Does short-term litter input manipulation affect soil respiration and its carbon-isotopic signature in a coniferous forest ecosystem of central China?

Applied Soil Ecology 113 (2017) 45–53

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Does short-term litter input manipulation affect soil respiration and its carbon-isotopic signature in a coniferous forest ecosystem of central China? Junjun Wu, Qian Zhang, Fan Yang, Yao lei, Quanfa Zhang, Xiaoli Cheng* Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, PR China

A R T I C L E I N F O

Article history: Received 2 December 2016 Received in revised form 20 January 2017 Accepted 21 January 2017 Available online xxx Keywords: Carbon isotope Litter inputs manipulation Microbial community structure Soil respiration Substrate availability

A B S T R A C T

Global change greatly alters the quality and quantity of plant litter inputs to soils and further impacts soil respiration. However, it is not fully understood how soil respiration may change with future shifts in litter input. The Detritus Input and Removal Treatment (DIRT) experiment were conducted in a coniferous forest (Platycladus orientalis (Linn.) Franco) ecosystem of central China to investigate the impact of aboveand belowground litter input on soil respiration and the carbon-isotopic signature of soil-respired CO2. Short-term litter input manipulation significantly affected soil respiration. Based on annual flux values, soil respiration decreased by 31.9%, 20.5% and 37.2% in treatments with no litter (NL), no roots (NR) and no roots and no litter (NRNL), respectively, compared to the control (CK) treatment. Conversely, the double litter (DL) treatment increased soil respiration by 9.1% compared to the CK treatment. The recalcitrance index of carbon (RIC) and the relative abundance of fungi increased under NL, NR and NRNL treatment compared to the CK treatment. The carbon-isotopic signature of soil-respired CO2 was enriched under NRNL treatment and was slightly depleted under DL treatment compared to the CK treatment. The soil respiration rate and its carbon-isotopic signature exhibited similar seasonal patterns among treatments with higher soil respiration rates and lower d13C values of soil-respired CO2 in the summer compared with other seasons. Basal soil respiration was positively related to labile C and microbial biomass and negatively related to RIC and the fungi-to-bacteria (F:B) ratio, whereas the d13C value of soil-respired CO2 was negatively correlated with soil temperature and water content. Our results suggest that short-term litter input manipulation can affect soil respiration by altering substrate availability and microbial community structure and can impact the carbon-isotopic signature of soil-respired CO2 possibly due to changes in the components of soil respiration and soil microclimate. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Soil respiration, the largest source of carbon (C) flux from terrestrial ecosystems to the atmosphere, is very important in regulating climate change, as well as the global ecosystem C balance (Luo and Zhou, 2006; Bond-Lamberty and Thomson, 2010). Soil respiration can be greatly affected by abiotic and biotic factors, such as soil temperature and moisture, the microbial community and the substrate supply; thus, it is very susceptible to global change (Bond-Lamberty and Thomson, 2010; Zhou et al., 2016; Sawada et al., 2016). Even a small change in soil respiration can have a large effect on atmospheric CO2 with potential feedbacks to climate change (Heimann and Reichstein, 2008). Although in

* Corresponding author. E-mail address: [email protected] (X. Cheng). http://dx.doi.org/10.1016/j.apsoil.2017.01.013 0929-1393/© 2017 Elsevier B.V. All rights reserved.

recent decades numerous studies have related soil respiration to global change (e.g., van Groenigen et al., 2011; Giardina et al., 2014; Xue et al., 2016), uncertainty remains about how abiotic (e.g., soil temperature and moisture) and biotic (e.g., the microbial community and substrate supply) factors interactively affect soil respiration under controlled field conditions. Soil respiration is largely influenced by substrate supply derived from litter and roots and by soil organic matter (SOM) (Sayer et al., 2011; van Groenigen et al., 2014). Alterations in the quality and quantity of plant litter inputs to soils have profound influences on SOM dynamics and soil respiration (Schlesinger et al., 2015). Increased inputs of fresh organic matter may result in a “priming effect,” which can in turn enhance the soil’s respiration (Fontaine et al., 2007; Kuzyakov, 2010). However, litter removal or root exclusion not only directly reduces the decomposition of litterfall or root respiration but also indirectly affects the biological process

