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\begin{document}

\title{Soil quality and C-S-R fungal communities in monoculture plantations
after conversion from subtropical forest}



\author[1]{Ting Liu}%
\author[1]{Xiao Wu}%
\author[1]{Huangwei Li}%
\author[1]{Chen Ning}%
\author[1]{Yong Li}%
\author[1]{Xuyuan Zhang}%
\author[1]{Jinsong  He}%
\author[2]{Filimonenko Ekaterina}%
\author[3]{Shu Chen}%
\author[4]{Xiaoyong  Chen}%
\author[3]{David  Gibson}%
\author[5]{Yakov Kuzyakov}%
\author[1]{Wende Yan}%
\affil[1]{Central South University of Forestry and Technology}%
\affil[2]{Tyumen State University}%
\affil[3]{Southern Illinois University Carbondale}%
\affil[4]{Governors State University}%
\affil[5]{Institute of Physicochemical and Biological Problems of Soil Science. Russian Academy of Sciences}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Intensive clear cutting of natural forests and conversion to monoculture
plantations are ongoing worldwide, leading to degradation of soil
quality and microbial functions. Here, we compared soil quality index
(SQI) and fungal community in a natural forest (Forest) and four
5-year-old monoculture plantations, including Camellia oleifera (Oil),
Amygdalus persica (Peach), Myrica rubra (Berry) and Cunninghamia
lanceolata (Fir), in a subtropical region of China. After conversion,
soil pH rose up to 0.31, but organic carbon, total nitrogen, sucrase,
acid protease, glutaminase and phosphatase activities decreased by 83\%,
59\%, 43\%, 31\%, 64\%, 66\% and 77\%, respectively, in the plantations.
Correspondingly, the SQI dropped by 65\%. High-throughput sequencing of
the ITS1 region demonstrated an increase in \selectlanguage{greek}α-\selectlanguage{english}diversity and a striking
difference in \selectlanguage{greek}β-\selectlanguage{english}diversity following conversion. Changes in the dominant
fungal taxa following forest conversion to plantations was interpreted
by Grime's C-S-R life history framework. Conversion increased the fungal
groups with stress-tolerant (S) and ruderal (R) strategies - mainly
copiotrophic saprophytes, such as Ascomycota and Zygomycota, but
decreased the fungal groups with competitor (C) strategies - mainly
oligotrophic saprophytes and mycorrhizal fungi, such as Basidiomycota.
Genera affiliated to those phyla including Pseudophialophora, Rhytisma
increased, but Russula decreased. Redundancy analysis and structural
equation modeling indicated that the diversity and composition of fungal
communities changed with soil quality degradation, which were mainly
driven by increased soil pH, decreased available carbon and nutrients
(N, P), and related enzymes activities.%
\end{abstract}%



\sloppy


\textbf{1 Introduction}

Worldwide, half of primary and secondary forests, which are crucial for
maintaining biodiversity and ecosystem functions, are being converted
into monoculture plantations for the cultivation of timber, fuel
materials and other commodities. The annual expanding rates of
monoculture plantations have increased to 2\% over the past 25 years
(FAO, 2015). Most conversion from natural forests to plantations has
occurred in subtropical regions(Lan et al., 2021) . Such conversion
results in alterations of ecosystem services and functions along with
changes in plant diversity and soil nutrients loss (Krashevska, Klarner,
Widyastuti, Maraun, \& Scheu, 2015; Yang, Zhu, Xu, \& Zheng, 2018). Soil
quality is defined as the capacity to sustain biological productivity,
maintain environmental quality, and promote plant and animal health
(Doran \& Parkin, 1996). Various methods such as visual qualitative and
quantitative approaches have been used to assess soil quality (B\selectlanguage{ngerman}ünemann
et al., 2018; Yakov et al., 2020). Quantitative evaluation incorporates
chemical, physical and biological indicators into the calculation of
soil quality index (SQI). Forest conversion may lead to reduced
vegetation coverage and forest productivity, soil quality deterioration
and erosion (Guillaume, Damris, \& Kuzyakov, 2015), all of which will
have a negative impact on soil quality and health (Zarafshar et al.,
2020). Intensive management and short rotations are employed during
conversion, including site reclamation, clear-cutting, residue mulching,
herbicide weeding and fertilization. These practices can modify soil
quality, but the consequences for microorganisms and nutrient cycling
are not clear (Armenise, Redmile-Gordon, Stellacci, Ciccarese, \&
Rubino, 2013). Thus, comprehensive and quantitative studies are needed
to better understand the impacts of forest conversion on soil quality
and functions in subtropical areas.

