Ecosystem respiration and its controlling factors in a coniferous and broad-leaved mixed forest in Dinghushan, China

ACTA ECOLOGICA SINICA Volume 27, Issue 7, July 2007 Online English edition of the Chinese language journal Cite this article as: Acta Ecologica Sinica, 2007, 27(7), 2659−2668.

RESEARCH PAPER

Ecosystem respiration and its controlling factors in a coniferous and broad-leaved mixed forest in Dinghushan, China Wang Chunlin1,2,*, Zhou Guoyi1, Tang Xuli1, Wang Xu1, Zhou Chuanyan1, Yu Guirui3, Tang Lisheng2, Meng Ze1 1 South China Botanical Garden, CAS, Guangzhou 510650, China 2 Guangdong Climate Center, Guangzhou 510080, China 3 Institute of Geographical Science and Natural Resources Research, CAS, Beijing 100101, China

Abstract:

Accurate estimation of ecosystem respiration (Reco) in forest ecosystems is critical for validating terrestrial carbon

models. Continuous eddy covariance measurements of Reco were conducted in a coniferous and broad-leaved mixed forest located in Dinghushan Nature Reserve of southern China. Reco was estimated and the controlling environmental factors were analyzed based on two years’ data from 2003 to 2004. Major results included that: (1) Reco was affected by soil temperature, soil moisture, canopy air temperature and humidity, where soil temperature at 5 cm depth was the dominant factor. (2) The exponential equation, Van’t Hoff equation, Arrhenius equation and Lyold-Talor equation can be used to describe the relationship between Reco and temperature factors with similar statistical significance, while Lyold-Talor equation was the most sensitive to the temperature index (Q10). (3) The multiplicative model driven by soil temperature (Ts) and soil moisture (Ms) was more corresponsive to Reco, which explained that there were more Reco variations than Lyold-Talor equation, both for higher and lower Ms. However, there was no statistical difference between the two models. (4) Annually accumulated Reco of the mixed forest in 2003 was estimated as 1100–1135.6 gC m–2 a–1 by using daytime data, which was 12%–25% higher than Reco (921–975 gC m–2 a–1) estimated by using nighttime data. The results suggested that using daytime data to estimate Reco can avoid the common underestimation problem caused by using eddy covariance methods. The study provides a basic method for further study on accurate estimation of net ecosystem CO2 exchange (NEE) in the coniferous and broad-leaved mixed forest in southern China. Key Words:

Dinghushan; ecosystem respiration; eddy covariance; ChinaFLUX

Long term measurement and accurate estimation of carbon exchange between the terrestrial ecosystems and the atmosphere were recognized as the essential requirements to evaluate whether ecosystems function as carbon sinks or sources at regional and global scales and to carry out research on large scale carbon flux modeling[1,2]. Therefore, this research has become a key scientific issue concerned commonly about the related disciplines of earth science, ecology and environment sciences[2–4]. Forest is the largest terrestrial ecosystem on the earth, CO2 flux research over forest ecosystems has become a hot research topic in the field of global change[5]. In the last 10 years, flux measurement technique and meth-

odology based on eddy covariance theory have been widely applied to flux measurement of carbon dioxide, water vapor and sensible heat in terrestrial ecosystem, and have become a standard method in FLUXNET[6]. Systematic observation and study in the field in China, however, actually started when Chinese Terrestrial Ecosystem Flux Observation Research Network (ChinaFLUX) was established in 2002[1,2,7]. Study on the forest ecosystem was mainly focused on temperate forests[7–9], mid-sub-tropical woody plants[9–12] and tropical forests[13,14], and a mass of flux data was collected in situ. Furthermore, great advance has been made in technology and methodology of eddy covariance, ecosystem flux patterns,

Received date: 2007-02-01; Accepted date: 2007-06-04 *Corresponding author. E-mail: [email protected] Copyright © 2007, Ecological Society of China. Published by Elsevier BV. All rights reserved.

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

simulation and scale conversion[2]. With respect to climate changes[15], studies on carbon dioxide flux of forest ecosystems in Dinghushan Nature Reserve are significant both scientifically and applicably owing to the high potential productivity and remarkable ecosystem balance regulating function in this area. Carbon flux observation and study on the southern sub-tropical area in China were mostly focused on soil respiration, mainly by static chamber[16] and alkali-lime absorption method[17–20], while ecosystem scale research in the field actually started when ChinaFLUX was established[12]. Ecosystem respiration and its variation characteristics as well as the controlling environmental factors were analyzed in this paper by using eddy covariance measurements of in situ CO2 flux. The goal of this paper is to provide a method basement for further study of accurate estimation of NEE in the forest, and to provide valid data for establishment and validation of the ecosystem carbon balance model[11,21].

