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\begin{document}

\title{Effects of stand age and soil organic matter quality on soil bacterial
and fungal community composition in \emph{Larix gmelinii} plantations,
Northeast China}



\author[1]{li jianwei}%
\author[1]{Sun Xiaoqian}%
\author[1]{Li Ming}%
\author[2]{Zou Jiying}%
\author[1]{Bian Hongfeng}%
\affil[1]{Northeast Normal University}%
\affil[2]{Jilin Institute of Chemical Technology}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
It is of great interest to elucidate the biogeographic patterns of soil
microorganisms and their driving forces, which is fundamental to
predicting alterations in microbial-mediated functions arising from
environment changes. Although the vertical movement of dissolved organic
matter (DOM) drives the cycle of nutrients such as soil carbon but, in
the restored ecosystem, the relationship between DOM and soil microbial
nutrient utilization remains to be determined. Here, we investigated the
changes of soil microbial community at 0-40 cm depth profile in three
stages (10-, 30-, 50-years) of succession in \emph{Larix olgensis}
plantations and the fluorescence spectrum composition of DOM. With the
increase of soil depth, the signal source of microorganisms increases.
In a coniferous forest soil environment, the possible main source of DOM
in deep soil is the production of microbial metabolism. Difficulty in
the decomposition of organic matter determines the distribution and
composition of microorganisms. Increasing forest age makes bacteria and
fungi more specific and bacterial-fungal associations greater. Overall,
our work contributes to the understanding of factors underlying
microbial community distribution in plantation forests and the
importance of DOM quality in building microbial communities.%
\end{abstract}%



\sloppy


\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{1 Introduction}

Returning farmland to forest is an effective means to protect the
ecological environment and prevent soil erosion; moreover, it
effectively improves vegetation coverage, species diversity, and primary
productivity (Wu et al., 2021). In Northeast China, afforestation
account for 70\% of the total forest area (Yang et al.,
2010).\emph{Larix(L.) gmelinii} has been widely used as a plantation
species because of its rapid growth and excellent wood quality (Li et
al., 2020; Yu and Liu, 2020). The response of soil microbial communities
to such returning farmland to forest little attention. It is of
particular importance to understand the effects of such conversion on
soil microbial community, which will further affect the maintenance of
ecosystem services and functions.

Soil microorganisms fundamentally determine soil organic carbon (SOC)
accumulation and renewal and affect soil carbon mineralization (Jiang et
al., 2018; Maron et al., 2018). Soil active microbial communities play a
central role in subsurface processes, especially in mediating soil
organic decomposition and nutrient cycling in forest ecosystems (Jiao et
al., 2018; Wagg et al., 2019). The interaction between forest SOC
decomposition and soil microbial communities is key to understanding the
feedback of terrestrial ecosystem processes to global climate change
(Cavicchioli et al., 2019; Geisen et al., 2021; Zheng et al., 2019). The
majority of soil microorganisms obtain carbon by decomposing different
types of SOC, which have different decomposition abilities and
mechanisms (Zhong et al., 2018).

In soil systems, the source and composition of dissolved organic matter
(DOM) change with varying soil depth, and the source may change from
plant to microbial with an increase in depth (Roth et al., 2019). DOM
can combine with fine soil particles to form mineralized SOC; it can
also stimulate soil microorganism activity and promote SOC decomposition
(Gross and Harrison, 2019; Kleber et al., 2021). The vertical movement
of DOM plays a crucial role in soil development, microbial metabolism,
carbon and nutrient cycling, and distribution (Ye et al., 2020), and it
drives the basic biogeochemical processes in forest soils (Kaiser and
Kalbitz, 2012). DOM is an important carbon source for soil
microorganisms. Solid organic macromolecules are decomposed into
molecules with relatively small water solubility under the action of
extracellular enzymes. Soluble substrates are a prerequisite for
microbial diffusion through cell membranes (H. Liu et al., 2021; Roth et
al., 2019). DOM is the most bioavailable organic matter in the soil, and
its composition is highly dynamic and sensitive to environmental change
(Fan et al., 2020). In addition, some studies have revealed that
microorganisms are unable to utilize all carbon resources and exhibit a
significant substrate preference (Huang et al., 2021).

Plantations play a vital role in supporting the timber industry,
promoting local economic development, and mitigating global climate
change (Tong et al., 2020; Zhu et al., 2017). With reduced logging and
effective wildfire control, plantations are increasing in age and, thus,
are storing more carbon. Most of the current studies on the change of
soil carbon focus on the surface layer, that is, 0-20 cm depth (Cheng et
al., 2017). However, the balance between soil carbon mineralization and
decomposition in the sub-deep layer (\textgreater{}20 cm) may play an
important role in the long-term carbon sequestration potential of SOC.
Therefore, it is necessary to quantify the vertical change in soil
carbon stability in plantations (Hou et al., 2019; Mayer et al., 2020;
Yang et al., 2020). Returning farmland to forest can significantly
change soil properties and nutrient status, and further soil microbial
community (Liu et al., 2020). However, there are strong interactions
between soil physico-chemical and biological properties. Thus, the
combined analysis of all these factors during the returning farmland to
forest can provide useful information for comprehensive evaluation the
effects of plantations on soil quality and functions.

