- Email: [email protected]
Rhizospheric and heterotrophic respiration of a warm-temperate oak chronosequence in China
Soil Biology & Biochemistry 43 (2011) 503e512
Contents lists available at ScienceDirect
Soil Biology & Biochemistry journal homepage: www.elsevier.com/locate/soilbio
Rhizospheric and heterotrophic respiration of a warm-temperate oak chronosequence in China Junwei Luan a, b,1, Shirong Liu a, b, *, Jingxin Wang c, Xueling Zhu d, Zuomin Shi a, b a
Key Laboratory of Forest Ecology and Environment, China’s State Forestry Administration, Beijing 100091, PR China The Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, PR China c West Virginia University, Division of Forestry and Natural Resources, Morgantown, WV 26506, USA d Baotianman Natural Reserve Administration, Neixiang county in Henan province 474350, PR China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 March 2010 Received in revised form 5 November 2010 Accepted 8 November 2010 Available online 4 December 2010
Plot trenching and root decomposition experiments were conducted in a warm-temperate oak chronosequence (40-year-old, 48-year-old, 80-year-old, and 143-year-old) in China. We partitioned total soil surface CO2 efflux (RS) into heterotrophic (RH) and rhizospheric (RR) components across the growing season of 2009. We found that the temporal variation of RR and RH can be well explained by soil temperature (T5) at 5 cm depth using exponential equations for all forests. However, RR of 40-year-old and 48-year-old forests peaked in September, while their T5 peaks occurred in August. RR of 80-year-old and 143-year-old forests showed a similar pattern to T5. The contribution of RR to RS (RC) of 40-year-old and 48-year-old forests presented a second peak in September. Seasonal variation of RR may be accounted for by the different successional stages. Cumulative RH and RR during the growing season varied with forest age. The estimated RH values for 40-year-old, 48-year-old, 80-year-old and 143-year-old forests averaged 431.72, 452.02, 484.62 and 678.93 g C m2, respectively, while the corresponding values of RR averaged 191.94, 206.51, 321.13 and 153.03 g C m2. The estimated RC increased from 30.78% in the 40-year-old forest to 39.85% in the 80-year-old forest and then declined to 18.39% in the 143-year-old forest. We found soil organic carbon (SOC), especially the light fraction organic carbon (LFOC), stock at 0e10 cm soil depth correlated well with RH. There was no significant relationship between RR and fine root biomass regardless of stand age. Measured apparent temperature sensitivity (Q10) of RH (3.93 0.27) was significantly higher than that of RR (2.78 0.73). Capillary porosity decreased as stand age increased and it was negatively correlated to cumulative RS. Our results emphasize the importance of partitioning soil respiration in evaluating the stand age effect on soil respiration and its significance to future model construction. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Soil respiration Heterotrophic respiration Root respiration Forest age Fine root biomass Soil organic carbon Light fraction organic carbon Q10
1. Introduction In forests, soil surface CO2 efflux (RS) accounts for approximately 69% of total ecosystem respiration (Janssens et al., 2001) and thus it is the second largest terrestrial carbon flux. RS is considered to be sensitive to climate and vegetation type as well as forest age (Raich and Potter, 1995; IPCC, 2001; Tang et al., 2009). Both autotrophic (e.g. roots) and heterotrophic (e.g. bacterial and fungal detritivore)
* Corresponding author. The Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, PR China. Tel.: þ86 10 62889311; fax: þ86 10 62884229. E-mail addresses: [email protected] (J. Luan), [email protected] (S. Liu), [email protected] (J. Wang), [email protected] (X. Zhu), [email protected] (Z. Shi). 1 Present address: Research Institute of Wetland, Chinese Academy of Forestry, Beijing 100091, PR China. 0038-0717/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2010.11.010
organisms contribute to soil surface CO2 efflux through respiration (Boone et al., 1998; Kelting et al., 1998; Hanson et al., 2000; Jassal and Black, 2006; Kuzyakov, 2006), and the autotrophic component accounts for 10e90% of total RS (Hanson et al., 2000). Differential responses of these components to environmental changes have profound implications for the soil and ecosystem C balance (Boone et al., 1998; Subke et al., 2006; Hartley et al., 2007). Few models can be used to estimate these two components separately (Bååth and Wallander, 2003), due to our limited understanding of the factors controlling each component (Davidson et al., 2006). To model long-term forest carbon dynamics and their coupling with the climate system, we need to understand not only the responses of forest ecosystems to the changing climate, but also the effects of forest successional status on carbon dynamics (Tang et al., 2009). Still, there has been substantial uncertainty regarding the effect of forest age on carbon flux, although it plays a distinguishing role in determining the distribution of carbon pools and fluxes in
504
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
different forested ecosystems. For example, several studies have found an increase in RS with stand age (Litton et al., 2003; Wiseman and Seiler, 2004; Jiang et al., 2005), while other studies found an opposite trend (Wang et al., 2002; Pregitzer and Euskirchen, 2004). On the other hand, Klopatek (2002) and Tang et al. (2009) reported a nonlinear trend of RS with stand age. Apparently, these confusions of age effects on RS were mainly attributed to the lack of respiration components partitioning. As we know, soil temperature and soil water content are recognized as the main factors controlling the temporal variation of RS (Davidson et al., 1998; Janssens et al., 2001). However, spatial variability of RS is more complex due to the origins of its components. The spatial distribution of fine roots (Saiz et al., 2006; Tang et al., 2009), plant photosynthesis (Tang et al., 2005), leaf area and primary productivity (Högberg et al., 2001; Rey et al., 2002; Yuste et al., 2004), allocation patterns of recent photosynthates to roots (Högberg et al., 2001; Bhupinderpal-singh et al., 2003), and plant phenology (Fu et al., 2002) all contribute to the variation in autotrophic respiration. The variation in heterotrophic respiration is mainly controlled by the soil biophysical environment and substrate availability, such as aboveground and belowground litter (Ryan and Law, 2005), soil organic carbon (Xu and Qi, 2001; Wang and Yang, 2007) or labile organic carbon (Laik et al., 2009). There is a need to partition RS into autotrophic and heterotrophic components in order to understand the effect of climate change and forest succession on C cycling (Bond-Lamberty et al., 2004b; Yi et al., 2007; Tang et al., 2009). In China, warm-temperate forests account for a large proportion of the total forested area, and forest landscape patterns are often composed of different forest successional stages due to long-term human disturbance. Research on RS of different forest successional stages in this area is of great importance to assess the forest C budget in China. However, no studies on partitioning RS along a forest age gradient have been conducted in warm-temperate forests in China. In this study, a warm-temperate oak chronosequence (40-year-old, 48-year-old, 80-year-old and c. 143-year-old) was selected to investigate the effects of forest successional stages on soil carbon dynamics by partitioning total RS into heterotrophic respiration (RH) and rhizospheric respiration (RR). The objectives of this study were to: 1) understand the seasonal pattern of respiration components at different forest stages; and 2) elucidate the contribution of respiration components and their controls across stands of different ages. 2. Material and methods 2.1. Study sites and experimental design The study sites were located at the Forest Ecological Research Station in the Baotianman Natural Reserve (111470 e112 040 E,
33 200 e33 360 N), Henan Province, China. The average elevation is 1450 m. Meteorological parameters were measured at the nearby weather station, less than 3 km away from the study site. The annual mean precipitation and air temperature are 900 mm and 15.1 C, respectively. Precipitation occurs mainly in summer (55e62%; Liu et al., 1998). Upland soils are dominated by mountain yellow-brown soil (Chinese classification). All the plots are dominated by Quercus acutidentata. A common set of stand attributes are summarized in Table 1. In addition to dominant species, other tree species include Carpinus cordata, Cornus controversa Hemsl, Lindera obtusiloba, and Tilia mongolica Maxim. Stand age was obtained from forest management records and increment borer core samples. Each stand typically covers more than 1 ha. Similar climate and soil properties among these stands form an ideal chronosequence to study age effects on soil carbon dynamics. The chronosequence for our study consisted of stands aged 40-years, 48-years, 80-years and c. 143-years in 2008. Three 20 m 20 m measurement plots for each age class were established in September 2008. Three 1 m 1 m subplots were randomly located in each plot. On the outside edges of the subplots, 60-cm deep trenches (below which few roots existed) were dug and lined with double-layer plastic sheets, then refilled with soil. Furthermore, all aboveground vegetation was carefully removed with minimal soil disturbance, and the trenched subplots were kept free of live vegetation throughout the study. At each trenched subplot, one PVC collar (19.6 cm inner diameter, 8 cm height) was installed to a depth of 5 cm for soil CO2 efflux sampling (RT). Another three soil CO2 efflux sampling PVC collars were installed randomly in each plot and soil CO2 efflux in these untrenched plots (RUT) was considered the total soil surface CO2 efflux (RS). The first measurements were conducted after one week of collar establishment. All the PVC collars were installed permanently throughout the observation campaigns. 2.2. Soil CO2 efflux, soil temperature and soil moisture We measured soil CO2 efflux from September 2008 to November 2009 using a Li-8100 soil CO2 flux system (LI-COR Inc., Lincoln, NE, USA) in trenched (RT) and untrenched (RUT) plots. Soil temperature at 5 cm depth (T5) was measured adjacent to each respiration collar with a portable temperature probe provided with the Li-8100. Soil volumetric water content at 0e5 cm (SWC) was measured with a portable time domain reflectometer MPKit-B soil moisture gauge (NTZT Inc., Nantong, China) at three points close to each chamber. The measurements were made twice a month during the growing season (15 Apr.e16 Oct., DOY 106e287) and once a month in other periods. No measurements were conducted during Dec. 2008, Jan. and Feb. 2009 due to snow coverage.
Table 1 Summary of stand characteristics measured in summer 2009.a Stand
BA (m2 ha1)
Density (trees ha1)
DBH (SD, cm)
LAI (SD)
BD(SD) g/cm3
SOC kg C m2
TN kg N m2
PH
40-year-old 1 40-year-old 2 40-year-old 3 48-year-old 1 48-year-old 2 48-year-old 3 80-year-old 1 80-year-old 2 80-year-old 3 143-year-old 1 143-year-old 2 143-year-old 3
52.0 46.5 57.3 60.0 53.2 46.5 65.5 77.6 61.0 80.1 85.4 100.9
1900 2100 2600 2700 1900 2000 2000 2600 2000 1200 2500 2000
16.2(9.5) 13.7(9.2) 15.0(7.7) 12.4(11.6) 16.2(10) 13.7(10.7) 17.02(11.57) 16.36(10.11) 15.03(13.08) 21.64(20.47) 12.59(16.44) 17.83(18.48)
3.34(0.23) 3.41(0.41) 3.04(0.40) 2.43(0.09) 2.39(0.17) 2.36(0.22) 2.97(0.19) 3.26(0.25) 2.84(0.23) 3.24(0.53) 3.07(0.44) 3.11(0.52)
0.88(0.05) 0.72(0.04) 0.60(0.08) 0.60(0.06) 0.66(0.06) 0.69(0.05) 0.66(0.02) 0.67(0.03) 0.62(0.03) 0.46(0.06) 0.73(0.08) 0.53(0.07)
31.10 31.24 25.31 35.91 34.28 35.81 36.65 35.84 29.58 41.98 51.34 28.45
2.32 2.26 1.78 2.44 2.79 2.22 2.74 2.39 2.33 2.33 2.94 1.51
4.65 4.43 4.44 4.84 4.56 5.02 4.49 4.47 4.57 4.66 5.24 4.88
a BA, DBH, LAI, BD, SOC and TN stand for basal area, mean tree diameter at breast height, leaf area index, bulk density, soil organic carbon and total nitrogen stock at 0e10 cm soil depth respectively. Quercus acutidentata dominated all stands.
