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Agricultural and Forest Meteorology 232 (2017) 623–634

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

j our na l ho me page: www.elsev ier .com/ locate /agr formet

omprehensive synthesis of spatial variability in carbon flux acrossonsoon Asian forests

asayuki Kondoa,∗, Taku M. Saitohb, Hisashi Satoa, Kazuhito Ichii a,c

Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanRiver Basin Research Center, Gifu University, Gifu, JapanCenter for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan

r t i c l e i n f o

rticle history:eceived 15 April 2016eceived in revised form 25 October 2016ccepted 26 October 2016vailable online 29 October 2016

eywords:onsoon Asian forests

arbon fluxarbon balanceynthesis analysispatial variability

a b s t r a c t

Forest ecosystems sequester large amounts of atmospheric CO2, and the contribution from forests in Asiais not negligible. Previous syntheses of carbon fluxes in Asian ecosystems mainly employed estimates ofeddy covariance measurements, net ecosystem production (NEP), gross primary production (GPP), andecosystem respiration (RE); however, to understand the variability within carbon cycles, fluxes such asautotropic respiration (AR), net primary production (NPP), litterfall, heterotrophic respiration (HR), andsoil respiration (SR) need to be analyzed comprehensively in conjunction with NEP, GPP, and RE. Here weinvestigated 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 fromthe literature for 22 forest sites in monsoon Asia. We found that mean annual temperature (MAT) largelyrelates to the spatial variability of component fluxes in monsoon Asian forests, with stronger positiveeffect in the mid–high latitude forests than in the low latitude forests, but even stronger relationshipswere identified between component fluxes regardless of regions. This finding suggests that the spatialvariability of carbon fluxes in monsoon Asia is certainly influenced by climatic factors such as MAT, butthat the overall spatial variability of AR, NPP, litterfall, HR, SR, and RE is rather controlled by that ofproductivity (i.e., GPP). Furthermore, component fluxes of the mid–high and low latitude forests showedpositive and negative relationships, respectively, with NEP. Further investigation identified a commonspatial variability in NEP and annual aboveground biomass changes with respect to GPP. The relationshipbetween GPP and NEP in the mid–high latitudes implies that productivity and net carbon sequestrationincrease 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, potentiallydue 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 generalvalidity. These unique features of monsoon Asian forest carbon fluxes provide useful information forimproving 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

. Introduction

Forest ecosystems play a key role in climate change mitigationy absorbing large amounts of atmospheric CO2 (Schimel et al.,

001; Luyssaert et al., 2007; Pan et al., 2011). In particular, Asianorests uptake 2.49 Pg C yr−1 of atmospheric CO2, which accountsor 27% of the global forest carbon balance (Yu et al., 2014). Because

∗ Corresponding author at: Department of Environmental Geochemical Cycleesearch, Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-achi, Kanazawa-ku, Yokohama, 236-0001, Japan.

E-mail address: redmk92@gmail.com (M. Kondo).

ttp://dx.doi.org/10.1016/j.agrformet.2016.10.020168-1923/© 2016 The Authors. Published by Elsevier B.V. This is an open access article

/).

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

of this substantial contribution to the global carbon balance, Asianforests are an integral part of the carbon cycle and climate changeresearch (e.g., Patra et al., 2005; Piao et al., 2009; Kondo et al., 2013,2015a; Yu et al., 2013). Owing to substantial efforts invested intofield measurements in past decades (e.g., inventories) in conjunc-tion with the still growing network of eddy flux observations, moredata on forest carbon cycle has become accessible to the scientificcommunity (Luyssaert et al., 2007; Baldocchi, 2008). Subsequently,in monsoon Asia, a growing number of synthesis studies have pro-

moted understanding of the temporal and spatial variability ofcarbon fluxes (Hirata et al., 2008; Kato and Tang, 2008; Saigusaet al., 2008, 2013; Chen et al., 2013; Yu et al., 2013). For example,climatic factors, particularly temperature, were found to be a major

under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.

6 orest Meteorology 232 (2017) 623–634

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Fig. 1. Distribution of 22 forest sites analyzed in this study. Six forest types areindicated by different markers: deciduous coniferous forests (BDC), deciduousbroad-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

24 M. Kondo et al. / Agricultural and F

river of the spatial variability in net ecosystem production (NEP),ross primary production (GPP), and ecosystem respiration (RE) inonsoon Asia (Kato and Tang, 2008; Chen et al., 2013; Yu et al.,

013). These relationships signify a uniqueness of monsoon Asiancosystems compared to European and North America ecosystems,here climate factors were weakly related to the spatial variability

f NEP, GPP, and RE (Law et al., 2002; Reichstein et al., 2007).Besides the recent syntheses in monsoon Asia, it is important

o further elucidate the spatial variability of components of forestarbon balance, such as autotrophic respiration (AR), net primaryroduction (NPP), litterfall, heterotrophic respiration (HR), and soilespiration (SR). Indeed, NEP, GPP, and RE are important variables ofhe carbon cycle, as they indicate the magnitude of carbon seques-ration by forests; however, their variability does not indicatenternal processes of carbon flow. It is possible that strong climaticontrol of the spatial variability of GPP, RE, and NEP may not be thease for component fluxes; rather, biotic factors such as stand agend biomass or productivity (i.e., GPP) may be more relevant factorsor the spatial variability of component fluxes (Janssens et al., 2001;

ichaletz et al., 2014). Elucidating this variability is particularlymportant when implementing site- and regional-scale character-stics of the forest carbon cycle to process-based model simulationse.g., Kondo et al., 2015a); thus, a comprehensive synthesis of car-on fluxes is required. During the past decades, the relationshipsetween carbon fluxes and climate, and the linkages between car-on fluxes have been analyzed through worldwide observationse.g., Davidson et al., 2002; Law et al., 2002; Van Dijk and Dolman,004; DeLucia et al., 2007; Baldocchi, 2008; Piao et al., 2010; Malhit al., 2011; Yu et al., 2013; Chen et al., 2013; Xu et al., 2013; Hant al., 2015). However, to the best of our knowledge, there is no com-rehensive analysis involving multiple carbon fluxes of multipleorest sites in the past.

