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