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\begin{document}

\title{Reduced interactivity during microbial community degradation lead to the
extinction of Tricholomas matsutake}



\author[1]{Hanchang  Zhou}%
\author[2]{Anzhou Ma}%
\author[1]{Liu  Guohua}%
\author[1]{Xiaorong Zhou}%
\author[1]{Jun Yin}%
\author[1]{Yu Liang}%
\author[3]{Feng Wang}%
\author[1]{Guoqiang Zhuang}%
\affil[1]{RCEES}%
\affil[2]{Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences}%
\affil[3]{Jiangsu University}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Ecosystem degradation is a process during which different ecosystem
components interact and affect each other. The microbial community, as a
component of the ecosystem whose members often display high reproduction
rates, is more readily able to respond to environmental stress at the
compositional and functional levels, thus potentially threatening other
ecosystem components. However, very little research has been carried out
on how microbial community degradation affects other ecosystem
components, which hampers the comprehensive understanding of ecosystems
as a whole. In this study, we investigated the variation in a soil
microbial community through the extinction gradient of an
ectomycorrhizal species (Tricholomas matsutake) and explored the
relationship between microbial community degradation and ectomycorrhizal
species extinction. The result showed that during degradation, the
microbial community switched from an interactive state to a stress
tolerance state, during which the interactivity of the microbial
community decreased, and the reduced community interactions with
T.matsutake marginalized it from a large central interactive module to a
small peripheral module, eventually leading to its extinction. This
study highlights the mechanisms of T.matsutake extinction due to the
loss of soil microbial community interactivity, offering valuable
information about soil microbial community degradation and the plant
ectomycorrhizal species conservation.%
\end{abstract}%



\sloppy


\textbf{Reduced interactivity during microbial community degradation
lead} \textbf{to the extinction of} \emph{Tricholomas matsutake}
\textbf{}

\textbf{Authors}

Hanchang Zhou\textsuperscript{a, b}, Anzhou Ma\textsuperscript{a, b, *},
Guohua Liu\textsuperscript{a, b}, Xiaorong Zhou\textsuperscript{a, b},
Jun Yin\textsuperscript{a, b}, Yu Liang\textsuperscript{a, b}, Feng
Wang\textsuperscript{c}, Guoqiang Zhuang\textsuperscript{a, b,*}

\textsuperscript{a} Research Center for Eco-Environmental Sciences,
Chinese Academy of Sciences, 100085 Beijing, China

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

\textsuperscript{c} School of Food and Biological Engineering, Jiangsu
University, 212013 Zhenjiang, China

\textbf{*Corresponding author:}

Anzhou Ma

Phone: 86-10-6284-9156, Fax No.: 86-10-6284-9156

Email: azma@rcees.ac.cn

Guoqiang Zhuang

Phone: 86-10-6284-9613, Fax No.: 86-10-6284-9613

Email: gqzhuang@rcees.ac.cn

\textbf{Abstract}

Ecosystem degradation is a process during which different ecosystem
components interact and affect each other. The microbial community, as a
component of the ecosystem whose members often display high reproduction
rates, is more readily able to respond to environmental stress at the
compositional and functional levels, thus potentially threatening other
ecosystem components. However, very little research has been carried out
on how microbial community degradation affects other ecosystem
components, which hampers the comprehensive understanding of ecosystems
as a whole. In this study, we investigated the variation in a soil
microbial community through the extinction gradient of an
ectomycorrhizal species (\emph{Tricholomas matsutake} ) and explored the
relationship between microbial community degradation and ectomycorrhizal
species extinction. The result showed that during degradation, the
microbial community switched from an interactive state to a stress
tolerance state, during which the interactivity of the microbial
community decreased, and the reduced community interactions
with\emph{T.matsutake} marginalized it from a large central interactive
module to a small peripheral module, eventually leading to its
extinction. This study highlights the mechanisms of
\emph{T.matsutake}extinction due to the loss of soil microbial community
interactivity, offering valuable information about soil microbial
community degradation and the plant ectomycorrhizal species
conservation.

\textbf{Key words:} microbial community, degradation, network,
interactivity, \emph{Tricholomas matsutake}

\textbf{Introduction}

Environmental stresses due to climate change and human activity are
threatening to degrade the biodiversity and services of terrestrial
ecosystems around the world (Singh \emph{et al.} , 2010, De \& Ashley,
2013, Jansson \& Hofmockel, 2020). An ecosystem contains a myriad of
components that interact and affect each other in complex ways. Thus,
the degradation of a few, or even just one component, could potentially
change the state of others and lead to holistic ecosystem degradation
(Moreno-Mateos \emph{et al.} , 2020, Cheng \emph{et al.} , 2021). Among
ecosystem components, the microbial community is unique. On the one
hand, its members have extremely high richness and reproduction rate,
which allows the microbial community to be responsive to stress as its
composition can adapt rapidly when facing environment fluctuations
(Jansson \& Hofmockel, 2020, Hall \emph{et al.} , 2018). On the other
hand, the microbial community plays an irreplaceable role in
decomposition, nutrient cycling, and regulation of above-ground
community diversity and activity, therefore, degradation of the
microbial community is a potential threat to related ecosystem functions
and other ecosystem components (Berendsen \emph{et al.} , 2012).With
rising awareness of the importance of the microbial community and
increasingly mature methodology, research on ecosystem degradation
related to microbial communities under climatic and anthropogenic stress
have begun to receive increasing attention (Firestone, 2006,
Hall\emph{et al.} , 2018).

