1 | INTRODUCTION
According to data from the Global Forest Resources Assessment (IPCC,
2013), the total amount of carbon sequestered by global forests is as
high as 1950-3150 Pg C each year; the carbon sequestration by vegetation
reaches 450-650 Gt, accounting for approximately 43.5% of the total. On
the other hand, Schlesinger (1990) noted that soil is the largest
reservoir of carbon in terrestrial ecosystems, storing two-thirds of
their organic carbon. The rhizosphere microecosystem is the link between
plants, soil and microorganisms, the most active part of the global
carbon biochemical cycle, and the focus of research on the systematic
mechanisms of the global carbon cycle (Schweinsberg-Mickan, Jorgensen,
& Muller, 2012; Carrillo, Dijkstra, Pendall, LeCain, & Tucker, 2014).
Photosynthetic products are transported from leaves to various tissues,
such as root tissues, for storage, and this transport occurs through
material flow caused by pressure differences. The remaining
photosynthetic products are released into the surrounding soil by the
plant root system as various organic and inorganic compounds that form
rhizodeposits. In addition, carbon that enters the soil through a series
of biochemical processes is circulated and redistributed among roots,
the soil and microorganisms to maintain the balance of rhizosphere
carbon absorption and release (Jones, Nguyen, & Finlay, 2009).
Studies have shown that carbon sequestration by plants is particularly
important for rhizosphere microorganisms. Over 40% of the complex
carbon produced by photosynthesis enters rhizosphere soil through plant
roots to nourish microorganisms and maintain their normal metabolic
functions (Rodriguez, et al., 2019). Cheng et al. (1996) noted that root
exudates, as carbon sources for microbial utilization, increase
respiration by rhizosphere microorganisms. The differences in carbon
utilization efficiency among microbial communities mainly depend on the
differences in their microbial functional families related to carbon
source utilization (Xu, 2012). Yin et al. (2018) found that increasing
the atmospheric CO2 concentration to promote carbon
metabolism in Kandelia candel did not significantly increase the
abundance of the rhizosphere bacterial community. Moreover, Xiao et al.
(2017) noted that carbon sequestration by Bothriochloa ischaemumsignificantly increased the contents of total PLFAs
(phospholipid-derived fatty acids) and fungal PLFAs in rhizosphere soil.
In addition, Feng et al. (2011) indicated that under increased carbon
metabolism conditions in rice, the proportions of aerobic, anaerobic and
phototrophic bacteria in the bulk soil increased (from 0.5% to 1.5%),
while no significant effects were observed in rhizosphere soil. It has
been noted that among rhizosphere microorganisms, the response of fungi
to photosynthetic carbon sequestration by plants is clearer than that of
bacteria (Xu, 2012) because fungal mycelia can accelerate the turnover
cycle of the fungal carbon metabolism (which takes approximately one
week), while bacteria generally need more than two weeks to turn over
carbon (Ostle, et al., 2003; Staddon,Ramsey, Ostle, Ineson, & Fitter,
2003).
Masson pine (Pinus
massoniana ) is a large perennial tree that is widely distributed in 17
provinces and autonomous regions in the southern Qinling Mountains in
China (Wu, et al., 2020). Masson pine thrives in light, is shade
intolerant and prefers a warm and humid climate. It can grow in red
soil, gravel soil and sandy soil and is used as a pioneer tree species
for forest restoration in barren mountainous areas (Wang, et al., 2019).
Previous studies have shown that Masson pine has a high carbon
sequestration ability. Elisa et al. (2003) showed that the carbon
sequestration in Masson pine organs ranged from 533.93 to 568.08
g·kg-1, which is higher than the carbon contents of 32
common tropical tree species (444.0-494.5 g·kg-1). The
carbon sequestration ability of plants directly affects the quantity of
root exudates (Ainsworth, 2008). However, the response of soil microbial
communities, particularly rhizosphere microorganisms, to plant carbon
sequestration has rarely been studied, especially under Masson pine. In
this study, based on Masson pine from different families, samples with
significant differences in carbon sequestration ability were selected as
experimental materials. The corresponding rhizosphere soil was obtained
for 16S rRNA and ITS sequencing to analyze the differences in in the
number and taxonomic diversity of bacteria and fungi and their patterns
in response environmental factors. This research provides guidance
toward further understanding the response of microorganisms to plant
carbon sequestration, which will be helpful in predicting the effects of
climate change on rhizosphere microbial communities.
2 | MATERIAL AND METHODS
2.1 | Study site
This study was conducted in the progeny test plantation of the Masson
pine seed orchard at the Baisha State-Owned Forest Farm (25°15’N,
116°62’E), Shanghang County, Fujian Province. The samples from the
forests were collected in 2001, and the experimental trees were planted
in 2003. There were 68 families (Kang, 2012). Before the experiment was
carried out, it was found that due to human activities, the number of
samples in some families did not meet the requirements for statistical
analysis. Therefore, given the situation, 24
families were selected as the
experimental families (Table S2).
2.2 | Estimation of carbon storage in
different Masson pine families
To avoid destroying trees, regression equations were used to estimate
the average carbon sequestration by each family. Approximately 30
individuals in each family were randomly selected as experimental
samples, and the height and DBH (diameter at breast height) of each
sample were measured. The regression equations in professional standards
released by China’s Forestry Administration (Cai, et al., 2014) were
used to estimate the biomass of each organ (including the
trunk, branches, leaves, bark, and
roots) based on the tree height and DBH. Then, the biomass of each organ
was multiplied by the corresponding carbon coefficient (trunk: 0.5186,
branches: 0.5174, leaves: 0.5785, bark: 0.4994, and roots: 0.5082) to
obtain the total carbon sequestration. The total carbon sequestered by a
single tree was obtained by adding the carbon sequestered in each organ.
