3.1.2 Changes in dominant phyla of prokaryotes and fungi
The
12 most abundant
prokaryotic
phyla (relative abundance higher
than 1%) and 6 fungal phyla
(> 0.1%) were selected to evaluate the effects of water
table drawdown and drainage age on
the most significant taxonomic shifts in the soil microbial community of
degraded peatlands
(Figure
2c and 2d, Table S1). We observed
the relative abundances of all prokaryotic and fungal phyla were changed
significantly with water table
(p< 0.05 for all, Table S1). Remarkably, the relative abundances
of Proteobacteria, Acidobacteri, Actinobacteria, Basidiomycota and
Zygomycota significantly increased, whereas Chloroflexi, Bacteroidetes,
Crenarchaeota and Ascomycota markedly decreased with the water table
drawdown (Figure 2c and 2d). Compared with short-term drainage,
Actinobacteria, Gemmatimonadetes and Nitrospirae showed more relative
abundance in long-term drainage sites, while the higher relative
abundances of Bacteroidetes, Crenarchaeota, Chytridiomycota and
Glomeromycota were observed in the short-term drainage sites (p< 0.05 for
all,
Table S1).
3.1.3Soil
microbiota as biomarkers
We subsequently analyzed whether we
could discriminate samples with
different water table based upon the microbiota composition using a
random-forest
machine learning analysis (Zhang et al., 2019). We performed a ten-fold
cross-validation to evaluate the
importance of indicator
prokaryotic
classes. We detected 14 classes as biomarker taxa when the
cross-validation error curve was lowest
(Figure 3a). Among theses, 8
classes had higher relative abundance in intermediate and high water
table treatments (IWT and HWT), while 6 classes showed higher relative
abundance in low-water table
treatments (LWT;
Figure
3b).
This
result demonstrates that soil microbial biomarker can be used as one of
the important indexes to evaluate peatland degradation.
3.1.4 Functional
annotation and distribution of prokaryotic community
FAPROTAX was further applied to evaluate the influence of water table on
soil C and N processes
(Figure
3c). A total of 69 functional
pathways were obtained from 1688 OTUs. We found no significant
difference in the relative abundances of functional pathway between the
IWT and HWT, so we further analyzed the difference between
the LWT and
IWT and HWT treatments, and found
that there were 42 functional pathways with significant differences
(Figure 3c).
The
results showed that the low water table treatments had higher nitrate
denitrification, nitrite
denitrification, nitrate reduction and respiration, but had lower
nitrite ammonification and nitrogen respiration. This imply that more
NO3− were used for denitrification in
LWT than IWT and HWT.
In
addition, we found the relative abundances of many carbon
metabolism-related pathways, including methanogenesis, methanotrophy,
fermentation, hydrocarbon degradation and etc., were significantly
higher in IWT and HWT than in LWT.
GHG emission and itstemperature
sensitivity
CO2 and N2O emission rates began low,
peaked at about day 21, and kept a relative subsequently during the
35-day incubation. In addition, we found that 10 °C warming brought
forward the peak of CO2 and N2O
emissions by a week (Figure S3).
3.2.1GHG
emission variation with water table and drainage age
The average GHG emission rate and
its response to warming changed significantly with water table and
drainage age (p < 0.01 for all, Table S2). At 8
°C and 18°C, the average rates of
CO2, CH4 and N2O
emissions from different water table treatments were significantly
higher at short-term drainage
sites than long-term drainage sites, respectively (Figure 4 and Table
S2). At the same time, we also found that
CO2 and
CH4 emission rates were all significantly higher under
IWT conditions than the other conditions at both two temperatures, while
the N2O emission rates were significantly higher under
LWT conditions (Figure 4 and Table
S2). Similar to the results of FAPROTAX analysis, the GHG emission
decreased with the increase of drainage age, and water table drawdown
reduced the CO2 and CH4 emission
rates, but increased
N2O emission rates.
