Agricultural and Forest Meteorology 232 (2017) 623–634
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Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Comprehensive synthesis of spatial variability in carbon flux across monsoon Asian forests Masayuki Kondo a,∗ , Taku M. Saitoh b , Hisashi Sato a , Kazuhito Ichii a,c a b c
Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan River Basin Research Center, Gifu University, Gifu, Japan Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
a r t i c l e
i n f o
Article history: Received 15 April 2016 Received in revised form 25 October 2016 Accepted 26 October 2016 Available online 29 October 2016 Keywords: Monsoon Asian forests Carbon flux Carbon balance Synthesis analysis Spatial variability
a b s t r a c t Forest ecosystems sequester large amounts of atmospheric CO2 , and the contribution from forests in Asia is not negligible. Previous syntheses of carbon fluxes in Asian ecosystems mainly employed estimates of eddy covariance measurements, net ecosystem production (NEP), gross primary production (GPP), and ecosystem respiration (RE); however, to understand the variability within carbon cycles, fluxes such as autotropic respiration (AR), net primary production (NPP), litterfall, heterotrophic respiration (HR), and soil respiration (SR) need to be analyzed comprehensively in conjunction with NEP, GPP, and RE. Here we investigated the spatial variability of component fluxes of carbon balance (GPP, AR, NPP, litterfall, HR, SR, and RE) in relation to climate factors, between carbon fluxes, and to NEP using observations compiled from the literature for 22 forest sites in monsoon Asia. We found that mean annual temperature (MAT) largely relates to the spatial variability of component fluxes in monsoon Asian forests, with stronger positive effect in the mid–high latitude forests than in the low latitude forests, but even stronger relationships were identified between component fluxes regardless of regions. This finding suggests that the spatial variability of carbon fluxes in monsoon Asia is certainly influenced by climatic factors such as MAT, but that the overall spatial variability of AR, NPP, litterfall, HR, SR, and RE is rather controlled by that of productivity (i.e., GPP). Furthermore, component fluxes of the mid–high and low latitude forests showed positive and negative relationships, respectively, with NEP. Further investigation identified a common spatial variability in NEP and annual aboveground biomass changes with respect to GPP. The relationship between GPP and NEP in the mid–high latitudes implies that productivity and net carbon sequestration increase simultaneously in boreal and temperate forests. Meanwhile, the relationship between GPP and NEP in the low latitudes indicates that net carbon sequestration decreases with productivity, potentially due to the regional contrast in nitrogen depositions and stand age within sub-tropical and tropical forests; however, it requires further data syntheses or modelling investigations for confirmation of its general validity. These unique features of monsoon Asian forest carbon fluxes provide useful information for improving ecosystem model simulations, which still differ in their predictability of carbon flux variability. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Forest ecosystems play a key role in climate change mitigation by absorbing large amounts of atmospheric CO2 (Schimel et al., 2001; Luyssaert et al., 2007; Pan et al., 2011). In particular, Asian forests uptake 2.49 Pg C yr−1 of atmospheric CO2 , which accounts for 27% of the global forest carbon balance (Yu et al., 2014). Because
∗ Corresponding author at: Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology, 3173-25, Showamachi, Kanazawa-ku, Yokohama, 236-0001, Japan. E-mail address: redmk92@gmail.com (M. Kondo).
of this substantial contribution to the global carbon balance, Asian forests are an integral part of the carbon cycle and climate change research (e.g., Patra et al., 2005; Piao et al., 2009; Kondo et al., 2013, 2015a; Yu et al., 2013). Owing to substantial efforts invested into field measurements in past decades (e.g., inventories) in conjunction with the still growing network of eddy flux observations, more data on forest carbon cycle has become accessible to the scientific community (Luyssaert et al., 2007; Baldocchi, 2008). Subsequently, in monsoon Asia, a growing number of synthesis studies have promoted understanding of the temporal and spatial variability of carbon fluxes (Hirata et al., 2008; Kato and Tang, 2008; Saigusa et al., 2008, 2013; Chen et al., 2013; Yu et al., 2013). For example, climatic factors, particularly temperature, were found to be a major
http://dx.doi.org/10.1016/j.agrformet.2016.10.020 0168-1923/© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4. 0/).
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driver of the spatial variability in net ecosystem production (NEP), gross primary production (GPP), and ecosystem respiration (RE) in monsoon Asia (Kato and Tang, 2008; Chen et al., 2013; Yu et al., 2013). These relationships signify a uniqueness of monsoon Asian ecosystems compared to European and North America ecosystems, where climate factors were weakly related to the spatial variability of NEP, GPP, and RE (Law et al., 2002; Reichstein et al., 2007). Besides the recent syntheses in monsoon Asia, it is important to further elucidate the spatial variability of components of forest carbon balance, such as autotrophic respiration (AR), net primary production (NPP), litterfall, heterotrophic respiration (HR), and soil respiration (SR). Indeed, NEP, GPP, and RE are important variables of the carbon cycle, as they indicate the magnitude of carbon sequestration by forests; however, their variability does not indicate internal processes of carbon flow. It is possible that strong climatic control of the spatial variability of GPP, RE, and NEP may not be the case for component fluxes; rather, biotic factors such as stand age and biomass or productivity (i.e., GPP) may be more relevant factors for the spatial variability of component fluxes (Janssens et al., 2001; Michaletz et al., 2014). Elucidating this variability is particularly important when implementing site- and regional-scale characteristics of the forest carbon cycle to process-based model simulations (e.g., Kondo et al., 2015a); thus, a comprehensive synthesis of carbon fluxes is required. During the past decades, the relationships between carbon fluxes and climate, and the linkages between carbon fluxes have been analyzed through worldwide observations (e.g., Davidson et al., 2002; Law et al., 2002; Van Dijk and Dolman, 2004; DeLucia et al., 2007; Baldocchi, 2008; Piao et al., 2010; Malhi et al., 2011; Yu et al., 2013; Chen et al., 2013; Xu et al., 2013; Han et al., 2015). However, to the best of our knowledge, there is no comprehensive analysis involving multiple carbon fluxes of multiple forest sites in the past. To elaborate on carbon flux syntheses in monsoon Asia, the present study comprehensively analyzes literature-based carbon fluxes from eddy-covariance, inventory, and chamber measurements (i.e., GPP, AR, NPP, litterfall, HR, SR, RE, and NEP) of the 22 monsoon Asian forests. The objectives of this study are to: (1) elucidate the factors controlling the spatial variability of component fluxes, (2) identify relationships between component fluxes, (3) and identify patterns of NEP variability in relation to component fluxes. The findings are presented via three regional classifications: forests across monsoon Asia (22 sites), the mid–high latitudes (14 sites), and the low latitudes (8 sites).
