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3-20-2015 The Linkages Between Photosynthesis, , Growth and in Lowland Amazonian Forests Yadvinder Malhi University of Oxford

Christopher E. Doughty University of Oxford

Gregory R. Goldsmith Chapman University, [email protected]

Daniel B. Metcalfe Swedish University of Agricultural Sciences

Cécile A.J. Girardin University of Oxford

See next page for additional authors

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Recommended Citation Malhi, Y., C.E. Doughty, G.R. Goldsmith, D.B. Metcalfe, C.A.J. Girardin, T.R. Marthews, J. del Aguila-Pasquel, L.E.O.C. Aragão, A. Araujo-Murakami, P. Brando, A.C.L. da Costa, J.E. Silva-Espejo, F.F. Amézquita, D.R. Galbraith, C.A. Quesada, W. Rocha, N. Salinas- Revilla, D. Silvério, P. Meir & O.L. Phillips. 2015. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Global Change Biology 21: 2283-2295. doi: 10.1111/gcb.12859

This Article is brought to you for free and open access by the Science and Technology Faculty Articles and Research at Chapman University Digital Commons. It has been accepted for inclusion in Biology, Chemistry, and Environmental Sciences Faculty Articles and Research by an authorized administrator of Chapman University Digital Commons. For more information, please contact [email protected]. The Linkages Between Photosynthesis, Productivity, Growth and Biomass in Lowland Amazonian Forests

Comments This is the accepted version of the following article:

Malhi, Y., C.E. Doughty, G.R. Goldsmith, D.B. Metcalfe, C.A.J. Girardin, T.R. Marthews, J. del Aguila-Pasquel, L.E.O.C. Aragão, A. Araujo-Murakami, P. Brando, A.C.L. da Costa, J.E. Silva-Espejo, F.F. Amézquita, D.R. Galbraith, C.A. Quesada, W. Rocha, N. Salinas-Revilla, D. Silvério, P. Meir & O.L. Phillips. 2015. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Global Change Biology 21: 2283-2295. doi: 10.1111/gcb.12859 which has been published in final form at DOI: 10.1111/gcb.12859. This article may be used for non- commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Copyright Wiley

Authors Yadvinder Malhi, Christopher E. Doughty, Gregory R. Goldsmith, Daniel B. Metcalfe, Cécile A.J. Girardin, Toby R. Marthews, Jhon del Aguila-Pasquel, Luiz E.O.C. Aragão, Alejandro Araujo-Murakami, Paulo Brando, Antonino C.L. da Costa, Javier E. Silva-Espejo, Filio Farfán Amézquita, David R. Galbraith, Carlos A. Quesada, Wanderley Rocha, Norma Salinas-Revilla, Divino Silvério, Patrick Meir, and Oliver L. Phillips

This article is available at Chapman University Digital Commons: http://digitalcommons.chapman.edu/sees_articles/185 This article isprot 10.1111/gcb.12859doi: todifferences version between this andthe ofPleasecite article Version Record. this as been and throughtypesetting, proofreadingwhich may thecopyediting, process, pagination This article undergone has for and accepted been publication fu Moreno, Bolivia Santa Cruz, 7 6 5 4 3 Sciences, Sweden Umea, 2 UK Oxford, Oxford, 1 Rocha Espejo Alejandro Araujo GirardinCécile A.J. Yadvinder Malhi Ru Amazonian linkages The between Article Research typeArticles :Primary Accepted Date :23 Revised Date :06 Received :31 Date ) Museo Unive deNoel Mercado, HistoriaKempff Natural Institute) NationalSpace for dos Research,Brazil Campos, São José ) Department ofGeography, Exeter, Exeter, UniversityUK of InstitutoInvestigaciones) de deIquitos laAmazonia Peruana, Peru ) ) DepartmentEcology Swedishand University ofForest Management, Agricultural of ) Environmental Chan

Universidad Nacional de la Amazonia Peruana, Iquitos,Universidadde laAmazonia Peruana, Nacional Peru Accepted Articlen ning 8,9 11 , Norma Salinas , Filio Farfán Amézquita Title: Title: forests Amazon fo Amazon 1 - , Christopher E.Doughty ected rights by All copyright. reserved. Murakami - - - Nov May

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productivityand cycle 1,11 11 , David Galbraith R. , Divino Silvério productivity, biomass and in growth 1 , do Jhon delJhon Aguila 1

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This article isprotected rights by All copyright. reserved. Acceptedrespiration autotrophic productivity explores such and interpretedas directly changes signifying in sink carbon biomass to expected 2002 biomass lower and growth slower to which photosynthesis, reduced and photosynthesis Articleunderstanding thegrowth of and controls biomass on little relatively receive of number considerable still much and biomass, and photosynthesis on processes forest LiDAR in changes variation spatial diameter tree convert inventories forest Huntingford 2012; change global of context the in photosynthesis of (i) The (i) instance For ) .

- A ae rmt sensing remote based this chainthis ofcausality inmore detail aohr example, another s an forest nrylce no abnbns through bonds carbon into locked stimulate stimulate assumed or GPP) GPP) or (Saatchi

, it is frequently assumed that that assumed frequently is it ,

n biomass in carbon balance carbon et al. et woody growth rates and biomass biomass and rates growth woody

woody growth rates and rates growth woody relationship forms a basis of basis a forms relationship (Lewis (Lewis

used for growth or maintenance or growth for used sci s atal (50 partially is intermediate processes that link photosynthesis and biomass and photosynthesis link that processes intermediate , 2013) , et al. et nii atnin n yt r euly or equally are yet and attention entific (Mitchard t al. et Asner 2007; , iig CO rising approaches . As examples of the focus on focus the of examples As . (Lewis 2004) , , density wood , 2 - t al. et et al. et

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may stimulate photosynthesis, which in turn turn in which photosynthesis, stimulate may . siae n mp ims ad ne cnrl on controls infer and biomass map and estimate

net primary et ovrey cags n rwh ae ae often are rates growth in changes Conversely,

(Malhi, 2012)

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, 2012) , o be to

inferences in inferences (Galbraith there

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n s my also may so and ( annual photosynthesis photosynthesis annual and height estimates into estimates of estimates into estimates height and , with accompanying release of CO of release accompanying with , for

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This article isprotected rights by All copyright. reserved. Acceptedfull their describe Malhi Previously, regions. three for forest data synthesized tropical major the of studied best the description ofthe autotrop time residence biomass woody respiration, autotrophic of components major the quantify biomass and photosynthesis between bybiomass divided above time residence biomass woody approximately Articlerelatively forest temperate disturbed (mortality). output and growth) biom woody only . and , and , fine , or NPP (30 partially and (iii) The relationship between woody growth and biomass is not direct. The standing live standing The direct. not is biomass and growth woody between relationship The (iii) woody particular in organs, various between allocated is productivity primary net The (ii) Here we focus our analysis on the lowland tropical forests of Amazonia (Fig.probably Amazonia 2),tropical offorestslowland the on analysis our focus Here we relationship the influencing in factors intermediate these of importance relative The around around )

(Marthews

old growth stands growth old 30 s o neoytm s euto oh nu woyboas erimn and biomass (woody input both of result a is an of ass - equivalent magnitude equivalent 50 - 50%) % of NPP isa NPP % of carbon cycle, including estimates of GPP and CUE. Working on a wider a on Working CUE. and GPP of estimates including cycle, carbon et al.

