Artificial planting changes soil microbial community dynamics
ZiWei Tao1 JinJuan Li2 XiangTai
Wang1 * GuoZhen Du1 *
1 Lanzhou Univ,Sch Life Sci, State Key Lab Grassland
& Agroecosyst, Lanzhou, Gansu, Peoples R China
2 Gansu Academy of Agricultural Sciences
* corresponding author:
XiangTai Wang: wangxt@lzu.edu.cn
GuoZhen Du: guozdu@lzu.edu.cn
Abstract:
The artificial planting of grassland serves as the most important means
of grassland ecological restoration; however, the impact of artificial
planting on soil microbial communities is not well understood. In this
study, the evolution of the microbial community structure was studied
using 16S and ITS gene sequencing techniques, and the microbial
community differences between different forage grasses were analyzed,
including different density cropping schemes, multi-year degraded
grassland and natural grassland. It was found that the high-density
planting scheme of multiple pastures exerts a great impact on soil
nutrients as well as on the soil microbial community, effectively
increasing the relative abundance of Actinobacteria and Basidiomycota,
while the microbial community structure was found to be similar to that
of natural grassland. However, in artificial planting treatment, the key
node microflora group was noted to be bacteria, which was different from
that in natural grassland, in which the key node microflora group was
fungi. In comparison, fungi were found to be more sensitive than
bacteria to different plantings.The rise in soil fungal diversity did
not improve phosphate mineralization.Overall, this study may contribute
to understanding the influence of artificial grassland on soil
properties as well as the succession of microbial communities, How to
accelerate the succession process of grassland ecosystem. which are of
great significance in promoting artificial technology to restore the
ecological environment.
Compared with secondary bare land, artificial grassland treatment can
increase soil water content by 6.3%, increase soil organic matter
content by 52.25%, increase soil total potassium content by 10.67%,
increase soil total phosphorus content by 22.58%, and increase soil
total nitrogen content by 51.31%. The content of available potassium in
the soil was 53.98%, the content of available phosphorus in the soil
was increased by 29.48%, and the content of alkaline hydrolyzed
nitrogen in the soil was increased by 72.66%. This experiment confirmed
the changing trend of soil microbial community affected by grassland
succession. The fungal community will show the community composition of
multiple dominant species coexisting with the succession process, and
the bacterial community will show the community structure of a single
dominant population with the succession process. The composition of soil
microbial community in the artificial grassland verification technology
block is between the sparse grass and dense grass stages, which proves
that the artificial grassland technology accelerates the succession of
soil microbial communities.
Keywords:
Artificial planting; Ecological restoration; Microbial community;
Community succession;Soil improvement
Introduction
Soil microorganisms serve as an important component of soil. They are
an important part of the nutrient cycle (Van Der Heijden et al, 2008)
and help improve nutrient access and hormonal stimulation in order to
promote plant growth (Berg 2009). Soil restoration usually refers to
the improvement of soil nutrients and succession of the soil microbial
community (Deng et al., 2014). Grassland, as a living and economic
production area for herders, plays an irreplaceable role. However, due
to the increasingly fragile ecological environment and long-term
widespread use by humans, the global grasslands have experienced
severe degradation (Babel et al., 2014; Hoppe et al., 2016;Kerven et
al., 2012;Nesper et al., 2015; Pereira et al., 2018). The grassland
ecological environment has deteriorated, and its ecological and
production functions have been greatly affected. In addition, the
regional ecological protection and socio-economic development
functions of grasslands have been weakened (Dorre and Andrei
2012).However, much reliance has been given to the restoration of
natural vegetation communities so as to improve the soil microbial
community structure and restore land productivity (Chavarria et al,
2016; Guo et al, 2018; Liu et al, 2019). Therefore, how to efficiently
carry out grassland restoration has become an important research topic
in ecological restoration.
The concept of environmental friendliness and sustainable development
has promoted research into soil microorganisms as biofertilizers (Kalayu
2019; Meena et al, 2017; Singh et al, 2020), though most studies have
focused on directly adding microorganisms to improve the soil
environment (Raymond et al, 2021; Lies et al, 2018; Itelima et al,
2018). The alpine meadow region in the eastern part of the Qinghai-Tibet
Plateau is one of the main producing areas of animal husbandry in China.