3.2.2 GHG emission
variation with warming
Warming significantly increased
the average rates of CO2 and N2O
emissions of all treatments at
short-term and long-term drainage
sites, but slightly decreased the CH4 emission rates,
even not
significantly
(Figure 4). We found the average CO2 emission rate
at short-term drainage site was
36.34
± 19.21 μg g-1 d-1 at 8 °C and 63.27
± 31.18 μg g-1 d-1 at 18 °C, and
significantly increased by 74.11% with ten degree warming, while the
average CO2 emission rate at long-term drainage site was
24.25 ± 10.21 μg g-1 d-1 at 8 °C and
42.57 ± 10.75 μg g-1 d-1 at 18 °C,
which significantly increased by 75.55% with 10 °C warming. In
addition, we also found warming significantly increased average
CO2 emission rate of LWT
conditions by 91.14% from 19.46 ±
6.13 μg g-1d-1 of 8 °C to 37.78 ± 8.66 μg g-1d-1 of 18 °C,
IWT conditions by 56.44% from
46.60 ± 13.20 μg g-1 d-1 of 8 °C to
72.90 ± 28.88 μg g-1 d-1 of 18 °C,
and HWT conditions by 93.60% from 24.83 ± 3.56 μg g-1d-1 of 8 °C to 48.07 ± 5.64 μg g-1d-1 of 18 °C. The results of temperature sensitivity
(Q10) of soil respiration also further proved that the
sensitivity of IWT conditions to temperature were less than the other
conditions (Figure S4).
Variation of DOC and TDN concentration during incubation
Compared
with the soil before incubation, the variations of DOC and TDN
concentrations after incubation showed different patterns (Figure 5).
While TDN concentration increased,
DOC concentration decreased with the
water table drawdown at both short-term and
long-term drainage sites during
incubation (p < 0.001 for all; Figure 5 and Table
S3). The DOC concentration of
different water table treatments at short-term drainage site were
significantly higher than long-term drainage site, respectively
(p < 0.001; Table S3). Warming decreased
the DOC concentration of all
treatments (p < 0.05 for all) , but only significantly
increased TDN concentration of LWT conditions (p <
0.05; Figure 5).
The influence
factors ofGHG
emissions
The average rates of CHG emissions during the incubation period was
expressed in CO2, CH4 and
N2O. CO2 and CH4 were
positively correlated with DOC concentration under 8 °C and 18 °C
(r
= 0.555, r = 0.483 for CO2; r = 0.725, r = 0.555 for
CH4; p < 0.05 for all), and were
negatively correlated with TDN (r = -0.495, r = -0.505 for
CO2; r =-0.449 for CH4 at 8 °C; p< 0.05 for all; Figure
6a, 6c and 6b,6d). No linear relationship was found among
N2O and DOC, but N2O were correlated
positively with TDN under 8 °C (r = 0.459, p < 0.05;
Figure 6e and 6f).
Spearman’s
correlation analysis were used to further evaluate microbial influences
on the GHG emissions, DOC and TDN concentrations
(Figure 7). An interesting result
was found that the relationship between the dominant microbial phyla (11
most abundant prokaryotic phyla and 5 fungal phyla) and
carbon and nitrogen mineralization
showed opposite trends (Figure 7). Firmicutes, Chloroflexi, Chlorobi,
Bacteroidetes and Crenarchaeota of prokaryotic phyla and Ascomycota,
Chytridiomycota and Glomeromycota of
fungal phyla showed a positive
relationship with carbon mineralization (including CO2,
CH4 and DOC), but a negative relationship with nitrogen
mineralization (including N2O and TDN). Proteobacteria,
Acidobacteria, Planctomycetes and Gemmatimonadetes of prokaryotic phyla
and Basidiomycota, Zygomycota of fungal phyla showed a negative
relationship with carbon mineralization (including
CO2,
CH4 and DOC), but a positive relationship with nitrogen
mineralization (including N2O and TDN).
Finally, SEM was applied to access
the direct and indirect effects of water table, drainage age, soil
properties and soil prokaryotic
and fungal communities on GHG
emissions (Figure 8). Drainage can
directly
influenced CO2 and
N2O emissions,
with little or no directly effect on CH4 (Figure 8).
Water table showed a directly
negative influence on N2O, with little or no directly
effect on CO2 and CH4. Drainage age were
directly negative related to CO2, with little or no
directly effect on CH4 and N2O. Drainage
can indirectly affected GHG emissions by directly influenced plant
biomass, TC, TN, DON, SWC, NH4+-N,
microbial C:N and soil prokaryotic and fungal communities (Figure 8).
Prokaryotic community showed a
directly positive associated with CO2,
CH4 and N2O emissions (Figure 8). SEM
suggested that prokaryotic community exhibited a larger impact on GHG
emissions than fungal community (Figure 8).