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J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

in the underlying soil (Lajtha et al., 2014; Fekete et al., 2014). For instance, litter removal or root exclusion may reduce SOM decomposition by reducing the substrates available for microbes (Leff et al., 2012; Philippot et al., 2013). Meanwhile, the d13C values of soil-respired CO2 are also affected by the change in litter input and substrate availability (Rousk and Frey, 2015). When a plant’s litter input changes, the d13C value of the substrates also changes (Yang et al., 2015). Thus, the d13C value of microbial respiration may also deviate somewhat due to the use of different substrates (Bowling et al., 2008; Rousk and Frey, 2015). Alterations in detrital inputs can also affect soil microclimatic conditions, especially soil temperature and moisture, both of which influence soil respiration and its carbon-isotopic signature (Sayer, 2006; Phillips et al., 2010; Fekete et al., 2014). For instance, litter addition enhances the thickness of the organic layer and can reduce the effects of extremes as well as moderate minimum and maximum soil temperatures (Fekete et al., 2016), which can benefit the microbial reparation processes of litter and SOM. Meanwhile, surface litter layer regulates soil water content by reducing evaporation from mineral soil while absorbing a fraction of the precipitation (Sayer, 2006), and root exclusion can increase soil moisture due to lose of transpiration (Fekete et al., 2016). Some studies have reported that the d13C value of soil-respired CO2 is enriched during dry conditions and depleted when the water level is high (Phillips et al., 2010). Soil moisture may affect the production of ectomycorrhizal extramatrical mycelium (Ekblad et al., 2005), and the ectomycorrhizal fungi are approximately 2m enriched relative to their plant hosts (Ekblad et al., 2016), ultimately affecting the d13C value of the substrates. The Detritus Input and Removal Treatment (DIRT) experiment was established to examine feedbacks between plants, microbes, and SOM through long-term manipulation of above- and belowground litter inputs in forest ecosystems (Bowden et al., 1993; Nadelhoffer et al., 2004; Veres et al., 2015). In addition, this experiment provides a unique opportunity to understand the influence of abiotic (e.g., soil temperature and moisture) and biotic (e.g., microbial community and substrate supply) factors on soil organic C dynamics. Here, our overall objective was to assess the effect of different litter input manipulation on soil respiration and its carbon-isotopic signature in a coniferous forest (Platycladus orientalis (Linn.) Franco) ecosystem in the Northern Subtropics of Central China. To achieve this goal, a DIRT experiment in coniferous forest ecosystem was established to test the two following hypotheses. First, we hypothesized that the increase in soil respiration induced by litter addition would outpace the decrease in soil respiration by litter removal due to priming effect. Second, we hypothesized that the carbon-isotopic signature of soilrespired CO2 would be more enriched in litter removal or in no input treatment and more depleted in litter addition treatment due to corresponding changes of substrates and soil microclimate under these treatments. 2. Materials and methods 2.1. Study site This study was conducted at the Wulongchi Experiment Station (32
450 N, 111
130 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
C, with monthly averages of 27.3
C in July and 4.2
C in January. The annual precipitation is 749.3 mm, of which 70–80% occurs between April and October. The soil is yellow-brown with 11% sand, 41% silt, and 48% clay in the top 30 cm. Approximately 20 years ago, following a reorganization of land use, a large