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This article isprotected rights by All copyright. reserved. deficit water structured structured poorly but s was sites 5 across plots we effort Malhi 2014; Murakami cycle carbon the of descriptions detailed ( network (GEM) Monitoring Ecosystems Global (gem.tropicalforests.ox.ac.uk) the detail, such in monitored being so do To better to forests tropical dry seasonally and humid of budgets carbon the contrast we particular, linkages the about old in growth and productivity photosynthesis, between assumptions and questions fundamental examine to order in forests be examine to aspects through ranging Aragão scale, Amazonian www.rainfor.org

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soil and one and soil om t al. et et al. et et al. et the subset of these plots that cover lowland old lowland cover that plots these of subset the , a subset of the of subset a , ,

, 2002; Phillips Phillips 2002; , , 2010; 2012) 2010; , i ad Peru. and bia (2009) but there has been no multi no been has there but et al. et et al. et rainfall set of plots plots of set o

, 2014) , set of plots of set , 2014; del Aguil del 2014; , f sites where the components of the carbon cycle are cycle carbon the of components the where sites f

published data on NPP and its allocation for allocation its and NPP on data published

at 16 RAINFOR 16 at

regime on GPP, NPP, woody growth and biomass. and growth woody NPP, GPP, on regime - months of intensive data collection data intensive of months . , where , This represents This Amazon forest inventory network RAINFOR RAINFOR network inventory forest Amazon Thus te conditions across Amazonia in eastern Amazonia Amazonia eastern in t al. et

in western Amazonia Amazonia western in

far each set of plots varies in its its in varies plots of set each 2009) , a , no study has combined these two two these combined has study no , - growth tropical forest ecosystems. In ecosystems. forest tropical growth

Pasquel Pasquel - GEM

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This article isprotected rights by All copyright. reserved. Amazonia NE and forest Araujo season; dry (moderate Peru SE through Amazonian (http://gem.tropicalforests.ox.ac.uk/data). website network GEM the at available (Ta Amazonia in regimes soil and contrasting rainfall in site) per plots (2 sites 5 at plots 10 from budget carbon the of components sites Field M following questions: to rather

Acceptedm aterials and Article 2. 1. 4. 3.

e olce svrl er o dt o poutvt, uorpi rsiain and respiration autotrophic productivity, on data of years several collected We

- Are therelative is What CUE, importanceofthe different carbon budget of aspects (GPP, or productivity primary net Can ext what To Murakami Murakami illustrate decreasegrowth and rates, woodyan inGPP increase or inmortality tropicalAmazonian forests? alloc NPP orGPP NPP is conversely, and fine roots) cycle

chiquitano the sites (da Costa Costa (da

relatively low values ofbiomassinseasonally lowvalues relatively caused dryby a forests tropical ( such as GPP, NPP, CUE, and NPP allocation to canopy, wood canopy, to allocation NPP and CUE, NPP, GPP, as such ethods agd rm E eu n dy season; dry (no Peru NE from ranged

t al. et h iprac o a ul ln cro bde prpcie by perspective budget carbon plant full a of importance the ation and residence time)in indetermining inbiomass spatialvariation n can ent

? If? not? not,why dry forest. The eastern Amazonian Amazonian eastern The forest. dry

2014) , the be predicted frombe et al. et woody growth growth woody e stand key , 2014) , , hc i lctd n h eooe ewe hmd Amazon humid between the on located is which

to dry forest SE Amazonia Amazonia SE forest dry to - level aspects of the the of aspects level

woody growth be reliably predicted from GPP, and, GPP, from predicted reliably be growth woody Malhi rainfall of l 1 Fig 1; ble

a e

rpcl oet stand forest tropical al. t patterns 2014) , .

2).

sites e Aguila del and from soil properties? and from properties? soil lowland Amazon Amazon lowland nelig aa il e made be will data Underlying

to Bolivia (strong dry season; season; dry (strong Bolivia to ranged from humid forest in forest humid from ranged (Rocha - Pasquel Pasquel ueu poy fo proxy useful a et al. et

rates? rates? , 2014) , , stem growth stem , t al. et oet carbon forest The western western The ask

2014) , , which which , ing r

the the its ,

This article isprotected rights by All copyright. reserved. woodygrowth rate(n= report 82plots) dataset componentregional biomass the lowlandof we and a context, Amazonian include larger For ofresidence theanalysis and and toplace time ina plots woody biomass, ourintensive yeareveryandsite every theother fe (Tanguro) accompanying site(Caxiuanã) Brazil At theNE dry of typical is soils shallow on plot the has soils deeper on plot o (Tambopata) site Peru SE Aguila (Allpahuayo) site Peru NE composition species and pseudo of issues biomass. mixed hosting structure, forest plot; experiment times turnover and fo affect also may which structure, physical weaker have generally soils eastern the while soils, fertile sits Accepted(Malhi terrace Pleistocene older an on is ther Article

T close to the dry forest dry the to close he small distance (typically a few km) between between km) few a (typically distance small he - Pasquel Pasquel

,

hr i getr iiaiy n pce composition species in similarity greater is there et al. et otat i seis opsto. t h S Baiin mzna site Amazonia Brazilian SE the At composition. species in contrasts - replication pedx S1 Appendix (Quesada (Quesada , 2014 , a species a with ,

- one plot is in is plot one , ) savanna ecotone. The western Amazonian Amazonian western The ecotone. savanna sufficient are there However, . , resulting in very different species composition species different very in resulting , one plot is on white sand white on is plot one et al. et in each pair to consider them independent sample points. At the At points. sample independent them consider to pair each in sites are sites ) show little evidence of anthropogenic of the the of disturbance anthropogenic of evidence little show ) ,

on , 2012) , composition typical of humid Amazonian forest, whereas whereas forest, Amazonian humid of typical composition e w decades ( w decades

plot is on cla ison plot

on very infertile soils (Table 1). Western Amazonian Amazonian Western 1). (Table soils infertile very on a palm a . The plots included in this analysis this in included plots The . chiquitano - age tree communities with little net increment in in increment net little with communities tree age ed byed Galbraith - et al et rich forest on Holocene floodplain, while the while floodplain, Holocene on forest rich Rocha y soils and the other on a sandy loam, with a with sandy loam, on y other and the soils .

, forests ( forests

the two plots plots two the 2014). At the Bolivia site Bolivia the At 2014). , et al.

while

differences in both in differences , 2014) Araujo ,

et al. the other the but

n plot one at - (2013) Murakami Murakami

each site each sites are sites is rest mortality rates mortality rest . on clay soils ( soils clay on .

