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Abstract
Abstract
Larch (Larix spp.) forests are predominantly distributed across high latitudes of Eurasia. They potentially have a strong influence on the terrestrial carbon and energy cycles, because of their vast area and the large carbon stocks in their peat soils in the permafrost. In this study, we elucidated intersite variation of ecosystem photosynthetic and respiratory parameters of eight larch forests in East Asia using the CarboEastAsia carbon flux and micrometeorology dataset. These parameters were determined using the empirical relationship between the carbon fluxes (photosynthesis and respiration) and micrometeorological variables (light and temperature). In addition, we examined leaf area index (LAI) determined by Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to explain the intersite variation. Linear or exponential relationships with annual mean temperature or seasonal maximum LAI at the study sites were found for the annual carbon fluxes (gross primary production [GPP] and total ecosystem respiration [RE]) as well as for four of the five seasonal maximum values of determined photosynthetic and respiratory parameters (maximum GPP at light saturation, initial slope of the light-response curve, daytime respiration, and RE at the reference temperature of 10°C). Phenological indices, such as start day of the growing season, growing season length and growing season degree days explained much of the intersite variation of GPP and RE of the studied larch forests; however, the relationship between MODIS LAI and photosynthetic or respiratory parameters implies that the intersite variation in GPP and RE was caused not only by the temperature variation (abiotic factor), but also by the variation in the photosynthetic and respiration activity by vegetation (biotic factor) through the change in leaf (or whole vegetation) biomass. Our analysis shows that MODIS LAI serves as a good index to explain the variation of the ecosystem photosynthetic and respiratory characteristics of East Asian larch forests.
1. INTRODUCTION
Larch (Larix spp.) forests are characterized by their deciduous habit, a trait that allows them to endure the extremely cold and dry winters across high latitudes of Eurasia, including the Siberian taiga (Gower and Richards 1990). These forests are considered to have a strong influence on the terrestrial carbon and energy cycles, because of their vast area and the potentially large carbon stocks in their peat soils in the permafrost (Schulze et al. 1999; Dolman et al. 2004; Ueyama et al. 2010). Siberian forests constitute 20% of the world’s forested area (Dolman et al. 2004), and larch forests cover 37% (Abaimov et al. 1998), 70% (Gunin et al. 1999) and 13.6% (Jiang and Zhou 2002) of forested areas in Russia, Mongolia and China, respectively. Larch forests on the permafrost region are considered to be a vulnerable ecosystem, because of the rapid warming during the past few decades due to climate change (Dolman et al. 2008) and irreversible degradation by permafrost melting (Demek 1994). Larix species also have been planted intensively throughout northern Japan and China, because of their high cold tolerance and timber productivity (Hirata et al. 2007). For these reasons, it is important to gain a comprehensive understanding of the carbon and energy exchange characteristics of larch forests (Li et al. 2005; Machimura et al. 2005; Wang et al. 2005b; Hirata et al. 2007; Nakai et al. 2008; Ohta et al. 2008).
In this synthetic study, we aimed to elucidate the spatial variation of the ecosystem photosynthetic and respiratory characteristics of larch forests in East Asia using the CarboEastAsia carbon flux dataset (Saigusa et al. 2013) and additional data obtained at two other larch forest sites (Neleger, Russia, and Fuji Hokuroku Flux Observation Site, Japan). Several previous studies were able to explain the spatial variation of carbon fluxes such as gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) in East Asian forests using temperature and precipitation as the explanatory variables (Hirata et al. 2008; Chen et al. 2013; Saigusa et al. 2013). However, because GPP, RE and NEE are the products of abiotic (environment) and biotic (vegetation) factors, in this study, we evaluated the biotic characteristics by inversely estimating photosynthetic and respiratory parameters based on carbon flux and micrometeorology datasets, as well as explaining the intersite variation of GPP, RE and NEE. Several studies have succeeded in relating the spatial variation in photosynthetic and respiratory parameters to environmental and plant functional indices and applying that relationship to develop semi-empirical models to estimate terrestrial carbon cycles at a large scale from micrometeorology data (Saito et al. 2009; Groenendijk et al. 2011a, 2011b) or vegetation indices (Ide et al. 2010). Our study uses similar approaches and is the first step to developing a semi-empirical model to estimate carbon cycles in larch forests in East Asia. We used a Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product (MOD15A2 Collection 5; Myneni et al. 2002) for the vegetation index to explain the intersite variations of carbon fluxes and their photosynthetic and respiratory parameters, because it can be a universal index reflecting the vegetation structure and the magnitude of the stand biomass, and can be obtained at every larch forest with a short interval period.
2. MATERIALS AND METHODS
2.1 Site description
Carbon dioxide (CO2) flux and micrometeorology data obtained at eight larch forest sites distributed over East Asia were used for the analysis (Fig. 1; Table 1). Three Russian sites (Tura (TUR), Neleger (NLG), Yakutsuk (YLF)) and Southern Khentei Taiga (SKT) in Mongolia are naturally regenerated forests, whereas three Japanese sites (CC-LaG experiment site (TSE), Tomakomai Flux Research site (TMK), Fuji Hokuroku Flux Observation site (FHK)) and Laoshan (LSH) in northeastern China are artificial plantations. These sites belong to the AsiaFlux network (Mizoguchi et al. 2009) and cover a broad range of climatic values, with total annual precipitation ranging from 249 mm (NLG) to 1800 mm (FHK) and annual mean air temperatures from – 9.4°C (NLG) to 9.2°C (FHK). Sites TUR, NLG and YLF are characterized by a severe continental boreal climate with cold winters and short, warm, dry summers, and the permafrost soil has an active-layer depth of about 1–1.2 m. Site SKT is characterized by a sub-boreal continental climate with cold winters, and hot and dry summers. A temperate monsoon climate dominates the LSH site, which has much more precipitation during the summer than does SKT. Sites TSE, TMK and FHK are characterized by a cool-temperate climate with a warm summer and snow cover in winter, although TSE has much more snow accumulation and colder winters than the other two sites.
