Changes in Methane Flux along a Permafrost Thaw Sequence on the

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Changes in methane flux along a permafrost thaw sequence on the Tibetan Plateau Guibiao Yang, Yunfeng Peng, David Olefeldt, Yongliang Chen, Guanqin Wang, Fei Li, Dianye Zhang, Jun Wang, Jianchun Yu, Li Liu, Shuqi Qin, Tianyang Sun, and Yuanhe Yang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04979 • Publication Date (Web): 25 Dec 2017 Downloaded from http://pubs.acs.org on December 25, 2017

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Environmental Science & Technology

1

Changes in methane flux along a permafrost thaw sequence on the Tibetan

2

Plateau

3 4

Guibiao Yang1,2, Yunfeng Peng1, David Olefeldt3, Yongliang Chen1, Guanqin Wang1,2,

5

Fei Li1,2, Dianye Zhang1,2, Jun Wang1,2, Jianchun Yu1,2, Li Liu1,2, Shuqi Qin1,2,

6

Tianyang Sun1,2, and Yuanhe Yang1,2*

7 8

1.

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

9 10

2.

University of Chinese Academy of Sciences, Beijing 100049, China

11

3.

Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada, T6G 2H1

12 13 14

*

15

10-6283 6632, E-mail: [email protected]

Corresponding author: Dr. Yuanhe Yang, tel.: + 86 10-6283 6638, fax: + 86

16

1

ACS Paragon Plus Environment


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ABSTRACT: Permafrost thaw alters the physical and environmental conditions of

18

soil and may thus cause a positive feedback to climate warming through increased

19

methane emissions. However, the current knowledge of methane emissions following

20

thermokarst development is primarily based on expanding lakes and wetlands, with

21

upland thermokarst being studied less often. In this study, we monitored the methane

22

emissions during the peak growing seasons of two consecutive years along a thaw

23

sequence within a thermo-erosion gully in a Tibetan swamp meadow. Both years had

24

consistent results, with the early and mid-thaw stages (3 to 12 years since thaw)

25

exhibiting low methane emissions that were similar to those in the undisturbed

26

meadow, while the emissions from the late thaw stage (20 years since thaw) were 3.5

27

times higher. Our results also showed that the soil water-filled pore space, rather than

28

the soil moisture per se, in combination with the sand content, were the main factors

29

that caused increased methane emissions. These findings differ from the traditional

30

view that upland thermokarst could reduce methane emissions owing to the

31

improvement of drainage conditions, suggesting that upland thermokarst development

32

does not always result in a decrease in methane emissions.

2


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TOC Art

34 35

Keywords: carbon cycle, climate feedback, methane, methanogens, methanotrophs,

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permafrost, thermokarst.

3



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1. INTRODUCTION

38

Permafrost ground in high altitude and high latitude regions contains more than half

39

the global soil organic carbon (C)1,

40

warming3-5. A warmer climate will cause widespread permafrost thaw, which can lead

41

to land surface collapse and erosion, i.e., thermokarst, in certain landscape settings6-8.

42

Thermokarst often causes abrupt changes in soil environmental conditions and thus

43

strongly influences the production rates of microbial greenhouse gases, including both

44

carbon dioxide (CO2) and methane (CH4)9-14. Understanding the impacts of

45

permafrost thaw on CH4 emissions is particularly critical, considering the 25 to

46

30-fold greater warming potential of CH4 compared to CO2 over a 100-year horizon15.

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However, thermokarst is considered one of the key uncertainties in our understanding

48

of future CH4 emissions and the overall permafrost carbon feedback to climate

49

change6, 10, 16.

2

and is considered vulnerable to climate

50 51

Thermokarst occurs due to the melting of excess ground ice7 and can cause the

52

development of a large number of distinct landforms depending on the landscape

53

characteristics and position. Broadly, these landforms have been grouped into wetland,

54

lake, and upland thermokarst landforms17. Upland thermokarst often occurs in upland

55

settings on moderate slopes or along watercourses and includes active layer

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detachment slides, retrogressive thaw slumps, and thermo-erosion gullies18-20. While

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several studies have reported increased CH4 emissions from recently formed wetland

58

and lake thermokarst landforms21-24, less is known about the impact on CH4 emissions 4


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from upland thermokarst landforms. It has been reported that upland ecosystems with

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stable permafrost are most often minor CH4 sources or CH4 sinks

61

improved water drainage26, 27 following the development of upland thermokarst would

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lead to further reduced CH4 emissions or an increased sink function16, 28, 29. However,

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a survey of 26 upland thermokarst landforms on the North Slope of Alaska found

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elevated CH4 concentrations in soil profiles compared to the nearby locations

65

unaffected by thermokarst10. This finding implies that upland thermokarst may also

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cause increased CH4 emissions, but direct observations of increasing CH4 emissions

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following upland thermokarst are still lacking.