In this study, the diversity in the composition of soil dissolved
organic carbon (DOC) in three different stands along with the soil
layers (0-40 cm) and explored the relationships between DOM composition
and bacterial and fungal communities (i.e., diversity, species
composition, and microbial abundance) by absorption and fluorescence
spectroscopy. The objectives of this study were (1) to determine the
effects in three different stands along with the soil layers (0-40 cm)
on soil chemical properties, (2) to compare and evaluate the influence
of returning farmland to forest on soil microbial community, and (3) to
determine whether DOM quality plays a predominant role in structuring
the soil microbial community.

\textbf{2 Materials and methods}

\textbf{2.1 Site description and soil sampling}

The Jinchuan Forest Farm (42\selectlanguage{ngerman}°20'N, 126°24'E) in Huinan County, southwest
of Changbai Mountain, is located in Northeast China (Fig. S1). The total
area is 22.5 km\textsuperscript{2}; the average altitude is 655 m; the
annual average temperature is 3.4 °C, and the annual average
precipitation is 1053.9 mm. Soil samples were collected in July 2020. We
selected three \emph{L. gmelinii} plantations with different restoration
years (10, 30, and 50 years). Homogenous climatic conditions throughout
the study site made it possible to compare and represent plantations
with a different number of restoration years and the associated soil
microbial communities.

Three sample plots (30 × 30 m) were randomly selected at each study
site. Soil samples were collected from five layers (10, 20, 30, and 40
cm) along with the soil profile. The samples were screened to 2 mm,
stored at 4 °C, and transported to the laboratory for immediate
analyses. A total of 36 samples were collected (3 cultivation years of
plantation × 3 repeated samples × 4 layer depths). Each sample was
divided into three sub-samples, namely, (1) those air-dried at room
temperature (20 °C) for analyses of pH, total nitrogen (TN), soil water
content (SWC), SOC and DOM; (2) those stored at 4 °C for analyses of
soil ammonia nitrogen (NH\textsubscript{4}\textsuperscript{+}) and
nitrate nitrogen (NO\textsubscript{3}\textsuperscript{-}); and (3) those
stored at -80 °C for analysis of eukaryotic and prokaryotic
microorganisms.

\textbf{2.2 Soil physical, chemical, and microbial analyses}

Soil pH was determined with the slurry method: air-dried soil was shaken
in water (soil: water {[}m/v{]}\selectlanguage{english} =\selectlanguage{english} 1:2.5) for 2\selectlanguage{english} min, allowed to stand for
30\selectlanguage{english} min, and pH was measured in the supernatant with an electrode. For
nitrate measurement, 10\selectlanguage{english} g fresh soil was extracted into 50\selectlanguage{english} mL of 2\selectlanguage{english} M KCl
(aq) with shaking for 1\selectlanguage{english} h\selectlanguage{english} at 25\selectlanguage{english} degC, followed by filtration; and, the
filtrate was analyzed on a Continuous Flow Analyzer (Technicon
Corporation, Oregon, U.S.A.). SOC content was determined by the wet
oxidation method using potassium dichromate in acid medium followed by
redox titration and TN using the Kjeldahl method.

The relative abundances of C compound classes were determined in
air-dried soils using Fourier-transformed infrared spectroscopy
(attenuated total reflection Fourier-transformed infrared spectroscopy),
with 64 scans by a Thermo-Scientific (Waltham, MA, USA), Nicolet iS50
FT-IR and peak assignments were made following Parikh (Parikh et al.,
2014; Qin et al., 2021). DOM extraction and fluorescence
excitation-emission (EEM) analysis were conducted with reference to
previous studies (Huang et al., 2020).

Briefly, 5 g of air-dried soil samples were placed in 50-mL centrifuge
tubes and mixed with 25 mL distilled water with a soil-to-water ratio of
1:2.5 (w/v). The mixture was then by a shaker at 25\selectlanguage{english} at 200 rpm for 12
hours in the dark. All of the extracts were centrifuged at 3000xg for 10
min and filtered through prewashed 0.45 \selectlanguage{greek}μ\selectlanguage{english}M cellulose acetate filters.
The concentration of DOM was determined by the TOC tester (Germany multi
N/C 3100). Fluorescence EEM was measured with a Hitachi
spectrophotometer (Hitachi, Tokyo, Japan) at ambient temperature, across
the excitation wavelengths 220-550 nm with an interval of 5 nm and the
emission wavelengths 250-550 nm with an interval of 5 nm and scanning
speed at 3000 nm min\textsuperscript{-1}. The filtered DOM samples were
transferred into brown glass sample bottles to prevent
photodecomposition and stored in a refrigerator at 4 \selectlanguage{ngerman}°C. All analyses
were conducted within 24 h after extraction.

