 d).

Three individual functional indexes (C, N, and P processes) were
evaluated (Figure S1). Overall, the C and N processes were higher at the
potential desertification (PD) sites but did not show different changes
between the moderate desertification (MD) and severe desertification
(SD) sites. However, the P process did not differ between the whole
superficial and deep layers along the desertification gradient. The C
process did not exhibit a significant difference between superficial
soil and deep soil at the MD and SD sites. The N process differed
between superficial soil and deep soil at only the SD sites. The C and N
processes typically decreased as the soil depth increased throughout the
entire profile.

\subsection*{3.2 Variation in soil
microbiomes}

{\label{variation-in-soil-microbiomes}}

We acquired a total of 5490761, 7562145, and 5357782 high-quality
bacterial, fungal, and archaeal sequences (from the 125 samples), which
were classified into 4890, 7723, and 3246

operational taxonomic units (OTUs), respectively. Bacteria were mainly
composed of the phyla Actinobacteria (40.06\%), Proteobacteria
(27.23\%), Chloroflexi (8.26\%), and Acidobacteria (7.89\%). Fungi were
mainly composed of the phyla Ascomycota (69.70\%), Basidiomycota
(9.85\%), and Zygomycota (5.39\%). Archaea were mainly composed of the
phyla Thaumarchaeota (81.34\%) and Euryarchaeota (15.27\%). Overall,
Acidobacteria, Chloroflexi, and Thaumarchaeota typically increased,
while Actinobacteria, Proteobacteria, and Euryarchaeota significantly
decreased during the process of desertification (Figure S2).

Regarding the soil profiles, distinct variation trends of the
microbiomes were observed along the desertification gradient. First,
Ascomycota, Basidiomycota, Euryarchaeota and Thaumarchaeota did not
differ between the superficial and deep layers at any desertification
stage. In contrast to Proteobacteria, Acidobacteria, Actinobacteria and
Chloroflexi in the superficial soils were much richer than those in the
deep soils (Figure S3). Second, Acidobacteria, Actinobacteria,
Chloroflexi, and Thaumarchaeota significantly decreased and
Proteobacteria significantly increased as soil depth increased (Figure
S4). The most abundant phyla of bacteria, fungi, and archaea showed
distinct variation trends as soil depth increased along the
desertification gradient (Figure S5). The significance of the primary
phyla composing the microbiomes was confirmed by a Kruskal-Wallis H test
and least-squares linear regression analysis.

The alpha-diversity levels of the soil microbiomes were all higher at
the PD and MD sites than at the SD sites, except for the Shannon index
for archaea (Fig. 2). As soil depth increased, bacterial, fungal and
archaeal diversity exhibited different fluctuating trends in the desert
ecosystems. The Shannon and phylogenetic diversity indexes of bacterial
diversity significantly decreased as soil depth increased, and the
phylogenetic diversity index of fungi significantly decreased, but the
Shannon diversity index of archaea significantly increased as soil depth
increased. Vertical variation tendencies were determined by
least-squares linear regression models. In addition, distinct variation
tendencies for alpha diversity were detected during the desertification
process (Figure S6). The Shannon index of bacteria at the SD sites and
the phylogenetic diversity index of bacteria did not exhibit different
changes between superficial and deep soils. The alpha diversity of fungi
in superficial and deep soils did not exhibit different changes, except
at the PD sites. The alpha diversity of archaea in superficial and deep
soils did not exhibit different changes, except for the Shannon index at
the MD sites.