13, 21, 25

, and

68 69

Upland thermokarst can lead to both land surface collapse and erosion, which together

70

abruptly alters the physiochemical characteristics of the soil, and thus potentially

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affect the processes controlling CH4 production, transport, and oxidation4,

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Concurrent changes to several soil characteristics may have counteracting influences

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that need to be considered to understand the resulting net CH4 emissions. For example,

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while thermokarst-induced lower volumetric water content (VWC) in the soil may

75

reduce CH4 flux, other factors could enhance CH4 emissions. For instance, soil

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collapse often increases soil bulk density by reducing soil porosity, which may

77

increase soil water-filled porespace (WFPS) in the soil30,

78

though soil compaction reduces the VWC, which has been linked to reduced CH4

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emissions11-13, the concurrent increase in the WFPS may cause increasing anaerobic

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conditions

that

are

beneficial

to

methanogens

and

10

.

31

. Consequently, even

adverse

for

aerobic 5



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methanotrophs32-34, increase CH4 production and inhibit aerobic CH4 oxidation35. At

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the same time, increases in both bulk density and WFPS increases the thermal

83

conductivity of the soil36, which would lead to higher soil temperatures and thus

84

stimulate microbial CH4 production. In addition, thermokarst may increase the sand

85

content as a result of the water scouring action37, 38, which may retain less CH4 and

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increase the potential for the transport of CH4 from soils to the atmosphere39, 40.

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Increased sand content may also influence the net CH4 emissions following shifts in

88

pH, since both methanogen and methanotroph activity increase as conditions go from

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acidic to neutral41, 42. However, it remains poorly understood about the factors that

90

dominate the response of net CH4 emissions following thermokarst development,

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which is a prerequisite for predicting future CH4 emissions from permafrost regions.

92 93

The Tibetan permafrost region represents approximately three quarters of the total

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alpine permafrost area in the Northern Hemisphere43, yet its vulnerability to climate

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change has attracted limited attention compared to the boreal and tundra permafrost

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regions1, 44-46. In this study, we conducted a two-year survey (2015 and 2016) of CH4

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emissions during the peak growing season in a thermo-erosion gully underlaid by

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discontinuous permafrost on the Tibetan Plateau. We also measured the biotic and

99

abiotic parameters relevant to the processes of CH4 production and consumption.

100

Structural equation modeling (SEM) was used to evaluate the relative importance of

101

various pathways that regulate CH4 emissions. Overall, our current study aimed to 1)

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examine how the CH4 emissions during peak growing season change along a thaw 6


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sequence; and 2) disentangle the biotic and abiotic regulating pathways of net CH4

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emission pattern along the thaw sequence.

105 106

2. MATERIALS AND METHODS

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2.1. Site description

108

This study was conducted within the permafrost region of the northeastern Tibetan

109

Plateau, China (N 37°28′, E 100°17′, altitude ~3900 m above sea level; Figure 1a).

110

The study site is located on a hillslope with a gentle south-facing 9° incline. The mean

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annual temperature is -3.3 °C and the average annual precipitation is 460 mm. The

112

vegetation type is swamp meadow dominated by Kobresia tibetica, K. royleana,

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Carex atrofuscoides, Saussurea pulchra Lipsch, Potentilla saundersiana Royle, etc.

114

The soil has a silty loam texture with a high organic carbon content (19.4 ± 0.5%) and

115

high-water content at a depth of 10 cm (seasonal mean VWC of 62.2 ± 4.4%

116

measured in 2015). Average active layer thickness (ALT) was estimated to be 0.86 m

117

based on the combination of GSSI georadar (SIR-20, Laurel, Santa Clara, USA) and

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thaw-probe measurements. The excess ground ice is classified as intrusive ice47.

119

Permafrost thaw and surface subsidence have been documented at the site since the

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early 1990s and have led to the initiation and expansion of the thermo-erosional gully.