However, most microbial community degradation related research has been
performed according to vegetation or soil degradation gradients, rather
than according to microbial community degradation itself (Zhang \emph{et
al.} , 2010, Cheng \emph{et al.} , 2021). These studies have also mainly
focused on the effects that vegetation or soil degradation exert on the
microbial community, either directly or indirectly, rather than the
effects that microbial community degradation exerts on other ecosystem
components (Zhang \emph{et al.} , 2010, Cheng \emph{et al.} , 2021).
Yet, due to the much higher reproduction rate of microbes than plants,
the microbial community could have already passed through numerous
generations and established new stages of succession before significant
degradation of vegetation or soil is detected (Curtis, 2006, Konopka,
2006, Berendsen \emph{et al.} , 2012). Thus, it is more plausible that
microbial community degradation occurs earlier than vegetation
degradation, and that the latter is a consequence of microbial community
degradation (Curtis, 2006, Konopka, 2006, Berendsen et al., 2012).
However, studies exploring the effects of microbial community
degradation on other ecosystem components, such as ectomycorrhizal
species, remain scarce.

Ectomycorrhizal fungi are fungal microbes that live in the interface
between plant roots and soils, and are possibly the first step that soil
microbial community degradation exert to plant community (Martin et al.,
2016). The cloze interactions between ectomycorrhizal fungi species and
host plants had been comprehensively studied from the molecular level to
ecological level (Martin et al., 2016, Hoeksema et al., 2018). However,
the interactions between ectomycorrhizal fungi species and soil
microbial community were seldom reported. Theoretically, since the
ectomycorrhizal fungi physically interact with the soil microbial
community, the ectomycorrhizal fungi species should also interact at the
biochemical or ecological level (Bowen \& Theodorou, 1979). In recent
years, climate change has resulted in the decreasing production and the
regional extinction of ectomycorrhizal fungi species in several
ecosystems even if the vegetation has not yet shown significant
degradation (Collado \emph{et al.} , 2019, Ohara \& Hamada, 1967,
Gill\emph{et al.} , 2000). Taking the potential influence from the soil
microbial community on ectomycorrhizal fungi into consideration, the
extinction of ectomycorrhizal fungi is a potential sign of soil
microbial community degradation, thereby linking the degradation of the
soil microbial community to other ecosystem components.

In order to elucidate the relationship of the extinction of
ectomycorrhizal fungi species with the degradation of a soil microbial
community, we chose Hengduan mountain as the sampling site. It is
located in the eastern edge of Tibet plateau and is a hotspot of global
biodiversity. Several types ectomycorrhizal fungi species,
especially\emph{T.matsutake} , have shown signs of regional extinction
due to climatic and anthropogenic stresses (Ruth \emph{et al.} , 2008,
Yin\emph{et al.} , 2020). We sampled the top-soil along a
\emph{T.matsutake}extinction gradient and investigated the variation in
the associated microbial communities.

\textbf{Materials and Methods}

\emph{Sample collection and basic soil physico-chemical properties}

Soil samples were collected from the Baima Snow Mountain National Park
in Yunnan Province, near Nanren village (99\selectlanguage{ngerman}°6´40.65'' E, 28°34´59.06''
N, at 3,534 m). The soils were sampled from an old alpine forest
dominated by \emph{Cyclobalanopsis} \emph{glauca (Thunb.) Oerst} on
August 9, 2019 (Fig. S1A). With help from native experienced mushroom
collectors, soils beneath \emph{T. matsutake} were collected and
considered as undegraded areas (UD). Degrading areas (DI) were
classified as location where\emph{T. matsutake} that did not fruit that
year, and degraded areas (DD) were where no \emph{T. matsutake} growth
had been observed for over three years. Over three sites were selected
for each soil type and divided into 10 subsamples around the fruit,
marked as 1 to 10, and each subsample was about 10cm away from the fruit
(Fig. S1B). Each subsample was collected from a depth of 0--20 cm. For
each soil type, the subsamples from each site of the same mark were
mixed to form 10 samples (Zhou \emph{et al.} , 2016). Using this
methodology, we were able to minimize the influences and errors caused
by heterogeneity and focus primarily on the state of the microbial
community under the same degree of \emph{T. matsutake} extinction.

Soil pH was measured using a pH meter (FE20-FiveEasyTM pH,
MettlerToledo, Germany) after shaking a soil water (1:5 w/v) suspension
for 30min. Soil moisture was measured gravimetrically. Total organic
carbon (TOC) and total nitrogen (TN) contents were measured using an
elemental analyzer (VarioMAX, Elementar, Germany). Ammonium
(NH\textsubscript{4}\textsuperscript{+}-N) and nitrate
(NO\textsubscript{3}\selectlanguage{english}\textsuperscript{--}N) were extracted at a ratio of
10g fresh soil to 100mL 2M KCl. After shaking for 1h,
NH\textsubscript{4}\textsuperscript{+}-N,
NO\textsubscript{3}\selectlanguage{english}\textsuperscript{-}-N contents in the filtered
extracts were analyzed using a continuous flow analytical system (San++
System, Skalar, Holland). Total phosphorus content (TP) was measured
using the alkali fusion--Mo-Sb Anti spectrophotometric method.

\emph{DNA extraction and amplicon sequencing}

Total DNA was extracted according to the protocol for the
FastDNA\textsuperscript{TM} SPIN kit (MP Biomedicals), followed by
measuring the DNA concentrations and quality using a Nano-100 NanoDrop
spectrophotometer. Primers for the 16S rRNA gene amplification targeted
the V3-V4 hypervariable region and included 338F
5'-ACTCCTACGGGAGGCAGCA-3' and 806R 5'-GGACTACHVGGGTWTCTAAT-3'. The
primers for ITS region amplification, 3F 5'-GCATCGATGAAGAACGCAGC-3' and
4R 5'-TCCTCCGCTTATTGATATGC-3', targeted the ITS2 genetic region. PCR
products were mixed in equimolar ratios according to the GeneTools
Analysis Software (Version 4.03.05.0, SynGene), and the pooled PCR
products were purified using an E.Z.N.A. Gel Extraction Kit (Omega,
USA). Sequencing libraries were generated using the NEBNext(r) Ultra\selectlanguage{english} II
DNA Library Prep Kit for Illumina(r) (New England Biolabs, MA, USA)
following manufacturer's recommendations, and indexing adapters were
added. Library quality was assessed with a Qubit@ 2.0 fluorometer
(Thermo Fisher Scientific, MA, USA). The library was then sequenced on
an Illumina Nova6000 platform to generate 250 bp paired-end reads
(Guangdong Magigene Biotechnology Co., Ltd., Guangzhou, China).