1

Site description

The Dinghushan Nature Reserve (hereafter referred to as DNR) is located in the middle west part of Guangdong Province, China. Favored by the humid monsoon climate of southern sub-tropical zones, the DNR has abundant resources of radiation, rainfall and heat. Annual mean global radiation in the DNR is 4665 MJ m–2 a–1, and annual mean sunshine duration is 1433 h. Annual mean temperature is 21.0℃ with a mean minimum of 12.0℃ in January and a mean maximum of 28.0℃ in July. Annual average precipitation is 1956 mm with a distinct pattern of wet season (from April to September), during which 76% of rain occurs, and a distinct pattern of relative dry season (from October to March). The study site (23°10′N, 112°32′E; altitude: 240 m) is located in the investigation plot of the evergreen coniferous and broad-leaved mixed forest, the kernel area of the DNR. The slope of the plot is about 10°, facing to the southeast, and the terrain is nearly flat, especially at the northeast, which is the prevailing wind direction of the site. Dominant species in canopy layers are Schima superba, Castanopisis chinensis, Pinus massoniana and so forth. The mean canopy height is about 17 m. The stand age of the forest is about 100a old, with the complicated forest structure of 4 layers: two arbor layers, one shrub layer and one herbage and seedling layer. The soil consists mainly of lateritic red-earth with a varied depth of 30–60 cm. Surface litter covers 80%–90% of the ground with a thickness of 1–3 cm and pH value of 3.86.

2

Materials and methods

2.1 Data collection and processing Open Path Eddy Covariance (OPEC) flux measurement was installed on a mast at 27 m in height. Three dimentional wind speed and virtual temperature were measured with a three-

dimensional sonic anemometer (Model CSAT3, Campbell Scientific Inc., USA (CSI)), and fluctuations of carbon dioxide and water vapor concentration in the air were measured with a fast response infrared gas analyzer (IRGA; Model Li-7500, LiCor Inc., USA) by using the open-path approach running at 10 Hz. The 10 Hz raw measurements were stored online by a CR5000 data logger (Compbell Scientific, Inc., USA), and half-hourly flux of CO2 (Fcb) was computed considering correction of cross-wind contamination of virtual temperature[22] and air density fluctuations[23]. Two-dimensional coordinate rotation[24] was employed to normalize the vertical velocity to the mean wind streamlines following the local terrain. With respect to flux measurement analysis, eddy covariance theory and its technical limitation, a data-screening procedure, were applied to remove problematic 30 min records owing to rainfall. Furthermore, nighttime (photosynthetically active radiation (PAR)<1 umol Photons m–2 s–1) data under weak turbulence condition (u*< 0.2 m s–1) was removed[12,25] to avoid systematic underestimation of CO2 flux owing to storage and advection effects[11,25]. Net ecosystem CO2 exchange (NEE) is defined as: (1) NEE = Fca + Fstor where Fca in Eq.(1) is CO2 flux measurement above canopy, and Fstor is the storage flux reflecting the accumulation and depletion of CO2 in the canopy volume. Calculation details of Fstor refer to literature[12]. According to micrometeorological sign convention, upward fluxes are positive while downward fluxes are negative in Eq. (1). Ecosystem respiration (Reco) equals to NEE at nighttime when none photosynthesis occurs. Routine Meteorology (RMET) measurements, such as air temperature, soil temperature and soil moisture etc., running at 0.5 Hz, were calculated on-line and stored half hourly by 4 dataloggers (model CR23X-TD/CR10X-TD,CSI). Data from 2003 to 2004 were used in this paper. 2.2 Ecosystem respiration model Temperature and vapor conditions are usually regarded as the main controlling environmental factors of ecosystem respiration. The response of ecosystem respiration (Reco) to temperature is commonly described by using Van’t Hoff (Eq.(1)), Arrhenius (Eq.(2)), Lloyd-Taylor (Eq.(3)) and exponential equations (Eq.(4))[26]: (2) Reco=Reco,ref exp(B(Tm–Tref))

⎛E ⎛ 1 1 ⎞⎞ − ⎟⎟ Reco = Reco,ref exp ⎜ a ⎜ ⎜ R ⎜ Tref Tm ⎟ ⎟ ⎠⎠ ⎝ ⎝

(3)

⎛ ⎛ 1 1 ⎞⎞ − Reco = Reco,ref exp ⎜ E0 ⎜ ⎟⎟ ⎜ ⎜ Tref − T0 Tm − T0 ⎟ ⎟ ⎠⎠ ⎝ ⎝

(4)

Reco=aexp(bTm)