2. Materials and methods 2.1. Carbon balance dataset This study uses observation-based annual carbon fluxes of forest sites in monsoon Asia. The flux data were obtained from the compilation data set of ecosystem functions in Asia version 1.1 (compiled by Dr. Taku M. Saitoh), which contains literature values for annual carbon fluxes, storages, and environmental characteristics of forests and grasslands across Asia. From the dataset, we analyze carbon fluxes (i.e., GPP, AR, NPP, litterfall, HR, SR, RE, and NEP) and climate data (mean annual temperature and precipitation: MAT and MAP, respectively) for the 22 forest ecosystems, consisting of: five boreal forests (deciduous coniferous forests: BDC); nine temperate forests (four deciduous broad-leaf forests: TDB, three evergreen coniferous forests: TEC, and two mixed forests: MF); and eight tropical forests (three sub-tropical coniferous forests: STR and five tropical evergreen broad-leaf forests: TR). The geographic locations of the forest sites range from latitude 64◦ 12 N–0◦ 52 S, to longitude 100◦ 27 E–141◦ 31 E (Fig. 1 and Table 1). The study sites cover
Fig. 1. Distribution of 22 forest sites analyzed in this study. Six forest types are indicated by different markers: deciduous coniferous forests (BDC), deciduous broad-leaf forests (TDB), evergreen coniferous forests (TEC), mixed forests (MF), sub-tropical coniferous forests (STR), and tropical evergreen broad-leaf forests (TR). BDC, TDB, TEC, and MF are classified as the mid–high latitude forests (triangles), and STR and TR as the low latitude forests (squares). Background shows the empirically upscaled mean seasonal GPP in May (based period 2001–2014) from Kondo et al. (2015b).
a diverse range of tree species, including Larix, Quercus, Betula, Pinus, Cryptomeria, Pometia, and Dipterocarp species, and include a wide range of developmental stages, from young migration and replanted forests to mature forests (Table 2). 2.2. Estimations of carbon fluxes NEP (=-NEE: Net Ecosystem Exchange) of the study sites was based on the eddy-covariance method, which calculates net CO2 exchange as the sum of eddy flux. Eddy flux was measured with three-dimensional sonic anemometer–thermometers and open- or closed-path CO2 /H2 O analyzers, or both, depending on the sites. A majority of the sites use a sampling frequency of 10 Hz, and some sites use 4 or 5 Hz. All the sites adopted the same averaging time of 30 min, except one of the Siberian sites (YLF), which adopted 13.67 min. The basic and site specific quality control and quality assurance was applied to measured eddy flux (e.g., Saigusa et al., 2013) and the standard gapfilling method such as an empirical model or a lookup table was used to replace low-quality data after friction velocity filterirng (e.g., Falge et al., 2001). GPP and RE were estimated by a common empirical model that involves temperature dependency curve for nighttime NEE and light response curve for daytime NEE. At several sites which experienced relative narrow temperature ranges, site specific methods were applied (e.g., Saitoh, 2005; Kosugi et al., 2008). NPP and litterfall were estimated from inventory measurements, which were obtained at sample plots established below or near a flux tower depending on sites. Annual NPP was estimated as the sum of annual increment biomass and litterfall. Annual increment biomass was estimated by measuring the annual change in diameter at breast height (DBH) of all trees or trees with DBH greater than a certain threshold, which differs by site (e.g., 2–9 cm: Ohtsuka et al., 2007; Kominami et al., 2008; Tan et al., 2010, 2011). The amount of carbon stored in aboveground components was estimated using allometric equations that relate DBH to stem, branch, and leaf biomass. The amount of carbon stored in belowground
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Table 1 The description of forest sites analyzed in the present study. A complete list of data sources is shown in Supplemental Materials. Site name (site code)
Country
Location
Forest typea
Elevation (m)
Mean annual temperature (◦ C)
Annual precipitation (mm)
Reference
Tura (TUR)
Russia
BDC
250
−9.0
360
Nelegel (NEL)
Russia
64◦ 12 N, 100◦ 27 E 62◦ 19 N, 129◦ 31 E
BDC
200
−10.0
237
Yakutsk Spasskaya Pad larch (YLF)
Russia
62◦ 15 N, 129◦ 14 E
BDC
220
−10.4
240
Laoshan (LSH)
China
45◦ 20 N, 127◦ 34 E
BDC
370
2.8
700
Tomakomai (TMK)
Japan
42◦ 44 N, 141◦ 31 E
BDC
140
6.3
1021
Sapporo (SAP)
Japan
TDB
165
6.5
1100
Appi (API)
Japan
TDB
825
5.9
2029
KoFlux Gwangneung Supersite deciduous forest (GDK) Takayama deciduous forest (TKY) Takayama coniferous forest (TKC) Fujiyoshida (FJY)
Korea
42◦ 59 N, 141◦ 23 E 40◦ 00 N, 140◦ 56 E 37◦ 45 N, 127◦ 09 E
TDB
260
11.5
1332