All sites studied All sites xeine fire experiences Likewise, a Likewise, (except one fire fire one (except soil conditions soil

could et al. et

on relatively on (Kenia) , 2014) , lead to lead , t the t

the del . This article isprotected rights by All copyright. reserved. fractions and of estimates by scaling then seasons, two in leaves shaded and to multiplier below area, surface stem measuring onmonthly stemrespiration and timescales level scaling byestimating tothe stand root of respiration the subtracting by estimated is respiration rhizosphere respiration, autotrophic above to factor multiplying a applying by productivity root root ( productivity root fine , live from material branch fallen freshly of months three regularinte (2 trees small and plot the in trees dbh) cm (≥10 above litterfall, of scans to bimonthly at traps litterfall S Table see site (for plot forest ha 1 each in timescales seasonal or monthly on respiration papersandabove,briefly. cited are summarised here only site the in as well as website, the on manual a in detail in described are Methods p field the adopt We Field methods Tanguro: Araujo firea dynamics multi at are age mixed forest

Acceptedes and months, three every harvestedand installed cores ingrowth from) Article h pooo maue ad us l maj all sums and measures protocol The - re ol rm ht f nlee si, above soil, unaltered of that from soil free rvals, the turnover of branches on live trees by conducting transect censuses every every censuses transectconducting bytrees live on branches of turnover the rvals, 1 R

and

stem - Murakaml Murakaml , and leaf and , pedx S1 Appendix s

with lit - ooo o te E ntok (http://gem.tropicalforests.ox.ac.uk). network GEM the of rotocol decadal scale has - - rudcas od poutvt ( productivity woody coarse ground ground course root and bole respiration is estimated by applying a applying by estimated is respiration bole and root course ground

dark respiration by measuring leaf dark respiration rates on sunlit on rates respiration dark leaf measuring by respiration dark et al tle evidence ofnon

monthly intervals, estimates of leaf loss to herbivory from from herbivory to loss leaf of estimates intervals, monthly . o NP ti icue cnp ltefl ( litterfall canopy includes this NPP, For ). .,

2014, Rocha

had

some influence on the driest sites (Kenia influence sites andsome onthedriest et al. - - equilibrium size structure. It size structure. equilibrium that possible is 10 cm dbh) in sub in dbh) cm 10 - r opnns f P ad autotrophic and NPP of components or rud od rsiain s siae by estimated is respiration woody ground ,

2014).

-

ground woody productivity. For For productivity. woody ground NPP - ACW plots via dendrometers at dendrometers via plots ) of all medium all of ) timation of courseof timation NPP - specific details specific canopy - specific specific NPP from ) - large fine CUE = NPP / GPP = =NPPCUE /GPP + /(NPP NPP This article isprotected rights by All copyright. reserved. six flux covariance eddy from is errors) measurements. inherent with (also GPP estimating to approach R and bio Our rather investedinNPP GPP as theproportion oftotal than (CUE) Efficiency calculateUse theCarbon Ra. We and NPP sum of aswe the GPP estimated non of loss or accumulation substantial no is there as long as stocks, carbon soil or biomass losing or gaining be still can plot the equilibrium, in be to carbohyd ( respiration plant autotrophic and NPP for used carbon of amount the (PCE), expenditure carbon plant to equal approximately non grossprimarystorage), net little is there where longer or timescales annual on (and respiration autotrophic NPP of components measured The uncertainties calculations through our propagate rigorously and measurement, each to uncertainties systematic th of many that recognise We respiration. dark of inhibition of correction a applying and

Accepted Article or sampling assign we uncertainties: systematic potential have measurements ese

sites a , each with their inherent sampling errors and systematic uncertainties. An alternative An uncertainties. systematic and errors sampling inherent their with each , metric estimate of GPP is indirect and depends on summing up components of NPP of components up summing on depends and indirect is GPP of estimate metric

(5 tropical temperate broadleaf) andone rates. Autotrophic steady state condition does not require the total plot carbon cycle carbon plot total the require not does condition state steady Autotrophic rates. Comparisons of Comparisons productivity (GPP), the carbon taken up via photosynthesis, should bephotosynthesis, via should up carbon takenthe (GPP), productivity R a

( Appendix S1 Appendix

biometric

R R a

) and Ra are then summed to estimate total NPP and and NPP total estimate to summed then are Ra and (Appendix S1) (Appendix a i tee s o e acmlto o non of accumulation net no is there if ) ). In plant In ). approaches

demonstrate - level autotrophic steady state conditions state steady autotrophic level .

with flux with

R

a - : structural ca structural

good -

or canopy canopy or (1) agreement ( agreement - tutrl structural

rbohydrates. Hence, rbohydrates. by quadrature quadrature by Table S2 - structural structural , Fig. , Fig. the in in

no largebiases or missing terms es independent with agreement close respiration autotrophic root fine to similar very is term NPP exudate this rhizosphere, the in microfauna soil and mycorrhizae by respired rapidly and NPP, as than rather respiration autotrophic rhizosphere as mycorrhizae to transfer and exudation NPP ha C Mg (0.13±0.06 budget carbon the of Amazonia central in site a At traps. non emissions, organic volatile measure as lost NPP as such terms, NPP small several neglects it because underestimation towards biased be may NPP of estimate any inevitably, Somewhat This article isprotected rights by All copyright. reserved. Acceptedbiomass production and total NPP. activ mycorrhizal review respiration is CUE our that note but BPE. to equivalent literature, older and wider the with compatible be to CUE of Efficiency Production Biomass the termed it VOCs, and exudates mycorrhizae, neglects and terms production biomass straightforward only includes NPP of estimate our Because Article of the autotrophic budgetare carbon being missed = slope S1; , the allocation to root exudates and to mycorrhizae is neglected. In effect, we treat root treat we effect, In neglected. is mycorrhizae to and exudates root to allocation the ,

which could potentially impact our CUE CUE our impact potentially could which Metcalfe , lte tapd n h cnp, r rpe from dropped or canopy, the in trapped litter d

a be evaluated been has 0.97

 t ae iey o otiue o mismatch a to contribute to likely are ity

At At 0.04, 0.04, t al et the . coefficient of determination of coefficient i prep in ,

few t few i hs aus rud 1 around values has it , ropical sites where the mycorrhizal component of rhizosphere rhizosphere of component mycorrhizal the where sites ropical

at the scale

, aration

timates of GPP (Fig. S1; Table S2) suggest that there are there that suggest S2) Table S1; (Fig. GPP of timates volatile emissions were found to be a minor component minor a be to found were emissions volatile . hs ugss ht site that suggests This ). ( - of the wholeof stand the BPE 1

has recently be recently has year estimates ;

. Vicca

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1 Malhi ; = 0. =

in terms of carbon cycling carbon of terms in - . Given . et al. et

M C ha C Mg 2 61 nesoe understorey ) t al. et , suggesting , en , 1 g ha C Mg 1 < .

201

proposed that proposed that these exudates are labile labile are exudates these that ,

2 09. o below For 2009). ). He ). - 1

- year to that no major terms terms major no that re - ie aitos in variations site

- - 1 we retain the use the retain we

1

eo te litter the below

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1 should be should t al et

between - ground fairly fairly . ,

in - This article isprotected rights by All copyright. reserved. follow calculated Maximum Mean necessary. where then was deficit, water season dry peak of measure a (MCWD), Deficit Water Climatological products rainfall satellite and data station weather locat each at precipitation measured we cycle, carbon autotrophic the of components major measuring to addition In approaches the are capturing for (as agree covariance eddy measurements independent seven of comprised estimate final the in uncertainty equivalent an in result can conditions, atmospheric stable in respiration of underestimation as such method, the in uncertainties any hence exchange); GP of estimate based in our measurement of GPP or NPP sum an potential uncertainties these of some measurement, each for independent are scalin and measurement substantial carry can terms these NPP, of components (the measurements independent seven of summation the on based is GPP of estimate our pr root fine growth, tree (litterfall, measurements independent that note to important is It

Accepted Articlefinal the in uncertainty manageable a in result to quadrature by propagate they and other naaebe netite i or siae o GP B cnrs, n dy covariance eddy an contrast, By GPP. of estimates our in uncertainties unmanageable

ay f h sts n Fig. in sites the of many f P. Hence GPP. of - based estimate comprised of one measurement. Where the two approaches two the Where measurement. one of comprised estimate based ing Aragão ing