Table 1 Site characteristics. Seasonal maximum leaf area index (LAI) values obatained by ground observation and evaluated from monthly average MODIS (Moderate Resolution Imaging Spectroradiometer) product LAI product (MOD15A2) are shown.
Published online:
05 February 2015![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0001_b.gif"})
Figure 1 Location of studied larch forests.
TUR is a uniformly aged (105-year-old) Larix gmelinii (Rupr.) Rupr. forest located in Tura in the Evenkia Autonomous District in central Siberia (Nakai et al. 2008). The stand density of living L. gmelinii is 5500 trees ha–1, and the mean height is 3.4 m. The ground surface is covered densely with lichens and mosses, and the understory is comprised of woody shrubs of Betula nana L., Ledum palustre L., Vaccinium uliginosum L., and Vaccinium vitis-idaea L. with heights < 1.5 m. The soil type is Cryosol, and the permafrost table is within 1 m depth.
YLF is a Larix cajanderi Mayr. forest located approximately 20 km north of Yakutsk in eastern Siberia (Ohta et al. 2001, 2008), where there is continuous permafrost and the active layer is approximately 1.2 m deep. The larch trees have a stand density of 840 trees ha–1 and a mean stand height of 18 m. Betula platyphylla Sukaczev. dominates the understory, and the forest floor is fully covered by V. vitis-idaea.
NLG is a L. cajanderi forest located about 25 km northwest of Yakutsk (Machimura et al. 2005, 2008). The forest is on continuous permafrost, with an active layer depth of approximately 1.0 m. The average and maximum tree heights are 8.6 and 21 m, respectively, and the density is 2100 trees ha−1. The forest floor is covered with mosses and shrubs including V. vitis-idaea, V. uliginosum and Pyrola incarnata (DC.) Fisch. ex Freyn. The soil texture is loamy, and soil organic carbon (C) content in the top 1-m layer was 13.2 kg C m−2 (Sawamoto et al. 2003).
SKT is a Larix sibirica Ledeb. forest, mixed with scattered or patchy B. platyphylla trees in some places. It is on a southwest-facing, gently sloping hill of the Khentii Mountains located about 25 km northeast of the Mongonmorit village in the Tov province of Mongolia (Li et al. 2005). The mean stand height and density are 20 m and 1120 trees ha−1, respectively. The dense understory forms a distinct layer of grasses and scattered shrubs. The grasses are dominated by Carex spp., Koeleria spp., and Chamaenerion angustifolium L., and the dominant shrub is Potentilla fruticosa L. The forest experienced a large-scale fire in 1996–1997, and approximately 37.5% of the trees have fire scars on their trunks. In addition, there were 570 ha−1 of tree stumps left after timber harvesting during the last three decades. As a result of these disturbances, the age structure of the forest spanned from 70 to > 150 years old, with the oldest trees > 300 years. The soil is a seasonal Cryosol with a coarse texture that is highly leached and gray in color.
LSH is an L. gmelinii plantation in northeastern China (Wang et al. 2005a, 2005b). The forest was established in 1969 on a complex terrain, and some broadleaf species (e.g., B. platyphylla and Fraxinus mandshurica Rupr.) are sparsely distributed throughout the canopy. The mean canopy height is about 17 m. Shrub species such as Ulmus propinqua Koidz., Corylus heterophylla Fish. ex Besser and Lonicera ruprechtiana Regel grow in the forest. The soil is classified as a typical dark brown forest soil.
TSE is a young hybrid larch (L. gmelinii × Larix kaempferi (Lamb.) Carriére) plantation located in northernmost Hokkaido, in northern Japan (Takagi et al. 2009). After clear-cutting trees covering an area of 13.7 ha and strip-cutting the former dense undergrowth of Sasa senanensis Rehd. and Sasa kurilensis (Rupr.) Makino et Shibata into alternating 4-m-wide cut and uncut rows in the clear-cut area in 2003, 2-year-old hybrid larch was planted in the strip-cut rows at a density of 2500 saplings ha–1. In 2011, the mean heights of larch and Sasa were 3.5 and 1.5 m, respectively, and the LAI values were 1.7 and 6.7 m2 m−2 at the seasonal maximum, showing the minor contribution of larch to the whole forest leaf area. The soil is a Gleyic Cambisol, and its surface organic horizon is about 10 cm thick.
TMK is a Japanese larch (L. kaempferi) plantation located 15 km northwest of Tomakomai, Hokkaido, Japan (Hirano et al. 2003; Hirata et al. 2007). Trees were about 45 years old at the time of the measurement. The forest includes scattered deciduous broadleaf trees (Betula ermanii Cham., B. platyphylla var. japonica (Miq.) Hara. and Ulmus japonica (Rehder) Sarg.) and spruce (Picea jezoensis (Siebold et Zucc.) Carriére), with a dense understory of ferns (Dryopteris crassirhizoma Nakai and Dryopteris austriaca (Jacq.) Woynar ex Schinz. et Thell. var. orientalis Fomin). In 1999, the canopy height was about 15 m and the stand densities were 673, 459, 18 and 1150 trees ha−1, respectively, for larch, broadleaf trees, spruce and all species. The soil is a volcanogenic Regosol with 1- to 2-cm thick fresh litter and a 5- to 10-cm thick decomposed organic layer.