(5)

where Reco,ref in Eq.(2–4) is the ecosystem respiration at ref-

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

erence temperature (Tref), Reco,ref and B in Eq.(2), activation energy Ea (J/mol) in Eq.(3), T0 in Eq.(4) and parameters of and b in Eq.(5) are all fitted site-specific parameters; Tm is soil temperature in K; R is the gas constant (8.134 J K–1 mol−1); in application, E0 is set to be 309 K. Water factors, especially soil moisture, are also important factors controlling ecosystem respiration. Taken as a predictor, soil moisture (Ms) is often coupled with temperature factors to construct ecosystem respiration models. At present, the multiplicative model and the Q10 model are frequently used. The multiplicative model[27] was selected in this study: ⎛ ⎛ 1 1 ⎞⎞ 2 − Reco=Reco,ref exp ⎜ E0 ⎜ ⎟ ⎟ exp(cMs+d Ms ) ⎜ ⎜ Tref − T0 Tm − T0 ⎟ ⎟ ⎠⎠ ⎝ ⎝ (6) where temperature response equation of Reco in Eq.(6) is exactly the Lloyd-Taylor equation (Eq.(4)), and soil moisture response equation of Reco is a two-order polynomial of Ms. The reference temperature Tref in Eq.(6) was set to be 283.16 K, and Reco,ref, T0, c and d were fitted site-specific parameters. Tm and Ms were input variables. 2.3 Response model of NEE to PAR Daytime net ecosystem CO2 exchange (NEE) is mainly controlled by PAR. Michaelis-Menten model based on the kinetics theory[28,29] was applied to simulate the response of NEE to PAR: NEE=– α ⋅ PAR ⋅ Amax +Reco α ⋅ PAR + Amax

(7)

where NEE was calculated by Eq.(1), α was the apparent quantum yield (the slope of the best fitted line when PAR = 0), Amax was the asymptotic value of gross primary productivity at saturation irradiance (PAR→∞), and Reco was the ecosystem respiration. PAR is the photosynthetically active radiation measured above the canopy at 21 m above the ground (on the

fourth platform). The three parameters Amax , α and Reco of the model were constrained monthly by daytime (PAR > 1) eddy covariance measurements with sufficient turbulent condition (u*>0.2 m s–1) (see Fig. 1).

3

Results and analysis

3.1 Response of Reco estimated by using nighttime data to temperature factors Relationships between Reco and temperature factors fitted by different models were shown in Table 1. Since all of the models were basically exponential equations, R2 of different models was exactly close. Both canopy air temperature (Ta) and soil temperature (Ts) related to Reco significantly. However, on the basis of R2 of the fitted equations, Ts has more significant relationship with Reco than Ta, indicating that soil respiration contributed more significantly to Reco than canopy did. Q10 derived from Van’t Hoff equation didn’t change with temperature, while Q10 derived from Arrhenius equation and Lloyd-Talor equation decreased with increasing temperature, which was consistent with known response of Reco to temperature[12]. Taking Lloyd-Talor equation with an input variable of Ts as an example, Q10 was 2.1, 1.8 and 1.5 when Ts was 10℃, 20℃ and 30℃, respectively. The values of Q10 are consistent with those in the study on other forest ecosystems with similar latitude[10,11], indicating that Lloyd-Talor equation driven by Ts performs better than the other two to describe response of Reco to temperature factors. 3.2 Response of Reco estimated with nighttime data to soil moisture Driven by Ts and Ms, the multiplicative model was fitted as:

1 ⎛ Reco = 0.008exp(309 × ⎜ − ⎝ 283.16 − 219.96 ⎞ 1 2 ⎟ × exp(17.77 M s − 38.885M s ), (Ts + 273.16) − 219.96 ⎠ R 2 = 0.023, n = 2375 2

Fig. 1 Daytime net ecosystem CO2 exchange (NEE) vs. photosynthetically active radiation (PAR) Best fitting curves were derived from Eq.(7) by data from Jan 30 to Feb 22, 2003 and from May 30 to June 3, 2003, respectively. The y-intercepts interpreted an estimation of Reco.