(Appendix S1) (Appendix

root productivity root or s el as well as i bsd n snl tp o maueet o nt ecosystem net (of measurement of type single a on based is P o, sn lcl uoai wahr ttos gap stations weather automatic local using ion,

major t al. et u cluain f P i bsd n h smain f four of summation the on based is NPP of calculation our ,

t ol b age ta a abn umto measurement summation carbon a that argued be could it . ef se ad hzshr maueet) Wie oe of some While measurements). rhizosphere and stem leaf, For exa For

components of thecarboncomponentscycle. of (2007) in our mple, while mple, S . 1) We prefer We ,

scaling scaling we can have increased confidence that both both that confidence increased have can we a p may there may be significant uncertainty in uncertainty significant be may there of stem respiration, this respiration, of stem the tnily e oe cuae than accurate more be otentially

g uncertainties, if the uncertainties the if uncertainties, g use of MCWD, rather than more than rather MCWD, of use oduction and branchfall) and and branchfall) and oduction

- ild ih other with filled

do ly es

cancel cancel

not result - time one

an -

This article isprotected rights by All copyright. reserved. Acceptedi.e. ( plots forest of censuses multiple from determined usually most productivity woody coarse ground turnover branch excludes but equations, allometric from here, NPP among ( productivity wood framework Analysis Article very high. not repo do but whole, a as dataset the to analysis statistical apply we Thus, interpretation. in and (n=10) for tropical lowland for cycling carbon of analysis controlled methodologically largest the represent presented dataset The closure, shedding leaf and there intensity season dry of metric simple a is it because precipitation, annual traditional

NPP t ttsis o individual for statistics rt ACW oe oe lsl lne t te ehnss ( mechanisms the to linked closely more fore NPP e.g.

To examine what parameters explain the variation in total NPP, above NPP, total in variation the explain parameters what examine To ie, e rsn a ytmtc rmwr t dcmoe h rltosi between relationship the decompose to framework systematic a present we sites,

= and GPP into several terms in a productivity a in terms several into GPP and

ACW Malhi

GPP as discussed above, discussed as

includes the net recruitment and g and recruitment net the includes

× et al. CUE

NPP

, etc. , 2004)

ACW ) that limit GPP. ) that limit . The framework follows as . The follows framework a rx fr re growth tree for proxy a ;

regions ests to date. Despite this, the number of plots analysed is low low is analysed plots of number the this, Despite date. to ests may be at risk of spatial autocorrelation; caution is warranted is caution autocorrelation; spatial of risk at be may

(n=6

in west west in rowth of small and large trees as calculated as trees large and small of rowth

e.g. n n=4 and - allocation –

water

ae) ad above and rates), hence it is equivalent to the above the to equivalent is it hence

n east in deficits deficits - turnover chain. As defined As chain. turnover

)

nes infcne is significance unless ht ed to lead that (3) (2) - ground biomass biomass ground

- ground coarse ground

stomatal - Core Team, 2013) was AGB, NPP, (GPP, structure error normal a with variables response those For specified. was link logit general of means by two the of (MCWD) deficits water climatological maximum of function a as cycle carbon the of components various between relationship the analysed We disturbance), assumption our a after (e.g. aggrading substantially still is plot the If plot. ha 1 a of terms) biomass (in rate inter at mortality than variability woody However, biomass). standing its to compared biomass in increasing significantly not is forest the (i.e. equal approximately are mortality and production woody the years) 10 (~ timescales appropriate over that is assumption The dete to rates growth woody and biomass standing use we plots, ha 1 by captured poorly be may and stochastic are events mortality tree large because However, trees. large and medium of rates mortality the by determined largely is time residence biomass The This article isprotected rights by All copyright. reserved. Accepted Articlebiomass can as: be expressed productivity woody by divided biomass woody as estimated above the biomass, in change net little is there and similar are rates mortality and growth biomass where forest, mature a For i.e.

NPP hsn y tpie vlain sn AC Al nlss ee odce i R 3.0.1 R in conducted were analyses All AIC. using evaluation stepwise by chosen ACW

= NPP .

× fractional× allocation toabove ), an identity link function was specified. The minimal adequate model model adequate minimal The specified. was function link identity an ),

will tendtounderestimatewill residence time.

- annual scales, and hence better represent the mean mortality mortality mean the represent better hence and scales, annual linear models. For CUE and carbon allocation fractions, a a fractions, allocation carbon and CUE For models. linear - rud od boas eiec tm ( time residence biomass woody ground

- gro

,

east vs. west vs. east und wood

production rates show much less less much show rates production

(Galbraith

(4) region

rmine residence times. times. residence rmine et al. et

and the interaction interaction the and , 2013) , , can be be can , . Hence . (R delineated the with ha MgC and S Table in presented are components cycle carbon of Estimates mm 1 from ranging ( mm 386 from ranging (MCWD) deficit water climatological maximum mm 2689 A R This article isprotected rights by All copyright. reserved. relationship plott but pr annual where soil or MCWD and NPP between in relationship reduction greater a (i.e. CUE in increase the by offset 0.002 = p 0.91; = sites, seasonal moderately the in negative) (more i ( CUE on west) vs. (east regime soil of effect significant no detected also ( MCWD and CUE between relationship overall significant no is There te etr (fertile) western the n Accepted Article mong esults significant (very

the western western the - ed highest MCWD highest 1 s

strong year trong by the y

against annual precipitation, in supplementary material (Fig S (Fig material supplementary in precipitation, annual against ea between r - - 770 to 2311 mm and MCWD from MCWD and mm 2311 to 770 1 1 negative relationship negative

seasona while , ecipitation is used rather than MCWD, we reproduce an equivalent to Fig to equivalent an reproduce we MCWD, than rather used is ecipitation

dry season; season; dry MCWD, ranging from around 0.35 in the least seasonal sites, to sites, seasonal least the in 0.35 around from ranging MCWD,

in the in dry season) east vs. westeast vs. ; Fig. 3 Fig. ;

(fertile) (fertile) GPP GPP sites with the lowest MCWD lowest the with sites sites b ( p ). and < 0.001, < . Table 1). The eastern eastern The 1). Table CE hw a tay n s and steady a shows CUE , H

sites

ence, the decline in GPP with increasing seasonality is largely is seasonality increasing with GPP in decline the ence, sites MCWD MCWD dfeecs n h tmn o te rcptto rsle i a in resulted precipitation the of timing the in differences l to around to ma ana peiiain MP rne fo 11 to 1310 from ranged (MAP) precipitation annual mean , ) on Fig with increasing water deficit, deficit, water increasing with is stronger thanannual precipitation GPP ( GPP .