uncultivated area was converted to a woodland plantation with coniferous plants (Platycladus orientalis (Linn.) Franco). 2.2. Experimental design and soil sampling The DIRT experimental plots were established in November, 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 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 September 2015, three soil cores (diameter = 5 cm) from each plot were collected from the top layer of soil (0–10 cm) after removing the litter and organic horizon. The cores were combined to yield one composite sample per plot. All soil samples were immediately sieved through a 2-mm mesh. A small subsample was removed from each soil sample and stored at 80
C for Phospholipid Fatty Acids (PLFAs) extraction. Another subsample was removed and stored at 4
C for incubation. The remaining soil samples were air-dried and stored in airtight plastic bags until analysis. 2.3. Soil respiration and its carbon-isotopic signature Fluxes of CO2 were measured twelve times from January 2015 to December 2015 using static chambers and the gas chromatography technique. Static chambers were inserted into each 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) that was placed on top to ensure the chamber was enclosed during sampling and removed afterwards. On the top wall of each chamber cover, a batteryoperated fan of 10-cm diameter was installed to mix the air in the chamber while the sample was collected. Generally, once the chambers were closed, 140-ml air samples were collected every 10 min using 100-ml plastic syringes. The samples were then injected into 150-ml pre-evacuated gas bags over half an hour. Simultaneously, the air temperature of each experimental plot was measured with a mercurial thermometer. Soil temperature and moisture were measured outside each chamber with a portable instrument that measured soil temperature and moisture (SINTH8, SinoMeasure, China). The concentration and d13C value of CO2 were analyzed with a CO2 Isotope Analyzer (912-0003, LGR, America). The CO2 fluxes were calculated using linear model regression analysis of the change in gas concentration in the chambers with time over a 30-min period with an average chamber temperature (Metcalfe et al., 2007): F = DC/Dt *273/(273 + T)*44/22.4*V/A 2

1

(1)

where F is the CO2 flux (mg m h ), T is the air temperature inside chambers, 44 is the molecular weight of CO2, 22.4 is the molar volume of an ideal gas at standard temperature and pressure (1 mol1), V is the chamber volume (m3) and A is the chamber area

J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

(m2). CO2 fluxes were discharged if the regression coefficients (r2) were below 0.9. The d13C of CO2, sampled at a series of time points, are evaluated using a two-component mixing model (Ohlsson et al., 2005):

dC ¼ d30 C30 d0 C0 C ¼ C30 C0

ð2Þ

where superscript * refers to bulk CO2 and subscripts 30 and 0 denote the concentration of chamber headspace CO2 at 30- and 0min time intervals, respectively. In addition, d refers to 13C value of CO2. For evaluation of the d value, the above models were rearranged:

d ¼ d30 C30 d0 C0 = C30 C0 ð3Þ The relationship between temperature and soil respiration is usually described using an exponential Eq. (4). When the relationship between temperature and soil respiration is fitted with an exponential function, the temperature sensitivity of soil respiration (Q10) can be estimated from coefficient b in Eq. (4) (Luo and Zhou, 2006). RS = ae

bT

Q10 = e10b

(4)

(5)

where RS refers to the soil respiration rate (mg C m2h1), T is soil temperature (
C), and a and b are constants. 2.4. 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. Total extractable PLFAs were used as microbial biomass. Specific PLFA markers were used to quantify the relative abundance of various

47

taxonomic groups in a manner similar to Xu et al. (2015). The PLFAs chosen to indicate gram-positive bacteria (G+ bacteria), gramnegative bacteria (G bacteria), actinomycetes and fungi were exhibited in appendices (Table A.1). And total bacteria were calculated as the sum of G+ bacteria, G bacteria and actinomycetes. 2.5. Basal respiration Basal soil respiration was determined by sieving (2-mm mesh) fresh soil (equivalent to 50-g dry mass) for each plot from each site. Duplicate soil subsamples were moistened to 60% water-filled pore space following light tamping in a PVC tube (3.5 cm in diameter and 10-cm deep) and incubated in 1-L glass jars in the dark at 25
C. All soils were pre-incubated for 7 days. Glass jars contained flasks with 10 mL of 0.5 M NaOH to absorb CO2. Alkali traps were replaced at 1, 3, 5, and 7 days, and then once a week for 60 days. Carbon dioxide evolution was determined by adding 5 mL of 1 M BaCl2 and titrating the residual alkali (NaOH) to pH 7 with 0.5 M HCl. 2.6. Recalcitrant C and N The air-dried soil samples were ground to a fine powder, treated with 1 N HCl solution for 24 h at room temperature to remove any carbonates, washed with distilled water, and oven-dried at 60
C 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 recalcitrant C and N pools (Rovira and Vallejo, 2000; Xu et al., 2015). Briefly, 500 mg of each soil sample were hydrolyzed with 25 mL of 5 N H2SO4 in sealed Pyrex tubes, in a block digester at 105
C 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 26 N H2SO4 were 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
C. After cooling, this second hydrolysate was recovered as described. The residue was washed and then dried at 60
C. The recalcitrant and total C and N concentrations were determined using an element analyzer (vario EL, Elementar Analysensysteme, Hanau, Germany). Similar to Rovira and Vallejo (2000), the recalcitrance indices for C and N (RIC and RIN, respectively) were calculated using the following equations: RIC (%) = (unhydrolyzed C/total OC)  100%

