\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[greek,ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{The Effect of Drainage and Afforestation on the Soil Microbial
Composition of Fens Is Greater than that of Bogs in Subtropical Moss
Peatlands}



\author[1]{Junheng Yang}%
\author[1]{Xunxun Shi}%
\author[1]{Haijun Cui}%
\author[1]{Weifeng Song}%
\author[1]{Putao Zhang}%
\author[1]{Xiaoting Bing}%
\affil[1]{Southwest Forestry University}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Subtropical moss peatlands have important ecological functions, and
their protection and restoration are urgent. In this study, typical
subtropical moss peatlands and the Cryptomeria swamp forest (CSF) formed
by long-term (more than 20 years) drainage and afforestation in the
Yunnan-Guizhou Plateau of China were selected as the research sites. 16S
rRNA high-throughput sequencing technology was used to study the
differences in soil bacterial community diversity and composition among
a natural Sphagnum fen (SF), Polytrichum bog (PB) and CSF to explore the
effects of drainage and afforestation on different types of moss
peatlands and its mechanism combined with soil physicochemical
properties. Results showed that (1) drainage and afforestation
significantly reduced the \selectlanguage{greek}α \selectlanguage{english}diversity of soil bacterial communities in
SF, while significantly increased the \selectlanguage{greek}α \selectlanguage{english}diversity of soil bacterial
communities in PB. Soil bacterial communities of SF had the highest \selectlanguage{greek}α
\selectlanguage{english}diversity and had many unique species or groups at different taxonomic
levels. (2) The impact of drainage and afforestation on the soil
bacterial community composition in SF was significantly higher than that
in PB. Drainage and afforestation caused significant changes in the
composition and relative abundance of dominant groups of soil bacteria
in SF at different taxonomic levels, such as significantly reducing the
relative abundance of Proteobacteria, significantly increasing the
relative abundance of Acidobacteria, and significantly reducing the
ratio of Proteobacteria to Acidobacteria, but did not have a significant
impact on the corresponding indicators of PB. The changes in the ratio
of Proteobacteria to Acidobacteria may reflect changes in the trophic
conditions of peatlands. (3) Soil moisture content, available phosphorus
content, and pH were key driving factors for changes in soil bacterial
community composition and diversity, which should be paid attention to
in the restoration of moss peatlands.%
\end{abstract}%



\sloppy


\textbf{The Effect of Drainage and Afforestation on the Soil Microbial
Composition of Fens Is Greater than that of Bogs in Subtropical Moss
Peatlands}

Junheng Yang\textsuperscript{1,2,3+}, Xunxun Shi\textsuperscript{1,2,3}
11\textsuperscript{+}Junheng Yang and Xunxun Shi should be considered
joint first author\emph{\textsuperscript{\textbf{+}}\textbf{, Haijun
Cui\textsuperscript{1,2,3}}} 22\textsuperscript{*}Correspondence: Haijun
Cui haijun\_cui@swfu.edu.cn, Weifeng Song\textsuperscript{1,2,3}, Putao
Zhang\textsuperscript{1,2,3} and Xiaoting Bi\textsuperscript{1,2,3}

\emph{\textsuperscript{1}Yunnan Key Laboratory of Plateau Wetland
Conservation, Restoration and Ecological Services, Southwest Forestry
University, Kunming, China}

\emph{\textsuperscript{2}National Plateau Wetlands Research
Center/College of Wetlands, Southwest Forestry University, Kunming,
China}

\emph{\textsuperscript{3}National Wetland Ecological Fixed Research
Station of Yunnan Dianchi, Southwest Forestry University, Kunming,
China}

\textbf{Abstract:} Subtropical moss peatlands have important ecological
functions, and their protection and restoration are urgent. In this
study, typical subtropical moss peatlands and the \emph{Cryptomeria}
swamp forest (CSF) formed by long-term (more than 20 years) drainage and
afforestation in the Yunnan-Guizhou Plateau of China were selected as
the research sites. 16S rRNA high-throughput sequencing technology was
used to study the differences in soil bacterial community diversity and
composition among a natural \emph{Sphagnum} fen (SF),\emph{Polytrichum}
bog (PB) and CSF to explore the effects of drainage and afforestation on
different types of moss peatlands and its mechanism combined with soil
physicochemical properties. Results showed that (1) drainage and
afforestation significantly reduced the \selectlanguage{greek}α \selectlanguage{english}diversity of soil bacterial
communities in SF, while significantly increased the \selectlanguage{greek}α \selectlanguage{english}diversity of soil
bacterial communities in PB. Soil bacterial communities of SF had the
highest \selectlanguage{greek}α \selectlanguage{english}diversity and had many unique species or groups at different
taxonomic levels. (2) The impact of drainage and afforestation on the
soil bacterial community composition in SF was significantly higher than
that in PB. Drainage and afforestation caused significant changes in the
composition and relative abundance of dominant groups of soil bacteria
in SF at different taxonomic levels, such as significantly reducing the
relative abundance of Proteobacteria, significantly increasing the
relative abundance of Acidobacteria, and significantly reducing the
ratio of Proteobacteria to Acidobacteria, but did not have a significant
impact on the corresponding indicators of PB. The changes in the ratio
of Proteobacteria to Acidobacteria may reflect changes in the trophic
conditions of peatlands. (3) Soil moisture content, available phosphorus
content, and pH were key driving factors for changes in soil bacterial
community composition and diversity, which should be paid attention to
in the restoration of moss peatlands.

\textbf{Keywords:} microbial diversity, \emph{Cryptomeria fortuneana} ,
bog, fen, swamp forest, soil physicochemical properties,\emph{Sphagnum}
, \emph{Polytrichum}

\textbf{1 Introduction}

Subtropical moss peatlands have important ecological functions, such as
biodiversity maintenance, carbon storage and water conservation, and are
characterized by their rarity (Li et al. 2018). Microtopography affects
the formation and characteristics of moss peatlands, and different
microtopography forms different types of moss peatlands (bogs and fens),
which are usually occupied by different dominant moss plants (Page et
al. 2016; Li et al. 2019). For example, in the Yunnan-Guizhou Plateau of
China, the typical subtropical moss peatlands form
ombrotrophic\emph{Polytrichum} bogs (PBs) in the plateau area and
minerotrophic \emph{Sphagnum} fens (SFs) in the low-lying area. Driven
by economic interests, moss peatlands all over the world are faced with
the disturbance threats of human activities such as agricultural
reclamation (Kandel et al. 2018), drainage and afforestation (Sloan et
al. 2019), peat mining (Vitovcova et al. 2022; Pospisilova et al. 2023)
and fire (Lynda et al. 2023). Drainage and afforestation poses the
greatest threat to the patchy moss peatlands in the subtropical region,
which greatly reduces their area and causes them to lose their
ecological function. Therefore, the protection and restoration of
subtropical moss peatlands are urgently needed (Wang et al. 2021).

Soil microorganisms are important drivers of element cycling in peatland
ecosystems (Andersen et al. 2013). Studies have found that the most
common bacteria in peat are representatives of Proteobacteria and
Acidobacteria, which have good adaptability to acidic environments and
exhibit a variety of different lifestyles (Lin et al. 2012; Andersen et
al. 2013; Urbanov\selectlanguage{ngerman}á et al. 2014). Other important bacterial groups
typically found in peatlands include Actinobacteria, Verrucomicrobia,
Planctomycetes, Chloroflexi, Firmicutes and Chlamydiae. The study of
peatlands in different climate zones has found that although there are
common dominant groups in different peatlands, the correlation between
microbial communities and environmental factors is different. In
primitive peatlands, the composition and function of soil microbial
communities have been shown to vary according to the hydrological
conditions, nutritional status, and vegetation composition of a site
(Andersen et al. 2013). For example, the relative abundances of
Acidobacteria and Proteobacteria showed opposite changes with changes in
pH and substrate availability (Smit et al. 2001; Hartman et al. 2008;
Urbanová et al. 2014). For example, the water table has been shown to
affect the structure of peatland microbial communities or their \selectlanguage{greek}α
\selectlanguage{english}diversity (Tian et al. 2019). Other environmental factors, such as
nitrogen content (Pankratov et al. 2008), organic matter content,
moisture, and phosphorus (Elliott et al. 2008), have also been shown to
have an impact on the microbial communities of peatland ecosystems.
Compared with high-latitude and tropical peatlands, there are few
studies related to subtropical moss peatlands due to their rarity (Alam
et al. 2022; Ifo and Garcin 2022; Wilkinson et al. 2023). A few studies
have reported that the microbial community of \emph{Sphagnum} peatlands
is affected by microhabitats, and it has been found that groundwater
level and total nitrogen content have significant effects on the soil
bacterial community of \emph{Sphagnum} peatlands (Tian et al. 2019).
There are few reports on the differences in soil microbial structure and
function of different types of subtropical moss peatlands (bogs and
fens) and their influencing factors.