NMDS analysis suggested that soil microbiomes at different
desertification~sites formed distinct clusters in ordination space (Fig.
3). The significant spatial variations in species composition were
further confirmed by ANOSIM and ADONIS tests (Table S3). These
variations were the highest for bacteria, followed by fungi and archaea,
suggesting that the bacterial communities were more likely to be
reshaped by desertification. We observed different changes in
microbiomes between superficial and deep soils (Fig. 3 and Table S4).
These changes were larger for bacteria and archaea than for fungi,
suggesting that the bacterial and archaeal communities had higher
feedbacks with soil depths. The significant variations in beta diversity
among different microbiomes were evaluated and identified (Fig. 3).
Fungi exhibited the highest beta diversity, suggesting a higher
community dispersion level. The vertical spatial variation (VDR) of
different microbiomes was compared among the desertification~sites. We
further found that the VDR slopes of the soil microbiomes at the PD
sites were steeper than those at the MD and SD sites. The bacterial and
archaeal VDR slopes were significantly steeper than the fungal VDR
slopes, suggesting that desertification markedly enhanced the vertical
variation in bacterial and archaeal communities but had~less~effect~on
the variation in fungal communities.

\subsection*{3.3 Links between the microbial community and
multifunctionality}

{\label{links-between-the-microbial-community-and-multifunctionality}}

The relationships between the microbial community and multifunctionality
in desert ecosystems were explored (Figs. 4 and 5). The dominant
bacterial and archaeal phyla were significantly (\emph{P} \textless{}
0.01) related to multifunctionality, but the dominant fungal phyla did
not exhibit different relationships (\emph{P} \textgreater{} 0.05) (Fig.
4). The soil microbial diversity of bacteria and fungi was positively
related to multifunctionality, while archaeal diversity showed the
reverse pattern (Fig. 5). Further analyses provided evidence that
dominant species (Acidobacteria and Chloroflexi) and the fungal
phylogenetic diversity index had significantly~stronger and more
positive correlations with multifunctionality than did microbial
diversity (Shannon and phylogenetic diversity indexes). Furthermore, we
found significantly stronger and more positive correlations between
microbial diversity and individual functions (i.e., C, N, and P
processes), as well as between this diversity and combinations of
functions (i.e., C and N, N and P, C and P, and C, N, and P processes)
(Table S5). In particular, Acidobacteria, Chloroflexi and fungal
phylogenetic diversity were positively and strongly related to the
individual functions and combinations of functions.

\subsection*{3.4 Accounting for soil multifunctionality drivers in desert
ecosystems}

{\label{accounting-for-soil-multifunctionality-drivers-in-desert-ecosystems}}

Random forest modelling was adopted to determine and compare the most
important predictors of multifunctionality in desert ecosystems. We
found that Acidobacteria and fungal phylogenetic diversity were more
important than the other multifunctionality predictors (Fig. 6 a). The
predominant predictors of multifunctionality differed between
superficial and deep soils (Fig. 6 b, c). While the fungal phylogenetic
diversity index best predicted multifunctionality in superficial soils,
Acidobacteria and Thaumarchaeota were instead pivotal in deep soils.
Further, the microbial species index was the predominant predictor of
multifunctionality (Fig. 6 d).

Our structural equation modelling (SEM) explained 65.0\% of the variance
in multifunctionality at the desertification sites (Fig. 7). The
microbial species index appeared to have a distinctly stronger total
positive effect than the biodiversity index on multifunctionality (Fig.
7 and Figure S7). Depth and soil electrical conductivity (EC) had the
strongest total positive effects on multifunctionality, as indicated by
the standardized total effects from SEM.

\section*{4. DISCUSSION}

{\label{discussion}}

In this study, we discovered that distinct variation trends of soil
microbiomes during desertification regulated the changes in
multifunctionality in vertical soil profiles. Notably, particular
microbial taxa rather than microbial diversity better predicted the
vertical profile variation in multifunctionality in desert ecosystems.
Our results highlight the significance of deep soil microbiomes for
buffering and regulating the multifunctionality of desert ecosystems.