3

a 0.45 in the driest sites driest the in 0.45 ). p >

There regime 0.1). (infertile)

- to is no significant effect of effect significant no is 203

around (Fig. 3 (Fig.

mm (moderate dry season) season) dry (moderate mm giiat nrae with increase ignificant sites c R 25 ). - a 3 6.2 ), leading to no significant significant no to leading ),

varied similarly, with MAP MAP with similarly, varied . Mg C ha MgC ( To compare with literature with compare To least squares regression squares least GPP GPP declining from around 40 40 around from declining mm (no dry season) season) dry (no mm p > p demonstrates 2 p > p - ). As expected, the expected, As ). 1 (Fig S

0.1, Fig. 3b). We We 3b). Fig. 0.1, year 0.1). However, However, 0.1). soil soil - 1 around 2

at the sitesthe at regime a). increasing

to a clear - 0.41 482 482 to

. : (as (as

3, r - 2

This article isprotected rights by All copyright. reserved. considerably inthe eastern varyfu stronglyas a Both the intensive and within wider w plotdatasets, none arefound. fact in where relationships, significant apparently finding of likelihood the enhance to tend as here autocorrelation for potential is there Again, NPP. total for proxy poor particularly a is growth 0.41, p (Malhi above production wood ground of this Within trend in NPP. Mg ha C particularly thefertile noticeable in western ±0.01.However,is 0.39 there variance among issubstantialresidual inCUE is sites.This forced thelinearWhen is regression origin, through the ofthe slope the(CUE) mean dataset relationships, illustratebut to between twovariables, these F

ig Accepted0.05) > Article P all NPP ure - p

rud od pouto tr ms feunl est frequently most term production woody ground 4 et al. NPP < 0.03) 0.03) < a - 1 ,

plots the plots

year while

ocation dataset, t dataset, , 2004) ACW

- 1

have more significant relationships to overall NPP (Fig. (Fig. NPP overall to relationships significant more have in five of the six plots (CUEbetween plots in five ofthe six with nocorresponding 0.34and 0.46), total canopy production production canopy total itself accounts for around 20 around for accounts itself

relationship between GPP relationship and NPP . (to wood, canopy or fine roots) roots) fine or canopy wood, (to nction of MCWD a nction of MCWD NPP here is no evidence for significant relationships between relationships significant forevidence no is here

ACW NPP sites

o the range of values possible amongthe range possible ofvalues thete shows no significant relationship with NPP or GPP (Fig orGPP relationshipNPP with shows nosignificant ACW ur main purpose here is not to establish theur to significance isnot main purpose here of (Fig

(Fig. 3 (Fig. .

3 e mongwestern the sites ). While residence). While (around islow 30 time d (r ) is similar at all sites. all at similar is ) 2 site

= 0.31, = - 30% of N of 30% s , where rangesGPP , thebetween 34and 42 oody time biomass residence p n MCWD and .

< 0.06) < In recognitionofthe autocorrelation

PP, but such autocorrelation would autocorrelation such but PP, mtd rm oet inventories forest from imated , whereas it varies it , whereas and fine root p root fine and NPP

(Fig ACW n different plots .

S is equivalent to the to equivalent is 3 5 . Hence ). ) the components the .

roduction ec woody Hence does not - 50 years)50 s . . above 4 (r b , 5 2

= - ; the higher the dry the in site dry and area leaf in decline the with ( significant plots dry the western the In analysis frameworkResults from for This article isprotected rights by All copyright. reserved. Accepted influence residen growth (Fig are nos consistent thetrend with inresidence time. Article ha C Inand aseasonal sites. eastern itrisesmore Amazonia, substantially toapeak of170 be mm) Itlow. western moderately 100 rises (around Amazonia in Biomass thedriest lowest around is in sites, 60 The pattern inresidence clarityas to among (around 80 in the western - 1

at MCWD =at MCWD

ce times (mortality times andce NPP rates), GPP, whereas countries (Fig. cause residence times increase, but shows no trend increase, betweencauseresidence themoderately butshows seasonal times ignificant .

CUE CUE - 12 . 21 their

S (Kenia) 0 yearsat 0 ± 5 sites sites ). 7 (+

edaphic The variationdominated biomassby is spatial in the % relationships betweenrelationships above 18 , theeastern , t , - ; 200 mm, with some hintofa withsome 200 mm, slight decline s at less seasonal ±

z and the humid plots humid the and here is no significant difference in mea in difference significant no is here 12 12 S - on on test, 4

- time isreflectedtime ofabove inthepatterns ). Plots in Venezuela were in ). Plots Colombia and excluded 200 mm).

% position (Araujo ; p p

= 0.001 = =0.07 sites increased -

in western oreastern Amazonia. Murakami Murakami The patterns

) of the drier sites, and by increased allocation of NPP to to NPP of allocation increased by and sites, drier the of ) above demonstrates highdemonstrates residence at times MCWD moderate ) decline in GPP in decline )

-

(Allpahuayo and Tambopata) and (Allpahuayo Due to Due ground ground stomatal et al. et - - ground biomass and GPP, NPP orwoodyground NPP biomass andGPP, 80 Mg ha 80 C observed are consistent

the stro , 2014) , growth closure

in the dry plots dry the in

woodygrowthrates - . However, this decline is offset by offset is decline this However, . ngeffect ofresidence there time,

and biomass 150 Mg ha C -

1 that was directly observed at the the at observed directly was that , because residence are times so n woody productivity between between productivity woody n - ground (Fig biomass

spatial variation in spatial

- , probably associated probably , both

1 (Fig

at MCWD =at MCWD due to

. within within have

6a ites, ) . There is a is There .

lack of negligible - and 220 Mg .

- 3 200 f ). ten of analysis dataset largest the presents study This D (Fig. east; in 61±8% west, in 47±10% halving; (approximate decrease strong east; the by the explained in overwhelmingly lower % 62±7 west, the in lower % (43±10 sites dry the at lower substantially is biomass Woody allocation and spatial effectively woodgrowth. NPP variations inGPP, decouple growth woody tree in shifts by offset both In than the increase inCUE. e growththe (atstem sites the between in increase site plots Caxiuanã humid more to relative A dry % sites(+7±4 stem This article isprotected rights by All copyright. reserved. iscussion ey iia pten s observed is pattern similar very Accepted Article

plots covering geographical, hydrological and edaphic contrasts in Amazonia. Amazonia. in contrasts edaphic and hydrological geographical, covering plots offset by an increase in increase an by offset

6 growth ). the eastern and western sites western and eastern the

the productivity and autotrophic carbon cycling of lowland tropical forests, with with forests, tropical lowland of cycling carbon autotrophic and productivity the CUE CUE

(+15±7 p

; +±3 % (+7±13 ( CUE < 10 < - p 3±6 % 3±6

= xpense of branchturnoverof xpense % rm e t dy sites. dry to wet from

- 0.06

; 5 and allocation, resulting in in resulting allocation, and ) of woody biomass r biomass woody of ) p

; = 0.02 = ) p . ;

= allocat woody p

0.3 = ), leading to a slight overall increase in woody growth at the the at growth woody in increase overall slight a to leading ),

0.3 ;

Fig. , the clear decline in GPP in the drier sites is completely is sites drier the in GPP in decline clear the , o te eastern the for ) , result , , with a with , 6b assembled ) o (1±0 % (+15±10 ion . In the eastern the In . ing

esidence time at the dry sites in both in sites dry the at time esidence 21 ec te opnaoy hfs n U and CUE in shifts compensatory the Hence –

± in no significant difference in difference significant no in see Figssee 9 either

% o date to sites

in both in ( p

no decline no

. = 0.009) =

3 comparing the dry Tanguro plots plots Tanguro dry the comparing ; sites e

p that and 3 and seasonal water deficit gradients deficit water seasonal p

=

,

10 < the increase in allocation to to allocation in increase the 0.06 provid decline in GPP at the drier drier the at GPP in decline g ,

