Seasonal contribution of C3 and C4 species to ecosystem respiration and photosynthesis estimated from isotopic measurements of atmospheric CO2 at a grassland in Japan

agricultural and forest meteorology 149 (2009) 603–613

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Seasonal contribution of C3 and C4 species to ecosystem respiration and photosynthesis estimated from isotopic measurements of atmospheric CO2 at a grassland in Japan Seiji Shimoda a,*, Shohei Murayama b, Wenhong Mo c, Takehisa Oikawa d a

National Agricultural Research Center for Western Region (WeNARC), National Agriculture and Food Research Organization (NARO), Nishifukatu 6-12-1, Fukuyama, Hiroshima 721-8514, Japan b National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan c National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki, Japan d Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan

article info

abstract

Article history:

Measurements of d13C in atmospheric CO2 and plant samples were made in 2003, along with

Received 21 September 2007

CO2 flux measurements, at a grassland site in Tsukuba in central Japan. The objective of the

Received in revised form

study was to obtain estimates of relative seasonal contributions of C3 and C4 plants to the net

7 October 2008

CO2 flux over a C3/C4 grassland area influenced by the Asian monsoon. C4 contribution to the

Accepted 10 October 2008

ecosystem respiration ( f4R) increased from June to September, and then became constant at 63–67% through October and November. The seasonal variation in f4R reflected the biomass composition of C3 and C4 plants at the measurement site. The seasonal C4 contribution to

Keywords:

photosynthesis ( f4P) was significantly different from its contribution to f4R in May (8%) while

Stable carbon isotope

it showed similar values to f4R after June. The seasonal variations in f4R and f4P reflect the

Asian monsoon

biomass composition ratio. Such seasonal transition from C3 to C4 of relative contribution to

CO2 flux

the carbon flux is similar to that observed in humid prairie ecosystems, while it is different

Photosynthetic pathway types

from that in dry prairie ecosystems where the contribution of C3 plants is lower and the

Temperate grassland

seasonal maximum of the C4-plant contribution occurs earlier compared to the humid prairie ecosystems. # 2008 Elsevier B.V. All rights reserved.

1.

Introduction

Temperate grasslands cover some 16% of east Asia (defined as the region between latitudes 408N and 108S, and longitudes 608E and 1808E (Oikawa and Ito, 2001)). In east Asia, the climate is often characterized by heavy rains and humid summers (Ninomiya and Akiyama, 1992), and water is available to most ecosystems during the growing season. For temperate grasslands in northern China, a positive correlation has been shown between summer precipitation, temperature and aboveground biomass productivity (Ni, 2004). High productivity is

expected in the temperate grasslands where high precipitation associated with the Asian summer monsoon occurs. In Japan (Numata, 1969) and in North China (Nui et al., 2005), a typical temperate grassland ecosystem is a mixture of C3 and C4 species. Ueno and Takeda (1992) have reported a positive correlation between the fraction of C4-plant area and the annual mean precipitation and temperature for C3/C4 mixed grasslands in Japan. In temperate C3/C4 mixed grasslands, the C4 fraction varies spatial and temporally (e.g., Saigusa et al., 1998) as the C3 plants are generally less well adapted to high temperature than C4 plants. In addition, due to the high Asian

* Corresponding author. Tel.: +81 84 923 4100; fax: +81 84 924 7893. E-mail address: [email protected] (S. Shimoda). 0168-1923/$ – see front matter # 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2008.10.007

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summer monsoon precipitation, Miscanthus-type C3/C4 mixed grasslands, distributed in fragments throughout Japan (Numata, 1969), show higher productivity than the prairie grasslands in North America (Shoji et al., 1990; Yazaki et al., 2004; Frank and Dugas, 2001). These observations across Japan suggest that humid C3/C4 mixed grasslands are characterized by carbon dynamics that are different from those in dry C4 grasslands. Therefore, it is of interest to investigate the humid C3/C4 mixed grasslands in the monsoon-influenced Asia and to compare them to other regions. Furthermore, since it has been pointed out that the differential responses of C3 and C4 plants to precipitation and temperature can lead to changes in the ecosystem structure and function in C3/C4 mixed communities under climate change (Nui et al., 2005), understanding how the relative contribution of the C3 and C4 plants in this region varies on a seasonal time scale would give a better estimate of their behavior under a warming climate. However, studies on micrometeorological and ecophysiological controls (C3/C4 contribution) on the carbon dynamics in C3/ C4 mixed grasslands under Asian monsoon climate are rare (e.g. Saigusa et al., 1998; Mo et al., 2004; Shimoda et al., 2005). Measurements of stable carbon isotopic ratios (d13C) atmospheric CO2 provides very useful information for partitioning the net flux obtained by eddy covariance measurements (Yakir and Wang, 1996; Bowling et al., 2001; Lai et al., 2003). Because of the differences in photosynthetic discrimination related to CO2 leaking out of the bundle sheath cells and refixed by Rubisco, C4 photosynthesis discriminates less against 13CO2 than C3 (Zhang et al., 2006; Farquhar et al., 1989). On average, the values of d13C are 28% and 12% for C3 and C4 plants (Farquhar et al., 1989). This large d13C difference allows us to partition the CO2 flux between two plant functional types over C3/C4 mixed ecosystems. Since the seasonal pattern of CO2 flux is modified by the composition of plant species, especially the relative contribution of C3 and C4