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
2.3. Soil carbon contents, light fraction organic carbon, root biomass and LAI Soil samples were collected at 0e10 cm, 10e30 cm and 30e50 cm depths using a 10-cm diameter corer in summer 2009. Three samples were taken within 1e2 m of the centre of the respiration collars in the trenched and untrenched plots and mixed thoroughly to give one sample per subplot and depth. Coarse (>5 mm), medium (2e5 mm) and fine (<2 mm) roots were manually separated from soil collected at 0e30 cm depth (>80% fine root biomass was distributed at 0e30 cm soil depth, unpublished data), and dry biomass was measured. We measured massbased soil carbon content (%) and bulk density (g m3) for each soil sample at the three depths across the chronosequence. Capillary porosity (CP) at 0e10 cm was determined as described by Liu et al. (2009) for each soil sample. Soil light fraction organic matter at the depth of 0e10 cm was obtained by density fractionation based on Six et al. (1998), but with a modification of using CaCl2 solution (density of 1.5 g ml1; Garten et al., 1999). Bulk soil and light fraction organic carbon contents were determined by the wet oxidation method with 133 mM K2Cr2O7 at 170e180 C (Lu, 2000). Soil carbon stocks (SOC; g m2) at 0e10, 10e30, and 30e50 cm depth and light fraction organic carbon stocks (LFOC; g m2) at 0e10 cm depth were calculated using carbon content and bulk density. Tree diameter at breast height (DBH) was measured for each tree in each stand. Leaf area index (LAI) was measured using hemispherical photographs in August 2009 along a 25 m transect in each stand with WinSCANOPY (Regent Instruments Inc., Quebec, Canada). 2.4. Root decomposition experiment
2.5. Data analysis We used an exponential equation (Eq. (1)) to describe the relationships between RUT, RT, estimated rhizospheric respiration (RR), or estimated heterotrophic respiration (RH) and soil temperature at 5 cm depth:
R ¼ aebT
(1)
where R is RUT, RT, RR, or RH, T is the soil temperature at 5 cm depth (T5), a and b are fitted parameters. The temperature sensitivity parameter, Q10, was calculated as:
Q 10 ¼ eð10bÞ
conducted a multilinear regression (soil temperature and SWC as two independent variables; ln(R) ¼ aþbT5þcSWC). T-tests was used to determine if the SWC term was significant in predicting soil respiration. 2.5.1. Growing season RS, RH, RR estimation Based on half-hour time steps, we used the plot-specific respiration models (RT or RUT) vs. T5 (Eq. (1)) to estimate growing season (DOY 106e287) cumulative RT and RUT (RS) for the 12 plots (3 in each forest type). The continuous measurements of T5 were used as the model inputs for T. Since there were no meteorological stations in the four forests to directly obtain continuous T5, we developed regression models of soil temperature between the four forests and the adjacent forest meteorological station based on discrete T5 measurements (R2 > 0.95, P < 0.001). Using these models and halfhour time step measurements of T5 in the adjacent meteorological station, we estimated continuous T5 for each forest. Cumulative soil CO2 effluxes were calculated using the empirical model of each plot for interpolation. CO2 efflux due to the decomposition of residual roots of each size class (Rd; g C m2 day1) at a given time t was calculated using the method described by Lee et al. (2003):
Rd ¼ Br aevðt1=365Þ aevt RD ¼
X
Rd
(2)
On the basis of year-round data, we calculated annual Q10 and R0 (defined as the basal respiration rate at 0 C) values. To test the effect of soil water content (SWC) on soil respiration, we added SWC or log-transformed SWC as a term in Eq. (1), and
(3) (4)
where Br: Root biomass (g C m2); root decomposition rates (n) were calculated using 0.64 as the mineralization rate (Nakane K et al., 1996) by:
v ¼ 0:64k
Plot trenching cuts off the roots from live plants, initially raising the measured CO2 flux from the trenched plots due to an increase of substrate supply (dead roots) for microbial respiration (Lee et al., 2003; Bond-Lamberty et al., 2004b). Thus, we used root biomass data and measured root decomposition to calculate the CO2 fluxes released from dead root decomposition (RD). Root samples collected in September 2008 were placed in bags (30 30 cm) made of 1-mm nylon mesh to estimate the relative loss rate constant (k) in the soil. A total of 45 bags were prepared with 15 bags each of fine, medium and coarse roots. They were buried at a soil depth of 10e20 cm in September 2008 and dry mass of the buried roots was measured every 2 months (3 bags sampled each time for each root size).
505
(5)
where k is the relative loss rate constant obtained by the exponential decay function:
X=X0 ¼ aekt
(6)
Heterotrophic respiration (RH), rhizospheric respiration (RR) and the relative contribution of RR to total RS (RC; %) were calculated based on data collected from March to November of 2009 as follows:
RH ¼ RT RD
(7)
RR ¼ RUT RH
(8)
RC ¼ RR =RUT 100
(9)
2.5.2. Comparisons of RT, RUT, RS, RH and RR Repeated measures GLMs were employed to compare the means of RT and RUT among forests across the growing season. Because of nonsimultaneous and periodic cross-stand measurement, which may change diurnally and seasonally, we relied on modeling results to compare the cumulative components of soil CO2 efflux among stands across the season. WallereDuncan k-ratio t-tests were performed to assess the age effect on growing season cumulative RS, RH, RR, RD, as well as CP and root biomass for each root size class. Linear regression analyses were used to examine the relationships between growing season cumulative RS, RH, RR and soil or stand properties. Linear regression of differences in SWC and differences in soil CO2 efflux between trenched and untrenched plots was also performed to evaluate the trenching effect on soil surface CO2 flux for each stand age class separately. A quadratic regression was used to describe the relationship between RS and
506
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
LAI. All statistical analyses were performed using the SPSS 13.0 software package for Windows (SPSS Inc., Chicago, USA).
(Fig. 3). Q10 of RH increased from 3.60 for the 40-year-old forest to 4.26 for the 80-year-old forest and then declined to 3.90 for the 143-year-old forest. Regardless of age effects, Q10 of RH (3.93 0.27) was significantly higher than that of RR (2.78 0.73; F7 ¼ 8.74, P ¼ 0.025).
3. Results 3.1. Seasonal dynamic of soil CO2 efflux
3.3. Growing season cumulative fluxes of respiration components The seasonal patterns of RT and RUT were similar to those of T5, they peaked in August and presented the lowest values in March and November (Fig. 1). RUT and RT of younger forests showed relatively little inter-seasonal variation (Fig. 1). The estimated RH of all forests showed a similar seasonal pattern to T5, peaking in August (from 3.03 for 48-year-old to 5.55 g C m2day1 for 143-year-old forests respectively; Fig. 2). The estimated RR of 80-year-old and 143-year-old forests also had a similar seasonal pattern to T5, while RR of 40-year-old and 48-year-old forests peaked a month later (Sept.) than T5 (Aug.; Fig. 2). The contribution of RR to RS (RC) in 2009 ranged from 12% to 52% and the four forests showed significantly different seasonal patterns of RC (P < 0.05). The RC of 40-year-old and 48-year-old forests presented a second peak in September (Fig. 2). The mean measured rates of RT and RUT were significantly different (P < 0.05) among forests (Table 2); both RT and RUT increased with forest age.
Among the four forests, the estimated growing season cumulative RS increased with forest age. RS in 143-year-old and 80-year-old forests was significantly higher than in 48-year-old and 40-year-old forests (P < 0.05; Table 2). Estimated growing season RH and RR also differed significantly among the four forests (P < 0.05; Table 2). RH increased with forest age, while RR increased 40-year-old to 80-year-old forests and then declined for 143-year-old forests (Table 2). The calculated RC increased from 40-year-old forest to 80-yearold forest and then declined for 143-year-old forests (Table 2). Growing season cumulative RS was explained by basal area (BA) with linear regression (R2 ¼ 0.589, P ¼ 0.004; Fig. 4a) and we found a marginal correlation between RS and SOC (R2 ¼ 0.265, P ¼ 0.087; Fig. 4b). LAI showed a marginally significant quadratic relationship with RS (P ¼ 0.074; Fig. 4b). Growing season cumulative RH was positively correlated with SOC at 0e10 cm depth (R2 ¼ 0.314, P ¼ 0.058) and with LFOC stocks (R2 ¼ 0.654, P ¼ 0.001; Fig. 4d). Basal respiration (R0) of trenched plots was explained by LFOC stock (R2 ¼ 0.592, P ¼ 0.003; Fig. 4f). No significant relationship between RR and fine root biomass was found (Fig. 4e). Lower capillary porosity (CP) was found in 80-year-old and 143-year-old forests compared to 40-year-old and 48-year-old forests (P < 0.05). Capillary porosity at 0e5 cm depth was negatively correlated to cumulative RS (RS ¼ 1143.72 CP þ 1370.32, R2 ¼ 0.441, P ¼ 0.019).
3.2. Relationship of soil CO2 efflux with soil temperature and moisture Temporal variation of soil CO2 efflux in trenched (RT) and untrenched plots (RUT) can be well described by T5 using an exponential equation, with R2 ranging from 0.60 to 0.88 for the trenched, and from 0.61 to 0.86 for the untrenched plots (P < 0.001). A multilinear regression (T5 and SWC as independent variables) indicated that soil moisture was not a significant factor in predicting soil CO2 efflux. Therefore, we used the models of RT or RUT against T5 for estimating growing season fluxes of RT and RUT. There were significant exponential relationships between estimated RR, RH and T5
3.4. Trenching effect on soil microclimate Plot trenching had no significant effect on soil temperature (P > 0.05), yet tended to raise soil moisture in the four forests
a
b
c
d
Fig. 1. Seasonal patterns of soil surface CO2 fluxes from trenched (RT) and untrenched (RUT) plots in the four forests during 2008e2009. The error bars represent standard deviations (n ¼ 9 for trenched and untrenched, n ¼ 3 for untrenched-trenched). **Denotes significant differences between trenched and untrenched plots at p ¼ 0.05.
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
507
a
b
c
d
Fig. 2. Seasonal patterns of heterotrophic respiration rates (RH), rhizospheric respiration rates (RR), root decomposition rates (RD; g C m2d1) and the contribution of RR to RS (RC %).
(Fig. 5). Most significant differences in soil moisture between the trenched and untrenched plots occurred in the 80-year-old forest (Fig. 5c). Significant relationships between the difference of SWC and the difference of soil CO2 efflux between trenched and untrenched plots were only found in 80-year-old forests (R2 ¼ 0.09, P ¼ 0.038; Fig. 6). 4. Discussion 4.1. Seasonality of soil CO2 efflux In this study, the measured soil CO2 efflux rates from trenched and untrenched plots were comparable to previous studies in temperate forests (Boone et al., 1998; Subke et al., 2006; Wang and Yang, 2007). The seasonality of soil CO2 efflux was affected by the soil environment (Davidson et al., 1998), vegetation structure (Wang and Yang, 2007), and forest age (Tang et al., 2009). The trenching treatment allowed us to explore seasonal patterns of each component of soil CO2 efflux among forests instead of merely total RS. The estimated RH showed a similar seasonal pattern to T5, hence the temporal variation of RH can be largely explained by T5 (R2 ¼ 0.83e0.93; Fig. 3). This indicates that soil temperature is a good indicator of temporal variation of heterotrophic respiration. In addition, we found that the 143-year-old forest had higher RH
compared to the other forests (Fig. 2d) and this led to a significantly higher cumulative RH (Table 2). However, we did not find higher seasonal coefficients of variance (CV) of RH in the 143-year-old forest compared to the other forests (P > 0.05). Therefore, we found that the Q10 of RH in the 143-year-old forest was similar to the other forests (Fig. 3), suggesting similar temperature responses of RH among the forests. As we found higher R0 of RH (0.469 m mol CO2 m2 s1) in the 143-year-old forest compared to the other forests (0.301 0.017 m mol CO2 m2 s1; Fig. 3), the higher RH in this forest may be accounted for by higher substrate availability rather than temperature. This was illustrated by the significant relationship between R0 of trenched plots and LFOC (Fig. 4f), as LFOC was considered as a good indicator of soil organic matter quality (Six et al., 2002; Luan et al., 2010). We found an apparent inconsistent pattern between RR and T5 in 40-year-old and 48-year-old forests, where RR peaked nearly one month later than T5 (Fig. 2a, b). Similar results were also found in a Pinus contorta forest (Scott-Denton et al., 2006) and in a series of temperate forests (Wang and Yang, 2007). Gaumont-Guay et al. (2008) explained that the lagged response may suggest relatively slow transport of photosynthates to roots. However, a recent 13C pulse-chase labeling experiment indicated that photosynthates were transferred to soil CO2 efflux in less than 4 days (Högberg et al., 2008; Plain et al., 2009). On the other hand, we did not find a similar RR lag for 80-year-old and 143-year-old forests, and
Table 2 Mean soil surface CO2 fluxes from trenched (RT) and untrenched plots (RUT) given as m mol CO2 m2 s1, cumulative growing season (DOY 106e287) total soil respiration (RS), rhizospheric (RR) and heterotrophic respiration (RH) in 2009 (g C m2), and relative contribution of RR to RS (RC%) for the four forest types.a, b Forest type
RUT
RT
RS
RD
RH
RR
RC (%)
40-year-old 48-year-old 80-year-old 143-year-old
2.801(0.106)a 3.105(0.109)a 3.619(0.153)b 4.007(0.173)b
2.227(0.097)a 2.345(0.078)a 2.388(0.091)a 3.560(0.148)b
623.66(37.56)a 658.54(11.03)a 805.74(24.44)b 831.96(22.79)b
40.70(0.52)a 40.41(1.19)a 38.67(1.28)a 45.17(0.21)b
431.72(31.86)a 452.02(19.57)a 484.62(12.42)a 678.93(40.97)b
191.94(9.23)a 206.51(29.95)a 321.13(36.68)b 153.03(41.35)a
30.78 31.36 39.85 18.39
a b
Values in parentheses are standard errors of means for N ¼ 3. Means with different letters within a column are significantly different at p ¼ 0.05.