In forest ecosystems, fungi play fundamental ecological roles as
decomposers, symbionts, and tree pathogens (Uroz, Buée, Deveau,
Mieszkin, \& Martin, 2016). They regulate organic matter decomposition
and nutrient cycling, and stimulate plant growth (Nakayama, Imamura,
Taniguchi, \& Tateno, 2019). The establishment of fungal community is
determined by both abiotic (i.e. climate, spatial heterogeneity and
edaphic conditions) (Steidinger et al., 2019; van der Linde et al.,
2018) and biotic (i.e. host species) factors (Duhamel et al., 2019). The
conversion from natural forest to plantations is accompanied by changes
in the identity and diversity of host tree species and soil properties,
thereby affecting the fungal community and tropical-guild composition
(Mcguire et al., 2015). Compared with extensive studies on bacterial
communities (Kerfahi, Tripathi, Dong, Go, \& Adams, 2016; Liu et al.,
2020), only a few studies have evaluated the response of soil fungi to
forest conversion. In addition, microbial activity is a valuable
indicator of soil quality (Bünemann et al., 2018). For instance,
microorganisms can maintain soil P bioavailability (Wu et al., 2019) and
alleviate N limitations (Johnson, Rowland, Corkidi, \& Allen, 2008).
Hence, it is necessary to evaluate the impacts of forest conversion on
the fungal community, which play a vital role in shaping ecosystem
functions (e.g., soil nutrient cycling, carbon sequestration).

The aim of our study was to quantify the changes in soil quality and the
fungal community following forest conversion. Physico-chemical and
biological properties as well as the microbial community composition
were measured under a natural forest and four monoculture plantations
common for subtropical climate. The objectives were as follows: (1) To
investigate the impacts of forest conversion on soil quality and assess
the main influencing factors; (2) To evaluate the differences in
diversity, composition, and key taxa of fungal community after
conversion; (3) To clarify the relationship between soil quality and the
fungal community, and explore main driving factors of community changes
after conversion.

\textbf{2 Materials and methods}

\textbf{2.1 Study site and sampling}

The study sites were located in Lutou National Station for Scientific
Observation and Research of Forest Ecosystems, Hunan Province, Central
South China (113°51\selectlanguage{english}'52\selectlanguage{english}"\textasciitilde{}113deg58\selectlanguage{english}'24\selectlanguage{english}"E,
28deg31\selectlanguage{english}'17\selectlanguage{english}"\textasciitilde{} 28deg38\selectlanguage{english}'00\selectlanguage{english}"N). The region has a typical
mid-subtropical monsoon climate with an annual temperature of 15.8 degC
and an annual precipitation of 1,450 mm. The soils are characterized as
Ferric and Orthic Acrisols (Liu et al., 2020). The subtropical natural
forest is dominated by \emph{Cinnamomum camphora} , \emph{Liquidambar
formosana} , \emph{Pinus massoniana} , \emph{Photinia serratifolia} ,
\emph{Ilex chinensis} ,\emph{Broussonetia papyrifera} ,
\emph{Loropetalum chinense} and\emph{Camellia japonica.} A large area of
the natural forest (Forest) was cleared and reclaimed to monoculture
plantations including\emph{Camellia oleifera} (Oil), \emph{Amygdalus
persica} (Peach),\emph{Myrica rubra} (Berry) and \emph{Cunninghamia
lanceolata} (Fir) in 2013 (Liu et al., 2020). After conversion,
management practices such as site reclamation, fertilization and weeding
were adopted. For the Berry and Peach plantations, 4,356 kg
ha\textsuperscript{-1} yr\textsuperscript{-1} organic fertilizers
(manure) were applied in October, and 545 kg
ha\textsuperscript{-1}yr\textsuperscript{-1} compound mineral
fertilizers (N/P/K ratios: 1:1:2) were additionally applied to the Peach
plantation in July each year. Only 163 kg ha\textsuperscript{-1}
yr\textsuperscript{-1} compound mineral fertilizers were applied to the
Oil plantation in May each year. The Fir plantation was subject to no
management practice.

In November 2018, three replicate plots (10 m x 10 m each one) were
randomly established for each forest type according to slope. After
removing the litter layer within each plot, soil was sampled from the
0-20 cm layer, homogenized and sieved with a 2-mm mesh. In total, 15
composite soil samples were divided into three parts: one was dried at
room temperature for soil physico-chemical analysis; the other two were
kept at 4 \selectlanguage{english} and -80 \selectlanguage{english} for enzyme activity and molecular analysis,
respectively.

\textbf{2.2 Analysis of soil}

\textbf{physico-chemical properties and enzyme activities}

Soil bulk density (BD) and moisture (SWC) were measured with a bulk
density ring and conventional dry wet weight methods, respectively. Soil
pH was measured at a soil-to-water ratio of 1:2.5 (w/v) with a pH meter
(FE20K, Mettler Toledo, China). Organic carbon (SOC) was determined
using the dichromate oxidation method. Dissolved organic carbon (DOC)
was extracted by potassium sulfate (0.5 mmol L\selectlanguage{english}\textsuperscript{-1}).
Total nitrogen (TN) was determined according to semi-micro Kjeldahl
method. Ammonium nitrogen (NH\textsubscript{4}\textsuperscript{+}) and
nitrate nitrogen (NO\textsubscript{3}\textsuperscript{-}) were measured
by a continuous flow analyzer (Skalar Analytical B.V, SAN++). Total
phosphorus (TP) and available phosphorus (AP) were assayed by the
molybdenum antimony anti-colorimetric method. Total potassium (TK),
available potassium (AK), exchangeable calcium (Ca) and magnesium (Mg)
were determined using the EDTA titrimetric method and an inductively
coupled plasma spectrometer (ICP-AES, Thermo, USA). Potential soil
sucrase (SC), urease (UR), acid protease (ACPT) and glutaminase (GLS)
activities were determined using the soil enzyme kit from Solarbio
Science \& Technology Co. (Beijing, China). Acid (ACP) and alkaline
phosphatase (ALP) activities were assayed by p-nitrophenyl phosphate
disodium method (PNPP) at pH = 6.5 and 11, respectively. Three parallel
measurements were performed for each sample to minimize experimental
errors.