(6)

Fig. 1. (a) Temporal variations of air and soil temperature at top soil (5 cm) and (b) annual average in soil temperature under different litter input manipulation treatments. Values are Mean  SE. Abbreviations: CK, control; NL, no litter; NR, no root; NRNL, no root and no litter; and DL, double litter.

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J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

(7)

2.7. Statistical analyses The data analysis was performed using IBM SPSS Statistics 21.0 (Armonk, NY, USA). 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 manipulation on soil respiration, substrate availability (SOC and SON, labile C and N, as well as RIC and RIN), and soil microbial community structure (total microbial biomass, bacterial and fungal PLFAs, and the F:B ratio). The two-way ANOVA test was used to test the statistical significance of litter input manipulation, seasonality and their interactive effects on soil respiration and its d13C values. Simple regressions were performed to calculate the relationship of the soil respiration and its d13C value with soil temperature and water content, as well as basal soil respiration with labile C, RIC, microbial biomass and the F:B ratio.

3.2. Temporal variation in soil respiration rates and the carbonisotopic signature of soil-respired CO2 The average soil respiration rate was 249.91, 169.59, 195.70, 156.39 and 271.88 mg m2 h1 in CK, NL, NR, NRNL, and DL treatments, respectively. Both aboveground litter removal and root exclusion significantly decreased the soil respiration rate compared to the CK treatment (F(4;328) = 6.34, P < 0.05; Fig. 3). Based on annual flux values, soil respiration was 31.9%, 20.5% and 37.2% lower in NL, NR and NRNL treatments, respectively, compared to the CK treatment (F(4;25) = 5.88; P < 0.05). In contrast to root exclusion, aboveground litter removal had a greater effect on reducing the soil respiration. The DL treatment increased soil respiration by 9.1% compared to the CK treatment. When compared with the CK treatment, both basal microbial respiration and microbial respiration rate on per-unit PLFA declined under NL, NR and NRNL treatments (F(4;25) = 4.28; P < 0.05) whereas the DL treatment did not significantly affect them (Fig. 4). The d13C value of soil-respired CO2 was not significantly affected by litter manipulation (F(4;328) = 0.85, P > 0.05), but small deviations occurred among the treatments (Table 3, Fig. 5b). The d13C value of soil-respired CO2 was most depleted in the DL treatment, and most enriched in the NLNR treatment. Both the soil respiration and its carbon-isotopic signature exhibited significant seasonal variations among different litter input manipulation treatments, with a higher soil respiration rate and a lower d13C value of soil-respired CO2 in the summer compared with other seasons (Table 3, Fig. 5a).

3. Results 3.1. Microclimate, soil carbon and nitrogen, and microbial biomass Air and soil temperatures varied greatly with seasons, with the highest air temperature occurring in July, the highest soil temperature occurring in August, and the lowest air and soil temperatures occurring in February (Fig. 1a). Litter input manipulation did not significantly affect the soil temperature (F(4;355) = 0.01, P > 0.05; Fig. 1b). Soil moisture showed similar patterns in all treatments, with higher values in the rainy seasons (from April to September) and lower values in the dry seasons (from October to March) (Fig. 2a). The annual mean soil moisture varied from 24.04% to 27.86%, with the highest soil moisture occurring in the DL treatment and the lowest occurring in the NRNL treatment (F(4;355) = 3.21, P < 0.05; Fig. 2b). Litter input manipulation did not significantly affect the soil organic C and N contents except for the recalcitrant N, which was significantly higher in the DL treatment compared with the NR treatment (F(4;25) = 2.94, P < 0.05; Table 1). The RIC ranged from 69.4% to 77.4%, with the highest RIC in the NRNL treatment, followed by RIC in NL and NR treatments, which were higher than CK and DL treatments(F(4;25) = 3.04, P < 0.05). Whereas the RIN was not significantly influenced by litter input manipulation (Table 1).

