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\begin{document}

\title{Relationships among rhizosphere microbial co-occurrence patterns, core
microbiota, and soil multinutrient cycling in Robinia pseudoacacia
plantations of different ages}



\author[1]{Li Jiajia}%
\author[2]{Le Yang}%
\author[2]{Miaochun  Fan}%
\author[2]{Zhouping Shangguan}%
\affil[1]{Northwest A\&F University}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 12, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Vegetation restoration alters soil biotic and abiotic factors. Plants
have evolved multiple strategies to adapt to nutrient limitation by
reshaping and recruiting nutrient cycling-associated microbial
communities in the rhizosphere. However, our understanding of the
co-occurrence patterns of rhizosphere microbial communities and their
role in soil multinutrient cycling during vegetation restoration remains
limited. The present study explored the co-occurrence patterns of
rhizosphere microbial communities along a chronosequence (15--45 years
old) in Robinia pseudoacacia plantations in China's Loess Plateau
region, the associations between core microbiota and multinutrient
cycling under spatiotemporal changes. Our results indicate that soil
multifunctionality influences microbial co-occurrence patterns in the R.
pseudoacacia rhizosphere, resulting in a more stable bacterial network
than fungal network, forest age was a major driver of modularized
distribution of nodes in bacterial and fungal networks, and forest
age-sensitive microbes were taxonomically diverse at the phylum level.
Meanwhile, we found that core microbiota play essential role in
rhizosphere soil multinutrient cycling under R. pseudoacacia
afforestation.%
\end{abstract}%



\sloppy


\textbf{Relationships among rhizosphere microbial co-occurrence
patterns, core microbiota, and soil multinutrient cycling in
\emph{Robinia pseudoacacia} plantations of different ages}

Jiajia Li\textsuperscript{a}, Le Yang\textsuperscript{a}, Miaochun
Fan\textsuperscript{b}, Zhouping Shangguan\textsuperscript{a}*

\textsuperscript{a} State Key Laboratory of Soil Erosion and Dryland
Farming on the Loess Plateau, Northwest A\&F University, Yangling,
Shaanxi 712100, P.R. China

\textsuperscript{b} Department of Grassland Science, Northwest A\&F
University, Yangling, Shaanxi 712100, P.R. China

* Corresponding author at: Institute of Soil and Water Conservation, 26
Xinong Road, Yangling, Shaanxi 712100, P.R. China; Phone:
+86-29-87019107; Fax: +86-29-87012210; \emph{E-mail address} :
shangguan@ms.iswc.ac.cn (Z.P. Shangguan)

\textbf{Abstract}

Vegetation restoration alters soil biotic and abiotic factors. Plants
have evolved multiple strategies to adapt to nutrient limitation by
reshaping and recruiting nutrient cycling-associated microbial
communities in the rhizosphere. However, our understanding of the
co-occurrence patterns of rhizosphere microbial communities and their
role in soil multinutrient cycling during vegetation restoration remains
limited. The present study explored the co-occurrence patterns of
rhizosphere microbial communities along a chronosequence (15--45 years
old) in \emph{Robinia pseudoacacia}plantations in China's Loess Plateau
region, the associations between core microbiota and multinutrient
cycling under spatiotemporal changes. Our results indicate that soil
multifunctionality influences microbial co-occurrence patterns in the
\emph{R. pseudoacacia} rhizosphere, resulting in a more stable bacterial
network than fungal network, forest age was a major driver of
modularized distribution of nodes in bacterial and fungal networks, and
forest age-sensitive microbes were taxonomically diverse at the phylum
level. Meanwhile, we found that core microbiota play essential role in
rhizosphere soil multinutrient cycling under \emph{R. pseudoacacia}
afforestation.

Keywords : Rhizosphere soil;\emph{Robinia pseudoacacia;}Microbial
community; Co-occurrence network.

\textbf{Introduction}

Soil microorganisms shape aboveground biodiversity and the functioning
of terrestrial ecosystems significantly. They also play a key role in
determining the ecological and evolutionary responses of terrestrial
ecosystems to current and future environmental change (6). The
rhizosphere, a narrow zone where plant and soil interact, is
intriguingly complex and dynamic, and recruits numerous microorganisms
(43). The assembly processes of rhizosphere microbial communities are
governed by both abiotic and biotic factors. Root exudates (2, 21, 39),
soil properties (1, 23, 45), plant type and development stage (30), and
fine roots (34) are major factors driving the assembly and
differentiation of rhizosphere microbial communities (7). Plant--soil
microbe feedback refers to the linkages between plants and soil
microbial communities. Plant root exudates contain secondary metabolites
with plant-defense functions that alter root-associated microbial
community structure and facilitate plant growth (22). Plants modify
their exudation patterns to adapt to microbial nutrient demands, which
in turn facilitates the meeting of plant nutrient demands during
different growth stages (55). Therefore, survival of any plant species
in a particular rhizosphere environment depends primarily on the
capacity of the plant to perceive changes in the local environment that
require adaptive responses (50).