) or even a net increase net a even or

appears - ) and a non a and ) 5 , u ti dcie is decline this but ), es

comprehensive a more important more

tm growth stem - significant significant gradients Previous ,

in

To what extent what To canbe budget keypredicted parameters carbon from in the cycle.carbon the ofunderstanding our bringsto CUE and Hence components. new tropical in time residence and analyses This article isprotected rights by All copyright. reserved. account into taken is trees Amazonian western of stature lower Accepted the when diminishes Amazonia western well less fertile, more the on higher is productivity woody that evidence propertie deficit water seasonal of function contrast In model tropical forest GPP de Coupe water seasonal modest with forests in limitation little but Amazonia, clear show data tower trees deciduous dry the by or stomata of in closure through either season, photosynthesis of reduction the of intensity and duration the to related likely so contrasting the in relationships similar Articlewith results Our

synthesis context et al. et

s allocation NPP of patterns explored have , though i though , w se no see we , , 2013) suggest

of the autotrophic carbon budg

s h ability the is

(Araujo

. , (Malhi (Malhi t

Th ht annual that on discussion our focus we is possible that a larger dataset would find a significant result. significant a find would dataset larger a that possible is reduction ese findings ese - te srn rltosis f te cro ccig aaees s a as parameters cycling carbon other of relationships strong other Murakami

as et al. et

a function of seasonality

to forests forests

, 2004; Quesada 2004; , of photosynthesis in the dry the in photosynthesis of vlae P ad U i rlto to relation in CUE and GPP evaluate GPP GPP . Surprisingly, we also do not find a significant effect significanta find not do also we Surprisingly, .

suggest it may be relatively straightforward to predict and predict relatively to be straightforward may it suggest et al. et (Galbraith increases , 2014; del Aguila del 2014; , et framewor ils of eastern and western Amazonia western and eastern of ils

et al. et the the linearly the novel the et al. et

(Aragão shedding of leaves in deciduous or semi or deciduous in leaves of shedding , 2013) , , irrespective of soil , irrespective of , 2012) ,

k presented inFig.

as We highlight four highlightfour We

insights

seasonal water water seasonal . A particular contribution of this this of contribution particular A . - et al. et

Pasquel season in the southern fringe of fringe southern the in season , though th though , , 2009; Malhi 2009; , that precipitation et al. et quantification of GPP of quantification

is regime othe -

structured soils of soils structured east , 2014) , 1 deficit emergentresults ficits :

cro cycle carbon r - west gradient gradient west . et al. et

and soils?

. (Restrepo decreases CO

. There is There

, 2011) , This is This of 2

f soil soil lux

- - , ,

This article isprotected rights by All copyright. reserved. Acceptedalso ma and production the with costs respiration high carrymay forests dynamic less in found strategies defensive conservative, More costs. respiration maintenance and biomass lower with stage younger faster reason The allocati have we Until rates. Running map and determine studies cycle carbon ecosystem NPP and CUE in variations important equally very and Amazonia Articleacross that show findings Our Can surprising, production root whereas NPP, total to relationship (Malhi sites, across consistency methodological (Feldpausch

help NPP - gro on in tropical forests, such attempts haveon intropical tobeinterpreted forests, withcaution. t al. et - cnmc setu:a mr yai sites dynamic more at spectrum: economics et al. et

explain why tropical forest tropical why explain wing species that prioritise growth over defence and are also also are and defence over growth prioritise that species wing or woody growth bereliablyGPP, predicted andvice versa? from

positiv U vre bten sites between varies CUE t al. et 2011) , 2004 , e which which 2012) , correlation between fine root production between andcorrelation root total NPP fine

) or relate spatial or temporal changes in GPP to changes in tree growth tree in changes to GPP in changes temporal or spatial relate or ) rpcl oet P fo stlie aa (e.g. data satellite from NPP forest tropical n improved an which , GPP has GPP s aey esrd n h cnet f oa NPP total of context the in measured rarely is . little power as a predictor of woody biomass production biomass woody of predictor a as power little u findi Our intenance of defence compounds defence of intenance ae lo eosrtd ht od got sos little shows growth woody that demonstrated also have Ti osrain a rmfctos o atmt to attempts for ramifications has observation This . only lit nesadn o te atr dtriig U ad NPP and CUE determining factors the of understanding s t

erfall ng appear to have to appear moderate o NP loain ae the have allocation NPP on s may be linked to life history dynamics and the the and dynamics history life to linked be may but are consistent are but

is a good predictor of total NPP. Our data on fine on data Our NPP. total of predictor good a is power

allocation

lower CUE than many temperate forests forests temperate many than CUE lower ,

as a predictor of of predictor a as the tree community is dominated by dominated is community tree the

with larger pan larger with

(Coley ht r rrl qatfe in quantified rarely are that the et al. et OI NP product NPP MODIS the variation in NPP in variation the eei of benefit

( , 1985) , ( Fig

n average on - als , tropical datasets tropical

.

5 o hint at a a at hint o ) . .

associated This complete complete , due to due , ) either

may t a at ;

This article isprotected rights by All copyright. reserved. AcceptedAmazonia divergent aspe other with 2013) Amazonia of predictors poor all are growth biomass forest woody Amazonian and NPP GPP, time. residence in variation s that show analyses Our trop andresidenceallocation time) biomassin variationin Amazonian spatial indetermining What(GPP, thecarbon of therelativeCUE,NPP is ofdifferentbudget aspects importance ofexplanation differencesbetween inCUE sites. in al., et (Doughty paper Articlepreparation forthcoming a in explored be will fluxes these of plausibility 2 as much Fig of Examination 2012). on BPE) (or CUE apparent of decrease a in result and NPP nutr on larger are fluxes missing these If CUE. and NPP in trends apparent explain partially may fluxes below of quantification of lack our addition, In management (DeLucia ical forests? ; however, t however, ; ,

-

et al. et and in particular, in and 4 Mg C ha C Mg 4 (e.g. (e.g. eainhp bten eiec time residence between relationships ), but it seems possible that such terms may terms such that possible seems it but ), .

ient

, 2007) , (Baker t o te abn ugt uh s GPP as such budget carbon the of cts

- he analysis here advances on th on advances here analysis he poor soils, this may be at the expense of other, more visible components of components visible more other, of expense the at be may this soils, poor - 1 ,

et al. et y which are often at a at often are which ea r

. patial variation variation patial

-

(Fig 1 the low residence time residence low the , 2004; Malhi 2004; , a ugss ht uh isn fue wud ed ifr b differ need would fluxes missing such that suggests 4a

between sites to account for all spatial differences in CUE. The CUE. in differences spatial all for account to sites between 3, .

Fig .

S 5 et al. et ) i . n stage of vigorous recovery after disturbance after recovery vigorous of stage T

ims i oewemnl dtrie by determined overwhelmingly is biomass - his has been noted noted been has his , 2006; Delbart 2006; ,

ground exudate and mycorrhizae and exudate ground s

is n wtr eii i esen n western and eastern in deficit water and

“hyperdynamic” belt around the southern the around belt “hyperdynamic” by providing a quantitative comparison quantitative a providing by

provide a pa a provide It .

also et al. et hw te consistent the shows n u prev our in - poor soils (Vicca soils poor rtial (but not complete) not (but rtial , 2010; Castanho 2010; , os work ious - associated associated et al et et al. et ,

as y but

or or in . , ,

This article isprotected rights by All copyright. reserved. Acceptedstrategies. drought rates fall resource Amazonia disturbances exogenous life with interacting system, limited ( rates mortality intrinsic both for account forest in question major a is death tree trees, large and medium times residence biomass do Why and there (Marimon sites Amazonian southern other at reported rates turnover stem high s The Article small. be to the that suggesting time, over increase biomass net little and structure age mixed a show sites both However, disturbance. time 2012) manifest be this sink carbon biosphere the of behaviour This NPP. and GPP of determinants priority understanding dry

rne f Amazonia of fringe are biased downwards downwards biased are ?