photosynthetic types (Ojima et al., 1993), estimation of the relative contribution using an isotopic approach is very useful. To simulate the biological CO2 flux on the global scale, numerical analyses have been performed using global modeling of carbon isotope ratios of canopy photosynthesis (Lloyd and Farquhar, 1994; Fung et al., 1997; Ito, 2003; Still et al., 2003a). On a regional scale, previous studies using isotopic measurements have focused on the contribution of the C3 and C4 plants in prairie grasslands (Still et al., 2003b; Lai et al., 2003, 2006) but not in humid C3/C4 mixed grasslands in the monsoon Asia region. In 2003, d13C of atmospheric CO2 and plant samples were measured, along with net ecosystem production (NEP) using an eddy covariance technique, over a Miscanthus-type C3/C4 mixed grassland in central Japan. The objectives of this study were: (1) to examine the variation of the atmospheric isotopic signal with the composition of C3 and C4 plants and (2) to elucidate the seasonal variation of ecophysiological characteristics in a humid temperate grassland in Japan. For this purpose, the Keeling plot analysis, combined with a footprint analysis, was performed on the measurements obtained at our small grassland site.

2.

Field experiment

2.1.

Study site

The study site was located in an observation field of the Terrestrial Environment Research Center (TERC), University of Tsukuba (368060 N, 1408060 E, 27 m above sea level) in central Japan. The observation field is a circular plot with a diameter of 160 m (20,000 m2). The facility has been used for meteorological and ecological field experiments over the past 25 years. The mean annual precipitation is 1300 mm, and the annual

Fig. 1 – (a) Monthly average of daytime/nighttime air temperature (Ta-day and Ta-night) and relative humidity (Rh), and (b) monthly precipitation and monthly average of volumetric soil water content (SWC) in 2003. The bars show the standard errors of the averages.

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mean temperature is 14 8C. During the growing season (from April to October) in 2003, the precipitation was 787 mm, and the mean temperature was 18.3 8C (Fig. 1a and b). The area is dominated by marine humid climate. The study site has been managed by mowing once a year in winter when most aboveground parts of the vegetation are dead, and the litter was left where it fell. The biomass and leaf area index (LAI) have been estimated every month throughout the growing season since 1993 (Saigusa et al., 1998). The results show that C3 species such as Solidago altissima (Compositae) and Artemisia princeps (Compositae) grow in spring and early summer, while C4 species such as Miscanthus sinensis and Imperata cylindrica (Gramineae) predominate during late summer and autumn (Shimoda and Oikawa, 2006). The soil is characterized as loam soils (Andosols).

2.2.

Micrometeorological measurements

To ensure adequate flux source area for meteorological measurements in the direction of the prevailing wind (southerly wind), micrometeorological data were collected 60 m north-northeast of the center of the field. Air temperature was measured by temperature probes (HMP45C, Vaisala, Finland) at a height of 2.0 m above the ground. Volumetric soil water content (SWC) was measured with time-domain reflectometers (TDR CS615, Campbell Scientific, USA) at a depth of 0.15 m under the soil surface at three different locations. These data were stored as 15-min averages on a data logger (CR23X, Campbell Scientific, USA). Photosynthetic photon flux density (PPFD) was measured at 2.4 m above the ground with a quantum sensor (LI-190SA, Li-Cor, USA). The PPFD was sampled every 5 s and stored as 5-min averages on a data logger (Thermodac EF 5001A, ETO Denki, Japan). NEP was measured continuously with an eddy covariance system. The system, which was mounted at fixed height (2.0 m) above the ground, consisted of a three-dimensional supersonic anemometer (DAT600, Kaijo, Japan) and an openpath infrared gas analyzer (LI-7500, LI-COR, USA). The anemometer and open-path infrared gas analyzer provided digital output of the fluctuations in the vertical wind speed ðw0 Þ and either CO2 density (c0 ) or water vapor density (q0 ), respectively. These values were measured at 10 Hz and the covariances were calculated every 15 min. A detailed description of the eddy covariance measurement system and protocol has been given in Shimoda et al. (2005, submitted). The WPL density correction was then applied to fluxes of CO2 and water vapor (Webb et al., 1980).

2.3.