To elaborate on carbon flux syntheses in monsoon Asia, theresent study comprehensively analyzes literature-based carbonuxes from eddy-covariance, inventory, and chamber measure-ents (i.e., GPP, AR, NPP, litterfall, HR, SR, RE, and NEP) of the 22onsoon Asian forests. The objectives of this study are to: (1) elu-

idate the factors controlling the spatial variability of componentuxes, (2) identify relationships between component fluxes, (3) and

dentify patterns of NEP variability in relation to component fluxes.he findings are presented via three regional classifications: forestscross monsoon Asia (22 sites), the mid–high latitudes (14 sites),nd the low latitudes (8 sites).

. Materials and methods

.1. Carbon balance dataset

This study uses observation-based annual carbon fluxes of for-st sites in monsoon Asia. The flux data were obtained from theompilation data set of ecosystem functions in Asia version 1.1compiled by Dr. Taku M. Saitoh), which contains literature valuesor annual carbon fluxes, storages, and environmental characteris-ics of forests and grasslands across Asia.

From the dataset, we analyze carbon fluxes (i.e., GPP, AR,PP, litterfall, HR, SR, RE, and NEP) and climate data (meannnual temperature and precipitation: MAT and MAP, respec-ively) for the 22 forest ecosystems, consisting of: five borealorests (deciduous coniferous forests: BDC); nine temperate forestsfour deciduous broad-leaf forests: TDB, three evergreen conifer-us forests: TEC, and two mixed forests: MF); and eight tropical

orests (three sub-tropical coniferous forests: STR and five trop-cal evergreen broad-leaf forests: TR). The geographic locationsf the forest sites range from latitude 64◦12′N–0◦52′S, to longi-ude 100◦27′E–141◦31′E (Fig. 1 and Table 1). The study sites cover

STR and TR as the low latitude forests (squares). Background shows the empiricallyupscaled 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 includea wide range of developmental stages, from young migration andreplanted forests to mature forests (Table 2).

2.2. Estimations of carbon fluxes

NEP (=-NEE: Net Ecosystem Exchange) of the study sites wasbased on the eddy-covariance method, which calculates net CO2exchange as the sum of eddy flux. Eddy flux was measured withthree-dimensional sonic anemometer–thermometers and open- orclosed-path CO2/H2O analyzers, or both, depending on the sites. Amajority of the sites use a sampling frequency of 10 Hz, and somesites use 4 or 5 Hz. All the sites adopted the same averaging timeof 30 min, except one of the Siberian sites (YLF), which adopted13.67 min. The basic and site specific quality control and qualityassurance was applied to measured eddy flux (e.g., Saigusa et al.,2013) and the standard gapfilling method such as an empiricalmodel or a lookup table was used to replace low-quality data afterfriction velocity filterirng (e.g., Falge et al., 2001). GPP and RE wereestimated by a common empirical model that involves tempera-ture dependency curve for nighttime NEE and light response curvefor daytime NEE. At several sites which experienced relative nar-row temperature ranges, site specific methods were applied (e.g.,Saitoh, 2005; Kosugi et al., 2008).

NPP and litterfall were estimated from inventory measure-ments, which were obtained at sample plots established below ornear a flux tower depending on sites. Annual NPP was estimated asthe sum of annual increment biomass and litterfall. Annual incre-ment biomass was estimated by measuring the annual change indiameter at breast height (DBH) of all trees or trees with DBHgreater 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 esti-mated using allometric equations that relate DBH to stem, branch,and leaf biomass. The amount of carbon stored in belowground

M. Kondo et al. / Agricultural and Forest Meteorology 232 (2017) 623–634 625

Table 1The description of forest sites analyzed in the present study. A complete list of data sources is shown in Supplemental Materials.

Site name (sitecode)

Country Location Forest typea Elevation (m) Mean annualtemperature(◦ C)

Annualprecipitation(mm)

Reference

Tura (TUR) Russia 64◦ 12′ N,100◦ 27′ E

BDC 250 −9.0 360 Osawa et al. (2010), Kajimotoet al. (2010), Hirata et al. (2008)

Nelegel (NEL) Russia 62◦ 19′ N,129◦ 31′ E

BDC 200 −10.0 237 Ueyama et al. (2010),Sawamoto et al. (2003),Machimura et al. (2005),Ueyama et al. (2010), UeyamaM. (personal communication)

Yakutsk SpasskayaPad larch (YLF)

Russia 62◦15′N,129◦14′E

BDC 220 −10.4 240 Maximov (2012), Dolman(2004), Matuura and Hirobe(2007)

Laoshan (LSH) China 45◦ 20′ N,127◦ 34′ E

BDC 370 2.8 700 Wang et al. (2008), Jomuraet al. (2009), Hirata et al.(2008), Matuura et al. (2007)

Tomakomai (TMK) Japan 42◦ 44′ N,141◦ 31′ E

BDC 140 6.3 1021 Hirata et al. (2008), Liang et al.(2010), Sakai et al. (2007),Yanagihara et al. (2006)