FHK is another Japanese larch (L. kaempferi) plantation, located at the northern foot of Mt. Fuji in central Japan (Ueyama et al. 2012). The stand age was 45–50 years old at the time of the measurement. The forest includes scattered coniferous trees (Pinus densiflora Siebold et Zucc. and Abies homolepis Siebold et Zucc.) with an understory of shrubs (Prunus incisa Thunb.). The canopy height was about 22 m, and the stand density was 433 trees ha−1.
2.2 Calculation of net ecosystem CO2 exchange
We used 15 years of data of 30-min NEE for this study (Table 2). NEE was calculated as the sum of the eddy CO2 flux (Fc) and the CO2 storage change in the air column below the flux measurement height (Fs), although Fc was measured directly as NEE at YLF and TSE because of the limitation of the instruments and short vegetation after clear-cutting, respectively. Fc was measured using the eddy covariance technique with 3D-sonic anemometer-thermometers and open-path (TUR, NLG, YLF, SKT) or closed-path (LSH, TSE, TMK, FHK) infrared gas analyzers. Measurement systems and calculation protocols were based on EUROFLUX methodology (Aubinet et al. 2000). The CO2 storage change was evaluated using the vertical profile of the atmospheric CO2 concentration (NLG, TMK, FHK) or a single height measurement of CO2 concentration at the Fc measurement height (TUR, SKT, LSH). The instruments and features for the calculation are listed in Table 2. Because of the severe winter climate, the flux was measured only from day of the year (DOY) 156 to 255 at TUR; from DOY 97 to 283 at YLF; and from DOY 104 to 282 (in 2003), DOY 122 to 274 (in 2004) and DOY 168 to 270 (in 2005) at NLG.
Table 2 Flux measurement, calculation and quality control and assurance (QC/QA) procedures for each site. Method used to determine carbon dioxide (CO2) storage change flux is also listed. See text for the determination of threshold value for friction velocity (u*) filtering.
2.3 Quality control, gap filling of net ecosystem CO2 exchange and flux partitioning
Quality control procedures were applied by each principal investigator of the site; these included the removal of spikes, performing tests on 10-Hz data (Vickers and Mahrt 1997) and instationarity ratio and turbulence characteristics tests (Foken and Wichura 1996), as well as footprint analysis (Kormann and Meixner 2001). Features of the quality control procedures are shown in Table 2.
Friction velocity (u*) filtering was applied to the quality-assured nighttime NEE data. The u* threshold was determined for each site and year according to the procedures proposed by Papale et al. (2006), but the highest values among years (Table 2) were used throughout the entire study period for sites with multiyear data.
Gaps in the remaining NEE data after u* filtration were filled, and the GPP and RE were determined using an online server (http://www.bgc-jena.mpg.de/bgc-mdi/html/eddyproc/) for gap-filling and flux-partitioning calculation (Reichstein et al. 2005; Papale et al. 2006; Takagi et al. 2007), which is the basic procedure of FLUXNET synthesis activity. The FLUXNET gap-filling strategy is a combination of several filling methods proposed by Falge et al. (2001), which primarily adopts lookup tables, where bin-averaged NEE for similar air temperature, vapor pressure deficit and solar radiation ranges is calculated within a time window of ± 7 d (or 14 d, if no similar meteorological conditions are present within the 7-d window). If none of these meteorological data are present, the missing NEE value is replaced by the average value at the same time of day. Refer to Appendix A in Reichstein et al. (2005) for more details. Nighttime NEE can be assumed to be equivalent to RE; thus, for the determination of RE, 30-min nighttime NEE (NEEnight) within a 15-d moving window (10-d overlap) was related to the air temperature using the Lloyd and Taylor (1994) equation as: 
(1)
where R10 is RE (μmol m−2 s−1) at the reference temperature (Tr: 283.15 K), Ea is the apparent temperature sensitivity (J mol−1), R is the universal gas constant (8.314 J mol−1 K−1), Ta is the air temperature (K) and T0 is 227.13 K. The coefficient R10 was determined every 5 d as the time variable, whereas Ea was determined as a constant for each year, and daytime RE was estimated every 30 min using this equation and air temperature at that time. GPP was calculated as RE − NEE. GPP and RE were calculated every 30 min, and the daily or annual sum was used for the following analyses. Owing to the limited measurement period under severe winter climate at the three Russian sites, gap-filled NEE, GPP and RE were determined for DOY 97 to 257 at TUR; DOY 45 to 333 at YLF; and DOY 45 to 341 (in 2003), DOY 63 to 333 (in 2004) and DOY 173 to 329 (in 2005) at NLG. These were then used as annual values. This treatment would decrease the annual sum of RE, thus increasing the annual NEE, although the magnitude would not be significant because of the low respiration rate during the severe winter.
2.4 Determination of ecosystem photosynthetic and respiratory parameters
Photosynthetic parameters, the maximum GPP at light saturation (Amax: μmol m−2 s−1), initial slope of the light-response curve (Φ: mol mol−1) and daytime respiration (Rd: μmol m−2 s−1) as the y-intercept of the light-response curve (Eq. 2) were determined every day by the least-squares method using daytime (Q ≥ 10 µmol m–2 s–1) 30-min NEE (quality controlled but not gap filled) and Q data within a 15-d moving window:
(2)
where Q is the photosynthetic photon flux density (μmol m−2 s−1) and θ is the convexity of the light-response curve. We used a fixed value (0.9) for θ to apply the least-squares method.
We used the same equation (Eq. 1) for the determination of respiratory parameters, the apparent temperature sensitivity (Ea) and RE at the reference temperature of 10ºC (R10), but these were determined every day by the least-squares method using nighttime (Q < 10 µmol m–2 s–1) 30-min NEE (quality controlled with u* filtration but not gap filled) and Ta data within a 29-d moving window.