Studies of northern peatlands have shown that fens have greater
microbial diversity due to additional nutrient input from groundwater,
higher pH, and different quality litter compared to nutrient-poor acidic
bogs, which mainly obtain nutrients from precipitation (Galand et al.
2005; Kim et al. 2008; Urbanov\selectlanguage{ngerman}á et al. 2011; Gupta et al. 2012; Lin et
al. 2012). Urbanová and Bárta (2016) studied the effects of long-term
drainage on different types of peatlands (bogs, fens, swamps) in the
Czech Republic and found that fens and swamps were more affected, while
bogs were less affected, and the soil microbial structure and function
of the three types of peatlands became similar after long-term drainage
and suggested that the subsurface microbial community in the drainage
sites seems to be driven primarily by the biogeochemical characteristics
of peat rather than plant community composition. Compared with long-term
drainage disturbance, vegetation is more homogeneous, and long-term
drainage and afforestation will make the soil microbial community
structure and function of affected bogs and fens more similar to each
other (Sloan et al. 2019). Are there significant differences in soil
microbial community structure and function between SFs and PBs in the
subtropics? Compared with the two types of natural moss peatlands, which
swamp forest formed by long-term drainage and afforestation has greater
changes than in the soil microbial community? The answers to the above
questions will enrich our understanding of the characteristics of
different types of moss peatlands in subtropical regions, help us
scientifically assess how easy it is for different types of peatlands
affected by drainage and afforestation to recover, and provide a
theoretical basis for the protection and restoration of moss peatlands
in subtropical regions.

This study selected typical subtropical moss peatlands and
the\emph{Cryptomeria} swamp forest (CSF) formed by long-term drainage
and afforestation in the Yunnan-Guizhou Plateau of China as the research
objects. By collecting the topsoil (0-10 cm) that is easily disturbed or
strongly affected by climate change, 16S rRNA gene Illumina sequencing
technology was used to study the differences in soil bacterial community
composition and structure between natural SF, PB and CSF habitats and
combined with soil physicochemical properties to explore the important
environmental factors affecting the soil bacterial community. We
hypothesized that (1) SF has a higher diversity of the soil bacterial
community than PB. (2) Compared with PB, the differences in soil
bacterial communities between CSF and SF were greater, and this
difference was caused by the differences in soil water and nutrients
between the different types of peatlands (soil water and nutrients are
important factors in this difference).

\textbf{2 Materials and methods}

\textbf{2.1 Study area}

The study area is in the Niangniangshan National Wetland Park
Conservation area in China. The wetland is located at the junction of
Yunnan-Guizhou Province with geographical coordinates of 104°45 '24
''E-104°51' 41'' E and 26°04 '25 ''N-26°8' 24'' N. It is a typical
subtropical karst mountain moss peatlands wetland in the Yunnan-Guizhou
Plateau. Moss peatlands are developed in the waterlogged areas of basalt
formations on subalpine platforms and have important functions such as
water conservation, biodiversity protection, climate regulation and
carbon sink. Before the 1990s, a large area of moss peatlands was
distributed in the wetland, and different topographic environments
formed different moss peatlands, including ombrotrophic PB in the
plateau area and minerotrophic SF in the low-lying area. However, in the
late 1990s, large-scale drainage and construction of
the\emph{Cryptomeria fortuneana} forest resulted in the disappearance of
a large area of moss peatlands. The vegetation was mainly swamp forest,
and the moss peatlands and herbaceous peatlands were mosaic landscapes.
The ecological function of wetlands declined significantly, and the
diversity and heterogeneity of vegetation and the environment decreased.

The climate of the area where the wetland is located is cool and humid,
which is a typical subtropical monsoon climate. The extreme maximum
temperature is 36.7 °C, the extreme minimum temperature is -7.9 °C, and
the annual average temperature is 15.2 °C. The average annual sunshine
duration is 1615 h, and the percentage of sunshine is 37\%. The average
annual rainfall is 1413.6 mm, and the average evaporation is 1526.7 mm.
The annual average relative humidity is 76\%, the maximum relative
humidity is 100\%, and the minimum relative humidity is 2\%. The annual
average frost-free period is 271 days. The main soil types in the
wetland conservation area are peat soil and scrub meadow soil.

\textbf{2.2 Site setting and sampling}

Typical SF, PB and CSF patches in wetland conservation areas were
selected as sites. The dominant species in the SF plant community
are\emph{Sphagnum} \emph{palustre} , \emph{Carex nemostachys}
,\emph{Oenanthe linearis} , \emph{Neanotis hirsuta} and \emph{Isachne
globosa} . The dominant species in the PB plant community
are\emph{Polytrichum commune} , \emph{Pteridium revolutum} ,
\emph{Gaultheria hookeri} , \emph{Smilax china} , and \emph{Cyanotis
vaga} . The CSF was a pure artificial forest with an average density of
1283.33 trees/hm\textsuperscript{2}, an average diameter at breast
height of 10.44 cm, an average tree height of 8.68 m, and an average
canopy density of 85\%. There were sparse shrubs and herbs under the
forest.

Three 1 m×1 m plots were randomly set near the centre of the SF and PB
patches. In each plot, 0-10 cm soil was collected by using the random
3-point sampling method to form a mixed repeat soil sample. Three 20
m×30 m plots were set in CSF, one plot was set in the high and flat area
with the terrain similar to that of SF, one plot was set in the
low-lying area with the terrain similar to that of PB, and one plot was
set in the transition zone between the two. Each plot was kept at least
10m away from the vegetation boundary between swamp forest and moss
peatlands to avoid edge effects. The random 3-point sampling method was
used to collect 0-10 cm soil to form a mixed repeat soil sample.
Therefore, three repeated soil samples were collected for each of the
three types of sites. Plant roots, litter and debris were removed
immediately and stored under appropriate conditions. Each repeat soil
sample was divided into 2 parts: one fresh soil sample was stored at 4°C
for soil physicochemical property analysis, and the other was stored at
-80°C for DNA analysis. Samples for the determination of soil bulk
density and water content were collected simultaneously with the
sampling.

\textbf{2.3 Determination of soil physicochemical properties}

The fresh soil sample was divided into two parts. One fresh soil sample
was used for the determination of soil ammonium nitrogen content
(NH\textsubscript{4}\textsuperscript{+}-N) and nitrate nitrogen content
(NO\textsubscript{3}\textsuperscript{-}-N), and the other was naturally
air-dried and screened for the determination of other indices. Soil
weight water content (SWW) and soil bulk density (SBD) were measured by
the drying method. Soil pH was determined by a glass electrode pH meter
(STARTER 300, Shanghai) (soil\selectlanguage{english}-water ratio 1:5). The soil total carbon
content (TC) was determined by a total organic carbon analyser (Vario,
German). The soil total nitrogen content (TN) was measured by the
Kjeldahl method. NH\textsubscript{4}\textsuperscript{+}-N and
NO\textsubscript{3}\textsuperscript{-}-N in soil were extracted by 1
mol/L KCl (soil\selectlanguage{english}-water ratio 1:10), and the extract was determined by a
continuous flow analyser (SEAL Analytical AA3, German). The content of
available phosphorus (AP) in soil was determined by the
molybdenum-antimony resistance colorimetric method after leaching with
sodium bicarbonate.