The humification index (HIX) describes the DOM humification degree, with
a higher value corresponding to lower H/C ratios and more ring
structure. The HIX was determined using interpolated values for
excitation at 254 nm, as the area under the emission peak from 435 to
480 nm was divided by the area under the peak from 330 to 345 nm (Ohno,
2002). The fluorescence index (FI), calculated as em 470 nm/em 520 nm at
ex 370 nm, is an indicator of precursor material to the humic portion of
OM, with higher values indicating microbe-dominated origins (FI\selectlanguage{english} \selectlanguage{english}[?]\selectlanguage{english} 1.8)
and lower values indicating plant-dominated (FI\selectlanguage{english} \selectlanguage{english}[?]\selectlanguage{english} 1.2) (T. Liu et al.,
2021; McKnight et al., 2001).

According to the instructions of the FastDNA SPIN Kit for Soil
(Q-BIOgene, Carlsbad, USA), we extracted microbial DNA. And then, we
detected the concentration and purity of DNA using an automatic
microplate reader (BioTek ELX 800, USA). The primers 338F
(5\selectlanguage{english}'-ACTCCTACGGGAGGCAGCA-3\selectlanguage{english}') and 806R (5\selectlanguage{english}'-GGACTACHVGGGTWTCTAAT-3\selectlanguage{english}') (Mori
et al., 2014) were used to amplify the V3--V4 hypervariable region of
the bacterial 16S rRNA gene. The primers ITS1F
(5\selectlanguage{english}'-CTTGGTCATTTAGAGGAAGTAA-3\selectlanguage{english}') and ITS2R (5\selectlanguage{english}'-GCTGCGTTCTTCATCGATGC-3\selectlanguage{english}')
(Adams et al., 2013) were used to amplify the fungal ITS1 region. The
PCR amplification steps were as following: pre-denaturation at 94\selectlanguage{english} degC for
5\selectlanguage{english} min, followed by 30\selectlanguage{english} cycles of deformation at 95\selectlanguage{english} degC for 30\selectlanguage{english} s, annealing
at 58\selectlanguage{english} degC for 20\selectlanguage{english} s, elongation at 72\selectlanguage{english} degC for 6\selectlanguage{english} s, and a final extension at
72\selectlanguage{english} degC for 7\selectlanguage{english} min. The obtained PCR product was further purified and
quantified using QuantiFluor\selectlanguage{english}-ST (Promega, USA) according to the
manufacturer's procedure. Finally, all the gene sequencing was performed
on an Illumina HiSeq PE150 system (Illumina Corporation, USA) by
Biomarker Technologies Co, LTD.

Primer sequences were trimmed after the raw sequences were de-noised,
sorted, and separated using Trimmomatic (version 0.33). The remaining
sequences were filtered for redundancy, and all unique sequences for
each sample were then clustered into operational taxonomic units (OTUs)
at similarities of 97\%. Sequences with putative chimeras and ambiguous
bases were discarded. The taxonomic identify (species level) of
representative sequences for each OTU was determined using the Silva
reference database (http://www.arb-silva.de) for the 16S rRNA genes and
the Unite reference database (http://unite.ut.ee/index.php) for the ITS
using the RDP naive Bayesian classifier with the BLAST tool in QIIME
(http://qiime.org/index.html).

\textbf{2.3 Statistical analysis}

Two-way ANOVAs were used to analyze the effects of varied tree ages,
soil depths, and their interaction on the soil properties and microbial
alpha diversities (number of OTUs and Simpson's diversity and
Shannon-Wiener indices). A Pearson correlation analysis assessed the
association between microbial alpha diversity and environmental factors.
A value of \emph{p} \selectlanguage{english} \textless{}\selectlanguage{english} 0.05 was considered significant. The
heterogeneity of the variance was tested, and the original data were
normalized by log-transformation or standardization prior to analysis
when necessary. Using non-metric multidimensional scaling (NMDS), the
dynamics of microbial community during the increase of \emph{L.
gmelinii} plantations age and then assessed through a permutational
multivariate analysis of variance (PERMANOVA). The correlations between
environmental parameters and microbial community profiles were then
assessed through Redundancy analysis (RDA). A Mantel test was used to
assess the correlations of microbial communities and environmental
variables using the Vegan package. A partial Mantel test in the Vegan
package was used to control the covarying effects of various factors. A
classification random forest (RF) analysis was used to identify
microbial taxa associated with stand age in \emph{L.
gmelinii}plantations. The analysis aimed to test which microbial taxa
are possible predictors of forest age change. Additionally, the
functional profiles of soil prokaryotes and fungi were separately
analyzed using FAPROTAX (Louca et al., 2016) and FUNGuild (Nguyen et
al., 2016). To estimate species coexistence across different \emph{L.
gmelinii}plantations, metacommunity co-occurrence networks consisting of
all the members of the three microbial groups were constructed. To
reduce rare OTUs in the dataset, we removed OTUs with a relative
abundance \textless{}0.5\%. Robust correlations with Spearman's
correlation coefficients (\selectlanguage{greek}ρ\selectlanguage{english}) \textgreater{} 0.7 and false discovery
rate-corrected p-values \textless{}0.05 were used to construct networks
(Jiao et al., 2020; Shi et al., 2020). Then, the networks were
constructed in R with the igraph package, and the related network
indices were calculated. Finally, the networks were visualized in Gephi
(version 0.9.3; https://gephi.org/). The node size was proportional to
the number of connections (i.e., degrees), and the node color
represented the microbial taxonomy. The link between each pair of nodes
represented a positive (in green) or a negative (in red) correlation.