The raw sequencing data generated in this study are available in the
NCBI Sequence Read Archive under the accessions PRJNA687558 (bacterial
undegraded stage-BUD library), PRJNA690166 (bacterial degrading
stage-BDI library), PRJNA690139 (bacterial degraded stage-BDD library),
PRJNA690207 (fungal undegraded stage-FUD library), PRJNA690181 (fungal
degrading stage-FDI library), and PRJNA690169 (fungal degraded stage-FDD
library).

\emph{Sequence data preprocessing and bioinformatics analyses}

Fastp (version 0.14.1) was used for quality control of the raw sequence
data using a sliding window (-W4-M20). Primers were also removed using
the cutadapt software program. Paired-end clean reads were then merged
using the usearch -fastq\_mergepairs command based on overlap between
paired-end reads. Merging criteria included requiring at least 16 bp of
overlap from reads generated from the opposite end of the same DNA
fragment with a maximum of 5 mismatches being allowed in the overlap
region. The merged sequences were identified as raw sequences. Fastp
(version 0.14.1) was then used again to perform quality control on the
raw sequence data using a sliding window (-W4-M20) to obtain paired-end
clean sequences. Sequences with more than 97\% nucleotide similarity
were clustered into operational taxonomic units (OTUs).

\emph{Bioinformatic analyses}

Network analyses were conducted according to the molecular ecological
network analysis pipeline (MENA, http://ieg2.ou.edu/MENA/) protocols. A
cutoff threshold of 0.88--0.89 was used, and threshold values ranging
from 0.01 to 0.99 over 0.01 intervals were applied to the Spearman
correlation matrix (Deng \& Zhou, 2015). Module hubs and connectors were
determined based on the criteria of Zi \textgreater{} 2.5 and Pi
\textgreater{} 0.625, respectively. The constructed network and
sub-networks were visualized using Cytoscape 3.3.0 (Xie \emph{et al.} ,
2020). Statistically significant differences in basic soil properties
and diversity indices among the three soil types were measured using
one-way ANOVA tests. In addition, differences in the relative abundances
of OTUs and functional gene modules were evaluated using the
Kruskal-Wallis method. Calculations of richness, Shannon index, and
equitability, in addition to Linear discriminant analysis Effect Size
(LEfSe) tests, principal coordinates analysis (PCoA) based on
Bray-Curtis distances, and 16S rRNA gene-based functional predictions
(by comparison to the database COG-cluster of orthologous groups), were
all conducted with the online platform
http://www.magichand.online/h5-BioCloud-site/\#/. Factor extractions
were conducted using a correlation matrix in the SPSS software suite.

\textbf{Results}

\emph{Variation in basic soil abiotic and biotic properties during
degradation}

The degradation of \emph{T. matsutake} habitat was accompanied by
significant variation in soil physico-chemical and microbial community
properties. The contents of TC, TN, TP, ammonia, nitrate, and pH
significantly increased with degradation status (Table S1). The most
prominent increase was for ammonia content that was nearly 42 times
higher in DD soils than in UD soils, while TC, TN, and TP increased by
only approximately 1.0-, 2.0-, and 0.5-fold, respectively. TN and
nitrate contents did not significantly differ between UD and DI soils.
The Shannon indices of the bacterial and fungal communities from UD
soils were 2.28 and 0.97, respectively, but increased to 2.77 and 1.60
in DI soils. The Shannon index values were lowest in DD soils, at 2.03
and 0.72 for bacterial and fungal communities, respectively. Similar
trends were also observed for richness and equitability metrics (Fig.
S2). Variations in the relative abundances (RA) of fungal and bacterial
populations were observed among communities (Fig. S3A and B). The RA of
\selectlanguage{greek}α-\selectlanguage{english}\emph{Proteobacteria} (the bacterial phylum with the highest RA)
increased from 21.9\% (UD) to 31.6\% (DI), and then decreased to 13.6\%
(DD) in the respective soil communities. \emph{Acidobacteria} RAs
significantly increased to as high as 62.7\% in DD soils. The RAs of
\selectlanguage{greek}β-\selectlanguage{english}\emph{Proteobacteria} decreased from 17.7\% in UD soils to 1.1\% in DD
soils, while the RAs of \emph{Actinobacteria} also decreased from 16.9\%
in UD soils to 4.4\% in DD soils.

The RAs of ``other'' fungi decreased from 28.1\% in UD soils to 6.7\% in
DD soils. Furthermore, the RAs of \emph{Eurotiales}
,\emph{Thelephorales,} and \emph{Hypocreales} exhibited similar
declining trends. \emph{Helotiales} RAs slightly increased from 4.2\% in
UD soils to 7.5\% in DD soils. The dominant \emph{Agaricales} (41.5\%
RA) of UD soils was replaced by \emph{Atheliales} (73.3\% RA in DD
soils).\emph{Sebacinales} and \emph{Russulales} exhibited the highest
RAs in DI soils (18.8\% and 33.6\%, respectively) which were
significantly higher (\emph{p} \textless{} 0.05) than in UD and DD
soils. The RA of\emph{T.matsutake} in the fungal community decreased
from 41\% in UD soils to 1.6\% in DD soils, highlighting the decreasing
dominance of\emph{T.matsutake} during the degradation process (Fig.
S3C). LEfSe analysis indicated that \emph{T.matsutake} was the most
enriched representative species in UD soils, with decreasing prominence
in degraded soils (Fig. S3D). Only the RAs of \emph{Piloderma
olivaceum}were significantly enriched in DD soils compared to the other
two degradation gradients.