Characterization of soil microbial community structure is often used to
determine the responses of soil to disturbance and assess ecosystem
stability (3). The functions of ecosystems are diverse rather than
specific. Since Hector and Bagchi (19) proposed the quantification of
ecosystem multifunctionality in 2007, numerous researchers have focused
on the issue. Evidence from research suggests a close relationship
between soil microbial diversity and ecosystem multifunctionality in
different environments (11, 49). Delgado-Baquerizo et al. (11) collected
soil samples from 179 locations in 78 global drylands across Scotland
and observed that soil microbial diversity increased with an increase in
ecosystem multifunctionality. Furthermore, Jiao et al. (26) reported
that soil bacterial \selectlanguage{greek}α-\selectlanguage{english}diversity and \selectlanguage{greek}β-\selectlanguage{english}diversity were correlated with
ecosystem multifunctionality in maize and rice soils, respectively, on a
continental scale. Because the rhizosphere is a hotspot of microbial
activity, stable structure and functioning of rhizosphere microbial
communities (plant--root--soil and associated microbes--hyphae) are
conducive to the activation, absorption, and utilization of soil
nutrients. They are also essential for achieving multi-interface
interactions and synergies, while facilitating the addressing of the
problems associated with food security and environmental protection
(47)(Shen et al, 2021).

Black locust (\emph{Robinia pseudoacacia} ) is one of the major
afforestation species in China's Loess Plateau region, and it is of
great significance for ecological restoration and water and soil
conservation. Some studies have reported that soil properties and
microbial community structure vary along the chronosequence in \emph{R.
pseudoacacia} plantations. Particularly,\emph{R} . \emph{pseudoacacia}
afforestation altered soil bacterial richness and community composition
in a forest stand aged 25 years (37). Moreover, restoration time and
vegetation type (abandoned land vs.\emph{R. pseudoacacia} plantation)
markedly influenced soil physical properties, nutrient contents, and
microbial biomass (54). Furthermore, rhizosphere microbial community
structure are influenced by soil properties and fine root parameters in
\emph{R. pseudoacacia}plantations. Soil available phosphorus (AP)
contents have been reported to be lower at forest ages of 25 and 35
years than at forest ages of 5 and 15 years, with soil AP and total
phosphorus (TP) contents of fine roots being the key factors regulating
rhizosphere microbial community structure in the root--soil system
following \emph{Robinia}afforestation (35).

Currently, there is a need to observe the trends in rhizosphere
microbial community structure along a chronosequence during vegetation
restoration. Because rhizosphere microbiota could respond rapidly to
changes in environmental conditions induced by ecological restoration,
their turnover dynamics could serve as sensitive indicators that could
facilitate the monitoring of the restoration process (3). Despite
numerous studies having been carried out on soil microbial diversity
in\emph{R. pseudoacacia} plantations (Castellano et al., 2021; Hamonts
et al., 2018; Lemanceu et al., 2017), little is known about the assembly
process of rhizosphere microbial communities and their co-occurrence
network patterns along a chronosequence. In addition, although the
changes in soil factors along a chronosequence are well documented
(Hamonts et al., 2018; Castellano et al., 2021), the relationship
between ecosystem functionality and rhizosphere microbial community
structure (especially core microbiota) in\emph{R. pseudoacacia}
plantations is poorly understood. Such a poor understanding of
rhizosphere community dynamics along a chronosequence in \emph{R.
pseudoacacia} plantations calls for the exploration of the role of core
microbiota in the interaction interfaces in the rhizosphere through the
identification core microbial taxa and predicting their contribution to
soil nutrient cycling activities.

Core microbiota refer to the major microbial components present in the
general community that are enriched, selected, and inherited through
evolutionary processes (32). Core microbiota sustain plant health and
increase productivity (15). In addition, core rhizosphere microbiota
(rhizobiome) have greater effects on soil nutrient cycling and plant
growth than other rhizobiomes in the rhizosphere (8). It has been
reported that soil bacterial community composition shifted
from\emph{Acidobacteria-} dominated to \emph{Proteobacteria-} dominated
and then \emph{Acidobacteria-} dominated communities with an increase
in\emph{R. pseudoacacia} forest age, whereas fungal communities were
dominated by \emph{Ascomycta} and \emph{Zygomycota} irrespective of
forest age (36, 37). However, the previous studies did not elucidate the
potential relationships between the dominant microbes and soil
multifunctionality, in addition to their role in the microbial network.
Therefore, it is required to identify core microbiota and predict their
contribution to rhizosphere soil nutrient cycling, which is essential
for the implementation of rational rhizosphere management strategies and
maximizing microbiota ecological function.

Here, we present a rhizosphere soil survey conducted in \emph{R.
pseudoacacia} plantations across different forest ages at the Huaiping
Forest Farm in Yongshou, Shaanxi Province, China. We analyzed bacterial
and fungal diversity using high-throughput sequencing technologies and
quantified rhizosphere soil multifunctionality based on measurements of
edaphic variables associated with nutrient cycling. The aims of the
present study were to (i) explore rhizosphere microbial diversity and
the driving factors along a chronosequence in the course of \emph{R.
pseudoacacia}vegetation restoration; (ii) explore the influence of
forest age on the co-occurrence patterns of rhizosphere microbes and the
network features of forest age-sensitive microbes; and (iii) identify
core microbiota and their potential contribution to rhizosphere soil
multinutrient cycling in \emph{R. pseudoacacia} plantations.