(8)

where in Eq.(8), R was higher than that fitted by soil temperature alone (Table 1), indicating that introducing the moisture factor was helpful to increase significance of the respiration equation. One- and two-time parameters of Ms were positive and negative, respectively, indicating that Reco increased with increasing Ms when Ms was relatively lower, while when Ms became higher, Ms would become a restraining factor for Reco. Comparing with other study on forest sites[11] in the country, Ms parameters had the same sign (Table 2). In terms of magnitude, Ms of the site was similar to that of the subtropical forest of Qianyanzhou, while considerably different from that of the temperate forest in Changbaishan (Table 2). 3.3 Reco estimated by using daytime data and its response to environmental factors Based on daytime (PAR>1 μmol Photons m–2 s–1) flux measurements, Reco was derived from Michaelis-Menten equation

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

Table 1 Nonlinear regression results of different ecosystem respiration models (nighttime (PAR<1 μmol Photons m–2 s–1) flux data of 2003 was used) Temperature factor Van’t Hoff equation

Arrhenius equation

Lloyd-Talor equation

Reco,ref (283.16K)

B/Ea/T0

Q10 10℃

20℃

30℃

0.063

1.88

1.88

1.88

R2

Soil temperature (Ts)

0.054

0.021

Canopy air temperature (Ta)

0.070

0.041

1.51

1.51

1.51

0.018

Soil temperature (Ts)

0.053

44162

1.92

1.84

1.77

0.021

Canopy air temperature (Ta)

0.069

28847

1.53

1.49

1.45

0.018

Soil temperature (Ts)

0.048

223.9

2.12

1.76

1.55

0.021

Canopy air temperature (Ta)

0.067

206.5

1.59

1.45

1.35

0.018

Table 2 Comparisons of model parameters derived from multiple ecosystem respiration models driven by soil water content and temperature in Dinghushan and other flux sites in China. Nighttime in Dinghushan was defined as PAR<1 μmol Photons m–2 s–1, and as global radiation <1Wm–2 in other two sites Observation site

Data period

Reco,ref (283.16K)

T0

c

d

Nighttime

0.008

219.96

17.77

–38.885

Daytime

0.034

214.61

9.894

–28.65

Qianyanzhou

Nighttime

0.019

215.38

18.196

–48.241

Changbaishan

Nighttime

0.138

231.89

0.818

–0.021

Dinhushan

Fig. 2 Annual variations of (a) photosynthetic active radiation (PAR), canopy air temperature (Ta) and soil temperature (Ts), and (b) ecosystem respiration (Reco). Each datum represents means from 100 observations. Reco was derived from Michaelis-Menten equation (Eq.7) and daytime (PAR>1 μmol Photons m–2 s–1) fluxes in 2003

(Eq.7) (see Fig. 1). Reco in rainy seasons was higher than in dry seasons (Fig. 2a), and annual variation of Reco was roughly the

same as that of Ta and Ts (Fig. 2b). Reco related to both Ta and Ts significantly (Fig. 3); however, sensitivity index of Reco to

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

Ts (Q10=2.0) was higher than that of Ta (Q10 =1.7), indicating that soil respiration contributed more to Reco than canopy vegetation did, which was consistent with the results deduced from nighttime data. Results of different respiration models were shown in Table 3. Q10 based on daytime flux data was less than Q10 based on nighttime data (Table 2), since respiration of leaves was restrained by high temperature and radiation during daytime[29]. On the basis of Reco derived from daytime flux data, the multiplicative model was fitted (Table 3), in which parameters of Ms were consistent with those derived from nighttime flux data, in terms of both sign and magnitude, indicating that Reco derived from both daytime and nighttime flux data showed similar ecological relationship to Ts and Ms. 3.4 Reco and its annual variation Based on daytime and nighttime flux data, and by using Tloyd-Taylor equation and the multiplicative model, respectively, daily accumulated Reco of 2003 showed similar annual variation (Fig.4) to Ts (Fig.5). Daily accumulated Reco calculated by using Tloyd-Taylor equation driven by Ts, varied little and was totally affected by Ts, while Reco estimated by using multiplicative model driven by two factors Ts and Ms varied much and showed the ability to reflect the synthetic effect of

water and heat conditions. Reco calculated by using multiplicative model was higher than that by using Lloyd-Talor equation when Ms was relatively higher; on the contrary, Reco calculated by using multiplicative model was lower than that by using Lloyd-Talor equation when Ms was low. In terms of annually accumulated Reco (Table 4), Reco estimated by using multiplicative model was mostly higher than that by using Tloyd-Taylor equation; however, there was no statistically significant difffernce between the two models, indicating that even in a relative drier year there was no obvious low-moisture stress existing in the coniferous and broad-leaved mixed forest in Dinghushan. Annually accumulated Reco estimated by using daytime flux data was 1100.9–1135.6 gC m–2 a–1 in 2003 and 1107.3–1137.5 gC m–2 a–1 in 2004, respectively. Reco of 2003 was higher than that of 2004 since annual mean temperature in 2003 (20.6℃) was higher than that in 2004 (19.9℃). Reco estimated by using daytime flux data was 12%–25% higher than that by using nighttime flux data (Table 4, Fig. 4), especially in dry periods or years. Accordingly, it was of significance to estimate Reco by using daytime flux data for reducing underestimation by using nighttime eddy flux measurements.