Volumetric soil water content (%)

50

(a)

40

3.3. Controls on soil respiration and its carbon-isotopic signature When all treatments were considered, soil respiration exhibited a significantly exponential relationship with soil temperature, and the relationship between soil respiration and soil moisture was fitted with a polynomial model: higher soil respiration corresponded to moderate soil moisture (Fig. 6a and b). The

40

(b)

CK NL NR NRNL DL

a

b

a

ab b

30

30

20

20

10

10

Volumetric soil water content (%)

RIN (%) = (unhydrolyzed N/total N)  100%

Microbial biomass was 29.8%, 11.5%, and 22.8% lower in NL, NR, and NRNL treatments, respectively, and the DL treatment increased microbial biomass by 17.9% compared with the CK treatment (F (4;25) = 4.16, P < 0.05). Similar to microbial biomass, the biomarkers of bacteria and fungi were both lowest in the NL treatment and highest in the DL treatment (Table 2). NL, NR and NRNL treatments increased the F:B ratio by 18.1%, 5.8% and 12.3%, respectively, and the DL treatment slightly decreased the F:B ratio compared with the CK treatment (F (4;25) = 2.86, P < 0.05).

0

Jan

Feb

Mar

Apr

May

Jun

Jul

Month

Aug

Sep

Oct

Nov

Dec

CK

NL

NR NRNL DL Treatment

Fig. 2. (a) Temporal variations of soil moisture (0–5 cm) and (b) annual average in soil moisture under different litter input manipulation treatments. Values are Mean  SE. See Fig. 1 for abbreviations.

J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

49

Table 1 Soil organic carbon (SOC) and nitrogen (SON), recalcitrant C (RC) and N (RN), as well as the recalcitrance indices for C (RIC) and N (RIN) under different litter input manipulation treatments. Treatment

SOC (g kg1)

RC (g kg1)

RIC (%)

SON (g kg1)

RN (g kg1)

RIN (%)

CK NL NR NRNL DL

24.99  2.00a 21.02  5.64a 20.87  0.59a 21.03  3.91a 22.23  5.52a

17.34  2.56a 15.13  1.80a 15.19  2.43a 15.78  2.95a 16.47  2.77a

69.4  7.5b 72.4  5.0b 72.7  11.0b 77.4  10.3a 70.3  0.9b

1.73  0.24a 1.59  0.57a 1.30  0.04a 1.64  0.37a 1.89  0.25a

0.86  0.07ab 0.80  0.18ab 0.69  0.21b 0.86  0.29ab 0.92  0.36a

49.8  3.1a 51.6  7.3a 52.3  5.1a 54.8  8.1a 50.1  4.1a

Note: Values are Means  SE. Different lowercase letters in the same column 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.

Table 2 Microbial biomass, taxonomic biomarkers (mg g1 dry soils) and F:B ratios under different litter input manipulation treatments. Treatment

Microbial biomass Bacteria (mg g1) (mg g1)

Fungi (mg g1)