Sapporo (SAP) Japan 42◦ 59′ N,141◦ 23′ E

TDB 165 6.5 1100 Nakai et al. (2003), Utsugi(2005), Ishizuka et al. (2006),

Appi (API) Japan 40◦ 00′ N, 140◦

56′ ETDB 825 5.9 2029 Yasuda et al. (2012), Koarashi

et al. (2009)KoFlux

GwangneungSupersitedeciduous forest(GDK)

Korea 37◦ 45′ N,127◦ 09′ E

TDB 260 11.5 1332 Kwon et al. (2010), Kim et al.(2010), Lim et al. (2003), Leeet al. (2010)

Takayamadeciduous forest(TKY)

Japan 36◦ 08′ N,137◦ 25′ E

TDB 1420 7.3 2400 Saigusa et al. (2008), Ohtsukaet al. (2007)

Takayamaconiferous forest(TKC)

Japan 36◦ 08′ N,137◦ 22′ E

TEC 800 9.6 1700 Saitoh et al. (2010), Yashiroet al. (2010)

Fujiyoshida (FJY) Japan 35◦ 27′ N,138◦ 46′ E

TEC 1030 9.7 2025 Mizuguchi et al. (2012),Ohtsuka et al. (2013), Tanabeet al. (2003)

KoFluxGwangneungSupersiteconiferous forest(GCK)

Korea 37◦ 45′ N, 127◦

09′ ETEC 128 11.3 1365 Thakuri et al. (2010), Lee et al.

(2010)

Changbaishan(CBS)

China 41◦ 29′ N,128◦ 05′ E

MF 738 3.6 695 Yu et al. (2008), Zhang et al.(2010), Li et al. (2010)

YamashiroExperimentalForest (YMS)

Japan 34◦ 47′ N,135◦ 51′ E

MF 220 15.5 1449 Kominami et al. (2008),Yamanoi et al. (2011),Kominami (2016)

Qianyanzhou (QYZ) China 26◦ 44′ N,115◦ 03′ E

STR 100 17.9 1489 Ma et al. (2008)

Ailao (ALF) China 24◦ 32′ N,101◦ 01′ E

STR 2476 11.3 1840 Tan et al. (2011), Schaefer et al.(2009)

Dinghushan (DHS) China 23◦ 10′ N,112◦ 32′ E

STR 300 20.9 1956 Yu et al. (2008), Zhang et al.(2010), Tang et al. (2011), Yanget al. (2006)

Xishuangbanna(BNS)

China 21◦ 57′ N,101◦ 12′ E

TR 750 21.7 1487 Zhang et al. (2010), Tan et al.(2010)

Sakaerat (SKR) Thailand 14◦ 29′ N,101◦ 55′ E

TR 535 24.0 1250 Saigusa et al. (2013), Tani et al.(2007)

Lambir HillsNational Park(LHP)

Malaysia 4◦ 20′ N,113◦ 50′ E

TR 200 27.0 2734 Saitoh (2005)

Pasoh (PSO) Malaysia 2◦ 58′ N,102◦ 18′ E

TR 75–150 26.3 1804 Kosugi et al. (2008), Hoshizakiet al. (2004), Tani et al. (2007)

Bulit Suharuto(BKS)

Indonesia 0◦ 52′ S,117◦ 03′ E

TR 20 27.0 3300 Kato and Tang (2008),Hiratsuka et al. (2006), Gamoet al. (2000)

forestc

cowgA

a BDC: boreal deciduous coniferous forests, TDB: temperate deciduous broad-leaf

oniferous forests, and TR: tropical evergreen broad-leaf forests.

omponents (i.e., stumps and lateral roots) was assumed to be 20%f that stored in the aboveground parts (e.g., Jomura et al., 2009) or

as derived from allometric equations that relate tree diameter at

round height to woody root biomass (e.g., Kominami et al., 2008).nnual litterfall was estimated using litter traps placed on the for-

s, TEC: temperate evergreen coniferous forests, MF: mixed forests, STR: sub-tropical

est floor and collected every month or only in one month duringdefoliation 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

626 M. Kondo et al. / Agricultural and Forest Meteorology 232 (2017) 623–634

Table 2Physiological 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 Betula nana, Ledum palstre, Vaccinium uliginosum,Vaccinium vitis-idaea

NEL 200 1.9 8.6 Larix cajanderiiYLF 160 2.0 18 Larix gmliniiLSH 35 2.5 17 Larix gmliniiTMK 45 5.6 15 Larix Kaempferi Sarg., Betula ermanii, Betula platyphylla,

Ulmus japonica, and Picea jezoensisTDB SAP 90 6.0 20 Betula platyphylla, Quercus crispula, Quercus mongolica

API 70 4.8 17–19 Fagus crenataGDK 80–200 3.7 18 Quercus species, Carpinus speciesTKY 50 5.0 13–20 Quercus crispura, Betula ermanii, and B. platyphylla var.

japonicaTEC TKC 50 5.0 20 Cyptomeria japonica, Chamaecyparis obtuse

FJY 90 3.5 20 Pinus densifloraGCK 93 4.0–7.6 (PAIa) 23 Abies holophylla

MF CBS 200 6.1 26 Pinus koraiensis, Tilia amurensis, Quercus mongolica,Fraxinus mandshurica, Acer mono

YMS 50–60 5.4 12 Quercus serrata, Ilex pedunculosaSTR QYZ 23 3.5 12 Pinus elliottii, Pinus massoniana

ALF > 300 5.0 9 Lithocarpus chintungensis, Rhododendron leptothrium,Vaccinium ducluoxii, Lithocarpus xylocarpus, Castanopsiswattii, Schima noronhae, Hartia sinensis, and Manglietiainsignsis

DHS 400 4.0 17 Schima superba, Castanopsis chinensi, Pinus massonianaTR BNS Mature 5.5 40 Pometia tomentosa, Terminalia myriocarpa, Gironniera

subaequalis and Garuga floribundaSKR Mature 4.0 35 Highly diverse dipterocarp speciesLHP Mature 6.2 40 Highly diverse dipterocarp speciesPSO Mature 6.52 35 Highly diverse dipterocarp species

10

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a PAI: plant area index.

or the conversion to flux. Most of the litterfall estimates excludeelowground components such as root litter.