Stable carbon isotope measurements

The air samples for the CO2 concentration ([CO2]) and carbon stable isotope measurements were collected from May to November 2003. Air samples were taken over a couple of sunny days within the first 5 days of each month. The air intakes were mounted at four different heights (0.5, 1.0, 2.0 and 3.5 m above the ground) on a meteorological tower. A diaphragm pump continuously drew the air from each height. Four air samples were collected from each height over 15 min. Water vapor was removed from the air samples by passage through a column of magnesium perchlorate. Each air sample

was collected in a 1000-ml Pyrex glass flask and pressurized to 2  105 Pa above the atmospheric pressure. To characterize the isotopic signals associated with ecosystem respiration (RE; plant respiration plus soil respiration) and NEP (i.e. RE plus photosynthesis) as described below, nighttime and daytime air samples were collected at 21:00, 00:00 and 3:00 and at 9:00, 12:00 and 15:00 JST, respectively. The flasks filled with sample air were returned to our laboratory for analysis. The CO2 concentrations of the samples were determined using an infrared CO2 analyzer (VIA-500R, Horiba Ltd., Japan) with a precision better than 0.1 ppm (Murayama et al., 2003). The CO2 extraction procedures for isotopic measurement were as follows: each flask containing sample air was connected to an extraction system through an airtight coupler with O-rings. After the temperature of the H2O and CO2 traps were increased to 120 8C and the system was evacuated sufficiently at pressures lower than 1.33  103 Pa, the traps were cooled to about 85 8C and 197 8C, respectively. Each sample was evacuated from the flask with a rotary pump first and then a turbo molecular pump through these traps, maintaining the pressure in the traps at lower than 133 Pa. After confirming that the pressures in the extraction system and the flask decreased to lower than 1  102 Pa by evacuation, the temperature of the CO2 trap was increased to 120 8C and sublimed CO2 was collected into a sample tube that was cooled to 197 8C. After the collection of CO2, the tube was flamed off by a gas burner. It took about 60 min to extract a CO2 sample from each flask. The carbon isotope ratio (d13C) of the air sample was determined with an isotope ratio mass spectrometer (Delta-S, Finnigan Mat, Germany). The isotopic component of d13C of CO2 in the VPDB (Vienna Peedee Belemnite) scale is defined as ( d13 C ð%Þ ¼

ð13 C=12 CÞsample ð13 C=12 CÞVPDB

) 1

 1000:

(1)

The external precision of the d13C analysis was estimated to be within 0.02% for air samples. The contribution of N2O to the measured values of d13C was corrected by using the formulae determined experimentally for our mass spectrometer and the concentration values of the atmospheric CO2 and N2O measured in this study (Mook and Van der Hoek, 1983; Friedli and Siegenthaler, 1988). The average N2O correction for d13C was +0.19%. Upper and lower-canopy leaves of four dominant species (C3 plants: Solidago altissima, Artemisia princeps; C4 plants: Miscanthus sinensis, Imperata cylindrica) were sampled in July 2003 and dried at 80 8C for 48 h. Dried samples were ground into fine powder with a vibration mill (Heiko Co. Ltd., Japan). The d13C values of the leaves were determined with an organic elemental analyzer (Flash EA 1112, Finnigan Mat, Germany) and an isotope ratio mass spectrometer (Delta Plus, Finnigan Mat, Germany). The precision of the carbon isotope measurements was estimated to be within 0.13% for organic carbon.

2.4.

Biomass measurements

To estimate the aboveground biomass of each grass species, 80 quadrats (2 m  2 m) were set in the observational field every month during the growing season (Li et al., 2005). Three

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representative sub-quadrats (0.5 m  0.5 m) were selected for each month and the vegetative coverage and the dry weight of aboveground parts were measured for each grass species in the sub-quadrats. By extrapolating the relationship of each species between the aboveground biomass and the relative vegetative coverage percentage obtained for the sub-quadrats to the whole of the field, the aboveground biomass of each 80 quadrats was estimated from the observed vegetative coverage percentage for each species. Dry weight was determined after oven drying at 80 8C for 48 h. The LAI was also measured using a hand-held plant canopy analyzer (LAI-2000, Li-Cor, USA) (Shimoda and Oikawa, 2008).

3.

Data analysis

3.1.

Estimation of isotopic composition for fluxes

(2)

Note that NEP is defined such that positive and negative values represent CO2 uptake by (downward flux) and release from (upward flux) the ecosystem, respectively. A single exponential regression relationship was established between RE (estimated from NEP observed at night) and the air temperature, as follows (Goulden et al., 1996): RE ¼ A expðBTÞ;

(3)

where A and B are empirical constants and T is the air temperature in K. A and B were estimated for three time windows (May–June, July–August, and September–October). A increased from 1.1 (May–June) to 5.3 gC m2 (September–October), while B varied relatively little, ranging from 0.039 to 0.085 K1 (Shimoda et al., submitted). To minimize the effect of poor turbulent mixing in our measurements of nighttime NEP, the regression analysis was made using only the data collected under the conditions of friction velocity (u*) greater than 0.15 m s1 (Shimoda et al., 2005). From extrapolation of Eq. (3), the daytime RE was estimated, and then GPP was calculated using Eq. (2). The isotopic composition of ecosystem respiration (d13CR) was calculated by using the ‘‘Keeling plot’’ approach (Keeling, 1958) where nocturnal d13C is plotted against the inverse of [CO2] and the intercept of the linear regression is d13CR; d13 CA ¼