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The gully had been experiencing thermo-erosion processes and retreating forward and

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laterally along the slope. The gully is present ~240 m long, with a maximum depth of

123

approximately 2 m. During the thawing season, a stream flows along the gully from

124

the head to the bottom where it exits into a larger watercourse. 7



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Four 15 × 20 m2 plots were established in 2015. One plot, referred to as the control,

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was located away from the thermo-erosional gully and showed no evidence of surface

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subsidence due to permafrost thaw. Another three plots were located within the gully,

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and reflected different stages of thaw, with the specific age since thaw initiation

130

estimated by two steps. We first estimated the rate of gully retreat (~8 m yr-1) by

131

comparing to satellite images from 2007 to 2013 and ground measurements between

132

2014 and 2016. We then determined the time since collapse for each thaw stage by

133

dividing the distance between the gully head and each site by the rate of gully retreat,

134

respectively. The early, mid- and late stage plots were thus estimated to have

135

undergone surface collapse due to thaw 3, 12, and 20 years prior to the study,

136

respectively (Figure 1c-f).

137 138

2.2. CH4 flux measurements

139

We measured the CH4 fluxes using the static chamber methodology48. Ten sampling

140

locations were randomly selected within each of the four plots. A collar (diameter 26

141

cm and height 12 cm) with an anchor ring was installed at each location to a depth of

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10 cm. The static chambers (25 cm tall) were made of PVC and were covered with

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insulation materials. A fan was installed on the top wall of each chamber to provide

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headspace mixing. Gas samples (50 ml) were taken from the headspace 0, 10, 20, 30

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and 40 min after chamber closure. The chamber was vented before it was moved to

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the next collar. During gas sampling, the soil temperature and VWC in the top 10 cm, 8


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as well as the ALT, were measured adjacent to each collar. To ensure that the

148

measurements were not biased by differences in the microclimate over time, or by

149

artificial and system errors, four people collected gas samples simultaneously in the

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four quadrats, and the people were rotated among the quadrats during gas collection.

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Gas samples were collected between 9 am and 12 noon in July and August of 2015

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(three times per month with 10-day intervals) and 2016 (once in early July, three

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times in a row at the end of July, and once at the end of August). Note that we

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conducted the gas measurements during three consecutive days near the end of July

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rather than at equal intervals in 2016, since we aimed to match the gas and soil

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samplings to better reveal the biotic and abiotic mechanisms regulating CH4 fluxes.

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All gas samples were analyzed for CH4 concentration on a gas chromatograph

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(Agilent 7890A, Agilent Technologies Inc., Santa Clara, California, USA). The CH4

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flux was then calculated according to Eq.148.

160

V P T 0 dC F = ρ× × × × t A P0 T dt

(1)

161

where F is the CH4 flux (mg m-2 h-1); ρ is the density of CH4 under standard

162

conditions (mg m-3); V and A are the volume of the chamber (m3) and the base area

163

(m2), respectively; P is the air pressure (hPa) and T is the air temperature (K). Po and

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To are the standard pressure (1013 hPa) and the standard temperature (273 K),

165

respectively. dCt/dt is the growth rate of the CH4 concentration (10-6 h-1).

166 167

2.3. Soil sampling and analyses

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Soil samples were collected from the upper 15 cm using a standard soil probe 9



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(inside-diameter 2.5 cm) at the end of July 2016 near each collar. The soil samples

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were sieved (2 mm), homogenized, and divided into two subsamples. One set of

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subsamples was immediately flash-frozen in liquid nitrogen and stored at -80° C for

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DNA extraction. The other set of subsamples was air-dried and used to determine soil

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pH and sand content. The soil pH was determined in a soil water suspension (1:5 dry

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soil to deionized water ratio) using a pH electrode (PB-10, Sartorius, Germany). The

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soil texture was measured using a particle size analyzer (Malvern Masterizer 2000,

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Malvern, Worcestershire, UK) after organic matter and carbonates were removed

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using 30% hydrogen peroxide and 30% hydrochloric acid, respectively49. The bulk

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density of each sample was obtained using a standard container with a fixed volume

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size of 100 cm3 and was then measured based on the dry soil weight after it was

180

oven-dried at 105 °C for 24 h. The WFPS was determined according to Eq.2.

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W FPS = SW C × BD /(1-BD/PD )

(2)

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In the equation, WFPS and BD represent the soil water-filled pore space (%) and soil

183

bulk density (g cm-3), respectively, while the soil water content (SWC) is calculated

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based on the oven-dry method (g g-1), and PD represents the particle density of the

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soil (2.65 g cm-3).