Annual SR was measured by infra-red gas analyzer (IGRA) in chamber system (e.g., Katayama et al., 2009; Lee et al., 2010),nd annual HR was estimated by the trenching method, whichartitions SR into HR by comparing SR rates between a trenchedlot and a control plot (e.g., Ohtsuka et al., 2007; Lee et al., 2010).here were no direct estimates of AR reported in the literaturehat we investigated. Further specific site information (e.g., instru-

entation, data collection, and data processing) is provided inhe references in Table 1 and the AsiaFlux webpage (http://www.siaflux.net/). From available annual carbon fluxes in literatures,e established a dataset by averaging multi-year values for eachux of the 22 sites when they are available; otherwise, we usedingle-year values (Table 3).

.3. Treatment of missing components and uncertainty of fluxes

Direct estimates of all carbon fluxes were not available fromach source. As shown in Table 3, the availability of the estimatesaries by flux: 22 for NEP, GPP, and RE, 17 for NPP and litterfallaboveground only), 20 for SR, 17 for HR, and 0 for AR. For missinguxes, we supplemented values using available fluxes. Specifically,e applied (NEP + HR) to missing NPP values, (NPP – NEP) to miss-

ng HR values, and (RE – HR) to AR values. Missing litterfall and SRalues were not filled because additional components (e.g., above-round or belowground AR, and annual biomass increment) areequired to calculate those fluxes.

Uncertainties are rarely reported for flux estimations, and wheneported the sources of uncertainty remain unclear (Luyssaert et al.,007). As a compensation approach, Luyssaert et al. (2007) calcu-

ated uncertainties for carbon fluxes based on multiplicative factorseflecting expert judgements. Although this approach provides aniform method for calculating uncertainty, it still lacks in phys-

cal basis and does not provide information useful for analyses.

Homalanthus populneus, Macaranga gigantea

To avoid unnecessary complications, we chose not to address theuncertainties of fluxes in the present study.

2.4. Estimations of annual carbon change in abovegroundbiomass and soil

From the dataset, we estimated annual carbon changes in above-ground vegetation and soil. Specifically, these are represented bydifferences between aboveground NPP and litterfall (abovegroundbiomass change: �ABM), and between litterfall and SR (partial soilorganic matter change: �SOMP). Using data from sites at whichboth aboveground NPP and NPP are available (n = 7, see Table 3),we first established the relationship between to aboveground NPPand NPP (aboveground NPP = 0.86NPP – 0.56, R2 = 0.95, p < 0.01).To obtain aboveground NPP for the rest of the sites, we appliedthe established relationship to the NPP data, and then calculated�ABM using the available litterfall data (n = 17) by (abovegroundNPP – litterfall).

�SOMP was calculated by (litterfall – SR) for the sites that bothlitterfall and SR data were available (n = 17). One caveat is that (lit-terfall – SR) does not represent the exact �SOM because it doesnot account for belowground components of litterfall or for car-bon leakage from the soil. However, the aboveground componentsof litterfall usually represent a larger carbon input to soil than thebelowground 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, weconsider that �SOMP still provides useful information about thespatial variability of �SOM.

2.5. Analysis

After preparing the data as described above, we evaluated howcomponent fluxes (GPP, AR, NPP, litterfall, HR, SR, and RE) relate toclimatic factors (MAT and MAP) and to net carbon balance (NEP) in

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Fig. 2.

Rad

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R,

SR,

and

RE)

and

climatic

factors (M

AT

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MA

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(a) all

the

forests, (b)

the

mid

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and

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Slopes

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(positive

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(P). Statistics

for th

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ental

Materials

(Tables S1

and

S2).

Table 3Availability 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 indicatedin each column. Estimates of flux that were not available in literatures are indicated by −.

GPP(n = 22)

RE(n = 22)

NEP(n = 22)

NPP(n = 17)

AbovegroundNPP(n = 7)

Litterfall(n = 17)

SR(n = 20)

HR(n = 17)

AR(n = 0)