\textbf{2.3 Soil quality evaluation}

The soil quality index (SQI) was evaluated by principal component
analysis (PCA) with soil physico-chemical properties, enzyme activities
and microelements as indicators. The determination procedure includes
three steps: (1) selecting minimum data set (MDS) of indicators; (2)
scoring the MDS indicators using a standard scoring function; (3)
calculating soil quality index (SQI) of five forest types. First, a
variety of soil properties including texture, bulk density, nutrients,
enzyme activities and microelements were added to the indicator dataset,
and correlation analysis was conducted. Secondly, all indicators were
analyzed by PCA, with a varimax rotation to enhance interpretability of
the uncorrelated components (Bernhard Flury \& Riedwyl, 1988). Only the
principal components (PC) with eigenvalues\selectlanguage{english}>1 and absolute loading
values \selectlanguage{english}[?]0.5 can identify representative MDS for further analysis
(Bastida, Luis Moreno, Hernandez, \& Garcia, 2006). Third, to avoid
information loss and data redundancy, the norm value of each indicator
was calculated. For each PC, the selected indicators obtained the norm
value within 10\% of the maximum weighted load for the MDS1. When more
than one indicator was retained in a PC, correlation analysis was
conducted to determine whether these indicators were redundant (Andrews,
Karlen, \& Mitchell, 2002). If high variables were highly correlated
(\emph{r} \selectlanguage{english}[?]0.6) with the maximum value, only the highest factor loading
was retained, and the rest would be eliminated from the dataset. Fourth,
a standard scoring function was employed for each indicator in MDS2
(ranging from 0 to 1) (Andrews et al., 2002). The weight of an indicator
was equal to the ratio of its communality with the sum of communalities
of all indicators.

Type S:

\begin{equation}
f(x_{i})=\left\{\begin{matrix}1.0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ,x_{i}\geq x_{2}\\
0.1+\frac{0.9\left(x_{i}-x_{1}\right)}{x_{2}-x_{1}}\ \ ,x_{1}<x_{i}x_{2}\\
0.1\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ,x_{i}\leq x_{1}\\
\end{matrix}\right.\ \nonumber \\
 \end{equation}

(2) Type reverse S:

\begin{equation}
f(x_{i})=\left\{\begin{matrix}0.1\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ,x_{i}\geq x_{2}\\
0.1+\frac{0.9\left(x_{2}-x_{i}\right)}{x_{2}-x_{1}}\ \ ,x_{1}<x_{i}x_{2}\\
1.0\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ,x_{i}\leq x_{1}\\
\end{matrix}\right.\ \nonumber \\
 \end{equation}

where \emph{f} (x) is the score of the indicator ranging from 0.1 to 1,
X is the value of soil indicator, X\textsubscript{1} and
X\textsubscript{2} are the minimum and maximum values of indicator,
respectively.

Finally, following scoring and weighting for all indicators in the MDS2,
SQI was calculated as follows:

SQI=\(\sum_{i=1}^{n}W_{i}\times S_{i}\)\(\sum_{i=1}^{n}W_{i}\times F_{i}\)

where SQI is the soil quality index ranging from 0 to 1.0,
W\textsubscript{i} is the weight of each indicator, F\textsubscript{i}is
the indicator score, and n is the number of indicators in the final MDS.

\textbf{2.4 DNA extraction, amplification, sequencing and data
processing}

Soil total DNA was extracted from 0.3 g of homogenized soil samples
using a DNA extraction kit (MoBio Laboratories Inc., CA, USA) following
the manufacturer's instructions. The quantity and quality of the
extracted DNA were assessed using a NanoDrop spectrophotometer (Thermo
Fisher Scientific, MA, USA), and were further checked with 1\% (w/v)
agarose gel. A primer set of ITS5-1737F (5'-GGAAGTAAAAGTCGTAACAAGG-3')
and ITS2-2043R (5'-GCTGCGTTCTTCATCGATGC-3') with adaptors and barcodes
was used to amplify fungal ITS hypervariable region (White, Bruns, Lee,
\& Taylor, 1990). Polymerase chain reaction (PCR) followed the
thermal-cycling conditions: initialization for 5 min at 94 \selectlanguage{ngerman}°C, 30 cycles
of denaturation for 30 s at 94 °C, annealing for 30 s at 52 °C, and
extension for 30 s at 72 °C, followed by a final elongation for 10 min
at 72 °C. PCR products were mixed in equal density ratios according to
the Gene Tools Analysis Software (Version4.03.05.0, SynGene), and were
purified using an E.Z.N.A\textsuperscript{®} Gel Extraction Kit (Omega
Bio-Tek, Norcross, Georgia, USA). The resultant PCR products were
sequenced on the IlluminaHiSeq2500 (250-bp paired-end reads) platform at
Magigene Biotechnology Co., Ltd. (Guangzhou, China).