\textbf{2.4 High-throughput sequencing}

\textbf{2.4.1 Soil bacterial DNA extraction and PCR amplification}

The total DNA of soil samples was extracted using the Power soil DNA
Isolation Kit (MOBIO, USA), with 9 DNA samples for 9 mixed soil samples.
Nanodrop 2000c was used to detect the quality of DNA, followed by PCR
amplification of the bacterial 16S rRNA gene. The amplification primers
were 338F (ACTCCTACGGGAGGCAGC AG) and 806R (GACTACHVGGGTWTCTAAT). The
PCR conditions were as follows: predenaturation at 98degC for 3 min;
denaturation for 30 s at 98degC; annealing at 50degC for 30 s; 72degC
extension for 30 s; 27 cycles; maintenance at 72degC for 5 min; and
storage at 4degC. The electrophoretic PCR products were gelled and
purified using an AxyPrep DNA gel recovery kit (AXYGEN). The recovered
and purified PCR products were then quantified using the
QuantiFluorTM-ST blue fluorescence quantification system (Promega, USA).
According to the quantitative results and sequencing quantity
requirements, PCR products were used to construct a sequencing library.
The constructed library was sequenced on the IlluminaPE300 sequencing
platform.

\textbf{2.4.2 Data processing}

Bacterial 16S rRNA gene sequences were analysed using the Quantitative
Insights Into Microbial Ecology (QIIME) platform. First, the
bidirectional sequences were spliced, the sequences with matching errors
were checked, the chimeric sequences were removed, and high-quality
sequences were obtained for the next analysis. According to UCLUST, all
gene sequences were clustered according to 97\% similarity, singleton
OTUs were removed, and a representative OTU sequence was obtained. The
species information was obtained by comparing the representative OTU
sequences with the RDP reference database.

\textbf{2.5 Statistical analysis}

Nonmetric multidimensional scale analysis (NMDS) was conducted based on
the relative abundance of all groups at the phylum, class, family and
genus levels of soil bacteria, and the significance of differences in
soil bacterial communities among different types of vegetation was test
by ANOSIM based on Bray\selectlanguage{english}-Curtis distance. One-way ANOVA was used to
analyse the significance of distance difference between any two of them,
and the LSD method (least significant difference method) was used for
posttest. The significance of differences in soil physicochemical
properties, the \selectlanguage{greek}α \selectlanguage{english}diversity indexes of the soil bacterial community and
the relative abundances of the top ten phyla, classes, families and
genera in the soil bacterial community among different vegetation types
was analysed by one-way ANOVA, and the LSD method was used for the
posttest. Based on the relative abundance data of major groups of phyla,
class, family and genus in soil bacterial communities of different
vegetation types, redundancy analysis (RDA) was used to explore the key
soil factors affecting the composition differentiation of major groups
at different classification levels of soil bacterial communities. The
significance of soil factors was determined by the Monte Carlo test.
Pearson correlation was used to analyse the correlation between soil
factors and the \selectlanguage{greek}α \selectlanguage{english}diversity index of soil bacterial communities and the
relative abundance of major groups at the phylum, class, family and
genus levels. Among them, NMDS, ANOSIM and RDA were all analysed using
the programme package ''vegan'' in R 4.2.2 software, and all other
analyses and mapping were completed in R 4.2.2.

\textbf{3 Results}

\textbf{3.1 Soil physicochemical properties}

There was no significant difference in TC and TN between SF and PB, and
the values in CSF were significantly lower than those in SF and PB (Fig.
1A, B). Soil pH was all acidic, and the values in SF were significantly
higher than those in PB, and the values in CSF were significantly lower
than those in SF but significantly higher than those in PB (Fig. 1C). AP
in SF was significantly lower than that in PB and that in CSF was
significantly lower than that in PB, but there was no significant
difference from that in SF (Fig. 1D).
NO\textsubscript{3}\textsuperscript{-}-N and
NH\textsubscript{4}\textsuperscript{+}-N were the highest in SF, and
NH\textsubscript{4}\textsuperscript{+}-N was significantly higher than
that in PB, but there was no significant difference in
NO\textsubscript{3}\textsuperscript{-}-N (Fig. 1E, F).
NO\textsubscript{3}\textsuperscript{-}-N and
NH\textsubscript{4}\textsuperscript{+}-N in CSF were significantly lower
than those in SF, but there was no significant difference between them
and the values in PB. SWW in SF was significantly higher than that in
PB, and its value in CSF was significantly lower than that in SF but had
no significant difference from that in PB (Fig. 1G). SBD in SF was
significantly lower than that in PB, and its value in CSF was
significantly higher than that in SF and PB (Fig. 1H).

\textbf{3.2 Soil bacterial community diversity indexes}

The indices, Sobs, Chao and ACE, which reflect the richness of the soil
bacterial community, have the same rule: the value in SF is
significantly higher than that in PB, and the value in CSF is
significantly lower than that in SF but is significantly higher than
that in PB (Fig. 2A, B, C). The indices reflecting bacterial community
diversity included Shannon, Simpson and pd indices. The Shannon index in
SF was significantly higher than that in PB, while the Shannon index in
CSF was not significantly different from that in SF but was
significantly higher than that in PB (Fig. 2D). There was no significant
difference in the Simpson index between SF, PB and CSF (Fig. 2E). Pd in
SF was significantly higher than that in PB, and Pd in CSF was
significantly lower than that in SF but significantly higher than that
in PB (Fig. 2F). The indices reflecting community evenness included
Shannoneven and Simpsoneven indices, and Shannoneven in CSF was
significantly higher than that in PB (Fig. 2H, I). The index reflecting
the community coverage was Coverage, and there was no significant
difference in the value of SF, PB and CSF, and the values were all close
to 1 (Fig. 2G).

\textbf{3.3 Correlation between soil physicochemical properties and soil
bacterial community diversity indexes}

Sobs, Chao and ACE were all significantly positively correlated with
soil pH and SWW and significantly negatively correlated with AP (Table
1). The Shannon index was positively correlated with soil pH but
significantly negatively correlated with TC, TN and AP. Pd was
significantly positively correlated with soil pH and SWW and
significantly negatively correlated with AP. The Simpson index was
significantly positively correlated with TC, TN and AP. The Shannoneven
index had a significant negative correlation with TC and TN, while the
Simpsoneven index had a significant negative correlation with TC and
NO\textsubscript{3}\textsuperscript{-}-N.

\selectlanguage{greek}\textbf{3.4 Soil bacterial community β diversity}\selectlanguage{english}

The results of NMDS analysis based on Bray-Curtis showed that the soil
bacterial communities at the genus, family, class and phylum levels
showed clear aggregation types in SF, PB and CSF (Fig. 3A, D, H, K). At
the class and phylum levels, the distance between SF and PB was not
significantly different from that between SF and CSF, both of which were
significantly higher than that between PB and CSF (Fig. 3C, F). At the
genus and family levels, the Bray-Curtis distance between the soil
bacterial communities of SF and PB was the largest, which was
significantly greater than the distance between SF and CSF, and the
distance between PB and CSF and the distance between CSF and SF were
significantly higher than the distance between CSF and PB (Fig. 3J, M).

\textbf{3.5 Indicator groups of soil bacterial communities}

A total of 33 phyla were found in the soil bacterial community of SF,
PB, and CSF, of which 21 phyla (63.6\%) were found to be common in all
three, 22 phyla (66.63\%) were found to be common in two of the three, 9
phyla (32.5\%) were unique to SF, and neither PB nor CSF had unique
phyla (Fig. 3B).

A total of 83 classes were found in the soil bacterial communities of
SF, PB, and CSF, among which 48 classes (52.9\%) were found to be common
in the three. There were 50 classes (60.21\%) common in SF and PB, 52
classes (62.62\%) common in CSF and SF, 50 classes (60.21\%) common in
CSF and PB, 27 classes (32.5\%) unique to SF, and neither PB nor CSF had
a unique class (Fig. 3E).