½CO2 B ðd13 CB  d13 CR Þ þ d13 CR ; ½CO2 A

d13 CN NEPDaytime ¼ ðd13 CB  D p Þ GPP þ d13 CR REDaytime ;

(5)

where d13CB is the d13C value of the background CO2 in the troposphere that was set to 8.0% (GLOBALVIEW-CO2, 2007), and d13CN is the isotopic composition of NEP. NEPDaytime and REDaytime are the NEP and RE during the daytime, respectively. According to Bowling et al. (2001), d13CN can be estimates as follows:

During daytime the lowest part of the atmospheric boundary layer is well mixed, thus CO2 concentration gradients are generally low, and vertical advection is negligible on daily time scale (Baldocchi, 2003). Also, since the height of our flux system was only 2 m high, the CO2 storage below the flux measurement height was negligible. Therefore, NEP was directly measured by the eddy covariance method. NEP represents the balance of gross primary production (GPP) and ecosystem respiration (RE): NEP ¼ GPP þ RE;

of respective components. By substituting the CO2 and d13C data collected during the nighttime of each day into the above equation, d13CR was obtained from the linear regression analysis between d13CA and 1/[CO2]A. Canopy scale photosynthetic discrimination (DP) has been estimated using a modified flux partitioning approach based on the conservation of 13CO2 (Yakir and Wang, 1996; Bowling et al., 2001; Lai et al., 2003). To obtain daytime average DP, we solved DP from the following equation:

(4)

where [CO2]A and [CO2]B are the atmospheric CO2 concentration observed near the surface and the background CO2 concentration, respectively, and d13CA and d13CB are the d13C values

d13 CN ¼ 2m½CO2  þ b;

(6)

where ½CO2  is the mean [CO2] during the daytime of each observed period, and m and b are the regression coefficients obtained from an empirical relationship between [CO2]A and d13CA for the daytime data: d13 CA ¼ m½CO2  þ b;

(7)

Bowling et al. (2001) demonstrated empirically that this method is robust for air samples collected across orders of timescales. We obtained Dp by substituting the observed daytime mean NEP, GPP, RE, d13CR and d13CN estimated above into Eq. (5). We rejected GPP and RE when the flux footprint length was more than the distance from the tower to the edge of the grassland. Footprint calculation will be described in Section 3.3. Keeling plot intercepts were calculated from the data obtained during a single day (daytime and nighttime). Footprint analysis showed that most of the concentration footprint lengths were within the grassland area for measurements taken from the two lower inlets (0.5 and 1.0 m above the ground surface from May to July; 1.0 and 2.0 m above the ground surface from August to November) as discussed in Section 4.2. Therefore, we used only the measurements from the two lower inlets for the analysis.

3.2.

C3/C4 contribution to the CO2 flux

The dissimilarity in the carbon isotope discrimination between C3 and C4 plants provide a significant difference in d13C of their respired CO2. We assumed that there is no significant discrimination during respiration (Lin and Ehleringer, 1997) and that d13C of respired CO2 is roughly similar to that of the organic matter. The fraction of C4 plants to ecosystem respiration during nighttime ( f4R) was estimated by using the following two-source mixing model (e.g., Lai et al., 2003):

f 4R ¼

d13 CR  d13 C3 ; d13 C4  d13 C3

(8)

agricultural and forest meteorology 149 (2009) 603–613

where d13C3 and d13C4 are the measured carbon isotope ratios of plant organic matter for C3 and C4 species, respectively. Respired CO2 represents RE, therefore f4R expresses the C4 fraction of the total ecosystem respiration (plant and soil). C4 fraction of the photosynthetic CO2 ( f4P) was determined similarly following Lai et al. (2003):

f 4P ¼

DP  D3 ; D4  D3

(9)

where D3 and D4 are the 13C discrimination of C3 and C4 plants during photosynthesis, respectively, which were obtained from the following function of the measured d13C3 and d13C4 (Buchmann and Ehleringer, 1998; Still et al., 2003b), respectively:

D3;4 ¼

d13 CB  d13 C3;4 1 þ d13 C3;4 =1000

(10)

The value of f4R and f4P was estimated under the assumption that d13C3 and d13C4 are constant during the air sampling periods.

3.3.