The paired-end raw reads were proceeded using Quantitative Insights Into
Microbial Ecology (QIIME) pipeline (Caporaso et al., 2010) for quality
filtering, trimming, and chimera checking. Low quality sequences
(ambiguous bases, nucleotide mismatches, an average quality
score\textless{} 20, length \textless{} 100 bp, total expected errors
\textgreater{} 0.1) were filtered. After discarding chimeras and
singletons, operational taxonomic units (OTUs) were identified using
UPARSE with a similarity threshold of 97\% (Edgar, 2013). Finally,
representative sequences of fungal OTU were selected for taxonomic
assignment by using BLAST against the UNITE database
(\emph{http://unite.ut.ee/index.php}) (Abarenkov et al., 2010), using
the Ribosomal Database Project (RDP) classifier (Caporaso et al., 2010).
Fungal functional guilds were tentatively assigned using the `FUNGuild'
algorithm (Nguyen et al., 2016). All raw sequences were deposited in the
NCBI Sequence Read Archive (SRA) database under accession number SRR
(SUB9976505).

\textbf{2.5 Statistical analyses}

A one-way analysis of variance (ANOVA) followed by a Duncan's test
(\emph{p \textless{}0.05} ) was applied to test the differences in soil
properties, fungal diversity and abundance across five sites. PCA and
Pearson's correlation was used to evaluate the SQI using the IBM SPSS
software (SPSS Inc., Chicago, USA). For the sequence data, the \selectlanguage{greek}α
\selectlanguage{english}diversity indices including Observed species, Shannon-Wiener's
diversity, Chao1 and species accumulation curves were calculated using
Mothur and visualized in Origin 2018 software (OriginLab, USA). The \selectlanguage{greek}β
\selectlanguage{english}diversity in the fungal community was visualized in CANOCO 5.0 software
(Microcomputer Power, Ithaca, New York) by using non-metric
multidimensional scaling (NMDS) ordinations based on Bray--Curtis
distance. Network visualized by Cytoscape (Vailaya et al., 2007) was
used to analyze the number of OTU which was cosmopolitan (present in all
forest types), specific to forest or plantations (i.e., only detected in
one forest type) or broadly dispersed (found in two or four sites).
Linear discriminant analysis (LDA) effect size (LEfse) was conducted to
identify the biomarkers between forest and plantations using an LDA
score \textgreater{} 4 and \emph{p} \textless{} 0.05 for the
nonparametric Kruskal--Wallis test. A heatmap was conducted in R with
the ``pheatmap'' package to compare the variation in dominant
``funguilds'' (top 35 in abundance). Relationships between soil
parameters and the fungal community were performed with redundancy
analysis (RDA) by using the CANOCO 5.0 software. Structural equation
model (SEM) was further used to determine indirect or direct
contributions of soil properties and fungal community with a
multivariate approach using AMOS software (IBM SPSS AMOS 20.0.0). In
this model, the Shannon index was selected as an informative estimate of
taxonomic diversity for the fungal communities (Delgado-Baquerizo et
al., 2016). The SEM fitness was examined on the basis of a
non-significant chi-square test (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}), \emph{p} value
(>0.05), the goodness-of-fit index (GFI, good fit when GFI>0.9) and
the root mean square error of approximation (RMSEA, good fit when
RMSEA<0.05) (Byrne \& Erlbaums, 2009). The fit of additional indices of
SEM is shown in Table S6.

\textbf{3 Results}

\textbf{3.1 Soil properties}

After forest conversion, soil BD was higher and SWC was lower in the
plantations compared with the forest (Fig. 1a). The SOC and DOC contents
were 83\% and 39\% lower, respectively (Fig. 1b, d). A similar
decreasing trend was found for TN,
NH\textsubscript{4}\textsuperscript{+}and
NO\textsubscript{3}\textsuperscript{-} contents (except Berry; Fig. 1b,
d). Consequently, the soil C:N ratio (C/N) strongly dropped by 11.9
(Fig. 1b). However, the highest TP, TK and AP, AK contents were observed
under Peach. Compared to the other three plantations, TP, TK and AP, AK
in Peach was 26\%, 12\%, 88\% and 43\% higher, respectively (Fig. 1b,
d). Compared with the forest, Ca\textsuperscript{2+} content in
plantations was reduced by 43\%.

Soil enzyme activities decreased after conversion, including SC, ACPT,
ACP, GLS, ACP and ALP (Fig. 1c). For instance, SC, ACPT and GLS
activities declined up to 40\%, 64\% and 67\%, respectively (Fig. 1c).
Similarly, soil phosphatase activities were lower in the plantations,
and ACP and ALP activities declined by approximately 93\% and 59\%,
respectively (Fig. 1c). Likewise, with the exception of Berry (Fig.1c),
the UR activities were lower in the plantation than in the forest (Fig.
1c).

\textbf{3.2 Soil quality index}

PCA results showed that the first three components with eigenvalues [?]1.0
explained 87\% of the total variation of soil properties (Table S1).
According to the Norm value, SOC, BD, DOC, TN,
NH\textsubscript{4}\textsuperscript{+}, C/N, ACPT, ACP and ALP were the
highly weighted variables in the PC1 and highly correlated with each
other. Thus, SOC was selected in the PC1 with the highest eigenvector.
Similarly, GLS and TP were selected for PC2 and PC3, respectively. In
short, SOC, GLS and TP were the three important indicators selected for
MDS2. The SQI was calculated after scoring and weighting the four
indicators (Table S2). The SQI values derived with the MDS method ranged
from 0.16 to 0.92. After conversion, the SQI of Forest decreased sharply
by 65\%. Within four plantations, the highest values were observed in
the Peach, the lowest in the Oil (Fig. 2).