\textbf{3 Results}

\textbf{3.1 Changes of soil physical and chemical properties in an
artificial forest with different recovery periods}

Soil properties including pH, NH\textsubscript{4}\textsuperscript{+},
and DOM quantity (represented by DOC concentration) and quality varied
with respect to soil depth and recovery time increased. The soil was
consistently acidic, with the lowest pH observed in the topsoil.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 1} soil properties: a-e; FT-IR: (f) fourier-transformed
infrared spectra of soil organic matter mixtures (mixtures were prepared
with replicate three samples). Mean values are shown with standard error
(n = 3). Y and D denote the effects of different ages and soil depth,
respectively (only significant effects based on two-way ANOVA are shown)

There were some differences in the concentrations of DOM quantity and
SOC in soil layers of the sampled stands along the \emph{L.
gmelinii}afforestation along the chronosequence. SOC and TN content in
the 0--10 cm soil layers was significantly higher than the 10--40 cm
soil layers in each afforestation (\emph{p} \textless{} 0.05; Figs. 1a
and 1c), and the SOC and TN content in the chronosequence were
significantly different, mainly in the deep layer 10--40 cm. DOC/SOC
ratio significantly increased with increasing forest age
(\emph{p}\textless{} 0.05). NH\textsubscript{4}\textsuperscript{+}
content ranged from 5.68 to 15.88 and increased with increasing forest
age (\emph{p}\textless{} 0.05) but did not affect the
NO\textsubscript{3}\textsuperscript{-} contents (\emph{p} \textgreater{}
0.05; Table S1). The quality of DOM (FI and HIX index) in the soil
changed with increasing forest age (Figs. 1d and 1e)). The FI index of
different forest ages fluctuated around 1.5. FI index in subsoil
significantly decreased over time, while in the topsoil, the change was
not significant (\emph{p} \textgreater{} 0.05). There were no
significant differences in HIX index between soil layers in the younger
stand (10 years old), but the HIX index was significantly higher in
topsoil than in subsoil at the older stands (30 and 50 years old)
(\emph{p} \textless{} 0.05). In comparison with the results of previous
studies (Gao et al., 2017), it can be inferred that the DOM of different
forest ages may have similar sources in the \emph{L. gmelinii} .

To investigate the structure of the soil organic matter, FT-IR
spectroscopy was used to examine, whereas the spectra are generally
similar between samples, the relative intensity of particular bands
differs, revealing the SOC chemical changes during in afforestation
(Hartman et al., 2017) (Fig. 1f). With increasing forest age
polysaccharides {[}1030-1080 cm\textsuperscript{-1}{]} weaken. 10 years
plantations had more abundant polysaccharides, abundant lignins
aromatics {[}1600--1650 cm\textsuperscript{-1}{]}, and less abundant
clay minerals {[}3691 cm\textsuperscript{-1}{]} than plantations aged 30
and 50 years. It indicated more cellulose (O-alkyl-C) plant material in
the younger stand vs. more organic--mineral compounds in the older
stands.

\textbf{3.2 Soil Microbial community diversity, composition, and
abundance of taxa covaried with different recovery periods and soil
depth}

A total of 1720 bacterial and 959 fungal operational taxonomic units
(OTUs) were obtained, and each sample harbored 1343 bacterial and 238
fungal OTUs on average. The number of fungal OTUs is significantly lower
than the bacterial OTUs.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 2} Shannon diversity and Chao1 richness of bacterial (Figs.
2a and 2c) and fungal (Figs. 2b and 2d) communities in plantation stands
of different ages. Different upper and lowercase letters indicate
different levels among different ages and soil depth, respectively (n =
3, \emph{p} \textless{} 0.05)