\textbf{Materials and methods}

\textbf{2.1 Study sites and sample collection}

Four forest sites with different ages were selected at the Huaiping
forest farm in Yongshou County, Shaanxi Province, China. \emph{R.
pseudoacacia} is the dominant tree species on the farm, which represents
a natural forest protection area on the Loess Plateau
(108\selectlanguage{ngerman}°5'6''--108°5\selectlanguage{english}'11'' E, 34deg12'--34deg50' N). The study area has a
temperature semi-arid climate, with a mean annual temperature of 11.30degC
and a mean annual precipitation of 569.90 mm. The elevation ranges from
1368.3 to 1393.6 m. The main undergrowth vegetation in the four sites
with different forest ages is summarized in Table S1.

Soil sampling was carried out in \emph{R. pseudoacacia} plantations aged
15, 25, 35, and 45 years (designated RS15, RS25, RS35, and RS45,
respectively) with gentle slopes (Table S1). Six 20 x 20-m plots for
each forest age treatment were established in July, 2020, and the
distance between any two plots was more than 50 m. Rhizosphere soil was
defined as the soil tightly adhering to fine roots (\textless{}2 mm
diameter) of \emph{R. pseudoacacia} after gentle mechanical disturbance
(28). After removing the litter horizon, rhizosphere soil samples were
collected from the top 20 cm. Six soil cores were collected in each plot
based on an ``S''-shaped pattern and mixed to form one composite sample.
Subsequently, each composite sample was divided into two parts. One part
was immediately frozen at --20degC with dry ice and transported to the
laboratory, where it was stored at --80degC until DNA extraction. The
other part was air-dried for used in physicochemical analyses after
transportation to the laboratory.

\textbf{2.2 Soil analyses}

A total of 11 soil physicochemical properties were measured to assess
the overall multifunctionality of the rhizosphere soil based on the soil
multinutrient index proposed by Jiao et al. (25). The soil
physicochemical properties measured were pH, soil organic matter (SOM),
soil organic carbon (SOC), total nitrogen (N; TN), AP, TP, nitrate N
(NO\textsubscript{3}\textsuperscript{-}-N), ammoniacal N
(NH\textsubscript{4}\textsuperscript{+}-N), microbial biomass carbon (C;
MBC), microbial biomass N (MBN), dissolvable organic C (DOC), and
dissolvable organic N (DON). Rhizosphere soil multinutrient index was
used to quantify soil multinutrient cycling, and the index values were
calculated based on rhizosphere soil nutrient properties that had been
normalized using log-transformation (11, 24).

Soil pH was measured using a glass electrode with a soil-water ratio of
1:2.5 (v/w). Soil SOM was determined by potassium dichromate oxidation
with external heating(4). Soil TN was determined using the Kjeldahl
method(4), and soil TP was determined using the molybdenum antimony
anticolorimetric method after perchloric acid--sulfuric acid digestion
(42). Soil NH\textsubscript{4}\textsuperscript{+}-N and
NO\textsubscript{3}\textsuperscript{-}-N were extracted using 0.5 mol
L\textsuperscript{-1} KCI and then analyzed using a continuous flow
analyzer (AutoAnalyzer-AA3; Seal Analytical, Norderstedt, Germany). Soil
MBC and MBN were determined using the chloroform fumigation--extraction
method with conversion factors of 0.45 and 0.54, respectively (29). Soil
DOC and DON were measured using a total organic C analyzer (vario TOC
select; Elementar Analysensysteme, Frankfurt, Germany) after extraction
with 0.5 mol L\textsuperscript{-1}
K\textsubscript{2}SO\textsubscript{4}(33).

\textbf{2.4 Bioinformatics analysis}

The bioinformatics analysis procedure is presented in Supplemental
Information Methods. All the samples produced an average 79,168
bacterial tags and 103,364 fungal tags through reads splicing. After
quality control, 76,509 bacterial effective reads and 85,563 fungal
effective reads were obtained across all samples. The numbers of
bacterial and fungal effective reads following the quality control were
49,093 and 64,266, respectively, while the quality control effective
rates were 62\% and 62\%, respectively. Sequences were clustered into
operational taxonomic units (OTUs) with a 97\% similarity cut-off. A
total of 13,034 bacterial OTUs and 8,705 fungal OTUs were obtained. The
OTUs were then annotated with the Silva132 (bacterial) database and
UNITE (fungi) database.

\textbf{2.5 Statistical analysis}

All data analyses were performed in R v4.0.4
(\emph{https://www.r-project.org/} ). One-way Analysis of Variation and
the Least Significant Difference multiple comparison test were used to
investigate differences in rhizosphere soil variables.

Microbial \selectlanguage{greek}α-\selectlanguage{english}diversity (OTU richness, Shannon index, and Simpson index)
was calculated using the ``\emph{vegan} '' and ``\emph{picante} ''
packages in R v4.0.4. Variation analysis was performed using
``\emph{EasyStat} '' and ``\emph{dplyr} '' packages, followed by
visualization using the ``\emph{ggplot2} '' package, in R v4.0.4.

Microbial \selectlanguage{greek}β-\selectlanguage{english}diversity was estimated based on the Bray-Curtis distances
between samples. Bray-Curtis dissimilarity index was used to assess
differences in \selectlanguage{greek}β-\selectlanguage{english}diversity, and Wilcoxon rank sum tests were performed
to investigate significant differences in microbial \selectlanguage{greek}β-\selectlanguage{english}diversity.
Unconstrained Principal Co-ordinate Analysis (PCoA) and Permutational
Multivariate Analysis of Variance (PERMANOVA) were carried out using the
``\emph{adonis} '' function in the ``\emph{vegan} '' package in R
v.4.0.4, and the results were visualized using the ``\emph{ggplot2''} in
R v4.0.4. Subsequently, the ``\emph{phyloseq} '' package was used to
extract normalized abundance of the top 10 phyla and phylogenetic trees
were constructed using the ``\emph{hclust''} function in the
``\emph{ggtree''} package in R v4.0.4 based on the Bray-Curtis distance.
Stacked bar plots and trees were combined using the ``\emph{faceted} ''
function in the ``\emph{ggplot2''} package. In addition, \selectlanguage{greek}β-\selectlanguage{english}Diversity of
the core microbiota and other indices were quantified using the first
two axes (PCoA1 and PCoA2) of a PCoA plot.