F: B

CK NL NR NRNL DL

9.32  0.14ab 6.54  1.49c 8.25  1.85b 7.20  1.34c 10.99  2.55a

1.65  0.09a 1.07  0.31b 1.47  0.33ab 1.40  0.53ab 1.72  0.59a

0.33  0.02b 0.40  0.02a 0.35  0.02ab 0.38  0.04ab 0.32  0.04b

4.94  0.05ab 2.68  0.66c 4.18  1.08b 3.76  0.84bc 5.51  1.04a

respiration decreased by 31.9% in litter removal treatment and increased by 9.1% in litter addition treatment compared with control treatments (Fig. 3b). Soil substrates availability, such as labile carbon, could be readily converted to CO2 by roots and microbes with short residence time. In contrast, inherent chemical recalcitrant materials, such as lignin, seem to resist decomposition (Luo and Zhou, 2006; Cotrufo et al., 2015). A lower labile C source from litter leachates or root exudates in litter removal or root exclusion treatments (Leff et al., 2012; Philippot et al., 2013; Kuzyakov and Blagodatskaya, 2015) would greatly reduce soil respiration. Conversely, litter addition could increase the labile C flux from forest floor to mineral soil (Klotzbücher et al., 2012), a result that would be favorable to microbial activity and then increase soil respiration. This speculation was supported by our result that basal soil respiration was correlated positively to labile C and negatively to RIC (Fig. 7a and c). However, decrease in the soil respiration rate induced by the removal of aboveground litter was greater than the increase in the soil respiration rate caused by litter addition, indicating that the priming effect caused by litter addition was negligible. This phenomenon was probably due to the following reasons: First, we measured soil respiration 4 months after litter addition. During such a short time interval, soil respiration could not have been significantly impacted by litter addition (Sulzman et al., 2005; Wang et al., 2013). Second, the lack of nutrients in fresh litter could limit the native C decomposition (Luo et al., 2015). In our study site, the fresh litter of coniferous species, having a high C:N ratio, was nutrient-limited (Cheng et al., 2013) probably limiting the priming effect. Fekete et al. (2014) have found the priming effect is negligible in SíkfÅkút DIRT site likely due to low precipitation which can hinder decomposition and leaching. Thus, another possible explanation of the negligible

Note: Values are Means  SE. Different lowercase letters in the same column for each variable indicate statistically significant differences at P < 0.05. See Table 1 for abbreviations.

temperature sensitivity (Q10) of soil respiration was significantly altered by the manipulation of litter inputs, ranging from 2.42 to 2.73 (F(4;25) = 2.83, P < 0.05; Table 4). When compared to the control treatment, aboveground litter removal treatments (NL and NRNL) increased the Q10 of soil respiration, while litter addition decreased it. Basal soil respiration was positively correlated with labile C and microbial biomass (Fig. 7a and b), and it was negatively related to the RIC and F:B ratios (Fig. 7c and d). The d13C value of soil-respired CO2 was negatively correlated with soil temperature and water content (Fig. 6c and d). 4. Discussions Litter input manipulation strongly affects soil substrate availability and input and hence soil respiration (Nadelhoffer et al., 2004; Sayer et al., 2011; Leff et al., 2012; Fekete et al., 2014). Consistent with previous studies on litter input manipulation (Leff et al., 2012; Leitner et al., 2016), our results revealed that soil

3000

(a)

(b)

-2 -1

Soil respiration (mg CO 2 m h )

600

500

ab

2500

bc c

400

-1

a

2000

c 1500

300 1000 200 500

100

0

-2

CK NL NR NRNL DL

Annual flux of soil respiration (g CO 2 m a )

700

0

Jan

Feb

Mar

Apr

May

Jun

Jul

Month

Aug

Sep

Oct

Nov

Dec

CK

NL

NR NRNL DL Treatment

Fig. 3. (a) Temporal variations and (b) annual average in soil respiration rates under different litter manipulation treatments. Values are Mean  SE. Different letters over the bars indicate statistically significant differences between litter input manipulation treatment. See Fig. 1 for abbreviations.

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J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

7000

Bas al s o il res ip rat io n (mg CO2 kg -1 s o il)

(a)

b

5000

b

b

4000

3000

2000

1000

0

Bas al s o il res p irat io n o n a p er-u n it PLFA b ais

a

a

6000

(b)

0.6

0.5

a

b ab

b

CK

NL

b

0.4

0.3

0.2

0.1

0.0 NR

NRNL

DL

Treatment Fig. 4. (a) Basal soil respiration and (b) its respiration rate on a per-unit PLFA basis under different litter input manipulation treatments. Values are Mean  SE. Different letters over the bars indicate statistically significant differences at P < 0.05 among litter input manipulation treatment. See Fig. 1 for abbreviations.