\subsection*{4.1 Variation in multifunctionality in desert
ecosystems}

{\label{variation-in-multifunctionality-in-desert-ecosystems}}

Deep soil multifunctionality was as important~as superficial soil
multifunctionality in the desert ecosystems (Fig. 1). The results of
this study confirmed our first hypothesis. Desertification can lead to
the loss of surface soil C, N and P and fine soil particles and
decreases in the water-holding capacity and vegetation cover (D'Odorico
et al., 2013; Ravi et al., 2010; Ward et al., 2018). Soil pores and
water infiltration in superficial soil exhibited continuous increases
due to the gradual loss of fine soil particles during desertification
(Allington \& Valone, 2010; D'Odorico et al., 2007), leading to enhanced
nutrient accumulation in deep soils from plant litter and superficial
soil nutrients. Furthermore, microbiomes play a vital role in regulating
soil multifunctionality by supporting functional processes (i.e., soil
nutrient cycling, litter decomposition, and N mineralization), which
allow the transfer of materials and carbon energy between above- and
belowground communities (Falkowski et al., 2008; Tedersoo et al., 2014).
Thus, the simultaneous changes in and coupling of soil physical
properties and vegetation and microbiomes jointly contributed to the
equivalent multifunctionalities of superficial and deep soils (D'Odorico
et al., 2019; Jiao et al., 2018; Ravi et al., 2010). In addition, soil
nutrients are mainly derived from the decomposition of plant litter and
root in both undisturbed and restored ecosystems (Barber et al., 2017;
Lozano et al., 2014). In this study, the plant litter and root biomass
gradually decreased during desertification (Figure S8), leading to
reduced soil nutrient accumulation from litter and root decomposition,
which was largely responsible for the significant decrease in
multifunctionality in the vertical soil profiles along the
desertification gradient (Fig. 1c).

\subsection*{4.2 Vertical variation in soil microbiomes in desert
ecosystems}

{\label{vertical-variation-in-soil-microbiomes-in-desert-ecosystems}}

Our results showed that the desertification process drove distinct
variation trends of the microbiomes in the vertical soil profiles.
Microbial survival and growth may be severely limited by continuous
abiotic stressors (i.e., limited bioavailability of water and C
substrates), frequent disturbances (i.e., drying-rewetting events), and
heterogeneous distributions of substrates across soil profiles (Fierer,
2017). In desert ecosystems, all these abiotic stressors often
synchronously appear during desertification, which is characterized by
decreasing plant biomass and fluctuating soil nutrients and soil
structure, leading to enhanced environmental stress gradients that
account for the distinct variation in microbiomes in vertical soil
profiles (D'Odorico et al., 2013; D'Odorico et al., 2019; Neilson et
al., 2017). Furthermore, the phyla Actinobacteria, Proteobacteria,
Chloroflexi, Acidobacteria, Ascomycota, Basidiomycota, Thaumarchaeota
and Euryarchaeota were the most abundant microbial taxa with distinct
responses in the desert ecosystems. These phylum-level profiles were
similar to those in other soils and environments (Jiao et al., 2018; Li
et al., 2014; Tedersoo et al., 2014; Upton et al., 2020). We further
found that bacteria are on average more resilient in the face of
disturbances and perturbations because of their relatively fast
intrinsic growth rates (Wardle, 2013), suggesting that they are more
sensitive to the environmental filtering driven by desertification. As
soil depth increased, the bacterial phyla Acidobacteria, Actinobacteria,
and Chloroflexi typically declined, while Proteobacteria significantly
increased (Figs. S4 and S5). The majority of Acidobacteria,
Actinobacteria, Proteobacteria, and Chloroflexi have been suggested to
be closely associated with organic substrates (Goldfarb et al., 2011).
Except for the response of Proteobacteria, these observed changes were
further confirmed by the findings of other studies (Jiao et al., 2018;
Li et al., 2014), suggesting that soil pH and nutrient bioavailability
are more likely to be the reasons for the decreased relative abundance
in soil profiles. In this study, Alpha-, Delta-, and Gammaproteobacteria
belonging to the class Proteobacteria were examined (Figure S2). Alpha-
and Deltaproteobacteria have been suggested to be negatively associated
with increased organic substrates, while Gammaproteobacteria are
positively associated with increased organic substrates (Goldfarb et
al., 2011). This contrasting pattern could reflect divergent ecological
niches and microbial synergism, which are more likely to be the reasons
for the enhanced abundance of Proteobacteria in the vertical soil
profiles, as reported by Li et al. (2014) in farmland ecosystems. In
addition, the most abundant fungal phyla (Ascomycota and Basidiomycota)
were less mobile in vertical soil profiles than bacterial and archaeal
phyla in the desert ecosystems, similar to previous findings (Tedersoo
et al., 2014), suggesting that the richness of fungi and functional
groups is not associated with plant productivity and that the plant-soil
feedback loop does not typically reshape fungal diversity in different
ecosystems. The archaeal phyla Thaumarchaeota and Euryarchaeota mainly
drive soil N cycling (Haroon et al., 2013; Leininger et al., 2006).
Thus, the gradual decrease in N substrate in vertical soil profiles is
more likely to be the reason for the decreased relative abundance of
Thaumarchaeota and Euryarchaeota.