2.5 LAI data and phenological parameters
Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MOD15A2 Collection 5; Myneni et al. 2002) was used as the LAI in each site to explain the intersite variations of C fluxes and their photosynthetic and respiratory parameters, because MODIS LAI includes the effect of understory leaves when it is estimated based on the radiative transfer theory (Myneni et al. 2002), and the methods of on-site LAI observation were site specific; some sites used plant area index including the shades by the stems, branches and culms in addition to the leaves of the canopy. We used MODIS 1-km resolution subset data sets (collection 5; http://daac.ornl.gov/MODIS/), each of which consisted of 7 × 7-km regions centered on the flux towers. At each time step, we averaged the MODIS observations by only using high-quality pixels (with the mandatory quality assurance – QA – flag being good in the QA data) based on the method of Yang et al. (2007) and Ichii et al. (2010), and missing data were replaced by a long-term average calculated using high-quality pixels. The original eight days’ composite products were converted to monthly averages during study period for each site, although LAI for TSE is that only in 2010.
We also determined the following phenological indices for each site: start (SG) and end (EG) day of the growing season, growing season length (GSL), growing season degree days (GSD) and degree days until SG (PGD). SG and EG were determined as the first and last day when GPP was continuously more than 1 g C m–2 d–1. GSL was determined as the day length between SG and EG. GSD and PGD were determined as the cumulative daily average air temperature above 5°C during GSL and until SG, respectively.
3. RESULTS AND DISCUSSION
3.1 Seasonal and intersite variation of carbon fluxes and MODIS LAI
Seasonal variation of GPP, RE and NEE for eight larch forests are shown in Fig. 2. Here, we show the maximum–minimum ranges of the interannual variation as shades to show the possible uncertainty caused by the different years’ observation. Photosynthesis began to increase in late May at the three Siberian sites (TUR, NLG, YLF) and the Mongolian site (SKT), whereas the increase began in late April at the other sites (Fig. 2). GPP reached its seasonal maximum during early to mid July at TUR, NLG, YLF and SKT, whereas this occurred during early to mid June at LSH, TMK and FHK. Thus, the start and peak of GPP at the Chinese and Japanese (except TSE) sites were about a month earlier than at the Siberian and Mongolian sites. TSE had a unique seasonal variation in GPP, which showed a gradual increase from May to July and a peak from July to August. This site was covered with dense Sasa dwarf bamboo, and the contribution of larch to ecosystem leaf area was minor. Thus, the GPP seems to be strongly affected by the seasonal variation of Sasa, which developed new leaves from July to August, and the LAI increased 1–3 months after that of the trees (Fukuzawa et al. 2007). On the other hand, RE was maximized from late July to early August at all study sites, owing to the seasonal maximum temperature during that period. As a result, a high sequestration rate (minimum NEE) was observed in June at most of the study sites (Fig. 2).
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05 February 2015Figure 2 Seasonal variation of daily gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) for eight Larch (Larix spp.) forests. The max–min range of interannual variation is shown for sites with multiple years of observation.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0002_b.gif"})
Figure 2 Seasonal variation of daily gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) for eight Larch (Larix spp.) forests. The max–min range of interannual variation is shown for sites with multiple years of observation.
MODIS LAI reached its seasonal maximum at a similar period to that of GPP except LSH, TSE and FHK (Fig. 3). This discrepancy for the three sites may partly be owing to the different spatial representativeness between MODIS (up to 7 km) and flux observation (up to several hundred meters, or kilometers).
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05 February 2015Figure 3 Seasonal variation of monthly MODIS LAI (MOD15A2) for eight larch (Larix spp.) forests. The max-min range of interannual variation is shown for sites with multiple years of observation, although LAI for TSE is that only in 2010.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0003_oc.jpg"})
Figure 3 Seasonal variation of monthly MODIS LAI (MOD15A2) for eight larch (Larix spp.) forests. The max-min range of interannual variation is shown for sites with multiple years of observation, although LAI for TSE is that only in 2010.
Seasonal maximum daily GPP and RE ranged from 4.3 g C m−2 d−1 (TUR) to 20.0 g C m−2 d−1 (TMK) and from 3.1 g C m−2 d−1 (TUR) to 14.3 g C m−2 d−1 (LSH), respectively (Table 3). Seasonal minimum daily NEE (maximum CO2 absorption) ranged from −11.3 g C m−2 d−1 (TMK) to −2.1 g C m−2 d−1 (TUR). Annual GPP, RE and NEE ranged from 234 g C m−2 yr−1 (TUR) to 1827 g C m−2 yr−1 (TMK), 145 g C m−2 yr−1 (TUR) to 1564 g C m−2 yr−1 (LSH) and −342 g C m−2 yr−1 (FHK) to 95 g C m−2 yr−1 (LSH), respectively.
Table 3 Carbon (C) fluxes of each study site. Average value and range are shown for sites with multiple years of observation.
GPP and RE increased exponentially with the increase in annual mean air temperature (r2 = 0.87, p < 0.001 for GPP and r2 = 0.84, p < 0.01 for RE) and linearly with the increase in seasonal maximum MODIS LAI of the site (r2 = 0.72, p < 0.01 for GPP and r2 = 0.77, p < 0.01 for RE) (Fig. 4). Except at the two larch plantations (LSH and TSE), net CO2 absorption rate (−NEE) tended to increase with the rise in annual mean air temperature or seasonal maximum LAI. Linear relationships were also observed between annual carbon fluxes (g C m−2 yr−1) and annual precipitation (Pr; mm yr−1) among study sites (GPP = 0.90 × Pr + 310, RE = 0.86 × Pr + 197, NEE = − 0.11 × Pr − 141 excluding the two plantations, LSH and TSE); however, the coefficients of the determination (r2 = 0.67, p < 0.05 for GPP, r2 = 0.61, p < 0.05 for RE, r2 = 0.69, p < 0.05 for NEE) were smaller than those for annual mean air temperature. There was no clear relationship (r2 < 0.12) between annual carbon fluxes and cumulative photosynthetic photon flux density (mol m−2) during the growing season (DOY153 − 257). Because we also found strong linear relationships of annual GPP and RE to SG (r2 = 0.94, p < 0.001 for GPP and r2 = 0.99, p < 0.001 for RE), GSL (r2 = 0.89, p < 0.001 for GPP and r2 = 0.92, p < 0.001 for RE) and GSD (r2 = 0.87, p < 0.001 for GPP and r2 = 0.93, p < 0.001 for RE) (Fig. 5), the GPP and RE were highly restricted by the length and temperature of the growing season, and the apparent relationships to mean annual temperature were likely observed. PGD, cumulative daily average air temperature above 5°C until SG, was not constant through the study sites and tended to increase with the delay in SG (Fig. 6). This implies that the larch forest requires more heat accumulation to start photosynthesis in colder regions, and this may be owing to the permafrost.