TUR *(2004) *(2004) *(2004) *(1995) *(1995) *(2004–2005) *(NA) *(NA) RE-HRNEL *(2003–2004) *(2003–2004) *(2003–2004) NEP + HR *(1997–2000) *(1998–1999) *(1997, 2000) *(1997, 2000) RE-HRYLF *(NA) *(NA) *(NA) NEP + HR – – *(NA) *(NA) RE-HRLSH *(2004) *(2004) *(2004) *(NA) – *(NA) *(NA) *(NA) RE-HRTMK *(2003) *(2003) *(2003) NEP + HR – *(1999–2000) *(2003) *(2003) RE-HRSAP *(2000) *(2000) *(2000) *(2000) *(2000) *(2000) *(1998–2000) NPP-NEP RE-HRAPI *(2000–2006) *(2000–2006) *(2000–2006) NEP + HR – – *(2005) *(2005) RE-HRGDK *(2006–2008) *(2006–2008) *(2006–2008) *(2006–2008) – *(1998–1999) *(2005–2006) *(2005–2006) RE-HRTKY *(1994–2002) *(1994–2002) *(1994–2002) *(1999–2003) *(1999–2003) *(1999–2003) *(1999–2003) *(1999–2003) RE-HRTKC *(2006–2007) *(2006–2007) *(2006–2007) *(2005–2009) *(2005–2009) *(2005–2009) *(2006–2007) *(2006–2007) RE-HRFJY *(2000–2008) *(2000–2008) *(2000–2008) *(2000–2007) – *(1999–2007) *(2006–2007) *(2006–2007) RE-HRGCK *(2008) *(2008) *(2008) NEP + HR – – *(2005) *(2005) RE-HRCBS *(2003–2005) *(2003–2005) *(2003–2005) *(2005–2007) – – – NPP-NEP RE-HRYMS *(2000–2002) *(2000–2002) *(2000–2002) *(1999– 2003) *(1999– 2003) *(1999–2002) *(2002–2003) *(2004–2005) RE-HRQYZ *(2003–2005) *(2003–2005) *(2003–2005) *(2003–2005) – *(2003–2005) *(2004–2005) NPP-NEP RE-HRALF *(2008–2009) *(2008–2009) *(2008–2009) * four years (2002/2003–2007) – *(2003–2007) *(2004–2007) *(2003–2007) RE-HRDHS *(2003–2005) *(2003–2005) *(2003–2005) *(1982–2004) – *(1982–2004) * one year (2003/2004) NPP-NEP RE-HRBNS *(2003–2006) *(2003–2006) *(2003–2006) *(2003–2006) *(2003–2006) *(2003–2006) *(2002–2007) *(2002–2007) RE-HRSKR *(2002) *(2002) *(2002) *(2002–2005) – – – *(NA) RE-HRLHP *(2001–2002) *(2001–2002) *(2001–2002) *(2001–2002) – *(2001–2002) *(2001–2002) *(2001–2002) RE-HRPSO *(2003–2005) *(2003–2005) *(2003–2005) *(2002–2006) – *(NA) *(2003–2005) NPP-NEP RE-HRBKS *(2001–2002) *(2001–2002) *(2001–2002) *(2000–2001) – *(2000–2001) *(2000–2001) *(2000–2001) RE-HR

6 orest

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28 M. Kondo et al. / Agricultural and F

onsoon Asian forests. In addition to general relationships acrossll the 22 forests, we analyzed the relationships of mid–high andow latitude forests: the former consisting of boreal and temper-te forests (BDC, TDB, TEC, and MF) and the latter sub-tropical andropical forests (STR and TR) (see Fig. 1).

. Results

.1. Correlation patterns of component fluxes with temperaturend precipitation

A correlation analysis using the data for all 22 of the forestites showed strong linear relationships between component fluxesnd MAT (Fig. 2a). The average value in coefficients of determi-ation (R2) between component fluxes and MAT was 0.54, withhe highest value occurring in GPP (R2 = 0.65) and the lowest valuen NPP (R2 = 0.46). Without exception, all component fluxes wereositively related with MAT (p < 0.01). Compared with MAT, com-onent fluxes were rather weakly related with MAP. The average2 between component fluxes and MAP was 0.23, with the highestalue occurring in GPP (0.30) and the lowest value in HR (0.13).owever, similarly to the results for MAT, all component fluxesere positively related with MAP.

Component fluxes of the mid–high latitude forests were over-ll more strongly related with MAT than MAP (Fig. 2b), and thisattern was also identified in the low latitude forests (Fig. 2c). Allomponent fluxes of the mid–high latitude forests were positivelyelated both with MAT and MAP. In the low latitude forests, a major-ty of component fluxes were also positively related to MAT, buthere were some fluxes that showed negative relationships to MATr MAP (i.e., NPP, litterfall, and HR); however, those relationshipsere 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 spa-

ial variability of component fluxes, there was a difference in thetrength of relationships with MAT between the two forest groups.n the mid–high latitude forests, the average R2 between compo-ent fluxes and MAT were 0.46, and in the low latitude forests, thoseere 0.28. In both forest groups, GPP, AR, litterfall, HR, SR and RE

howed moderately strong relationships with MAT, but those rela-ionships were overall stronger in the mid–high latitude forestsR2 = 0.38–0.58) than the low latitude forests (R2 = 0.19–0.45). Con-rary to those fluxes, a relationship between NPP and MAT wasubstantially weak in both the forest groups: R2 = 0.19 for theid–high latitude forests and R2 = 0.02 for the low latitude forests.

.2. Correlation patterns of NEP with component fluxes

Correlation patterns between component fluxes, and betweenomponent fluxes and NEP, are shown in Fig. 3. For all the forests,e found that all component fluxes were positively related with

ach other (Fig. 3a). Particular strong relationships were foundetween GPP and RE (R2 = 0.96), GPP and AR (R2 = 0.91), and RE andR (R2 = 0.94). However, despite the strong relationships betweenomponent fluxes, none of component flux is related with NEP with

significant level (R2 = 0.13 at most, p > 0.05).A majority of component fluxes of the mid–high latitude forests

howed overall strong positive relationships with each otherFig. 3b). Likewise, a similar pattern was found in the low latitudeorests (Fig. 3c), except that relationships between NPP and otheruxes were rather weak. Regardless of the common positive rela-

ionship between component fluxes, however, component fluxesf the mid–high and low latitude forests related differently withEP. We found that all component fluxes of the mid–high latitude

orests were positively related with NEP (Fig. 3b), whereas those

Meteorology 232 (2017) 623–634

of the low latitude forests were negatively with NEP (Fig. 3c). Inthe mid–high latitude forests, GPP showed the strongest positiverelationship with NEP (R2 = 0.49), followed by RE (R2 = 0.35). In thelow latitude forests, RE showed the strongest negative relationshipwith NEP (R2 = 0.68), followed by AR (R2 = 0.58) and GPP (R2 = 0.54).