Osawa et al. (2010), Kajimoto et al. (2010), Hirata et al. (2008) Ueyama et al. (2010), Sawamoto et al. (2003), Machimura et al. (2005), Ueyama et al. (2010), Ueyama M. (personal communication) Maximov (2012), Dolman (2004), Matuura and Hirobe (2007) Wang et al. (2008), Jomura et al. (2009), Hirata et al. (2008), Matuura et al. (2007) Hirata et al. (2008), Liang et al. (2010), Sakai et al. (2007), Yanagihara et al. (2006) Nakai et al. (2003), Utsugi (2005), Ishizuka et al. (2006), Yasuda et al. (2012), Koarashi et al. (2009) Kwon et al. (2010), Kim et al. (2010), Lim et al. (2003), Lee et al. (2010)
Japan
36◦ 08 N, 137◦ 25 E
TDB
1420
7.3
2400
Saigusa et al. (2008), Ohtsuka et al. (2007)
Japan
36◦ 08 N, 137◦ 22 E
TEC
800
9.6
1700
Saitoh et al. (2010), Yashiro et al. (2010)
Japan
35◦ 27 N, 138◦ 46 E
TEC
1030
9.7
2025
Mizuguchi et al. (2012), Ohtsuka et al. (2013), Tanabe et al. (2003) Thakuri et al. (2010), Lee et al. (2010)
KoFlux Gwangneung Supersite coniferous forest (GCK) Changbaishan (CBS) Yamashiro Experimental Forest (YMS) Qianyanzhou (QYZ)
Korea
37◦ 45 N, 127◦ 09 E
TEC
128
11.3
1365
China
41◦ 29 N, 128◦ 05 E 34◦ 47 N, 135◦ 51 E
MF
738
3.6
695
MF
220
15.5
1449
STR
100
17.9
1489
Ailao (ALF)
China
STR
2476
11.3
1840
Dinghushan (DHS)
China
26◦ 44 N, 115◦ 03 E 24◦ 32 N, 101◦ 01 E 23◦ 10 N, 112◦ 32 E
STR
300
20.9
1956
Xishuangbanna (BNS) Sakaerat (SKR)
China
TR
750
21.7
1487
TR
535
24.0
1250
Lambir Hills National Park (LHP) Pasoh (PSO)
Malaysia
21◦ 57 N, 101◦ 12 E 14◦ 29 N, 101◦ 55 E 4◦ 20 N, 113◦ 50 E
TR
200
27.0
2734
TR
75–150
26.3
1804
Bulit Suharuto (BKS)
Indonesia
TR
20
27.0
3300
Japan
China
Thailand
Malaysia
2◦ 58 N, 102◦ 18 E 0◦ 52 S, 117◦ 03 E
Yu et al. (2008), Zhang et al. (2010), Li et al. (2010) Kominami et al. (2008), Yamanoi et al. (2011), Kominami (2016) Ma et al. (2008) Tan et al. (2011), Schaefer et al. (2009) Yu et al. (2008), Zhang et al. (2010), Tang et al. (2011), Yang et al. (2006) Zhang et al. (2010), Tan et al. (2010) Saigusa et al. (2013), Tani et al. (2007) Saitoh (2005)
Kosugi et al. (2008), Hoshizaki et al. (2004), Tani et al. (2007) Kato and Tang (2008), Hiratsuka et al. (2006), Gamo et al. (2000)
a BDC: boreal deciduous coniferous forests, TDB: temperate deciduous broad-leaf forests, TEC: temperate evergreen coniferous forests, MF: mixed forests, STR: sub-tropical coniferous forests, and TR: tropical evergreen broad-leaf forests.
components (i.e., stumps and lateral roots) was assumed to be 20% of that stored in the aboveground parts (e.g., Jomura et al., 2009) or was derived from allometric equations that relate tree diameter at ground height to woody root biomass (e.g., Kominami et al., 2008). Annual litterfall was estimated using litter traps placed on the for-
est floor and collected every month or only in one month during defoliation period; the methodologies vary slightly by site (e.g., size and number of traps; Ohtsuka et al., 2007; Kominami et al., 2008; Tan et al., 2010). The collected litters were dried and then weighed
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Table 2 Physiological properties of the study sites. Site information are obtained from literatures listed in Table 1. Forest type
Site code
Forest age (year)
LAI (m2 m−2 )
Tree height (m)
Dominant species
BDC
TUR
105
0.6
3.4
NEL YLF LSH TMK
200 160 35 45
1.9 2.0 2.5 5.6
8.6 18 17 15
TDB
SAP API GDK TKY
90 70 80–200 50
6.0 4.8 3.7 5.0
20 17–19 18 13–20
TEC
TKC FJY GCK CBS
50 90 93 200
5.0 3.5 4.0–7.6 (PAIa ) 6.1
20 20 23 26
YMS QYZ ALF
50–60 23 > 300
5.4 3.5 5.0
12 12 9
DHS BNS
400 Mature
4.0 5.5
17 40
SKR LHP PSO BKS
Mature Mature Mature 17
4.0 6.2 6.52 3.5–4.0
35 40 35 10
Betula nana, Ledum palstre, Vaccinium uliginosum, Vaccinium vitis-idaea Larix cajanderii Larix gmlinii Larix gmlinii Larix Kaempferi Sarg., Betula ermanii, Betula platyphylla, Ulmus japonica, and Picea jezoensis Betula platyphylla, Quercus crispula, Quercus mongolica Fagus crenata Quercus species, Carpinus species Quercus crispura, Betula ermanii, and B. platyphylla var. japonica Cyptomeria japonica, Chamaecyparis obtuse Pinus densiflora Abies holophylla Pinus koraiensis, Tilia amurensis, Quercus mongolica, Fraxinus mandshurica, Acer mono Quercus serrata, Ilex pedunculosa Pinus elliottii, Pinus massoniana Lithocarpus chintungensis, Rhododendron leptothrium, Vaccinium ducluoxii, Lithocarpus xylocarpus, Castanopsis wattii, Schima noronhae, Hartia sinensis, and Manglietia insignsis Schima superba, Castanopsis chinensi, Pinus massoniana Pometia tomentosa, Terminalia myriocarpa, Gironniera subaequalis and Garuga floribunda Highly diverse dipterocarp species Highly diverse dipterocarp species Highly diverse dipterocarp species Homalanthus populneus, Macaranga gigantea
MF
STR
TR
a
PAI: plant area index.
