Fig. 1. Location of sampling points (Fig. A). Each grassland
type includes a paired no-grazing and grazing site. Fig B shows a
temperate desert sample site; Fig C shows a temperate grassland sample
site; Fig D shows a montane meadow sample site. Fig E shows the sample
layout.
2.2. Experimental design and field sampling
In July 2021, three areas (Fukang City, Mubi County and Qitai County) on
the eastern section of the northern slope of Tianshan were selected,
each area corresponds to one grassland type: temperate desert, temperate
steppe and mountain meadow. The test sites are all located within the
national fixed monitoring sites and have been fenced since 2012. The
fenced areas are surrounded by spring and autumn grazing areas for
sheep, and the grazing intensity is medium (0.6-1.0
sheep/hm2). At each site, paired plots were sampled,
which were long-term grazing plots and nearby grazing exclusion plots.
All paired plots had the same soil type and similar geographical
conditions including slope, elevation and topography. Three typical
sample lines were set up at 50 m intervals in each of the grazing
exclusion and grazing areas, three 1 m x 1 m herbaceous samples were
laid out at approximately 50 m intervals in each sample line (Total of
54 small samples). If there are shrubs in the plot, 5 additional shrub
plots (10m×10m) are measured. Field vegetation collections were carried
out to record the species present in each square and to determine cover,
height, density and biomass by species. Plant coverage was measured by
the projection method; natural heights were measured with the help of a
ruler, and individual quantities (densities) of each specie were
recorded with the help of statistical methods. Above ground biomass of
each specie (only the green plant parts) were figured out by clipping
the whole plant from the soil surface using scissors in each sampling
plot and brought back to the laboratory for treatment(dried at 80°C for
24 h to constant weight). The specific plant data have all been obtained
(Supplementary Table 1)
Soil samples were taken in layers of 0-5 cm and 5-10 cm depth, and each
sample line was mixed separately and placed in sealed bags. Some of the
samples were stored in a refrigerator at 4°C, while the remainder was
dried indoors by picking out plant roots, gravel and other debris, and
then ground and mixed, and stored in 1 mm and 0.25 mm sieves for indoor
analysis. Soil microbial samples were collected from 0-5 and 5-10 cm
soil layers in the sample plots, and each sample line was evenly mixed
in a sealed bag and taken back to the laboratory in a vehicle
refrigerator (-20°C).
2.3. Soil property analysis
The soil pH was determined using a ratio of 2.5:1 water to soil with a
standard pH meter (Cao et al., 2017); soil water content(SWC) was
determined gravimetrically by drying the soil samples (105◦C, 24 h); and
bulk density(BD) was measured gravimetrically after oven-drying (105 ℃,
24 h). The soil organic carbon(SOC) was determined using the dichromate
oxidation method (Walkley and Black, 1934). Total phosphorus (TP) was
measured with Mo-Sb colorimetricmethod using a spectrophotometer
(Lambda25 UV-visspectrometer, United States). Total nitrogen(TN) was
determined using the Kjeldahl method.
2.4. DNA extraction and high-throughput sequencing
High-throughput sequencing was performed using the Illumina MiSeq PE 300
platform to analyse the soil fungal community. Total DNA was extracted
from the samples using the macrogenomic DNA extraction kit; the variable
region sequence of the ITS1-1F region of the fungal ITS rRNA gene was
used as the target, and the fungal primers were ITS1-1F-F and ITS1-1F-R.
PCR amplification was performed to obtain the PCR products; the PCR
products were quantified and the library was constructed to obtain the
fungal variable region base sequence information. The microbial
sequencing analyses in this study were all done on the biochemical cloud
platform of Shenzhen Microcomputer Technology Group
Co.(https://bioincloud.tech/pipelines)
2.5. Statistics and analysis
All data are shown as mean and standard error. Data on soil
physicochemical properties and microbial diversity indices were
subjected to two-way variance comparisons and Pearson correlations using
SPSS 26.0. Bar graphs were generated in OriginPro 2021 (originlab
Corporation, USA). Principal Coordinate Analysis (PCoAs) from BrayCurtis
distances were used to visualise the effects of grazing exclusion and
grassland type on fungal community composition. PERMANOVA was performed
in the ’vegan’ package using the ANOSIM function to test for
significance of differences in community composition and plotted using
the ’ggplot2’ package in R 4.2.1. Finally, we implemented structural
equation modelling (SEM) based on the lavaan package (Rosseel., 2011) to
assess the effects of grazing exclusion and grassland type on fungal
diversity through changes in plant and soil abiotic variables, the
statistical analyses of which were carried out using R version 4.2.1.
Results
3.1. Soil physicochemical properties as affected by grazing exclusion
and grassland type
In the 0-5 cm soil layer (Table 1), grazing exclusion significantly
increased 139.34% of SOC and 36.36% of TP in temperate desert
(P < 0.05), while significantly increasing temperate
steppe SWC、 SOC and mountain meadow TN (P < 0.05), but
grazing exclusion significantly reduced 16.67% of BD in temperate
steppe (P < 0.05). Before the grazing exclusion, soil pH and
BD were significantly higher in temperate desert than in temperate
steppe and montane meadow, while the differences between temperate
steppe and montane meadow were not significant. C:N did not differ
significantly between the three grassland types. All other soil
physicochemical properties were significantly higher in temperate steppe
and mountain meadow than in temperate desert. After exclusion, grazing
exclusion significantly increased the differences in SWC, SOC, TN and
N:P between the three grassland types, while the other soil chemistry
properties were consistent with the results before exclusion. The
interaction of grazing exclusion and grassland type significantly
altered TN and N:P (P < 0.05; Supplementary Table 2).
Soil physicochemical properties were all highly significantly influenced
by different grassland types in general compared to grazing exclusion,
while grazing exclusion, grassland type and the interaction between the
two did not significantly alter C:N.
In the 5-10 cm soil layer, grazing exclusion significantly increased TP
in temperate deserts by 34.1%, while significantly decreasing BD by
9.8% and N:P by 47.1%. In temperate steppes, none of the effects of
grazing exclusion on the measured soil physicochemical properties were
significant. In mountain meadows, grazing exclusion resulted in a
significant increase in SWC and SOC. BD and SOC were significantly
different between the three grassland types before the grazing
exclusion, while C:N was not significantly different. All other soil
physicochemical properties were significantly different between
temperate desert and temperate grassland、 mountain meadow, while the
differences between temperate steppe and mountain meadow were not
significant. After grazing exclusion, C:N was significantly higher in
temperate deserts than in temperate steppe and mountain meadows, while
SOC and BD were not significantly different between temperate steppe and
mountain meadows. The interaction of grazing exclusion and grassland
type significantly altered BD, SOC, TP and C:N (P <
0.05; Supplementary Table 3).
Table 1 Soil physicochemical properties as affected by grazing
exclusion and grassland type.