The microbial community in DD exhibited a greater number of predicted
gene modules related to stress resistance and basic metabolism than in
DI and UD, but also exhibited fewer predicted modules related to
interaction, reproduction, and cofactor metabolism. In the \emph{T.
matsutake} degraded habitats, the RAs of gene modules related to basic
metabolites (i.e., carbohydrates, lipids, and amino acids) were
enhanced, while carbohydrate metabolite-related gene modules exhibited
the largest increased abundances, increasing to approximately 25\%
higher in the DD soils compared to UD soils. The predicted RAs of
defense and motility related gene modules were also higher in DD (Fig.
1). In contrast, the predicted RAs of replication-related gene modules
decreased from UD to DD, the largest decrement was chromatin structure
and dynamics-related gene modules, with the largest decreases associated
with chromatin structure and dynamics-related gene modules the RA in DD
soils were only 77.5\% of levels in UD soils. The RAs of community
interaction and cofactor metabolite-related gene modules were also
predicted as lower in DD (Fig.1).

\emph{Microbial community network variation during degradation and
interactions with T.matsutake}

Microbial networks become less interactive along the\emph{T.matsutake}
degradation gradient, although the largest network was observed for DI
communities. The network topological parameters (Table S2) demonstrated
that DI soil networks exhibited the most nodes (1,280) and interactions
(1,414), although it is not the most interactive network among the three
soil types. Across the degradation gradient, the average degree (avgK)
decreased from 3.54 (UD) to 1.63 (DD), indicating a lower interaction
density (Deng \& Zhou, 2015). Furthermore, the average clustering
coefficient (avgCC) decreased from 0.192 (UD) to 0.112 (DD), while the
centralization of eigenvector centrality (CE) increased from 0.231 (UD)
to 0.572 (DD). These observations indicate that the size proportion of
unit modules in the network decreased across the degradation gradient,
with an increase in the number of smaller modules (Deng \& Zhou, 2015).
The transitivity (trans) decreased from 0.506 (UD) to 0.241 (DD) across
the gradient, reflecting a potentially decreased ability to communicate
and cooperate among network members (Deng \& Zhou, 2015). In addition, a
mantel test indicated the presence of significant differences between
the empirical network and random networks (\emph{p} \textless{} 0.05),
suggesting that the empirical networks were reasonable.

The form and state of interactions in the network varied during
degradation (Fig. 2). The UD network contained four module hubs (BOTU79,
BOTU257, BOTU99 and FOTU7) and two bacterial connectors (BOTU175 and
BOTU2394), while the DI network contained twelve module hubs and two
fungal connectors (FOTU1608 and FOTU1160), and the DD network had no
connectors and only a single module hub (BOTU37). The ratio of module
hubs/connectors increased from UD to DD, indicating that the interaction
types shifted from an inter-module focused state to an intra-module
focused state (Fig. 2A and Fig. 2B).

The ratios of total positive to negative interactions (p/n) in the UD,
DI, and DD networks were nearly equivalent, while the DI network had a
slightly higher ratio of negative interactions than the UD and DD
networks (Fig. 3A). However, the p/n of bacterial-fungal interactions
decreased from 1.18 (UD) to 0.82 (DD), while the p/n of
bacterial-bacterial interactions increased from 0.89 (UD) to 1.08 (DD).
The p/n of fungal-fungal interactions was lowest in the DI network
(1.07) compared to 1.72 and 1.85 in the UD and DD networks, respectively
(Fig. 3A). This indicated interactions between bacterial and fungi
transitioned from a more cooperative to a more competitive state during
degradation. The proportion of bacterial-fungal interactions to total
interactions was similar among the three networks, but the proportion of
fungal-fungal interactions decreased from 28.18\% in the UD network to
24.31\% in the DD network, while the proportion of bacterial-bacterial
interactions increased from 26.01\% in the UD network to 31.33\% in the
DD network (Fig. 3B). This observation indicates the potential
decreasing relative importance of fungal interactions compared to
bacterial interactions in the network along the degradation gradient.

Furthermore, the RAs of OTUs that were directly associated
with\emph{T.matsutake} encompassed 13 negatively interacting OTUs that
had significantly lower RAs in the DD communities compared to the UD
communities (Fig. 3C). However, half of the positively associated OTUs
(80\% were bacterial) exhibited increased RAs along the degradation
gradient, indicating that they improved their fitness once they no
longer had interactions with \emph{T.matsutake} . Only BOTU43 and
FOTU304 exhibited decreased RAs with the loss of \emph{T.matsutake}
dominance, indicating that they may have intimate and irreplaceable
interactions with\emph{T.matsutake} (Fig. 3C). Thus, the location of
\emph{T.matsutake}in the networks became marginalized as degradation
increased (Fig. 2B), moving from a large central module in the UD
network that had tight interactions, to a small peripheral module that
had few interactions with other nodes and modules in the DD network.
This change indicated the loss of functional interactions with
\emph{T.matsutake} during habitat degradation.

\emph{The contribution of microbial community interactivity to
T.matsutake extinction}

A strong linear relationship was observed between \emph{T.matsutake} RAs
and network structures, with R\textsuperscript{2} values as high as
0.909 (\emph{p} \textless{} 0.05), further implicating the influence of
network interactions on \emph{T.matsutake} RAs (Fig. 4). However,
correlations of soil properties in addition to the diversity and
composition metrics for the bacterial and fungal communities exhibited
weak linear relationships to \emph{T.matsutake} RAs. The lowest
R\textsuperscript{2} was observed for the relationship between community
structure and \emph{T.matsutake} RAs (0.006). Furthermore, a weak
positive relationship was observed between community diversity
and\emph{T.matsutake} RAs (R\textsuperscript{2}=0.302), while a weak
negative relationship was observed between soil properties
and\emph{T.matsutake} RAs (R\textsuperscript{2}=0.417).