for the conversion to flux. Most of the litterfall estimates exclude belowground components such as root litter. Annual SR was measured by infra-red gas analyzer (IGRA) in a chamber system (e.g., Katayama et al., 2009; Lee et al., 2010), and annual HR was estimated by the trenching method, which partitions SR into HR by comparing SR rates between a trenched plot and a control plot (e.g., Ohtsuka et al., 2007; Lee et al., 2010). There were no direct estimates of AR reported in the literature that we investigated. Further specific site information (e.g., instrumentation, data collection, and data processing) is provided in the references in Table 1 and the AsiaFlux webpage (http://www. asiaflux.net/). From available annual carbon fluxes in literatures, we established a dataset by averaging multi-year values for each flux of the 22 sites when they are available; otherwise, we used single-year values (Table 3). 2.3. Treatment of missing components and uncertainty of fluxes Direct estimates of all carbon fluxes were not available from each source. As shown in Table 3, the availability of the estimates varies by flux: 22 for NEP, GPP, and RE, 17 for NPP and litterfall (aboveground only), 20 for SR, 17 for HR, and 0 for AR. For missing fluxes, we supplemented values using available fluxes. Specifically, we applied (NEP + HR) to missing NPP values, (NPP – NEP) to missing HR values, and (RE – HR) to AR values. Missing litterfall and SR values were not filled because additional components (e.g., aboveground or belowground AR, and annual biomass increment) are required to calculate those fluxes. Uncertainties are rarely reported for flux estimations, and when reported the sources of uncertainty remain unclear (Luyssaert et al., 2007). As a compensation approach, Luyssaert et al. (2007) calculated uncertainties for carbon fluxes based on multiplicative factors reflecting expert judgements. Although this approach provides a uniform method for calculating uncertainty, it still lacks in physical basis and does not provide information useful for analyses.
To avoid unnecessary complications, we chose not to address the uncertainties of fluxes in the present study. 2.4. Estimations of annual carbon change in aboveground biomass and soil From the dataset, we estimated annual carbon changes in aboveground vegetation and soil. Specifically, these are represented by differences between aboveground NPP and litterfall (aboveground biomass change: ABM), and between litterfall and SR (partial soil organic matter change: SOMP ). Using data from sites at which both aboveground NPP and NPP are available (n = 7, see Table 3), we first established the relationship between to aboveground NPP and NPP (aboveground NPP = 0.86NPP – 0.56, R2 = 0.95, p < 0.01). To obtain aboveground NPP for the rest of the sites, we applied the established relationship to the NPP data, and then calculated ABM using the available litterfall data (n = 17) by (aboveground NPP – litterfall). SOMP was calculated by (litterfall – SR) for the sites that both litterfall and SR data were available (n = 17). One caveat is that (litterfall – SR) does not represent the exact SOM because it does not account for belowground components of litterfall or for carbon leakage from the soil. However, the aboveground components of litterfall usually represent a larger carbon input to soil than the belowground components (Ohtsuka et al., 2007, 2013; Tan et al., 2010), and carbon leakage usually contributes a small amount to SOM of forest ecosystems (Kindler et al., 2011). Therefore, we consider that SOMP still provides useful information about the spatial variability of SOM. 2.5. Analysis After preparing the data as described above, we evaluated how component fluxes (GPP, AR, NPP, litterfall, HR, SR, and RE) relate to climatic factors (MAT and MAP) and to net carbon balance (NEP) in
RE (n = 22)
NEP (n = 22)
NPP (n = 17)
Aboveground NPP (n = 7)
Litterfall (n = 17)
SR (n = 20)
HR (n = 17)
AR (n = 0)
*(2004) *(2003–2004) *(NA) *(2004) *(2003) *(2000) *(2000–2006) *(2006–2008) *(1994–2002) *(2006–2007) *(2000–2008) *(2008) *(2003–2005) *(2000–2002) *(2003–2005) *(2008–2009) *(2003–2005) *(2003–2006) *(2002) *(2001–2002) *(2003–2005) *(2001–2002)
*(2004) *(2003–2004) *(NA) *(2004) *(2003) *(2000) *(2000–2006) *(2006–2008) *(1994–2002) *(2006–2007) *(2000–2008) *(2008) *(2003–2005) *(2000–2002) *(2003–2005) *(2008–2009) *(2003–2005) *(2003–2006) *(2002) *(2001–2002) *(2003–2005) *(2001–2002)
*(2004) *(2003–2004) *(NA) *(2004) *(2003) *(2000) *(2000–2006) *(2006–2008) *(1994–2002) *(2006–2007) *(2000–2008) *(2008) *(2003–2005) *(2000–2002) *(2003–2005) *(2008–2009) *(2003–2005) *(2003–2006) *(2002) *(2001–2002) *(2003–2005) *(2001–2002)
*(1995) NEP + HR NEP + HR *(NA) NEP + HR *(2000) NEP + HR *(2006–2008) *(1999–2003) *(2005–2009) *(2000–2007) NEP + HR *(2005–2007) *(1999– 2003) *(2003–2005) * four years (2002/2003–2007) *(1982–2004) *(2003–2006) *(2002–2005) *(2001–2002) *(2002–2006) *(2000–2001)
*(1995) *(1997–2000) – – – *(2000) – – *(1999–2003) *(2005–2009) – – – *(1999– 2003) – – – *(2003–2006) – – – –