Correlation analyses between microbial community composition (based on
Bray-Curtis distance) and soil environmental properties were conducted
using the ``\emph{mental test} '' function in the ``\emph{ggcor} ''
package in R v4.0.4. In the Mantel test, microbial community structure
and soil environmental properties were considered distance matrices; and
the correlation between the two matrices was tested.

Forest age-sensitive OTUs (fsOTU) were identified according to the
method of Harman (17). First, we used the ``\emph{Indicspecies} ''
package in R v4.0.4 to identify the indicator species. Subsequently, we
tested for differences in OTU abundances of bacteria and fungi between
two or more forest ages using the likelihood ratio test with the
``\emph{edgeR} '' package in R v4.0.4. The OTUs with significantly
different relative abundances between two or more forest ages (corrected
false discovery rate p \textless{} 0.05) were considered responsive to
forest age. Afterward, OTUs that were confirmed by both indicator
species analysis and likelihood ratio test were defined as forest
age-sensitive OTUs (fsOTU).

Microbial co-occurrence networks were constructed for bacteria and fungi
separately and visualized using the ``\emph{igraph} '' package in R
v4.0.4 based on the Fruchterman-Reingold layout. The trimmed mean of
M-values was used to normalize relative abundance to counts per million.
The Spearman correlation between every two OTUs was estimated. Robust
correlations with Spearman correlation coefficients \textgreater{}0.7
and p values \textless{}0.01 were identified for use in constructing
networks, in which each node represents one OTU (16S\_OTU for bacteria
and ITS\_OTU for fungi) and each edge represents a strong and
significant correlation between two nodes. The descriptive and
topological properties of the networks, including node number, edge
number, and co-occurrence degree were calculated.

Core microbiota were identified in rhizosphere bacterial and fungal
communities in \emph{R. pseudoacacia} plantations with different ages.
In all the samples, the ubiquitous (present in [?] 80\% of samples) and
highly abundant (top 10 in terms of relative abundance) OTUs were
selected (5, 10, 25). For the core microbiota, co-occurrence networks
were constructed for bacteria and fungi among all samples after removing
OTUs with low abundance (\textless{}0.001\%) from the dataset. Spearman
rank correlations were conducted between OTUs, and significant positive
correlations (r \textgreater{} 0.07, p \textless{} 0.001) were
visualized. The node-level topological feature (degree) was calculated
for each node.

The associations between core microbiota and rhizosphere soil
multinutrient cycling were evaluated using random forest analysis (5).
Microbial \selectlanguage{greek}α-\selectlanguage{english}diversity and \selectlanguage{greek}β-\selectlanguage{english}diversity served as predictors of the
rhizosphere soil multinutrient cycling index for estimating the
contributions of the microbiota to ecosystem functions (25).
Subsequently, multiple regression modelling with variance decomposition
analysis was used to estimate the importance of the major taxonomic
categories (class) of the core microbiota in explaining rhizosphere soil
nutrient properties, based on the backward filtering approach. The
``\emph{rfPermute} '' package in R v4.0.4 was used to estimate the
significance of the influence of the environmental variables on core
microbiota abundance.

\textbf{Results}

\textbf{3.1 Variation in rhizosphere soil properties at different forest
ages}

During the restoration of \emph{R. pseudoacacia} , rhizosphere soil
properties typically changed along the chronosequence (Table S2). Across
all samples, most soil properties (SOC, SOM, TN, AP,
NO\textsubscript{3}\textsuperscript{-}-N,
NH\textsubscript{4}\textsuperscript{+}-N, MBC, MBN, DOC, and DON)
increased significantly from RS15 to RS35. In contrast, SOC, SOM, and TN
contents were significantly lower in RS45 than in RS35. Furthermore,
soil pH values decreased significantly from RS15 to RS35, while there
was no considerable change in soil pH values between RS45 and RS15.

\textbf{3.2 Shifts in rhizosphere microbial diversity during forest
restoration}

There were 3035 bacterial and 647 fungal OTUs commonly identified at all
forest ages. The numbers of unique bacterial OTUs identified in the
RS15, RS25, RS35, and RS45 rhizosphere soils were 1027, 839, 783, and
979, respectively; the corresponding numbers of unique fungal OTUs were
395, 602, 1218, and 931, respectively (Fig. 1).

There was no significant difference in rhizosphere bacterial \selectlanguage{greek}α-\selectlanguage{english}diversity
among the four forest ages (Fig. S1A). The Shannon and Simpson indices
for bacteria peaked in the RS15 rhizosphere soil, while bacteria
richness did not change significantly among forest ages. Overall, fungal
\selectlanguage{greek}α-\selectlanguage{english}diversity increased first and then decreased with an increase in
forest age, and the peak values were observed in the RS35 rhizosphere
soil (Fig. S1B). As for fungi \selectlanguage{greek}α-\selectlanguage{english}diversity, fungal richness was
significantly different between the RS35 and RS45 rhizosphere soils, but
not between the RS15 and RS25 rhizosphere soils. However, Shannon index
changed significantly between the RS15 and RS25 rhizosphere soils,
although there was no difference between the other two treatments.
Simpson index did not differ significantly among the RS25, RS35, and
RS45 rhizosphere soils.