Alpha-diversity can characterize the number of microbial taxa within
sample sites, while beta diversity can describe the variation trend of
microbial composition across sample sites (de Carvalho et al., 2016;
Legendre \& De C\selectlanguage{ngerman}áceres, 2013). Previous work has suggested that the
alpha-diversity (i.e., Shannon and OTU richness indexes) of bacteria and
fungi decreases while archaeal diversity typically increases with
increasing soil depth in different systems (i.e., grassland, forest and
farmland) (Eilers et al., 2012; Jiao et al., 2018). Our results further
indicated that bacterial diversity decreased, archaeal diversity
increased, and fungal diversity fluctuated with increasing soil depth
along a desertification gradient (Fig. 2). These discordant patterns of
soil microbiomes were due to their distinct ecological niches and
differences in oxygen tolerance, showing that bacteria and fungi are
mainly aerobic, while archaea are mainly anaerobic (Haroon et al., 2013;
Upton et al., 2020). The oxygen content gradually decreased with soil
depth in the desert ecosystems, given the increasing water content with
increasing depth resulting from high soil water infiltration (D'Odorico
et al., 2007). In addition, ecological restoration characterized by
increased availability of soil C and N significantly enhanced the alpha-
and beta-diversity of bacteria and archaea but not fungi (Barber et al.,
2017; Jiao et al., 2018; Lozano et al., 2014). In contrast to ecological
restoration, desertification is characterized by decreasing soil C and
N, which may be the reason for the decreased alpha- and beta-diversity
of bacteria and archaea in the vertical soil profiles as desertification
proceeded. In this study, the indistinctive alpha- and beta-diversity of
fungi suggested the stable performance of fungal communities in desert
ecosystems (Figs. 2 and 3). Fungi are heterotrophic microorganisms that
play fundamental ecological roles as decomposers, such as the
decomposition of litter and senescence or death of roots (Tedersoo et
al., 2014). In this study, the litter and root supplies decreased as
desertification progressed, and this unfavourable and variable habitat
was ineffective in completely restraining the growth and enrichment of
fungi. The fungal kingdom contains a large proportion of various niche
strategies ranging from saprotrophy through mutualism to parasitism
across trophic levels (Nilsson et al., 2019). In addition, fungal
communities with filamentous growth may show different interactions
because of dispersal limitation and greater tolerance of desiccation
(Austin et al., 2004; Fukami et al., 2010; Powell et al., 2015), leading
to their co-enrichment and distinct vertical distributions.

\subsection*{4.3 Main predictors of ecosystem multifunctionality in
desert
ecosystems}