*(2004–2005) *(1998–1999) – *(NA) *(1999–2000) *(2000) – *(1998–1999) *(1999–2003) *(2005–2009) *(1999–2007) – – *(1999–2002) *(2003–2005) *(2003–2007) *(1982–2004) *(2003–2006) – *(2001–2002) *(NA) *(2000–2001)
*(NA) *(1997, 2000) *(NA) *(NA) *(2003) *(1998–2000) *(2005) *(2005–2006) *(1999–2003) *(2006–2007) *(2006–2007) *(2005) – *(2002–2003) *(2004–2005) *(2004–2007) * one year (2003/2004) *(2002–2007) – *(2001–2002) *(2003–2005) *(2000–2001)
*(NA) *(1997, 2000) *(NA) *(NA) *(2003) NPP-NEP *(2005) *(2005–2006) *(1999–2003) *(2006–2007) *(2006–2007) *(2005) NPP-NEP *(2004–2005) NPP-NEP *(2003–2007) NPP-NEP *(2002–2007) *(NA) *(2001–2002) NPP-NEP *(2000–2001)
RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR RE-HR
627
TUR NEL YLF LSH TMK SAP API GDK TKY TKC FJY GCK CBS YMS QYZ ALF DHS BNS SKR LHP PSO BKS
GPP (n = 22)
M. Kondo et al. / Agricultural and Forest Meteorology 232 (2017) 623–634
Fig. 2. Radar plots illustrating strength of linear relationships (R2 ) between carbon fluxes (GPP, AR, NPP, litterfall, HR, SR, and RE) and climatic factors (MAT and MAP) for (a) all the forests, (b) the mid–high latitude forests, and (c) the low latitude forests. Slopes of linear regression (positive or negative) are indicated by + and—for each relationship with MAT (T) and MAP (P). Statistics for these relationships are provided in Supplemental Materials (Tables S1 and S2). Table 3 Availability of carbon fluxes data in literatures (indicated by *), year(s) of data values (NA if it is not explicitly stated in literatures), and supplementary methods for filling missing values. Numbers of available data are indicated in each column. Estimates of flux that were not available in literatures are indicated by −.
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monsoon Asian forests. In addition to general relationships across all the 22 forests, we analyzed the relationships of mid–high and low latitude forests: the former consisting of boreal and temperate forests (BDC, TDB, TEC, and MF) and the latter sub-tropical and tropical forests (STR and TR) (see Fig. 1). 3. Results 3.1. Correlation patterns of component fluxes with temperature and precipitation A correlation analysis using the data for all 22 of the forest sites showed strong linear relationships between component fluxes and MAT (Fig. 2a). The average value in coefficients of determination (R2 ) between component fluxes and MAT was 0.54, with the highest value occurring in GPP (R2 = 0.65) and the lowest value in NPP (R2 = 0.46). Without exception, all component fluxes were positively related with MAT (p < 0.01). Compared with MAT, component fluxes were rather weakly related with MAP. The average R2 between component fluxes and MAP was 0.23, with the highest value occurring in GPP (0.30) and the lowest value in HR (0.13). However, similarly to the results for MAT, all component fluxes were positively related with MAP. Component fluxes of the mid–high latitude forests were overall more strongly related with MAT than MAP (Fig. 2b), and this pattern was also identified in the low latitude forests (Fig. 2c). All component fluxes of the mid–high latitude forests were positively related both with MAT and MAP. In the low latitude forests, a majority of component fluxes were also positively related to MAT, but there were some fluxes that showed negative relationships to MAT or MAP (i.e., NPP, litterfall, and HR); however, those relationships were rather weak (R2 = 0.17 at most) and statistically insignificant (p > 0.05) (Supplemental Materials). Although MAT was found a strong climatic factor to the spatial variability of component fluxes, there was a difference in the strength of relationships with MAT between the two forest groups. In the mid–high latitude forests, the average R2 between component fluxes and MAT were 0.46, and in the low latitude forests, those were 0.28. In both forest groups, GPP, AR, litterfall, HR, SR and RE showed moderately strong relationships with MAT, but those relationships were overall stronger in the mid–high latitude forests (R2 = 0.38–0.58) than the low latitude forests (R2 = 0.19–0.45). Contrary to those fluxes, a relationship between NPP and MAT was substantially weak in both the forest groups: R2 = 0.19 for the mid–high latitude forests and R2 = 0.02 for the low latitude forests. 3.2. Correlation patterns of NEP with component fluxes Correlation patterns between component fluxes, and between component fluxes and NEP, are shown in Fig. 3. For all the forests, we found that all component fluxes were positively related with each other (Fig. 3a). Particular strong relationships were found between GPP and RE (R2 = 0.96), GPP and AR (R2 = 0.91), and RE and AR (R2 = 0.94). However, despite the strong relationships between component fluxes, none of component flux is related with NEP with a significant level (R2 = 0.13 at most, p > 0.05). A majority of component fluxes of the mid–high latitude forests showed overall strong positive relationships with each other (Fig. 3b). Likewise, a similar pattern was found in the low latitude forests (Fig. 3c), except that relationships between NPP and other fluxes were rather weak. Regardless of the common positive relationship between component fluxes, however, component fluxes of the mid–high and low latitude forests related differently with NEP. We found that all component fluxes of the mid–high latitude forests were positively related with NEP (Fig. 3b), whereas those