The mean value of all samples from the same family was used as an index
to evaluate the carbon sequestration level of the family. The relevant
regression equations are provided in Table S1.
2.3 | Soil
sampling
The families with high, low and intermediate carbon sequestration were
selected for follow-up experiments. Three
individuals with carbon
sequestration values that were close to the mean value for each
experimental family were selected as the samples. Five sampling points
near each sample were chosen, and 5~10 cm bulk soil was
dug up. The roots were carefully pulled out of the soil with a shovel,
and the loosely attached soil was gently shaken off. The rhizosphere
soil was closely attached to the roots. Litter and humus were removed
from the soil surface before soil sampling, and the rhizosphere soil
from the five sites near each sample tree was mixed together to form a
composite soil sample. The soil samples were divided into two parts; one
part was placed into a 5 ml freezing tube and immediately frozen in dry
ice for sequencing, and the other part was loaded into a 50 ml
centrifuge tube for analyses of soil physical and chemical properties.
2.4 | Detection of the physical and chemical
properties of rhizosphere soil
The rhizosphere soil total organic carbon was determined by the
combustion oxidation nondispersive infrared absorption method according
to the Chinese Environmental Protection Standard (Jian, Zhai, Wang, &
Cai, 2020). The experimental temperature was set at 900°C, and the
oxygen pressure was 900 Mbar. The gas flow rate in the analysis module
was 150-165 mL·min-1. The soil total nitrogen was
determined based on the Kjeldahl method as provided in the Chinese
Environmental Protection Standard (Zhang, et al., 2015). To determine
the rhizosphere soil pH, 5 g soil samples were ground and passed through
a 100 mesh sieve, and 12.5 mL ddH2O was added. The
samples were mixed by vortexing and oscillation and centrifuged at 5000
r·min-1 for 5 min, and the supernatant was separated
and directly tested with a pH meter. To determine the rhizosphere soil
moisture, soil samples (20 g) were weighed and dried at 105°C for 6 h.
After cooling to room temperature and weighing again, the difference
between the two weights was divided by the fresh soil weight (20 g) to
obtain the moisture content.
2.5 | DNA extraction, high-throughput
sequencing and analysis
The FastDNATM Spin Kit for Soil (MP Biomedicals,
California, USA) was used to extract DNA from 0.5 g soil samples. The
operation process was performed in strict accordance with the
instruction manual. The barcode primers 515F (5′-GTGCCAGCMGCCGCGG-3′)
and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′)
were used to amplify the bacterial
16S rDNA. ITS1F
(5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′)
were used to amplify the fungal ITS sequence. The PCR amplification
procedure was carried out according to the instructions forTransStart ® FastPfu DNA Polymerase
(TransGen Biotech, Beijing). Each
sample was subjected to PCR three times. The PCR products from the same
sample were mixed and detected with 2% agar-gel electrophoresis,
recovered using the AxyPrepDNA gel recovery kit (Axygen Biosciences, CA,
USA), and purified using the agar-gel DNA purification kit (TransGen
Biotech, Beijing). The purified PCR products were sequenced according to
the default parameters on the MiSeq PE300 platform. The original data
were stored in the NCBI Sequence Read Archive database (accession number
PRJNA662187 for bacteria and PRJNA662212 for fungi).
The raw sequence data were analyzed and quality-controlled using fastp
(version 0.19.6, https://github.com/OpenGene/fastp). Bioinformatics
statistical analysis was performed using Usearch (version 7.0,
http://drive5.com/uparse/) for OTUs (operational taxonomic units) at
97% similarity. The OTUs were subsampled according to the minimum
sample sequence number (35,000). The taxonomic analysis of the OTU
representative sequences was carried out by the RDP classifier Bayesian
algorithm (version 2.2, http://sourceforge.net/projects/rdp-classifier/,
the default confidence threshold value was 0.7), and the community
composition of each sample was counted at different taxonomic levels.
The Silva bacterial 16S comparison database and the Unite fungal ITS
comparison database were used.
2.6 | Statistical analyses
A Venn diagram of the microbial community diversity was constructed with
the R Venn diagram package (Chen, & Boutros, 2011) based on the common
and unique OTUs in the different samples. The Chao and Shannon index of
α-diversity were calculated according to the corresponding formulas
shown in Table S1. The relative
abundance histogram at the phylum
level was plotted using the R ggplot2 package (Kahle, & Wickham, 2013)
based on the data sheet in the tax_summary_a folder.
Welch
T-tests were conducted to determine the significance of differences
among families (Garcia-Lledo, Vilar-Sanz, Trias, Hallin, & Baneras,
2011). The Bonferroni method was used to conduct multiple test
corrections to evaluate the significance level of taxonomic abundance
differences and to identify the significantly different phyla among
samples. For the core microbiome analyses at the genus level, common
OTUs with relative richness values higher than 1% were extracted from
different samples for graphical purposes. Pie and box charts were
generated with the R ggplot2 package and SPSS 16.0, respectively
(Perez-Jaramillo, et al., 2019). RDA (redundancy analysis) was used to
clarify the relationships between soil physicochemical properties and
the rhizosphere microbial community using the R vegan package (Ng, et
al., 2014). Correlation heatmap analysis was performed by calculating
the Pearson correlation coefficients between the environmental factors
and the selected taxa and drawing the heatmap diagram using the R
Pheatmap package. One-way ANOVA and Duncan multiple comparison tests
were conducted using SPSS 16.0.
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