508
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
a
b
c
d
Fig. 3. Relationships between soil temperature at 5 cm depth (T5) and RH and RR for 40-year-old (a), 48-year-old (b), 80-year-old (c) and 143-year-old (d) forests (n ¼ 15).
a
b
c
d
e
f
Fig. 4. Relationships between growing season cumulative gross soil respiration (RS) and basal area (a), RS and SOC (b), RS and LAI (c), RH and soil organic carbon (SOC) and light fraction organic carbon (LFOC) stocks at 0e10 cm depth (d), rhizospheric respiration (RR) and fine root biomass (e), and basal respiration (R0) of trenched plots and LFOC stocks (f).
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
a
b
c
d
509
Fig. 5. Seasonal patterns of soil temperature (T5) and soil water content (SWC) of trenched and untrenched plots, error bars represent standard errors of means (n ¼ 3). *, ** and ***Denote significant difference between trenched and untrenched plots at p ¼ 0.05, p ¼ 0.01, or p ¼ 0.001, respectively.
therefore complex biophysical processes may dominate the seasonality of RR differently among forests. Fu et al. (2002) reported that rhizospheric respiration varied not only with plant species but also with plant phenology, and Högberg et al. (2001) also showed that forest type and associated metabolic activities (e.g. phenology, photosynthate allocation, etc.) may be more important in controlling seasonal patterns of RR than of RH. Therefore, differences in seasonal patterns of RR and RC among forests in our study may suggest different physiological processes among forest successional stages. Accurate measurements of long-distance phloem transport
of photosynthates to roots or a new technique to measure rhizospheric respiration are necessary to validate our results. We also found lower seasonal variation of RR for the 143-year-old compared to the other forests and only a marginally significant exponential relationship between RR and T5, which may explain the lower temperature response of RR (Q10 ¼ 1.75; Fig. 3). We speculate that the temperature response of RR in the old growth forest is lower than that in the young stands. All of the above suggest that seasonal variations in RH and RR are probably driven by different mechanisms. 4.2. Components of soil CO2 efflux and their controls along the chronosequence Our estimated seasonal fluxes of RS, RH and RR, as well as the RC values for the four forests were within the range of reported values for temperate forests (Hanson et al., 2000; Bond-Lamberty et al., 2004a; Subke et al., 2006). However, their variation and controlling factors were different among stands. Fu et al. (2002) reported that rhizospheric respiration varied with plant species. In our study, the basal area of dominant species (Q. acutidentata) accounted for 95e98% of the total basal area for the four forests, and we speculate that the difference in species composition among the stands would not have a great impact on respiration partitioning.
Fig. 6. Relationships between differences in soil water content between trenched and untrenched plots (SWCtrenched SWCuntrenched) and the differences in soil CO2 efflux between trenched and untrenched plots (RT RUT) for four forests.
4.2.1. Variation of RS Cumulative RS varied significantly across the four forests and stand age had a positive influence on RS (Table 2); this is consistent with previous studies (Litton et al., 2003; Wiseman and Seiler, 2004; Jiang et al., 2005). Zhou et al. (2006) reported that old growth forest in subtropical China can still accumulate soil organic carbon. Our study supports this, as higher SOC stock in the topsoil was found in the old growth oak forest, which was marginally correlated with the inter-site variation of cumulative RS (Fig. 4a). Soil carbon
510
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
decomposes much slower in deeper soil, probably due to lower soil temperatures during the growing season, low oxygen availability, and high soil aggregation (Six et al., 2002). Therefore, we did not find a significant relationship between RS and SOC stock at the depths of 10e30, 30e50 cm. There was a positive relationship between RS and BA (a proxy of aboveground biomass), which was consistent with the result reported by Nsabimana et al. (2009). This suggests that the correlation between soil CO2 efflux and gross primary production (Janssens et al., 2001; Reichstein et al., 2003). We did not find the significant linear relationship between RS and LAI as reported by Tang et al. (2009). However, we found a marginally significant quadratic relationship between RS and LAI (R2 ¼ 0.40, P ¼ 0.074; Fig. 4c). LAI in relation to gross primary production may drive respiration over a short period of time (days to months; Högberg et al., 2001; Tang et al., 2005) and root biomass has larger direct effects on RS than LAI does (Tang et al., 2009). On the other hand, CP declined significantly with increasing stand age and this will possibly influence soil surface CO2 efflux. It was found that lower CP in 80-year-old and 143-year-old forests partly contributed to the higher cumulative RS and we speculate that the restriction of soil CO2 overflow by CP may be as a result of capillary hanging water. 4.2.2. Variation of RH Similar to Jiang et al. (2005), we found a positive age effect on RH as RH was significantly higher in the 143-year-old forest than in other forests (Table 2). Our results were also consistent with Law et al.’s (2003) results, who found that heterotrophic respiration in ponderosa pine (Pinus ponderosa) stands in Oregon increased with forest age from 9e23 years old to 95e106 years old. In our study, the cumulated soil carbon stock, especially the LFOC, in the topsoil, explained the pattern of RH across the chronosequence (Fig. 4d). Furthermore, a higher slope of LFOC vs. RH than SOC vs. RH was found, and this may indicate the higher contribution of labile organic carbon to soil respiration. In addition, the basal respiration (R0) of trenched plots was highly dependent on LFOC (Fig. 4f). This further illustrated that the variations in RH across the chronosequence can be mainly attributed to different substrate (i.e. labile carbon) availability (Wang et al., 2010) and organic matter quality (Cisneros-Dozal et al., 2006; Wang and Yang, 2007). 4.2.3. Variation of RR Unlike RS and RH, the age effect on RR was nonlinear in our study. RR was significantly higher in the 80-year-old forest than in the other forests (Table 2). We did not find a significant relationship between RR and fine root biomass, in contrast to Wang and Yang (2007). This may be explained by the following reasons: Firstly, both respiration and root biomass were very dynamic over the growing season. Although we sampled root biomass at the peak in soil temperature, which is associated with maximum root production (Wang et al., 2006), a single sampling of fine root biomass during the growing season could not account completely for the cumulative estimate of RR. Secondly, we did not separate roots between trees and grasses when sampling, which may lead to inaccurate estimation of the root biomass as the root biomass composition of woody plants and grasses varied with season and forest age. Finally, it is believed that complex mechanisms underpin the variation in RR, for example, photosynthesis and transport of photosynthates to roots both influence root respiration (Högberg et al., 2001; Gaumont-Guay et al., 2008). Thus, further research is needed to elucidate the variation of RR among forests of different age. 4.3. Trenching effects on microclimate and soil CO2 efflux Our study showed that trenching modified soil environmental conditions, but the degree of trenching effects varied with forest
type. Unlike Wang and Yang (2007), we found no significant trenching effect on soil temperature in our study (Fig. 6). Trenching increased soil water content, especially for the 80-year-old forest (Fig. 5c), which is consistent with other studies (Ngao et al., 2007; Wang and Yang, 2007) and could be the result of the elimination of root uptake of soil water after trenching. However, since we did not find a significant relationship between RS and SWC, changes in SWC due to trenching may have had no significant effect on soil CO2 efflux. The correlation between the difference in soil surface CO2 efflux (RT RUT) and the difference in SWC (SWCtrenched SWCuntrenched) further confirmed that trenching did not significantly affect soil CO2 efflux through its effects on soil microclimate (Fig. 6). It was reported that severed fine roots may quickly decompose after trenching, leading to a rapid CO2 flush (Rey et al., 2002; Lee et al., 2003; Ngao et al., 2007). In our study, two methods were employed to mitigate this impact on our results. First, we estimated RH and RR from April 16, after 8 months trenching when the transient CO2 flush had disappeared (Lee et al., 2003; Wang and Yang, 2007). Second, we estimated the C released from dead root decomposition during the same period using root decomposition bags. We found significantly higher fine root biomass in the 143-year-old forest compared to the other forests (P < 0.05), and this resulted in a higher RD in the 143-year-old forest (Table 2). However, RD is unlikely to have affected our estimation of RH as RD contributed little to RS (5e7%; Fig. 2, Table 2). Our estimation of RD 8 months after trenching was lower than that reported in previous studies, e.g. in temperate deciduous forests (14e24%; Epron et al., 1999 and 16%; Lee et al., 2003) and temperate evergreen forests (8%; Ohashi et al., 2000). Furthermore, priming effects from dead roots should be negligible so long after trenching (Kuzyakov and Bol, 2006). 4.4. Models of rhizospheric and heterotrophic respiration Growing season Q10 values for heterotrophic and rhizospheric respiration ranged from 3.60 to 4.26 and from 1.75 to 3.35, respectively (Fig. 3), which were well within the range reported for other temperate forests (Kirschbaum, 1995; Davidson et al., 1998). If the age effect was ignored, we found significantly higher Q10 of RH compared to RR. This indicates that microbial respiration is more temperature-sensitive compared to rhizospheric respiration, which has implications for future climate warming. Until now, no consensus has been reached on whether the temperature response of rhizospheric or heterotrophic respiration is higher (Boone et al., 1998; Bååth and Wallander, 2003; Hartley et al., 2007). However, the Q10 function is still a valid empirical method to simulate soil CO2 efflux in a specific site, which is not limited by water content, especially, when the simulation is made through interpolation rather than extrapolation (Tang et al., 2009). The variability of Q10 among forests and between components will affect cumulative soil respiration estimation across stands. Therefore, rhizospheric and heterotrophic respiration components and their respective Q10 values should be separately incorporated models of ecosystem C cycling. 5. Conclusions Soil heterotrophic and rhizospheric respiration varied with forest age. Soil environmental conditions and labile organic carbon stocks were good indicators for predicting temporal and spatial variations of heterotrophic respiration, whereas controlling mechanisms on the temporal and spatial variation of rhizospheric respiration were more complex. New techniques (e.g. stable 13C isotope tracing) are needed to elucidate the mechanism underlying
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