Data selection of

13

C values

d13CR in Eq. (4) and m and b in Eq. (7) were calculated using the standard linear regression method. The data from air samples collected at the lower two heights above the canopy through nighttime and daytime were used to obtain d13CR and the values of m and b, respectively. Zobitz et al. (2006) showed that the Keeling plot intercepts are more affected by the uncertainty of d13C than by the uncertainty in [CO2], and that the geometric mean regression technique (Pataki et al., 2003) has skewed bias for small [CO2] range. Therefore, we performed standard linear regression analyses for all the data. The standard error of the intercept was calculated by the ordinary least squares. Outliers were excluded using an iterative residual analysis in which sample residual values larger than three times the standard deviation were removed (Knohl et al., 2005), leading to a reduction of the sample size by 1.2%. Our observation field was surrounded by some C3 sources such as crops and forests, the influence of the wind direction and the surrounding vegetation on the observed isotopic signal therefore needed to be considered (Still et al., 2003b; Lai et al., 2003). During the measurements, air was collected 60 m north-northeast of the center of the circular field. To ensure adequate fetch for air sampling in the direction of the prevailing wind, as described above, the surrounding sources could still contaminate the air samples when wind was blowing from the northern sector (0–908 and 270–3608). In this way, we rejected all the data collected under these wind conditions. In all, we rejected 19.9% of the total number of collected data through the data selection procedure. Flux and concentration footprints were estimated to avoid the problems caused by the variations of sampling footprint and isotopic signals over time. In order to compute the concentration and flux footprints relatively easily, we used passive scalar source area models (SAM and FSAM; Schmid, 1997).

4.

607

Results

4.1. Seasonal variations in microclimate conditions and plants Fig. 1a shows monthly averages of daytime and nighttime mean air temperatures and daily mean relative humidity from April to November. The daytime mean air temperature reached a seasonal maximum of 26.5 8C in August, followed by 24.8 8C in September and a minimum of 13.4 8C in November (Fig. 1a). The daily mean humidity increased from 63% (April) to 82% (July). The monthly precipitation was more than 100 mm during the rainy season from April to August (Fig. 1b). SWC remained above 0.40 throughout the observation period (Fig. 1b). Mean canopy height and LAI increased from June to September (Fig. 2a and b). Mean canopy height reached more than 0.8 m in September, and then was almost constant until November (Fig. 2a). LAI was 0.5 m2 m2 in May, and reached the seasonal maximum of 4.2 m2 m2 in September (Fig. 2b). Aboveground biomass increased linearly from May and reached a maximum in September (1117 g d.w. (dry weight) m2) at the study site (Fig. 2c). This biomass production was similar in dry weight to other Miscanthus-type grasslands in Japan (Shoji et al., 1990; Yazaki et al., 2004). As seen in Fig. 2d, Solidago altissima (C3), Miscanthus sinensis (C4), and Imperata cylindrica (C4) dominated the grassland species at the study site. Among these species, Miscanthus sinensis showed the largest increase in aboveground biomass during the growing season. Its biomass reached a maximum in October (605 g d.w. m2), occupying approximately 60% of the total aboveground biomass. The d13C of leaf biomass for the dominant species are displayed in Table 1. The differences in d13C among the C3 species were within 3%, with the C4 species showing similarly small differences. The differences in d13C between C3 and C4 plant biomass were fairly large, reaching more than 17%.

4.2.

Estimated values of d13CR and DP

Fig. 3 shows the daytime NEP measured by the eddy covariance method and the estimated GPP and daytime RE (REDaytime) for each sampling day. The values of NEP and GPP increased from April to August. GPP increased with vegetation growth, reaching a peak of 13.3 gC m2 day1 in August. This peak value of GPP is five times larger than those observed at a shortgrass prairie site in Oklahoma (Meyers, 2001), and twice the value at a tallgrass prairie site in Oklahoma (Suyker et al., 2003), both located at similar latitude. Thus, humid temperate grassland in Tsukuba has a high CO2 uptake potential. REDaytime reached a minimum value of 9.1 gC m2 day1 in August, which is probably attributable to high temperature conditions. To determine the reasonable sample inlet heights and sampling periods for the analyses of the CO2 exchange in our grassland site, we estimated the concentration footprint length (Fig. 4). The concentration footprint lengths for an inlet height of 3.5 m were generally greater than 300 m (Fig. 4). Most of the calculated concentration footprint showing 90% of the source contributing to the measurements obtained from the lower two inlets (0.5 and 1.0 m above the ground from May

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Fig. 2 – Seasonal variations of (a) mean canopy height, (b) leaf area index (LAI), (c) aboveground biomass, and (d) percentage of aboveground biomass of each species in 2003.

Table 1 – d13C values of leaf biomass samples. Species name

13

d C (%)

(S.E.) n

C4 plants Miscanthus sinensis Imperata cylindrical

11.8 12.1

(0.3) 9 (0.1) 9

C3 plants Solidago altissima Artemisia princeps

30.2 27.0

(0.3) 9 (0.7) 9

to July; 1.0 and 2.0 m above the ground from August to November) lay within the study site area (140 m from main wind direction) under typical atmospheric conditions. To

reduce the contribution from the surrounding area on the observed d13C, Keeling plot intercepts were estimated using only the measurements from the lower two heights obtained during a single day (and night). The concentration footprint lengths for the lowest inlets were greater than 20 m. The dominant species in our grassland site were distributed randomly at the several meter scale (Shimoda and Oikawa, 2006), thus avoiding bias depending on the proximity to C3 or C4 species. The concentration footprint lengths were longer than the distance from the tower to the edge of the grassland for only two nights of 2 and 3 August, and four sampling periods under strongly stable conditions. In all, we used only 34.5% of the total number of collected data.