{\label{main-predictors-of-ecosystem-multifunctionality-in-desert-ecosystems}}

Our results indicated that particular microbial phyla rather than total
microbial diversity better predicted and explained the vertical profile
variation in soil multifunctionality in desert ecosystems. The results
of this study confirmed our second hypothesis. Experiments at the
microcosm and global scales showed that microbial diversity variables
(i.e., Shannon and phylogenetic diversity indexes and OTU richness) are
important predictors of multifunctionality and are positively linked to
superficial soil multifunctionality (Delgado-Baquerizo et al., 2016; Li
et al., 2019; Wagg et al., 2014; Zheng et al., 2019), suggesting that
microbial communities with higher richness perform better under varying
conditions and better protect against the loss of taxa. However, our
knowledge is largely based on microbial diversity and dominance in
superficial soil, and less attention has been paid to deep soils of
desert ecosystems. Our results suggested that individual bacterial and
archaeal species are more important predictors of multifunctionality in
desert soils, especially in deep soils (20-100 cm). These individual
bacterial and archaeal species in deep soils may play a leading role in
driving soil multifunctionality, which to some extent could explain why
the process of desertification significantly decreased soil
multifunctionality (Fig. 1) and dominant bacterial and fungal phyla
maintained synchronous positive feedbacks (Figures. S3, S4, and S5). In
contrast to those of bacterial and archaeal taxa, the links between
individual species of fungi and ecosystem function are dependent on the
presence of other species and a result of multiple interactions (i.e.,
positive and negative as well as direct and indirect) between the
various species that as a whole regulate potential ecosystem functions
(Tedersoo et al., 2014; Wagg et al., 2019).

Intriguingly, our results showed a disproportionate role of individual
species (i.e., Acidobacteria or their strategic alliances) in
multifunctionality, which seems counter-intuitive given their higher
than expected ecological importance in soil microbial communities (Figs.
4 and 5). Acidobacteria in soil habitats are considered ubiquitous and
physiologically active but are rarely cultured and consequently remain a
poorly studied phylum (Goldfarb et al., 2011; Naether et al., 2012). The
phylogenetic diversity and relative abundance of Acidobacteria in
diverse habitats have suggested their vital role in driving
biogeochemical processes and diverse metabolic functions (Naether et
al., 2012). High C bioavailability is negatively associated with
acidobacterial abundance in various soils (Fierer et al., 2007; Goldfarb
et al., 2011), suggesting that Acidobacteria are adapted to habitats
with poor substrates and often slow-growing oligotrophs. Indeed, the
acidobacterial community can be energetically adapted to C-limited soils
and may be predominant in oligotrophic habitats, where decreasing plant
biomass results in a decrease in the availability of plant-derived C
sources (Castro et al., 2010).

Microbial diversity can maintain and regulate multifunctionality in a
variety of ways, suggesting that microbial communities with higher total
richness perform better in progressively developed and less-stressed
soils (i.e., forest, cropland, and wetland soils) (Delgado-Baquerizo et
al., 2016; Jiao et al., 2018; Li et al., 2019; Wagg et al., 2014).
Conversely, multifunctionality may be controlled by particular microbial
taxa (relative abundance of phylotypes) but not the total richness and
abundance of microbial communities in dryland soils (Delgado-Baquerizo
et al., 2017). Interestingly, our results further support the notion
that particular microbial phyla (microbial species index in Figs. 4 and
5) rather than total microbial diversity best predict and explain the
vertical profile variation in soil multifunctionality in desert
ecosystems.

\section*{5. CONCLUSIONS}

{\label{conclusions}}

Altogether, our findings provide strong empirical evidence that deep
soil multifunctionality, which has largely been ignored in desert
ecosystems, differs from the multifunctionality of whole superficial
soils and slightly decreases as soil depth increases throughout the
entire profile, suggesting fundamental roles in regulating and buffering
overall ecological service functions. Particular microbial phyla rather
than total microbial diversity can best predict and explain the vertical
profile variation in multifunctionality in desert ecosystems. By
identifying and characterizing the quantitative relationships between
soil microbiomes and multifunctionality in vertical soil profiles, our
results advance knowledge of pivotal ecological factors such as
microbial community-multifunctionality relationships and will assist
microbial ecologists in predicting and explaining the slight variation
in ecosystem service functions in desert ecosystems.

\section*{Declaration of competing
interest}

{\label{declaration-of-competing-interest}}

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.

\section*{Acknowledgements}

{\label{acknowledgements}}

This work was supported by the Strategic Priority Research Program of
the Chinese Academy of Sciences (XDA23070201) and the National Science
Foundation of China (NO. 41830755 and 41807042).