We further investigated the different patterns in relationshipsbetween component fluxes and NEP (positive relationships in themid–high latitude forests and negative relationships in the low lat-itude forests) by examining the variability of �ABM and �SOMPwith respect to GPP. As indicated in Fig. 3b and c, NEP of the two for-est groups showed different variation patterns that it increases withincreasing GPP in the mid–high latitude forests, while it decreaseswith increasing GPP in the low latitude forests (Fig. 4a). Corre-spondingly, we found that �ABM of the two forest groups formed avariation pattern similar to that of NEP with high statistical signif-icance (p < 0.01); �ABM of the mid–high latitude forests increasesand that of the low latitude forests decreases with increasing GPP(Fig. 4b). Contrarily, �SOMP commonly decrease with GPP in boththe 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 isone of unique tropical forests characterized by an exceptionallyhigh SR rate despite of a low litterfall rate (Katayama et al., 2009).Nevertheless, including this site or not does not change a decreas-ing pattern of �SOMP of the low latitude forests. These resultsindicate that the variability in the GPP–NEP relationship is largelyattributable to aboveground processes.

4. Discussion

4.1. Relationship between carbon fluxes and temperature

Previous syntheses of monsoon Asian ecosystems identifiedstrong 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 themajor factor related to the spatial variability of other componentfluxes. MAT contributed 46–65% of the spatial variability of com-ponent fluxes (GPP, AR, NPP, litterfall, HR, SR, and RE), whereasMAP contributed rather less (13–30%) (Fig. 2a). The strong influ-ence of temperature on the spatial viability of component fluxes ispossibly related to the reduction in water stress attributable to theEast Asia monsoon. The East Asia monsoon brings humid air to thecontinent and islands of Asia, consequently inducing seasonal highrainfall during the summer; thus, monsoon Asian forests are lessexposed to water stress during the growing season. Additionally,the wider temperature gradient in monsoon Asia than in Europeand North America may strengthen the control of temperature onthe spatial variability of carbon fluxes in monsoon Asia (Kato andTang, 2008). In certain regions in Asia, precipitation plays a criticalrole in forest productivity (e.g., semi-arid Northwestern China andthe Mongolian Steppe, Poulter et al., 2013), and sites representingthose regions are not included in this study. However, the sensi-tivity specific to semi-arid ecosystems is unlikely to influence theoverall pattern of sensitivity across monsoon Asia.

A similar pattern in relationships between component fluxesand MAT was identified for the mid–high and low latitude forests,which signifies the temperature dependence of carbon fluxes inmonsoon Asia. Overall, we found that component fluxes weremoderately related to MAT regardless of region, but fluxes of themid–high latitude forests were more sensitive to changes in tem-

perature than those of the low latitude forests (Fig. 2b and c). Thisdifferent control on carbon fluxes is expected because the sitesat the mid–high latitudes are rather cooler than those at the lowlatitudes (Table 1).

M. Kondo et al. / Agricultural and Forest Meteorology 232 (2017) 623–634 629

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 areindicated 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 relationshipsare 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 versionof this article.).

630 M. Kondo et al. / Agricultural and Forest Meteorology 232 (2017) 623–634

Fig. 4. Variability of (a) NEP, (b) �ABM, and (c) �SOCp with respect to GPP. Regres-sion lines and 95% confidence ellipses are shown separately for the mid–high latitudeforests (red line and shade) and low latitude forests (green line and shade). Borealforests (four BDCs) and temperate forests (four TDBs, three TEC, and two MFs)are indicated by red lower triangles and circles, respectively. Sub-tropical (threeSTRs) 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, oneexcluding an outlier (green solid line) and the other including the outlier (blackdashed line). (For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)

Fig. 5. Variability of NPP with respect to GPP. The figure configuration is as same asin Fig. 4, except an addition regression line for all the forests (black line).

One exception to the appreciable relationships between com-ponent fluxes and MAT was found in NPP, which exhibited notablyinsignificant relations with MAT in both the mid–high and low lat-itude forests (Fig. 2b and c). Contrary to those regional-specificresults, however, NPP of all the forests was related to MAT witha level of significance comparable to other fluxes (Fig. 2a). This dis-crepancy is due to large regional variability in NPP. As indicatedin Fig. 2a, a scatter plot between NPP and MAT shows a positivelinear relationship for a range of MAT from −9 to 27 ◦C, but thisrelationship accompanies large variations in NPP for each value ofMAT (Fig. 5). Correspondingly, we found large NPP variations indata representing the high latitude forests (cluster around MATof −10 ◦C), the middle latitude forests (MAT between −2.8 and15.5 ◦C), and the low latitude forests (MAT between 11.3 and 27 ◦C).This result suggests that the relationship between NPP and temper-ature may be valid when considering a wide temperature gradientin monsoon Asia, but it is uncertain for specific regions representingcomparatively narrow temperature gradients.

4.2. Relationship of productivity to the spatial variability ofcarbon fluxes

Despite the positive relationships that were found between themajority of carbon fluxes and MAT, we suggest that GPP is the fluxmore directly associated with changes in temperature, and thatother component fluxes are influenced by changes in GPP ratherthan temperature. Our results indicate that component fluxes wereoverall 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 signifi-cant than that with MAT (R2 = 0.37–0.5: Fig. 2a–c). Likewise, othercomponent fluxes consistently exhibited stronger relations withGPP than MAT (refer to Supplemental Materials).