Accordingly, its extensive use has led to serious grassland degradation
(Wen et al, 2018; Ma et al, 2020). The use of biofertilizer involves
numerous costs, and herdsmen are usually not accustomed to investing in
the pasture (Yan et al, 2011).
Artificial planting of grassland is the most effective method for
grassland restoration (Wu et al, 2010); however, few studies exist that
pertain to the response of soil microbial communities to different
artificial planting schemes. In this study, the effects of different
planting schemes on the soil microbial community were examined at the
Alpine Meadow Wetland Ecosystem Positioning Research Station of Lanzhou
University. Here, four main local pastures were used with a variety of
unicast mixed sowing schemes for artificial planting. Moreover, a
degraded alopecia areata that has been exposed for more than 7 years was
added along with a piece of land. Natural succession occurred for over
10 years, and a comparison of top community blocks with stable community
structure was conducted in order to understand the interaction of
vegetation and microbial communities in the complete succession link.
Accordingly, experiments were conducted at the Alpine Meadow Wetland
Ecosystem Positioning Research Station of Lanzhou University. Four major
local pastures were selected, and a variety of unicast and mixed sowing
schemes were used for artificial planting. In addition, two comparative
blocks were added: an alopecia areata plot that was degraded and exposed
for more than 7 years, and a climax block that had natural succession
for more than 10 years. Afterward, using high-throughput sequencing of
16S and ITS rRNA genes, the bacterial and fungal community structures
were investigated, and their composition in different planting schemes
were examined.
So, which planting method can effectively promote the process of
grassland ecological succession and accelerate the restoration of
grassland?
2.1. Experimental materials
Four local wild fine pastures with complementary morphological
characteristics were selected as test materials, namely Avena sativa,
Poa pratensis, Elymus nutans and Festuca sinensis.
2.2. Experimental area
The experiment was set up in the Alpine Meadow Wetland Ecosystem
Research Station of Lanzhou University (34°55′N, 102°53′E). The site was
located in the eastern part of the Qinghai-Tibet Plateau, in which the
altitude was 2900 m, average annual temperature was 2.0 ℃, and average
annual precipitation was 557.8 mm.
2.3. Experimental program
2.3.1. Planting plan
The experiment is divided into two parts: unicast and mixed broadcast. 4
types of unicast: Avena sativa (As), Elymus nutans (En), Poa pratensis
(Pp) and Festuca sinensis (Fs). Three kinds of mixed broadcasting:
As+En+Pp (AEP), As+Fs+Pp (AFP) and As+En+Fs+Pp (AEFP). See Table 1 for
seeding density.
For seeds in April 2019, when sowing, each treatment was broadcasted in
three blocks with a block area of 4m2 (2m×2m), and
0.5m intervals were left between the plots. After the seeds were sowed,
no mowing, grazing, fertilization, watering, and artificial intervention
occurred during the growth period.
Table 1. Artificial planting seeding density
2.3.2 Sample Collection
Sampling was carried out in September 2019, which consisted of 22 plots
that had previously been artificially constructed and planted and a
barren areata plot that had been degraded and exposed for more than 7
years. In addition, a top community block that had a stable community
structure after natural succession for more than 10 years was added for
comparison.
After removing the plant residues on the soil surface of each plot, 5
samples were drilled along the S-shaped curve from the surface (0-30cm)
using a soil drill (6cm in diameter). After getting the soil samples,
they were mixed into one sample, which was then divided into two parts,
for which one part was dried for 15 days and sieved using a 2 mm sieve
to analyze the soil composition. After removing the sand and roots, the
other part was divided into 3 parts and stored at -80°C in order to
conduct an analysis of the microbial information.