DISCUSSION
- Microbial
characteristics varied in relation toduration ofdrainage and water
table drawdown
The
response of microbes to drainage is likely to depend on peatland type,
the alteration of water table fluction, and the extent of spatiotemporal
variation (Andersen, Chapman, & Artz, 2013; Krista Jaatinen et al.,
2008; Minick et al., 2019; Peltoniemi, Fritze, & Laiho, 2009).
Our study found that both water
table and drainage age significantly affected the microbial community
structure and compositions (Figure 2), which was consistent with many
studies (Jaatinen et al., 2007; Urbanova & Barta, 2016).
Drainage can directly lead to water
table drawdown in peatlands, resulting in persistent aerobic conditions
(Holden, Chapman, Lane, & Brookes, 2006), while with long-term drainage
may further change the vegetation composition, thus affecting microbial
composition and function (Miller, Benscoter, & Turetsky, 2015; Urbanova
& Barta, 2016). Our results showed that water
table drawdown significantly
increased the relative abundances of Proteobacteria,
Acidobacteria,
Actinobacteria and Basidiomycota
(Figure 2c and 2d). In oxic
conditions, Members of
Proteobacteria (Di Lonardo, De Boer,
Klein Gunnewiek, Hannula, & Van der Wal, 2017), Acidobacteria(Dedysh,
2011), Actinobacteria (Goodfellow
& Williams, 1983) and Basidiomycota (Ludley & Robinson, 2008)
are considered to be the principal
decomposers of soil organic matter, that can degrade recalcitrant
organic materials (e.g. lignin, cellulose and humic materials) (Chen et
al., 2018; Pankratov, Dedysh, & Zavarzin, 2006). So these microbes play
an important role in GHG emissions, and this results consistent with
previous research (Schimel & Gulledge, 1998). In addition, we also
found long-term drainage significantly increased the abundance
Actinobacteria of compared to short-term drainage. Relevant studies have
found that Actinobacteria are more sensitive to long-term water table
drawdown than short-term water table drawdown(Jaatinen et al., 2008;
Peltoniemi et al., 2009).
Microbes are ubiquitous and mediate the macroscopic characteristics of
the ecosystems (Thompson, Johansen, Dunbar, & Munsky, 2019). Because of
the complexity of microbial communities make it necessary to explore
functional relationships between specific
microbes and ecosystem
characteristics. Here, we have used machine learning techniques to find
that specific microbial taxa could be used as one of the important
indexes to evaluate peatland degradation. The relative abundance of 4
classes of the phylum Proteobacteria were significantly higher in the
LWT compared to IWT and HWT (Figure 3b). As the
members of Proteobacteria are
involved in bacteriochlorophyll-dependent photosynthesis, they also
considered to be the major decomposers of soil organic matter, and
showed important role in nutritionally limited or arid environments (Ren
et al., 2018). In addition, as an important member of
Deltaproteobacteria, methanotroph can effectively oxidize methane in
nature (Hanson & Hanson, 1996). In consistent with other studies (Cao
et al., 2018; Siljanen, Saari, Bodrossy, & Martikainen, 2012; Zhong et
al., 2020), we also found the relative abundance of Deltaproteobacteria
increased with water table drawdown.
Influence of duration of drainage and water table on GHG
emissions
The emissions of
CO2, CH4 and N2O
were significantly different
between short-term drainage sites and long-term drainage
sites
(Figure 4 and Table S2).
The
long-term drainage peatlands are less suitable for microbial use
compared with short-term drainage peatlands because of the poor quality
of organic matter and low decomposability due to drainage over many
decades of grazing history (Andersen et al., 2013; Leifeld, Steffens, &
Galego-Sala, 2012; Urbanova & Barta, 2016). So far, few studies have
examined the impact of duration of
drainage on GHG emissions of
peatland. Most researches have focused on the effect of drainage on GHG
emissions in disturbed peatland compared to
natural peatland (Cao et al.,
2018; Maljanen, Hytönen, & Martikainen, 2001; Nieveen, Campbell,
Schipper, & Blair, 2005; Zhou, Cui, Wang, & Li, 2017), or the effect
of the water table drawdown on GHG emissions due to drainage (Chen,
Borken, Stange, & Matzner, 2012; Hou et al., 2013; Laiho, Silvan,
Cárcamo, & Vasander, 2001; Saurich et al., 2019; Wang, Siciliano,
Helgason, & Bedard-Haughn, 2017).