186 187

2.4. DNA extraction and quantitative PCR analysis

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We quantified the abundance of CH4-related functional genes (mcrA and pmoA) to

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examine the effects of specific microorganisms on the CH4 fluxes. The abundance of

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the mcrA gene has been widely used to quantify methanogens34, 35, as it codes for a 10


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subunit of Methyl-coenzymeM reductase, which is a crucial enzyme in CH4

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production. Conversely, the abundance of the pmoA gene is used as an indicator of the

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abundance of methanotrophs32,

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monooxygenase, which is a key enzyme for CH4 oxidation. During the quantification

195

process, the DNA was extracted from 0.4 g of soil preserved at -80 °C using a Power

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Soil® DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA) according to the

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manufacturer protocol. The quality of the DNA was assessed based on 260/280 nm

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and 260/230 nm absorbance values using a NanoDrop-2000 (Thermo Fisher Scientific,

199

Madison, WI, USA). The abundances of the pmoA and mcrA genes were determined

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on a StepOne Plus real-time PCR system (Applied Biosystems, Inc., CA, USA). The

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20 µl reaction mixture contained 10 µl SYBR Premix Ex Taq (Tli RNaseH Plus), 0.4

202

µl forward and reverse primers, 0.4 µl ROX Reference Dye (50×), 6.8 µl sterile water

203

and 2 µl five-fold diluted template DNA. The thermal-cycling conditions, primer pairs,

204

number of cycles and references used to quantify the abundances of the pmoA and

205

mcrA genes were shown in SI Table S1. The standard curves were generated using

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ten-fold serial dilutions of purified plasmids containing the respective genes with r2 >

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0.9. The copy numbers in the samples were calculated based on the comparison to the

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threshold cycle values of the standard curve, and were given in per gram soil (dry

209

weight).

35

, as it codes for a subunit of methane

210 211

2.5. Statistical analyses

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The data were analyzed following three steps. First, a repeated-measures analysis of 11



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variance (ANOVA) with interactions was carried out with the thaw stage as the main

214

(between-subject) factor and sampling date as the within-subject factor to assess the

215

effect of the main factors on the CH4 fluxes during the peak growing seasons of 2015

216

and 2016. One-way ANOVAs were used to determine the differences in the biotic

217

factors (the mcrA and pmoA abundances) and abiotic factors (soil temperature, VWC,

218

WFPS, ALT, pH, bulk density, and sand content) among the different thaw stages.

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Multi-comparison of Tukey's HSD was conducted to test whether each parameter

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exhibited significant differences among the thaw stages at the significance level of P

221

< 0.05.

222 223

Second, linear regressions were performed to explore the relationships of the CH4

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fluxes with the biotic and abiotic factors, in which we adopted the mean values of

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CH4 fluxes, soil temperature, VWC and ALT measured three times in a row at the end

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of July, and the abundances of the mcrA and pmoA genes, WFPS, pH, bulk density,

227

and sand content in the soils collected during this time. The mean CH4 fluxes were

228

log-transformed to achieve a normal distribution of the data to meet the assumptions

229

of the analysis. All abovementioned statistics were performed using SPSS 20.0 (IBM

230

SPSS, Chicago, IL, USA).

231 232

Third, structural equation modeling (SEM) was used to determine the major

233

controlling pathways regulating the CH4 fluxes along the thaw sequence. SEM is an

234

extension of traditional regression and pathway analyses that is used to model 12


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multivariate relations based on a collection of simultaneous procedures that determine

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the hypothetical pathways of direct and indirect influence among many variables

237

using the covariance among those variables50, 51. A base model was established on the

238

basis of the current understanding of the key CH4 emission controls as described in

239

the literature (SI Figure 1). In the base model, we assumed that: 1) methanogen

240

microbial abundance, as expressed by the mcrA gene, has a direct effect on the CH4

241

flux. By contrast, the CH4 oxidation process was not included in the model because no

242

significant correlation was observed between the CH4 fluxes and the abundance of the

243

pmoA gene (Figure 4g). 2) Soil pH affects microbial growth and enzyme activities and

244

thus controls the CH4 emissions through both direct and indirect pathways via the

245

mcrA gene abundance. 3) Sand content, VWC and WFPS are linked to CH4 emissions

246

directly and indirectly through microbial abundance and pH. The model χ2 test and

247

root mean squared error approximation (RMSEA) value were used to evaluate the fit

248

of the final model. The model was considered to have a good fit when the χ2 test was

249

not significant (P>0.05) and the RMSEA was between 0 and 0.150. The coefficient of

250

each pathway was presented as a standardized coefficient in the final model, and was

251

determined using the analysis of the correlation matrices. The pathway coefficients

252

reflect how many standard deviations the effect variable would be changed when the

253

causal variable was changed by one standard deviation. SEM analyses were

254

conducted using AMOS 21.0 (Amos Development Corporation, Chicago, IL, USA).