\textbf{3.3 Diversity, composition and} \textbf{functional guilds of the
fungal community}

After quality filtering, we obtained 1,252,350 sequences for downstream
analysis (range, 83,490 \selectlanguage{ngerman}± 369 reads per sample) from a total of
1,251,620 raw sequencing reads. A total of 3,933 OTUs were clustered
with \selectlanguage{english}[?] 97\% identity from good-quality sequences. Rarefaction curves
demonstrated that the sequencing depth was sufficient to capture species
richness of the community (Fig. S1). Following forest conversion,
Observed species, Shannon-Wiener and Chao1 indices of \selectlanguage{greek}α-\selectlanguage{english}diversity
increased by 3.5\%, 6.5\% and 2.9\%, respectively. All indices were
higher in Berry and Peach than the other three plantations (\emph{p}
\emph{\textless{}} 0.05; Fig. 3a, 3b, 3c). Moreover, Chao1, Observed
species, and Shannon-Wiener indices increased with pH, UR and Mg
(\emph{p} \textless{}0.05; Table S3). According to NMDS analysis, the
dissimilarity of the fungal community structure changed substantially
after conversion (Fig. 3d).

In total, 15 fungal phyla and 696 genera were identified but only 4
phyla and 20 genera were recorded with relative abundances of over 1\%.
Ascomycota, Basidiomycota, Glomeromycota and Zygomycota were dominant
phyla, accounting for approximately 87\% of the fungal sequences (Fig.
4a). After conversion, phyla such as Ascomycota, Zygomycota, and
Glomeromycota increased, but Basidiomycota and Rozellomycota decreased
by 57\% and 32\%, respectively (Fig. 4a)\emph{.} Ascomycota increased
with TK, but decreased with
NO\textsubscript{3}\textsuperscript{-}(\emph{p} \emph{\textless{}} 0.05;
Table S5). Basidiomycota declined with SOC, DOC, C/N,
NH\textsubscript{4}\textsuperscript{+}, GLS and ACP, and Rozellomycota
fell with NO\textsubscript{3}\textsuperscript{-}, UR, GLS and Ca, while
Glomeromycota increased with BD (\emph{p}\textless{}0.05; Table S5). At
the genus level, relative abundance of\emph{Pseudochaetosphaeronema} and
\emph{Russula} decreased by approximately 99\%, while
\emph{Pseudophialophora, Rhytisma} and\emph{Lepidostroma} increased
after conversion (Fig. 4b; Table S4).

Most fungi were classified as saprotrophs (35\%), undefined saprotrophs
(29\%) or ectomycorrhizal fungi (13\%). Comparisons within the top 35
guilds revealed that three functional guilds (ectomycorrhizal, wood
saprotroph, fungal parasites) were more abundant in Forest than in
plantations, whereas undefined saprotrophs, arbuscular mycorrhizal,
bryophyte parasite and plant pathogen fungi were enriched in plantations
(Fig. 5).

The majority of the top 50 OTUs were either broadly dispersed across
five sites (32\%) or only occurred in the plantations (32\%) (Fig. 6).
Specific OTUs for Forest, Oil, Peach, Berry and Fir plantations were
six, five, one, four and two, respectively.
Specifically,\emph{Pseudochaetosphaeronema} OTU\_4 and \emph{Russula}
OTU\_7 were distinct from others for Forest. \emph{Biscogniauxia}
OTU\_55 and\emph{Inocybe} OTU\_25 were special to Oil. \emph{Cistella}
OTU\_100 was unique to Peach. \emph{Simplicillium} OTU\_48,
\emph{Subulicystidium}OTU\_125, \emph{Dendrochytridium} OTU\_90 and
\emph{Melanogaster}OTU\_88 were four particular taxa for Berry.
\emph{Lepidostroma} OTU\_38 and \emph{Tomentella} OTU\_17 were specific
for Fir (Fig. 6). LDA analysis showed that the Agaricomycetes class and
Russulaceae family were identified as biomarkers between Forest and Oil,
Berry, respectively. Compared with the Forest, Russulales order
(belonging to Basidiomycota) was identified as a biomarker in Peach and
Fir plantations (Fig. S2).

\textbf{3.4 Effects of key soil properties on fungal community
composition}

Soil properties explained 37.4\% of the total variance of the fungal
community composition at the OTU level (RDA analysis). The community
structure of Forest was more closely associated with soil SWC, SOC, DOC,
C/N, TN, and NH\textsubscript{4}\textsuperscript{+}, and activities of
SC, ACPT, ACP, and ALP (Fig. 7a). By contrast, those of the Berry and
Peach were more strongly affected by pH, TP, AP,
NO\textsubscript{3}\textsuperscript{-}, Ca, Mg, TK, AK, BD, GLS and UR
activities (\emph{p} \textless{} 0.05). Pearson's correlation indicated
that the dominant phyla had strong correlation with soil quality
indicators (Fig. 7b; Table S5). For instance, the relative abundance of
Basidiomycota decreased with the reduction of SOC content
(\emph{p}=0.033) and GLS (\emph{p} =0.042).