The \selectlanguage{greek}α-\selectlanguage{english}diversity (represented by Shannon's diversity and Chao1 richness)
of the soil bacteria community differed among 10-40 cm in 30 and 50
years plantations but not in 0-10 cm soil (Figs. 2a and 2c), and, the
diversity measurements such as the Shannon index and Chao1 richness
indicate that 0-10 cm has a higher \selectlanguage{greek}α-\selectlanguage{english}diversity than 10-40 cm. However, a
major dissimilarity was observed among the fungal communities in the
soil microhabitat samples collected from different stands (Figs. 2b and
2d), indicating that the fungal communities were highly diverse in soil
from different soil layers.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 3} (a)-(b): Compositions and structures of soil microbial
communities between different stand ages in the \emph{L.
gmelinii}plantations. The NMDS, the dynamics of microbial community
during the increase of \emph{L. gmelinii} plantations age and then
assessed through a PERMANOVA.

The plot of NMDS (Figs. 3c and 3d) suggests variations in microbial beta
diversity among the soil samples, with the response of soil bacterial
beta diversity to afforestation time being larger than that of fungal
beta diversity (bacteria, PERMANOVA, r = 0.48,\emph{p}
 \textless{} 0.001; fungal, PERMANOVA, r = 0.31,\emph{p}
 \textless{} 0.001), despite the significant variation in both species
with afforestation time, and the differences in diversity between the
restorative stages were much larger than the differences in diversity
among soil depths.

Six bacterial phyla (Acidobacteria, Actinobacteria, Proteobacteria,
Rokubacteria, Gemmatimonadetes, and Chloroflex) dominated forest soils,
accounting for 95\% of bacterial abundance. The relative abundances of
these taxa varied by forest ages stages and soil depth. Notably,
Rokubacteria significantly increased with increasing forest ages.
Proteobacteria showed the opposite trend. Other phyla showed different
responses with increasing stand age but these responses were
insignificant. In the case of the fungal community, fungi could be
classified into two different phyla, predominantly Ascomycota and
Basidiomycota. The relative abundance of Basidiomycota increased but
Ascomycota decreased with increasing forest age. The relative abundance
of Basidiomycota gradually decreased with increasing soil depth in the
younger stand (10 years old).

Using random-forest machine learning to discriminate samples with
different samples based upon the microbiota composition (Zhang et al.,
2018). The importance of indicator prokaryotic classes was assessed by
10-fold cross-validation (Xue et al., 2021). When the cross-validation
error curve was at its lowest, a total of 24 OTUs were detected as
biomarker fungi taxa and 42 OTUs as biomarker bacteria taxa. Among these
classes, taxa exhibited different relative abundances in soils of
different stand ages, and these results suggest that these taxa may be
useful as biomarkers of soil microorganisms in planted forests (Fig.
S2).

Using FUNGuild, 11 of all OTUs were assigned to putative fungal
functional groups, which accounted for 54.5\% of total biomarker fungi
taxa. Pathotroph-saprotroph-symbiotroph (45.8\%) were the abundant
functional groups across all samples (Fig. S2). For trophic modes, the
relative abundances of some dominant taxonomic groups changed apparently
with forest ages. The relative abundances of saprotroph and symbiotroph
in 30 and 50 years forests were greater than those of 10 years forest
group, while the relative abundance of pathotroph aerobic
chemoheterotrophy (AC) and aromatic compound degradation (ACD) was
significantly lower than that of 10 years forest. Relative abundance of
aerobic nitrite oxidation (ANO) significantly increased with stand age.
These changes are closely related to soil nutrient limitation.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Fig. 4} Network of co-occurring bacteria and fungi OTUs across
three different stand ages in the \emph{L. gmelinii} plantations.

\textbf{Table 1} Properties of soil bacteria and fungi co-occurrence
networks\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& OTUs (Number of network nodes) & OTUs (Number of network nodes) &
Connections (Number of network edges) & Connections (Number of network
edges) & Connections (Number of network edges) & Interaction &
Interaction & Density\tabularnewline
\midrule
\endhead
Community & Bacteria & Fungi & Bac-Bac & Fun-Fun & Bac-Fun & Positive &
Negative &\tabularnewline
10 years & 216 & 87 & 8866 & 532 & 60 & 56.23\% & 43.77\% &
0.206\tabularnewline
30 years & 172 & 26 & 1127 & 8 & 77 & 59.29\% & 40.71\% &
0.062\tabularnewline
50 years & 172 & 51 & 2088 & 29 & 135 & 56.17\% & 43.83\% &
0.090\tabularnewline
\bottomrule
\end{longtable}

Network complexity varied considerably across the three forest ages
types (Table 1). The number of network edges (the number of bacterial
and fungal connections) increased with increasing stand age (Fig. 4).
The network complexity varied greatly among the three stand ages. The 30
years old microbial community had lower network complexity, fewer edges
(1211), lower mean (12.23), and lower graph density (0.062) compared to
the 10 and 50 years forest soils. 10 years forest soils had the most
complex network with a higher number of nodes and edges and higher graph
density.