2.3.3 Analysis of soil physical and chemical properties
Soil pH and moisture were determined using conventional methods (Liu et
al, 2019), and soil organic matter (SOM) was quantified through
potassium dichromate oxidation (Bao et al, 2011). Total nitrogen (TN)
was determined via Kjeldahl nitrogen determination method (Purcell and
King, 1996). Total potassium (TPo) was measured using alkaline fusion
– flame photometry (Banerjee et al, 2020), while total phosphorus
(TPh) was determined via alkaline fusion - molybdenum-antimony
anticolorimetry (Qu et al., 2020). The content of alkali-hydrolyzed
nitrogen (AN) was measured using alkali-hydrolyzed diffusion (Zhao et
al, 2017). Determination of available potassium (APo) was carried out by
ammonium acetate extraction-flame photometry (Sun, 2008). Finally,
available phosphorus (AP) was extracted by sodium bicarbonate and then
identified via anticolorimetry (Stevens et al., 2005).
2.3.4 Nucleic acid extraction
The genomic DNA was extracted using the PowerSoil DNA isolation kit (Mo
Bio Laboratories, Solana Beach, CA, USA) according to the manufacturer’s
instructions. Three parts of each compost sample were extracted, after
which the three extracts were mixed and detected using 1% agarose gel
electrophoresis. The concentration and purity of the DNA were then
determined via spectrophotometry with a micro-ultraviolet
spectrophotometer (Nano Drop Technologies, USA). The absorbance ratios
of all DNA samples were A260:A230>1.7 and
A260:A280>1.8.
2.3.5 High-throughput sequencing
Quantitative PCR analysis of the 16S rRNA gene of the bacteria and ITS
gene of the fungus was then carried out using the following primers:
338F: 5’-ACTCCTACGGGAGGCAGCAG-3’/806R:5’-GGACTACHVGGGTWTCTAAT-3’ and
ITS1F: 5’-CTTGGTCATTTAGAGGAAGTAA-3 ’/ITS2R:5’-GCTGCGTTCTTCATCGATGC-3’.
PCR was performed on the GeneAmp 9700 PCR system (Applied Biosystems,
Foster City) (California, USA). The PCR reaction conditions occurred in
the following order: 95°C (3 minutes), 95°C (30 s), 62°C (30 s), 72°C
(45 s) for 30 cycles, and finally at 72°C for 10 minutes. Three copies
of each sample were amplified, and the amplified products were mixed
into one sample and detected by 2% agarose gel electrophoresis. The
amplified products were then purified using the AxyPrep DNA gel
extraction kit (Axygen Biosciences, Union City) (California, USA), which
were then detected by 2% agarose gel electrophoresis. High-throughput
sequencing was performed by Shanghai Meiji Biopharmaceutical Technology
Co., Ltd. (Shanghai, China) on the Illumina MiSeq platform (San Diego,
California, U.S.).
2.3.6 Sequencing data processing
In order to obtain higher quality and more accurate results in the
biological information analysis, effective sequences were mixed, while
optimized sequences were obtained for the data statistics. The sequence
was less than 200 bp, the base was fuzzy or the average quality was less
than 25. The chimeric sequence was deleted using mothur software
(Schloss et al, 2010). Finally, sequence readings for each sample were
clustered with 97% similarity as an operable classification unit (OTU)
(Edgar et al., 2013). Based on the SILVA and Unite databases, the
sequences represented by OTU were then sorted, which included bacterial
and fungal ribosomal RNA sequences (version 119) (Pruesse et al. 2007)
using the RDP classifier (Wang Q et al, 2007).
2.3.7 Statistical analysis
Based on OTU, R software was used to calculate the species composition
of different samples at various taxonomic levels in order to understand
the dominant species contained in each sample at the same taxonomic
level as well as the relative abundance of each dominant species in the
sample. In order to ascertain the similarities and differences of the
flora of all groups under different treatment factors, PCoA analysis was
performed on the samples. Circos software was then used to make the
relationship diagram between the samples and species to obtain the
composition proportion of dominant species in each sample and understand
the distribution proportion of dominant species in different samples.