There are several lines of evidence suggesting that productiv-ity exerts stronger effects on the variability of component fluxesthan climate factors do. Recent synthesis studies indicated that,despite apparent influence from climatic factors, the variability incomponent fluxes such as AR and litterfall are more closely relatedto productivity (Liu et al., 2004; Piao et al., 2010). An early workin European forests provided an observation-based evidence that

forest productivity exerts stronger influence on SR than climaticfactors do (Janssens et al., 2001), and their finding was evalu-ated indirectly via litter manipulation experiments in China, whichfound that SR was more sensitive to changes in litterfall than to

orest

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M. Kondo et al. / Agricultural and F

limatic factors (Xu et al., 2013; Han et al., 2015). Furthermore, theight relationship between component fluxes and GPP are explic-tly demonstrated in Free-Air CO2 Enrichment (FACE) experiments,

hich indicated that increased atmospheric CO2 concentrationtimulates GPP, which in turn accompanies increases in compo-ent fluxes such as NPP and SR; in comparison, climatic factors are

ess closely related to those increases (e.g., DeLucia et al., 1999;insworth and Long, 2005; King et al., 2004; Norby et al., 2005;ittig et al., 2005). These results, in conjunction with the results of

he present study, indicate that climatic factors certainly influencearbon fluxes, but that their influences are generally overshadowedy the variability in GPP.

.3. Regional difference in the spatial variability of NEP

Although component fluxes were positively related with eachther, the spatial variability of NEP clearly indicated the oppositeariation patterns between the mid–high and low latitude forestsFig. 4a). As we found for the mid–high latitude forests in mon-oon Asia, GPP and NEP were positively related for boreal andemperate ecosystems in Europe and North America (Law et al.,002) and in China (Yu et al., 2013). The global metadata synthesis

nfers that a similar relationship between GPP and NEP is valid glob-lly, but up to the GPP value of 22 Mg C ha−1 yr−1, which roughlyegregates GPP values of boreal and temperature forests to thosef sub-tropical and humid tropical forests (Luyssaert et al., 2007).hese studies commonly infer that GPP and NEP have a positive lin-ar relationship in boreal and temperate ecosystems globally, andhat monsoon Asian forests are not an exception to this relationship.

Meanwhile, the negative linear relationship between GPP andEP found in the low latitude forests may reflect a unique fea-

ure of monsoon Asian forests. Unlike the case of monsoon Asianorests, the global synthesis of carbon fluxes by Luyssaert et al.2007) indicate neither a negative relationship nor any clear rela-ionship between GPP and NEP at GPP values greater than 22 Mg

ha−1 yr−1, due to large variability in NEP data. This differenceetween the two meta-analyses may be attributable to combiningll tropical flux data in the result of Luyssaert et al. (2007), con-rary to the region-specific results offered by the present study. Inhe GPP–NEP relationship of the low latitude forests (green markersnd line in Fig. 4a), four sites showing high NEP (> 4 Mg C ha−1 yr−1)orrespond to sub-tropical forests (QYZ, ALF, and DHS) and youngropical forest (BKS), while the other four sites showing low NEP (< 2

g C ha−1 yr−1) correspond to mature tropical forests (PSO, LHP,KR, and BNS). Previously, it was found that sub-tropical forestsn monsoon Asia uptake an exceptionally high amount of carbonegardless of moderate GPP, owing to high nitrogen deposition inhe surrounding region (Yu et al., 2014). The forest sites includedn this study may reflect this regional characteristic; for instance,itrogen deposition at DHS (25.39 kg N ha−1 yr−1) is approximately

our times larger than those at temperate and tropical forests suchs CBS and BNS (5.27, and 8.89 kg N ha−1 yr−1, respectively) (Sup-orting information in Yu et al., 2014). Additionally, productiveoung secondary forests constitute a large fraction of sub-tropicalnd tropical forests in monsoon Asia because of extensive on-goinghanges in land use (Achard et al., 2002; Mayaux et al., 2005; Kohnd Wilcove, 2007; Carlson et al., 2012) and frequent occurrence ofarge-scale forest fires (Van Der Werf et al., 2003; Patra et al., 2005),nd they are represented by QYZ (a plantation forest) and BKS (aegenerated forest after fire) in our dataset. Therefore, QYZ, ALF,HS, and BKS represent the high carbon-uptake pattern in tropicalonsoon Asia, resulting from the potential effects of high nitrogen

eposition and disturbance. Those sites are distinct from the lowarbon uptake of mature forests, resulting in the negative linearPP–NEP relationship for the low latitude forests. The variability innnual carbon storage changes (�ABM and �SOMP) suggests that

Meteorology 232 (2017) 623–634 631

the GPP-NEP relationship found in this study may be realistic. Asshown in Fig. 4, NEP and �ABM follow the same pattern of vari-ation with increasing GPP (Fig. 4b), while �SOMP decreases withincreasing GPP (Fig. 4b). It is physiologically reasonable that above-ground biomass is more susceptible to the variation in NEP than tosoil organic carbon (Goulden et al., 2006, 2011).

However, with limited available data for sub-tropical and trop-ical forests, it is still difficult to confirm general validity of thenegative GPP−NEP relationship in the low latitudes. Although themetadata of this study contains multiple carbon fluxes at multi-ple sites in monsoon Asia, it only covers three sub-tropical andfive topical forests. These site data are not sufficient to draw aclear conclusion about the potential nitrogen deposition and standage effects on the negative GPP−NEP relationship. To overcomethis difficulty, further expanded data syntheses and model-basedinvestigations are required; particularly, the latter is importantto confirm individual contributions from nitrogen deposition andstand age on the negative GPP−NEP relationship.