A total of 276 families were found in the soil bacterial communities of
SF, PB, and CSF, among which 146 families (52.9\%) were found to be
common in the three. There were 153 families (55.44\%) common in SF and
PB, 167 families (60.51\%) common in CSF and SF, 161 families (58.33\%)
common in CSF and PB, 78 families (28.3\%) unique to SF, 1 family
(0.362\%) unique to PB, and 8 families (2.9\%) unique to CSF (Fig. 3I).

A total of 392 genera were found in the soil bacterial communities of
SF, PB, and CSF, among which 183 genera (46.7\% of the total genera)
were found to be common in the three (Fig. 3C, F). There were 195 genera
(49.76\%) common in SF and PB, 221 genera (56.39\%) common in CSF and
SF, 208 genera (53.08\%) common in CSF and PB, 115 genera (29.3\%)
unique to SF, 7 genera (1.79\%) unique to PB, and 12 genera (3.06\%)
unique to CSF (Fig. 3L).

LEfSe analysis identified the indicator groups of soil bacterial
communities for three types of sites (Fig. 4). Twenty-four indicator
species of SF were identified, which belong to 2 phyla, 4 classes, 6
orders, 7 families, and 5 genera. Three indicator species of PB were
identified, which belong to 1 order, 1 family, and 1 genus. Fifteen
indicator species in CSF were identified, which belonged to 1 phylum, 2
classes, 1 order, 5 families, and 6 genera.

\textbf{3.6 Composition of the main groups of soil bacterial communities
at different taxonomic levels}

Fig. 5 reveals the differences in the relative abundance and ranking of
major groups of soil bacterial communities at different taxonomic levels
among the three types of sites. The top ten phyla with relative
abundance ranking for each of the three types of sites were reserved,
with a total of 14 phyla (Fig. 5A). There were significant differences
in the ranking and relative abundance of these phyla among the three
types of sites. Except for Verrucomimicrobia and Saccharibacteria, there
were significant differences in relative abundance among the three types
of sites in the other groups. The relative abundances of Proteobacteria,
Bacteroidetes, Elusimicrobia and Parcubacteria were significantly higher
in SF, the relative abundances of unclassified\_k\_\_norank and
Chlamydiae were significantly higher in PB, and the relative abundance
of Chloroflexi was significantly higher in CSF.

\textbf{3.7 Relationship between soil bacterial community composition
and soil physicochemical properties}

RDA revealed that soil pH, AP, NH\textsubscript{4}\textsuperscript{+}-N,
SWW and SBD were significant factors driving the composition of major
groups of soil bacterial communities at the phylum, class, family and
genus levels (\emph{P} \textless{}0.05) (Fig. 6, Table 2). The relative
abundances of Bacteroidetes and Elusimicrobia were significantly
positively correlated with soil pH, while the relative abundances of
Actinobacteria, Planctomycotes, Chlamydiae, and Firmicutes were
significantly negatively correlated with soil pH (Fig. 6A, Table S1).
The relative abundances of Actinobacteria, unclassified\_k\_\_norank,
Planctomycetes and Chlamydiae were significantly positively correlated
with AP. The relative abundance of Proteobacteria, Bacteroidetes and
Parcubacteria was significantly positively correlated with
NH\textsubscript{4}\textsuperscript{+}-N, while the relative abundance
of Acidobacter, Actinobacteria and Firmicutes was significantly
negatively correlated with NH\textsubscript{4}\textsuperscript{+}-N. The
relative abundances of Proteobacteria, Bacteroidetes, Elusimicrobia,
Parcubacteria and Saccharibacteria were significantly positively
correlated with SWW, while the relative abundances of Acidobacter,
Actinobacteria, Planctomycotes and Firmicutes were significantly
negatively correlated with SWW. The relative abundances of
Acidobacterium and Chloroflexi were significantly positively correlated
with SBD.

\textbf{4 Discussion}

\selectlanguage{greek}\textbf{4.1 Soil bacterial community α diversity and its influencing
factors}\selectlanguage{english}

The results of this study support the first hypothesis that SF has
higher soil bacterial community diversity than PB. The values of Sobs,
Chao, and ACE, which reflect the richness of the soil bacterial
community, and the values of the Shannon and Pd indices, which reflect
the diversity of the community, in SF were significantly higher than
those in PB, indicating that the diversity of the soil bacterial
community in SF was significantly higher than that in PB (Fig. 2). Our
research also found that the values of Sobs, Chao, ACE and Pd in CSF
were significantly lower than those in SF and were significantly higher
than those in PB. This shows that, in terms of soil bacterial community
diversity, CSF is located between SF and PB. Long-term drainage and
construction of the \emph{Cryptomeria fortunei} forest significantly
reduced the soil bacterial community diversity of SF and significantly
increased the soil bacterial community diversity of PB. The number of
bacterial taxa observed at the genus, family, class and phylum levels in
various types of sites also exhibited similar patterns. A total of 348
genera, 252 families, 81 classes and 32 phyla were found in SF soil
bacteria; 258 genera, 190 families, 54 classes and 23 phyla were found
in CSF soil bacteria; 227 genera, 169 families, 52 classes and 23 phyla
were found in PB soil bacteria; and SF soil bacteria had many unique
groups at the genus, family, class and phylum levels (Fig. 3). The above
analysis shows that, compared with PB and CSF, SF has rich soil
bacterial diversity and high uniqueness, so it is very important to
protect and restore this type of wetland.

The results of this study showed that most indices reflecting the
richness and diversity of soil bacterial communities were significantly
positively correlated with soil pH and SWW and negatively correlated
with AP, suggesting that soil pH, SWW and AP were important factors
affecting the \selectlanguage{greek}α \selectlanguage{english}diversity of soil bacterial communities (Table 1).
Hartman et al. (2008) conducted a comprehensive analysis of various
types of wetlands in the United States and found that soil bacterial
community diversity was closely related to soil pH, and soil pH
predicted the diversity of phyla and species at all the sites they
studied. Urbanov\selectlanguage{ngerman}á and Bárta (2014) reported a significant increase in
species richness and diversity in Czech peatlands from natural fen and
spruce forest swamps to bogs, reflecting changes in peat pH, nutrient
availability, and peat decomposition ability and that the higher the pH,
the higher the species richness and diversity. Urbanová and Bárta (2016)
found in their study of Czech peatlands that the pH values of fen and
spruce forest swamps significantly decreased after long-term drainage,
and species richness and diversity significantly decreased. These
studies all support that the pH value of peatland soil is an important
factor affecting the diversity of soil bacterial communities. This study
found that SWW and AP were significantly positively correlated and
extremely significantly negatively correlated with the pH value,
respectively, indicating that soil pH is the best predictor of soil
bacterial community diversity (Fig. S1) (Fierer et al. 2008; Hartman et
al. 2008). AP was significantly higher in PB (Fig. 1D), which may be
related to the Ericaceae plant \emph{Gaultheria hookeri} . It is known
that Ericaceae plants can form ericoid mycorrhizal symbionts with soil
fungi. These symbionts can mobilize N and P complexes in recalcitrant
organic matter, promote plant absorption, and lead to an increase in the
concentration of phosphorus in soil solution (Ka\selectlanguage{polish}š\selectlanguage{english}tovsk\selectlanguage{ngerman}á et al. 2018;
Perotto et al. 2018). This is also an important factor for Ericaceae
plants to survive and even dominate in poor, acidic and other harsh
environments.

This study shows that long-term drainage and construction of
the\emph{Cryptomeria fortunei} forest significantly reduce the diversity
of the soil bacterial community in SF and significantly increase the
diversity of the soil bacterial community in PB. Therefore, restoring
peatland to its natural SF state, which is affected by long-term
drainage and afforestation, will increase soil microbial diversity,
while restoring peatland to its natural PB state will reduce soil
microbial diversity. Urbanová and Bárta (2016) found similar results in
their long-term drainage study of peatlands in the Czech Republic, where
long-term drainage significantly reduced the diversity of soil bacteria
in fens and significantly increased the diversity of soil bacteria in
bogs. Hartman et al. (2008) studied three types of wetlands in North
Carolina, USA, and found that restoration of wetlands from agricultural
use reduced soil bacterial diversity. This suggests that unlike
terrestrial ecosystem restoration, which generally increases diversity
(DeGrood et al. 2005; Mckinley et al. 2005), wetland restoration does
not necessarily increase soil bacterial diversity, depending on the type
of disturbance and the type of wetland.

