Soil net methane uptake rates in response to short-term litter input change in a coniferous forest ecosystem of central China

Agricultural and Forest Meteorology 271 (2019) 307–315

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Soil net methane uptake rates in response to short-term litter input change in a coniferous forest ecosystem of central China

T

⁎

Junjun Wua, Meng Luc, , Jiao Fenga, Dandan Zhanga,b, Qiong Chena,b, Qianxi Lia, ⁎⁎ Chunyan Longa,b, Quanfa Zhanga, Xiaoli Chenga,c, a

Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences (CAS), Wuhan 430074, PR China Graduate University of Chinese Academy of Sciences, Beijing, 10039, PR China c School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, PR China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Coniferous forest Litter input manipulations Methane uptake Methanotrophs Soil moisture

The uptake of CH4 by well-drained soil plays a vital role in mitigating the atmospheric CH4, but the impacts of shift in plant litter input on uptake of CH4 and the underlying mechanism are not fully understood. Here, we conducted in situ measurements of soil CH4 flux rates monthly throughout the year after the short-term litter input manipulations (i.e. Detritus Input and Removal Treatment-DIRT: control, CK; double litter, DL; no litter, NL; no roots, NR; and no aboveground litter and no roots, NRNL) in a coniferous forest (Platycladus orientalis (Linn.) Franco) ecosystem in subtropical China. The associated microclimates, soil properties and microbial PLFAs were also measured. Our results showed that soils acted as CH4 sink in all litter manipulation treatments, and the CH4 sink capacity significantly differed under litter manipulation treatments. Based on annual average values, net CH4 uptake rates decreased by 37.7 ± 4.9% and 41.7 ± 5.8% in the NL and NRNL treatments (i.e. litter layer removal), respectively, compared to the CK treatment. Thus, the net CH4 uptake induced by litter layer approximately accounted for 37.7 ± 4.9% of the total net CH4 uptake rate. The net CH4 uptake rate was not significantly influenced by the root exclusion (NR) treatment. In contrast, the effect of litter addition on net CH4 uptake rate was strongly depended on soil water content. During the dry season, litter addition did not significantly affect net CH4 uptake rate. In contrast, during the wet season, the net CH4 uptake rate decreased by 47.1 ± 4.9% in the DL treatment compared to the CK treatment. There was no significant difference in net CH4 uptake rate between dry and wet season under other litter input manipulation treatments. The net CH4 uptake rate was positively correlated with the abundance of methanotrophic bacteria across all litter input manipulation treatments, whereas the significant negative relationship between net CH4 uptake rate and water filled pore space (WFPS) was only found in the DL treatment. Overall, our results suggest that aboveground organic layer (i.e. litter) is more important in regulating the soils acting as atmospheric CH4 sink than roots, while the regulating function primarily depends on soil dry/wet conditions and the abundance of methanotrophic bacteria.

1. Introduction Methane (CH4), next to carbon dioxide (CO2), is the second most important anthropogenic greenhouse gas (IPCC, 2013; Bodelier and Steenbergh, 2014). Globally, the averaged concentration of atmospheric CH4 reached 1853 ppb in the end of 2016 and nearly up to 257% of pre-industrial (before 1750) level (World Meteorological Organization, 2017). Although its concentration is far less than CO2 (403 ppm), its global warming potential (GWP) is 28–34 times higher than CO2 estimated on a mass basis over a time scale of 100 years

(IPCC, 2013). As a consequence, it is believed that CH4 accounts for approximately 20% of the total global warming forcing (Kirschke et al., 2013). Thus, even a small change in the CH4 flux would have a profound effect on future climate (Ghosh et al., 2015; Tian et al., 2016). The uptake of CH4 by aerobic natural soils is the largest biological sink of atmospheric CH4, which is primarily dominated by high affinity methanotrophs (Nazaries et al., 2013). This biological process is mainly regulated by soil moisture and permeability by affecting gas diffusion rate from soil (Hiltbrunner et al., 2012; Fest et al., 2015) and the methanotrophic activity (Kravchenko and Sukhacheva, 2017; Schnyder

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Corresponding author. Corresponding author at: Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, PR China. E-mail addresses: [email protected] (M. Lu), [email protected] (X. Cheng). ⁎⁎

https://doi.org/10.1016/j.agrformet.2019.03.017 Received 11 September 2018; Received in revised form 20 March 2019; Accepted 21 March 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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of gas diffusion barrier, while litter addition may decrease it; 2) root exclusion has little effect on net CH4 uptake rate owing to the root exudates facilitating both the production and oxidation of CH4. To test these hypotheses, we conducted monthly measurement of CH4 in the coniferous ecosystem throughout the year. We also investigated the microclimate, soil properties and microbial biomass to explore potential control on the CH4 uptake capacity of soils.