The SEM model indicated that the key predictors collectively accounted
for 42\% of the variation in fungal community diversity (Fig. 8). This
model depended on the contents of pH, TP, TN and SOC affected by
conversion. In particular, fungal diversity was directly and positively
mediated by the key soil quality indicators including TP (path
coefficient = 0.54, \emph{p} \textless{}0.01) and pH (path coefficient =
0.51, \emph{p} \textless{}0.001), and probably indirectly affected by
the contents of SOC, DOC and TN (Fig. 8). The fungal diversity also had
a direct negative impact on DOC contents (path coefficient =
-0.19,\emph{p} \textless{}0.001), SC (path coefficient =-0.38,
\emph{p}\textless{}0.01) and ALP activities (Fig. 8). The key soil
quality indicator SOC was positively and directly related to TN, DOC and
SC activities (Table S7), and it also increased with ALP activities via
SC. However, SOC exerted a direct positive impact on TP (Fig. 8).

\textbf{4 Discussion}

\textbf{4.1 Effects of forest conversion on soil quality}

The soil C, N contents dropped by 83\% and 59\% after conversion,
respectively (Fig. 1), similar to previous studies that were focused on
rainforest and temperate forest conversion into monoculture plantations
(Guillaume et al., 2015; Yang et al., 2018). Firstly, soil erosion
increased when understory vegetation and litter layer were removed after
conversion, coupled with a large amount of precipitation in the region.
These factors caused a sharp drop in C and N contents (Guillaume et al.,
2015). Secondly, the decomposition of SOC increased due to nutrient
release from the dead or burnt biomass, and the labile organic matter
released from destroyed soil aggregates (Berhe, Harte, Harden, \& Torn,
2007). Therefore, soil SOC content decreased following conversion.
Third, C and N were reduced in plantations because low plant diversity
prevented the formation of developed organic horizons, and reduced the
input of labile organic compounds, leading to a decrease in microbial
biomass (Mcguire et al., 2015). In contrast, soil pH increased after
conversion, which was ascribed to three factors: 1) Conversion reduces
plant species, resulting in a decline in root and ectomycorrhizal
exudates which contain a lot of organic acids (Grayston, Vaughan, \&
Jones, 1997). Thus, soil pH increased after conversion. 2) Soil erosion
removes the surface layer, which is usually more acidic than the
subsoil. 3) The increase of exchangeable cations results in an increase
in soil pH, as a fertilizer following conversion (Tripathi et al.,
2016). In addition, soil TP, AP, TK, AK were the highest in the Peach
plantation (Fig. 1) because of intensive application of mineral and
organic fertilizers.

Compared with Forest, the enzymatic activity of SC, ACPT, GLS, UR, ACP
and ALP decreased in plantations (Fig. 1). Soil SC is a C-degrading
enzyme involved in organic matter hydrolysis. Thus, the SC activities
declined with the reduction of SOC after conversion. Soil ACPT, UR and
GLS are key enzymes involved in N mineralization. ACPT acts on
proteolysis and breaks down organic N such as proteins and peptides into
amino acids (Watanabe, 2010). UR converts organic N to mineral nitrogen
that can be used by plants and microorganisms (Fraser, Hallett, Wookey,
Hartley, \& Hopkins, 2013). In the Forest, high SOC content accelerates
N mineralization, resulting in higher N mineralization activity,
including ACPT and UR (Yang, Yang, \& Yu, 2018). Soil GLS involves the
hydrolysis of L-glutamine to produce L-glutamic acid and
NH\textsubscript{3}, and its activity is positively correlated with
organic C and total N (Frankenberger \& Tabatabai, 1991). Thus, the
decrease in GLS activity is attributed to the drop in SOC and TN
contents in plantations. After conversion, the phosphatase activity
decreased with the increase of AP content. Compared with plantations,
lower AP stimulated the microbial community to secrete phosphatase to
mobilize P from organic sources, leading to higher phosphatase activity
in the Forest (Wu et al., 2019).

The decline in soil quality after conversion, revealed by the SQI,
showed that GLS was the biggest contributor to SQI, followed by SOC and
TP (Fig. 2, Table S2). GLS is a key enzyme of N metabolism, as it
catalyzes the hydrolysis of NH\textsubscript{3} group from amino acid
and degrades L-glutamine into L-glutamic acid and ammonium ions, which
maybe a reliable indicator of soil fertility (Kanazawa \& Kiyota, 2000).
Hence, this enzyme could be used as a biological indicator of SQI in
subtropical areas. SOC is often used as a proxy of soil quality and
productivity due to its key role in multiple soil processes such as
nutrient cycling as well as plant and microbial growth (Raiesi, 2017). A
big decrease in GLS activities and SOC contents was responsible for the
sharp drop in SQI after conversion. Forest conversion accelerated soil
erosion, reduced organic matter inputs and C, N contents, resulting in a
decrease in SQI. TP was another key indicator that contributed to SQI
(Zhang et al., 2019). Among the four plantations, SQI of Peach and Berry
was superior than the other two, indicating that fertilization was
beneficial for soil quality after conversion by increasing TP contents.