Kang et al. (2018) showed that
N2O flux was found to be inconsistent under drought
conditions, which may be caused by SOC variations by drought. This is in
accordance with our results, We found N2O emission in
long-term drained peatland was lower than that in short-term drained
peatland (Figure 4). Huang, Zou, Zheng, Wang, and Xu (2004) found
long-term drought conditions may induced the SOC to be recalcitrant,
indicating a reduced supply of organic carbon for microbial activity.
Therefore, nitrification and denitrification processes were probably
inhibited to produce less N2O under the long-term
drought conditions.
Many researches have found that water table drawdown caused by drainage
make peatlands into sources for CO2 and
N2O, whereas CH4emission is known to decrease(Cao
et al., 2018; Saurich et al., 2019; Tiemeyer et al., 2016). In contrast,
Knorr, Glaser, and Blodau (2008) and Muhr, Höhle, Otieno, and Borken
(2011) found the emissions of CO2 from minerotrophic fen
peatland had no change after water table drawdown. In accordance with
other previous researches (Hou et al., 2013; Maljanen et al., 2001;
Regina, Silvola, & Martikainen, 1999),
we
also found the N2O
emission increased with the water table drawdown. The nitrogen cycle of
peatland soil is very sensitive to the fluctuation of water table (Pal,
Stres, Danevčič, Leskovec, & Mandic-Mulec, 2010). It is well known that
soil nitrate is reduced to N2, NO and
N2O through microbial processes (Knowles, 1982).
N2 is the end product under anaerobic conditions, while
N2 generally replaced by N2O at higher
oxygen levels (Weil & Brady, 2017). The results of FAPROTAX also
further supported our result, which we found had higher nitrous oxide
denitrification, nitrate denitrification, nitrite denitrification,
nitrate reduction and respiration, but had lower nitrite ammonification
and nitrogen respiration (Figure 3c). In addition, we also found that
multiple carbon metabolism-related pathways were significantly higher in
IWT and HWT than those of LWT, which was partly supports the results of
the carbon emissions (Figure 3c and Figure 4). Freeman, Lock, and
Reynolds (1992) and Kwon et al. (2013) pointed out that water table
drawdown changes anaerobic surface peat into aerobic, increasing the
decomposition rates, microbial activity and aerobic respiration, and
then increasing CO2 emissions. However,
our results showed that
CO2 emissions first increased and then decreased along
the water table gradient.
Similarly, Laiho (2006) showed that
short-term water level drawdown increased CO2 emissions,
but the longterm changes of the water level drawdown caused by the
drought decreased the CO2 emissions of peatland. Hou et
al. (2013) also found that water depth of 5 cm below surface increased
the CO2 emission of peatlands, but the continued water
table drawdown was no significant influenced or even decreased the
CO2 emission. Swails et al. (2018) further proved that
degradation of soil organic matter quality and nutrients associated with
drainage may decrease substrate driven CO2 production
from peat decomposition. Therefore,
water table drawdown increased the
emissions of CO2, but this impact maybe offset by
long-term drought.
Influence of temperature on GHG emissions
Our
study results consistent with previous studies, which found warming
increased CO2 (Laine, Makiranta, et al., 2019) and
N2O emissions (Cui et al., 2018; Duval & Radu, 2018),
but have no significant impact on CH4 emission
(Johnson,
Pypker, Hribljan, & Chimner, 2013; Pearson et al., 2015). As for the
impact of temperature on CO2, it is now generally
accepted that warming can significantly increase CO2emissions in both drained and undrained peatlands (Hopple et al., 2020;
Laine, Makiranta, et al., 2019; Salm, Kimmel, Uri, & Mander, 2009).
However, warming appears to have a complex impact on the emission of
CH4 from peatlands (Yang et al., 2014). Turetsky et al.
(2008) found a positive correlation between warming and
CH4 emissions, some reported a negative relationship
(Eriksson, Öquist, & Nilsson, 2010; Peltoniemi et al., 2016), and while
some other studies have showed no effect (Laine, Mehtatalo, Tolvanen,
Frolking, & Tuittila, 2019; Pearson et al.,
2015).
Recenly, many studies have found that warming effect on CH4 emissions
varied with the fluction of water table (Laine, Makiranta, et al., 2019;
Peltoniemi et al., 2016; Yang et al., 2014). Gill, Giasson, Yu, and
Finzi (2017) and Munir and Strack (2014) reported that warming increased
the CH4 emisssion in water-saturated conditions, while
the opposite phenomenon has been found from drier hummocks. This is
partly consistent with our research, we only found that warming
increased CH4 emission of HWT conditionin short-term
drained peatlands, although not significant. Peltoniemi et al. (2016)
concluded that the effect of warming under different moisture conditions
on the activity and community of microorganisms regulating the methane
cycle are not directly.