et al., 2018). Meanwhile, other multiple factors such as soil substrate and nutrient availability, temperature can also regulate the CH4 flux between soil and atmosphere (Savi et al., 2016; Zheng et al., 2016; D’Imperio et al., 2017). For instance, numerous studies have found that atmospheric CH4 oxidizers can use low molecular weight compounds when CH4 is scarce (Sullivan et al., 2013). Soil CH4 oxidation capacity can be stimulated when improving the status of nitrogen (Aronson and Helliker, 2010; Tate, 2015). Among the various terrestrial ecosystems, forest soils have been viewed as the most efficient sinks of atmospheric CH4 (Kolb, 2009; Levine et al., 2011). Global change profoundly influences the primary productivity of forest ecosystems and consequently alters the above- and belowground litter inputs to soils (Peng et al., 2017; Yue et al., 2017). Thus, shifts in plant litter inputs can directly or indirectly affect the biotic and abiotic drivers of soil CH4 oxidation, and may ultimately have a long-lasting effect on the CH4 sink strength of forest soils. The litter layer can influence soil CH4 uptake by regulating gas diffusion into soil (Gritsch et al., 2016; Pedersen et al., 2017). Most studies have found that litter layer removal increases the CH4 uptake by facilitating the diffusion of atmospheric CH4 into soil (Peichl et al., 2010; Wang et al., 2013; Leitner et al., 2016). Litter layers of well drained forests have generally been reported to have little CH4 oxidation capacities (Steinkamp et al., 2001; Tang et al., 2006; Wang et al., 2013; Leitner et al., 2016), and the microbial CH4 uptake is primarily dependent on the mineral soil (Liu et al., 2008). However, a previous study conducted in tropical montane forest has indicated that there is substantial high affinity methane oxidation occurred in the thick organic layers (Wolf et al., 2012). On the contrary, the moist litter layer may create an anaerobic microsite where it can be the source for CH4 production (Peichl et al., 2010). Thus, the influence of litter layer on CH4 uptake can be moisture-dependent (Wang et al., 2013; Pedersen et al., 2017). Meanwhile, there are only few studies focused on the effects of rhizosphere on atmospheric CH4 uptake with contradictory results (Fender et al., 2013; Praeg et al., 2016; Subke et al., 2018). For instance, Subke et al. (2018) found that root exclusion significantly reduced the CH4 uptake capacity. Halmeenmäki et al. (2017) indicated that the presence of plant roots enhanced the methanotrophic activity and thus the soil uptake of CH4. While, Praeg et al. (2016) observed that the uncovered soil revealed the greatest potential to oxidize CH4 than that with plant cover, and this was possibly because the root exudates could also influence the abundance and activity of methanogenic microorganisms. These contradictory results suggest that the underlying mechanism of the CH4 sinks with a changing climate remains uncertain, and thus, there is an urgent need to study the response of CH4 uptake to the shifts in above- and belowground plant litter systematically and integrally. The Danjiangkou Reservoir, established in the 1970s, is the main water source for the Middle Route of South-to-North Water Transfer Project of China (Zhang et al., 2009). Human activities such as conventional tillage and deforestation around the reservoir have resulted in numerous environmental problems. To preserve the ecological environment, a large area of coniferous (Platycladus orientalis (Linn.) Franco) plantation has been established around the Danjiangkou reservoir area (Cheng et al., 2013; Deng et al., 2014). Our previous studies in this ecosystem have found that aboveground litter removal and root exclusion significantly reduced the CO2 flux, while litter addition had negligible effect on it (Wu et al., 2017). However, the key controlling factors for soil CO2 and CH4 flux are different, and little is known about how plant litter input changes affect the role of soils acting as CH4 sink. To identify how soil net CH4 uptake capacity respond to future shift in plant litter inputs, we have conducted a Detritus Input and Removal Treatment (DIRT) experiment in this coniferous plantation since September, 2014. The DIRT experiment provided a unique approach to explore the issues mentioned above. The objectives of this study were to test the following hypotheses: 1) aboveground litter removal can increase the net CH4 uptake rate due to the absence