the variation in rhizospheric respiration. Different temperature responses between respiration components and among forests with different stand ages indicate the importance of component partitioning for accurate estimation of soil CO2 efflux in forest ecosystems. Acknowledgement We gratefully acknowledge the support from Baotianman National Nature Reserve for their help in field monitoring and sampling, they are Yin Wu, Ye Tian, Xinfang Yang, Xiaojing Liu, and Jiguo Liu. We also thank the two anonymous reviewers and Dr. Clay Altizer for their valuable comments and suggestions. This study was funded by the China’s National Natural Science Foundation (No. 30590383), the China’s Ministry of Finance (No. 200804001; No. 201104006) and the Ministry of Science and Technology of China (No. 2008DFA32070; No. 2011CB403205; No. 2006BAD03A04). References Bååth, E., Wallander, H., 2003. Soil and rhizosphere microorganisms have the same Q10 for respiration in a model system. Global Change Biology 9, 1788e1791. Bhupinderpal-singh, Nordgren, A., LöVenius, M.O., Höberg, M.N., Mellander, P.-E., Höberg, P., 2003. Tree root and soil heterotrophic respiration as revealed by girdling of boreal Scots pine forest: extending observations beyond the first year. Plant, Cell & Environment 26, 1287e1296. Bond-Lamberty, B., Wang, C., Gower, S.T., 2004a. A global relationship between the heterotrophic and autotrophic components of soil respiration. Global Change Biology 10, 1756e1766. Bond-Lamberty, B., Wang, C., Gower, S.T., 2004b. Contribution of root respiration to soil surface CO2 flux in a boreal black spruce chronosequence. Tree Physiology 24, 1387e1395. Boone, R.D., Nadelhoffer, K.J., Canary, J.D., Kaye, J.P., 1998. Roots exert a strong influence on the temperature sensitivity of soil respiration. Nature 396, 570e572. Cisneros-Dozal, L.M., Trumbore, S., Hanson, P.J., 2006. Partitioning sources of soilrespired CO2 and their seasonal variation using a unique radiocarbon tracer. Global Change Biology 12, 194e204. Davidson, E.A., Belk, E., Boone, R.D., 1998. Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest. Global Change Biology 4, 217e227. Davidson, E.A., Janssens, I.A., Luo, Y., 2006. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Global Change Biology 12, 154e164. Epron, D., Farque, L., Lucot, E., Badot, P.-M., 1999. Soil CO2 efflux in a beech forest: the contribution of root respiration. Annals of Forest Science, 289e295. Fu, S., Cheng, W., Susfalk, R., 2002. Rhizosphere respiration varies with plant species and phenology: a greenhouse pot experiment. Plant and Soil 239, 133e140. Garten, C.T., Post, W.M., Hanson, P.J., Cooper, L.W., 1999. Forest soil carbon inventories and dynamics along an elevation gradient in the southern Appalachian mountains. Biogeochemistry 45, 115e145. Gaumont-Guay, D., Black, T.A., Barr, A.G., Jassal, R.S., Nesic, Z., 2008. Biophysical controls on rhizospheric and heterotrophic components of soil respiration in a boreal black spruce stand. Tree Physiology 28, 161e171. Hanson, P.J., Edwards, N.T., Garten, C.T., Andrews, J.A., 2000. Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48, 115e146. Hartley, I.P., Heinemeyer, A., Evans, S.P., Ineson, P., 2007. The effect of soil warming on bulk soil vs. rhizosphere respiration. Global Change Biology 13, 2654e2667. Högberg, P., Höberg, M.N., Götlicher, S.G., Betson, N.R., Keel, S.G., Metcalfe, D.B., Campbell, C., Schindlbacher, A., Hurry, V., Lundmark, T., Linder, S., Näholm, T., 2008. High temporal resolution tracing of photosynthate carbon from the tree canopy to forest soil microorganisms. New Phytologist 177, 220e228. Högberg, P., Nordgren, A., Buchmann, N., Taylor, A.F.S., Ekblad, A., Hogberg, M.N., Nyberg, G., Ottosson-Lofvenius, M., Read, D.J., 2001. Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature 411, 789e792. IPCC, 2001. Climate Change 2001: the Scientific Basis. Cambridge University Press, Cambridge, UK. Janssens, I.A., Lankreijer, H., Matteucci, G., Kowalski, A.S., Buchmann, N., Epron, D., Pilegaard, K., Kutsch, W., Longdoz, B., Grunwald, T., Montagnani, L., Dore, S., Rebmann, C., Moors, E.J., Grelle, A., Rannik, U., Morgenstern, K., Oltchev, S., Clement, R., Gudmundsson, J., Minerbi, S., Berbigier, P., Ibrom, A., Moncrieff, J., Aubinet, M., Bernhofer, C., Jensen, N.O., Vesala, T., Granier, A., Schulze, E.D., Lindroth, A., Dolman, A.J., Jarvis, P.G., Ceulemans, R., Valentini, R., 2001. Productivity overshadows temperature in determining soil and ecosystem respiration across European forests. Global Change Biology 7, 269e278. Jassal, R.S., Black, T.A., 2006. Estimating heterotrophic and autotrophic soil respiration using small-area trenched plot technique: theory and practice. Agricultural and Forest Meteorology 140, 193e202.
511
Jiang, L., Shi, F., Li, B., Luo, Y., Chen, J., Chen, J., 2005. Separating rhizosphere respiration from total soil respiration in two larch plantations in northeastern China. Tree Physiology 25, 1187e1195. Kelting, D.L., Burger, J.A., Edwards, G.S., 1998. Estimating root respiration, microbial respiration in the rhizosphere, and root-free soil respiration in forest soils. Soil Biology and Biochemistry 30, 961e968. Kirschbaum, M.U.F., 1995. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biology and Biochemistry 27, 753e760. Klopatek, J.M., 2002. Belowground carbon pools and processes in different age stands of Douglas-fir. Tree Physiology 22, 197e204. Kuzyakov, Y., 2006. Sources of CO2 efflux from soil and review of partitioning methods. Soil Biology and Biochemistry 38, 425e448. Kuzyakov, Y., Bol, R., 2006. Sources and mechanisms of priming effect induced in two grassland soils amended with slurry and sugar. Soil Biology and Biochemistry 38, 747e758. Laik, R., Kumar, K., Das, D.K., Chaturvedi, O.P., 2009. Labile soil organic matter pools in a calciorthent after 18 years of afforestation by different plantations. Applied Soil Ecology 42, 71e78. Law, B.E., Sun, O.J., Campbell, J., Van Tuyl, S., Thornton, P.E., 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology 9, 510e524. Lee, M.-S., Nakane, K., Nakatsubo, T., Koizumi, H., 2003. Seasonal changes in the contribution of root respiration to total soil respiration in a cool-temperate deciduous forest. Plant and Soil 255, 311e318. Litton, C.M., Ryan, M.G., Knight, D.H., Stahl, P.D., 2003. Soil-surface carbon dioxide efflux and microbial biomass in relation to tree density 13 years after a stand replacing fire in a lodgepole pine ecosystem. Global Change Biology 9, 680e696. Liu, S., Jiang, Y., Shi, Z., 1998. Overview of the Baotianman nature reserve. In: A Study on the Biological Diversity in Warm Temperate Forest in China. China Science and Technology Press, Beijing, p. 45. Liu, X., Zhang, G., Heathman, G.C., Wang, Y., Huang, C.-h, 2009. Fractal features of soil particle-size distribution as affected by plant communities in the forested region of Mountain Yimeng, China. Geoderma 154, 123e130. Lu, R., 2000. Soil and Agricultural Chemistry Analysis Methods. Chinese Agricultural Scientific and Technology Press, Beijing (in Chinese). Luan, J., Xiang, C., Liu, S., Luo, Z., Gong, Y., Zhu, X., 2010. Assessments of the impacts of Chinese fir plantation and natural regenerated forest on soil organic matter quality at Longmen mountain, Sichuan, China. Geoderma 156, 228e236. Nakane, K., Kohno, T., Horikoshi, T., 1996. Root respiration before and just after clear-felling in a mature deciduous, broad-leaved forest. Ecological Research 11, 111e119. Ngao, J., Longdoz, B., Granier, A., Epron, D., 2007. Estimation of autotrophic and heterotrophic components of soil respiration by trenching is sensitive to corrections for root decomposition and changes in soil water content. Plant and Soil 301, 99e110. Nsabimana, D., Klemedtson, L., Kaplin, B.A., Wallin, G., 2009. Soil CO2 flux in six monospecific forest plantations in Southern Rwanda. Soil Biology and Biochemistry 41, 396e402. Ohashi, M., Gyokusen, K., Saito, A., 2000. Contribution of root respiration to total soil respiration in a Japanese cedar (Cryptomeria japonica D. Don) artificial forest. Ecological Research 15, 323e333. Plain, C., Gerant, D., Maillard, P., Dannoura, M., Dong, Y., Zeller, B., Priault, P., Parent, F., Epron, D., 2009. Tracing of recently assimilated carbon in respiration at high temporal resolution in the field with a tuneable diode laser absorption spectrometer after in situ 13CO2 pulse labelling of 20-year-old beech trees. Tree Physiology 29, 1433e1445. Pregitzer, K.S., Euskirchen, E.S., 2004. Carbon cycling and storage in world forests: biome patterns related to forest age. Global Change Biology 10, 2052e2077. Raich, J.W., Potter, C.S., 1995. Global patterns of carbon dioxide emissions from soils. Global Biogeochemical Cycles 9, 23e36. Reichstein, M., Rey, A., Freibauer, A., Tenhunen, J., Valentini, R., Banza, J., Casals, P., Cheng, Y., Grünzweig, J.M., Irvine, J., Joffre, R., Law, B.E., Loustau, D., Miglietta, F., Oechel, W., Ourcival, J.-M., Pereira, J.S., Peressotti, A., Ponti, F., Qi, Y., Rambal, S., Rayment, M., Romanya, J., Rossi, F., Tedeschi, V., Tirone, G., Xu, M., Yakir, D., 2003. Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Global Biogeochemical Cycles 17, 1104. Rey, A., Pegoraro, E., Tedeschi, V., De Parri, I., Jarvis, P.G., Valentini, R., 2002. Annual variation in soil respiration and its components in a coppice oak forest in Central Italy. Global Change Biology 8, 851e866. Ryan, M.G., Law, B.E., 2005. Interpreting, measuring, and modeling soil respiration. Biogeochemistry 73, 3e27. Saiz, G., Green, C., Butterbach-Bahl, K., Kiese, R., Avitabile, V., Farrell, E., 2006. Seasonal and spatial variability of soil respiration in four Sitka spruce stands. Plant and Soil 287, 161e176. Scott-Denton, L.E., Rosenstiel, T.N., Monson, R.K., 2006. Differential controls by climate and substrate over the heterotrophic and rhizospheric components of soil respiration. Global Change Biology 12, 205e216. Six, J., Callewaert, P., Lenders, S., 2002. Measuring and understanding carbon storage in afforested soils by physical fractionation. Soil Science Society of America Journal 66, 1981e1987. Six, J., Elliott, E.T., Paustian, K., Doran, J.W., 1998. Aggregation and soil organic matter accumulation in cultivated and native grassland soils. Soil Science Society of America Journal 62, 1367e1377.
512
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
Subke, J.-A., Inglima, I., Cotrufo, M.F., 2006. Trends and methodological impacts in soil CO2 efflux partitioning: a meta analytical review. Global Change Biology 12, 921e943. Tang, J., Baldocchi, D.D., Xu, L., 2005. Tree photosynthesis modulates soil respiration on a diurnal time scale. Global Change Biology 11, 1298e1304. Tang, J., Bolstad, P.V., Martin, J.G., 2009. Soil carbon fluxes and stocks in a Great Lakes forest chronosequence. Global Change Biology 15, 145e155. Wang, C., Bond-Lamberty, B., Gower, S.T., 2002. Soil surface CO2 flux in a boreal black spruce fire chronosequence. Journal of Geophysical Research 107, 1e8. Wang, C., Yang, J., 2007. Rhizospheric and heterotrophic components of soil respiration in six Chinese temperate forests. Global Change Biology 13, 123e131. Wang, C., Yang, J., Zhang, Q., 2006. Soil respiration in six temperate forests in China. Global Change Biology 12, 2103e2114. Wang, W., Peng, S., Wang, T., Fang, J., 2010. Winter soil CO2 efflux and its contribution to annual soil respiration in different ecosystems of a forest-steppe ecotone, north China. Soil Biology and Biochemistry 42, 451e458.
Wiseman, P.E., Seiler, J.R., 2004. Soil CO2 efflux across four age classes of plantation loblolly pine (Pinus taeda L.) on the Virginia Piedmont. Forest Ecology and Management 192, 297e311. Xu, M., Qi, Y., 2001. Soil-surface CO2 efflux and its spatial and temporal variations in a young ponderosa pine plantation in northern California. Global Change Biology 7, 667e677. Yi, Z., Fu, S., Yi, W., Zhou, G., Mo, J., Zhang, D., Ding, M., Wang, X., Zhou, L., 2007. Partitioning soil respiration of subtropical forests with different successional stages in south China. Forest Ecology and Management 243, 178e186. Yuste, J.C., Janssens, I.A., Carrara, A., Ceulemans, R., 2004. Annual Q10 of soil respiration reflects plant phenological patterns as well as temperature sensitivity. Global Change Biology 10, 161e169. Zhou, G., Liu, S., Li, Z., Zhang, D., Tang, X., Zhou, C., Yan, J., Mo, J., 2006. Old-growth forests can accumulate carbon in soils. Science 314 1417e1417.