Fig. 3 Relationships between ecosystem respiration (Reco) and soil temperature (a) and canopy air temperature (b), respectively. Reco was derived from Michaelis-Menten equation (Eq. 7) and daytime (PAR>1 μmol Photons m–2 s–1) flux data in 2003 Table 3 Nonlinear regression results of different ecosystem respiration models (where ecosystem respiration was derived from daytime (PAR>1 μmol Photons m–2 s–1) flux data of 2003 and Michaelis-Menten equation (Eq. 7) (n=40) Ecosystem respiration model Van’t Hoff equation

Arrhenius equation

Lloyd-Taylor equation

Temperature factor

Reco,ref (283.16K)

B/Ea/T0

Q10 10℃

20℃

30℃

R2

Soil temperature (Ts)

0.0745

0.0523

1.69

1.69

1.69

0.246

Canopy air temperature (Ta)

0.0820

0.0392

1.48

1.48

1.48

0.210

Soil temperature (Ts)

0.0730

37864

1.75

1.69

1.63

0.248

Canopy air temperature (Ta)

0.0803

27976

1.51

1.47

1.44

0.209

Soil temperature (Ts)

0.0680

217.7

1.87

1.61

1.46

0.260

Canopy air temperature (Ta)

0.0756

207.7

1.61

1.46

1.36

0.220

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

Fig. 4 Daily ecosystem respiration in 2003 derived by different models

Fig. 5 Daily average soil temperature and soil moisture in 2003 Table 4 Annual ecosystem respiration of 2003–2004 in the coniferous and broad-leaved mixed forest in Dinghushan derived by different methods Year

Data period Nighttime

2003 Daytime Nighttime 2004 Daytime

4

Model

Average respiration (mgCO2 m–2 s–1)

Accumulated respiration (gC m–2 a–1)

Lloyd-Talor equation

0.107±0.03

Multiplicative model

0.113±0.032

975.1

Lloyd-Talor equation

0.132±0.032

1135.6

Multiplicative model

0.128±0.034

1100.9

Lloyd-Talor equation

0.104±0.031

894.6

921.1

Multiplicative model

0.105±0.037

907.5

Lloyd-Talor equation

0.128±0.033

1107.3

Multiplicative model

0.132±0.031

1137.5

Discussion

4.1 Reco influenced by environmental factors Ecosystem respiration (Reco) was co-affected by multiple environmental factors such as soil temperature, soil moisture, and canopy air temperature and humidity. Both nighttime and daytime flux data were used, and a variety of ecosystem respiration models were fitted in this research. Results showed that soil temperature was a main factor affecting Reco as a

whole, which was similar to temperate broad-leaved pine mixed forest[11] in Changbaishan, China. Further study suggested that the reponse pattern of Reco to environmental factors changed seasonally. Multi-factor regression model of Reco in dry seasons (from October to March) with 4 environmental factors, soil temperature at 5 cm depth (Ts), soil moisture at 5 cm depth (Ms), canopy air temperature (Ta) and canopy relative humidity (RH), was fitted by using the step regression method. Coefficients of Ts

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

and Ms of the regression were positive and more significant statistically, comparing with coefficients of Ta and RH, which were negative and less significant. The results suggested that soil temperature and moisture were main factors affecting Reco, and therefore Reco was mainly contributed by soil respiration in dry seasons. For rainy seasons (from April to September), among the 4 factors Ts , Ms , Ta and RH in the multi-factor regression model of Reco, only Ta was remained when the other 3 factors were removed, indicating that canopy respiration could contribute more to Reco, which was contrary to dry seasons. Analysis above showed that response patterns of Reco to environmental factors changed seasonally; Reco was mainly contributed by soil respiration in dry seasons, while mainly by canopy vegetation in rainy seasons. Reco of temperate broad-leaved Korean pine mixed forest in Changbaishan related most to ground surface temperature, while Reco of sub-tropical evergreen coniferous plantation in Qianyanzhou related most to air temperature[11]. Response patterns of Reco to environmental factors of the two cases were consistent with the coniferous and broad-leaved mixed forest in Dinghushan in dry seasons and rainy seasons, respectively. 4.2 Comparison of different ecosystem respiration models Commonly-used models such as Van’t Hoff equation, Arrhenius equation, Lloyd-Talor equation and simple exponential equation can be used to describe relationship between Reco and temperature factors with similar statistical significance, since the four equations are basically of exponential form. However, temperature sensitivity index Q10 of Van’t Hoff equation and simple exponential equation doesn’t change with temperature, while Q10 of Arrhenius equation and Lloyd-Talor equation decreases with temperature, which reflects commonly recognized responses of ecosystem respiration to temperature[12]; therefore Arrhenius equation and Lloyd-Talor equation are better than the other two. Furthermore, in terms of magnitude, Q10 derived from Lloyd-Talor equation is comparable with that from other forest ecosystems with similar latitude, indicating that Lloyd-Talor equation is better than Arrhenius equation for describing response of Reco to temperature factors in Dinghushan mixed forest. Fairly good performance of Lloyd-Talor equation simulating Reco has been validated in the field. Exponential equation has been widely applied practically owing to its simplicity of forms and easiness to use[31]. In the ecosystem respiration model where temperature and moisture are coupled by using the multiplicative way, temperature response equation is exactly the Lloyd-Taylor equation, and soil moisture response equation is a two-order polynomial of Ms. Reco calculated by using multiplicative model is higher than that by using Lloyd-Talor equation when Ms is relatively high; on the contrary, Reco calculated by using multiplicative model is lower than that by using Lloyd-Talor