Lefse software was used to detect the characteristics of significant
abundance difference according to non-parametric factorial
Kruskal-Wallis (KW) Sum-rank test (non-parametric factorial
Kruskal-Wallis rank sum test), for which the groups with significant
abundance difference were found. Finally, linear discriminant analysis
(LDA) was used to estimate the impact of each species’ abundance on the
effect of the difference. Spearman correlation of each OTU was paired
with Gephi (R>0.8), after which the construction of a
microbial symbiosis network and network module was performed.
3.Results
3.1 Influence of different planting schemes on soil properties
Different planting schemes have different effects on soil properties
(Table 2). Compared to other single-seeding treatments and low-density
planting treatments for a variety of pastures, T7 treatments for
high-density planting of four pastures in conjunction with the T22
treatments overgrown with weeds were found to significantly increase the
organic matter (SOM) content in the soil. Compared to T22 treatment, T7
and T5 treatment of high-density planting of the three pastures were
noted to greatly increase the content of alkali hydrolysable nitrogen
(AN) in the soil. Moreover, T2 and T7 treatment of high-density planting
of Elymus nutans (Elymus nutans) were observed to significantly raise
the available phosphorus (AP) content in the soil. Compared to T23
treatment, which was barren for many years, and T24 treatment with top
stable vegetation communities, T7 and T22 treatment had the highest
levels of total soil nitrogen (TN) content. Furthermore, compared to T24
treatment, the soil moisture content of other treatments was found to
have a large difference.
Table 2 Soil properties of different establishment blocks
3.2 Composition of soil microbial community
We selected five stages in the succession process from secondary bare
land to natural grassland: secondary bare land stage (T23), primary and
biennial weedy stage (T22), pioneer planting stage (T1), sparse bush
Grass stage (T20), dense grass stage (T24) (Xiao et al., 1982; Sun,
1992), measure the bacterial and fungal gene sequences of soil samples,
and understand the composition of soil microbial communities in
different succession stages. The bacterial and fungal gene sequences of
soil samples treated with T7 were measured to understand the similarity
between the microbial community composition of this treatment and the
five stages of grassland succession, and to verify whether artificial
grassland technology can accelerate the succession process of grassland
ecosystems.
Accordingly, 1,054,960 valid bacterial gene sequences were obtained from
all soil samples, with an effective base number of 440,512,479 and an
average sequence length of 417. Through clustering, 5498 OTUs were then
obtained, which belonged to 33 Phylum, 92 Class, 239 Order, 405 Family,
and 757 Genus. Similarly, 1,275,477 effective fungal gene sequences were
obtained from the soil samples, in which the number of effective bases
was 300,395,036 and the average sequence length was 235. Following
clustering, 1965 OTUs were obtained, which belonged to 12 Phylum, 38
Class, 84 Order, 190 Family, and 361 Genus.
According to the influence of different treatments on soil
characteristics, the 6 most representative treatment methods were then
selected for analysis. As seen in Figure 1A, the main bacterial
communities in the soil were found to be Proteobacteria, Actinomycetes,
Acidobacteria and Chlorocurbillus, while other populations, including
Bacteroidetes, Bacillomonas and Corynebacterium, were not observed to
contribute much to the bacterial community in the soil. Compared to the
bacterial community, the composition of the fungal community was
observed to be relatively simple, in which the main fungal communities
were Ascomycetes, Morphomyces and Basidiomycetes, while the abundance of
Olpidiomycota, Chytrid fungi and Rozomycetes was noted to be relatively
low (Figure 1B).
According to another perspective, the six treatments selected simulated
the complete restoration and succession process of the grassland
vegetation community from the barren land, invasion of weeds, and
establishment of dominant species to the attainment of the top grassland
vegetation community. From the barren T23 treatment to the T24 treatment
with top community structure, no significant differences were present in
microbial community composition at the Phylum level; however, there was
a significant difference in the abundance of each Phylum (p<0.05) (Figure 1).
Figure 1. Relationship between soil microbial Circos samples and
species. The figure reflects the composition proportion of dominant
species in each sample as well as the distribution proportion of each
dominant species in different samples.
3.3. Relationships between soil properties and microbial taxa
The correlations among the soil properties and top 24 phyla of bacteria
and fungi were then analyzed (Figure 2). Here, the Spearman’s rank
correlation coefficients showed that seven of the dominant phyla were
positively correlated with most physicochemical factors, while six of
the dominant phyla were negatively correlated with these factors.