of the low latitude forests were negatively with NEP (Fig. 3c). In the mid–high latitude forests, GPP showed the strongest positive relationship with NEP (R2 = 0.49), followed by RE (R2 = 0.35). In the low latitude forests, RE showed the strongest negative relationship with NEP (R2 = 0.68), followed by AR (R2 = 0.58) and GPP (R2 = 0.54). We further investigated the different patterns in relationships between component fluxes and NEP (positive relationships in the mid–high latitude forests and negative relationships in the low latitude forests) by examining the variability of ABM and SOMP with respect to GPP. As indicated in Fig. 3b and c, NEP of the two forest groups showed different variation patterns that it increases with increasing GPP in the mid–high latitude forests, while it decreases with increasing GPP in the low latitude forests (Fig. 4a). Correspondingly, we found that ABM of the two forest groups formed a variation pattern similar to that of NEP with high statistical significance (p < 0.01); ABM of the mid–high latitude forests increases and that of the low latitude forests decreases with increasing GPP (Fig. 4b). Contrarily, SOMP commonly decrease with GPP in both the forest groups (Fig. 4c). One of mature tropical forests (i.e., LHP) indicated substantially smaller SOMP value than other forests, which we regarded as an outlier of the regression because LHP is one of unique tropical forests characterized by an exceptionally high SR rate despite of a low litterfall rate (Katayama et al., 2009). Nevertheless, including this site or not does not change a decreasing pattern of SOMP of the low latitude forests. These results indicate that the variability in the GPP–NEP relationship is largely attributable to aboveground processes.
4. Discussion 4.1. Relationship between carbon fluxes and temperature Previous syntheses of monsoon Asian ecosystems identified strong temperature control on the spatial variability of GPP, RE, and NEP (Hirata et al., 2008; Kato and Tang, 2008; Chen et al., 2013; Yu et al., 2013), and this study found that temperature is also the major factor related to the spatial variability of other component fluxes. MAT contributed 46–65% of the spatial variability of component fluxes (GPP, AR, NPP, litterfall, HR, SR, and RE), whereas MAP contributed rather less (13–30%) (Fig. 2a). The strong influence of temperature on the spatial viability of component fluxes is possibly related to the reduction in water stress attributable to the East Asia monsoon. The East Asia monsoon brings humid air to the continent and islands of Asia, consequently inducing seasonal high rainfall during the summer; thus, monsoon Asian forests are less exposed to water stress during the growing season. Additionally, the wider temperature gradient in monsoon Asia than in Europe and North America may strengthen the control of temperature on the spatial variability of carbon fluxes in monsoon Asia (Kato and Tang, 2008). In certain regions in Asia, precipitation plays a critical role in forest productivity (e.g., semi-arid Northwestern China and the Mongolian Steppe, Poulter et al., 2013), and sites representing those regions are not included in this study. However, the sensitivity specific to semi-arid ecosystems is unlikely to influence the overall pattern of sensitivity across monsoon Asia. A similar pattern in relationships between component fluxes and MAT was identified for the mid–high and low latitude forests, which signifies the temperature dependence of carbon fluxes in monsoon Asia. Overall, we found that component fluxes were moderately related to MAT regardless of region, but fluxes of the mid–high latitude forests were more sensitive to changes in temperature than those of the low latitude forests (Fig. 2b and c). This different control on carbon fluxes is expected because the sites at the mid–high latitudes are rather cooler than those at the low latitudes (Table 1).
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Fig. 3. Correlation matrices (in term of R2 ) of carbon fluxes for (a) all the forests, (b) the mid–high latitude forests, and (c) the low latitude forests. Positive correlations are indicated by a red scale and negative correlations by a blue scale. Statistical significances are indicated by ** (p < 0.01) and * (p < 0.05). Detailed statistics for these relationships are provided in Supplemental Materials (Tables S3, S4, and S5). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).
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Fig. 5. Variability of NPP with respect to GPP. The figure configuration is as same as in Fig. 4, except an addition regression line for all the forests (black line).