Published online:
05 February 2015Figure 4 Relationship of annual gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) with annual mean air temperature (Ta) and seasonal maximum of monthly MODIS LAI (MOD15A2). The average and the max–min range of interannual variation of carbon (C) fluxes are shown as symbols and bars, respectively for sites with multiple years of observation. Regression equation between NEE and Ta or LAI was obtained while excluding two larch (Larix spp.) plantations (LSH and TSE).
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0004_b.gif"})
Figure 4 Relationship of annual gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) with annual mean air temperature (Ta) and seasonal maximum of monthly MODIS LAI (MOD15A2). The average and the max–min range of interannual variation of carbon (C) fluxes are shown as symbols and bars, respectively for sites with multiple years of observation. Regression equation between NEE and Ta or LAI was obtained while excluding two larch (Larix spp.) plantations (LSH and TSE).
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05 February 2015Figure 5 Relationship of annual gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) with start day of the growing season (SG; left), growing season length (GSL; middle) and growing season degree days (GSD; right). The average and the max–min range of interannual variation of carbon fluxes and phenological indices are shown as symbols and bars, respectively for sites with multiple years of observation. DOY: day of the year.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0005_b.gif"})
Figure 5 Relationship of annual gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) with start day of the growing season (SG; left), growing season length (GSL; middle) and growing season degree days (GSD; right). The average and the max–min range of interannual variation of carbon fluxes and phenological indices are shown as symbols and bars, respectively for sites with multiple years of observation. DOY: day of the year.
Published online:
05 February 2015Figure 6 Relationship between start day of the growing season (SG) and degree days until SG (PGD) for each site. The average and the max–min range of interannual variation of phenological indices are shown as symbols and bars, respectively for sites with multiple years of observation. DOY: day of the year.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0006_b.gif"})
Figure 6 Relationship between start day of the growing season (SG) and degree days until SG (PGD) for each site. The average and the max–min range of interannual variation of phenological indices are shown as symbols and bars, respectively for sites with multiple years of observation. DOY: day of the year.
Ueyama et al. (2013) also reported that the MODIS LAI is the best vegetation index to explain the spatial variation of GPP in Arctic and boreal ecosystems in Alaska, and they suggested that the inclusion of the understory leaves in the MODIS LAI estimation was the probable reason for the better performance to explain the intersite variation of photosynthetic capacity in Alaska, where the understory vegetation plays an important role in the total ecosystem photosynthesis, than other vegetation indices such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), which are corrected for the canopy background signal. Transfer functions from NDVI and EVI to LAI were different in different vegetation types (Street et al. 2007), and this would be another possible reason. However, we need to test applicability to other ecosystems to confirm universal applicability.
Hirata et al. (2008) and Chen et al. (2013) reported linear increase in GPP and exponential increases in RE, with an increase in the annual mean air temperature of the studied forests across East Asia. However, we obtained better correlation coefficients by exponential regression of both GPP and RE to the annual mean air temperature, similar to those reported by Saigusa et al. (2013), within the limited forest type and temperature range (from −9.4°C [NLG] to 9.2°C [FHK]).
Compared with the GPP and RE, it was difficult to find an apparent relationship between annual NEE and temperature or LAI (Fig. 4 and 5), because of the two outliers (LSH and TSE). The TSE site was a 7-year-old larch plantation after clear-cutting in 2003, when data were acquired, and former Sasa undergrowth still strongly affected the C fluxes. The LSH site is also a relatively young plantation (35 years old) compared with the other study sites (> 45 years old at TMK), and the C fluxes have possible uncertainty caused by the complex terrain. Although the annual GPP and RE increased with the stand age at the first stage of the stand development (< 50 years old), these tended to decrease with the stand age (Fig. 7); therefore, the disturbance history may partly affect the relationship of NEE to temperature or LAI, as suggested by process-based ecosystem model studies (Ueyama et al. 2010; Ichii et al. 2013). However, the effects of other environmental factors are included in this apparent relationship, and it is difficult to distinguish these effects in our bulk analyses; thus, the age or management effect must be clarified by the comparison among different-aged forest stands under similar environments in future studies.
Published online:
05 February 2015Figure 7 Relationship of annual gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) with the stand age. The average and the max–min range of interannual variation of carbon (C) fluxes are shown as symbols and bars, respectively, for sites with multiple years of observation.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0007_b.gif"})
Figure 7 Relationship of annual gross primary production (GPP), total ecosystem respiration (RE) and net ecosystem carbon dioxide exchange (NEE) with the stand age. The average and the max–min range of interannual variation of carbon (C) fluxes are shown as symbols and bars, respectively, for sites with multiple years of observation.