Consisitent with other ecosystems, for example alpine swamp meadow (Chen
et al., 2017), high arctic tundra (Gong, Wu, Vogt, & Le, 2019) and
subarctic tundra (Voigt, Lamprecht, et al., 2017), we found that warming
increased N2O emissions with different water table
treatments in both short-term and long-term drained peatlands at
seasonal frozen soil. Butterbach-Bahl et al. (2013) reported that
denitrification and consequently N2O emissions have high
temperature sensitivity. The emission of N2O is
controlled by of mineral nitrogen availability and influenced by
microbial nitrification and denitrification processes (Bouwman, 1990). N
mineralization from SOM can be accelerated by warming and leaching of
nitrate and ammonium may occur (Bai et al., 2013). Thus, increased soil
temperature promoted N2O emissions via
denitrification(Voigt, Marushchak, et al., 2017). In addition, we also
found Q10 values for GHG emissions were all lowest in
IWT conditions (Figure S4).
This
indicates that water table is also an important index affecting the
temperature sensitivity of GHG.
The role of
biogeochemical factors in GHG emissions
Many researches have showed that
drainage affected the processes of the carbon (C) and
nitrogen (N) mineralization of
peatland, and thus changed its C and N sink and sources function (Chen
et al., 2012; Chimner, Pypker, Hribljan, Moore, & Waddington, 2017;
Laine, Makiranta, et al., 2019; Zhang et al., 2020). The fluction of
water table can directly affect soil biogeochemical properties,
including soil basic physical and chemical properties, soil substrate,
microbial community and enzyme activity, which lead to the change of GHG
emissions (de Vries et al., 2018; Mpamah, Taipale, Rissanen, Biasi, &
Nykanen, 2017; Swails et al., 2018; Wen et al., 2019). The concentration
of DOC and TDN are the balance between soil organic matter production
and soil microbial consumption, and DOC and TDN will be further
decomposed and discharged in the form of CHG (van den Berg, Shotbolt, &
Ashmore, 2012). Therefore, DOC and TDN concentration in soil can reflect
carbon and nitrogen loss (Boothroyd, Worrall, & Allott, 2015). The
results of our study found DOC and TDN concentration were strongly
correlated with CO2 and CH4 emissions,
while no linear relationship was found between N2O and
DOC, N2O was only correlated positively with TDN (Figure
6). This suggests that soil substrate availability is crucial in GHG
emission from soil. We further analyzed the relationship between soil
microorganisms and substrate availability and GHG emission. An
interesting result was found that some microbial taxa were contribute
significantly to carbon mineralization, and others contribute
significantly to nitrogen mineralization (Figure 7). Soil microorganisms
affect the mineralization of soil organic C and N by secreting
extracellular enzymes (Chapman, Cadillo-Quiroz, Childers, Turetsky, &
Waldrop, 2017; Schnecker et al., 2015). Abatenh, Gizaw, Tsegaye, and
Genene (2018) reported that microbial processes have a central role in
GHG emissions and specific functional microorganisms are responsible for
the related biochemical processes, and possibly a rapid response to
climate change. In addition, SEM was used to analyze the impact of
environmental factors on the emission of CHG, and it was found that the
prokaryotic microbial community had the greatest impact on GHG, which
also further supported the important role of microorganisms in soil GHG
emissions (Singh, Bardgett, Smith, & Reay, 2010).
CONCLUSION
The emissions of CO2, CH4 and
N2O response to drainage is inconsistent.
CO2 and CH4 emission rates first
increased and then decreased along
the water table drawdown gradient, while N2O increased
along the water table gradient. The emissions of CO2,
CH4 and N2O from different water table
treatments were significantly higher at short-term drainage sites than
long-term drainage sites. The results of FAPROTAX analysis also
supported this perspective. In addition, warming significantly increased
the average rates of CO2 and N2O
emissions of all treatments at short-term and long-term drainage sites,
but not significantly decreased the CH4 emission rates.
The Q10 values of GHG emissions were lowest in IWT
conditions compared to other conditions. Microbial community composition
was the primary factor affecting GHG emissions from peatlands,
especially prokaryotes. Collectively,
Our results further reveal the
mechanism of climate change and human activities on the emisssions of
GHG in alpine peatland ecosystem, which can provide support for the
sustainable management of alpine peatland in the future.