\textbf{4.2 Soil bacterial community composition and its influencing
factors}

The difference analysis of the soil bacterial community composition
between the three types of sites showed that the soil bacterial
community composition of SF and PB had the largest difference, and the
difference in the soil bacterial community between CSF and SF was
significantly greater than the difference in the soil bacterial
community between CSF and PB (Fig. 3). This suggests that the
restoration of the soil bacterial community in SF affected by drainage
and afforestation may be more difficult than that in PB.

The results of this study showed that the dominant groups of bacteria at
the phylum level in the soil of SF, PB and CSF were all Proteobacteria
and Acidobacteria (Fig. 5A), which was consistent with the results of
other peatlands (Dedysh et al. 2006; Kraigher et al. 2006; Morales et
al. 2006; Ausec et al. 2009; Pankratov al. 2011; Serkebaeva al. 2013;
Sun al. 2014; Danilova et al. 2016). However, the relative abundance of
Proteobacteria was the highest in SF, and the relative abundance of
Acidobacteriota was the highest in PB and CSF. The relative abundance of
Proteobacteria in SF was significantly higher than that in PB and CSF,
and the relative abundance of Acidobacteriota in PB and CSF was
obviously higher than that in SF, reaching a significant level in CSF.
There was no significant difference in the relative abundance of
Proteobacteria and Acidobacteria in PB and CSF. Acidobacteria are known
to prefer acidic environments and can grow under poor nutrient
conditions (Philippot et al. 2010; Dedysh et al. 2011; Andersen et al.
2013), while Proteobacteria are associated with higher C availability
(Fierer et al. 2007; Leff et al. 2015). Several studies have found a
negative response of Acidobacteria relative abundance to pH (Hartman et
al. 2008; Urbanová et al. 2016). This study found that the relative
abundance of Proteobacteria was significantly positively correlated with
NH\textsubscript{4}\textsuperscript{+}-N and SWW, while the relative
abundance of Acidobacteriota was significantly negatively correlated
with NO\textsubscript{3}\textsuperscript{-}-N,
NH\textsubscript{4}\textsuperscript{+}-N and SWW and significantly
positively correlated with SBD. The relative abundances of
Proteobacteria and Acidobacteria were positively correlated and
negatively correlated with soil pH, respectively, which did not reach a
significant level. The difference in the relative abundance of
Proteobacteria and Acidobacteria among the three types of sites may
reflect their different environmental conditions, such as pH and
nutrient status (substrate availability).

In addition, the ratio between Proteobacteria and Acidobacteria is
considered to indicate the nutrient status of soil ecosystems and
different peatlands, and the higher the ratio is, the richer the
nutrient is, and vice versa (Smit et al. 2001; Hartman et al. 2008;
Urbanová et al. 2014). In general, species richness and microbial
diversity in peat sediments increase with the improvement in nutritional
status (Hartman et al. 2008). In addition, differences in nutritional
status may also lead to changes in the bacterial microbiome in different
microhabitats (Hartman et al. 2008; Urbanová et al. 2016). In this
study, the ratios of Proteobacteria and Acidobacteria were 2.02, 0.86
and 0.76 in SF, PB and CSF, respectively, and the values in SF were
significantly higher, while the values in PB and CSF had no significant
difference (Fig. 1S). The results showed that the nutrient status of SF
was significantly better than that of PB and CSF. Drainage construction
of \emph{Cryptomeria fortuneana} forest will significantly reduce the
nutrient status in SF but has no significant impact on PB. The nutrient
status may also be an important factor for the significant difference
between the soil bacterial community diversity and composition of SF and
PB and CSF.

This study found that the relative abundance of Actinomycetota in PB and
CSF was significantly higher than that in SF, but there was no
significant difference between their values (Fig. 5A). This study also
found that the relative abundance of Actinomycetota was significantly
negatively correlated with soil pH,
NH\textsubscript{4}\textsuperscript{+}-N, and SWW and was extremely
significantly positively correlated with AP (Fig. 6A, Table S1). Members
of Actinomycetota can produce extracellular enzymes and have the same
enzymatic ability as fungi (le Roes-Hill et al. 2011). Heterotrophic
actinomycetes can degrade recalcitrant polymer substances such as
lignin, chitin, pectin, aromatic hydrocarbon and humic acids under
aerobic conditions, so they thrive in the oxygen-bearing layer of acidic
peatland (Jaatinen et al. 2007). Tian et al. (2019) found that a
decrease in water level increased the thickness of the aerobic layer of
peat, leading to an increase in the abundance of actinomycetes,
supporting our research findings. The relative abundance of
Actinomycetota in PB and CSF was significantly higher, which may
indirectly indicate that their soil carbon quality was significantly
lower, and their stable carbon or recalcitrant carbon components were
significantly higher. Research has found that long-term drainage and
tree growth lead to a decrease in the decomposability of peat and an
increase in the content of recalcitrant compounds such as carboxylic
acids, aromatics, and phenols (Blodau et al. 2012; Mastny et al. 2016;
Urbanová et al. 2018). Acidobacterium has been found to be a dominant
phylum of bacteria under nutrient-poor conditions, and it is believed
that it is involved in the degradation of cellulose and aromatic
compounds (Ausec et al. 2009; Pankratov et al. 2011). Therefore, the
higher abundance of Acidobacteriota in PB and CSF also indirectly
supports this hypothesis (Fig. 5A).

This study analysed the differences in soil bacterial community
composition among different treatments at the genus, family, class, and
phylum levels using the top ten relative abundance rankings of various
types of sites (Fig. 5). This analysis method is superior to the
analysis method of ''using the top ten groups with relative abundance
ranking of all samples'' (Lin et al. 2012). Because there may be
significant differences in the dominant species of soil microbial
communities among different treatments, the latter cannot clearly
display the composition of dominant species in specific treatments and
the relative abundance differences of dominant species among different
treatments. The use of relative abundance thresholds also has drawbacks,
as there may be significant differences in the dominance of soil
microbial communities among different treatments (Urbanová et al. 2016;
Tian et al. 2019). As shown in the results of this study,
Planctomycetes, Saccharibacteria, Chlamydiae, and Firmicutes were not
the dominant groups in SF (relative abundance ranking is not in the top
ten), but they were the dominant groups in PB or CSF (Fig. 5A). The
relative abundance of Planctomycetes ranked seventh and sixth in PB and
CSF, respectively. The relative abundance of Saccharibacteria ranked
10th in CSF, the relative abundance of Chlamydiae ranked 9th in PB, and
the relative abundance of Firmicutes ranked 4th and 5th in PB and CSF,
respectively. The relative abundance of Firmicutes in PB and CSF was not
significantly different but was significantly higher than that in SF.

The RDA results show that soil pH, AP,
NH\textsubscript{4}\textsuperscript{+}-N, SWW, and VW are all
significant influencing factors for the composition of major groups of
soil bacterial communities at the phylum, class, family, and genus
levels (Fig. 6, Table 2). This further demonstrates the important
effects of pH, nutrient level, and water conditions on the composition
of major groups of soil bacterial communities at different
classification levels. In conclusion, this study shows that the
diversity and composition of the soil bacterial community in CSF are in
the middle of the corresponding values of SF and PB, and the difference
between CSF and SF is significantly greater than that between CSF and
PB, which is closely related to the soil pH, nutrient level and water
conditions of different types of peatlands. This supports our second
hypothesis, that is, ''Compared with that between CSF and PB, the
difference in the soil bacterial community between CSF and SF is
greater, which is caused by the difference in soil moisture and
nutrients between different types of peatlands''. Urbanová and Bárta
(2014) found that the diversity and composition of soil bacterial
communities in spruce swamp forest were between those of bogs and fens
in their study of different types of peatlands in the Czech Republic.
They believe that this reflects changes in soil pH, nutrient
availability, and peat decomposition ability. Hartman et al. (2008)
found a strong correlation between soil bacterial composition and
diversity and soil pH in swamps and bogs in North Carolina and fens in
the Everglades in Florida. Tian et al. (2019) studied the \emph{Sphagnum
palustre} peatlands in Dajiuhu Lake of Shennongjia, China, and found
that the groundwater level and total nitrogen content had a significant
impact on the soil microbial community of the \emph{Sphagnum
palustre}peatlands. The above studies all indicate that environmental
conditions have a strong impact on the diversity and composition of soil
microbial communities in peatlands, and significant environmental
factors vary depending on the specific research system.