Fig. 3 – Daily net ecosystem production during daytime (NEPDaytime), gross primary production (GPP), and daytime respiration (REDaytime) for each sampling day.

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Table 3 – The seasonal variation of 13C discrimination during photosynthesis (DP) estimated by d13CR in Table 1 and m and b in Eq. (7). Standard error (S.E.) of the estimated DP, the range and the number (n) of the [CO2] data used to determine m and b in Eq. (7) through the regression analyses are also given as well as the values of square of the correlation coefficient (R2) for the regression. The DP values are not estimated for 2 and 3 August and 31 October due to non-availability of d13CR data, and for 4 June due to data rejection based on wind direction. Period

Fig. 4 – Concentration footprint lengths constituting 90% of the source during each sampling period at 4 sampling heights (0.5, 1.0, 2.0, and 3.5 m). The nighttime footprint length at the 3.5 m height is more than 1000 m on 2 and 3 August, and 31 October. The daytime data are for 9:00, 12:00, and 15:00 local time and the nighttime for 21:00, 0:00, and 3:00 local time.

d13CR, DP values estimated from standard linear regression are shown in Tables 2 and 3. Pataki et al. (2003) pointed out that decreasing sample [CO2] range increases the uncertainty on Keeling plot intercepts. In Fig. 5, the standard error of the intercept during nighttime is shown as a function of the [CO2] range of the

Table 2 – The seasonal variation of d13C of RE (d13CR) estimated by standard linear regression. Standard error (S.E.) of the estimated d13CR, CO2 concentration range ([CO2] range) of the data, number of samples (n), and the square of the correlation coefficient (R2) for the regression are also shown. No available d13CR are for 2 and 3 August and 31 October due to long concentration footprint length. Period

May 3 May 4 June 3 June 4 July 2 July 3 August 2 August 3 September 3 September 4 October 1 October 2 October 31 November 1

d13CR(%) Values

(S.E.)

21.8 22.2 23.8 21.9 24.8 21.9 – – 18.5 18.9 18.3 18.3 – 17.6

(0.8) (1.2) (0.6) (1.1) (3.7) (2.1) – – (1.3) (1.2) (0.9) (1.0) – (0.7)

[CO2] range (ppm)

n

R2

157 137 54 95 6 18 – – 176 140 56 171 – 101

6 6 6 6 6 6 – – 6 4 6 6 – 4

0.99 0.99 1.00 0.99 0.91 0.95 – – 0.94 0.99 0.96 0.96 – 0.95

May 3 May 4 June 3 June 4 July 2 July 3 August 2 August 3 September 3 September 4 October 1 October 2 October 31 November 1

DP (%) Values

(S.E.)

20.2 20.1 15.7 – 20.4 14.5 – – 6.5 10.6 13.0 12.6 – 20.4

(3.9) (4.3) (2.6) – (36.0) (6.4) – – (50.1) (3.3) (6.4) (2.3) – (8.5)

[CO2] range (ppm)

n

R2

13 20 35 – 3 30 – – 3 20 9 20 – 15

4 6 6 – 5 5 – – 4 4 5 6 – 4

1.00 0.98 0.98 – 0.62 0.94 – – 0.61 0.96 0.92 0.99 – 0.99

data used for each Keeling plot. The data in Fig. 5 suggest that, to reliably maintain a standard error in d13C signal below 2%, a [CO2] range of more than approximately 20 ppm is needed. These conditions occur most commonly under stable conditions during the nighttime sampling period. Thus, to minimize the effect of the uncertainty of Keeling plot in our measurements, we restricted the analysis to those data measured when the [CO2] range was greater than 20 ppm (Fig. 6). The value of d13CR ranged between 21.8 and 23.0% in May, and showed a similar value in June. The value of d13CR increased as the growing season proceeded, reaching 17.6% in November.

Fig. 5 – Relationship between standard error of the Keeling plot intercept calculated from the nighttime data and the range of CO2 concentrations for each day.

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Fig. 6 – Seasonal pattern of d13CR. Plots are restricted to the nighttime data when [CO2] range was greater than 20 ppm. The bars show the standard errors of the d13CR estimates.

Daytime data presents the difficulty of obtaining a sufficiently large [CO2] range for a Keeling plot analysis (Table 3). To estimate as reasonable DP values as possible, we use DP data obtained only when the daytime and nighttime [CO2] range was greater than 20 ppm following d13CR analysis. The seasonal variation of such DP data is shown in Fig. 7. The estimated value of DP decreased sharply from May (20.4%) to October (12.6%), with the lowest value in September (10.8%). In this connection, some reports have indicated that the mismatch between the flux and concentration footprints is problematic for flux partitioning with Keeling plot (Griffis et al., 2007). However, the footprint lengths calculated for the flux source (at the 90% contribution level) were found to lie within the distance from the tower to the edge of the grassland, as shown in Fig. 8.