2. Materials and methods 2.1. Study site This study was conducted at the Wulongchi Experiment Station (32°45′N, 111°13′E; 280–400 m a.s.l) in the Danjiangkou Reservoir area. The climate in this area belongs to the subtropical monsoon of the north subtropical zone. The mean annual temperature is 15.7℃, with monthly averages of 27.3℃ in July and 4.2℃ in January. The annual precipitation is 749.3 mm, of which 70–80% occurs between April and October (Fig. S1). The soil is classified as yellow-brown soil in Chinese soil classification, equivalent to Haplic Luvisols in the USDA Soil Taxonomy. The mean annual production of leaf litter was 369.5 ± 70.6 g m–2 yr–1 from 2014 to 2016. 2.2. Experimental design and soil sampling The DIRT experimental plots were established in September, 2014. We randomly selected six 10 m × 10 m study sites. In each study site, five 1 m × 1 m plots free of trees and saplings were randomly selected, and above- and belowground plant C inputs were manipulated in a number of ways. The treatments included control (CK, normal annual aboveground litter inputs), double litter (DL, twice the litter inputs of the control plots), no litter (NL, annual aboveground litter inputs excluded), no roots (NR, plots trenched and root regrowth into plots prevented), and no input (NRNL, plots trenched and annual aboveground litter excluded). The thickness of litter layer in the CK treatments ranged from 1 to 2 cm. Three-year continuous litter addition resulted in a thicker litter layer (3–4 cm) in the DL treatments. In the NL and NRNL plots, the whole organic layer was removed carefully, but decomposed liter which was potentially unidentifiable or too small to remove was left on the mineral soil surface. The fresh litter in these plots was excluded with 1 mm mesh screens placed 0.5 m above the plots. For the NR and NRNL plots, we dug a trench of 0.1 m width and 0.6-0.8 m in depth (reaching to the bottom of the rooting zone and bedrock) along the four sides of the plot. Then 0.35 mm-thick polyethylene sheets were placed along the sides of the trench to prevent roots from entering the plots. Aboveground litter inputs were augmented in the DL plots monthly by adding litter taken from the NL plots. The surface solar radiation was approximately the same in all treatments, as the distribution and slope of land was not greatly different (average slope of 5°) and the site faced south. Therefore, the climatic effect was similar in all plots. Moreover, the plots were established at random, thus reducing the effects of incidental minor differences. In October 2016 and 2017, three soil cores (diameter = 3 cm) from each plot were collected from the top layer of soil (0–10 cm) after removing the litter and organic horizon, respectively. The cores were combined to yield one composite sample per plot and taken back to the laboratory with dry ice. After the removal of visible plant residues and stones, the soil samples were passed through a 10-mm screen by gently breaking the large clods by hand along the natural fractures. A small subsample (approximately 50 g) was removed from each soil sample, immediately sieved through a 2-mm mesh, and stored at ‒80℃ for Phospholipid Fatty Acids (PLFAs) extraction. The left soil samples were air dried. A small air-dried subsample was used for soil organic carbon (SOC), labile C and recalcitrant C measurement. Another air-dried subsample (approximately 200 g) was used for measuring the fractions 308

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treated with 1 M HCl solution for 24 h at room temperature to remove any carbonates. Next, the fine powders were washed with distilled water to remove the remaining HCl, and oven-dried at 60℃ for 24 h prior to organic C and N measurements (Cheng et al., 2006). We used a two-step acid hydrolysis procedure with H2SO4 as the extractant to determine the labile and recalcitrant C and N (LC, LN and RC, RN) pools (Rovira and Vallejo, 2000; Xu et al., 2015). Briefly, 500 mg of each soil sample which had been treated with HCl was hydrolyzed with 25 mL of 2.5 M H2SO4 in sealed Pyrex tubes, in a block digester at 105℃ for 30 min, with occasional shaking. After cooling, tubes were centrifuged and the liquid was decanted. The residue was washed twice with 25 mL water (homogenization, centrifugation and decantation) and dried. Then, 2 mL of 13 M H2SO4 was added, and the tubes were placed in an end-over-end shaker overnight. After dilution of acid with water, the residue was hydrolyzed for 3 h at 105℃. After cooling, this second hydrolysate was recovered as described. The residue was washed and then dried at 60℃. The total and recalcitrant C and N concentrations were determined using an element analyzer (vario EL, Elementar Analysensysteme, Hanau, Germany).