\textbf{3.3 Microbial community diversity and composition of taxa
covaried with soil} \textbf{properties}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Fig. 5} Redundancy analyses (RDA) using pooling data of
bacterial (a) and fungal (b) community and abiotic variables (arrows).
The values of Axes 1 and 2 are percentages that the corresponding axis
can explain.

Redundancy analysis (RDA) and Monte Carlo permutation test were used to
provide a statistical analysis of the microbial community response to
changes in different forest ages (\emph{p} \textless{} 0.01) (Fig. 5).
According to the RDA results the first two axes explained 52.12\% of the
total variation in the soil bacterial community (RDA1, 40.89\%; RDA2,
11.23\%), and HIX, FI, SOC, and SWC were the main factors influencing
the structure of the soil bacterial community. The structure of bacteria
was significantly correlated with soil FI index (\emph{p} \textless{}
0.01) in 10 years plantations, but was more strongly associated with
NO\textsubscript{3}\textsuperscript{-} and FI index (\emph{p}\textless{}
0.05) in 30 years plantations, then SWC and
NH\textsubscript{4}\textsuperscript{+} (\emph{p} \textless{} 0.01) had
the greatest effect on bacterial community structure in 50 years
plantations.

Differences in fungal community composition were mainly determined by
SWC (\emph{p} \textless{} 0.01), HIX (\emph{p} \textless{} 0.01), and FI
(\emph{p} \textless{} 0.05), with HIX and FI indexes influencing the
distribution of fungal communities in 10 years plantations and SWC and
NH\textsubscript{4}\textsuperscript{+} (\emph{p} \textless{} 0.01)
having a greater impact on fungal diversity in 50 years plantations. The
first two axes explained 23.7\% of the total variation in the soil
fungal community (RDA1, 13.34\%; RDA2, 10.36\%).

\textbf{Table 2} Mantel tests and partial Mantel tests showing the
relationships between dissimilarities of microbial community composition
and individual environmental variables after controlling the potential
effect of age stand. Mantel statistic r and associated \emph{p} were
determined by partial Mantel tests based on Spearman's correlation with
9999 permutations\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Edaphic variables & Bacterial composition & Fungi
composition\tabularnewline
\midrule
\endhead
Years & 0.48*** & 0.58***\tabularnewline
Deep & 0.21*** & 0.01\tabularnewline
Year x Deep & 0.55*** & 0.52***\tabularnewline
pH & -0.16 & -0.05\tabularnewline
SWC & -0.36 & 0.07\tabularnewline
DOC & -0.08 & -0.19\tabularnewline
SOC & 0.11* & -0.25\tabularnewline
TN & -0.01 & -0.26\tabularnewline
NO\textsubscript{3}\textsuperscript{-} & 0.03 & -0.12\tabularnewline
NH\textsubscript{4}\textsuperscript{+} & 0.08 & 0.08\tabularnewline
FI & 0.17** & 0.14*\tabularnewline
HIX & 0.23** & -0.08\tabularnewline
\bottomrule
\end{longtable}

Significance levels: ***: \emph{p} \textless{} 0.001; **:
\emph{p}\textless{} 0.01; *: \emph{p} \textless{} 0.05.

Since forest age and soil depth also contributed to building microbial
community composition, partial Mantel tests were performed to estimate
the independent contribution of each environmental factor after removing
the effects of forest age and soil depth. Partial Mantel test results
showed that FI (r = 0.11, \emph{p} \textless{} 0.01) and HIX (r =
0.23,\emph{p} \textless{} 0.01) were environmental factors significantly
associated with bacterial community composition (Table 2) and soil SOC
(r = 0.11, \emph{p} \textless{} 0.05) but their effects were apparently
much smaller; FI was the key to influence the distribution of fungal
communities (r = 0.14, \emph{p} \textless{} 0.05) and other
environmental factors were not significantly influenced.

Linear regression analyses showed that the diversity of bacteria and
fungi all increased significantly as DOM quantity increased. Diversity
of fungi all declined as FI increased (r = 0.32, \emph{p} = 0.058; Fig.
S3e) and bacteria increased as HIX increased (r = 0.71,
\emph{p}\textless{} 0.05; Fig. S3c). This indicated that fungal
communities were more diverse when DOM was more plant-dominated in
origin than those microbial sources dominated and increasing
humification of DOM significantly enhanced bacteria diversity.

\textbf{4.} \textbf{Discussion}

\textbf{4.1 Influence of recovery period on SOC stability of larch
plantations}

This study shows that the recovery time of larch trees affects the
microorganisms in the soil, and soil properties and microbial
communities change with increasing stand age, with bacteria and fungi
responding differently to increase in age with the stand. There is a
very strong link between soil microbial communities and soil properties
in plantations. The DOM quality of the soil, especially the source of
DOM and the level of humification, strongly influenced the structural
and functional diversity of bacteria and fungi.