Fig. 8 – Daytime flux footprint lengths (at the 90% contribution level) of the flux source when the daytime [CO2] range was greater than 20 ppm.

because high temperature favors C4 plants (Ehleringer et al., 1997).

5.

Discussion

5.1. Relationship of seasonal variations in f4R and f4P with those in C3 and C4 plant biomass

Seasonal patterns of relative C4 contribution to ecosystem respiration ( f4R) and photosynthesis ( f4P) are shown in Fig. 9, together with the fractional contribution to the total aboveground biomass. The value of f4R increased as the growing season proceeded, reaching 67% in November. f4P increased from May (9%) to September (62%), and then decreased slightly thereafter. The fraction of the C4 plant biomass gradually increased from 20% in May to above 50% in August, reaching a plateau of around 72% in October and November (Fig. 9). Such a shift of dominant photosynthetic type from C3 to C4 grasses may be primarily attributed to the increase in air temperature,

The increasing tendency of f4R from June to November corresponded with the increasing aboveground biomass of C4 species (Figs. 2d and 9). Although there is a positive correlation between f4R and C4 biomass, a more detailed description of the seasonal displacement of C3- and C4-derived ecosystem respiration at our site from June to November can be obtained from examining the corresponding temporal variation in d13CR. The proportion of C4 aboveground biomass increased from 20% in May to 45% in June. Despite this increase in C4 biomass during this period, d13CR ranged from 21.9 to 23.8% in May and June (Fig. 6), which was 6% higher than the d13C value of C3 leaf biomass. Furthermore, f4R in May was 36%, while the proportion of C4 aboveground biomass was only 20%. A synthesis of these results suggests that d13CR was noticeably influenced by d13C of soil-respired CO2. d13C of CO2 released as soil respiration would have a value somewhere between those of C3 and C4 plant biomasses, reflecting the soil composition of C3 and C4 plant detritus accumulated over the past 25 years at the study site. The previous year’s litter on the soil surface may also have an influence on d13C of RE. In May, the ratio of

Fig. 7 – Seasonal pattern of DP. Plots are restricted to the daytime data when [CO2] range was greater than 20 ppm. The bars show the standard errors of the DP estimates.

Fig. 9 – Seasonal patterns of the fraction of C4 contribution to the ecosystem respiration ( f4R) (closed circle), and photosynthesis ( f4P) (open circle). The bars show the standard errors of f4R and f4P estimates, respectively.

4.3.

C3/C4 contribution

agricultural and forest meteorology 149 (2009) 603–613

soil respiration to the total ecosystem respiration would be expected to be high due to the small amount of the aboveground biomass (30 d.w. g m2). As a result, d13CR would attain a higher value than the value expected from the C4 ratio of the aboveground biomass, and approach the value of soil-respired d13C. To improve precision of our discussions, additional measurements such as isotopic measurements of soil respiratory CO2 and soil organic matters may be needed. In this study we have adopted the assumption that there is no fractionation during respiration (Still et al., 2003b; Lai et al., 2003). Klumpp et al. (2005) concluded from investigation of respired CO2 discrimination at a shoot level that the discrimination related to the total plant respiration was very small (0.7% enrichment for 13C). Even if d13C of respired CO2 would show a 1% isotopic enrichment relative to d13C of source organic matters, f4R estimated in our analysis would uniformly decrease only by about 6% throughout the observed period. Therefore, even if the no-fractionation assumption is wrong, its influence on the qualitative pattern of the seasonal variation of the calculated f4R is estimated to be negligible. Compared with f4R, f4P showed a similar seasonal pattern. However, the value of f4R in May is approximately 30% higher than f4P (Fig. 9). The difference between f4R and f4P may be explained by the difference in influence of photosynthesis and ecosystem respiration on atmospheric 13CO2. While f4R reflect the respired CO2 from whole ecosystem (live biomass, litter and soil), f4P is be strongly influenced by the CO2 exchange in live biomass composition of C3 and C4 plant. Thus, the increase in f4P from May to October corresponded with the growth of C4 aboveground biomass. On the other hand, factors other than aboveground biomass also have large influence on f4R especially during the small aboveground biomass period. Therefore, large difference between f4R and f4P may be observed in May. Although the f4P increased from May to September, it slightly decreased in October (Fig. 9). This could be explained by well-known facts that the optimum temperature for photosynthesis is higher for C4 plant than C3 plant (Ehleringer et al., 1997; Collets et al., 1998). Another interpretation of this result is the large uncertainty associated with the estimation of f4P. The daytime d13C signal may be influenced by the isotopic disequilibria (Still et al., 2003b; Oge´e et al., 2004) or a change in air mass trajectory (Lai et al., 2006). Zhang et al. (2006) reported that there might be a limitation on the extrapolation of nighttime d13CR to daytime values in the isotopic flux partitioning. To examine the effects of these factors, further detailed analyses are needed.