Table 3 Significance of the effects of litter input manipulation treatments, seasons and their interactions on soil respiration and d13C values of soil respiration based on the twoway ANOVA test (*P < 0.05; **P < 0.01; numbers are F-values). Source

Soil respiration

d13C-CO2

Treatment Season Treatment  season

4.58** 49.60** 0.50

2.48 38.90** 0.19

priming effect was the humidity of our study site (with annual precipitation of 749.3mm). The respiration of roots and their associated microorganisms contributed 10–90% of total soil respiration across different ecosystems with a mean contribution of 48% in forest ecosystems (Subke et al., 2006). However, in this study, respiration of roots and their associated microorganisms accounted for total soil respiration of 26.2% (Table A.2), which was lower than what was found in other studies (Li et al., 2004; Atarashi-Andoh et al., 2012). The decomposition of dead roots in the trenched plots could lead to higher soil respiration (Subke et al., 2006). Additionally, the additional decay of trenched roots for the second year following trenching introduced an approximate estimate of error, indicating that heterotrophic respiration might be overestimated by as much as 20% (Bond-Lamberty et al., 2004). This finding suggested that the contribution of roots and their associated microorganisms to

total soil respiration would be underestimated when the delay time between treatment and measurement was not sufficiently long. In the present study, the measurement of soil respiration began at 4 months after root trenching and was likely higher than the actual value. The d13C value of soil-respired CO2 was enriched in the litter removal and no input treatments, and slightly depleted in the litter addition treatment (Fig. 5b), partly due to the change in the components of soil respiration. The d13C values of soil organic matter were more enriched than those of plant litter because of the microbial decomposition process and the incorporation of 13Cenriched microbes into soil organic matter (Yang et al., 2015). The litter removal or no input treatments thus reduced the relatively 13 C-depleted plant-derived C incorporation into soil organic matter (Rousk and Frey, 2015). Furthermore, root respiration was reported to have been more depleted in d13C value than that of bulk leaf (Bowling et al., 2008; Rascher et al., 2010); therefore, root exclusion could increase the d13C value of soil-respired CO2. When the substrate signature changed, the d13C value of soil-respired CO2 followed. Soil temperature and moisture are the main abiotic drivers for soil respiration (e.g., Bond-Lamberty and Thomson, 2010). Soil respiration showed significant exponential relationships with soil temperature across litter input manipulation treatments (Table 4; Fig. 6a). Similar results have also been found in other forest ecosystems (Sulzman et al., 2005; Wang et al., 2013). This result suggested that the seasonality of soil respiration was possibly due to the thermal regulation of soil microbial activity, rather than the seasonality of litter input or root respiration. Whereas the relationship between soil respiration and soil moisture was more complicated; both positive and negative relationships have been reported (Liu et al., 2009; Wang et al., 2013). In the present study, higher soil respiration was matched with moderate soil moisture. Moisture that was too high or too low would limit soil respiration (Fig. 6b). Soil respiration’s sensitivity to temperature as expressed by Q10 in the litter removal (NL) and no input (NRNL) treatments was higher, which was similar to previous studies (Wang et al., 2013; Leitner et al., 2016), indicating that the temperature sensitivity of litter C was lower than that of soil C. This result supports the theory that with decreasing substrate quality, the temperature sensitivity of soil CO2 increases because more enzymatic steps are required to breakdown low-quality organic matter, and each of these steps in turn is temperature sensitive due to microbial enzyme kinetics (Fierer et al., 2005; Davidson and Janssens, 2006). Soil moisture was a main factor regulating the isotopic composition of soil respiration with the d13C value of soilrespired CO2 when it was greater enriched under low-moisture conditions (Phillips et al., 2010). The d13C value of soil-respired CO2 was slightly enriched in litter removal and no input treatments (Fig. 5b), probably due to low soil moisture in these treatments (Fig. 2b). This phenomenon was also supported by our results that the d13C values were negatively correlated with soil moisture (Fig. 6d). The d13C value of soil-respired CO2 showed similar seasonal patterns among treatments, with higher values in winter and lower in the summer. This result was probably due to the change in temperature-induced microbial activities. As explained above, the decomposition of low-quality litter was more sensitive to temperature (Fierer et al., 2005; Davidson and Janssens, 2006). In winter, when the temperature was low, labile C, such as sugar and starch (isotopically enriched relative to lignin), was prior to decomposition (Frey et al., 2013), resulting in the increased d13C value of soil-respired CO2. When the temperature was high, the decomposition of low-quality litter, such as lignin, was enhanced, resulting in the decrease of d13C value of soil-respired CO2. Soil microbes play key roles in nutrient cycling and organic matter dynamics (Averill and Hawkes, 2016; Kögel-Knabner, 2016). Litter input manipulation may have also elicited indirect changes

J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

51

Treatment Fig. 5. (a) Temporal variations and (b) annual average in the carbon-isotopic signature of soil-respired CO2 under different litter input manipulation treatments. Values are Mean  SE. Different letters over the bars indicate statistically significant differences at P < 0.05 among litter input manipulation treatments. See Fig. 1 for abbreviations.