\textbf{4.2 Effects of forest conversion on fungal communities}

After forest conversion, \selectlanguage{greek}α-\selectlanguage{english}diversity of fungi increased in Berry and
Peach plantations (Fig. 3a-c), and there were significant differences in
community structure among the five types of plantations (Fig. 3d). The
increase of \selectlanguage{greek}α-\selectlanguage{english}diversity was consistent with the conversion of which
forest to which plantation (Nakayama et al., 2019). We explain this as
follows: 1) Acidity reduced the taxonomic diversity of the arbuscular
mycorrhiza fungi (AM) by decreasing sporulation (Coughlan, Dalp\selectlanguage{ngerman}é,
Lapointe, \& Piché, 2000). Hence, the gradual increase in soil pH led to
a higher diversity in plantations (Fig. 3a-c, Fig. 8). 2) Fertilization
was responsible for the increase of \selectlanguage{greek}α-\selectlanguage{english}diversity in Berry and Peach. The
continuous application of manure increased fungal biomass and diversity
by increasing substrate variety and C input (Kamaa et al., 2011). The
exogenous fungi from the manure (organic resources) may increase fungal
diversity of plantation (Sun et al., 2016). According to the
\selectlanguage{greek}β-\selectlanguage{english}diversity, notable divergence was observed in the composition between
Forest and four plantations (Fig. 3d). Forest conversion affects fungal
community structure via changes in soil chemical properties (e.g., pH,
C, N contents), root exudates and litter input (Prescott \& Grayston,
2013).

Due to specific functional attributes and survival strategies, the
abundance of dominant fungi responded differently to forest conversion
(Fig. 4, 6). For instance, the abundance of Basidiomycota decreased
while that of Ascomycota and Zygomycota increased. We can characterize
these fungi based on Grime's C-S-R (Competitor, Stress-tolerant,
Ruderal) life history strategy to explain succession dynamics and the
spatial structure of fungal assemblages (Chagnon, Bradley, Maherali, \&
Klironomos, 2013; Grime, 1979). Zygomycota (R-strategists) are primary
saprotrophic fungi with ruderal strategies, which are only active in
habitats with low levels of competition and stress. They are
characterized by a short growth period, high reproductive potential, and
effective use of accessible substrates (Cooke \& Rayner, 1984). Thus,
Zygomycota are more abundant in plantations with low C and N content.
Basidiomycota and Ascomycota are secondary saprotrophic fungi, and their
\emph{cbhI} gene is a reliable indicator of cellulolytic ability. Both
fungi groups can degrade part of plant residues and promote soil C
accumulation (Stursova, Zifcakova, Leigh, Burgess, \& Baldrian, 2012).
By contrast, most Basidiomycota (C-strategists) are white and brown rot
fungi, which are late-successional and oligotrophic species with slow
growth and favor resource-rich conditions and better substrate quality
(high C/N). They are usually involved in decomposition of lignified
plant detritus (Lundell, Makela, \& Hilden, 2010). In addition, the
lignin content in old forest litter is high (Osono, 2006). Therefore,
C-strategists Basidiomycota decrease with reduction of C/N ratio and
litter after conversion. Subsequently, Basidiomycota such as the typical
ectomycorrhizal group (\emph{Russula)} and other genera belonging to the
Basidiomycota declined sharply. In contrast, Ascomycotas are S-strategy
fungi, with survival related stress tolerance characteristics, and use
resources more efficiently under low-nutrient conditions. So they
exhibit a more stress-tolerant strategy to C substrate than
Basidiomycetes (Wang et al., 2021). The Ascomycotas are mainly soft rot
fungi that can decompose complex organic matters such as cellulose,
hemicellulose and the lignin in the soil (Stursova et al., 2012). As a
fast-growing copiotrophic fungi, the S-strategists Ascomycota increased
with stressful conditions (low C, N contents) in plantations. Thereby,
the abundance of genera affiliated Ascomycota:\emph{Pseudophialophora}
and\emph{Rhytisma} increased. Moreover, among the root-associated fungi,
the change in the abundance of Ascomycota related to Basidiomycota also
implied that the ratio of ericoid mycorrhizal fungi (mainly Ascomycota)
and ectomycorrhizal (mainly Basidiomycota) had changed (Sterkenburg,
Bahr, Brandstrom, Clemmensen, \& Lindahl, 2015).

\textbf{4.3 Main drivers of fungal community under forest conversion}

According to the RDA analysis, shifts of fungal composition were
associated with changes in key soil quality properties, such as SOC
(Fig. 7, Table S5). Fungal diversity was directly regulated by the soil
pH, TP and TN, but indirectly affected by SOC according to the SEM (Fig.
8). Soil pH directly shapes the fungal community by affecting nutrients
uptake over the cell plasma membrane, secretase activity or mycorrhizal
colonization (Deacon, 2006), or affecting multiple soil chemical
parameters such as ion concentrations, base cations and available P. For
example, an increase in pH reduces bioavailable P pool via decreased
production of extracellular enzymes used by soil microorganisms to
access organic phosphate, and then affects mycorrhizal colonization,
biomass and fungal community structure (Carrino-Kyker et al., 2016).
Base cation (e.g., Ca\textsuperscript{2+}) also increased AM (mostly
belonging to Glomeromycota) colonization by affecting spore germination,
mycelia production and hyphal regrowth (Coughlan et al., 2000; Hepper,
1984). Moreover, Ca\textsuperscript{2+} influences the turnover rate of
soil organic matter, and increases colonization and biomass of
Ectomycorrhizal (ECM) fungi, particular Basidiomycota (P\selectlanguage{ngerman}õlme et al.,
2013). Alternatively, soil pH had an indirect effect on the fungal
community. Since the more acidic soil in Forest inhibits the growth of
bacteria, fungi are more competitive in the use of substrates, thus
establishing a variety of niches (Gunina, Smith, Godbold, Jones, \&
Kuzyakov, 2017).