Distinct variation in rhizosphere microbial \selectlanguage{greek}β-\selectlanguage{english}diversity was observed
among forests with different ages. In the case of bacteria, \selectlanguage{greek}β-\selectlanguage{english}diversity
first decreased and then increased, and finally decreased, with an
increase in forest age (Fig. 2A). In the case of fungi, \selectlanguage{greek}β-\selectlanguage{english}diversity
increased from RS15 to RS35, and then deceased in RS45 (Fig. 2B).
Subsequently, we visualized and quantified differences among rhizosphere
microbial communities in terms of bacterial and fungal \selectlanguage{greek}β-\selectlanguage{english}diversity using
PCoA and PERMANOVA. The bacterial communities were clearly separated
into two groups along PCoA 1 (explain ratio 51.98\%), but the fungal
communities were not (explain ratio 43.99\%) (Fig. 2C-D).

Across all samples, \emph{Proteobacteria}
(27.7\%),\emph{Acidobacteriota} (21.0\%), and \emph{Actinobacteriota}
(7.7\%) were the dominant phyla in the bacterial communities
(Supplemental information Fig.S2). The relative abundances of the taxa
increased with an increase in forest age. Analysis of bacterial
community composition based on Bray-Curtis distances across different
forest ages revealed that the bacterial communities were clustered based
on forest age; one cluster comprised RS15 and RS25 samples, while the
other cluster contained RS35 and RS45 samples. In the case of
fungi,\emph{Basidiomycota} (57.4\%) and \emph{Ascomycota} (20.2\%) were
the dominant phyla among all the samples (Supplemental information
Fig.S3).\emph{Basidiomycota} abundance decreased with an increase in
forest age, while \emph{Ascomycota} abundance exhibited an opposite
trend. Conversely, fungal community composition was not clearly
clustered based on forest age.

\textbf{3.3 Factors driving rhizosphere microbial community structure
and assembly}

We analyzed the correlations between rhizosphere microbial community
structure and soil environmental properties using the Mantel test and
Spearman correlation analysis. Bacterial community structure was
significantly correlated with MBC, AP, TP,
NH\textsubscript{4}\textsuperscript{+}-N,
ON\textsubscript{3}\textsuperscript{-}-N, and SOC contents
(\emph{P}\textless{} 0.01), whereas fungal community structure was
significantly correlated with NH\textsubscript{4}\textsuperscript{+}-N,
TN, TP, and SOC contents (\emph{P} \textless{} 0.01; Supplemental
information Fig. S4). Furthermore, we used random forest analysis to
predict the contribution of soil environmental properties to \selectlanguage{greek}β-\selectlanguage{english}diversity
in rhizosphere bacterial and fungal communities. TP and
NH\textsubscript{4}\textsuperscript{+}-N were the major factors driving
bacterial \selectlanguage{greek}β-\selectlanguage{english}diversity (Fig. 2E), whereas
NH\textsubscript{4}\textsuperscript{+}-N and pH were the major factors
driving fungal \selectlanguage{greek}β-\selectlanguage{english}diversity (Fig. 2F).

\textbf{3.4 Co-occurrence patterns of rhizosphere microbial communities
across different forest ages}

We constructed co-occurrence networks for rhizosphere bacterial and
fungal communities separately, and determined network topographical
properties to explore the effects of different forest ages. Consistent
with the results of \selectlanguage{greek}β-\selectlanguage{english}diversity analysis, RS15 and RS25 samples
clustered in a module, while RS35 and RS45 samples clustered in another
module for both bacteria and fungi (Fig. 3). We also mapped the fsOTUs
(Fig. S5) into the rhizosphere microbial networks, and observed their
clustering according to forest age (Fig. 3A--B).

We then explored the distribution patterns of fsOTUs in the
co-occurrence networks of rhizosphere bacterial and fungal communities
(Fig. 3, Table1). The abundances of inter-kingdom microbial associations
also varied based on forest age, and there are difference among
different modules (Fig. 3C--D). The module members were sensitive to
forest age and their distribution in the network partially reflected the
drivers of community dissimilarity illustrated in the PCoA plot (Fig.
2). Module 1 mainly contained fsOTUs specific to RS15 and RS25, which
were not fully separated from other modules (Module 3; Fig. 3A--B).
Numerous fsOTUs assigned to RS35 and RS45 were predominantly located in
Module 2 (Fig. 3A--B). Our results suggested that the species with
similar community phylogeny had more similar community structure, closer
symbiotic relationship and easier aggregation in the network.

All the forest age-responsive modules comprised a taxonomically broad
set of bacteria (Supplemental information Fig. S6A), revealing that the
different forest ages did not target specific bacterial lineages.
However, in the case of fungi, \emph{Olpidiomycota}
and\emph{Kickxellomycota} only existed in Module 1,
and\emph{Aphelidiomycota} only existed in Module 2,
while\emph{Zoopagomycota} mainly existed in both Module 1 and Module 2
(Supplemental information Fig. S6B).