\textbf{Discussion}

\emph{4.1 The properties of soil microbial community degradation}

In this study, microbial community degradation was accompanied by
significant variation in community composition, \selectlanguage{greek}α-\selectlanguage{english}diversity and
functions. The \selectlanguage{greek}α-\selectlanguage{english}diversity rose and then decreased again, which
indicated the potential error to describe microbial community
degradation by \selectlanguage{greek}α-\selectlanguage{english}diversity as a proxy (Fig. S2). Degradation is
triggered by stresses (Li \emph{et al.} , 2013), which alters soil
properties in a spatially and temporally heterogeneous manner (Pedersen
\emph{et al.} , 2015, Jian\emph{et al.} , 2018, Li \emph{et al.} ,
2020), creating new niches that could harbor new species (Seaton
\emph{et al.} , 2020). However, with increasing degradation, soil
properties are more likely to be altered to an extent that the number of
niches decreases, filtering species (Orrock, 2020). Thus, the
relationship between \selectlanguage{greek}α-\selectlanguage{english}diversity and degradation varies from the extent
(or stage) of degradation.

We found that during degradation, microbial functions focused more on
stress resistance and basic metabolite, and less on interaction and
reproduction (Fig.1). This indicated microbial community was under
pressure such that its members reduced energy utilization efficiency to
overcome the stressor(s) (Malik \emph{et al.} , 2018). When facing
acidic stresses, microbes expend extra energy to synthesis stress
tolerance protein and, thus, leaving less energy for reproduction (Malik
\emph{et al.} , 2018). Further, our results supported that the microbial
community lost metabolite cooperation during degradation as shown by the
decrease in the relative abundance of gene modules related to microbial
interaction and increase in the relative abundance of gene modules
related to basic metabolism, which coincided with the decrease in
microbial community interactivity (Fig.1, Fig.2B and Table S2). This
indicate that under UD, members in the network were able to conserve
energy by scaling back the synthesis enzymes and proteins for entire
pathways, achieve high energy acquisition and reproduction rate through
the exchange of intermediate metabolites (Damore \& Gore, 2012,
Malik\emph{et al.} , 2018). The broken metabolite cooperation under
stress conditions forced members to express a great number of basic
metabolite related gene to ensure survival (Damore \& Gore, 2012, Malik
\emph{et al.} , 2018). We found the inhibited metabolite cooperation
occurred more frequently between fungi and bacteria than between fungi
and fungi or bacteria and bacteria, since the positive interaction ratio
of the former consistently decreased over the degradation gradient
(Fig.3A). In nature, fungi play an important role in litter degradation
and contribute the main driving force for organic carbon release to the
soil (Taylor \& Osborn, 1996), while bacteria play a larger role in
various ecosystem functions such as ammonia oxidization and nitrogen
fixation. (Galloway \emph{et al.} , 2004). The decreased cooperation
between fungi and bacteria is a potential indicator of decoupling
between their respective functions (Taylor \& Osborn, 1996, Galloway
\emph{et al.} , 2004, Kuypers \emph{et al.} , 2018). This was further
supported by the loss of inter-module interactions (Fig. 2A and Fig.
2B), since members within the same module are more likely to be linked
to a single function, decreasing inter-module interaction indicates
decoupling of different functions (Deng \& Zhou, 2015). As discussed
above, our result support that the interactivity, rather than simple
diversity, is better able to indicate microbial community degradation.
Due to the extremely high richness and functional redundancy of the
microbial community, it is more meaningful to understand who's working
or interacting together rather than who is simple present (Curtis,
2006).

\emph{4.2 Reduced interactivity lead to T.matsutake extinction}

Interactions among community members are an important contributor to
community stability and species diversity (Mougi \& Kondoh, 2012,
Hale\emph{et al.} , 2020). The interactions could be cooperation and
competition in metabolism, immune, stress tolerance, and appeared as
positive or negative interactions (Mougi \& Kondoh, 2012).

As interactivity within the soil microbial community decreased during
degradation, \emph{T.matsutake}lost cooperative interactions (Fig.3C)
that may have been capable of enhancing the rates of certain ecological
processes (Corbett \& Watkin, 2018, Cui \emph{et al.} , 2019),
stimulating energy conservation by individual members (Malik \emph{et
al.} , 2018), and even allowing the coexistence of certain species (Cai
\emph{et al.} , 2020). The loss of cooperation increases the cost of
survival for that species. We found that \emph{T.matsutake} was
marginalized from a central module to a peripheral one within the
networks (Fig.2B). As a module is a collection of members (nodes) that
are potentially highly functionally related (Deng \& Zhou, 2015), this
indicated that \emph{T.matsutake} could not rely on the original
functional groups for metabolic cooperation, and resulted in extinction.
(Gill \emph{et al.} , 2000, Xu \emph{et al.} , 2015). Thus, our result
support the hypothesis that loss of cooperation due to reduced
interactivity of the microbial community was responsible for the
extinction of \emph{T.matsutake} (Gill \emph{et al.} , 2000, Xu\emph{et
al.} , 2015). This was further confirmed by regression analysis that
showed the extent of \emph{T.matsutake} extinction was most linearly
correlated to network interaction indicators than to soil properties,
community composition or biodiversity (Fig.4).

