INTRODUCTION
Peatland
are an important global carbon pool that contain 1,055 Gt of soil
carbon, and covering 3 % of the Earth’s land surface (Nichols &
Peteet, 2019). With higher carbon densities than other ecosystems, they
release more GHG emissions (Danevčič, Mandic-Mulec, Stres, Stopar, &
Hacin, 2010). In particular, peatlands were destabilized by natural and
hunman factors in recent years, such as climate change, land-use change
and other disturbances, and more carbon was emitted into the atmosphere
(Chen et al., 2014; Ward, Bardgett, McNamara, Adamson, & Ostle, 2007).
Becase
of the special altitude, alpine peatland ecosystem have higher
sensitivity to global change (Ise, Dunn, Wofsy, & Moorcroft, 2008).
The peatlands in this area are in a
low temperature and anoxia all year round, and climate warming have
changed peatlands as a carbon sink to a carbon source (Dise & Phoenix,
2011). Warming-induced the
acceleration of carbon and nitrogen (C and N) decomposition in peatland,
which further exacerbating climate change (Wen et al., 2019). So far,
the research on C and N cycling of peatland induced by climate warming
has most focused on undisturbed peatlands (McPartland et al., 2019;
Weedon et al., 2013). In addition, many researchers have explored the
response of carbon and nitrogen cycling to water table fluctuation (Cao,
Chen, Wu, Zhou, & Sun, 2018; de Vries et al., 2018; Rhymes et al.,
2016; Zhang et al., 2018). However, there are relatively few studies on
the effects of climate warming on drained peatlands. A recent study
found in the period 2020-2100, the impact of emissions from drained
peatlands could be as high as 41% of the GHG emissions budget (Leifeld,
Wüst-Galley, & Page, 2019). Drainage peatlands become hotspots for both
CO2 and N2O emissions from soils, as
well as a minor part of CH4 source or even carbon sink
(Saurich, Tiemeyer, Dettmann, & Don, 2019).
Zoige peatlands is one of the largest alpine peatlands in the world,
covered about 4605 km2 area, and store approximately
0.477 Pg of carbon (Chen et al., 2014). Similar to other peatlands
(Hooijer et al., 2010; Urbanova & Barta, 2016), it is currently
experiencing ubiquitous warming and intensified anthropogenic
activities. Since the 1960s, nearly
half of the Zoige peatland have drained due to pasture expansion (Dong,
Hu, Yan, Wang, & Lu, 2010). Meanwhile, global warming had doubled the
rate of warming on Tibetan Plateau over the past century (Qiu, 2007).
Anaerobic
surface peat changes to aerobic with the soil drainage, which increased
in C and N decomposition rate (Borken & Matzner, 2009). The magnitude
of GHG emissions and microbial activity increase as soils become
oxygenated (Chapuis-Lardy, Wrage, Metay, Chotte, & Bernoux, 2007;
Oertel, Matschullat, Zurba, Zimmermann, & Erasmi, 2016). Many studies
have found that drained peatlands can release a lot of dissolved organic
carbon (DOC) (Fenner & Freeman, 2011; Liu et al., 2019) and
CO2 (Gatis et al.,
2019).
In addition, peatlands drainage significantly reduced the emission of
CH4 (Laine et al., 1996), but substantially increased
N2O fluxes (Martikainen, Nykänen, Crill, & Silvola,
1993). Drainage changes the biogeochemical and hydrological processes of
peatlands and shifting peatland from a carbon sink to a source of GHG
emissions (Norberg, Berglund, & Berglund, 2018; Tiemeyer et al., 2016).
Many
researches have shown that GHG emissions from soils increase with
anthropogenic disturbance (Cai & Chang, 2020; Peng et al., 2013;
Saurich et al., 2019). Global
warming can accelerate the decomposition of recalcitrant organic matter
and old aged carbon, which resulting in the C and N loss from these
ecosystems (Craine, Fierer, & McLauchlan, 2010; Dillon, Wang, & Huey,
2010). However, how the GHG production and responses to temperature in
drained peatland are still uncertain.
Soil microbes play a critical role in the processes involved in the
cycling of C and N, and they are sensitive to environmental changes
(Anthony, Crowther, Maynard, van den Hoogen, & Averill, 2020). It was
found that microorganisms are very sensitive to the availability of
water and oxygen in wetland ecosystems (Jaatinen, Fritze, Laine, &
Laiho, 2007). Water table drawdown enhances the activities of
extracellular enzyme (Wiedermann, Kane, Potvin, & Lilleskov, 2017),
increases microbial biomass(Minick, Mitra, Li, Noormets, & King, 2019),
and thus changes the GHG emissions resulting from microbial activities
(Wang et al., 2017; Zhong et al., 2017). As well the vulnerability or
resilience of microbial communities to water table
drawdown is likely to depend on
duration of drainage. In the
longterm, microbial structure and function will change due to the shifts
in the composition of vegetation, which caused by water table drawdown
(Murphy, Laiho, & Moore, 2009), which will alter (Kwon, Haraguchi, &
Kang, 2013).