Specifically, SOM, AP, TN, and TPh were found to be positively
correlated with Proteobacteria, Acidobacteria, Latescibacteria, and
Ascomycota, which also had positive correlations with Gemmatimonadetes
and Firmicutes. Firmicutes and Armatimonadetes were noted to be
negatively correlated, while AN and APo were found to be positively
correlated with Chloroflexi, Rokubacteria, and Basidiomycota and
negatively correlated with Bacteroidetes and Mortierellomycota.
Figure 2. Correlations between environmental factors and dominant
microbial taxa based on the Spearman correlation coefficient. The color
represents the value of the Spearman correlation coefficient. ”B_”
represents the bacterial Phylum, and ”F_” represents the fungal Phylum.
3.4 Differences in microbial communities among different treatments
The similarity of the bacterial community and fungal community under OTU
water classification among different treatments were then compared
(Figure 3). Comparing the composition of the bacterial community (Figure
3A) and composition of the fungal community (Figure 3B) of each
treatment, T7 treatment with high-density establishment of the four
pastures was found to possess the highest similarity with T24 treatment.
Figure 3. PCoA analysis of species diversity among microbial communities
in different treatments
In order to refine and understand the differences among the microbial
community samples of each treatment, a LEFSE analysis was conducted.
Comparing the differences in bacterial community abundance of each
treatment, the difference in relative abundance between T23 and T24
treatment was found to be most significant (Figure 4A). The biomarkers
having a LDA value greater than 2 was T23 dealt with 24, while the top
three LDA values were O__chloroflexales, F__Roseiflexaceae and
P_armatimonadetes. T24 treated 17, and the top three LDA values were
C__Actinobacteria, P__Actinobacteria and F__Micrococcaceae.
Comparing the difference in fungal community abundance, T24 treatment
was observed to have the most significant difference in relative
abundance (Figure 4, B). There were 30 biomarkers with a LDA value
greater than 2, while the top three with the largest LDA values were
c__Agaricomycetes, p__Basidiomycota and o__Agaricales.
Figure 4. Discriminant analysis of LEFSE multi-energy level differences
in soil microbial communities. A LEFSE linear discriminant analysis
(LDA) was conducted on the samples according to taxonomic composition
and different grouping conditions in order to ascertain the communities
or species with significant differences in sample division. Nodes with
different colors indicated microbial groups that were significantly
enriched in the corresponding group and had a significant impact on
differences between groups. Here, light yellow nodes indicated microbial
groups with no significant differences in different groups or no
significant impact on differences between the groups.
3.5 Interaction network between soil microbial communities
In order to further understand the correlation and difference between
the dominant microbial groups (taxa) of each treatment, three treatments
were selected: grasslands that have been degraded for many years,
artificial planting treatments that have the greatest impact on soil
properties, and natural top grassland communities. A network diagram was
then constructed at the OTU classification level (Figure 5) in order to
understand the interaction of microbial communities at the three key
nodes of ecological restoration of degraded grasslands. Accordingly, the
modulo quickening index of the three treatments was found to be greater
than 0.6, serving as a typical module structure (Newman, 2006). Compared
to the mean degree, the mean degree of T24 treatment was noted to be
higher than that of T23 and T7, indicating that the microbial community
of T24 treatment had a higher degree of interdependence.
Figure 5. Interaction network and modular network of the soil microbial
community. Each connection represents a strong positive correlation
(Spearman’s ρ>0.8). The size of each node in A is
proportional to the number of connections (degrees), and the connections
between nodes are colored according to the different degrees of
connection of the nodes. Picture B is colored according to modular
classification. In the picture, the text ”OTUB” represents the bacterial
group, while ”OTUF” represents the fungal group.