SOC, TN and TP were important regulators of fungal communities after
forest conversion (Fig. 8). As non-autotrophic microorganisms, fungi
mainly rely on C sources from fallen leaves, wood detritus and root
exudates, which are the three key ways that aboveground plants affect
soil fungal communities. Firstly, litter quality affected degradation
rates and composition of saprotrophic (mainly Ascomycota) and ECM fungi
(mainly Basidiomycota) (Prescott \& Grayston, 2013). Secondly, the
reduction of wood detritus led to a decline in the abundance of
Basidiomycota, which mainly decomposed lignin (Cooke \& Rayner, 1984).
Thirdly, some C in soil came from root exudates, including primary
metabolites (sugars, amino acids and organic acids) and secondary
metabolites (Jones, Hodge, \& Kuzyakov, 2004). The reduction of organic
substrates in root exudates may alter the fungal community composition
(Prescott \& Grayston, 2013). Hence, as the plant diversity decreases,
and understory plants and litter are removed after conversion, the
diversity of organic substrates entering the soil is reduced, thereby
reducing the ability to establish highly diverse heterotrophic fungi
(Peay, Baraloto, \& Fine, 2013). Moreover, high levels of soil erosion
in the plantations reduced the SOC and TN stock, and accelerated SOC
mineralization and mineral N leaching, resulting in lowered C and N
contents (Guillaume et al., 2015). Dominant taxa fungal growth was
consequentially reduced. For instance, the abundance of Basidiomycota
decreased with the decrease of C and N content under plantations
(Lauber, Strickland, Bradford, \& Fierer, 2008). In addition, P is a key
regulator of the biogeographical pattern of fungal communities. Higher P
content increases plant root growth, mycelia development and
mycorrhizas, especially for special functional groups such as AM and ECM
fungi, thereby enhancing fungal diversity (Baldrian, 2017). Therefore,
after the forest was converted into plantations, the synchronization
effect caused changes in the biodiversity of the fungal community.

\textbf{5 Conclusions}

The conversion from natural forest to monoculture plantations decreased
C and N contents and related enzymes activities (such as sucrase, acid
protease, urease, glutaminase, acid and alkaline phosphatase
activities), thereby reducing soil quality. The conversion resulted in
markedly differences in the structure of the fungal community between
Forest and plantations, and increased microbial \selectlanguage{greek}α-\selectlanguage{english}diversity. According
to the life history classification system based on functional traits -
Grime's C-S-R (competitor, stress-tolerator, ruderal) life history
framework, forest conversion decreased the abundance of dominant fungal
taxa including Basidiomycota (C-strategists), but increased that in
Ascomycota (S-strategists) and Zygomycota (R-strategists). The decrease
of C-strategists Basidiomycota indicated that forest conversion had a
negative impact on the fungal lignin decomposition ability and
competitive intensity, especially for oligotrophic taxa. The increase of
S-strategists Ascomycota and R-strategists Zygomycota demonstrated that
some taxa with stress-tolerating and ruderal survival strategies, such
as copiotrophic fungi, maximize occupation and exploitation of available
resources after conversion. Correspondingly, \emph{Pseudophialophora,
Rhytisma} and other genera (mostly belonging to Ascomycota) increased,
while \emph{Russula} (mostly belonging to Basidiomycota) decreased.
Correlation analysis showed that, in addition to soil pH, the key
indicators of SQI, including organic carbon, glutaminase, and
phosphorus, were main drivers for fungal community characteristics in
subtropical forest conversion. To conclude, forest conversion to
monoculture plantations decreased soil quality, but increased the
dominance of S and R strategists in a more diverse fungal community,
retarding lignin decomposition and increasing the depletion of available
resources.

\textbf{Acknowledgment}

This study was supported by the Key Research and Development program of
Hunan Province (2020NK2022), National Natural Science Foundation of
China (32171761), Scientific Innovation Fund for Post-graduates of
Central South University of Forestry and Technology (CX20191006,
CX202102023), and Special Funding for Innovative Construction in Hunan
Province (2021ZK4226). Thanks also to the staff in Lutou National
Station for Scientific Observation and Research of Forest Ecosystems and
National Engineering Laboratory of Applied Technology for Forestry \&
Ecology in Southern China for field sampling and laboratory analysis. We
appreciate the West-Siberian Interregional Science and Education
Center's (project 89-DON) and CarboRus project (075-15-2021-610).
Special thanks to the Ministry of Education of the People's Republic of
China providing Yakov Kuzyakov the Changjiang Professorship Award. The
publication was supported by the Government Program of Competitive
Growth of Kazan Federal University and with the support of the ``RUDN
University Strategic Academic Leadership program''.

Declaration of interests

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

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\textbf{Hosted file}

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