Because
of the limited data about C and N dynamics of alpine peatlands, and
together with ubiquitous global change, it may be of great significance
to study the C and N dynamics in Zoige peatlands. Drainage lead to rapid
peatland degradation and carbon losses, and the emissions of GHG are
likely to increase further with the climate warming. Therefore, we
wanted to answer the following questions in this study: 1) Whether the
degradation of peatland caused by drainage change the microbial
community? 2) what is the effect of
drainage on CHG emission rates in Zoige peatlands? 3)
Whether warming further increase the
GHG emissions from the drained peatlands? 4) What are the influencing
factors of GHG emissions from drained peatlands?
MATERIALS AND METHODS
Site and soil sampling
Samples were collected from two sites with different drainage histories
in the Ruokeba peatland demonstration area in Hongyuan County town
(33°04′ N, 102°34′ E; avg. 3472 m a.s.l., Figure 1). The mean annual
temperature was 1.6°C, and the mean annual precipitation was 760 mm for
the period 1961-2016 (Cao et al., 2018). Zoige plateau has experienced
significant climate change and human activities in the past 50 years,
and temperature increased 0.4 °C per decade since 1970 (Yang et al.,
2014). The plant communities in this area is over 70% and consists
mostly of Grass, Sedge, Gentianaceae, Rosaceae, Ranunculaceae,
Leguminosae and Forb.
The
long-term
drainage site
(L;
drainage age = 48 years) was used
as a grazing pasture in the 1970s, and the ditch is now 0.3-0.5 m deep
and 1-1.5m wide. The adjacent short-term drainage site (S; drainage age
= 3 years) was drained in 2015 (duration of drainage = 3 years) to
analysis the effect of drainage age on GHG emissions, and the ditch is
now 0.3-0.5 m deep and 0.5-1.0 m wide.
In July 2018, we sampled a total of six
plots
that were spaced 2, 10, and 50 m from the drainage ditch in the short-
and long-term drainage sites. 0-15 cm soil columns were randomly sampled
in quadruplicate using an auger at each plot. We used a grid layout to
monitor
the
water table in these sites and collected data at different distances
from the two ditches between 2016 and 2018. During the growing season,
average water table depths at the
short-term site were -31.38 cm (S2), -18.50 cm (S10), and -8.13 cm
(S50), while depths at the long-term site were -31.75 cm
(L2), -10 cm (L10), and -6.13 cm
(L50) (Figure S1). Based on these values,
the L2 and S2 treatments were
considered as the low water table treatments (L); L10 and S10,
intermediate water table treatments (I); and L50 and S50, high water
table treatments (H). The collected soil samples were kept at 4°C. Moist
soils were sieved through a mesh (2 mm) to remove impurities and to
further homogenize the samples before subdivision for analysis. The soil
was separated into three sections: one section was for incubation
experiment, one section was stored at 4°C to analyze the physicochemical
properties and the last was frozen at -80°C for microbiological
analysis.
Soilparameters
and microbial commum.ity composition analysis
Soil parameters including soil
water content (% SWC), pH, total
carbon (TC), dissolved organic C (DOC), total dissolved nitrogen (TDN),
total nitrogen (TN), soil ammonium N
(NH4+-N), soil nitrate N
(NO3−-N) and microbial biomass C and N
(MBC and MBN) were examined in 24 samples
(4 replicates × 3 plots × 2 sites)
before incubation. Soil samples were dried for at least 12 h at 105°C to
measure SWC; pH was measured using an acidity meter (Sartorius PB-10,
Göttingen,, Germany); TC and TN were determined using an elemental
analyzer (Elementar, Langenselbold,
Germany); Concentrations of
NH4+-N
and
NO3−-N
were extracted with 2M KCl as the extractant and measured using a
continuous
flow analyzer (San++, Skalar, Breda, Netherlands);
DOC
and
TDN were measured using a TOC/TN
analyzer (Multi N/C 2100, Analytik, Jena, Germany) based on a water
extraction method (Pinsonneault et al., 2016); Chloroform fumigation
extraction method were used to detect MBC and MBN (Vance et al., 1987).
Aboveground plant biomass of each plots was detected on August 2018, and
weighed after oven dring samples for 36 h at 65°C.
For microbial diversity, about 0.25 g of each sample soil was used for
DNA extraction by PowerSoil DNA Isolation Kit (MoBio Laboratories,
Carlsbad, CA, USA). High- throughput sequencing was carried out on the
Illumina MiSeq platform using the MiSeq V2 Reagent Kit (Illumina, Inc.,
San Diego, CA), after amplified by
prokaryote and fungi primer pairs
515F/909R and ITS4/gITS7F(Baker et al., 2003; Ihrmark et al., 2012). The
sequencing data was trimed via QIIME pipeline
(http://qiime.org/tutorials/tutorial.html).