By comparing the three microbial groups with the highest degree of
connection among different treatments (Fig. 5), T23 treatment, which was
deserted for many years, was found to belong to two groups of Phylum:
Bacteroidetes and Chloroflexi, which were not among the top three Phylum
in the bacterial community in terms of abundance (Figure 1A, ChloroFlexi
13.94% and Bacteroidetes 5.75%). A possible reason for the low
abundance but very active group is that the microbial community of T23
did not reach the top stable community level after many years of
succession, while the inferior population with low abundance kept
competing for a higher ecological niche. Compared to T24 treatment, the
three species groups with the highest activity and the two species with
the highest abundance were Phylum, in which the community structure was
found to be more stable. T7 treatment was observed to be between the
two.
As seen in Fig. 5B, the network structure of T24 treatment was noted to
be clearer and more stable, while the network topology of T23 treatment
was disorderly and T7 treatment was between T24 and T23 treatment.
Another interesting phenomenon was that the three groups with the
highest connectedness in T7 treatment were all bacterial groups, while
those in T24 treatment were all fungal groups.
4.
Discussion
Artificial planting has been demonstrated to be an important ecological
restoration scheme according to previous studies (Yin et al, 2009; Li et
al., 2017). Due to the interaction between plants and microorganisms
(Trivedi et al, 2020), a top-level and stable grassland ecosystem should
include top-level vegetation communities and top-level microbial
communities. In this study, artificial planting was found to restore the
aboveground vegetation community while influencing the soil’s physical
and chemical properties as well as the composition of soil microbial
community, similar to previous studies (Menyailo et al, 2002; Han et
al., 2007; Zhang et al., 2016). One possible reason is that litter and
root biomass provide suitable habitat and sufficient energy for the soil
microbial community to drive the succession of the microbial population
(Yuan et al, 2015; Kyaschenko et al, 2017; Schirawski et al. al, 2018;
Pathma et al, 2020). The increase in the abundance of microbial
populations accelerates the cycle of carbon, nitrogen, and phosphorus
(Xu et al, 2013; Karhu et al, 2014; Brabcová et al, 2018). Moreover,
different planting schemes have different degrees of influence on soil
characteristics, which was also confirmed in this study (Table 2).
In general, different cropping schemes have different effects on the
microbial community due to the varied responses of different microbial
groups to the environment (Fig. 1). The results of this study showed
that the soil bacterial community was mainly comprised of Proteobacteria
and Actinobacteria (Fig. 1), similar to previous studies (Spain et al.,
2009; Barka et al, 2016). Proteobacteria, actinomycetes and
acidobacteria were found to respond significantly to different
treatments (Figure 1), while other studies have also proven that
Proteobacteria, actinomycetes and acidobacteria are more sensitive to
environmental changes (Verzeaux et al, 2016). In regard to the T7
treatment of high-density planting of the four pastures, the bacterial
community structure was noted to be most similar to the natural
top-level community T24 treatment (Figure 1).
Different planting schemes had varying degrees of influence on soil
properties, which was also confirmed in this study (Table 2). Compared
with the secondary bare land T23 treatment, the artificial grassland
treatment can increase the soil water content by 6.3%, the soil organic
matter content by 52.25%, the soil total potassium content by 10.67%,
the soil total phosphorus content by 22.58%, and the soil total
nitrogen content by 51.31%. The content of soil available potassium was
increased by 53.98%, the content of soil available phosphorus was
increased by 29.48%, and the content of soil alkali-hydrolyzed nitrogen
was increased by 72.66%.
The experimental results illustrated that the soil fungal community was
dominated by Ascomycota (Fig. 1), which was also confirmed in previous
studies (Zhou et al., 2017), indicating that Ascomycota can be
considered as the dominant population and indicator species, as
confirmed through the interaction network (Fig. 5). The ascomycete
community has also been shown to respond very positively to artificial
planting (Li et al, 2017; Brundrett and Tedersoo 2018). However, the
results of the present study demonstrated that by comparing the
abundance of Mortierella species treated with T24 and T23 and other
artificial establishment treatments, the abundance of Mortierella
treated with T24 was lowest, which was different from the findings of
previous studies. Specifically, ”The relative abundance of Ascomycota is
higher in disaffected soils, while Mortierellomycota is more abundant in
healthy soils (Yuan et al, 2020)” was different compared to this study.