255 256

3. RESULTS 13



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3.1. Variability in CH4 emissions along the thaw sequence

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Our field observations showed that permafrost collapse had significant effects on CH4

259

emissions (SI Table S2), with consistent trends along the permafrost thaw sequence

260

during the 2015 and 2016 peak growing season. In both years, significantly increased

261

CH4 emissions compared to the control were only observed at the late thaw stage,

262

where surface collapse due to permafrost thaw had occurred 20 years prior (Figure 2).

263 264

3.2 Effects of upland thermokarst on biophysical variables

265

Thermokarst significantly altered the soil properties, including soil temperature, VWC,

266

WFPS, bulk density, sand content and pH (Table 1). Compared with the control, the

267

soil temperatures in the top 10 cm at the middle and late thaw stages were 0.4 and

268

0.6 °C higher, respectively. Both the VWC and WFPS were lower at the early thaw

269

stage compared to the control, but progressively increased at the mid- and late thaw

270

stages. Interestingly, at the late stage, the WFPS was significantly higher, but the

271

VWC was still lower than the control. Both bulk density and sand content

272

progressively increased along the permafrost thaw sequence, ranging from 0.27 to

273

0.32 g cm-3 and from 44.3% to 58.7%, respectively. The soil pH was slightly acidic,

274

ranging from 5.5 to 5.8, with a slight decrease at the early thaw stage compared to the

275

control, but progressively higher at the mid- and late thaw stages. In contrast,

276

permafrost collapse did not cause any significant change in the ALT among the thaw

277

stages, possibly because a large amount of material in the active layer eroded during

278

thermokarst development. 14


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279 280

Accompanying the changes in the soil properties, the abundances of the mcrA gene

281

were substantially altered following thermokarst formation (Figure 3). Specifically,

282

while the abundances of the mcrA were not significantly different among the control,

283

early and mid-thaw stages (Figure 3a), but significantly higher at the late stage of

284

permafrost collapse (Figure 3a). In contrast, thermokarst development did not exert a

285

significant effect on the abundance of the pmoA gene (Figure 3b).

286 287

3.3 Linking CH4 fluxes to biotic and abiotic variables

288

The regression analyses were performed between the average CH4 fluxes during three

289

consecutive days and the biophysical variables measured in late July 2016. The CH4

290

fluxes exhibited positive correlations with the WFPS (r2 = 0.43, P < 0.001; Figure 4c),

291

pH (r2 = 0.53, P < 0.001; Figure 4d) and sand content (r2 = 0.64, P < 0.001; Figure 4e),

292

but did not show any significant associations with either soil temperature (P = 0.054;

293

Figure 4a) or VWC (P = 0.094; Figure 4b). Moreover, the CH4 fluxes significantly

294

increased with higher mcrA gene abundances (r2 = 0.58, P < 0.001; Figure 4f).

295

However, there was not a significant relationship between CH4 fluxes and pmoA gene

296

abundance (P = 0.31; Figure 4g).

297 298

The SEM analysis indicated that the fit of this model was good (χ2 = 5.5, df = 5, P =

299

0.35; RMSEA = 0.05; Figure 5). The combination of biotic (the abundance of the

300

mcrA gene) and abiotic factors (sand content, VWC, WFPS and pH) explained 79% of 15



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the total variance in CH4 emission along the permafrost thaw sequence. Among the

302

explanatory variables, sand content, WFPS, pH and the abundance of the mcrA gene

303

had direct positive effects on CH4 emissions, with standardized coefficients ranging

304

from 22% to 48% (SI Figure 2a), whereas VWC, WFPS and sand content had indirect

305

positive effects, ranging from 18% to 32% (SI Figure 2b). Specifically, sand content

306

and VWC affected CH4 emissions by modifying the WFPS, soil pH and mcrA gene

307

copies. The WFPS regulated CH4 emissions by altering the soil pH and the abundance

308

of the mcrA gene. Of these biotic and abiotic variables, sand content was the most

309

dominant factor responsible for the variations in the CH4 fluxes along the permafrost

310

thaw sequence (SI Figure 2c).