of silt and clay-sized aggregates by wet sieving method (Yamashita et al., 2006). In October 2017, after removing the litter and organic horizon, one undisturbed soil sample was taken from the topsoil (0–10 cm) in each plot using steel cylinders with a volume of 100 cm3. Soil samples were sieved (< 2 mm) and oven-dried at 105℃, and then soil bulk density was quantified with the mass of the oven-dried soil divided by the core volume. 2.3. CH4 flux measurement Fluxes of CH4 were measured fourteen times from October 2016 to December 2017 (once every month) using static chambers and the gas chromatography technique. Static chambers were inserted into each plot of debris input and removal treatment. The static chamber comprised two parts: (1) a cylindrical bottom pedestal (diameter = 0.25 m, height = 0.2 m) that was permanently inserted in the soil and (2) a removable cover (diameter = 0.25 m, height = 0.3 m) on which had a gasket to ensure air-tightness during sampling and removed afterwards. On the top wall of each chamber cover, a battery-operated fan of 10-cm diameter was installed to mix the air in the chamber when the sample was collected. There was a hole located on the top wall of the upper chamber, and the hole was connected with a 12 cm long silicic tube (5 mm in diameter), which was used for air collection. The chamber was maintained leak tight by a bulldog clip during the measuring period. CH4 fluxes were calculated from measurement of the CH4 mole fraction change in the enclosed headspace. Four air samples with the volume of 15 mL were collected by syringe throughout a 45 min incubation period (at 0, 15, 30, and 45 min) and transferred to 20 mL preevacuated tedlar (aluminium foil compound membrane, Delin gas packing Co., Ltd, Dalian, China) bags. Simultaneously, the air temperature of each experimental plot was measured with a mercurial thermometer. The temperature (at 10 cm soil depth) and volumetric water content (0–10 cm) of mineral soil were measured outside each chamber with a portable instrument that measured soil temperature and moisture (SIN-TH8, SinoMeasure, China). CH4 concentrations of the gas samples stored in tedlar bags were measured with a flame ionization detector (FID) in a gas chromatography (Agilent 7890B, Santa Clara, CA, USA). The CH4 fluxes were calculated using linear model regression analysis of the change in gas concentration in the chambers with time over a 45-min period with an average chamber temperature (Zhang et al., 2008):

F=

dc 16 273 V × × × dt 22.4 273+T A

2.5. PLFAs analysis PLFAs were extracted using the method described by Bossio and Scow (1998). Briefly, lipids were extracted from 8-g freeze-dried soils in 23-mL extraction mixture containing chloroform: methanol: phosphate buffer (1: 2: 0.8 v/v/v). Next, phospholipids were split into neutral, glyco- and phospho-lipids. To recover fatty acid methyl esters, phospholipids were subjected to a mild-alkali methanolysis. The extraction was transferred to a separatory funnel to separate overnight. Samples were then re-dissolved in hexane solvent containing nonadecanoic acid methyl ester (19:0) as an internal standard and were analyzed with an Agilent 6890 Gas Chromatograph equipped with an Ultra 2-methylpolysiloxane column. Bacterial fatty acid standards and the MIDI eukaryotic method with Sherlock software (MIDI, Inc., Newark, DE) were used to identify peaks. We calculated the concentrations of each PLFA based on the 19:0 internal standard concentrations. Specific PLFA marker 18:1ω7c was used to quantify the relative abundance of methanotrophic bacteria (Smith et al., 2014, 2015). 2.6. Statistical analyses The data analysis was performed using IBM SPSS Statistics 21.0 (Armonk, NY, USA). The data were checked for normality and homogeneity of variances and transformed when necessary. The one-way Analysis of Variance (ANOVA) test with Tukey’s multiple comparison test (HSD) were performed to examine the statistical significance of litter input manipulations on soil physical and chemical properties (e.g. SOC, TN, LC, LN, soil bulk density and clay content) and the abundance of specific PLFA biomarker (18:1ω7c), as well as the net CH4 uptake rates. The two-way ANOVA test was used to test the statistical significance of litter input manipulation treatments, seasons and their interactions on soil temperature, WFPS and the net CH4 uptake rates; the two-way ANOVA test was also used to test the statistical significance of litter input manipulation treatments, duration of the treatments and their interactions on the abundance of specific PLFA biomarker (18:1ω7c). Simple regressions were performed to explore the relationship of net CH4 uptake rate with soil temperature, water filled pore space and the abundance of methanotrophic bacteria. Collinearity between the drivers of net CH4 uptake rates was diagnosed by using IBM SPSS Statistics 21.0 (Armonk, NY, USA).

(1) −2

is the rate of change in gas where F is the CH4 flux (μg·m h ), concentration inside the chamber, T is the air temperature inside chambers, 16 is the molecular weight of CH4, 22.4 is the molar volume of an ideal gas at standard temperature and pressure (1 mol‒1), V is the chamber volume (m3) and A is the chamber area (m2). Almost all the coefficients of determination (r2) of linear regression were greater than 0.9 (P < 0.05). Occasionally, data from individual chambers were excluded if the regression coefficients (r2) were below 0.9 (P > 0.05). The water filled pore space (WFPS) was determined according to Eq.2.

WFPS (%) =

-1

dc dt

Wvol BD

1 – 2.65

(2) 3

–3

where Wvol is the volumetric water content (cm cm ), BD is the bulk density (g cm–3) and 2.65 is the soil particle density (g cm–3).