In comparison, fungi were found to be more sensitive than bacteria to
different plantings. Compared to different artificial planting blocks,
the difference in the fungal diversity coefficient was observed to be
significantly higher than that of the bacterial diversity coefficient
(Table 1). A possible reason may be due to fungi’s important role in the
degradation of plant residues (Bastida et al, 2016; Liang et al, 2017).
Ascomycota and Basidiomycota are key microbial groups for the
degradation of complex plant compounds (Bastida et al., 2016).
Soil microbial communities are also regulated by interspecific network
relationships (Poole et al, 2018; Kuypers et al, 2018). In this
experiment, differences were noted in the modularity, number of nodes
and number of connections of soil microbial community network between
artificial planting and natural grassland and degraded grassland (Figure
5). Compared to artificial grassland and degraded grassland, the
microbial groups of natural grassland were found to be more closely
related. Since the modularization index was greater than 0.4 and the
average clustering coefficient was greater than 0.6, it can be inferred
that these relationship networks are modular (Fig.4), which has been
previously confirmed by past studies (Koskella et al., 2017; Belin et
al., 2018). Compared to artificial grassland and degraded grassland, the
modular classification of microbial groups in natural grassland was also
clearer (Figure 5). However, in contrast to the experimental
expectations, the key microorganism group in the artificially
constructed T7 treatment was found to be the bacteria group, while that
of the T24 treatment was fungi (Figure 5).
The rise in soil fungal diversity did not improve phosphate
mineralization. Compared to T7 treatment, T1 had a higher index of soil
fungal diversity (Table 1), while the available phosphorus content of T1
treatment was found to be lower than that of T7 treatment (Table 2). A
possible reason is that the increased abundance of microbial populations
may have led to increased competition, thereby reducing the utilization
of organic matter (Maynard et al., 2017; Maynard &Crowther et al,
2017). Therefore, improving soil microbial diversity alone cannot
improve soil restoration efficiency. This experiment confirmed the
changing trend of soil microbial community affected by grassland
succession. With the advancement of grassland succession, the highest
abundance of Ascomycota in the fungal community accounted for 52.5% in
the secondary bare land stage, and decreased to 44.95% in the dense
grass stage, and the abundance was the second The third Basidiomycota
accounted for 6.67% in the secondary bare field stage, and increased to
34% in the dense grass stage. The fungal community will present a
community composition in which multiple dominant species coexist with
the succession process. The abundance of Proteobacteria, which accounted
for 25.28% of the bacterial community in the secondary bare land stage,
further increased with the succession process, reaching 36.74% in the
dense grass stage. Actinobacteria and The abundance of Acidobacteria
decreased continuously, and the bacterial community showed a community
structure of a single dominant population with the succession process.
The comparison found that the composition of soil microbial community in
the artificial grassland verification technology block was between the
stage of sparse grass and the stage of dense grass, which proved that
the artificial grassland technology accelerated the succession of soil
microbial community.
In this work, we have gained some basic understanding of the
relationship between cultivated grasslands, natural grasslands, degraded
grasslands and soil microbes. But there are still some problems to be
studied. Under the conditions of this experiment, the fungal community
will show a community composition of multiple dominant species
coexisting with the grassland succession process, and the bacterial
community will show a community structure of a single dominant species
with the succession process. In other environments and under different
plant community conditions, more experiments are needed to confirm
whether the microbial community still exhibits the same trend.
5. Conclusion
The results demonstrated that the high-density planting plan of multiple
pastures is able to alter soil properties and soil microbial community
structure more effectively than the planting plan of a single species of
pasture schemes, and accelerate the succession process of the grassland
ecosystem. As a result, this study confirmed that artificial grassland
planting serves as an important means of grassland ecological
restoration, which has great research benefit.
Acknowledgments Du Guozhen,XiangTai Wang is the project manager, Li
Jinjuan helped organize the data, and Tao Ziwei wrote this article
Funding:the National Key Research and Development Program of China
(Grant No. 2017YFC0504801)
Conflict of Interest: The authors declare that they have no conflict of
interest.