- At the driest sites (Tanguro and Kenia), it is possible that our estimates our that possible is it Kenia), and (Tanguro sites driest the At to understand the determinants of mortality rates and residence times, rather than the than rather times, residence and rates mortality of determinants the understand to - soitd mortality associated fore demanding (Quesada

is almost certainly linked to soil substrate, whether through higher fertility driving fertility higher through whether substrate, soil to certainlylinked almost is

likely reflect a genuine a likely reflect phenomenon.

of

primarily the spatial variation of biomass in old in biomass of variation spatial the hort residence times at the dry margin report margin dry the at times residence hort et al. et ,

high mortality strategies, or the we the or mortalitystrategies, high , 2012) ,

vary so much across Amazonia? Understanding the determinants of of determinants the Understanding Amazonia? across much so vary (e.g.

as an increase in biomass, or an increase in turnover turnover in increase an or biomass, in increase an as (Marimon if the forests ar forests the if wih lo n u fvuig ih growth high favouring up end also which , y

are not in a strong secondary stage and any such bias is likely is bias such any and stage secondary strong a in not are . The short residence times residence short The . blow ,

whic - downs - may t al. et itr trade history – ae any oiae b te mortality the by dominated mainly are h

if rising atmosphe rising if e s e e.g. ) lo ha also . 201 , till far from biomass equilibrium following fire following equilibrium biomass from far till T

self e tog otat ewe es ad west and east between contrast strong he (Stephenson (Stephenson

4 - ve thinning in a light a in thinning ) - . offs

implications - f e r t dvlp predictive a develop to are we If growth forests, it is it forests, growth aker soil structure driving higher tree drivinghigher structure soil aker

at the dry fringe may be driven by driven be may fringe dry the at cos speci across ric CO ric et al. et ed

here are consistent with the with consistent are here , 2011) ,

for predicting the future future the predicting for 2

stimulates GPP, would GPP, stimulates - limited es ; ad the and )

explanations explanations prob ,

hr lifetime short and resource and ably a higher higher a ably

rates et al. et of residence of

rates of of rates role (Malhi, , 2014) must

of -

csses ad atclry o n te toia frs rgos Tee e plot ten These regions. forest tropical other in so particularly and ecosystems, however, where Pan 2010; allocation time, residence of components conclusion In This article isprotected rights by All copyright. reserved. response toatmosphericchange. structure isanimportantforests andneglected ofdry component inunderstanding their defence.investment in favour inherentlytrees with shorter life highand mortalityrates thehighand/or woody such a CUEallocation; system dynamic may these plots, Mortality rates(i.e. residence allocation), but completelyor is lower this (they GPP bywoody do,but and/or inCUE offset increases 6 sites because are dynamic they Our results showthat decrease r andwoodygrowth inGPP Are seasonally dry therelatively biomassin caused of tropicalforests values by low a ) Accepted Article.

The biomassnotbecause dry havegrowth plots rates (they they low donot) lower have

in both in both we we

the insights gained are probably equally applicable for other forest and woodland and forest other for applicable equally probably are gained insights the conducted n boas ad h sail aito o tee parameters these of variation spatial the and biomass, and t al. et

photosynthesis orgrowth seasonal water deficit ,

hs td demonstrates study this because the treesbecause the shorter inthesehave more lifetimes plots and quickly. die , 2013) , ec if we are to understand understand to are we if osystem osystem

a comprehensive quantification of the of quantification comprehensive a the Understanding the role of mortality Understanding therole and of .

W s , notbecause they are unproductive easonally dry forests tropical ousdo nqedtst of dataset unique a on focussed e forest

times

gradients abn budget carbon ) overwhelming

ates, or an increase in mortality rates? ates, oranincrease inmortalityrates? do not - history strategies, anhistory strategies, early mean stage, life and less the . , despite little little , despite

eainhp bten htsnhss growth photosynthesis, between relationships Again, thereAgain, a betw may directlink be critical , particularly s, ly

motne of importance explain spatial variaexplain studied here

decline

carb .

B

turnover in determining in turnover the ten iomass is lower at lower theiomass dry is on cycle on carbon use efficiency and and efficiency use carbon in stemproductivity

lowland Amazonia lowland

have low biomass low have considering the various various the considering

(Landsberg & Sands, Sands, & (Landsberg (Malhi (Malhi tion in biomass in inbiomass tion

et al. et een the s , 2009) ,

provide n plots n (Fig. ; ,

R AdvancedGrant a Research Award. Royal and SocietyWolfson Merit Award. Investigator Advanced Council Research European a by and Foundation Jackson the by supported is YM Foundation. Moore Betty and Gordon ( Council Research Environment Natural UK consortia. research RAINFOR and (GEM) Monitoring Ecosystems Global the of product a is work This A varyingconditions. environmental models and step next bein is that something protocol, tropics the across needed are plots Further biomass. and growth sufficient insights new substantial This article isprotected rights by All copyright. reserved. Araujo Roman AragãoLEOC, MalhiY, AragãoL, Y,Metcalfe Above DBet Malhi al.(2009) cknowledgements Acceptedeferences Article Research Letters (2007) Spatial fire patterns droughts. and responseofrecentAmazonian 2778. productivity forests tenAmazonian across onco - Murakami A Murakami will will

in We thank S. Abele for GIS assistance. F assistance. GIS for Abele S. thank We

for how GPP, autotrophic respiration, al respiration, autotrophic GPP, how for n of and be and toemploy other testhypotheses sitestodevelop from and new data these

, DoughtyMetcalfe al.(2014) CE, DBet The productivity, allocation , themselves 34

, L07701. , n rpeet mjr aa olcin effort collection data major a represent and - network GEM the by attempted g Cuesta SaatchiLO, RM, Anderson S, Shimabukuro YE to untangle the spatial variability of links between GPP, GPP, between links of variability spatial the untangle to

ED12X1 n NE/D014174/1 and NE/D01025X/1 ieldwork was funded by grant by funded was ieldwork ntrasting soils. - location and residence time time residence and location and below

P s upre b a ER an by supported is OP - ground primary net Biogeosciences Mroe, n important an Moreover, . e ggn a ngaging

bt r cery not clearly are but , Geophysical standardised standardised , respond to respond s from the from s ) and the the and ) 6 , 2759 – C This article isprotected rights by All copyright. reserved. AcceptedDeLucia Thomas DrakeRB, Gonzalez EH, JE, Delbart Viovy ChaveLe P, J, N,Ciais N, MalhiToanMortality Y, T(2010) a keyas dr del Aguila daDB, ACL, Costa Metcalfe Coley Chapin Bryant PD, FS (1985)availability JP, Resourceantiherbivore and plant ArticleImproving MH,Castanho Costa MalhiY,GalbraithD,Quesada ADA, (2013) CA CoeMT, Beer C,Reic OL,Baker wooddensityMalhi Yet in TR, Phillips al.(2004) Variation determines spatial Asner Mascaro GP, et Clark JK, al.(2012) J High 13 is respiration a cons dynamic vegetationmodel. of thespatialofaboveground forest: from inAmazonian distribution a results biomass (Iquitos,Amazonia Peru). productivity,a andcarbon wet north aseasonalforest in dynamics forest.Amazon primary productivityafter 8 defense. biophysical parameters. forestsimulated Amazon global andcovariation withclimate. distribution patterns forest inAmazonian biomass. Amazon.in the Colombian &Ecology Diversity and cyclingforestsofBolivia. theAmazon ofcarbon forest at in the dry margin in , 1157 - Pasquel J, DoughtyPasquel J, Metcalfe al.(2014 CE, DBet hstein M, Tomelleri E et al. (2010) Terrestrial gross uptake: Eetal.hstein M,Tomelleri (2010) grosscarbon Terrestrial dioxide – Science 1167.