\textbf{Figure captions}

\textbf{Fig. 1} Variation in multifunctionality in the desert
ecosystems. (a): Whole superficial soils (0-20 cm) and deep soils
(20-100 cm); (b): vertical soil multifunctionality during the process of
desertification; (c): multifunctionality in different desertification
stages; (d) superficial and deep soil multifunctionality in different
desertification stages. Significant differences between desertification
sites were~based on a one-way ANOVA followed by an LSD test. Linear
least-squares regression relationships between multifunctionality and
soil depth were estimated. The bold lines denote the least-squares
linear regressions across soil depth, with their 95\% confidence
intervals (grey-shaded areas). S: slope; PD: potential desertification;
MD: moderate desertification; SD: severe desertification.

\textbf{Fig. 2} Variation in microbial alpha-diversity during
desertification progression. Significant differences between
desertification sites were~based on a one-way ANOVA followed by an LSD
test. Vertical variation in the Shannon and phylogenetic diversity
indexes for bacterial, archaeal, and fungal communities was estimated
via linear least-squares regression. The bold lines denote the
least-squares linear regressions across soil depth, with their 95\%
confidence intervals (grey-shaded areas). S: slope; PD: potential
desertification; MD: moderate desertification; SD: severe
desertification.

\textbf{Fig. 3} General patterns of microbial beta-diversity in
superficial and deep soils during the process of desertification. NMDS
showed the variation in the microbial community for soil bacteria (A),
fungi (B), and archaea (C). 95\% confidence ellipses are shown around
the sites. Differences in beta-diversity among the bacteria, fungi, and
archaea were estimated based on a Bray-Curtis distance matrix of all
soil samples. Community similarity was calculated based on
1--{[}dissimilarity of the Bray-Curtis distance metric{]}. The lines
denote the least-squares linear regressions across soil depth, with
their 95\% confidence intervals (grey-shaded areas). ***:
\emph{P}\textless{} 0.001. Significant differences of beta-diversity
between desertification sites were~based on a one-way ANOVA followed by
an LSD test.

\textbf{Fig. 4} Relationships between the dominant phyla of soil
microbiomes and multifunctionality.

The red lines denote the least-squares linear regressions with their
95\% confidence intervals (grey-shaded areas). S: slope.

\textbf{Fig. 5} Relationships between microbial diversity and
multifunctionality. The red lines denote the least-squares linear
regressions with their 95\% confidence intervals (grey-shaded areas). S:
slope.

\textbf{Fig. 6} Main predictors of soil multifunctionality in the desert
ecosystems. The figure shows the random forest mean predictor importance
(\%increase in MSE) of soil variables and microbial community and
diversity (Shannon and phylogenetic diversity indexes) for
multifunctionality for all data sets. The abundances of standardized
dominant phyla of soil bacteria (Chloroflexi and Acidobacteria), fungi
(Ascomycota and Basidiomycota), and archaea (Thaumarchaeota and
Euryarchaeota) were then averaged to obtain an overall microbial species
index. These standardized diversity indexes (Shannon and phylogenetic
diversity) of soil bacteria, fungi, and archaea were then averaged to
obtain an overall biodiversity index. The significance levels of each
predictor are as follows: *: \emph{P} \textless{} 0.05; **:
\emph{P}\textless{} 0.01; ***: \emph{P} \textless{} 0.001. EC:
electrical conductivity.

\textbf{Fig. 7} Direct and indirect effects of soil depth, pH,
electrical conductivity (EC), bulk density (BD), microbial species
index, and biodiversity index on multifunctionality. Structural equation
models are shown for all data sets. Numbers adjacent to arrows are
indicative of the effect size (bootstrap \emph{P} value) of the
relationship. R\textsuperscript{2} denotes the proportion of variance
explained. Standardized total effects (direct plus indirect effects)
derived from the structural equation models depicted above. The
abundances of standardized dominant phyla of soil bacteria (Chloroflexi
and Acidobacteria), fungi (Ascomycota and Basidiomycota), and archaea
(Thaumarchaeota and Euryarchaeota) were then averaged to obtain an
overall microbial species index. These standardized diversity indexes
(Shannon and phylogenetic diversity) of soil bacteria, fungi, and
archaea were then averaged to obtain an overall biodiversity index.