To reveal the effects of soil multinutrient cycling on the structures of
sensitive bacteria and fungi, we used rhizosphere soil multinutrient
index for regression analysis with normalized abundances of fsOTUs in
different modules. The abundances of fsOTUs exhibited positive
correlations with rhizosphere soil multinutrient index in Module 1
(bacteria: \emph{R} \textsuperscript{2} = 31, fungi:\emph{R}
\textsuperscript{2} = 30); however, in Module 2 there were negative
correlations (bacteria: \emph{R} \textsuperscript{2} = 24, fungi:
\emph{R} \textsuperscript{2} = 38, Fig. 3E-F).

\textbf{3.5 Identification of core microbiota and their potential roles
in soil nutrient cycling}

We identified 37 bacterial and 16 fungal classes as core microbiota
(Fig. 4). \emph{Nitrososphaeria} , \emph{Nitrospiria} , \emph{Verrucom-
icrobiae} , \emph{Alphaproteobacteria, Gemmatimonadetes}
,\emph{Bacteriodia,} \emph{Bacillus} , and \emph{Acidimicrobiia} were
considered core bacterial microbiota. \emph{Agaricomycetes}
,\emph{Tremellomycetes} , \emph{Motrierellomycetes}
,\emph{Glomeromycetes} , and \emph{Sordariomycetes} were considered core
fungal microbiota. Subsequently, we explored the role of core microbiota
in rhizosphere soil nutrient cycling through co-occurrence network and
random forest analyses. The co-occurrence networks of core bacteria and
fungi were established based on correlation analysis (Fig. 4A). Core
bacterial microbiota comprised 272 nodes, and other bacterial microbiota
had 2,967 nodes. Core fungal microbiota contained 64 nodes, and other
fungal microbiota had 1532 nodes. The degrees of different
sub-communities were significantly higher (P \textless{} 0.001; Wilcoxon
rank sum tests) in core microbiota than in other microbiota in both
fungi and bacteria (Fig. 4B-C).

To disentangle the linkages between core microbiota and soil nutrient
cycling, we quantified the contributions of core and other
sub-communities to the rhizosphere soil multinutrient cycling index
(Fig. 4C). In the course of forest restoration, the bacterial and fungal
diversities of the core microbiota were more important than those of
non-core microbiota. Furthermore, multiple regression modeling was used
to evaluate the associations of core microbiota and rhizosphere soil
nutrient contents. Different bacterial and fungal classes contributed to
the variation in rhizosphere soil nutrient contents (Fig. 5).
Particularly, TN, SOM, SOC, and NH\textsubscript{4}\textsuperscript{+}-N
contents were major factors associated with fungal core microbiota.
NH\textsubscript{4}\textsuperscript{+}-N content was a major factor
associated with most bacterial core microbiota, and was positively
correlated with \emph{Acidobacteriae, Alphaproteobacteria} ,
and\emph{Verrucomicrobiae} abundance, and negatively correlated with the
abundance of most core bacteria.

\textbf{Discussion}

\textbf{4.1 Dynamic changes in rhizosphere microbial diversity at
different forest ages}

In the course of \emph{R. pseudoacacia} vegetation restoration, we
observed that rhizosphere bacterial \selectlanguage{greek}β-\selectlanguage{english}diversity decreased over time,
whereas bacterial richness did not change significantly under different
restoration stages. Moreover, rhizosphere fungal \selectlanguage{greek}β-\selectlanguage{english}diversity was much
greater at the 45-year-old forest site than at the 15-year-old site, and
fungal richness changed significantly with an increase in restoration
stage. At the phylum level, the relative abundances of dominant
bacteria, such as \emph{Acidobacteria} and \emph{Proteobacteria} , did
not change significantly under different restoration stages.
Nonetheless, among the dominant fungi, the relative abundance
of\emph{Basidiomycota} was much higher at the 15-year-old forest site
than at other sites, while the relative abundance of \emph{Ascomycota}
was the highest at the 35-year-old forest site. The trends we observed
in rhizosphere microbial community structure could be attributed to
improvements in rhizosphere soil nutrient conditions induced by \emph{R.
pseudoacacia} growth from a forest age of 15 years to a forest age of 35
years.

Large-scale planting of \emph{R. pseudoacacia} could result in the
deterioration of soil structure and an increase in soil nutrients; soil
phosphorus and fine root phosphorus contents were the key factors
regulating soil microbial community composition under a \emph{R.
pseudoacacia} plantation no more than 35 years old (34, 38). The finding
is partly inconsistent with our results in the present study. Here, the
key soil factors regulating rhizosphere microbial \selectlanguage{greek}β-\selectlanguage{english}diversity associated
with \emph{R. pseudoacacia} included soil
NH\textsubscript{4}\textsuperscript{+}-N, TN, and TP contents. Soil
NH\textsubscript{4}\textsuperscript{+}-N content was a primary factor
driving \selectlanguage{greek}β-\selectlanguage{english}diversity of rhizosphere microbial communities, whereas soil
TP and pH were secondary factors driving bacterial and fungal
\selectlanguage{greek}β-\selectlanguage{english}diversity, respectively. The discrepancy between the findings of the
previous studies and the present study could be attributed to the
different forest ages selected (15--35 years vs. 15--45 years), as well
as the potential influencing factors analyzed (soil and plant variables
vs. soil nutrient and pH levels).