3.2 Ecosystem photosynthetic and respiratory parameters
Seasonal variations of daily Amax, Φ, Rd, R10 and Ea for eight larch forests are shown in Fig. 8. Again, we showed the maximum–minimum range of the interannual variation to show the possible uncertainty caused by the different years’ observation. The two photosynthetic parameters (Amax and Φ) showed peaks from late June to early July and gradually decreased thereafter at all the sites, except the young plantation dominated by Sasa (TSE). Seasonal maximum values of Amax and Φ ranged from 5.9 µmol m−2 s−1 (TUR) to 45.2 µmol m−2 s−1 (TMK) and 0.025 mol mol−1 (TUR, NLG, and SKT) to 0.065 mol mol−1 (TMK), respectively (Table 4). The reference respiration rate (R10) showed seasonal variation similar to that of Amax and Φ, although an apparent decrease was observed from July to August at some sites (LSH and FHK). Compared with R10, a delayed increase was observed in the seasonal variation of the daytime respiration rate (Rd). Because Rd is a temperature-dependent parameter, it was likely enhanced by the temperature increase during July to August, although the respiratory parameter normalized by temperature (R10) tended to be suppressed by temperature increases at some sites (LSH and FHK). Ea did not show clear seasonal and interannual variation, but decreased during midsummer at some sites (NLG, YLF, SKT and TMK). The decrease in the ecosystem respiratory parameters can be caused by the hot and dry environment (Janssens and Pilegaard 2003) or the decreased plant respiratory activity (Liang et al. 2010), as observed in soil respiration studies. Seasonal maximum values of Rd, R10 and Ea ranged from 1.7 µmol m−2 s−1 (TUR) to 9.4 µmol m−2 s−1 (TSE), 1.1 µmol m−2 s−1 (TUR) to 6.6 µmol m−2 s−1 (TMK) and 1.9 kJ mol−1 (TUR) to 5.5 kJ mol−1 (FHK), respectively (Table 4).
Table 4 Ecosystem photosynthetic and respiratory parameters. Seasonal maximum values were obtained by averaging the top 10 daily values. Average value and range are shown for sites with multiple years of observation.
Published online:
05 February 2015Figure 8 Seasonal variation of daily Amax (maximum gross primary production at light saturation), Φ (initial slope of the light-response curve), Rd (daytime respiration), R10 (total ecosystem respiration at the reference temperature of 10ºC) and Ea (apparent temperature sensitivity of RE) for eight larch (Larix spp.) forests. The max–min range of interannual variation of the daily values is shown for sites with multiple years of observation. DOY: day of the year.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0008_oc.jpg"})
Figure 8 Seasonal variation of daily Amax (maximum gross primary production at light saturation), Φ (initial slope of the light-response curve), Rd (daytime respiration), R10 (total ecosystem respiration at the reference temperature of 10ºC) and Ea (apparent temperature sensitivity of RE) for eight larch (Larix spp.) forests. The max–min range of interannual variation of the daily values is shown for sites with multiple years of observation. DOY: day of the year.
We observed a linear or exponential increasing trend for seasonal maximum Amax, Φ, Rd and R10 with the increase in annual mean air temperature (r2 = 0.77, p < 0.01 for Amax, r2 = 0.83, p < 0.01 for Rd and r2 = 0.74, p < 0.01 for R10) and seasonal maximum LAI (r2 = 0.73, p < 0.01 for Amax, r2 = 0.66, p < 0.05 for Φ, r2 = 0.78, p < 0.01 for Rd and r2 = 0.81, p < 0.01 for R10) across the eight sites (Fig. 9). Because an increasing trend with temperature or LAI was observed not only for the C fluxes (GPP and RE), but also for the ecosystem photosynthetic and respiratory parameters, it is clear that the increase in GPP and RE along the latitudinal gradient was caused by the enhancement of vegetation activity (photosynthesis and respiration; biotic factor) through the increase in leaf (or whole vegetation) biomass as well as the increased temperature (abiotic factor), although the biomass itself also depends on the temperature. On the other hand, the temperature sensitivity of RE (Ea) was likely independent of the temperature or LAI gradients of the study sites. Because four (Amax, Φ, Rd and R10) of the five parameters were proportional to the magnitude of seasonal maximum LAI, we divided the values of the parameters in Fig. 9 by the seasonal maximum of monthly MODIS LAI (parameters were normalized by the LAI) in order to cancel the biomass effect on these parameters (Fig. 10). Linear increase with the temperature was still observed for Amax and Rd; however, this relationship disappeared for Φ and R10. This means that the abiotic factor (temperature) effect is dominant in the intersite variation of Amax, while this effect is not important in the variation of Φ or the temperature normalized respiratory parameter (R10).
Published online:
05 February 2015Figure 9 Relationship of seasonal maximum daily Amax (maximum gross primary production at light saturation), Φ (initial slope of the light-response curve), Rd (daytime respiration), R10 (total ecosystem respiration at the reference temperature of 10°C) and Ea (apparent temperature sensitivity) with annual mean air temperature and seasonal maximum of monthly MODIS LAI (MOD15A2). Seasonal maximum values for each photosynthetic and respiratory parameter were determined by averaging the top 10 daily values. The average and the max–min range of interannual variation are shown as symbols and bars, respectively, for sites with multiple years of observation.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0009_b.gif"})
Figure 9 Relationship of seasonal maximum daily Amax (maximum gross primary production at light saturation), Φ (initial slope of the light-response curve), Rd (daytime respiration), R10 (total ecosystem respiration at the reference temperature of 10°C) and Ea (apparent temperature sensitivity) with annual mean air temperature and seasonal maximum of monthly MODIS LAI (MOD15A2). Seasonal maximum values for each photosynthetic and respiratory parameter were determined by averaging the top 10 daily values. The average and the max–min range of interannual variation are shown as symbols and bars, respectively, for sites with multiple years of observation.