\section*{Additional Files captions}

{\label{additional-files-captions}}

\textbf{Table S1} Coverage of plant species at the different
desertification stages.

\textbf{Table S2} Primers and thermal profiles used for real-time PCR
quantification of the different phylogenetic and functional genes.

\textbf{Table S3} ANOSIM and \emph{ADONIS} analyses of microbial
beta-diversities between desertification stages.

\textbf{Table S4} ANOSIM and \emph{ADONIS} analyses of microbial
beta-diversities between superficial and deep layers.

\textbf{Table S5} Results of least-squares linear regressions analysing
ecosystem functions and community metrics. The multifunctionality index
calculated with all possible combinations of one, two and three
functions.

\textbf{Figure S1} Vertical variation in C, N, and P functions during
desertification. Significant differences between desertification sites
were~based on a one-way ANOVA followed by an LSD test. Linear
least-squares regression relationships between multifunctionality and
soil depth were estimated. The bold lines denote the least-squares
linear regressions across soil depth, with their 95\% confidence
intervals (grey-shaded areas). PD: potential desertification; MD:
moderate desertification; SD: severe desertification.

\textbf{Figure S2} Variation in dominant phyla of soil microbiomes
during desertification development.

(A): Bacteria; (b): fungi; (C): archaea. Significant differences between
desertification stages were~tested by the Kruskal-Wallis H test. PD:
potential desertification; MD: moderate desertification; SD: severe
desertification.

\textbf{Figure S3} Variation in dominant phyla of soil microbiomes in
superficial and deep soils during desertification. Significant
differences between desertification sites were~based on a one-way ANOVA
followed by an LSD test. PD: potential desertification; MD: moderate
desertification; SD: severe desertification.

\textbf{Figure S4} Relationships between the dominant phyla of soil
microbiomes and multifunctionality at all sites. The red lines denote
the least-squares linear regressions with their 95\% confidence
intervals (grey-shaded areas).

\textbf{Figure S5} Relationships between the dominant phyla of soil
microbiomes and multifunctionality at different desertification stages.
The red lines denote the least-squares linear regressions with their
95\% confidence intervals (grey-shaded areas). \emph{S} : slope. PD:
potential desertification; MD: moderate desertification; SD: severe
desertification.

\textbf{Figure S6} Variation in microbial alpha-diversity in superficial
and deep soils during desertification. Significant differences between
desertification sites were~based on a one-way ANOVA followed by an LSD
test. PD: potential desertification; MD: moderate desertification; SD:
severe desertification.

\textbf{Figure S7} Direct and indirect effects of soil depth, pH,
electrical conductivity (EC), bulk density (BD), microbial species index
or biodiversity index on multifunctionality. Structural equation models
are shown for the all data sets. Numbers adjacent to arrows are
indicative of the effect-size (bootstrap \emph{P} value) of the
relationship. R\textsuperscript{2} denotes the proportion of variance
explained. Standardized total effects (direct plus indirect effects)
derived from the structural equation models depicted above. The
abundances of standardized dominant phyla of soil bacteria (Chloroflexi
and Acidobacteria), fungi (Ascomycota and Basidiomycota), and archaea
(Thaumarchaeota and Euryarchaeota) were then averaged to obtain an
overall microbial species index. These standardized diversity indexes
(Shannon and phylogenetic diversity) of soil bacteria, fungi, and
archaea were then averaged to obtain an overall biodiversity index.

\textbf{Figure S8} Changes of plant litter and belowground biomass
during desertification development. Significant differences between
desertification sites were~based on a one-way ANOVA followed by an LSD
test. The plant was dug up and the aboveground litter were clipped and
dried to obtain the litter biomass, and the roots were washed with tap
water and dried at 70 °C for 48 h to obtain belowground biomass.

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