Contents lists available at ScienceDirect
Soil Biology & Biochemistry journal homepage: www.elsevier.com/locate/soilbio
Rhizospheric and heterotrophic respiration of a warm-temperate oak chronosequence in China Junwei Luan a, b,1, Shirong Liu a, b, *, Jingxin Wang c, Xueling Zhu d, Zuomin Shi a, b a
Key Laboratory of Forest Ecology and Environment, China’s State Forestry Administration, Beijing 100091, PR China The Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, PR China c West Virginia University, Division of Forestry and Natural Resources, Morgantown, WV 26506, USA d Baotianman Natural Reserve Administration, Neixiang county in Henan province 474350, PR China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 March 2010 Received in revised form 5 November 2010 Accepted 8 November 2010 Available online 4 December 2010
Plot trenching and root decomposition experiments were conducted in a warm-temperate oak chronosequence (40-year-old, 48-year-old, 80-year-old, and 143-year-old) in China. We partitioned total soil surface CO2 efflux (RS) into heterotrophic (RH) and rhizospheric (RR) components across the growing season of 2009. We found that the temporal variation of RR and RH can be well explained by soil temperature (T5) at 5 cm depth using exponential equations for all forests. However, RR of 40-year-old and 48-year-old forests peaked in September, while their T5 peaks occurred in August. RR of 80-year-old and 143-year-old forests showed a similar pattern to T5. The contribution of RR to RS (RC) of 40-year-old and 48-year-old forests presented a second peak in September. Seasonal variation of RR may be accounted for by the different successional stages. Cumulative RH and RR during the growing season varied with forest age. The estimated RH values for 40-year-old, 48-year-old, 80-year-old and 143-year-old forests averaged 431.72, 452.02, 484.62 and 678.93 g C m2, respectively, while the corresponding values of RR averaged 191.94, 206.51, 321.13 and 153.03 g C m2. The estimated RC increased from 30.78% in the 40-year-old forest to 39.85% in the 80-year-old forest and then declined to 18.39% in the 143-year-old forest. We found soil organic carbon (SOC), especially the light fraction organic carbon (LFOC), stock at 0e10 cm soil depth correlated well with RH. There was no significant relationship between RR and fine root biomass regardless of stand age. Measured apparent temperature sensitivity (Q10) of RH (3.93 0.27) was significantly higher than that of RR (2.78 0.73). Capillary porosity decreased as stand age increased and it was negatively correlated to cumulative RS. Our results emphasize the importance of partitioning soil respiration in evaluating the stand age effect on soil respiration and its significance to future model construction. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Soil respiration Heterotrophic respiration Root respiration Forest age Fine root biomass Soil organic carbon Light fraction organic carbon Q10
1. Introduction In forests, soil surface CO2 efflux (RS) accounts for approximately 69% of total ecosystem respiration (Janssens et al., 2001) and thus it is the second largest terrestrial carbon flux. RS is considered to be sensitive to climate and vegetation type as well as forest age (Raich and Potter, 1995; IPCC, 2001; Tang et al., 2009). Both autotrophic (e.g. roots) and heterotrophic (e.g. bacterial and fungal detritivore)
* Corresponding author. The Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, PR China. Tel.: þ86 10 62889311; fax: þ86 10 62884229. E-mail addresses: [email protected] (J. Luan), [email protected] (S. Liu), [email protected] (J. Wang), [email protected] (X. Zhu), [email protected] (Z. Shi). 1 Present address: Research Institute of Wetland, Chinese Academy of Forestry, Beijing 100091, PR China. 0038-0717/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2010.11.010
organisms contribute to soil surface CO2 efflux through respiration (Boone et al., 1998; Kelting et al., 1998; Hanson et al., 2000; Jassal and Black, 2006; Kuzyakov, 2006), and the autotrophic component accounts for 10e90% of total RS (Hanson et al., 2000). Differential responses of these components to environmental changes have profound implications for the soil and ecosystem C balance (Boone et al., 1998; Subke et al., 2006; Hartley et al., 2007). Few models can be used to estimate these two components separately (Bååth and Wallander, 2003), due to our limited understanding of the factors controlling each component (Davidson et al., 2006). To model long-term forest carbon dynamics and their coupling with the climate system, we need to understand not only the responses of forest ecosystems to the changing climate, but also the effects of forest successional status on carbon dynamics (Tang et al., 2009). Still, there has been substantial uncertainty regarding the effect of forest age on carbon flux, although it plays a distinguishing role in determining the distribution of carbon pools and fluxes in
504
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
different forested ecosystems. For example, several studies have found an increase in RS with stand age (Litton et al., 2003; Wiseman and Seiler, 2004; Jiang et al., 2005), while other studies found an opposite trend (Wang et al., 2002; Pregitzer and Euskirchen, 2004). On the other hand, Klopatek (2002) and Tang et al. (2009) reported a nonlinear trend of RS with stand age. Apparently, these confusions of age effects on RS were mainly attributed to the lack of respiration components partitioning. As we know, soil temperature and soil water content are recognized as the main factors controlling the temporal variation of RS (Davidson et al., 1998; Janssens et al., 2001). However, spatial variability of RS is more complex due to the origins of its components. The spatial distribution of fine roots (Saiz et al., 2006; Tang et al., 2009), plant photosynthesis (Tang et al., 2005), leaf area and primary productivity (Högberg et al., 2001; Rey et al., 2002; Yuste et al., 2004), allocation patterns of recent photosynthates to roots (Högberg et al., 2001; Bhupinderpal-singh et al., 2003), and plant phenology (Fu et al., 2002) all contribute to the variation in autotrophic respiration. The variation in heterotrophic respiration is mainly controlled by the soil biophysical environment and substrate availability, such as aboveground and belowground litter (Ryan and Law, 2005), soil organic carbon (Xu and Qi, 2001; Wang and Yang, 2007) or labile organic carbon (Laik et al., 2009). There is a need to partition RS into autotrophic and heterotrophic components in order to understand the effect of climate change and forest succession on C cycling (Bond-Lamberty et al., 2004b; Yi et al., 2007; Tang et al., 2009). In China, warm-temperate forests account for a large proportion of the total forested area, and forest landscape patterns are often composed of different forest successional stages due to long-term human disturbance. Research on RS of different forest successional stages in this area is of great importance to assess the forest C budget in China. However, no studies on partitioning RS along a forest age gradient have been conducted in warm-temperate forests in China. In this study, a warm-temperate oak chronosequence (40-year-old, 48-year-old, 80-year-old and c. 143-year-old) was selected to investigate the effects of forest successional stages on soil carbon dynamics by partitioning total RS into heterotrophic respiration (RH) and rhizospheric respiration (RR). The objectives of this study were to: 1) understand the seasonal pattern of respiration components at different forest stages; and 2) elucidate the contribution of respiration components and their controls across stands of different ages. 2. Material and methods 2.1. Study sites and experimental design The study sites were located at the Forest Ecological Research Station in the Baotianman Natural Reserve (111470 e112 040 E,
33 200 e33 360 N), Henan Province, China. The average elevation is 1450 m. Meteorological parameters were measured at the nearby weather station, less than 3 km away from the study site. The annual mean precipitation and air temperature are 900 mm and 15.1 C, respectively. Precipitation occurs mainly in summer (55e62%; Liu et al., 1998). Upland soils are dominated by mountain yellow-brown soil (Chinese classification). All the plots are dominated by Quercus acutidentata. A common set of stand attributes are summarized in Table 1. In addition to dominant species, other tree species include Carpinus cordata, Cornus controversa Hemsl, Lindera obtusiloba, and Tilia mongolica Maxim. Stand age was obtained from forest management records and increment borer core samples. Each stand typically covers more than 1 ha. Similar climate and soil properties among these stands form an ideal chronosequence to study age effects on soil carbon dynamics. The chronosequence for our study consisted of stands aged 40-years, 48-years, 80-years and c. 143-years in 2008. Three 20 m 20 m measurement plots for each age class were established in September 2008. Three 1 m 1 m subplots were randomly located in each plot. On the outside edges of the subplots, 60-cm deep trenches (below which few roots existed) were dug and lined with double-layer plastic sheets, then refilled with soil. Furthermore, all aboveground vegetation was carefully removed with minimal soil disturbance, and the trenched subplots were kept free of live vegetation throughout the study. At each trenched subplot, one PVC collar (19.6 cm inner diameter, 8 cm height) was installed to a depth of 5 cm for soil CO2 efflux sampling (RT). Another three soil CO2 efflux sampling PVC collars were installed randomly in each plot and soil CO2 efflux in these untrenched plots (RUT) was considered the total soil surface CO2 efflux (RS). The first measurements were conducted after one week of collar establishment. All the PVC collars were installed permanently throughout the observation campaigns. 2.2. Soil CO2 efflux, soil temperature and soil moisture We measured soil CO2 efflux from September 2008 to November 2009 using a Li-8100 soil CO2 flux system (LI-COR Inc., Lincoln, NE, USA) in trenched (RT) and untrenched (RUT) plots. Soil temperature at 5 cm depth (T5) was measured adjacent to each respiration collar with a portable temperature probe provided with the Li-8100. Soil volumetric water content at 0e5 cm (SWC) was measured with a portable time domain reflectometer MPKit-B soil moisture gauge (NTZT Inc., Nantong, China) at three points close to each chamber. The measurements were made twice a month during the growing season (15 Apr.e16 Oct., DOY 106e287) and once a month in other periods. No measurements were conducted during Dec. 2008, Jan. and Feb. 2009 due to snow coverage.
Table 1 Summary of stand characteristics measured in summer 2009.a Stand
BA (m2 ha1)
Density (trees ha1)
DBH (SD, cm)
LAI (SD)
BD(SD) g/cm3
SOC kg C m2
TN kg N m2
PH
40-year-old 1 40-year-old 2 40-year-old 3 48-year-old 1 48-year-old 2 48-year-old 3 80-year-old 1 80-year-old 2 80-year-old 3 143-year-old 1 143-year-old 2 143-year-old 3
52.0 46.5 57.3 60.0 53.2 46.5 65.5 77.6 61.0 80.1 85.4 100.9
1900 2100 2600 2700 1900 2000 2000 2600 2000 1200 2500 2000
16.2(9.5) 13.7(9.2) 15.0(7.7) 12.4(11.6) 16.2(10) 13.7(10.7) 17.02(11.57) 16.36(10.11) 15.03(13.08) 21.64(20.47) 12.59(16.44) 17.83(18.48)
3.34(0.23) 3.41(0.41) 3.04(0.40) 2.43(0.09) 2.39(0.17) 2.36(0.22) 2.97(0.19) 3.26(0.25) 2.84(0.23) 3.24(0.53) 3.07(0.44) 3.11(0.52)
0.88(0.05) 0.72(0.04) 0.60(0.08) 0.60(0.06) 0.66(0.06) 0.69(0.05) 0.66(0.02) 0.67(0.03) 0.62(0.03) 0.46(0.06) 0.73(0.08) 0.53(0.07)
31.10 31.24 25.31 35.91 34.28 35.81 36.65 35.84 29.58 41.98 51.34 28.45
2.32 2.26 1.78 2.44 2.79 2.22 2.74 2.39 2.33 2.33 2.94 1.51
4.65 4.43 4.44 4.84 4.56 5.02 4.49 4.47 4.57 4.66 5.24 4.88
a BA, DBH, LAI, BD, SOC and TN stand for basal area, mean tree diameter at breast height, leaf area index, bulk density, soil organic carbon and total nitrogen stock at 0e10 cm soil depth respectively. Quercus acutidentata dominated all stands.