3. Results

2.4. Labile and recalcitrant C and N

3.1. Microclimate, soil properties and microbial biomass

The air-dried soil subsamples were ground to fine powders, and then

Air and soil temperature under all litter input manipulation 309

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Fig. 1. Monthly average of air, soil temperature (a) and water filled pore space (WFPS) (c) at top soil (10 cm), as well as their annual averages (b, d) under different litter input manipulation treatments. Error bars represent standard errors of the mean values (n = 6), different lowercase letters over the bars indicate statistically significant differences at P < 0.05 among litter input manipulation treatments. Abbreviations: CK, control; NL, no litter; NR, no root; NRNL, no root and no litter; and DL, double litter.

treatments changed with the seasons, with the highest air and soil temperatures in August, 2017, and the lowest levels in December, 2016 (Fig. 1a). Litter input manipulations did not significantly affect soil temperature (Fig. 1b; Table 2). WFPS fluctuated remarkably with the seasons, but it was relatively lower in summer compared with other seasons (Fig. 1c; Table 2). The mean annual WFPS ranged from 52.1 ± 3.8% to 61.0 ± 2.9%, with the highest soil moisture in the DL treatment and the lowest value in the NR treatment (Fig. 1d). Litter input manipulation did not significantly affect the soil organic C, total N contents and the C:N ratio, as well as soil bulk density (Table 1). However, the labile C decreased significantly (P < 0.05) in the NL treatment and marginally (P = 0.087 and 0.073, respectively for NR and NRNL) in root exclusion treatments compared with the CK treatment. The labile N was significantly (P < 0.05) and marginally (P = 0.069) lower in the NR treatment compared with the DL and CK treatment, respectively (Table 1). While, neither labile C nor N was significantly (P > 0.05) was influenced by the DL treatment. Root exclusion (NR and NRNL) significantly (P < 0.05) increased the silt and clay-sized aggregates compared with the CK treatment (Table 1). The content of specific PLFA biomarker (18:1ω7c) which represented methanotrophic bacteria was also significantly affected by litter input manipulations, with significantly (P < 0.05) lower content in the NL and NRNL treatments after two and three-year litter input

Table 2 Soil temperature, water filled pore space and CH4 uptake rate, as well as the significance of the effects of litter input manipulation treatments, seasons and their interactions on soil temperature, water filled pore space and CH4 uptake rate based on the two-way ANOVA test. Treatment

Soil temperature (℃)

Water filled pore space (WFPS) (%)

CH4 uptake rate (ug·m−2·h-1)

CK NL NR NRNL DL Source of variance Treatment (T) Season (S) T×S

19.24 19.27 19.41 19.23 19.15

59.02 53.36 52.08 57.17 60.95

34.10 21.23 33.40 19.88 22.15

n.s ** n.s

± ± ± ± ±

2.69a 2.71a 2.70a 2.68a 2.61a

± ± ± ± ±

3.81a 3.77ab 3.53b 3.97ab 2.91a

* ** n.s

± ± ± ± ±

4.23a 2.11b 2.54a 1.83b 2.11b

** * n.s

Note: Values are Means ± SE, n = 6. Different lowercase letters in the same column for each variable indicate statistically significant differences at P < 0.05. n.s means no significant, * means P < 0.05, ** means P < 0.01. See Table 1 for abbreviations.

Table 1 Soil organic carbon (SOC), total nitrogen (TN), C to N ratio, labile C (LC) and N (LN), as well as the soil bulk density (BD) and silt and clay content under different litter input manipulation treatments. Properties

Treatment

–1

SOC (g kg ) TN (g kg–1) C:N LC (g kg–1) LN (g kg–1) BD (g cm–3) Silt and Clay (%)

CK

NL

NR

NRNL

DL

24.13 ± 2.71a 1.87 ± 0.33a 12.92 ± 0.55a 7.30 ± 0.46a 0.87 ± 0.10ab 1.38 ± 0.14a 9.6 ± 1.5b

22.06 ± 1.13a 1.65 ± 0.30a 13.34 ± 0.36a 5.41 ± 1.31b 0.79 ± 0.23ab 1.40 ± 0.08a 9.4 ± 2.2b

18.84 ± 3.02a 1.39 ± 0.05a 13.59 ± 0.42a 5.94 ± 0.40ab 0.62 ± 0.10b 1.36 ± 0.12a 13.9 ± 1.9a

18.09 ± 3.36a 1.26 ± 0.28a 14.37 ± 0.29a 5.93 ± 0.03ab 0.78 ± 0.28ab 1.44 ± 0.05a 15.9 ± 0.7a

22.57 ± 2.04a 1.78 ± 0.46a 12.65 ± 0.70a 6.93 ± 2.58ab 0.97 ± 0.21a 1.27 ± 0.23a 8.5 ± 4.2b

Note: Values are Means ± SE, n = 6. Different lowercase letters in the same row for each variable indicate statistically significant differences at P < 0.05. CK, control; NL, no litter; NR, no root; NRNL, no root and no litter; and DL, double litter. 310

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input manipulation treatments (Fig. 4).