311 312

4. DISCUSSION

313

We found that permafrost thaw and the development of a thermo-erosion gully in a

314

Tibetan swamp meadow led to a 3.5-fold increase in methane emissions, but elevated

315

CH4 emissions emerged only 20 years following the thaw. The CH4 emissions from

316

the undisturbed meadow during peak growing season were relatively low and similar

317

to the emissions from the early and mid-thaw stages within the thermo-erosion gully

318

(0.20 mg m-2 h-1 in 2015; 0.22 mg m-2 h-1 in 2016), and also similar to those observed

319

in an arctic upland permafrost region13, 52 (Figure 2). The methane emissions from the

320

late thaw stage were moderate (0.74 mg m-2 h-1 in 2015; 1.33 mg m-2 h-1 in 2016) and

321

more similar in magnitude to the emissions from boreal bogs (Figure 2). Our finding

322

is supported by Abbott et al. (2015) who also demonstrated an increase in soil CH4 16


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concentrations after thermokarst formation in upland regions. However, our finding is

324

conflicted by a recent study that reported a decrease in soil CH4 fluxes in another

325

thermo-erosion gully on the Tibetan Plateau29. This difference may be related to

326

different hydrological conditions of the soils in the two study sites. Specifically, the

327

volumetric water content (VWC) observed in this study was higher than that reported

328

by Mu et al. (2017) (~67% vs. ~40%), and may have caused the greater sand content

329

by increasing the potential for the erosion of finer material37, 38. The higher sand

330

content at the late thaw stage has been indicated to enhance the CH4 emissions at our

331

study site39, 40. Such a difference between the two study sites could also result from

332

the different thermokarst-induced changes in the soil bulk density. Specifically, soil

333

bulk density has been reported to be relatively stable after permafrost collapse by Mu

334

et al. (2017). However, the soil bulk density increased significantly at our study site

335

(Table 1), which resulted in decreased soil porosity and increased WFPS. The increase

336

in the WFPS likely enhanced CH4 production at our study site53. Lastly, the

337

thermokarst-induced changes in methanogen abundance could differ between the two

338

study sites, which may also be responsible for the different CH4 flux responses to

339

thermokarst formation. Nevertheless, this possibility should be tested in future studies.

340 341

Our results showed that sand content was the dominant driver of CH4 emissions along

342

the thaw sequence. The close association between CH4 fluxes and sand content

343

observed in this study could be attributed to the following three aspects. First, sand

344

content could directly regulate CH4 emissions by controlling the transport from soils 17



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to the atmosphere40, 41. It is widely accepted that coarse soil textures are usually

346

favorable for CH4 diffusion, and thus increase CH4 emissions39, 40. Although increased

347

sand content could also stimulate methanotrophs by favoring the transportation for

348

oxygen import54, it could inhibit methanotrophs in other ways. For instance, the

349

increase in sand content was accompanied by the decrease in soil nitrogen content

350

along the thaw sequence (SI Figure 4), which could then increase the competition for

351

nitrogen resources between methanotrophs and lead to a lower gene abundance of

352

methanotrophs55 (SI Figure 5). Nevertheless, considering the non-significant

353

relationship between CH4 fluxes and methanotrophs (P = 0.31), the increased sand

354

content led to greater CH4 emissions along the thaw sequence mainly by enhancing its

355

transportation from soils to the atmosphere. Second, sand content could exert indirect

356

effects on CH4 release by adsorbing less H+, increasing the soil pH, and thus

357

promoting the CH4 fluxes via regulating CH4 production in the acidic soil39, 42. Third,

358

sand content could further regulate CH4 emissions by indirectly increasing the

359

WFPS56, which has been demonstrated to be another key factor that determines CH4

360

emissions along the thaw sequence (Figure 5). Taken together, our results highlight

361

that changes in soil structure cannot be overlooked when examining CH4 dynamics

362

under upland thermokarst.

363 364

Our results also revealed that upland thermokarst formation increased CH4 emission

365

despite the decreased VWC. The soil water status regulates CH4 emission primarily

366

via controlling CH4 production and the oxidation process30, 31. Actually, the WFPS, 18


Page 19 of 39


367

rather than the VWC itself, may better reflect these processes because the WFPS is

368

not only related to the VWC but also significantly influenced by soil bulk density.