However, our study found that all of the 13 OTUs that competed
with\emph{T.matsutake} had also decreased in their relative abundance
during degradation, which indicated that groups with functions similar
to\emph{T.matsutake} were also facing the threat of extinction due to
environmental stress. Competition also contributes to community
stability as it is involved in resource partitioning and functional
redundancy among community members (Pianka, 1981, Pinsky, 2019). The
community could retain functional stability by trade-offs between the
functions of several phylogenetic groups under stress (Pianka, 1981,
Pinsky, 2019). Thus, it became an open question as to whether the
community possessed any other groups that could functionally
replace\emph{T.matsutake} and its competitors. As the ectomycorrhizal
species of the keystone plant (Gill \emph{et al.} , 2000),
\emph{T.matsutake} and its competitors were helpful for maintaining the
dominance of the host plant by assisting in nutrient uptake and defense
against stress and disease (Liang \emph{et al.} , 2020). Over the long
term, the extinction of \emph{T.matsutake} and its competitors are a
potential threat to keystone vegetation and even other animals that
inhabit the local environment (Gill \emph{et al.} , 2000, Weisskopf
\emph{et al.} , 2021).

In summary, the microbial community, during the degradation
of\emph{T.matsutake} , was demostrated variation in composition and
diversity, and a switch from an interactive state where metabolite and
signal functions predominated to stress tolerance state. The decreased
interactivity marginalized\emph{T.matsutake} from a large center module
to a peripheral module and reduced its interactions, leading to its
extinction. The extinction caused by the degradation of microbial
community directly impacts the ecosystem and is a potential long term
threat to aboveground biodiversity through rhizosphere activity and the
food-web. This study highlights that the key characteristic of microbial
community degradation and the mechanism for microbial species extinction
during degradation is the reduced interactivity. For future studies
focusing on conservation and on microbial community, an understanding of
the relationships among stress, microbial community properties, and the
extent of the microbial community degradation, will allow for improved
comprehensive conservation measures that target both below- and
above-ground communities.

\textbf{Acknowledgements}

This work was supported by the Second Tibetan Plateau Scientific
Expedition and Research Program (2019QZKK0402, 2019QZKK0307); the
National Key Research and Development Program of China (2016YFC0502104,
2018YFA0901200); and the National Natural Science Foundation of China
(41473079, 41673082, 41671270).

\textbf{References}

Berendsen RL, Pieterse CMJ \& Bakker PAHM (2012) The rhizosphere
microbiome and plant health. \emph{Trends in Plant Science} \textbf{17}
: 478-486. https://doi.org/10.1016/j.tplants.2012.04.001

Bowen GD \& Theodorou C (1979) Interactions between bacteria and
ectomycorrhizal fungi. \emph{Soil Biology and Biochemistry} \textbf{11}
: 119-126. https://doi.org/10.1016/0038-0717(79)90087-7

Cai W, Snyder J, Hastings A \& D'Souza RM (2020) Mutualistic networks
emerging from adaptive niche-based interactions. \emph{Nature
Communications} \textbf{11} : 5470.
https://doi.org/10.1038/s41467-020-19154-5

Cheng C, Gao M, Zhang Y, Long M, Wu Y \& Li X (2021) Effects of
disturbance to moss biocrusts on soil nutrients, enzyme activities, and
microbial communities in degraded karst landscapes in southwest
China.\emph{Soil Biology and Biochemistry} \textbf{152} : 108065.
https://doi.org/10.1016/j.soilbio.2020.108065

Collado E, Bonet JA, Camarero JJ, Egli S, Peter M, Salo K, Martinez-Pena
F, Ohenoja E, Martin-Pinto P \& Primicia I (2019) Mushroom productivity
trends in relation to tree growth and climate across different European
forest biomes. \emph{The Science of the Total Environment} \textbf{689}
: 602-615. https://doi.org/10.1016/j.scitotenv.2019.06.471

Corbett M \& Watkin E (2018) Microbial cooperation improves bioleaching
recovery rates. \emph{Microbiology Australia} \textbf{39} :50-52.
https://doi.org/10.1071/MA18013

Cui J, Mai G, Wang Z, Liu Q, Zhou Y, Ma Y \& Liu C (2019) Metagenomic
insights into a cellulose-rich niche reveal microbial cooperation in
cellulose degradation. \emph{Frontiers in microbiology} \textbf{10} :
618. https://doi.org/10.3389/fmicb.2019.00618

Curtis T (2006) Microbial ecologists: it's time to 'go
large'.\emph{Nature Reviews Microbiology} \textbf{4} : 488-488.
https://doi.org/10.1038/nrmicro1455

Damore JA \& Gore J (2012) Understanding microbial
cooperation.\emph{Journal of Theoretical Biology} \textbf{299} : 31-41.
https://doi.org/10.1016/j.jtbi.2011.03.008

De VFT \& Ashley S (2013) Controls on soil microbial community stability
under climate change. \emph{Frontiers in microbiology} \textbf{4} :1-16.
https://doi.org/10.3389/fmicb.2013.00265

Deng Y \& Zhou J (2015) Molecular ecological network of microbial
communities. \emph{Encyclopedia of Metagenomics: Genes, Genomes and
Metagenomes: Basics, Methods, Databases and Tools,} (Nelson KE, ed.)
p.\^{}pp. 504-510. Springer US, Boston, MA.

Firestone MPWK (2006) Response of Microbial Community Composition and
Function to Soil Climate Change. \emph{Microbial Ecology} \textbf{52} :
716-724. https://doi.org/10.1007/s00248-006-9103-3

Galloway JN, Dentener FJ, Capone DG, Boyer EW, Howarth RW, Seitzinger
SP, Asner GP, Cleveland CC, Green PA \& Holland EA (2004) \emph{Nitrogen
Cycles: Past, Present, and Future} . Royal Horticultural Society.