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
2.3. Soil carbon contents, light fraction organic carbon, root biomass and LAI Soil samples were collected at 0e10 cm, 10e30 cm and 30e50 cm depths using a 10-cm diameter corer in summer 2009. Three samples were taken within 1e2 m of the centre of the respiration collars in the trenched and untrenched plots and mixed thoroughly to give one sample per subplot and depth. Coarse (>5 mm), medium (2e5 mm) and fine (<2 mm) roots were manually separated from soil collected at 0e30 cm depth (>80% fine root biomass was distributed at 0e30 cm soil depth, unpublished data), and dry biomass was measured. We measured massbased soil carbon content (%) and bulk density (g m3) for each soil sample at the three depths across the chronosequence. Capillary porosity (CP) at 0e10 cm was determined as described by Liu et al. (2009) for each soil sample. Soil light fraction organic matter at the depth of 0e10 cm was obtained by density fractionation based on Six et al. (1998), but with a modification of using CaCl2 solution (density of 1.5 g ml1; Garten et al., 1999). Bulk soil and light fraction organic carbon contents were determined by the wet oxidation method with 133 mM K2Cr2O7 at 170e180 C (Lu, 2000). Soil carbon stocks (SOC; g m2) at 0e10, 10e30, and 30e50 cm depth and light fraction organic carbon stocks (LFOC; g m2) at 0e10 cm depth were calculated using carbon content and bulk density. Tree diameter at breast height (DBH) was measured for each tree in each stand. Leaf area index (LAI) was measured using hemispherical photographs in August 2009 along a 25 m transect in each stand with WinSCANOPY (Regent Instruments Inc., Quebec, Canada). 2.4. Root decomposition experiment
2.5. Data analysis We used an exponential equation (Eq. (1)) to describe the relationships between RUT, RT, estimated rhizospheric respiration (RR), or estimated heterotrophic respiration (RH) and soil temperature at 5 cm depth:
R ¼ aebT
(1)
where R is RUT, RT, RR, or RH, T is the soil temperature at 5 cm depth (T5), a and b are fitted parameters. The temperature sensitivity parameter, Q10, was calculated as:
Q 10 ¼ eð10bÞ
conducted a multilinear regression (soil temperature and SWC as two independent variables; ln(R) ¼ aþbT5þcSWC). T-tests was used to determine if the SWC term was significant in predicting soil respiration. 2.5.1. Growing season RS, RH, RR estimation Based on half-hour time steps, we used the plot-specific respiration models (RT or RUT) vs. T5 (Eq. (1)) to estimate growing season (DOY 106e287) cumulative RT and RUT (RS) for the 12 plots (3 in each forest type). The continuous measurements of T5 were used as the model inputs for T. Since there were no meteorological stations in the four forests to directly obtain continuous T5, we developed regression models of soil temperature between the four forests and the adjacent forest meteorological station based on discrete T5 measurements (R2 > 0.95, P < 0.001). Using these models and halfhour time step measurements of T5 in the adjacent meteorological station, we estimated continuous T5 for each forest. Cumulative soil CO2 effluxes were calculated using the empirical model of each plot for interpolation. CO2 efflux due to the decomposition of residual roots of each size class (Rd; g C m2 day1) at a given time t was calculated using the method described by Lee et al. (2003):
Rd ¼ Br aevðt1=365Þ aevt RD ¼
X
Rd
(2)
On the basis of year-round data, we calculated annual Q10 and R0 (defined as the basal respiration rate at 0 C) values. To test the effect of soil water content (SWC) on soil respiration, we added SWC or log-transformed SWC as a term in Eq. (1), and
(3) (4)
where Br: Root biomass (g C m2); root decomposition rates (n) were calculated using 0.64 as the mineralization rate (Nakane K et al., 1996) by:
v ¼ 0:64k
Plot trenching cuts off the roots from live plants, initially raising the measured CO2 flux from the trenched plots due to an increase of substrate supply (dead roots) for microbial respiration (Lee et al., 2003; Bond-Lamberty et al., 2004b). Thus, we used root biomass data and measured root decomposition to calculate the CO2 fluxes released from dead root decomposition (RD). Root samples collected in September 2008 were placed in bags (30 30 cm) made of 1-mm nylon mesh to estimate the relative loss rate constant (k) in the soil. A total of 45 bags were prepared with 15 bags each of fine, medium and coarse roots. They were buried at a soil depth of 10e20 cm in September 2008 and dry mass of the buried roots was measured every 2 months (3 bags sampled each time for each root size).
505
(5)
where k is the relative loss rate constant obtained by the exponential decay function:
X=X0 ¼ aekt
(6)
Heterotrophic respiration (RH), rhizospheric respiration (RR) and the relative contribution of RR to total RS (RC; %) were calculated based on data collected from March to November of 2009 as follows:
RH ¼ RT RD
(7)
RR ¼ RUT RH
(8)
RC ¼ RR =RUT 100
(9)
2.5.2. Comparisons of RT, RUT, RS, RH and RR Repeated measures GLMs were employed to compare the means of RT and RUT among forests across the growing season. Because of nonsimultaneous and periodic cross-stand measurement, which may change diurnally and seasonally, we relied on modeling results to compare the cumulative components of soil CO2 efflux among stands across the season. WallereDuncan k-ratio t-tests were performed to assess the age effect on growing season cumulative RS, RH, RR, RD, as well as CP and root biomass for each root size class. Linear regression analyses were used to examine the relationships between growing season cumulative RS, RH, RR and soil or stand properties. Linear regression of differences in SWC and differences in soil CO2 efflux between trenched and untrenched plots was also performed to evaluate the trenching effect on soil surface CO2 flux for each stand age class separately. A quadratic regression was used to describe the relationship between RS and
506
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
LAI. All statistical analyses were performed using the SPSS 13.0 software package for Windows (SPSS Inc., Chicago, USA).
(Fig. 3). Q10 of RH increased from 3.60 for the 40-year-old forest to 4.26 for the 80-year-old forest and then declined to 3.90 for the 143-year-old forest. Regardless of age effects, Q10 of RH (3.93 0.27) was significantly higher than that of RR (2.78 0.73; F7 ¼ 8.74, P ¼ 0.025).
3. Results 3.1. Seasonal dynamic of soil CO2 efflux
3.3. Growing season cumulative fluxes of respiration components The seasonal patterns of RT and RUT were similar to those of T5, they peaked in August and presented the lowest values in March and November (Fig. 1). RUT and RT of younger forests showed relatively little inter-seasonal variation (Fig. 1). The estimated RH of all forests showed a similar seasonal pattern to T5, peaking in August (from 3.03 for 48-year-old to 5.55 g C m2day1 for 143-year-old forests respectively; Fig. 2). The estimated RR of 80-year-old and 143-year-old forests also had a similar seasonal pattern to T5, while RR of 40-year-old and 48-year-old forests peaked a month later (Sept.) than T5 (Aug.; Fig. 2). The contribution of RR to RS (RC) in 2009 ranged from 12% to 52% and the four forests showed significantly different seasonal patterns of RC (P < 0.05). The RC of 40-year-old and 48-year-old forests presented a second peak in September (Fig. 2). The mean measured rates of RT and RUT were significantly different (P < 0.05) among forests (Table 2); both RT and RUT increased with forest age.
Among the four forests, the estimated growing season cumulative RS increased with forest age. RS in 143-year-old and 80-year-old forests was significantly higher than in 48-year-old and 40-year-old forests (P < 0.05; Table 2). Estimated growing season RH and RR also differed significantly among the four forests (P < 0.05; Table 2). RH increased with forest age, while RR increased 40-year-old to 80-year-old forests and then declined for 143-year-old forests (Table 2). The calculated RC increased from 40-year-old forest to 80-yearold forest and then declined for 143-year-old forests (Table 2). Growing season cumulative RS was explained by basal area (BA) with linear regression (R2 ¼ 0.589, P ¼ 0.004; Fig. 4a) and we found a marginal correlation between RS and SOC (R2 ¼ 0.265, P ¼ 0.087; Fig. 4b). LAI showed a marginally significant quadratic relationship with RS (P ¼ 0.074; Fig. 4b). Growing season cumulative RH was positively correlated with SOC at 0e10 cm depth (R2 ¼ 0.314, P ¼ 0.058) and with LFOC stocks (R2 ¼ 0.654, P ¼ 0.001; Fig. 4d). Basal respiration (R0) of trenched plots was explained by LFOC stock (R2 ¼ 0.592, P ¼ 0.003; Fig. 4f). No significant relationship between RR and fine root biomass was found (Fig. 4e). Lower capillary porosity (CP) was found in 80-year-old and 143-year-old forests compared to 40-year-old and 48-year-old forests (P < 0.05). Capillary porosity at 0e5 cm depth was negatively correlated to cumulative RS (RS ¼ 1143.72 CP þ 1370.32, R2 ¼ 0.441, P ¼ 0.019).
3.2. Relationship of soil CO2 efflux with soil temperature and moisture Temporal variation of soil CO2 efflux in trenched (RT) and untrenched plots (RUT) can be well described by T5 using an exponential equation, with R2 ranging from 0.60 to 0.88 for the trenched, and from 0.61 to 0.86 for the untrenched plots (P < 0.001). A multilinear regression (T5 and SWC as independent variables) indicated that soil moisture was not a significant factor in predicting soil CO2 efflux. Therefore, we used the models of RT or RUT against T5 for estimating growing season fluxes of RT and RUT. There were significant exponential relationships between estimated RR, RH and T5
3.4. Trenching effect on soil microclimate Plot trenching had no significant effect on soil temperature (P > 0.05), yet tended to raise soil moisture in the four forests
a
b
c
d
Fig. 1. Seasonal patterns of soil surface CO2 fluxes from trenched (RT) and untrenched (RUT) plots in the four forests during 2008e2009. The error bars represent standard deviations (n ¼ 9 for trenched and untrenched, n ¼ 3 for untrenched-trenched). **Denotes significant differences between trenched and untrenched plots at p ¼ 0.05.
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
507
a
b
c
d
Fig. 2. Seasonal patterns of heterotrophic respiration rates (RH), rhizospheric respiration rates (RR), root decomposition rates (RD; g C m2d1) and the contribution of RR to RS (RC %).
(Fig. 5). Most significant differences in soil moisture between the trenched and untrenched plots occurred in the 80-year-old forest (Fig. 5c). Significant relationships between the difference of SWC and the difference of soil CO2 efflux between trenched and untrenched plots were only found in 80-year-old forests (R2 ¼ 0.09, P ¼ 0.038; Fig. 6). 4. Discussion 4.1. Seasonality of soil CO2 efflux In this study, the measured soil CO2 efflux rates from trenched and untrenched plots were comparable to previous studies in temperate forests (Boone et al., 1998; Subke et al., 2006; Wang and Yang, 2007). The seasonality of soil CO2 efflux was affected by the soil environment (Davidson et al., 1998), vegetation structure (Wang and Yang, 2007), and forest age (Tang et al., 2009). The trenching treatment allowed us to explore seasonal patterns of each component of soil CO2 efflux among forests instead of merely total RS. The estimated RH showed a similar seasonal pattern to T5, hence the temporal variation of RH can be largely explained by T5 (R2 ¼ 0.83e0.93; Fig. 3). This indicates that soil temperature is a good indicator of temporal variation of heterotrophic respiration. In addition, we found that the 143-year-old forest had higher RH
compared to the other forests (Fig. 2d) and this led to a significantly higher cumulative RH (Table 2). However, we did not find higher seasonal coefficients of variance (CV) of RH in the 143-year-old forest compared to the other forests (P > 0.05). Therefore, we found that the Q10 of RH in the 143-year-old forest was similar to the other forests (Fig. 3), suggesting similar temperature responses of RH among the forests. As we found higher R0 of RH (0.469 m mol CO2 m2 s1) in the 143-year-old forest compared to the other forests (0.301 0.017 m mol CO2 m2 s1; Fig. 3), the higher RH in this forest may be accounted for by higher substrate availability rather than temperature. This was illustrated by the significant relationship between R0 of trenched plots and LFOC (Fig. 4f), as LFOC was considered as a good indicator of soil organic matter quality (Six et al., 2002; Luan et al., 2010). We found an apparent inconsistent pattern between RR and T5 in 40-year-old and 48-year-old forests, where RR peaked nearly one month later than T5 (Fig. 2a, b). Similar results were also found in a Pinus contorta forest (Scott-Denton et al., 2006) and in a series of temperate forests (Wang and Yang, 2007). Gaumont-Guay et al. (2008) explained that the lagged response may suggest relatively slow transport of photosynthates to roots. However, a recent 13C pulse-chase labeling experiment indicated that photosynthates were transferred to soil CO2 efflux in less than 4 days (Högberg et al., 2008; Plain et al., 2009). On the other hand, we did not find a similar RR lag for 80-year-old and 143-year-old forests, and
Table 2 Mean soil surface CO2 fluxes from trenched (RT) and untrenched plots (RUT) given as m mol CO2 m2 s1, cumulative growing season (DOY 106e287) total soil respiration (RS), rhizospheric (RR) and heterotrophic respiration (RH) in 2009 (g C m2), and relative contribution of RR to RS (RC%) for the four forest types.a, b Forest type
RUT
RT
RS
RD
RH
RR
RC (%)
40-year-old 48-year-old 80-year-old 143-year-old
2.801(0.106)a 3.105(0.109)a 3.619(0.153)b 4.007(0.173)b
2.227(0.097)a 2.345(0.078)a 2.388(0.091)a 3.560(0.148)b
623.66(37.56)a 658.54(11.03)a 805.74(24.44)b 831.96(22.79)b
40.70(0.52)a 40.41(1.19)a 38.67(1.28)a 45.17(0.21)b
431.72(31.86)a 452.02(19.57)a 484.62(12.42)a 678.93(40.97)b
191.94(9.23)a 206.51(29.95)a 321.13(36.68)b 153.03(41.35)a
30.78 31.36 39.85 18.39
a b
Values in parentheses are standard errors of means for N ¼ 3. Means with different letters within a column are significantly different at p ¼ 0.05.