The soil--plant--microbe system is a complex multitrophic system, and
its stability is key to maximizing ecosystem functionality (53).
Strategies of enhancing the robustness of the soil--plant--microbe
system through artificial intervention or regulation is an emerging
research topic. A previous study conducted in the Less Plateau region
reported that soil microbial communities shifted their survival strategy
(from r- to k-strategists) both at the phylum and genus levels, which in
turn influenced the expression of genes related to C and N metabolism
during secondary succession in \emph{Quercus liaotungensis} forests
(55). Another study reported that active plant-associated microbial
communities shifted from bacteria-dominated to fungi-dominated
communities, and the fungal community shifted from fast-growing and
pathogenic species to a community consisting of beneficial and
relatively slow-growing fungi during secondary succession following
agriculture abandonment (16). In the present study, the relative
abundances of the phyla \emph{Basidiomycota} and
\emph{Proteobacteria}(r-stategists) (16, 55) decreased gradually, while
the relative abundances of \emph{Ascomycota} and \emph{Acidobacteria}
(k-stategists) (14, 51) increased with an increase in \emph{R.
pseudoacacia} forest age. Accordingly, we hypothesized that rhizosphere
microbial communities could undergo dynamic changes in terms of life
strategies or trophic levels in the course of \emph{R. pseudoacacia}
vegetation restoration, thereby mediating ecosystem functionality. Our
findings provide support for the rational regulation and control of
ecosystem function stability in \emph{R. pseudoacacia} plantations.

\textbf{4.2 Divergent drivers and co-occurrence patterns for microbial
communities along the chronosequence}

In the past, researchers mainly focused on how restoration stages or
forest ages influence soil microbial diversity, but rarely reported
microbial co-occurrence patterns (especially in the rhizosphere) and the
divergent drivers along a chronosequence, for example, in \emph{R.
pseudoacacia} plantations. In the present study, we observed that from
25 years to 45 years of \emph{R. pseudoacacia} forest age, different
clusters of rhizosphere microbial communities formed distinct modules in
the microbial network. Modulization indicates habitat heterogeneity,
aggregation of phylogenetically related species, niche overlap, and
species coevolution (41). Therefore, a similar module means a similar
habitat, and species in the same module due to niche overlap have more
interactions with each other. Such an interaction may in the form of
cooperation or competition for resources. Conversely, there are
relatively less interactions between modules, probably due to isolation,
which means that the co-occurrence of species is inhibited. Our results
suggest that clusters located on the same branch of a phylogenetic tree
form unified modules in the microbial network, which indicates that
similarity in rhizosphere microbial community structure plays a role in
determining the modularized structure of the microbial network.

We observed tight interactions and close aggregations among microbes in
Module 1 and Module 2 for both bacteria and fungi in the rhizosphere
soils in the \emph{R. pseudoacacia} plantation. However, Module 1 and
Module 2 were not fully separated from Module 3, indicating that forest
age was not the only factor driving microbial community module
differentiation. In addition, the relationship between rhizosphere soil
multinutrient index and bacterial and fungal taxa relative abundance
varied in different modules. Consequently, rhizosphere soil functions
potentially influenced microbial co-occurrence patterns in different
modules; the microbial responses to rhizosphere soil functions varied
over time and space. Furthermore, we observed that different forest ages
targeted specific fungal lineages, but not bacterial lineages. Not only
the stability but also the connectivity of the bacterial network was
higher than those of the fungal network.

Notably, the microbial co-occurrence networks illustrated the
correlations among different taxa, including real ecological
interactions and non-random processes; therefore, the network may not
really reflect the direct interactions between different groups (13,
40). Because habitats have complex regulatory mechanisms, microbial
networks are useful tools for exploring complex microbial community
structures, particularly in rhizosphere microenvironments with complex
interactions. Microbial networks may also facilitate the exploration of
the interactions among multi-level biological systems in the future.
Long-term observations are still required to select microbes with
specific functions that could facilitate plant selection of mutually
beneficial microbiota, which would enhance plant adaptation without
adversely affecting plant growth.

\textbf{4.3 Role of core microbiota in rhizosphere soil nutrient
cycling}

Exploring core microbiota can enhance our understanding of the influence
of microbial community assembly and stability on rhizosphere soil
nutrient cycling (27) Therefore, in the present study, we identified the
core taxa in the rhizosphere microbial communities associated
with\emph{R. pseudoacacia} plantations. In the microbial network, a high
value represents a node and a central position, while a low value
indicates a peripheral position (12, 48). We observed core taxa in
rhizosphere microbial communities, which exhibited high connectivity.
The results indicate that the core microbiota had a central ecological
position in the rhizosphere microbial network.

High soil microbial diversity could promote multifunctionality, as
reported in other terrestrial ecosystems (11, 19, 31, 49). Jiao et al.
reported that core microbiota played a major role in the maintenance of
microbial interactions, community stability, and belowground
multinutrient cycling in agricultural fields across eastern China (25).
Our results indicated that the diversity of core taxa has a greater
impact on rhizosphere soil multinutrient cycling than the diversity of
non-core taxa, based on random forest module. Accordingly, we concluded
that the core microbiota participated in rhizosphere soil multinutrient
cycling, while maintaining rhizosphere microbial network complexity.
Multiple regression modeling and variance decomposition analysis
revealed the potential influences of core microbiota, at the class
level, on soil nutrient properties.