369

Given that soil bulk density was elevated at the late stage of permafrost collapse

370

(Table 1), the soil environment can still become more anaerobic despite the decrease

371

in the VWC, thus increasing CH4 production and reducing oxidation of CH4 when it

372

passes through the soil pores. In support of this deduction, our results revealed that the

373

CH4 flux had a non-significant correlation with the VWC (Figure 4b), and the VWC

374

was only indirectly related to CH4 emissions via the WFPS (Figure 5). Thereby,

375

thermokarst-induced increases in the WFPS could be responsible for the higher CH4

376

emission rates occurred at the late stage of permafrost collapse.

377 378

Our results further illustrated that CH4 flux was significantly related to the

379

methanogen abundance, while it had no relationship with the abundance of

380

methanotroph genes (Figure 4f-g), demonstrating that the CH4 production process

381

dominates the CH4 flux pattern along this permafrost thaw sequence. The

382

methanogens are an important group of archaea that convert acetate, CO2 and H2 into

383

CH4 under anaerobic conditions57,

384

abundance exhibited a pattern similar to that of the CH4 emissions along the

385

permafrost thaw sequence (Figure 3a). At the early stage of permafrost collapse, the

386

abrupt changes in the soil environments caused the decline in the WFPS, which was

387

not beneficial for the growth of methanogens, thus decreasing the abundance of the

388

mcrA genes. After a certain period of disturbance, the abundance of microorganisms

58

. We found that the changes in methanogen

19



Page 20 of 39

389

would gradually recover (the mid-stage), and at the late stage of permafrost thaw, the

390

more anaerobic conditions may lead to an increased abundance of methanogens32.

391

Thus, the thermokarst-induced changes in methanogen abundance affected the CH4

392

production potential (SI Figure 3) and the in situ CH4 emission rates (Figure 4f;

393

Figure 5). Nevertheless, the microbial gene abundances at the DNA level cannot

394

represent the activity of microbes although they were widely linked to greenhouse gas

395

emissions in previous studies35, 59, 60. Further experiments at the RNA or protein levels

396

should be conducted in future studies to better reflect the activity of microbes.

397 398

It is generally assumed that soil temperature is one of the major factors that control

399

CH4 emissions across permafrost zones13. However, we did not find any significant

400

correlation between CH4 fluxes and soil temperature along the thaw sequence. This is

401

likely due to the narrow range of soil temperature measured during the end of July,

402

which was only approximately 1.5 °C among the various thaw stages. Actually, the

403

soil temperature may be found to regulate CH4 emissions if the data from other times

404

of day or year are included. To test this point, we compared the seasonal dynamics of

405

soil temperature and CH4 fluxes and found that they displayed consistent trends (SI

406

Figure 6a). Moreover, a significant linear relationship was observed between CH4

407

fluxes and soil temperature (r2 = 0.12, P < 0.05; SI Figure 6b). Therefore, soil

408

temperature dynamics are still important to CH4 fluxes across upland permafrost

409

regions.

410 20


Page 21 of 39


411

In conclusion, the results from our two-year field survey indicated that upland

412

thermokarst significantly increased CH4 emissions during the peak growing season

413

from a typical thermo-erosion gully on the Tibetan Plateau, particularly at the later

414

thaw stage (Figure 2). Our results also revealed that the WFPS, rather than the VWC

415

per se, regulated the CH4 emission patterns along the thaw sequence, and sand content

416

was the most dominant factor that influenced CH4 flux by modifying the transport

417

processes (Figure 5). These findings have two important implications for

418

understanding the carbon-climate feedback in permafrost regions. First, upland

419

thermokarst enhanced the CH4 emissions in our study, which differ from the

420

traditional view that upland thermokarst could result in a decrease in CH4 flux. These

421

findings suggest that thermokarst-associated changes in CH4 emissions vary across

422

upland permafrost regions, and projecting CH4 emissions after the development of

423

upland thermokarst is more challenging than previously thought. Second, the changes

424

to the soil structure following upland thermokarst cannot be ignored as a major driver

425

of the response of methane emissions. Despite the fact that previous studies

426

emphasized that the VWC was an important factor in regulating CH4 emissions in

427

permafrost thaw regions12, 13, 61, our study highlights that the changes in soil structure

428

(e.g., sand content and bulk density) may become predominant following the

429

development of upland thermokarst despite the condition of decreased VWC. Hence,

430

the effects of thermokarst on soil structure should be considered in Earth System

431

Models used to project the permafrost carbon-climate feedback.

432 21



Page 22 of 39

433

ACKNOWLEDGMENTS

434

This work was supported by the National Basic Research Program of China on Global

435

Change (2014CB954001), National Natural Science Foundation of China (31670482),

436

Key Research Program of Frontier Sciences, Chinese Academy of Sciences

437

(QYZDB-SSW-SMC049), Chinese Academy of Sciences-Peking University Pioneer

438

Cooperation Team, and Thousand Young Talents Program.