equation when Ms is low; however, there is no statistical difference between the two models, indicating that Ms is not a stress factor in the ecosystem of the site as a whole. 4.3 Uncertainty of ecosytem respiration evaluation It has been well recognized in the field that CO2 flux measured by using eddy covariance technique is often underestimated. The problem remains even if u*-correction is considered[32]. Eddy covariance technique acquires CO2 flux between vegetation and atmosphere directly through measuring fluctuation of vertical wind speed and CO2 concentration, and only the turbulence signal of atmosphere can be captured by the sensors, therefore neglecting those non-turbulence signals. This non-turbulence course usually causes systematic underestimation of CO2 flux[29,33,34]; the selective systematic underestimation could be 4%–36% even if storage effects are considered[32]. Lee[34] also pointed out that for high vegetation with a high vertical CO2 concentration gradient the eddy covariance method tends to underestimate CO2 flux. In this study, annually accumulated Reco estimated by using nighttime flux data was 12%–25% lower than that by using daytime data, indicating that there existed underestimation of nighttime flux measurement in the Dinghushan mixed forest. Deriving Reco from daytime eddy covariance data based on the relationship between daytime NEE and PAR (i.e., Michaelis-Menten-type function) is an alternative way to deal with what is often referred to as the ‘night time problem’. Wohlfahrt’s study on meadow ecosystem[36] showed that Reco derived from daytime dada was 4% lower than that from nighttime data, and 12.5% lower than that from chamber methods. Study on forest ecosystem[31,35] showed that Reco derived from daytime data was normally 20% (or less) underestimated, and was less sensitive to temperature since respiration of vegetation leaves was restrained under higher temperature and radiation conditions. However, in this study, Reco derived from daytime data was 12%–25% higher than that from nighttime data, and thus more reasonable, indicating that there could exist unknown reasons at the site which caused systematic underestimation of Reco[12,37]. Annually accumulated Reco in 2003 at the site was estimated as 1118.3 gC m–2 a–1, which was 9.6% lower than that of subtropical evergreen coniferous plantation in Qianyanzhou (1237.4 gC m–2 a–1); however there was no statistical difference between them. It is worthy of noticing that annual variation of Reco was mostly affected by soil temperature and its annual biological effects could be covered partially by temperature effects[35]. Therfore, further studies should explore: 1) covariation of temperature and moisture as well as its non-linear effects on Reco; 2) difference between respiration models and relationship with ecological course such as LAI (leaf area index) variation.

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

5

Conclusions

(1) Reco of coniferous and broad-leaved mixed forest in Dinghushan was co-affected by soil temperature, soil moisture, canopy air temperature and humidity. Reponse pattern of Reco to environmental factors changed seasonally, and Reco was mainly contributed by soil respiration in dry seasons, while mainly by canopy vegetation in rainy seasons. As a whole, soil temperature at 5 cm depth was the dominant factor affecting Reco. (2) Exponential equation, Van’t Hoff equation, Arrhenius equation and Lyold-Talor equation can be used to describe the relationship between Reco and temperature factors with similar statistical significance, while Lyold-Talor equation is the most sensitive to temperature index (Q10). (3) The multiplicative model driven by soil temperature (Ts) and soil moisture (Ms) was more corresponsive to Reco, and explained Reco variations more than Lyold-Talor equation, both for higher and lower Ms. Reco calculated by using multiplicative model was higher than that by using Lloyd-Talor equation when Ms was relatively higher; on the contrary, Reco calculated by using multiplicative model was lower than that by using Lloyd-Talor equation when Ms was low; however, there was no statistical difference between the two models. (4) Annually accumulated Reco of the coniferous and broadleaved mixed forest in 2003 was estimated as 1100–1135.6 gC m–2 a–1 by using daytime data, which was 12%–25% higher than Reco (921–975 gC m–2 a–1) estimated by using nighttime data. The results suggested that using daytime data to estimate Reco can avoid the common underestimation problem of eddy covariance methods.