Table 3 The total abundances of the specific PLFA (18:1ω7c) after two-year and threeyear litter input manipulations, as well as the significance of the effects of litter input manipulation treatments, duration of the treatment and their interactions on methanotrophic bacteria based on the two-way ANOVA test. Treatment

2-year after treatment

3-year after treatment

CK NL NR NRNL DL Source of variance Treatment (T) Year (Y) T×Y

2.40 1.83 1.99 1.94 1.82

3.53 2.93 3.25 2.78 2.62

* ** n.s

± ± ± ± ±

0.27a 0.34b 0.40ab 0.38ab 0.24b

± ± ± ± ±

3.4. Controls on net CH4 uptake rates The relationships between net CH4 uptake rates and WFPS varied with the different litter input manipulation treatments (Fig. 5a-e). The net CH4 uptake rates were marginally correlated with WFPS (P < 0.1) in the CK, NL and NRNL treatment, respectively. But no relationship between these two parameters was found in the NR treatment. While for the DL treatment, the net CH4 uptake rate was significantly correlated with WFPS (P < 0.01). When all treatments were considered, the net CH4 uptake rate exhibited no relationship with soil temperature (Fig. 5f). Further research showed that there was significantly positive relationship between the net CH4 uptake rate and the 18:1ω7c content, and the relationship was fitted with a linear model for both two and three-year after the litter input manipulations, (Fig. 6). Additionally, the abundance of methanotrophs was positively related to the amount of labile C and N contents, but negatively related to the WFPS (Fig. S2).

0.18a 0.16ab 0.01ab 0.37b 0.39b

– – –

Note: Values are Means ± SE, n = 3. Different lowercase letters in the same column for each variable indicate statistically significant differences at P < 0.05. n.s means no significant, * means P < 0.05, ** means P < 0.01. See Table 1 for abbreviations.

4. Discussion

manipulations, respectively. Litter addition also significantly decreased (P < 0.05) the content of 18:1ω7c after two and three years of the treatment, respectively, when compared with the CK treatment (Table 3).

We found positive net CH4 uptake rates in soils across all litter input manipulation treatments, rendering our study site a net CH4 sink throughout one-year observation period. Our results agree well with the evidences that the well-aerated upland soils are CH4 sinks and can oxidize CH4 at atmospheric level (Kou et al., 2017; Martins et al., 2017; Gutlein et al., 2018). However, the soil net CH4 uptake capacity significantly differed among litter input manipulation treatments. Particularly, the net CH4 uptake rates were significantly lower in the litter layer removal (NL and NRNL) treatments compared to the control (CK) treatment, disagreeing with other studies which showed that litter layer removal enhances the CH4 uptake rate (Wang et al., 2013; Leitner et al., 2016). It has been suggested that litter layer itself can act as a barrier against diffusion of atmospheric CH4 into the mineral soil (Peichl et al., 2010; Leitner et al., 2016). However, Wolf et al. (2012) found that the substantial high affinity CH4 oxidation occurs in the deepest organic layer overlying the mineral soil. Our results partly supported their finding, implying that there was a certain amount of CH4 uptake which was induced by litter layer in our study site. The significant lower CH4 uptake rate in the NL treatment can be traced back to the following reasons: firstly, organic layers have substantial capacity to oxidize atmospheric CH4, and with the highest methanotrophic activity located in the lowest organic layers right above the mineral soil (Wolf et al., 2012), and hence the completely removal of organic layers in the NL and NRNL treatment could directly reduce the net CH4 uptake rates. Secondly, it is widely accepted that facultative methanotrophs could subsist on the low molecular weight compounds (i.e. single carbon, or ‘C1’ molecules) (Semrau et al., 2011; Sullivan et al., 2014). The exudation of labile C compounds from aboveground litter was reduced by litter removal, possibly leading to lower net CH4 uptake rates.

3.2. Temporal variation in net CH4 uptake rates and litter-induced CH4 flux The net CH4 uptake rates exhibited seasonal variations across all litter input manipulation treatments, with relatively higher net CH4 uptake rates in spring and summer and lower levels in autumn and winter (Fig. 2a; Table 2). Based on the annual average values, the net CH4 uptake rates significantly (P < 0.05) decreased by 37.7 ± 4.9%, 41.7 ± 5.8% and 35.1 ± 8.5% in the NL, NRNL and DL treatment, respectively, compared with the CK treatment. Whereas the excluded roots treatment (NR) did not significantly change net CH4 uptake rate (Fig. 2b). Furthermore, the annual average of organic layer induced net CH4 uptake rate (the difference between CK and NL treatment) was 12.86 ± 2.85 μg·m−2 h‒1, accounting for about 37.7 ± 4.9% of the total net CH4 uptake rate (Fig. 3). 3.3. Net CH4 uptake rates during dry and wet seasons During the dry seasons (the monthly WFPS below 50%), litter addition did not significantly affect net CH4 uptake rate (Fig. 4a). In contrast, during the wet seasons (the monthly WFPS above 50%), litter addition significantly (P < 0.05) decreased the net CH4 uptake rate by 47.1 ± 4.7%, compared with the CK treatment. Furthermore, the mean net CH4 uptake rate in litter addition plots during the dry seasons was 63.9% higher than that during the wet seasons. However, there were no significant differences between dry and wet seasons for other litter