439 440

Supporting Information

441

CH4 production and oxidation potential measurements; q-PCR conditions for pmoA

442

and mcrA genes; results of repeated-measures ANOVA for CH4 fluxes; base model for

443

CH4 fluxes; standardized effects of biotic and abiotic variables on CH4 fluxes;

444

changes in CH4 production and oxidation potentials along the thaw sequence;

445

relationship between TN and sand content; relationship between pmoA gene

446

abundance and TN content; seasonal dynamics of soil temperature and its effects on

447

CH4 fluxes.

448 449

This information is available free of charge via the Internet at http://pubs.acs.org.

450 451

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452

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31



643

Page 32 of 39

Table 1 Changes in soil physical parameters in different thaw stages. Parameter

Control

3 years

12 years

20 years

pH

5.6±0.03ab

5.5±0.03b

5.7±0.02a

5.8±0.06a

Soil Temperature (°C)

8.6±0.13c

8.8±0.10bc 9.0±0.12ab

9.2±0.13a

Volume Water Content (%)

67.6±0.6a

48.8±2.1c

62.8±1.1b

63.1±1.8b

Soil Water-filled Pore Space (%)

77.2±1.8b

62.7±2.8c

79.1±3.8ab

84.1±1.5a

Active Layer Thickness (cm)

68.5±5.1a

76.2±17.5a

73.7±7.5a

68.7±2.2a

Soil Bulk Density (g cm-3) Sand Content (%)

0.27±0.008b 0.32±0.014a 0.28±0.012b 0.32±0.021a 44.3±0.4b

46.5±0.9b

49.7±1.8b

58.7±4.2a

644

Data are expressed as the means of ten replicate plots (±1 standard error). Different

645

letters indicate significant differences among the different stages since permafrost

646

collapse (one-way ANOVA, P < 0.05).

32


Page 33 of 39


647

Figure legends

648

Figure 1. Location map of the study area (a), image of the thermo-erosion gully (b),

649

and photographs of the different thaw stages (c-f). The red dot indicates our study site,

650

and the image of the thermo-erosion gully was obtained by a high-resolution

651

topographic model with LiDAR (VZ-400, Riegl, Horn, Austria, analyzed with Riscan

652

pro 2.0 software).

653 654

Figure 2. Changes in CH4 fluxes along the thaw sequence in 2015 (a) and 2016 (b).

655

The error bars represent the standard error determined among replicates (n = 10). The

656

colors correspond to the different collapse times. The gray line indicates the mean

657

value of the CH4 flux in an arctic dry tundra, and the red line indicates the mean value

658

of the CH4 flux in an arctic bog (data from Olefeldt et al.13). Significant differences

659

were denoted by different letters among the different stages since permafrost collapse

660

(repeated-measures ANOVA, P < 0.05). The insert panels show the site-averaged CH4

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fluxes for each sampling time during the entire sampling period, with the error bars

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representing the standard error determined among replicates.

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Figure 3. Changes in the abundance of the mcrA (a) and pmoA (b) genes along the

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thaw sequence. The error bars represent the standard error determined among

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replicates (n = 10). The different letters indicate significant differences among the

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different thaw stages (one-way ANOVA, P < 0.05).

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Figure 4. Relationships between the logarithm transformed CH4 fluxes and soil

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temperature (ST), volume water content (VWC), soil water-filled porespace (WFPS),

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soil pH, sand content, and the gene copies of mcrA and pmoA. The black lines and

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shades represent the regression lines with 95% confidence intervals. Statistics (r2 and

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P values) for the linear regression are shown (***P < 0.001).

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Figure 5. Final results of structural equation model (SEM) analysis examining the

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effects of edaphic and microbial properties on CH4 flux. In the model, square boxes

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indicate variables, and the abbreviations of the variables are explained in Table 1. The

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arrows connecting the boxes indicate the direction of causation. The red arrows denote

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positive relationships. The arrow widths are proportional to the standardized path

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coefficients, which reflect the importance of the factors in the model. The proportion

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of explained variance (r2) is below each response variable in the model. The final

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model fit was evaluated by a χ2 test and RMSEA value. * P < 0.05, ** P < 0.01, ***

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P < 0.001.

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Changes in Methane Flux along a Permafrost Thaw Sequence on the

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