Fig. 6. The relationships between (a, c) soil respiration or its d13C values and soil temperature, (b, d) soil moisture at top soil (5 cm), respectively, for all different treatments.

Table 4 Exponential regression models between soil respiration and soil temperature and temperature sensitivity of soil respiration under each treatment. Treatment CK NL NR NRNL DL

Equation 0.0917T

Y = 39.525e Y = 21.784e0.1044T Y = 33.017e0.0909T Y = 25.586e0.0968T Y = 48.775e0.0884T

R2

Q10 value

0.589** 0.749** 0.587** 0.687** 0.620**

2.51  0.03ab 2.73  0.21a 2.48  0.14ab 2.63  0.29ab 2.42  0.13b

Note: Different letters in the same column for each variable indicate statistically significant differences at P < 0.05. T, soil temperature at 5 cm depth. ** P < 0.01. See Table 1 for abbreviations.

in soil respiration by affecting soil microbial biomass and community structure (Leff et al., 2012). In agreement with other studies (Li et al., 2004; Feng et al., 2009), microbial biomass decreased by 29.8% in the litter removal treatment, 11.5% in the root exclusion treatment, 22.8% in the no input treatment and increased by 17.9% in the litter addition treatment (Table 2). We found that microbial biomass was positively correlated with basal soil respiration across all treatments, suggesting that changes in microbial biomass in response to litter input manipulation could contribute to variations in soil respiration. Fungi and bacteria are different in their strategies for using C. For instance, fungi are characterized by their low respiration quotients (qCO2) and higher

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J. Wu et al. / Applied Soil Ecology 113 (2017) 45–53

Fig. 7. The relationships between (a) basal soil respiration and labile carbon, (b) microbial biomass, (c) recalcitrance indices for carbon (RIC) (d) or F: B ratios for all different treatments.

Acknowledgments

efficiency in their use of C (they produce more biomass C per unit of C metabolized than do bacteria) (Strickland and Rousk, 2010; Deng et al., 2016). Our finding that basal soil respiration was negatively correlated with F:B ratios (Fig. 7d) further revealed that alteration in the relative abundance of fungi and bacteria under litter input manipulation would further affect soil respiration (Wang et al., 2013; Han et al., 2015).

This research was financially supported by the National Natural Science Foundation of China (31470557, 31270550, 31130010) and the “Strategic Priority Research Program B of the Chinese Academy of Sciences” (XDB15010200). We thank Xi Han and Ming Du for assistance in the field and laboratory analyses.

5. Conclusions

Appendix A. Supplementary data

Short-term litter input manipulation affected soil respiration and the carbon-isotopic signature due to changes in substrate availability and soil carbon input. However, our results demonstrated that soil respiration was susceptible to decrease in litter inputs but was relatively resistant to litter increase, suggesting the priming effect was negligible. The underlying mechanisms were partly because substrate availability was more influential in litter removal or root exclusion treatment compared with litter addition. Meanwhile, litter removal or root exclusion increased the relative abundance of fungi but lowered respiration quotients (qCO2), resulting in lower soil respiration. Conversely, this was not the case in the litter addition treatment. The carbon-isotope signature of soil-respired CO2 was enriched in the litter removal and no input treatments and slightly depleted in the litter addition treatment, primarily resulting from the changing component of soil respiration and soil microclimate under litter input manipulation conditions. The soil respiration rate and its carbon-isotopic signature showed similar seasonal patterns among treatments with higher soil respiration rates and lower d13C values of soilrespired CO2 in the summer compared with other seasons. Overall, this study enhances our process-based understanding of how soil respiration may change with future shifts in plant litter in response to either global changes or human activities in Northern subtropical coniferous forests.

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