For detailed analysis procedures refered to Xue et al. (2016). Taxonomy
was assigned using the Greengenes database for prokaryote and the Unites
database for fungi. 1,266,722 and 1,298,041 assembled paired-end reads
were identified in 24 samples through sequencing of 16S rRNA gene and
ITS2 amplicons, respectively. Subsampling (17,150 for
prokaryote and 18,170 for fungi,
the lowest) was performed to calculate microbial diversity. The
sequencing datas have been deposited in Sequence Read Archive (SRA) in
NCBI under the accession numbers SRP255927 and SRP256039.
Soil incubation
experiment and GHG measurements
The soil incubation experiment was conducted under aerobic condition
with two temperatures (8°C and
18°C) and four replicates (2 temperatures × 4
replicates
× 3 plots × 2 sites =
48).
Peat was sieved by a 2 mm mesh sieve, and the remaining coarse roots and
stones were carefully removed and
discarded. The soil was stored at
8°C (Average temperature in growing season) in the dark for one week of
pre-incubation before the experiment. 20 g soil was puted in a 100 ml
glass
bottle,
sealed with rubber stopper and flushed with CO2-free air
for 8 min to maintain aerobic environment. During the incubation period
of 35 days, 5 ml headair was collected every 24 h for
CO2, CH4 and N2O
concentration measurement. The GHG emissions from the soil of peatland
were detected on day 1, 3, 5, 7, 14, 21, 28 and 35 using gas
chromatography (Agilent 7890A, Agilent Co., USA). After each sampling,
the headspace was flushed with
CO2-free air again
to fully remove the accumulated GHG.
The greenhouse gases emissions
were calculated as (Liu et al., 2016):
\begin{equation}
F=\frac{\text{PPM.}M_{0}}{22.4}\cdot\frac{T_{0}}{T}\cdot\frac{P}{P_{0}}\cdot\frac{V_{0}}{m}\cdot\frac{1}{d}\nonumber \\
\end{equation}
where F (µg.d-1g-1) is the rate of
greenhouse gas eemission; M0: molar mass of
CO2, CH4 and N2O; T and
P: temperature and atmosphere pressure in headspace of the bottle;
T0 and P0: standard temperature and
atmosphere pressure; V0: the bottle capacity is
100×10-6 m3; m: the weight of the
dry soil; d: the incubation time is 1 d. TDN and DOC were analyzed after
35-day incubation.
Data analysis
NMDS
and PERMANOVA based on Bray-Curtis distance matric were used to evaluate
the overall structural alteration of prokaryote and fungi. Random forest
analysis was performed to select the important features that may
contribute to the differences in GHG emissions among three different
water table treatments (Breiman, 2001). Cross-validation was used for
feature selection. Functional profiles of prokaryotic taxa were
annotated using FAPROTAX v.1.1 (Louca, Parfrey, & Doebeli, 2016) to
perdict the process of the carbon, hydrogen, nitrogen, phosphorus and
sulfur cycle of environmental samples.
The
temperature sensitivity of the GHG
emission is defined as the variation of GHG emission response to 10°C
temperature gradient. Varied DOC and TDN represented the changes of DOC
and TDN concentrations in the
incubation period. The difference of GHG emission, DOC and TDN
concentration between two temperatures calculated by t -Test.
Differences in soil microbial compositions, GHG emissions,
Q10, varied DOC
and TDN concentrations based on duration of drainage and water table
drawdown were assessed using multi-factor analysis of variance (ANOVA)
in SPSS 21.0 (IBM, Armonk, NY, USA). Correlation analysis was performed
to evaluate the impact of varied DOC and TDN concentration, soil
properties and microbial characteristics on GHG emission rate using R
3.5.1. (R Development Core Team, 2018).
Direct and indirect contributions of water table, drainage age, soil
properties and microbial characteristics to GHG emissions used by
Structural equation modeling (SEM) (Grace, 2006). The SEM of differet
factors affecting GHG emission was developed from a priori models based
on literature, knowledge and correlation analysis data (Figure S2). The
prokaryotic and fungal community dissimilarity were obtained by NMDS of
the Bray-Curtis distance matrix, and the first axes (NMDS 1) were used
in the subsequent SEM analysis. SEM analysis was performed by
AMOS
25.0 sofeware (AMOS IBM, USA) using the robust maximum likelihood
evaluation method. The SEM fitness was evaluted by the indexes of a
non-significant chi-square test (df > 5; p> 0.05), the root mean square error of approximation (RMSEA
< 0.05) and the goodness of fit index (GFI) (Byrne, 2016).
SPSS 21.0 (IBM, Armonk, NY, USA), R 3.5.1. software (R Development Core
Team, 2018) and GraphPad Prism 8.0.2 (GraphPad Software, San Diego, CA,
USA) were used to all statistical analysis and charting. p< 0.05 was considered significant differences.
RESULTS
Microbial
characteristics