\textbf{5 Conclusion}

Long-term drainage and afforestation had a greater impact on the
composition and diversity of soil bacterial communities in SF than in
PB. Soil moisture content, available phosphorus content, and pH were key
driving factors for changes in soil bacterial community composition and
diversity. The restoration of soil bacterial community composition and
diversity in moss peatlands affected by drainage and afforestation
should not only focus on vegetation restoration, but also on the
restoration of soil moisture conditions for SF and nutrient conditions
for PB.

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Junheng Yang:} Data curation (equal); investigation (equal);
software(equal); visualization (equal); writing -- original draft
(equal). \textbf{Xunxun Shi:} Data curation (equal); investigation
(equal); software(equal); visualization (equal); writing -- original
draft (equal). \textbf{Haijun Cui:} Funding acquisition(equal); project
administration(equal); supervision (equal); writing -- review and
editing (equal). \textbf{Weifeng Song:} Project administration(equal);
supervision (equal); writing -- review and editing (equal).\textbf{Putao
Zhang:} Formal analysis (equal); investigation (equal); software(equal).
\textbf{Xiaoting Bi:} Formal analysis (equal); investigation (equal);
software(equal).

\textbf{ACKNOWLEDGMENTS}

We sincerely thank the staff of the Niangniangshan National Wetland Park
Management Office in Liupanshui for their assistance in field sampling.

\textbf{FUNDING INFORMATION}

This work is supported by the Opening Foundation of Yunnan Key
Laboratory of Plateau Wetland Conservation, Restoration and Ecological
Services (202105AG070002) and the subproject of Yunnan Province Key R\&D
Program (202203AC100002-03) and the National Natural Science Foundation
of China (32160272).

\textbf{CONFLICT OF INTEREST STATEMENT}

The authors declare that there are no conflicts of interest.

\textbf{DATA AVAILABILITY STATEMENT}

The datasets generated for this study can be found in National Center
for Biotechnology Information(NCBI), PRJNA1003219.

\textbf{References}

Alam MJ, Rengasamy N, Dahalan MPB, Halim SA, Nath TK (2022)
Socio-economic and ecological outcomes of a community-based restoration
of peatland swamp forests in Peninsular Malaysia: A 5Rs approach. Land
Use Policy 122. doi: 10.1016/j.lusepol.2022.106390

Andersen R, Chapman SJ, Artz RRE (2013) Microbial communities in natural
and disturbed peatlands: a review. Soil Biol Biochem 57, 979-994. doi:
10.1016/j.soilbio.2012.10.003

Ausec L, Kraigher B, Mandic-Mulec I (2009) Differences in the activity
and bacterial community structure of drained grassland and forest peat
soils. Soil Biol Biochem 41, 1874-1881. doi:
10.1016/j.soilbio.2009.06.010

Blodau C, Siems M (2012) Drainage-induced forest growth alters
beloSWWround carbon biogeochemistry in the Mer Bleue bog, Canada.
Biogeochemistry 107, 107-123. doi: 10.1007/s10533-010-9535-1

Danilova OV, Belova SE, Gagarinova IV, Dedysh SN (2016) Microbial
community composition and methanotroph diversity of a subarctic wetland
in Russia. Microbiology 85, 545-554. doi: 10.1134/S0026261716050039

Dedysh SN, Pankratov TA, Belova SE, Kulichevskaya IS, Liesack W (2006)
Phylogenetic analysis and in situ identification of Bacteria community
composition in an acidic Sphagnum peat bog. Appl. Enviro Microb 72,
2110-2117. doi:10.1128/AEM.72.3.2110-2117.2006

Dedysh SN (2011) Cultivating uncultured bacteria from northern wetlands:
knowledge gained and remaining gaps. Front Microbiol 2:184. doi:
10.3389/fmicb.2011.00184

DeGrood SH, Claassen VP, Scow KM (2005) Microbial community composition
on native and drastically disturbed serpentine soils. Soil Biol Biochem
37, 1427-1435. doi:10.1016/j.soilbio.2004.12.013

Elliott DR, Caporn SJM, Nwaishi F, Nilsson RH, Sen R (2015) Bacterial
and fungal communities in a degraded ombrotrophic peatland undergoing
natural and managed re-vegetation. Plos One 10. doi:
10.1371/journal.pone.0124726

Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological
classification of soil bacteria. Ecolog 88, 1354-1364. doi:
10.1890/05-1839

Fierer N, Jackson RB (2006) The diversity and biogeography of soil
bacterial communities. P Natl Acad Sci USA 103, 626-631. doi:
10.1073/pnas.0507535103

Galand PE, Fritze H, Conrad R, Yrjala K (2005) Pathways for
methanogenesis and diversity of methanogenic archaea in three boreal
peatland ecosystems. Appl Enviro Microb 71, 2195-2198. doi:
10.1128/AEM.71.4.2195-2198.2005

Gupta V, Smemo KA, Yavitt JB, Basiliko N (2012) Active methanotrophs in
two contrasting North American peatland ecosystems revealed using
DNA-SIP. Microb Ecol 63, 438-445. doi: 10.1007/s00248-011-9902-z

Hartman WH, Richardson CJ, Vilgalys R, Bruland GL (2008) Environmental
and anthropogenic control of bacterial communities in wetland soils. P
Natl Acad Sci USA 105, 17842-17847. doi: 10.1073/pnas.0808254105

Ifo SA, Garcin Y (2022) Peat decomposition in central Congo was
triggered by a drying climate. Nature. doi: 10.1038/d41586-022-03481-2

Jaatinen K, Fritze H, Laine J, Laiho R (2007) Effects of short- and
long-term water-level drawdown on the populations and activity of
aerobic decomposers in a boreal peatland. Global Change Biol 13,
491-510. doi: 10.1111/j.1365-2486.2006.01312.x

Kandel TP, Laerke PE, Elsgaard L (2018) Annual emissions of CO2, CH4 and
N2O from a temperate peat bog: Comparison of an undrained and four
drained sites under permanent grass and arable crop rotations with
cereals and potato. Agr Forest Meteorol 256, 470-481. doi:
10.1016/j.agrformet.2018.03.021

Ka\selectlanguage{polish}š\selectlanguage{english}tovsk\selectlanguage{ngerman}á E, Straková P, Edwards K, Urbanová Z, Bárta J, Mastný J (2018)
Cotton-grass and blueberry have opposite effect on peat characteristics
and nutrient transformation in peatland. Ecosystems 21, 443-458. doi:
10.1007/s10021-017-0159-3

Kim SY, Lee SH, Freeman C, Fenner N, Kang H (2008) Comparative analysis
of soil microbial communities and their responses to the short-term
drought in bog, fen, and riparian wetlands. Soil Biol Biochem 40,
2874-2880. doi: 10.1016/j.soilbio.2008.08.004

Kraigher B, Stres B, Hacin J, Ausec L, Mahne I, van Elsas JD (2006)
Microbial activity and community structure in two drained fen soils in
the Ljubljana Marsh. Soil Biol Biochem 38, 2762-2771. doi: 10.1016/j.
soilbio.2006.04.031

Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL (2015)
Consistent responses of soil microbial communities to elevated nutrient
inputs in grasslands across the globe. P Natl Acad Sci USA 112,
10967-10972. doi: 10.1073/pnas.1508382112

le Roes-Hill M, Khan N, Burton SG (2011) Actinobacterial peroxidases: an
unexplored resource for biocatalysis. Appl. Enviro Microb 164, 681-713.
doi: 10.1007/s12010-011-9167-5

Lin X, Green S, Tfaily MM, Prakash O, Konstantinidis KT, Corbett JE
(2012) Microbial community structure and activity linked to contrasting
biogeochemical gradients in bog and fen environments of the Glacial Lake
Agassiz Peatland. Appl Environ Microb 78, 7023-7031.
doi:10.1128/AEM.01750-12

Li TT, Lei Y, Dai C, Yang LF, Li ZQ, Wang ZX (2018) Effects of both
substrate and nitrogen and phosphorus fertilizer on Sphagnum palustre
growth in subtropical high-mountain regions and implications for
peatland recovery. Wetl Ecol Manag 26, 651-663.
doi:10.1007/s11273-018-9598-7

Li TT, Wang ZX, Bu GJ, Lin LQ, Lei Y, Liu CY (2019) Effects of
microtopography and water table on Sphagnum palustre L. in subtropical
high mountains and implications for peatland restoration. J Bryol 41,
121-134. doi: 10.1080/03736687.2019.1601446

Lynda DP, Scott CN, Grant JW, David MB (2023) Post-fire restoration of
Sphagnum bogs in the Tasmanian Wilderness World Heritage Area,
Australia. Restor Ecol 31. doi: 10.1111/rec.13797

Mastný J, Urbanová Z, Ka\selectlanguage{polish}š\selectlanguage{english}tovsk\selectlanguage{ngerman}á E, Straková P, \selectlanguage{polish}Š\selectlanguage{english}antr\selectlanguage{polish}ůč\selectlanguage{english}kov\selectlanguage{ngerman}á H, Edwards KR
(2016) Soil organic matter quality and microbial activities in spruce
swamp forests affected by drainage and water regime restoration. Soil
Use Manage 32, 200-209. doi:10.1111/sum.12260

Mckinley VL, Peacock AD, White DC (2005) Microbial community PLFA and
PHB responses to ecosystem restoration in tallgrass prairie soils. Soil
Biol Biochem 37, 1946-1958. doi: 10.1016/j.soilbio.2005.02.033

Morales SE, Mouser PJ, Ward N, Hudman SP, Gotelli NJ, Ross DS, Lewis TA
(2006) Comparison of bacterial communities in New England Sphagnum bogs
using terminal restriction fragment length polymorphism (T-RFLP). Microb
Ecol 52, 34-44. doi:10.1007/s00248-005-0264-2

Page SE, Baird AJ (2016) Peatlands and global change: Response and
resilience. Annu Rev Env Resour 41, 35-57.
doi:10.1146/annurev-environ-110615-085520

Pankratov TA, Ivanova AO, Dedysh SN, Liesack W (2011) Bacterial
populations and environmental factors controlling cellulose degradation
in an acidic Sphagnum peat. Environ Microbiol 13, 1800-1814.
doi:10.1111/j.1462-2920.2011.02491.x

Pankratov TA, Serkebaeva YM, Kulichevskaya IS, Liesack W, Dedysh SN
(2008) Substrate-induced growth and isolation of Acidobacteria from
acidic Sphagnum peat. ISME J 2, 551-560. doi: 10.1038/ismej.2008.7

Perotto S, Daghino S, Martino E (2018) Ericoid mycorrhizal fungi and
their genomes: another side to the mycorrhizal symbiosis? New Phytol
220, 1141-1147. doi: 10.1111/nph.15218

Philippot L, Andersson SG, Battin TJ, Prosser JI, Schimel JP, Whitman WB
(2010) The ecological coherence of high bacterial taxonomic ranks. Nat
Rev Microbiol 8, 523-529. doi: 10.1038/nrmicro2367

Pospisilova P, Vitovcova K, Prach K (2023) Importance of repeated
sampling: vegetation analyses after 10 years revealed different
restoration trends in formerly extracted peatlands. Restor Ecol 31:
e13720. doi: 10.1111/rec.13720

Serkebaeva YM, Kim Y, Liesack W, Dedysh SN (2013) Pyrosequencing-based
assessment of the bacteria diversity in surface and subsurface peat
layers of a Northern Wetland, with focus on poorly studied phyla and
candidate divisions. Plos One 8. doi: 10.1371/journal.pone.0063994

Sloan TJ, Payne RJ, Anderson AR, Bain C, Chapman S, Cowie N (2019)
Peatland afforestation in the UK and consequences for carbon storage.
Mires Peat 23:1. doi: 10.19189/MaP.2017.OMB.315

Smit E, Leeflang P, Gommans S, van den Broek J, van Mil S, Wernars K
(2001) Diversity and seasonal fluctuations of the dominant members of
the bacterial soil community in a wheat field as determined by
cultivation and molecular methods. Appl Enviro Microb 67, 2284-2291.
doi: 10.1128/AEM.67.5.2284-2291.2001

Sun H, Terhonen E, Koskinen K, Paulin L, Kasanen R, Asiegbu FO (2014)
Bacterial diversity and community structure along different peat soils
in boreal forest. Appl Soil Ecol 74, 37-45. doi:
10.1016/j.apsoil.2013.09.010

Tian W, Wang H, Xiang X, Wang RC, Xu Y (2019) Structural Variations of
Bacterial Community Driven by Sphagnum Microhabitat Differentiation in a
Subalpine Peatland. Front Microbiol 10:1661.
doi:10.3389/fmicb.2019.01661

Urbanová Z, Bárta J (2016) Effects of long-term drainage on microbial
community composition vary between peatland types. Soil Biol Biochem 92,
16-26. doi: 10.1016/j.soilbio.2015.09.017

Urbanová Z, Bárta J (2014) Microbial community composition and in silico
predicted metabolic potential reflect biogeochemical gradients between
distinct peatland types. FEMS Microbiol Ecol 90, 633-646.
doi:10.1111/1574-6941.12422

Urbanová Z, Picek T, Bárta J (2011) Effect of peat re-wetting on carbon
and nutrient fluxes, greenhouse gas production and diversity of
methanogenic archaeal community. Ecol Eng 37, 1017-1026. doi:
10.1016/j.ecoleng.2010.07.012

Urbanová Z, Straková P, Ka\selectlanguage{polish}š\selectlanguage{english}tovsk\selectlanguage{ngerman}á E (2018) Response of peat
biogeochemistry and soil organic matter quality to rewetting in bogs and
spruce swamp forests. Eur J Soil Biol 85, 12-22. doi:
10.1016/j.ejsobi.2017.12.004

Vitovcova K, Liparova J, Manukjanova A, Vasutova M, Vrba P, Prach K
(2022) Biodiversity restoration of formerly extracted raised bogs:
vegetation succession and recovery of other trophic groups. Wetl Ecol
Manag 30, 207-237. doi: 10.1007/s11273-021-09847-z

Wang H, Li TT, Ran N, He MY, Jiang HQ, Wang ZX (2021) Peat swamp
biodiversity in the Qizimei Mountain National Nature Reserve, China.
Mires Peat 27. doi: 10.19189/MaP.2020.OMB.StA.2095

Wilkinson SL, Andersen R, Moore PA, Davidson SJ, Granath G, Waddington
JM (2023) Wildfire and degradation accelerate northern peatland carbon
release. Nat Clim Change. doi: 10.1038/s41558-023-01657-w