Fig. 2. Monthly average (a) and annual average (b) of net CH4 uptake rates under different litter input manipulation treatments. Error bars represent standard errors of the mean values (n = 6), different lowercase letters over the bars indicate statistically significant differences at P < 0.05 among litter input manipulation treatments. See Fig. 1 for abbreviations.

311

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Fig. 3. Monthly (a) and annual average (b) of net CH4 uptake rates from control and litter removal plots, as well as litterinduced (CK–NL) net CH4 uptake rates. Error bars represent standard errors of the mean values (n = 6), different lowercase letters over the bars indicate statistically significant differences at P < 0.05 among litter input manipulation treatments. See Fig. 1 for abbreviations.

The significant lower CH4 uptake rate in litter addition treatment during the wet and cold seasons can also be explained by the fact that two contrasting process can occur simultaneously, i.e., CH4 oxidation and production in the thick litter layer (Gritsch et al., 2016). Numerous studies have found that litter layer can emit CH4 by biological and nonbiological processes (Corteselli et al., 2017; Wang et al., 2017). Thus, the emission of CH4 from litter layer could offset a proportion of the CH4 uptake capacity, possibly leading to a lower CH4 uptake rate in litter addition treatment. Meanwhile, it was also worth mentioning that neither the available N for obligate methanotrophs nor the amount of labile C for facultative methanotrophs was improved by litter addition, and the total abundance of methanotrophs was significantly reduced by the litter addition. As methanotrophs are very sensitive to high soil moisture (Malyan et al., 2016; Kwon et al., 2016), and most of the high affinity methanotrophs are obligate microorganisms, and they can only grow utilizing CH4 at atmospheric level (Kolb, 2009; Nazaries et al., 2013). During the wet and cold seasons, the thick litter layer of litter addition treatment not only created an unfavorable habitat for methanotrophs, but also reduced the substrate availability for methanotrophs. In the coniferous forest, leachates such as monoterpenes from the thick litter layer in litter addition treatments could also suppress CH4 oxidation in mineral soils (Maurer et al., 2008; Degelmann et al., 2010). Therefore, shifts in precipitation patterns and soil moisture regimes due to climate change can directly affect the moisture content of litter layer, and then potentially alter the forest soils to act as a sink or source of atmospheric CH4. The significant difference in net CH4 uptake rate between dry and wet seasons was only observed in litter addition treatment, further confirming the relatively thick litter layer played an important role in regulating CH4 uptake capacity. Thus, we tentatively concluded that litter layer did not significantly impact net CH4 uptake rate in dry and warm soil conditions, whereas this situation reversed once the soil temperature started to decrease and soil water content increase in other seasons. Interestingly, unlike the aboveground litter removal, the reduction of roots input had little effect on net CH4 uptake rate. This finding was inconsistent with a recent study (Subke et al., 2018) which showed that

Moreover, litter removal can limit the formation of soil aggregates, especially the macro-aggregates (Mayzelle et al., 2014; Trivedi et al., 2017), and hence hinder overall pore connectivity which can prevent the transport of atmospheric of CH4 to methanotrophs (Bronick and Lal, 2005). The lower abundance of methanotrophs in the litter removal plots together with the positive relationship between the abundance of methanotrophs and net CH4 uptake rates further supported this point. The annual average net CH4 uptake rate was also significantly reduced by litter addition compared with the CK treatment. But this was not consistent throughout the year: when the monthly WFPS was below 50% (especially during the summer), net CH4 uptake rate was not impacted by litter addition. Once the WFPS was above 50%, CH4 uptake capacity was significantly reduced by litter addition. The three-year consecutive addition of plant litter created a thick organic layer (> 3 cm) above the mineral soil, and the thick organic layer could act as a moisture-induced regulator for atmospheric CH4 uptake (Peichl et al., 2010; Wang et al., 2013; Pedersen et al., 2017). During the summer, although the rainfall was higher than other seasons, the extreme high-temperature resulted in more intense water evaporation at both soil and litter layers, consequently, the litter layer failed to act as a diffusion barrier for CH4 oxidation. In contrast, the litter layer could absorb more water which was evaporated from soil when the soil water content was higher during the cold seasons (Sayer, 2006; Fekete et al., 2016). Meanwhile, Rosenkranz et al. (2006) found that there was a negative relationship between the organic layer water content and CH4 uptake. Additionally, the thick litter layer in the DL treatment could not only inhibit the diffusion of atmospheric CH4 into the soil, but also could lower the oxygen (O2) concentration (Wang et al., 2013; Leitner et al., 2016). The low soil O2 could favor the activity of methanogens and then decrease the net CH4 uptake rate (Nazaries et al., 2013). Thus, compared with the CK treatment, the significant lower net CH4 uptake rate in the DL treatment was mainly resulted from the much lower net CH4 uptake rate during wet seasons. During these seasons, the thicker organic layer could inhibit the diffusion of atmospheric CH4 and O2 into mineral soil and followed by the lower net CH4 uptake rate (Wang et al., 2013; Leitner et al., 2016).