[ 4 ] Steffen W, Noble I, Canadell J, et al. The terrestrial carbon cycle: implications for the Kyoto protocol. Science, 1998, 280: 1393–1394. [ 5 ] Fang J Y, Pu S L, Zhao P Q. The carbon sink: the role of the middle and high latitude terrestrial ecosystems in the northern hemisphere. Acta Phytoecologica Sinica, 2001, 26: 594–602. [ 6 ] Baldocchi D D, Falge E, Gu L H, et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 2001, 82(11): 2415–2434. [ 7 ] Wen X F, Yu G R, Sun X M, et al. Net water vapour exchange and environment control over a coniferous-broad leaved forest during autumn-winter transition in Changbai Mountains. Journal of Geographical Sciences, 2003, 13(4): 463–468. [ 8 ] Guan D X, Wu J B, Yu G R, et al. Meteorological control on CO2 flux above broad-leaved Korean pine mixed forest in Changbai Mountains. Science in China, Ser. D: Earth Sciences, 2005, 48(Supp I): 116–122. [9]

Zhang L M, Yu G R, Sun X M, et al. Seasonal variation of carbon exchange of typical forest ecosystems along the eastern forest transect in China. Science in China, Ser. D: Earth Sciences, 2006, 49(Supp II): 47–62.

[10]

Liu Y F, Yu G R, Wen X F, et al. Seasonal dynamics of CO2 fluxes from sub-tropical plantation coniferous ecosystem. Science in China, Ser. D: Earth Sciences, 2006, 49(Supp II): 99–109.

[11] Yu G R, Wen X F, Li Q K, et al. Seasonal patterns and environmental control of ecosystem respiration in sub-tropical and temperate forests in China. Science in China, Ser. D: Earth

Acknowledgements

Sciences, 2004, 34(Supp II): 84–94.

The project was jointly supported by the National Key Fundamental Research Development Layout Project (No. 2002CB412501), the Knowledge Innovation Funds of the Chinese Academy of Sciences (No. KZCX1-SW-01-01A and KSCX2-SW-120), and the Natural Science Foundation of Guangdong Province, China (No. 010567).

[12] Wang C L, Yu G R, Zhou G Y, et al. CO2 flux evaluation over

References

[14]

the evergreen coniferous and broad-leaved mixed forest in Dinghushan, China. Science in China, Ser. D: Earth Sciences, 2006, 49(Supp II): 127–138. [13]

China, Ser. D: Earth Sciences, 2004, 34(Supp II): 167–174.

forest of Xishuangbanna, SW China. Science in China, Ser.

directions of ChinaFLUX. Science in China, Ser. D: Earth [ 2 ] Yu G R, Zhang L M, Sun X M, et al. Advances in carbon flux

D: Earth Sciences, 2006, 49(Supp II): 150–162. [15]

[ 3 ] Houghton R A, Davidson A E, Woodwell G M. Missing si-

922. [16]

chemical Cycling, 1998, 12: 25–34.

Yi Z G, Yi W M, Zhou G Y, et al. Soil carbon effluxes of three major vegetation types in Dinghushan Biosphere Reserve.

nks, feedbacks, and understanding the role of terrestrial ecosystems in the global carbon balance. Global Biogeo-

Zhou G S, Wang Y H, Xu Z Z. Progress on NECT researches. Advances in Natural Sciences, 2003, 13(9): 917–

observation and research in Asia. Science in China, Ser. D: Earth Sciences, 2004, 34(Supp Ⅱ): 15–29.

Zhang Y P, Sha L Q, Yu G R, et al. Annual variation of carbon flux and impact factors in the tropical seasonal rain

[ 1 ] Yu G R, Fu Y L, Sun X M, et al. Recent progress and future Sciences, 2006, 36(Supp II): 1–23.