Changes in soil microbial diversity ware controlled by the quality of
soil carbon in silvicultural ecosystems, and resource use by soil
microorganisms was largely dependent on the availability of organic
matter, and previous studies had shown a strong relationship between
soil carbon content and microbial community diversity (Shen et al.,
2019).

The variation in soil microbial community diversity among different pine
stand ages varied between soil depths, which may be due to differences
in the quantity and quality of soil organic matter. Overall, the HIX
index decreased with the increase of soil depth, which indicated that
the increase of forest age DOM was more humified in the surface soil and
the accumulated humus was gradually transformed by microbial
decomposition with soil depth. In contrast, a large amount of newly
generated organic matter was buried in the deep soil environment of the
10 years forest due to the influence of anthropogenic activities and the
input of organic fertilizers and crop straw (Kramer and Gleixner, 2008).
Along with this perennial plant residue lingering on the soil surface,
which may increase anaerobic microsites and affect soil moisture and
temperature and thus the decomposition of plant residues (Ram\selectlanguage{ngerman}írez et
al., 2020a), making a higher proportion of lignin and aromatic compounds
in the surface soil, when the SOC is more difficult to be decomposed (Li
et al., 2021). As restoration time and soil depth increased,
humification in the soil would decrease, carbon from plants will
increase, and organic matter in the soil will be more difficult to
decompose.

\textbf{4.2 Effects of soil depth and plantation restoration period on
microbial community structure}

The microbial community structure changed at different stand ages, and
the microbial diversity was higher in the surface soils, with different
community structures and species composition at different soil depths.
Vertical gradients in microbial community structure have been reported
in other soil environments that are associated with differences in soil
properties with depth (Du et al., 2021; Sun et al., 2020).

Microorganisms promote the decomposition of soil humus and accelerate
the turnover rate of soil organic matter, therefor afforestation leads
to changes in soil microbial diversity and also affects the soil carbon
content in afforested ecosystems (Chen et al., 2021; Kang et al., 2018).
Significant differences in the microbial communities were found in the
different sampling sites in NMDS plots.

Changes in bacterial diversity were only expressed in the deeper soils,
probably due to the richer nutrient content and composition of the
topsoil, which can support a more diverse microbial community. The
relative abundance of Acidobateria in the soil was significantly higher
in the later stages of afforestation restoration (50 years) compared to
the early stages of restoration (10 years). Acidobateria was thought to
be able to use soil polysaccharides (especially microbially synthesized
polysaccharides) to supply its production, and organic matter in the
soil is more present in the form of microbially synthesized carbon as
the stand age increases (Shao et al., 2019). Proteobacteria showed a
gradual decreased in relative abundance with increasing recovery time,
and this was closely related to the organic matter availability content
(DOC/SOC). Changes in the composition and availability of soil organic
matter determine the role of these taxa.

In general, fungi exhibited slower biomass turnover rates but higher
decomposition efficiency than bacteria in early silviculture, and as the
age of the forest increases, the structure and amount of organic matter
in the deep soil changes and humification decreases the diversity of
fungi (Bastida et al., 2021; Heijboer et al., 2018). It was reported
that in the acidic soil fungi represented by Ascomycota and
Basidiomycota are the main decomposers (Shi et al., 2021; Zhou et al.,
2017). Basidiomycota played an important role in degrading lignin (Zhang
et al., 2020), and the relative abundance of Basidiomycota is higher in
plantation forests with younger stand ages (in 10 years old soil
environments) due to the high cellulose content and aromatic compounds
in the topsoil. While in the deep soil layer due to the new carbon
generated by the disturbance of early anthropogenic activities was
transferred and buried deeper in the soil (Lorenz and Lal, 2005), the
lignin content in the soil was low relative to the surface soil, and
Basidiomycota phylum would be gradually replaced by Ascomycota
(S-strategist) (Ho et al., 2017; Li et al., 2021).

The transfer and decomposition of soil organic matter should be carried
out from top to bottom, and the organic matter of the substratum of the
short-term vegetation restoration was still dominated by the organic
matter imported under human disturbance.

With increasing afforestation age, litter input from old vegetation
would be terminated and replaced by litter from new vegetation, while
the soil C derived from the former litter would be decomposed and
mineralized by soil microbes (Ramírez et al., 2020b), the content of
easily available ammoniacal nitrogen in the soil increased and the
content of soil nutrients increased (Hoffland et al., 2020). Organic
matter breakdown results from a combination of physical fragmentation
and the activities of microorganisms (bacteria and fungi).