Fig. 4. Mean net CH4 uptake rates of different litter input manipulation treatments during dry (a) and wet (b) seasons, respectively. Error bars represent standard errors of the mean values (n = 6), different lowercase letters over the bars indicate statistically significant differences at P < 0.05 among litter input manipulation treatments. The dashed lines indicated the 95% confidence interval of linear regression fits. See Fig. 1 for abbreviations.

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Fig. 5. The relationships between the net CH4 uptake rate and water filled pore space (WFPS) of each litter input manipulation treatment (a–e) (n = 14), as well as the relationship between the CH4 uptake rate and soil temperature across all litter input manipulation treatments (f) (n = 70). The dashed lines indicated the 95% confidence interval of linear regression fits. See Fig. 1 for abbreviations.

Fig. 6. The relationships between the net CH4 uptake rate and the abundance of methanotrophic bacteria after two-year (a) (n = 15) and three-year (b) (n = 15) litter input manipulation treatments.

(Nazaries et al., 2013), the low diffusion of atmospheric CH4 can suppress the activities of methanotrophs. Conversely, root exclusion also deceased the release of monoterpene by roots (Maurer et al., 2008; Degelmann et al., 2010), and then facilitated the activities of methanotrophs. The contrasting mechanisms underlying this phenomenon could neutralize each other and hence lead to negligible response of net CH4 uptake to root exclusion.

in the presence of intact plant root, net CH4 uptake was even 3 times that of bare soil. They attributed the reason to soil with plant root can provide more alternate sources of labile C or greater sources of nutrients for methanotrophs (Veraart et al., 2015). However, the phenomenon aforementioned did not always happen. For instance, Praeg et al. (2016) have found that upland soils without plant cover have relatively high CH4 uptake rates, and girdling followed by felling increases the CH4 uptake rates (Krause et al., 2013). Meanwhile, results from double-split-root rhizotrons indicated that CH4 uptake rates from plant rhizotrons do not differ from the root-free soils (Fender et al., 2013). The net CH4 flux primarily depends on the two inversed processes: methanogenesis and methanotrophy (Hiltbrunner et al., 2012; Fest et al., 2015; Yang et al., 2018). Root exudates not only favor the facultative methanotrophs (Pratscher et al., 2011; Semrau et al., 2011), but also methanogenic microbes (Praeg et al., 2016). Root exclusion terminated the supply of photosynthesis to methanogenic microbes, and thus would facilitate the net CH4 uptake. However, this scenario did not appear in our results. Thus, there could be some other explanations for the observed phenomenon. Plant roots, especially the fine roots could create channels for atmospheric CH4 to diffuse downward in soils (Fender et al., 2013). Severed and decomposed roots by trenching would result in the decline of these channels, consequently, reduced the diffusion of atmospheric CH4 to the mineral soil. Given the majority of obligate methanotrophs use CH4 as their sole source of C and energy

5. Conclusions This study investigated the response of net CH4 uptake rates to the shifts in above- and belowground plant litter, and found that the net CH4 uptake rate responded differently to the manipulation of aboveand belowground litter. The litter layer removal treatments significantly lowed net CH4 uptake rate, while the root exclusion treatment did not affect the net CH4 uptake rate. The underlying mechanism was that the litter layer removal significantly reduced soil substrates availability and the abundance methanotrophs, and hence lowered net CH4 uptake rate. Our results highlighted the significant contribution of litter layer to CH4 uptake capacity. Interestingly, significant difference in net CH4 uptake rate between the dry and wet conditons was only found in the litter addtion treatment. The litter addition significantly reduced the net CH4 uptake rate under wet conditions, but no significant effect was found under dry conditions. This was possibly 313

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because the litter addition had little effect on substrate availability, but significantly reduced the abundance of methanotrophic bacteria under wet conditions. Overall, in the context of global change, our results suggest that the shifts in aboveground litter input combined with the increased frequency of extreme wet and dry events could have important effects on the strength of atmospheric CH4 uptake by soils of subtropical plantations.

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