508
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
a
b
c
d
Fig. 3. Relationships between soil temperature at 5 cm depth (T5) and RH and RR for 40-year-old (a), 48-year-old (b), 80-year-old (c) and 143-year-old (d) forests (n ¼ 15).
a
b
c
d
e
f
Fig. 4. Relationships between growing season cumulative gross soil respiration (RS) and basal area (a), RS and SOC (b), RS and LAI (c), RH and soil organic carbon (SOC) and light fraction organic carbon (LFOC) stocks at 0e10 cm depth (d), rhizospheric respiration (RR) and fine root biomass (e), and basal respiration (R0) of trenched plots and LFOC stocks (f).
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
a
b
c
d
509
Fig. 5. Seasonal patterns of soil temperature (T5) and soil water content (SWC) of trenched and untrenched plots, error bars represent standard errors of means (n ¼ 3). *, ** and ***Denote significant difference between trenched and untrenched plots at p ¼ 0.05, p ¼ 0.01, or p ¼ 0.001, respectively.
therefore complex biophysical processes may dominate the seasonality of RR differently among forests. Fu et al. (2002) reported that rhizospheric respiration varied not only with plant species but also with plant phenology, and Högberg et al. (2001) also showed that forest type and associated metabolic activities (e.g. phenology, photosynthate allocation, etc.) may be more important in controlling seasonal patterns of RR than of RH. Therefore, differences in seasonal patterns of RR and RC among forests in our study may suggest different physiological processes among forest successional stages. Accurate measurements of long-distance phloem transport
of photosynthates to roots or a new technique to measure rhizospheric respiration are necessary to validate our results. We also found lower seasonal variation of RR for the 143-year-old compared to the other forests and only a marginally significant exponential relationship between RR and T5, which may explain the lower temperature response of RR (Q10 ¼ 1.75; Fig. 3). We speculate that the temperature response of RR in the old growth forest is lower than that in the young stands. All of the above suggest that seasonal variations in RH and RR are probably driven by different mechanisms. 4.2. Components of soil CO2 efflux and their controls along the chronosequence Our estimated seasonal fluxes of RS, RH and RR, as well as the RC values for the four forests were within the range of reported values for temperate forests (Hanson et al., 2000; Bond-Lamberty et al., 2004a; Subke et al., 2006). However, their variation and controlling factors were different among stands. Fu et al. (2002) reported that rhizospheric respiration varied with plant species. In our study, the basal area of dominant species (Q. acutidentata) accounted for 95e98% of the total basal area for the four forests, and we speculate that the difference in species composition among the stands would not have a great impact on respiration partitioning.
Fig. 6. Relationships between differences in soil water content between trenched and untrenched plots (SWCtrenched SWCuntrenched) and the differences in soil CO2 efflux between trenched and untrenched plots (RT RUT) for four forests.
4.2.1. Variation of RS Cumulative RS varied significantly across the four forests and stand age had a positive influence on RS (Table 2); this is consistent with previous studies (Litton et al., 2003; Wiseman and Seiler, 2004; Jiang et al., 2005). Zhou et al. (2006) reported that old growth forest in subtropical China can still accumulate soil organic carbon. Our study supports this, as higher SOC stock in the topsoil was found in the old growth oak forest, which was marginally correlated with the inter-site variation of cumulative RS (Fig. 4a). Soil carbon
510
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
decomposes much slower in deeper soil, probably due to lower soil temperatures during the growing season, low oxygen availability, and high soil aggregation (Six et al., 2002). Therefore, we did not find a significant relationship between RS and SOC stock at the depths of 10e30, 30e50 cm. There was a positive relationship between RS and BA (a proxy of aboveground biomass), which was consistent with the result reported by Nsabimana et al. (2009). This suggests that the correlation between soil CO2 efflux and gross primary production (Janssens et al., 2001; Reichstein et al., 2003). We did not find the significant linear relationship between RS and LAI as reported by Tang et al. (2009). However, we found a marginally significant quadratic relationship between RS and LAI (R2 ¼ 0.40, P ¼ 0.074; Fig. 4c). LAI in relation to gross primary production may drive respiration over a short period of time (days to months; Högberg et al., 2001; Tang et al., 2005) and root biomass has larger direct effects on RS than LAI does (Tang et al., 2009). On the other hand, CP declined significantly with increasing stand age and this will possibly influence soil surface CO2 efflux. It was found that lower CP in 80-year-old and 143-year-old forests partly contributed to the higher cumulative RS and we speculate that the restriction of soil CO2 overflow by CP may be as a result of capillary hanging water. 4.2.2. Variation of RH Similar to Jiang et al. (2005), we found a positive age effect on RH as RH was significantly higher in the 143-year-old forest than in other forests (Table 2). Our results were also consistent with Law et al.’s (2003) results, who found that heterotrophic respiration in ponderosa pine (Pinus ponderosa) stands in Oregon increased with forest age from 9e23 years old to 95e106 years old. In our study, the cumulated soil carbon stock, especially the LFOC, in the topsoil, explained the pattern of RH across the chronosequence (Fig. 4d). Furthermore, a higher slope of LFOC vs. RH than SOC vs. RH was found, and this may indicate the higher contribution of labile organic carbon to soil respiration. In addition, the basal respiration (R0) of trenched plots was highly dependent on LFOC (Fig. 4f). This further illustrated that the variations in RH across the chronosequence can be mainly attributed to different substrate (i.e. labile carbon) availability (Wang et al., 2010) and organic matter quality (Cisneros-Dozal et al., 2006; Wang and Yang, 2007). 4.2.3. Variation of RR Unlike RS and RH, the age effect on RR was nonlinear in our study. RR was significantly higher in the 80-year-old forest than in the other forests (Table 2). We did not find a significant relationship between RR and fine root biomass, in contrast to Wang and Yang (2007). This may be explained by the following reasons: Firstly, both respiration and root biomass were very dynamic over the growing season. Although we sampled root biomass at the peak in soil temperature, which is associated with maximum root production (Wang et al., 2006), a single sampling of fine root biomass during the growing season could not account completely for the cumulative estimate of RR. Secondly, we did not separate roots between trees and grasses when sampling, which may lead to inaccurate estimation of the root biomass as the root biomass composition of woody plants and grasses varied with season and forest age. Finally, it is believed that complex mechanisms underpin the variation in RR, for example, photosynthesis and transport of photosynthates to roots both influence root respiration (Högberg et al., 2001; Gaumont-Guay et al., 2008). Thus, further research is needed to elucidate the variation of RR among forests of different age. 4.3. Trenching effects on microclimate and soil CO2 efflux Our study showed that trenching modified soil environmental conditions, but the degree of trenching effects varied with forest
type. Unlike Wang and Yang (2007), we found no significant trenching effect on soil temperature in our study (Fig. 6). Trenching increased soil water content, especially for the 80-year-old forest (Fig. 5c), which is consistent with other studies (Ngao et al., 2007; Wang and Yang, 2007) and could be the result of the elimination of root uptake of soil water after trenching. However, since we did not find a significant relationship between RS and SWC, changes in SWC due to trenching may have had no significant effect on soil CO2 efflux. The correlation between the difference in soil surface CO2 efflux (RT RUT) and the difference in SWC (SWCtrenched SWCuntrenched) further confirmed that trenching did not significantly affect soil CO2 efflux through its effects on soil microclimate (Fig. 6). It was reported that severed fine roots may quickly decompose after trenching, leading to a rapid CO2 flush (Rey et al., 2002; Lee et al., 2003; Ngao et al., 2007). In our study, two methods were employed to mitigate this impact on our results. First, we estimated RH and RR from April 16, after 8 months trenching when the transient CO2 flush had disappeared (Lee et al., 2003; Wang and Yang, 2007). Second, we estimated the C released from dead root decomposition during the same period using root decomposition bags. We found significantly higher fine root biomass in the 143-year-old forest compared to the other forests (P < 0.05), and this resulted in a higher RD in the 143-year-old forest (Table 2). However, RD is unlikely to have affected our estimation of RH as RD contributed little to RS (5e7%; Fig. 2, Table 2). Our estimation of RD 8 months after trenching was lower than that reported in previous studies, e.g. in temperate deciduous forests (14e24%; Epron et al., 1999 and 16%; Lee et al., 2003) and temperate evergreen forests (8%; Ohashi et al., 2000). Furthermore, priming effects from dead roots should be negligible so long after trenching (Kuzyakov and Bol, 2006). 4.4. Models of rhizospheric and heterotrophic respiration Growing season Q10 values for heterotrophic and rhizospheric respiration ranged from 3.60 to 4.26 and from 1.75 to 3.35, respectively (Fig. 3), which were well within the range reported for other temperate forests (Kirschbaum, 1995; Davidson et al., 1998). If the age effect was ignored, we found significantly higher Q10 of RH compared to RR. This indicates that microbial respiration is more temperature-sensitive compared to rhizospheric respiration, which has implications for future climate warming. Until now, no consensus has been reached on whether the temperature response of rhizospheric or heterotrophic respiration is higher (Boone et al., 1998; Bååth and Wallander, 2003; Hartley et al., 2007). However, the Q10 function is still a valid empirical method to simulate soil CO2 efflux in a specific site, which is not limited by water content, especially, when the simulation is made through interpolation rather than extrapolation (Tang et al., 2009). The variability of Q10 among forests and between components will affect cumulative soil respiration estimation across stands. Therefore, rhizospheric and heterotrophic respiration components and their respective Q10 values should be separately incorporated models of ecosystem C cycling. 5. Conclusions Soil heterotrophic and rhizospheric respiration varied with forest age. Soil environmental conditions and labile organic carbon stocks were good indicators for predicting temporal and spatial variations of heterotrophic respiration, whereas controlling mechanisms on the temporal and spatial variation of rhizospheric respiration were more complex. New techniques (e.g. stable 13C isotope tracing) are needed to elucidate the mechanism underlying
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