Bacterial community structure was linked to the function of the
bacterial community, the function of degradation of aromatic compounds
was increased with increasing afforestation age, and the relative
abundance of functional flora associated with nitrogen cycling was also
increased. In contrast, the bacterial community of chemical heterotrophs
needs to consume a large number of organic molecules in the soil as a
source of energy (Ma et al., 2020), and the composition of organic
matter in the soil environment of the 10 years afforestation forest was
significantly more complex than that of the 30 and 50 years ones, so the
relative abundance of chemical heterotrophic bacteria was higher in the
10 years forest. For fungi, the relative abundance of Saprotroph and
Symbiotroph increased with the age of the stand. Fungi decomposing
organic matter in the soil to obtain nutrients, and nitrogen in the soil
which is mainly produced by the decomposition of organic matter by
symbiotic fungi (Wang et al., 2021). Increasing forest age makes fungi
simple and efficient, and humification of organic matter may be
particularly important in the functions performed by fungi in these
ecosystems.

In a coniferous forest soil environment, the early apoplastic material
was not easy to be decomposed, result in the high content of aromatic
and cellulose in the soil to be high (Jílková et al., 2019). As the age
of the forest increased, the substances in the soil that were difficult
to be decomposed gradually decreased, and the role played by
microorganisms changes from poor to eutrophic, and in the soil
difficulty in the decomposition of organic matter determines the
distribution and composition of microorganisms (Amarasinghe et al.,
2021).

In this study, the association of bacteria and fungi increased with
increasing forest age, the bacterial-fungal diversity network became
simple and specialized, the products to be metabolized and decomposed in
the soil decreased with increasing forest age, difficulty to decompose
substances such as lignin increased, and the multiplicity of network
relationships gradually increased with the singularity of nutrient
substrates (Jiang et al., 2021; Zheng et al., 2021). Increasing the age
of the trees did not increase the complexity of the microbial network,
and those different ages possessed different microbiota, indicating that
microbes were highly selected by apoplastic properties and reflecting
that increasing stand age makes microbial decomposition of apoplastic
material more specialized and efficient. Although only speculative
(Freilich et al., 2018), increasing forest age makes bacteria and fungi
more specific and bacterial-fungal associations greater.

Afforestation/reforestation was recognized as an important approach for
agricultural land restoration (Austin et al., 2020). While previous
studies had mainly provided analyses of soil microbial community
abundance and diversity in forests of different ages, the use of network
analysis to assess soil microbial differences due to stand age can
provide new insights into the potential mechanisms between recovery time
and microbes during vegetation restoration.

\textbf{4 Conclusions}

In our study, the diversity and abundance of the soil microbial
community increased continually during vegetation restoration. With
time, vegetation restoration led to a significantly altered soil
microbial community structure. Dissolved organic matter content and
structure are related to standing age, and its changes (degree of
humification, organic matter composition) are key drivers of changes in
microbial community composition, and that organic matter decomposition
in larch forest environments is likely to occur top-down, and that the
interactions between bacterial and fungal relationships increase with
increasing vegetation recovery time. The study provided a deeper
understanding of the vertical movement of dissolved organic matter and
microbial interactions in the process of vegetation restoration.

\textbf{Acknowledgements}

\textbf{Data accessibility:} Data share can be contacted with Prof. Bian
(E-mail: bianhf108@nenu.edu.cn).

\textbf{Funding} :~This study was jointly supported by the projects of
the National Natural Science Foundation of China (41630749)

\textbf{Availability of data and material} : The data that support the
findings of this study are available from the corresponding author upon
reasonable request.

\textbf{Conflicts of interest/Competing 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.

\textbf{Appendix A. Supplementary data}

\textbf{Table S1.} Basic physical and chemical properties of soil

\textbf{Fig. S1} Soil sampling sites across the Jinchuan Forest Farm
(42°20'N, 126°24'E) in Huinan County, southwest of Changbai Mountain in
China

\textbf{Fig. S2} Microbial biomarkers and predicted functional profiles
of microbes under different stand ages in the \emph{L.
gmelinii}plantations. Top 24 fungal OTUs and 42 bacterial OTUs were
identified using random-forest classification of the relative abundance
of all OTUs. Heatmap of the relative abundance of biomarker families in
the individual samples. Differences in biomarker functions in the
FUNGuild and FAPROTAX database under different stand ages. Sap:
Saprotroph; Sym: Symbiotroph; Pat: Pathotroph; ACD: aromatic compound
degradation; ANO: aerobic nitrite oxidation; AAO: aerobic ammonia
oxidation; Nit: nitrification; SR: sulfate respiration; AC: aerobic
chemoheterotrophy

\textbf{Fig. S3} Relationships between DOC, FI, HIX, and microbial
diversity based on linear regression analysis. r and associated P values
in each model are shown on the figure. (a), (b), (c): Bacteria richness.
(d), (e), (f): Fungi richness.

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