\textbf{TABLES}

\textbf{Table 1.} Pearson correlation analysis between \selectlanguage{greek}α \selectlanguage{english}diversity of
soil bacteria and soil physicochemical properties\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& \textbf{Sobs} & \textbf{ACE} & \textbf{Chao} & \textbf{Shannon} &
\textbf{Simpson} & \textbf{Pd} & \textbf{Shannoneven} &
\textbf{Simpsoneven} & \textbf{Coverage}\tabularnewline
\midrule
\endhead
\textbf{TC} & -0.364 & -0.357 & -0.392 & \textbf{-0.689*} &
\textbf{0.731*} & -0.304 & \textbf{-0.791*} & \textbf{-0.726*} &
0.365\tabularnewline
\textbf{TN} & -0.397 & -0.400 & -0.447 & \textbf{-0.668*} &
\textbf{0.710*} & -0.356 & \textbf{-0.716*} & -0.659 &
0.446\tabularnewline
\textbf{pH} & \textbf{0.948**} & \textbf{0.958**} & \textbf{0.952**} &
\textbf{0.835**} & -0.423 & \textbf{0.931**} & 0.436 & 0.087 &
-0.349\tabularnewline
\textbf{AP} & \textbf{-0.795*} & \textbf{-0.836**} & \textbf{-0.845**} &
\textbf{-0.786*} & 0.420 & \textbf{-0.792*} & -0.484 & -0.134 &
0.625\tabularnewline
\textbf{NO\textsubscript{3}\textsuperscript{---}N} & 0.070 & 0.084 &
0.027 & -0.300 & 0.656 & 0.135 & -0.581 & \textbf{-0.778*} &
0.252\tabularnewline
\textbf{NH\textsubscript{4}\textsuperscript{+}-N} & 0.534 & 0.592 &
0.559 & 0.189 & 0.292 & 0.612 & -0.254 & -0.637 & -0.329\tabularnewline
\textbf{SWW} & \textbf{0.806**} & \textbf{0.804**} & \textbf{0.786*} &
0.424 & 0.015 & \textbf{0.836**} & -0.101 & -0.376 &
0.027\tabularnewline
\textbf{SBD} & -0.259 & -0.257 & -0.230 & 0.153 & -0.386 & -0.303 &
0.509 & 0.604 & -0.218\tabularnewline
\bottomrule
\end{longtable}

Note: \textsuperscript{*}\emph{P}
\textless{}0.05,\textsuperscript{**}\emph{P} \textless{}0.001. TC---soil
total carbon content, TN---soil total nitrogen content, AP---soil
available phosphorus content,
NO\textsubscript{3}\textsuperscript{-}N---soil nitrate nitrogen content,
NH\textsubscript{4}\textsuperscript{+}-N---soil ammonium nitrogen
content, SWW---soil weight water content, SBD---soil bulk density.

\textbf{Table 2.} Monte Carlo permutation test between soil
physicochemical properties and composition of main groups of soil
bacteria at phylum, class, family and genus level\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& \textbf{Genus} & \textbf{Genus} & \textbf{Family} & \textbf{Family} &
\textbf{Class} & \textbf{Class} & \textbf{Phylum} &
\textbf{Phylum}\tabularnewline
\midrule
\endhead
& \textbf{r\textsuperscript{2}} & p & \textbf{r\textsuperscript{2}} & p
& \textbf{r\textsuperscript{2}} & p & \textbf{r\textsuperscript{2}} &
p\tabularnewline
\textbf{TC} & 0.718 & \textbf{0.031} & 0.892 & \textbf{0.002} & 0.784 &
\textbf{0.016} & 0.372 & 0.259\tabularnewline
\textbf{TN} & 0.673 & \textbf{0.039} & 0.837 & \textbf{0.003} & 0.736 &
\textbf{0.013} & 0.364 & 0.263\tabularnewline
\textbf{pH} & 0.816 & \textbf{0.009} & 0.806 & \textbf{0.014} & 0.797 &
\textbf{0.016} & 0.758 & \textbf{0.020}\tabularnewline
\textbf{AP} & 0.862 & \textbf{0.004} & 0.875 & \textbf{0.012} & 0.912 &
\textbf{0.005} & 0.830 & \textbf{0.011}\tabularnewline
\textbf{NO\textsubscript{3}\textsuperscript{-}-N} & 0.478 & 0.144 &
0.687 & \textbf{0.025} & 0.609 & 0.051 & 0.391 & 0.243\tabularnewline
\textbf{NH\textsubscript{4}\textsuperscript{+}-N} & 0.909 &
\textbf{0.005} & 0.875 & \textbf{0.008} & 0.894 & \textbf{0.010} & 0.715
& \textbf{0.024}\tabularnewline
\textbf{SWW} & 0.789 & \textbf{0.022} & 0.910 & \textbf{0.018} & 0.841 &
\textbf{0.023} & 0.975 & \textbf{0.001}\tabularnewline
\textbf{SBD} & 0.736 & \textbf{0.021} & 0.934 & \textbf{0.001} & 0.819 &
\textbf{0.013} & 0.687 & \textbf{0.047}\tabularnewline
\bottomrule
\end{longtable}

Note: TC---soil total carbon content, TN---soil total nitrogen content,
AP---soil available phosphorus content,
NO\textsubscript{3}\textsuperscript{-}N---soil nitrate nitrogen content,
NH\textsubscript{4}\textsuperscript{+}-N---soil ammonium nitrogen
content, SWW---soil weight water content, SBD---soil bulk density.

\textbf{FIGURES}

\textbf{Figures Captions}

\textbf{Fig. 1} Comparison of soil physicochemical properties
among\emph{Sphagnum} fen (SF), \emph{Polytrichum} bog (PB) and
\emph{Cryptomeria} swamp forest (CSF). TC---soil total carbon content,
TN---soil total nitrogen content, AP---soil available phosphorus
content, NO\textsubscript{3}\textsuperscript{-}N---soil nitrate nitrogen
content, NH\textsubscript{4}\textsuperscript{+}-N---soil ammonium
nitrogen content, SWW---soil weight water content, SBD---soil bulk
density. Error bars indicate the standard error (n=3). Lowercase letters
represent significant differences at 95\% confident interval as
indicated by ANOVA with LSD\emph{post hoc} comparisons.

\textbf{Fig. 2} Comparison of soil bacterial \selectlanguage{greek}α \selectlanguage{english}diversity
among\emph{Sphagnum} fen (SF), \emph{Polytrichum} bog (PB) and
\emph{Cryptomeria} swamp forest (CSF). Error bars indicate the standard
error(n=3). Lowercase letters represent significant differences at 95\%
confident interval as indicated by ANOVA with LSD \emph{post
hoc}comparisons.

\textbf{Fig. 3} Variations of soil bacterial composition
among\emph{Sphagnum} fen (SF), \emph{Polytrichum} bog (PB) and
\emph{Cryptomeria} swamp forest (CSF) at phylum, class, family and genus
level. (1) Comparison of soil bacterial composition among different
types of sites by NMDS based on Bray-Curtis distance (A, D, H, K). (2)
The number of shared and unique taxa across different types of sites (B,
E, I, L). (3) Comparison of the dissimilarities of soil bacterial
communities between different types of sites (C, F, J, M). Each box plot
represents the maximum, minimum, 75th, and 25th quartiles respectively,
the line of each box plot represents the median, and the red point of
each box plot represents the mean (n=9). Lowercase letters indicate
significances at the 95\% confidence interval according to ANOVA with
LSD post hoc test.

\textbf{Fig. 4} Indicator groups analysis of bacterial communities
in\emph{Sphagnum} fen (SF), \emph{Polytrichum} bog (PB) and
\emph{Cryptomeria} swamp forest (CSF) with LDA SCORE \textgreater{} 3.5.

\textbf{Fig. 5} Comparison of the relative abundance of major groups of
soil bacteria in \emph{Sphagnum} fen (SF),\emph{Polytrichum} bog (PB)
and \emph{Cryptomeria}swamp forest (CSF) at phylum, class, family and
genus level. The main groups of soil bacteria were composed of the top
ten groups in the relative abundance of each type of site.

\textbf{Fig. 6} Redundancy analysis showing the relationship between
soil physicochemical properties and major groups of soil bacterial
communities in all types of sites at phylum, class, family and genus
level. SF-\emph{Sphagnum} fen, PB-\emph{Polytrichum} bog,
CSF-\emph{Cryptomeria} swamp forest.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6/Figure-6}

\end{center}
\end{figure}

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