Published online:
05 February 2015Figure 10 Relationship of seasonal maximum of LAI-normalized daily Amax (maximum gross primary production at light saturation), Φ (initial slope of the light-response curve), Rd (daytime respiration), R10 (total ecosystem respiration at the reference temperature of 10°C), and Ea (apparent temperature sensitivity) with annual mean air temperature. Seasonal maximum values for each photosynthetic and respiratory parameter were determined by averaging the top 10 daily values, then divided by the seasonal maximum of monthly MODIS LAI (MOD15A2). The average and the max-min range of interannual variation are shown as symbols and bars, respectively, for sites with multiple years of observation.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tssp20/2015/tssp20.v061.i01/00380768.2014.990349/20150205/images/medium/tssp_a_990349_f0010_b.gif"})
Figure 10 Relationship of seasonal maximum of LAI-normalized daily Amax (maximum gross primary production at light saturation), Φ (initial slope of the light-response curve), Rd (daytime respiration), R10 (total ecosystem respiration at the reference temperature of 10°C), and Ea (apparent temperature sensitivity) with annual mean air temperature. Seasonal maximum values for each photosynthetic and respiratory parameter were determined by averaging the top 10 daily values, then divided by the seasonal maximum of monthly MODIS LAI (MOD15A2). The average and the max-min range of interannual variation are shown as symbols and bars, respectively, for sites with multiple years of observation.
4. CONCLUSIONS
Temperature and MODIS LAI explained much of the spatial variation of GPP and RE of larch forests distributed over East Asia. Taken together, our findings indicate that the intersite variation of ecosystem photosynthetic and respiratory parameters of larch forests can be explained well by MODIS LAI, suggesting that the intersite variation in GPP and RE was caused not only by the temperature variation (abiotic factor), but also by the variation in the photosynthetic and respiration activity by vegetation (biotic factor) through the change in leaf (or whole vegetation) biomass, and the applicability of this index to ecosystem C cycle models.
| Seasonal max of canopy LAI (m2 m–2) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Site | Site code | Location | Elevation (m) | Annual mean air temperature (°C) | Annual precipitation (mm) | Stand height (m) | Stand age (yr) | MODIS† | Ground observation | Species | Reference |
| Tura, Russia | TUR | 64°12ʹN, 100°27ʹE | 250 | –8.9†† | 360†† | 3.4 | 105 | 1.6 | < 0.3 | Larix gmelinii (Rupr.) Rupr. | Nakai et al. (2008) |
| Neleger, Russia | NLG | 62°19ʹN, 129°31ʹE | 200 | –9.4†† | 249†† | 8.6 | >200 | 2.6 (2.4–2.8) | 1.9 | Larix cajanderi Mayr. | Machimura et al. (2005, 2008) |
| Yakutsuk, Russia | YLF | 62°15ʹN, 129°14ʹE | 220 | –9.1†† | 260†† | 18 | 180 | 2.8 | 1.6 | Larix cajanderi Mayr. | Ohta et al. (2001, 2008) |
| Southern Khentei Taiga, Mongolia | SKT | 48°21ʹN, 108°39ʹE | 1630 | –1.5 | 296 | 20 | 110 | 1.9 (1.7–2.1) | 2.7 | Larix sibirica Ledeb. | Li et al. (2005) |
| Laoshan, China | LSH | 45°20ʹN, 127°34ʹE | 370 | 4.8 | 724 | 17 | 35 | 4 | 2.5 | Larix gmelinii (Rupr.) Rupr. | Wang et al. (2005a, 2005b) |
| CC-LaG experiment site, Japan | TSE | 45°03ʹN, 142°06ʹE | 70 | 6.0 | 1278 | 3.5 | 7 | 3.9††† | 1.7†††† | Larix gmelinii (Rupr.) Rupr.× Larix kaempferi (Lamb.) Carriére | Takagi et al. (2009) |
| Tomakomai Flux Research site, Japan | TMK | 42°44ʹN, 141°31ʹE | 140 | 6.3 | 1040 | 15 | 45 | 4.9 (4.5–5.3) | 5.6 | Larix kaempferi (Lamb.) Carriére | Hirata et al. (2007) |
| Fuji Hokuroku Flux Observation site, Japan | FHK | 35°26ʹN, 138°45ʹE | 1100 | 9.2 | 1800 | 20–25 | 50 | 3.3 | 2.9 | Larix kaempferi (Lamb.) Carriére | Ueyama et al. (2012) |
†Average value with the range in parentheses are shown for sites with multiple years of observation.
††Data gaps in the winter were extrapolated using the data obtained at the nearby weather station.
†††Data in 2010.
††††Plant area index including trunks and branches.