Some core taxa can participate in the maintenance of plant health and
plant production (20, 52), and such taxa play important roles in soil
multifunctionality maintenance through the promotion of nutrient
exchange and facilitation of resource availability in agroecosystems
(52). The core microbiota identified in the present study have been
reported in other environments. For example, \emph{Acidimicrobiia} are
observed extensively in acidic wastewater, marine lakes, deserts, and
other environments with important ecological functions and potential
development values (18). \emph{Bacillus} can degrade
Polyhydroxy-alkanoates and promote plant growth (9, 44). So, identifying
core microbiota facilitates the understanding of microbial responses to
environmental disturbance (46).

\textbf{5. Conclusions}

According to the results of the present study, pH value and TP content
were key soil environmental factors driving variation in rhizosphere
fungal and bacterial \selectlanguage{greek}β-\selectlanguage{english}diversity, respectively, during the restoration
of \emph{R. pseudoacacia} plantations. Meanwhile, soil
NH\textsubscript{4}\textsuperscript{+}-N content influenced both
bacterial and fungal \selectlanguage{greek}β-\selectlanguage{english}diversity in the rhizosphere of \emph{R.
pseudoacacia} . With an increase in forest age, rhizosphere microbial
communities tended to adjust their survival strategies, at the phylum
level. Compared to the fungal community network, the bacterial community
network was more stable. In addition, the bacterial network was more
complex, with higher connectivity than the fungal network. The microbial
networks had better aggregation in the nearly-mature and mature forests
(15 and 25 years old), and in the over-mature forests (35 and 45 years
old). Fungi, but not bacteria, had specific lineage changes in the
course of \emph{R. pseudoacacia} forest restoration. Furthermore, the
results of the present study demonstrated the influence of soil
multifunction on microbial network modules, and the importance of core
microbiota diversity in rhizosphere soil multinutrient cycling
activities under \emph{R. pseudoacacia} plantations.

\textbf{Acknowledgements}

This work was supported by National Natural Science Foundation of China
(41771549, 42077452, 41907205), Class A Strategic Priority Science and
Technology Project of Chinese Academy of Sciences (XDA23070201), and
Chinese Universities Scientific Fund (2452020227). Meanwhile, I am very
grateful to my teachers and team for their support and help in this
work.

\textbf{Declaration of Competing Interest}

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

\textbf{Appendix file}

Supplementary data to this article be found online, which include
supplement information and supplement table.

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\textbf{Table 1.} Topological features of microbial co-occurrence
networks constructed based on operational taxonomic units (OTUs) of
rhizosphere bacteria and fungi under \emph{R. pseudoacacia} plantations\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Edges\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Connections\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Average degree\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Forest age-sensitive OTUs\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Nodes\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Degrees\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
116592\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
116592\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
94.25384\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1753\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2602\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2747\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fungi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
18569\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
39783\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
60.19125\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
915\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1644\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
617\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure captions}

\textbf{Fig. 1} The number of common and unique operational taxonomic
units (OTUs) of rhizosphere bacterial (A) and fungi (B) under \emph{R.
pseudoacacia} plantations with different ages. RS15, RS25, RS35, and
RS45 indicate the 15-, 25-, 35-, and 45-year-old age groups,
respectively.

\textbf{Fig. 2} Variation in bacterial (A, C) and fungal (B, D)
\selectlanguage{greek}β-\selectlanguage{english}diversity in the rhizosphere soils of \emph{R.
pseudoacacia}plantations at different ages, and the contribution of soil
environmental properties to microbial \selectlanguage{greek}β-\selectlanguage{english}diversity based on random forest
analysis (E, F).

\textbf{Fig. 3} Co-occurrence patterns of forest age-sensitive
operational taxonomic units (fsOTUs) in the rhizosphere microbiota. (A,
B) Co-occurrence networks of bacterial (left) and fungal (right) fsOTUs
showing significant correlations (r \textgreater{} 0.07, p \textless{}
0.001). (C, D) Cumulative relative abundances (as counts per million;
y-axis in \selectlanguage{ngerman}×1000) of bacterial (left) and fungal (right) fsOTUs in
different network modules. The cumulative relative abundances in RS15
(brownish red), RS25 (blue), RS35 (green), and RS45 (purple) samples
indicate the overall responses of forest age-sensitive modules to the
different forest ages. (E, F) Abundances of bacterial (left) and fungal
(right) fsOTUs following regression with rhizosphere soil multinutrient
cycling index in different modules.

\textbf{Fig. 4} Identification of rhizosphere core microbiota (A, B) and
their contribution to soil multinutrient cycling index (C). Figures of
core bacteria are shown in the top panel and figures of core fungi are
in the bottom panel. Red color indicates core microbiota and blue color
indicates non-core (other) microbiota. Co-occurrence networks of core
and non-core microbiotas were constructed based on correlation analysis.
Network degrees of core bacteria and fungi were calculated.
Contributions of core and non-core microbial diversity to rhizosphere
soil multinutrient cycling were estimated based on random forest
analysis.

\textbf{Fig. 5} Potential contributions of the core microbiota to
rhizosphere soil nutrient properties based on correlation and optimal
multiple regression model for major classes of the core bacteria and
fungi (A-B). Color gradient represents Spearman's correlation
coefficients. Circle size represents the variable's importance, i.e.,
proportion of explained variation calculated via multiple regression
modeling and variance decomposition analysis.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig/Fig}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fih/Fih}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fii/Fii}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fij/Fij}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fik/Fik}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