5.2. Seasonal variation of ecophysiological characteristics in a humid temperate grassland in Japan Only a few studies have investigated the seasonal contributions of C3 and C4 plants to CO2 flux (Lai et al., 2003; Still et al., 2003b). Lai et al. (2003) have shown that C4-contribution to NEP increased in C3–C4 tall grass prairie in Kansas from 69% in March to 100% in late July as the air temperature increased. Kemp and Williams (1980) and others have also shown that severe dry conditions favor C4 plants over C3 in short grass prairie environments (e.g. Sage et al., 1999). C3 plants are generally less well adapted to conditions of elevated temperature and limited soil moisture than C4 plants. Under

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suitable conditions, C3 species demand much higher water consumption to fix the same amount of CO2 than C4 species. Therefore, high air temperature and low soil water content in the prairies during summer favor a larger contribution of C4 plants to the total CO2 flux than C3 plants. On the other hand, at our site, the contribution of C4 species to the CO2 flux and biomass production increased as the growing season proceeded. Such seasonal variation in the relative contribution of photosynthesis to ecosystem respiration is similar to that of tallgrass prairie in Oklahoma (Still et al., 2003b), and a similar seasonal pattern of biomass production has been observed in upland communities in South Dakota prairie (Ode et al., 1980). In this regard, it is interesting to note that the seasonal pattern of precipitation is similar at all three sites, where approximately 80% of the annual precipitation occurs during the growing season. For our site, as a result of high precipitation during summer Asian monsoon, SWC remained above 0.40 throughout the observation period (Fig. 1). Such humid conditions, thus, may lead to higher contribution of C3 plants and a delayed seasonal maximum of the C4-plant contribution to the ecosystem respiration and photosynthesis compared to the dryer Kansas prairie ecosystem (Lai et al., 2003). If the climate change would have any impact on humid climatic conditions in this region, temporal and spatial distributions of dominant species (C3 and C4 plants) could be shifted in the grasslands, and would result in a significantly altered carbon budget in the monsoon Asian region. From simultaneous isotopic and C3- and C4-biomass measurements in this study, we have found that the seasonal variation in the relative contribution among different photosynthetic types to the total CO2 flux reflects, to a large extent, the biomass composition ratio. Such a seasonal variation of the relative C3- and C4-contributions to the total CO2 flux at our site is attributed to the optimum mechanism of the competition between C3 and C4 plants to adapt to seasonally varied environmental conditions. Moreover, the seasonal displacement may reinforce canopy gross primary productivity and biomass production to at our site.

6.

Conclusions

In order to investigate how the atmospheric isotopic signal of CO2 varies with the seasonal change in the dominant species from C3 to C4 photosynthetic types in a grassland, d13C of atmospheric CO2 and plant samples were measured, in conjunction with CO2 flux measurements, in central Japan. We estimated the relative contribution of C3 and C4 plants to ecosystem respiration and photosynthesis during the growing season in 2003. The f4R from June to November corresponded to the increase in the ratio of C4 aboveground biomass. The apparent positive correlation from June to November between f4R and C4 biomass proportion probably indicated that the seasonal variation in biomass composition of photosynthetic plant type has a strong influence on the relative contribution of C3 and C4 plants to the ecosystem respiration. In May, f4R showed a higher value than the value one would expect from the C4 biomass ratio and f4P. It is suggested that such a high f4R value resulted from the facts that during the early period of the

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growing season the ratio of soil respiration to the total ecosystem respiration was large due to small amount of the aboveground biomass and that, as a result, d13CR approached the value of soil-respired d13C. With respect to f4P, its seasonal pattern was similar to that of f4R except for May, reflecting the biomass composition ratio. The replacement of dominant species from C3 to C4 species at our grassland site is similar to that observed in humid prairie grasslands, but different from that in dry prairie grasslands. The humid conditions at our site and the humid prairie grasslands may cause higher contribution of C3 plants and a delayed seasonal maximum of the C4plant contribution to the ecosystem respiration and photosynthesis compared to the dryer prairie ecosystems. Such a seasonal variation may mutually reinforce photosynthetic CO2 uptake between C3 and C4 plants, and as a result promote high production at our site. For a better understanding of the carbon cycle in C3/C4 mixed grasslands in the Asian monsoon region, it is necessary to investigate seasonal and inter-annual variations of carbon exchange processes and ecophysiological characteristics. To achieve these objectives, long-term systematic observations at different grassland sites in this region would be useful. It has been pointed out that the differential responses of C3 and C4 plants to climatic conditions can lead to changes in the ecosystem structure and function in the C3/C4 mixed grasslands in East Asia much influenced by the Asian Monsoon under climate change. Therefore, the data obtained from such measurements will provide very useful information for more precise carbon cycle modeling in mixed grasslands in this region.

Acknowledgements This study was financially supported by the Ministry of the Environment of Japan (Global Environment Research Fund S-l: Integrated Study for Terrestrial Carbon Management of Asia in the 21st Century Based on Scientific Advancements). The authors would like to thank Dr. Kaz Higuchi of Environment Canada for valuable comments. The Terrestrial Environmental Research Center, University of Tsukuba, provided us with its routine meteorological data.

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