| Site code | Sonic anemometer model | Infrared gas analyzer model | Flux measurement height (m) | Coordinate rotation | Corrections for flux calculation | QC/QA | CO2 storage change | u* threshold (m s−1) | Observation year |
|---|---|---|---|---|---|---|---|---|---|
| TUR | R-3a | LI-7500c | 20 | Double rotationd | Angle of attack errorsf/linear trend removing/humidity correctiong/WPL correctionh | Absolute valuep/ precipitation/raw data spikes and outliersp /inclination of wind flowq | CO2 concentration at flux measurement height | 0.1 | 2004 |
| NLG | R-3a | LI-7500c | 21 | Double rotationd | Linear trend removing/WPL correctionh | Absolute valuep/ instationarityq/ footprintr | CO2 concentration at 6 heights | 0.28 (0.21–0.28) | 2003–2005 |
| YLF | R-3a | LI-7500c | 32 | Double rotationd | WPL correctionh | Absolute valuep/ precipitation | Not considered | 0.23 | 2004 |
| SKT | SAT-550b | LI-7500c | 30 | Double rotationd | WPL correctionh/cospectral correctioni | Absolute valuep/ precipitation | CO2 concentration at flux measurement height | 0.1 | 2004–2005 |
| LSH | SAT-550b | LI-7000c | 29 | Double rotationd | Time lag/WPL correctionj | Absolute valuep/ raw data spikesp | CO2 concentration at flux measurement height | 0.1 | 2004 |
| TSE | DA600-3TVb | LI-7000c | 5.7 | Planar fite | Cross wind effectk/humidity correctiong/WPL correctionj/time lag/high frequency lossesl/band pass correctionm | Absolute valuep/ precipitation/ raw data spikes and outliersp/ instationarityq/ integral turbulence characteristicsq /footprintr | Not considered | 0.13 (0.1–0.13) | 2010–2012 |
| TMK | DA600-3TVb | LI-6262c | 27 | Planar fite | Flow distortionn/cross wind effectk/humidity correctiong/time lag/high frequency lossesl/empirical transfer function° | Absolute valuep/ precipitation/ raw data spikes and outliersp/ instationarityq/ integral turbulence characteristicsq/ wind directionq | CO2 concentration at 7 heights | 0.13 (0.1–0.13) | 2001–2003 |
| FHK | DA600-3TVb | LI-6262c | 35 | Planar fite | Flow distortionn/cross wind effectk/humidity correctiong/time lag/high frequency lossesl/empirical transfer function° | Absolute valuep/ precipitation/ raw data spikes and outliersp/ instationarityq/ integral turbulence characteristicsq/ wind directionq | CO2 concentration at 10 heights | 0.1 | 2006 |
aGill Instruments Ltd., Lymington, UK. bKAIJOSONIC Corporation, Tokyo, Japan. cLI-COR Inc., Lincoln, USA. dMcMillen (1988). eWilczak et al. (2001). fNakai et al. (2006). gHignett (1992). hWebb et al. (1980). iEugster and Senn (1995). jLeuning and King (1992). kKaimal and Gaynor (1991). lMoore (1986). mKowalski et al. (2003). nShimizu et al. (1999). °Aubinet et al. (2000). pVickers and Mahrt (1997). qFoken and Wichura (1996). rKormann and Meixner (2001).
| Site code | Seasonal max. daily GPP (g C m–2 d–1) | Seasonal max. daily RE (g C m–2 d–1) | Seasonal min. daily NEE (g C m–2 d–1) | Annual GPP (g C m–2 yr–1) | Annual RE (g C m–2 yr–1) | Annual NEE (g C m–2 yr–1) |
|---|---|---|---|---|---|---|
| TUR | 4.3 | 3.1 | –2.1 | 234† | 145† | –89† |
| NLG | 9.3 (5.9 to 13.1) | 6.6 (4.0 to 10.9) | –4.5 (–4.9 to – 3.7) | 463† (415† to 539†) | 286† (211† to 379†) | –177† (–224† to – 148†) |
| YLF | 5.7 | 3.1 | –4.3 | 470† | 267† | –203† |
| SKT | 8.8 (7.5 to 10.1) | 5.8 (4.8 to 6.9) | –4.4 (–4.9 to – 3.9) | 586 (562 to 610) | 371 (327 to 416) | –215 (–236 to – 194) |
| LSH | 15.4 | 14.3 | –7.5 | 1469 | 1564 | 95 |
| TSE | 11.4 (10.2 to 12.8) | 11.9 (11.0 to 12.6) | –3.9 (–4.3 to – 3.6) | 1092 (988 to 1190) | 1120 (1032 to 1193) | 28 (3.8 to 44) |
| TMK | 20.0 (18.9 to 21.0) | 12.4 (11.7 to 13.0) | –11.3 (–11.9 to – 10.3) | 1827(1761 to 1933) | 1551 (1494 to 1616) | –277 (–317 to – 219) |
| FHK | 16.3 | 10.7 | –9.9 | 1758 | 1416 | –342 |
†Limitted period for the determination (see text).
| Site code | Seasonal max. Amax (μmol m–2 s–1) | Seasonal max. Φ (mol mol–1) | Seasonal max. Rd (μmol m–2 s–1) | Seasonal max. R10 (μmol m–2 s–1) | Seasonal max. Ea (kJ mol–1) |
|---|---|---|---|---|---|
| TUR | 5.9 | 0.025 | 1.7 | 1.1 | 1.9 |
| NLG | 12.3 (9.6 to 14.1) | 0.025 (0.023 to 0.028) | 3.4 (2.5 to 3.9) | 2.8 (2.2 to 3.7) | 4.4 (4.2 to 4.6) |
| YLF | 9.9 | 0.054 | 3.0 | 2.2 | 3.7 |
| SKT | 13.8(13.2 to 14.4) | 0.025 (0.021 to 0.028) | 3.1 (2.3 to 3.8) | 3.4 (3.0 to 3.8) | 3.7 (3.1 to 4.4) |
| LSH | 27.3 | 0.040 | 6.7 | 4.5 | 4.2 |
| TSE | 22.3(19.8 to 24.8) | 0.054 (0.050 to 0.063) | 9.4 (8.8 to 10.6) | 4.9 (4.7 to 5.3) | 4.2 (4.0 to 4.3) |
| TMK | 45.2(42.6 to 49.9) | 0.065 (0.062 to 0.070) | 8.7 (8.6 to 8.9) | 6.6 (6.3 to 6.9) | 4.2 (3.9 to 4.7) |
| FHK | 36.3 | 0.053 | 7.9 | 4.5 | 5.5 |
Supplemental Materials
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This research was financially supported by the A3 Foresight Program (CarboEastAsia) of the Japan Society for the Promotion of Science, and in part by Grants-in-Aid for Scientific Research (Nos. 23255009 and 25241002) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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