One exception to the appreciable relationships between component fluxes and MAT was found in NPP, which exhibited notably insignificant relations with MAT in both the mid–high and low latitude forests (Fig. 2b and c). Contrary to those regional-specific results, however, NPP of all the forests was related to MAT with a level of significance comparable to other fluxes (Fig. 2a). This discrepancy is due to large regional variability in NPP. As indicated in Fig. 2a, a scatter plot between NPP and MAT shows a positive linear relationship for a range of MAT from −9 to 27 ◦ C, but this relationship accompanies large variations in NPP for each value of MAT (Fig. 5). Correspondingly, we found large NPP variations in data representing the high latitude forests (cluster around MAT of −10 ◦ C), the middle latitude forests (MAT between −2.8 and 15.5 ◦ C), and the low latitude forests (MAT between 11.3 and 27 ◦ C). This result suggests that the relationship between NPP and temperature may be valid when considering a wide temperature gradient in monsoon Asia, but it is uncertain for specific regions representing comparatively narrow temperature gradients. 4.2. Relationship of productivity to the spatial variability of carbon fluxes
Fig. 4. Variability of (a) NEP, (b) ABM, and (c) SOCp with respect to GPP. Regression lines and 95% confidence ellipses are shown separately for the mid–high latitude forests (red line and shade) and low latitude forests (green line and shade). Boreal forests (four BDCs) and temperate forests (four TDBs, three TEC, and two MFs) are indicated by red lower triangles and circles, respectively. Sub-tropical (three STRs) and tropical (four TRs) are indicated by green upper triangles and squares, respectively. In (c), two regression lines are shown for the low latitude forests, one excluding an outlier (green solid line) and the other including the outlier (black dashed line). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Despite the positive relationships that were found between the majority of carbon fluxes and MAT, we suggest that GPP is the flux more directly associated with changes in temperature, and that other component fluxes are influenced by changes in GPP rather than temperature. Our results indicate that component fluxes were overall more strongly related with GPP than MAT (Figs. 2 and 3). For example, AR is one of the fluxes most strongly related to GPP (R2 = 0.83–0.94: Fig. 3a–c), and this relationship was more significant than that with MAT (R2 = 0.37–0.5: Fig. 2a–c). Likewise, other component fluxes consistently exhibited stronger relations with GPP than MAT (refer to Supplemental Materials). There are several lines of evidence suggesting that productivity exerts stronger effects on the variability of component fluxes than climate factors do. Recent synthesis studies indicated that, despite apparent influence from climatic factors, the variability in component fluxes such as AR and litterfall are more closely related to productivity (Liu et al., 2004; Piao et al., 2010). An early work in European forests provided an observation-based evidence that forest productivity exerts stronger influence on SR than climatic factors do (Janssens et al., 2001), and their finding was evaluated indirectly via litter manipulation experiments in China, which found that SR was more sensitive to changes in litterfall than to
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climatic factors (Xu et al., 2013; Han et al., 2015). Furthermore, the tight relationship between component fluxes and GPP are explicitly demonstrated in Free-Air CO2 Enrichment (FACE) experiments, which indicated that increased atmospheric CO2 concentration stimulates GPP, which in turn accompanies increases in component fluxes such as NPP and SR; in comparison, climatic factors are less closely related to those increases (e.g., DeLucia et al., 1999; Ainsworth and Long, 2005; King et al., 2004; Norby et al., 2005; Wittig et al., 2005). These results, in conjunction with the results of the present study, indicate that climatic factors certainly influence carbon fluxes, but that their influences are generally overshadowed by the variability in GPP. 4.3. Regional difference in the spatial variability of NEP Although component fluxes were positively related with each other, the spatial variability of NEP clearly indicated the opposite variation patterns between the mid–high and low latitude forests (Fig. 4a). As we found for the mid–high latitude forests in monsoon Asia, GPP and NEP were positively related for boreal and temperate ecosystems in Europe and North America (Law et al., 2002) and in China (Yu et al., 2013). The global metadata synthesis infers that a similar relationship between GPP and NEP is valid globally, but up to the GPP value of 22 Mg C ha−1 yr−1 , which roughly segregates GPP values of boreal and temperature forests to those of sub-tropical and humid tropical forests (Luyssaert et al., 2007). These studies commonly infer that GPP and NEP have a positive linear relationship in boreal and temperate ecosystems globally, and that monsoon Asian forests are not an exception to this relationship. Meanwhile, the negative linear relationship between GPP and NEP found in the low latitude forests may reflect a unique feature of monsoon Asian forests. Unlike the case of monsoon Asian forests, the global synthesis of carbon fluxes by Luyssaert et al. (2007) indicate neither a negative relationship nor any clear relationship between GPP and NEP at GPP values greater than 22 Mg C ha−1 yr−1 , due to large variability in NEP data. This difference between the two meta-analyses may be attributable to combining all tropical flux data in the result of Luyssaert et al. (2007), contrary to the region-specific results offered by the present study. In the GPP–NEP relationship of the low latitude forests (green markers and line in Fig. 4a), four sites showing high NEP (> 4 Mg C ha−1 yr−1 ) correspond to sub-tropical forests (QYZ, ALF, and DHS) and young tropical forest (BKS), while the other four sites showing low NEP (< 2 Mg C ha−1 yr−1 ) correspond to mature tropical forests (PSO, LHP, SKR, and BNS). Previously, it was found that sub-tropical forests in monsoon Asia uptake an exceptionally high amount of carbon regardless of moderate GPP, owing to high nitrogen deposition in the surrounding region (Yu et al., 2014). The forest sites included in this study may reflect this regional characteristic; for instance, nitrogen deposition at DHS (25.39 kg N ha−1 yr−1 ) is approximately four times larger than those at temperate and tropical forests such as CBS and BNS (5.27, and 8.89 kg N ha−1 yr−1 , respectively) (Supporting information in Yu et al., 2014). Additionally, productive young secondary forests constitute a large fraction of sub-tropical and tropical forests in monsoon Asia because of extensive on-going changes in land use (Achard et al., 2002; Mayaux et al., 2005; Koh and Wilcove, 2007; Carlson et al., 2012) and frequent occurrence of large-scale forest fires (Van Der Werf et al., 2003; Patra et al., 2005), and they are represented by QYZ (a plantation forest) and BKS (a regenerated forest after fire) in our dataset. Therefore, QYZ, ALF, DHS, and BKS represent the high carbon-uptake pattern in tropical monsoon Asia, resulting from the potential effects of high nitrogen deposition and disturbance. Those sites are distinct from the low carbon uptake of mature forests, resulting in the negative linear GPP–NEP relationship for the low latitude forests. The variability in annual carbon storage changes ( ABM and SOMP ) suggests that