the variation in rhizospheric respiration. Different temperature responses between respiration components and among forests with different stand ages indicate the importance of component partitioning for accurate estimation of soil CO2 efflux in forest ecosystems. Acknowledgement We gratefully acknowledge the support from Baotianman National Nature Reserve for their help in field monitoring and sampling, they are Yin Wu, Ye Tian, Xinfang Yang, Xiaojing Liu, and Jiguo Liu. We also thank the two anonymous reviewers and Dr. Clay Altizer for their valuable comments and suggestions. This study was funded by the China’s National Natural Science Foundation (No. 30590383), the China’s Ministry of Finance (No. 200804001; No. 201104006) and the Ministry of Science and Technology of China (No. 2008DFA32070; No. 2011CB403205; No. 2006BAD03A04). References Bååth, E., Wallander, H., 2003. Soil and rhizosphere microorganisms have the same Q10 for respiration in a model system. Global Change Biology 9, 1788e1791. Bhupinderpal-singh, Nordgren, A., LöVenius, M.O., Höberg, M.N., Mellander, P.-E., Höberg, P., 2003. Tree root and soil heterotrophic respiration as revealed by girdling of boreal Scots pine forest: extending observations beyond the first year. Plant, Cell & Environment 26, 1287e1296. Bond-Lamberty, B., Wang, C., Gower, S.T., 2004a. A global relationship between the heterotrophic and autotrophic components of soil respiration. Global Change Biology 10, 1756e1766. Bond-Lamberty, B., Wang, C., Gower, S.T., 2004b. Contribution of root respiration to soil surface CO2 flux in a boreal black spruce chronosequence. Tree Physiology 24, 1387e1395. Boone, R.D., Nadelhoffer, K.J., Canary, J.D., Kaye, J.P., 1998. Roots exert a strong influence on the temperature sensitivity of soil respiration. Nature 396, 570e572. Cisneros-Dozal, L.M., Trumbore, S., Hanson, P.J., 2006. Partitioning sources of soilrespired CO2 and their seasonal variation using a unique radiocarbon tracer. Global Change Biology 12, 194e204. Davidson, E.A., Belk, E., Boone, R.D., 1998. Soil water content and temperature as independent or confounded factors controlling soil respiration in a temperate mixed hardwood forest. Global Change Biology 4, 217e227. Davidson, E.A., Janssens, I.A., Luo, Y., 2006. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Global Change Biology 12, 154e164. Epron, D., Farque, L., Lucot, E., Badot, P.-M., 1999. Soil CO2 efflux in a beech forest: the contribution of root respiration. Annals of Forest Science, 289e295. Fu, S., Cheng, W., Susfalk, R., 2002. Rhizosphere respiration varies with plant species and phenology: a greenhouse pot experiment. Plant and Soil 239, 133e140. Garten, C.T., Post, W.M., Hanson, P.J., Cooper, L.W., 1999. Forest soil carbon inventories and dynamics along an elevation gradient in the southern Appalachian mountains. Biogeochemistry 45, 115e145. Gaumont-Guay, D., Black, T.A., Barr, A.G., Jassal, R.S., Nesic, Z., 2008. Biophysical controls on rhizospheric and heterotrophic components of soil respiration in a boreal black spruce stand. Tree Physiology 28, 161e171. Hanson, P.J., Edwards, N.T., Garten, C.T., Andrews, J.A., 2000. Separating root and soil microbial contributions to soil respiration: a review of methods and observations. Biogeochemistry 48, 115e146. Hartley, I.P., Heinemeyer, A., Evans, S.P., Ineson, P., 2007. The effect of soil warming on bulk soil vs. rhizosphere respiration. Global Change Biology 13, 2654e2667. Högberg, P., Höberg, M.N., Götlicher, S.G., Betson, N.R., Keel, S.G., Metcalfe, D.B., Campbell, C., Schindlbacher, A., Hurry, V., Lundmark, T., Linder, S., Näholm, T., 2008. High temporal resolution tracing of photosynthate carbon from the tree canopy to forest soil microorganisms. New Phytologist 177, 220e228. Högberg, P., Nordgren, A., Buchmann, N., Taylor, A.F.S., Ekblad, A., Hogberg, M.N., Nyberg, G., Ottosson-Lofvenius, M., Read, D.J., 2001. Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature 411, 789e792. IPCC, 2001. Climate Change 2001: the Scientific Basis. Cambridge University Press, Cambridge, UK. Janssens, I.A., Lankreijer, H., Matteucci, G., Kowalski, A.S., Buchmann, N., Epron, D., Pilegaard, K., Kutsch, W., Longdoz, B., Grunwald, T., Montagnani, L., Dore, S., Rebmann, C., Moors, E.J., Grelle, A., Rannik, U., Morgenstern, K., Oltchev, S., Clement, R., Gudmundsson, J., Minerbi, S., Berbigier, P., Ibrom, A., Moncrieff, J., Aubinet, M., Bernhofer, C., Jensen, N.O., Vesala, T., Granier, A., Schulze, E.D., Lindroth, A., Dolman, A.J., Jarvis, P.G., Ceulemans, R., Valentini, R., 2001. Productivity overshadows temperature in determining soil and ecosystem respiration across European forests. Global Change Biology 7, 269e278. Jassal, R.S., Black, T.A., 2006. Estimating heterotrophic and autotrophic soil respiration using small-area trenched plot technique: theory and practice. Agricultural and Forest Meteorology 140, 193e202.
511
Jiang, L., Shi, F., Li, B., Luo, Y., Chen, J., Chen, J., 2005. Separating rhizosphere respiration from total soil respiration in two larch plantations in northeastern China. Tree Physiology 25, 1187e1195. Kelting, D.L., Burger, J.A., Edwards, G.S., 1998. Estimating root respiration, microbial respiration in the rhizosphere, and root-free soil respiration in forest soils. Soil Biology and Biochemistry 30, 961e968. Kirschbaum, M.U.F., 1995. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biology and Biochemistry 27, 753e760. Klopatek, J.M., 2002. Belowground carbon pools and processes in different age stands of Douglas-fir. Tree Physiology 22, 197e204. Kuzyakov, Y., 2006. Sources of CO2 efflux from soil and review of partitioning methods. Soil Biology and Biochemistry 38, 425e448. Kuzyakov, Y., Bol, R., 2006. Sources and mechanisms of priming effect induced in two grassland soils amended with slurry and sugar. Soil Biology and Biochemistry 38, 747e758. Laik, R., Kumar, K., Das, D.K., Chaturvedi, O.P., 2009. Labile soil organic matter pools in a calciorthent after 18 years of afforestation by different plantations. Applied Soil Ecology 42, 71e78. Law, B.E., Sun, O.J., Campbell, J., Van Tuyl, S., Thornton, P.E., 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Global Change Biology 9, 510e524. Lee, M.-S., Nakane, K., Nakatsubo, T., Koizumi, H., 2003. Seasonal changes in the contribution of root respiration to total soil respiration in a cool-temperate deciduous forest. Plant and Soil 255, 311e318. Litton, C.M., Ryan, M.G., Knight, D.H., Stahl, P.D., 2003. Soil-surface carbon dioxide efflux and microbial biomass in relation to tree density 13 years after a stand replacing fire in a lodgepole pine ecosystem. Global Change Biology 9, 680e696. Liu, S., Jiang, Y., Shi, Z., 1998. Overview of the Baotianman nature reserve. In: A Study on the Biological Diversity in Warm Temperate Forest in China. China Science and Technology Press, Beijing, p. 45. Liu, X., Zhang, G., Heathman, G.C., Wang, Y., Huang, C.-h, 2009. Fractal features of soil particle-size distribution as affected by plant communities in the forested region of Mountain Yimeng, China. Geoderma 154, 123e130. Lu, R., 2000. Soil and Agricultural Chemistry Analysis Methods. Chinese Agricultural Scientific and Technology Press, Beijing (in Chinese). Luan, J., Xiang, C., Liu, S., Luo, Z., Gong, Y., Zhu, X., 2010. Assessments of the impacts of Chinese fir plantation and natural regenerated forest on soil organic matter quality at Longmen mountain, Sichuan, China. Geoderma 156, 228e236. Nakane, K., Kohno, T., Horikoshi, T., 1996. Root respiration before and just after clear-felling in a mature deciduous, broad-leaved forest. Ecological Research 11, 111e119. Ngao, J., Longdoz, B., Granier, A., Epron, D., 2007. Estimation of autotrophic and heterotrophic components of soil respiration by trenching is sensitive to corrections for root decomposition and changes in soil water content. Plant and Soil 301, 99e110. Nsabimana, D., Klemedtson, L., Kaplin, B.A., Wallin, G., 2009. Soil CO2 flux in six monospecific forest plantations in Southern Rwanda. Soil Biology and Biochemistry 41, 396e402. Ohashi, M., Gyokusen, K., Saito, A., 2000. Contribution of root respiration to total soil respiration in a Japanese cedar (Cryptomeria japonica D. Don) artificial forest. Ecological Research 15, 323e333. Plain, C., Gerant, D., Maillard, P., Dannoura, M., Dong, Y., Zeller, B., Priault, P., Parent, F., Epron, D., 2009. Tracing of recently assimilated carbon in respiration at high temporal resolution in the field with a tuneable diode laser absorption spectrometer after in situ 13CO2 pulse labelling of 20-year-old beech trees. Tree Physiology 29, 1433e1445. Pregitzer, K.S., Euskirchen, E.S., 2004. Carbon cycling and storage in world forests: biome patterns related to forest age. Global Change Biology 10, 2052e2077. Raich, J.W., Potter, C.S., 1995. Global patterns of carbon dioxide emissions from soils. Global Biogeochemical Cycles 9, 23e36. Reichstein, M., Rey, A., Freibauer, A., Tenhunen, J., Valentini, R., Banza, J., Casals, P., Cheng, Y., Grünzweig, J.M., Irvine, J., Joffre, R., Law, B.E., Loustau, D., Miglietta, F., Oechel, W., Ourcival, J.-M., Pereira, J.S., Peressotti, A., Ponti, F., Qi, Y., Rambal, S., Rayment, M., Romanya, J., Rossi, F., Tedeschi, V., Tirone, G., Xu, M., Yakir, D., 2003. Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Global Biogeochemical Cycles 17, 1104. Rey, A., Pegoraro, E., Tedeschi, V., De Parri, I., Jarvis, P.G., Valentini, R., 2002. Annual variation in soil respiration and its components in a coppice oak forest in Central Italy. Global Change Biology 8, 851e866. Ryan, M.G., Law, B.E., 2005. Interpreting, measuring, and modeling soil respiration. Biogeochemistry 73, 3e27. Saiz, G., Green, C., Butterbach-Bahl, K., Kiese, R., Avitabile, V., Farrell, E., 2006. Seasonal and spatial variability of soil respiration in four Sitka spruce stands. Plant and Soil 287, 161e176. Scott-Denton, L.E., Rosenstiel, T.N., Monson, R.K., 2006. Differential controls by climate and substrate over the heterotrophic and rhizospheric components of soil respiration. Global Change Biology 12, 205e216. Six, J., Callewaert, P., Lenders, S., 2002. Measuring and understanding carbon storage in afforested soils by physical fractionation. Soil Science Society of America Journal 66, 1981e1987. Six, J., Elliott, E.T., Paustian, K., Doran, J.W., 1998. Aggregation and soil organic matter accumulation in cultivated and native grassland soils. Soil Science Society of America Journal 62, 1367e1377.
512
J. Luan et al. / Soil Biology & Biochemistry 43 (2011) 503e512
Subke, J.-A., Inglima, I., Cotrufo, M.F., 2006. Trends and methodological impacts in soil CO2 efflux partitioning: a meta analytical review. Global Change Biology 12, 921e943. Tang, J., Baldocchi, D.D., Xu, L., 2005. Tree photosynthesis modulates soil respiration on a diurnal time scale. Global Change Biology 11, 1298e1304. Tang, J., Bolstad, P.V., Martin, J.G., 2009. Soil carbon fluxes and stocks in a Great Lakes forest chronosequence. Global Change Biology 15, 145e155. Wang, C., Bond-Lamberty, B., Gower, S.T., 2002. Soil surface CO2 flux in a boreal black spruce fire chronosequence. Journal of Geophysical Research 107, 1e8. Wang, C., Yang, J., 2007. Rhizospheric and heterotrophic components of soil respiration in six Chinese temperate forests. Global Change Biology 13, 123e131. Wang, C., Yang, J., Zhang, Q., 2006. Soil respiration in six temperate forests in China. Global Change Biology 12, 2103e2114. Wang, W., Peng, S., Wang, T., Fang, J., 2010. Winter soil CO2 efflux and its contribution to annual soil respiration in different ecosystems of a forest-steppe ecotone, north China. Soil Biology and Biochemistry 42, 451e458.
Wiseman, P.E., Seiler, J.R., 2004. Soil CO2 efflux across four age classes of plantation loblolly pine (Pinus taeda L.) on the Virginia Piedmont. Forest Ecology and Management 192, 297e311. Xu, M., Qi, Y., 2001. Soil-surface CO2 efflux and its spatial and temporal variations in a young ponderosa pine plantation in northern California. Global Change Biology 7, 667e677. Yi, Z., Fu, S., Yi, W., Zhou, G., Mo, J., Zhang, D., Ding, M., Wang, X., Zhou, L., 2007. Partitioning soil respiration of subtropical forests with different successional stages in south China. Forest Ecology and Management 243, 178e186. Yuste, J.C., Janssens, I.A., Carrara, A., Ceulemans, R., 2004. Annual Q10 of soil respiration reflects plant phenological patterns as well as temperature sensitivity. Global Change Biology 10, 161e169. Zhou, G., Liu, S., Li, Z., Zhang, D., Tang, X., Zhou, C., Yan, J., Mo, J., 2006. Old-growth forests can accumulate carbon in soils. Science 314 1417e1417.