Gill, WM, Guerin-Laguette, Lapeyrie \& Suzuki (2000) Matsutake -
morphological evidence of ectomycorrhiza formation between Tricholoma
matsutake and host roots in a pure Pinus densiflora forest
stand.\emph{NEW PHYTOL} \textbf{147} :381 - 388.
https://doi.org/10.1046/j.1469-8137.2000.00707.x

Hale KRS, Valdovinos FS \& Martinez ND (2020) Mutualism increases
diversity, stability, and function of multiplex networks that integrate
pollinators into food webs. \emph{Nature Communications} \textbf{11} :
2182. https://doi.org/10.1038/s41467-020-15688-w

Hall EK, Bernhardt ES, Bier RL\emph{, et al.} (2018) Understanding how
microbiomes influence the systems they inhabit. \emph{Nature
Microbiology} \textbf{3} : 977-982.
https://doi.org/10.1038/s41564-018-0201-z

Hoeksema JD, Bever JD, Chakraborty S, et al. (2018) Evolutionary history
of plant hosts and fungal symbionts predicts the strength of mycorrhizal
mutualism. \emph{Communications Biology} \textbf{1} : 116.
https://doi.org/10.1038/s42003-018-0120-9

Jansson JK \& Hofmockel KS (2020) Soil microbiomes and climate
change.\emph{Nature Reviews Microbiology} \textbf{18} : 35-46.
https://doi.org/10.1038/s41579-019-0265-7

Jian J, Steele MK, Day SD, Quinn Thomas R \& Hodges SC (2018)
Measurement strategies to account for soil respiration temporal
heterogeneity across diverse regions. \emph{Soil Biology and
Biochemistry} \textbf{125} : 167-177.
https://doi.org/10.1016/j.soilbio.2018.07.003

Konopka A (2006) Microbial ecology: searching for
principles.\emph{Microbe} \textbf{1} : 175-179.

Kuypers MMM, Marchant HK \& Kartal B (2018) The microbial
nitrogen-cycling network. \emph{Nature Reviews Microbiology}\textbf{16}
: 263-276. https://doi.org/10.1038/nrmicro.2018.9

Li J, Pei J, Pendall E, Fang C \& Nie M (2020) Spatial heterogeneity of
temperature sensitivity of soil respiration: A global analysis of field
observations. \emph{Soil Biology and Biochemistry} \textbf{141} :
107675. https://doi.org/10.1016/j.soilbio.2019.107675

Li XL, Gao J, Brierley G, Qiao YM, Zhang J \& Yang YW (2013) Rangeland
degradation on the Qinghai-Tibet plateua: implications for
rehabilitation. \emph{Land Degradation \& Development} \textbf{24} :
72-80. https://doi.org/10.1002/ldr.1108

Liang M, Johnson D, Burslem DFRP, Yu S, Fang M, Taylor JD, Taylor AFS,
Helgason T \& Liu X (2020) Soil fungal networks maintain local dominance
of ectomycorrhizal trees. \emph{Nature Communications} \textbf{11} :
2636. https://doi.org/10.1038/s41467-020-16507-y

Malik AA, Jeremy P, Buckeridge KM, Tim G, Nico J, Somak C, Soon GH,
Peyton JM, Mason KE \& Maaike VA (2018) Land use driven change in soil
pH affects microbial carbon cycling processes. \emph{Nature
Communications} \textbf{9} : 3591.
https://doi.org/10.1038/s41467-018-05980-1

Martin F, Kohler A, Murat C, Veneault-Fourrey C \& Hibbett DS (2016)
Unearthing the roots of ectomycorrhizal symbioses. \emph{Nature Reviews
Microbiology} \textbf{14} : 760-773.
https://doi.org/10.1038/nrmicro.2016.149

Moreno-Mateos D, Alberdi A, Morri\selectlanguage{ngerman}ën E, van der Putten WH, Rodríguez-Uña
A \& Montoya D (2020) The long-term restoration of ecosystem
complexity.\emph{Nature Ecology \& Evolution} \textbf{4} : 676-685.
https://doi.org/10.1038/s41559-020-1154-1

Mougi A \& Kondoh M (2012) Diversity of interaction types and ecological
community stability. \emph{Science} \textbf{337} : 349.
https://doi.org/10.1126/science.1220529

Ohara H \& Hamada M (1967) Disappearance of bacteria from the zone of
active mycorrhizas in \emph{Tricholoma matsutake} (S. Ito et Imai)
Singer. \emph{Nature} \textbf{213} : 528-529.
https://doi.org/10.1038/213528a0

Orrock JL (2020) Deterministic insights from stochastic
interactions.\emph{Proceedings of the National Academy of Sciences}
\textbf{117} : 6965. https://doi.org/10.1073/pnas.2001848117

Pedersen LL, Smets BF \& Dechesne A (2015) Measuring biogeochemical
heterogeneity at the micro scale in soils and sediments. \emph{Soil
Biology and Biochemistry} \textbf{90} : 122-138. https://doi.org/
10.1016/j.soilbio.2015.08.003

Pianka E (1981) Competition and niche theory. Vol. 8 p.\^{}pp. 167-196.

Pinsky ML (2019) Species coexistence through competition and rapid
evolution. \emph{Proceedings of the National Academy of
Sciences}\textbf{116} : 2407. https://doi.org/10.1073/pnas.1822091116

Ruth S, Renee M, Li H, Fang Z \& Wang Y (2008) Spatial patterns of plant
diversity and communities in Alpine ecosystems of the Hengduan
Mountains, Northwest Yunnan, China. \emph{Journal of Plant
Ecology}\textbf{2} :117-136. https://doi.org/10.1093/jpe/rtn012

Seaton FM, George PBL, Lebron I, Jones DL, Creer S \& Robinson DA (2020)
Soil textural heterogeneity impacts bacterial but not fungal
diversity.\emph{Soil Biology and Biochemistry} \textbf{144} : 107766.
https://doi.org/10.1016/j.soilbio.2020.107766

Singh BK, Bardgett RD, Smith P \& Reay DS (2010) Microorganisms and
climate change: terrestrial feedbacks and mitigation
options.\emph{Nature Reviews Microbiology} \textbf{8} : 779-790.
https://doi.org/10.1262/jrd.17004