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the GPP-NEP relationship found in this study may be realistic. As shown in Fig. 4, NEP and ABM follow the same pattern of variation with increasing GPP (Fig. 4b), while SOMP decreases with increasing GPP (Fig. 4b). It is physiologically reasonable that aboveground biomass is more susceptible to the variation in NEP than to soil organic carbon (Goulden et al., 2006, 2011). However, with limited available data for sub-tropical and tropical forests, it is still difficult to confirm general validity of the negative GPP−NEP relationship in the low latitudes. Although the metadata of this study contains multiple carbon fluxes at multiple sites in monsoon Asia, it only covers three sub-tropical and five topical forests. These site data are not sufficient to draw a clear conclusion about the potential nitrogen deposition and stand age effects on the negative GPP−NEP relationship. To overcome this difficulty, further expanded data syntheses and model-based investigations are required; particularly, the latter is important to confirm individual contributions from nitrogen deposition and stand age on the negative GPP−NEP relationship. 4.4. Implications for modelling The results of this study provide useful information for modelling forest carbon cycles in monsoon Asia. The previous inter-model comparison of GPP at 157 sites around the globe has demonstrated differences in spatial variability between model estimates of GPP (Yuan et al., 2014), which in turn implies differences in climate sensitivities to GPP between models. Although it is not explicitly demonstrated before, we regard that monsoon Asia is not an exception to this result. Moreover, it has been indicated that current ecosystem models failed to reproduce the high carbon uptake in sub-tropical ecosystems in monsoon Asia because they do not account for the effects of nitrogen deposition and disturbances in carbon flux simulations (Yu et al., 2014). The observation-based results offered from this study in conjunction with previous syntheses in monsoon Asia (e.g., Kato and Tang, 2008; Yu et al., 2013, 2014) can be used to improve the reproducibility of the spatial variability of carbon flux simulations. Another implication of the present findings for future modelling studies is that component fluxes were strongly linked regardless of regions in monsoon Asia. A previous global metadata synthesis suggests the strong linkage between component fluxes and GPP; specifically, component fluxes increase with increasing GPP regardless of forest type, resource supply, or stand age (Litton et al., 2007). As suggested by this work, we confirmed that the strong relationships between carbon fluxes that component fluxes increase with increasing GPP in monsoon Asia. Thus, although the magnitude of carbon fluxes differs substantially between mid–high and low latitude forests and between all the sites (Fig. 6a), the patterns of flux variations becomes comparatively similar when carbon fluxes are normalized by GPP (Fig. 6b). These similar patterns in normalized carbon fluxes are useful for parameterizing relationships between carbon fluxes. 4.5. Limitations This study represents the first attempt toward comprehensive analysis of carbon fluxes; as such, the metadata-based approach of this study has several limitations. First, it is difficult to address physical-based uncertainties by means of literature-based values for carbon fluxes. As described in Section 2.3, uncertainties for flux estimations are rarely reported, and when reported, the sources of uncertainty often remain unclear. The consistency with findings of the previous syntheses (i.e., sensitivity of carbon fluxes to MAT, and positive relationships between component fluxes) suggests the reliability of the findings and the dataset used in this study. However, in the absence of an uncertainty analysis, the
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5. Conclusions Our findings identified unique features of carbon fluxes in monsoon Asian forests: (1) Temperature is the major factor in the spatial variability of carbon fluxes, including not only GPP and RE, but also AR, NPP, litterfall, HR, and SR, of monsoon Asian forests, whereas precipitation has less influence on those carbon fluxes. (2) Component fluxes are positively related to each other across a wide range of monsoon Asia. The correlation analysis indicated that, despite the relationship with temperature, the spatial variability of component fluxes is rather strongly influenced by productivity (i.e., GPP). (3) NEP of monsoon Asian forests shows a unique pattern, varying positively with component fluxes in the mid–high latitude forests, but negatively in low latitude forests potentially due to the regional contrast in nitrogen deposition and stand age. These findings for monsoon Asian forests are encouraging for conducting comprehensive analyses of carbon fluxes for other terrestrial regions, which are expected to shed light on unique regional features of the spatial variability of carbon fluxes, leading to greater understanding of the global-scale variability of carbon fluxes. Acknowledgements This research was supported by the JSPS KAKENHI (grant No. 25281003) and Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan. The compilation data set of ecosystem functions in Asia (version 1.1) is available from the co-author of this article (Dr. Taku M. Saitoh: taku@green.gifu-u.ac.jp). Appendix A. Supplementary data
Fig. 6. (a) Radar plot illustrating variation patterns of averaged carbon fluxes (Mg C ha−1 yr−1 ) for all the forests (blue), the mid–high latitude forests (red), and the low latitude forests (green). (b) The same configuration as in (a), but carbon fluxes are normalized by GPP. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
robustness of the findings cannot be ascertained. Second, differences in flux estimation methods potentially affect the results of this study. The carbon fluxes analyzed in this study were obtained by methods that differ between components and sites (refer to Section 2.2). In particular, uncertainties in different methods of flux measurements (i.e., eddy flux, inventory, and chamber measurements) propagate in the calculated fluxes (AR, NPP, and HR in Table 3), which may result in large uncertainties in the calculated fluxes and their spatial variability. In addition, even with the same method of flux measurements, inconsistent techniques such as gap-filling of eddy fluxes possibly induce non-negligible differences in annual carbon fluxes between sites (Moffat et al., 2007). Third, years of flux measurements in literatures do not necessarily coincide even for same sites (Table 3). Although we averaged multiple year fluxes when they are available in literatures, ˜ Southern Oscillation it is still possible that variations in El Nino (ENSO) induce biases in carbon fluxes collected from literatures. These issues cannot be resolved as long as we conduct syntheses based on literature-based values of carbon fluxes. To elaborate a carbon flux synthesis further, collaboration with site managers is needed, such as for uncertainty assessment, consistent processing of raw-level data, and judgement on balance between carbon fluxes.
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