Sha L Q, Zheng Z, Tang J W, et al. Soil respiration in tropical seasonal rain forest in Xishuangbanna, SW China. Science in

Acta Ecologica Sinica, 2003, 23(8): 1673–1678. [17]

Zhou C Y, Zhang D Q, Qang Y S, et al. Diurnal variations of fluxes of the greenhouse gases from a coniferous and broad-

WANG Chunlin et al. / Acta Ecologica Sinica, 2007, 27(7): 2659–2668

leaved mixed forest soil in Dinghushan. Acta Ecologica Sinica, 2004, 24(8): 1738–1741. [18] Zhou C Y, Zhou G Y, Wang Y H, et al. Soil respiration of a

physical Research, 1997, 102: 28987–28996. [29] Falge E, Baldocchi D D, Olson R J, et al. Gapfilling strategies

Mountain. Journal of Beijing Forestry University, 2005, 27(4):

for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 2001, 107: 43–69.

Zhou C Y, Zhou G Y, Wang Y H, et al. CO2 efflux from dif-

[30] Aubinet M, Grelle A, Ibrom A, et al. Estimates of the annual

ferent forest soils and impact factors in Dinghu Mountain,

net carbon and water exchange of Europeran forests: the EU-

China. Science in China, Ser. D: Earth Sciences, 2004,

ROFLUX methodology. Advances in Ecological Research,

34(Supp II): 175–182. [20]

sponses of a black spruce forest to weather. Journal of Geo-

coniferous and broad-leaved mixed forest in Dinghushan 23–27. [19]

[28] Michael L G, Bruce C D, Fan S M, et al. Physiological re-

2000, 30: 113–175.

Zhang D Q, Sun X M, Zhou G Y, et al. Seasonal dynamics

[31] Falge E, Baldocchi D, Tenhunen J, et al. Seasonality of eco-

of soil CO2 effluxes with responses to environmental factors

system respiration and gross primary production as derived

in lower sub-tropical forests of China. Science in China,

from FLUXNET measurements. Agricultural and Forest Me-

Ser. D: Earth Sciences, 2006, 36(Supp II): 139–149.

teorology, 2002, 113: 53–74.

[21] Law B E, Falge E, Gu L, et al. Environmental controls over

[32] Aubinet M, Chermanne B, Vandenhaute M, et al. Long term

carbon dioxide and water vapor exchange of terrestrial vegeta-

carbon dioxide exchange above a mixed forest in the Belgian

tion. Agricultural and Forest Meteorology, 2002, 113: 97–120.

Ardennes. Agricultural and Forest Meteorology, 2001, 108:

[22] Schotanus P H, Nieuwstadt F T M, de Bruin H A R. Temperature measurements with a sonic anemometer and its application

293–315. [33] Carrara A, Kowalski A S, Neirynck J, et al. Net ecosystem

to heat and moisture fluxes. Boundary-Layer Meteorology,

CO2 exchange of mixed forest in Belgium over 5 years. Agri-

1983, 26, 81–93.

cultural and Forest Meteorology, 2003, 119(3-4): 209–227.

[23] Webb E K, Pearman G I, Leuning R. Correction of flux meas-

[34] Lee X H, Jose D F, Ralf M S, et al. Long-term obvervation of

urements for density effects due to heat and water vapour

the atmospheric exchange of CO2 with a temperate deciduous

transfer. Quarterly Journal of the Royal Meteorological Society,

forest in southern Ontario, Canada. Journal of Geophysical

1980, 106: 85–100.

Research, 1999, 104: 15975–15984.

[24] Wilczak J M, Oncley S P, Stage S A. Sonic anemometer tilt

[35] Griffis T J, Black T A, Morgenstern K, et al. Ecophysiological

correction algorithms. Boundary-Layer Meteorology, 2001,

controls on the carbon balances of three southern boreal forests.

99(1): 127–150.

Agricultural and Forest Meteorology, 2003, 117: 53–71.

[25] Wen X F, Yu G R, Sun X M, et al. Turbulence flux measure-

[36] Wohlfahrt G, Anfang C, Bahn M, et al. Quantifying nighttime

ment above the overstory of a sub-tropical Pinus plantation

ecosystem respiration of a meadow using eddy covariance,

over the hilly region in southeastern China, Science in China,

chambers and modelling. Agricultural and Forest Meteorology,

Ser. D: Earth Sciences, 2005, 48(Supp I): 63–73.

2005, 128(3-4): 141–162.

[26] Falge E, Baldocchi D D, Olson R J, et al. Gapfilling strategies

[37] Wang C L, Zhou G Y, Wang Xu, et al. Below-canopy CO2

for defensible annual sums of net ecosystem exchange. Agri-

flux and its environmental response characteristics in a conif-

cultural and Forest Meteorology, 2001, 107: 43–69.

erous and broad-leaved mixed forest in Dinghushan, China.

[27] Fang C, Moncrieff J B. A model for soil CO2 production and transport, I: model development. Agriculture and Forest Meteorology, 1999, 95: 225–236.

Acta Ecologica Sinica, 2007, 27(3): 846–854.