4.4. Implications for modelling

The results of this study provide useful information formodelling forest carbon cycles in monsoon Asia. The previousinter-model comparison of GPP at 157 sites around the globe hasdemonstrated differences in spatial variability between model esti-mates of GPP (Yuan et al., 2014), which in turn implies differencesin climate sensitivities to GPP between models. Although it is notexplicitly demonstrated before, we regard that monsoon Asia is notan exception to this result. Moreover, it has been indicated that cur-rent ecosystem models failed to reproduce the high carbon uptakein sub-tropical ecosystems in monsoon Asia because they do notaccount for the effects of nitrogen deposition and disturbances incarbon flux simulations (Yu et al., 2014). The observation-basedresults offered from this study in conjunction with previous synthe-ses in monsoon Asia (e.g., Kato and Tang, 2008; Yu et al., 2013, 2014)can be used to improve the reproducibility of the spatial variabilityof carbon flux simulations.

Another implication of the present findings for future modellingstudies is that component fluxes were strongly linked regardlessof regions in monsoon Asia. A previous global metadata synthesissuggests the strong linkage between component fluxes and GPP;specifically, component fluxes increase with increasing GPP regard-less of forest type, resource supply, or stand age (Litton et al., 2007).As suggested by this work, we confirmed that the strong relation-ships between carbon fluxes that component fluxes increase withincreasing GPP in monsoon Asia. Thus, although the magnitude ofcarbon fluxes differs substantially between mid–high and low lati-tude forests and between all the sites (Fig. 6a), the patterns of fluxvariations becomes comparatively similar when carbon fluxes arenormalized by GPP (Fig. 6b). These similar patterns in normalizedcarbon fluxes are useful for parameterizing relationships betweencarbon fluxes.

4.5. Limitations

This study represents the first attempt toward comprehensiveanalysis of carbon fluxes; as such, the metadata-based approachof this study has several limitations. First, it is difficult to addressphysical-based uncertainties by means of literature-based valuesfor carbon fluxes. As described in Section 2.3, uncertainties for fluxestimations are rarely reported, and when reported, the sourcesof uncertainty often remain unclear. The consistency with find-

ings of the previous syntheses (i.e., sensitivity of carbon fluxes toMAT, and positive relationships between component fluxes) sug-gests the reliability of the findings and the dataset used in thisstudy. However, in the absence of an uncertainty analysis, the

632 M. Kondo et al. / Agricultural and Forest

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 thelow latitude forests (green). (b) The same configuration as in (a), but carbon fluxesare normalized by GPP. (For interpretation of the references to colour in this figurelegend, the reader is referred to the web version of this article.)

retbSflsictsd2nai(Tsaiifl

Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G.,Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P., Grunwald, T.,Hollinger, D., Jensen, N.O., Katul, G., Keronen, P., Kowalski, A., Lai, C.T., Law, B.E.,Meyers, T., Moncrieff, H., Moors, E., Munger, J.W., Pilegaard, K., Rannik, U.,

obustness of the findings cannot be ascertained. Second, differ-nces in flux estimation methods potentially affect the results ofhis study. The carbon fluxes analyzed in this study were obtainedy methods that differ between components and sites (refer toection 2.2). In particular, uncertainties in different methods ofux measurements (i.e., eddy flux, inventory, and chamber mea-urements) propagate in the calculated fluxes (AR, NPP, and HRn Table 3), which may result in large uncertainties in the cal-ulated fluxes and their spatial variability. In addition, even withhe same method of flux measurements, inconsistent techniquesuch as gap-filling of eddy fluxes possibly induce non-negligibleifferences in annual carbon fluxes between sites (Moffat et al.,007). Third, years of flux measurements in literatures do notecessarily coincide even for same sites (Table 3). Although weveraged multiple year fluxes when they are available in literatures,t is still possible that variations in El Nino Southern OscillationENSO) induce biases in carbon fluxes collected from literatures.hese issues cannot be resolved as long as we conduct synthe-es based on literature-based values of carbon fluxes. To elaborate

carbon flux synthesis further, collaboration with site managerss needed, such as for uncertainty assessment, consistent process-ng of raw-level data, and judgement on balance between carbon

uxes.

Meteorology 232 (2017) 623–634

5. Conclusions

Our findings identified unique features of carbon fluxes in mon-soon Asian forests: (1) Temperature is the major factor in the spatialvariability of carbon fluxes, including not only GPP and RE, but alsoAR, NPP, litterfall, HR, and SR, of monsoon Asian forests, whereasprecipitation has less influence on those carbon fluxes. (2) Compo-nent fluxes are positively related to each other across a wide rangeof monsoon Asia. The correlation analysis indicated that, despite therelationship with temperature, the spatial variability of componentfluxes is rather strongly influenced by productivity (i.e., GPP). (3)NEP of monsoon Asian forests shows a unique pattern, varying pos-itively with component fluxes in the mid–high latitude forests, butnegatively in low latitude forests potentially due to the regionalcontrast in nitrogen deposition and stand age. These findings formonsoon Asian forests are encouraging for conducting comprehen-sive analyses of carbon fluxes for other terrestrial regions, whichare expected to shed light on unique regional features of the spa-tial variability of carbon fluxes, leading to greater understanding ofthe global-scale variability of carbon fluxes.

Acknowledgements

This research was supported by the JSPS KAKENHI (grant No.25281003) and Environment Research and Technology Develop-ment Funds (2-1401) from the Ministry of the Environment ofJapan. The compilation data set of ecosystem functions in Asia (ver-sion 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

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.agrformet.2016.10.020.

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