Taylor TN \& Osborn JM (1996) The importance of fungi in shaping the
paleoecosystem. \emph{Review of Palaeobotany and Palynology}\textbf{90}
: 249-262. https://doi.org/10.1016/0034-6667(95)00086-0

Weisskopf L, Schulz S \& Garbeva P (2021) Microbial volatile organic
compounds in intra-kingdom and inter-kingdom interactions. \emph{Nature
Reviews Microbiology} . https://doi.org/10.1038/s41579-020-00508-1

Xie F, Ma A, Zhou H, Liang Y, Yin J, Ma K, Zhuang X \& Zhuang G (2020)
Niche differentiation of denitrifying anaerobic methane oxidizing
bacteria and archaea leads to effective methane filtration in a Tibetan
alpine wetland. \emph{Environment International} \textbf{140} : 105764.
https://doi.org/10.1016/j.envint.2020.105764

Xu C, Xiaofei L, Deping LV \& Mingying G (2015) Climate niche
characteristics and potential distribution of \emph{Tricholoma
matsutake} in China. \emph{Acta Edulis Fungi} .

Yin L, Dai E, Zheng D, Wang Y \& Tong M (2020) What drives the
vegetation dynamics in the Hengduan Mountain region, southwest China:
Climate change or human activity? \emph{Ecological
Indicators}\textbf{112} : 106013.
https://doi.org/10.1016/j.ecolind.2019.106013

Yun~Wang, Fuqiang~Yu, Chunxiang~Zhang \& Shuhong~Li
(2017)\emph{Tricholoma matsutake} : an edible mycorrhizal mushroom of
high socioeconomic relevance in China. \emph{Revista mexicana de
micología}\textbf{46} : 55-61.

Zhang B, Deng H, Wang H-l, Yin R, Hallett PD, Griffiths BS \& Daniell TJ
(2010) Does microbial habitat or community structure drive the
functional stability of microbes to stresses following re-vegetation of
a severely degraded soil? \emph{Soil Biology and
Biochemistry}\textbf{42} : 850-859.
https://doi.org/10.1016/j.soilbio.2010.02.004

Zhou J, Deng Y, Shen L\emph{, et al.} (2016) Temperature mediates
continental-scale diversity of microbes in forest soils. \emph{Nature
communications} \textbf{7} :
12083.\emph{https://doi.org/10.1038/ncomms12083}

\textbf{Figure Caption}

\textbf{Fig. 1} . The relative abundances of gene modules. The letters
a, b, and c indicate statistically significant differences between
groups (p \textless{} 0.05). Functional modules correspond to the
following designations: A-energy production and conversion, B-amino acid
transport and metabolism, C-nucleotide transport and metabolism,
D-carbohydrate transport and metabolism, E-intracellular trafficking,
secretion, and vesicular transport, F-lipid transport and metabolism,
G-transcription, H-cell wall or membrane or envelope biogenesis,
I-extracellular structures, J-signal transduction mechanisms, K-coenzyme
transport and metabolism, L-inorganic ion transport and metabolism,
M-RNA processing and modification, N-secondary metabolite biosynthesis,
transport, and catabolism, O-cell motility, P-defense mechanisms,
Q-general function prediction only, R-function unknown, S-cell cycle
control, cell division, chromosome partitioning, T-post-translational
modification, protein turnover, and chaperones, U-replication,
recombination and repair, V-chromatin structure and dynamics, and
W-translation, ribosomal structure, and biogenesis. Gene modules with
very low RAs were amplified 10--1000 times to better visualize their
relative ratios across groups (M, I and V). The modules can be
characterized into six general groups including basic metabolism-related
(dark yellow), interaction-related (gray), cofactor metabolite-related
(yellow), environmental resistance-related (brown), other (blue), and
reproduction-related (green) functions.

\textbf{Fig. 2} . Important nodes and modules within the soil microbial
networks. Fig.2A shows the identity of the module hubs and connectors
for the three soil types. The nodes with Zi \textgreater{} 2.5 were
considered module hubs, and nodes with Pi \textgreater{} 0.625 were
considered connectors, while nodes with both Zi \textgreater{} 2.5 and
Pi \textgreater{} 0.625 were considered network hubs. The module hub and
connector node identities at the phylum level are shown on the right
side of the panels. Fig.2B shows the modularity of soil networks and the
location of T.matsutake within the networks. Blue circles show
T.matsutake, yellow circles show bacterial OTUs, and green circles show
fungal OTUs. Yellow and green lines show positive and negative
interactions, respectively. Several smaller modules that were highly
marginalized are not included in the figure to allow for better
visualization of each stage's network.

\textbf{Fig. 3} . The interactions and interactive OTUs associated with
T.matsutake. Fig.3A shows the ratio of positive to negative
interactions. Fig.3B shows the relative abundances of different
interaction types across the T.matsutake habitat degradation gradient. +
and - indicate positive and negative interactions, respectively. B and F
indicate bacterial and fungal nodes, respectively, wherein B+F indicates
inferred positive interactions between bacterial OTUs and fungal OTUs.
Fig.3C shows variation in the relative abundances of OTUs that
originally interacted with T.matsutake, *** indicates statistically
significant differences among groups (p \textless{} 0.001).

\textbf{Fig. 4.} The linear relationships between \emph{T.matsutake} RAs
and soil properties, community diversity, community structure, and
community network structures. Upward arrows indicate components that
were positively related to extraction. BE, BR, and BS indicate bacterial
equitability, richness, and Shannon index values, respectively, while
FE, FR, and FS indicate the respective fungal counterparts. Community
structure was extracted from PC1 of the bacterial and fungal PCoAs,
while network structure was comprised of the values from the network
indices avgK, avgCC, and Tran. Shaded areas show 95\% confidence
intervals.\selectlanguage{english}
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