Plant Ecology & Ecology Plant Diversity , 230 tant fraction of gross ? grossproduction? tant fraction of primary , , 895 7 , 1 Biogeosciences

– Doughty CE et al. (2014) Doughty et CE Ecosystem al.(2014) and respiration net biomass andproductivity in by spatialvariation including – & EcologyPlant Diversity 15. Biogeosciences Biogeosciences 899. – 10 years through10 experimental of

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- west Plant Plant iver iver , This article isprotected rights by All copyright. reserved. AcceptedLewis OL SL, Fingerprinting MalhiY,Phillips global change theimpacts (2004) of on LewisLopez SL, LawLV and BE,Falge E,et water Gu al.(2002) controls dioxide over Environmental carbon (2010) Sands P Landsberg JJ, LK,Kho (in carbon review) Malhi Y,Tan a in Bornean Comprehensive S budget lowland Huntingford GalbraithZelazowski P, C, ArticleGalbraith D,Malhi Y,Affum Galbraith P, Levy MeirWilliams D, M, S,HuntingfordP Sitch PE, (2010) Multiple C,Cox LloydFeldpausch Lewis TR, et SL al.(2 J, NurFeeley WrightAR, SJ, Supardi MN,Kassim KJ, Doughty Metcalfe CE, The DB, daet allocation MC Costa al.(2014) production, and cycling 359 tropical forests. African forests. tropical 120. vapor exchange terrestrial . of Dipterocarp forest. toCO tropical forests. models under climate change. mechanisms three ofAmazonian forestglob dynamic losses in biomass biomass estimates. on adjacent soil. infertile of carbona terrafertile forest onpreta witha ineasterncompared in Amazonia soil in tropical forest trees. , 437

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Philosophical Transactions of the of Transactions Philosophical & Ecology Plant Diversity 2 - induced climateinduced change. Biogeosciences

Nature Ecology Letters, Ecology - Plant Ecology & EcologyPlant Diversity Ph BaffoeK et Residence al.(2013) of times woody biomass in ysiological Ecology ofForestProduction ysiological Ecology New Phytologist , 457 ,

9 , 1003 D et resilienceSimulated oftropical al.(2013) , 3381 012) Tree heightpantropical integratedinto forest Agricultural and Forest and Meteorology Agricultural 10 – , – Geoscience Nature 1006. 6 , 461 3403. , 139 , 187 -

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Royal Sciences SocietyBiological B: 157. , 647 ,

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This article isprotected rights by All copyright. reserved. Accepted et al.MitchardFeldpausch ETA, TR,Brienen RJW Simu MarthewsGirardin CAet MalhiY, TR, al.(2012) Ma Malhi Y,W OL,Malhi Y,Phillips Lloyd et the al.(2002)tomonitor structure, J Aninternational network ArticleMalhi Y,Doughty D(2011) Galbraith C, alloca The Malhi Y,BakerOLal. TR, Phillips (2004)above et The LEOC,MetcalfeMalhi Y,Aragão ComprehensiveDBet al.(2009) ofcarbon assessment Malhi Y,Amézquita FF,Doughty an The productivity, et CE al.(2014) metabolism carbonMalhi Y(2012) metabolism and Thecycle of vegetation. productivity, forest tropical rimon BS,Marimon BenHur Change Biology neotropical and carbon temperature elevationaltransect: efficiency. variation use & EcologyPlant Diversity hyperdynamic at treeturnover the forest old biomass in Science compositi SciencesBiological productivity intropical forests. 104 Neotropical plots. forest 15 productivity, storage and allocation Amazon inthree &Ecology Diversity cycle forestsouth ofin plots twolowlandtropical Journal ofEcology , 1255 , ood – 13 on and dynamics ofAmazonian forests(RAINFOR). 1274. , 439

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, This article isprotected rights by All copyright. reserved. Accepted NemaniRunning HeinschReeves(2004) RR, SW, FA,Zhao A M,HashimotoH Rocha Metcalfe W, Doughty Ecosystem DB,etand CE carbon al.(2014) productivity Restrepo Language CoreR TeamA (2013)and R: Environ Quesada OL, Schwarz Phillips Metal.Basin CA, (2012) ArticleQuesadaLloyd Schwarz chemical in Metal. physical CA, J, (2010) Variations and Philli Pan Y,Birdsey OL, Phillips RA, RBand The Jackson (2013) biomass structure,distribution, Nepstad Dias P, DC, Moutinho rainforestBrazil). Grosso, (Mato cycling andannually ofthe inintact burnt atthelimit Amazon forest dry southern 183 Brasilmeasurements network. the flux from Across basin? Amazon photosynthesis the across 2246. structurefunction are and mediated and by climate. bothsoils 1541. inrelation theirproperties to genesis. forest soils of Amazon Science 622. of the world's forests. forestAmazon ps OL,LE,Lewis Aragãoet SL al.(2009) .Amazon Drought the sensitivity of Amazon forest.Amazon oncanopyexclusion processes, and aboveground biogeochemistr production, , 128

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, , biomass The 1. Fig. F This article isprotected rights by All copyright. reserved. site. from derived neighboring a data Soil † comm.). Fine, pers. (P.V.A. observed high productivity the to thus and contribute soil more fertile deeper, a to access anon located siteis * This (%) Silt (%) Clay exchange (%) Sand (mmolcapacity Cation (cm) depth layer organic Soil from 0 ha C (Mg stock C Soil C(%) Total N(%) Total P type Soil (%) moisture Soil Mea (mmPrecipitation yr (°C) temperature air Mean annual yr m radiation (GJ Solar (mElevation asl) Longitude Latitude code RAINFOR site are of each availablefor sites. pair Datain eastern f westernAmazonia. and 1. Table

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Western Amazonia Transect Western

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1900 - 24.4 4.8 259 edn fo pooytei t sadn lv woody live standing to photosynthesis from leading

Tambopat 35.5 ± 0.4 35.5

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KEN01 62.730 16.015 Cambi 19.7 ± 19.7 Kenia Kenia 22.82 19.13 58.05 75.58 447.1 74.8 0.22 Wet 384 2.4 0.4 sol 32 1 8 - -

1310 - 23.4

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KEN02 62.730 16.015

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Vetic AcrisolVetic 22.4 ± 0.1 22.4 Caxiuanã Caxiuanã - - 51.4570 Control CAX04 1.7160 10.68 83.69 5.64 1.34 0.83 0.06 37.4 47 30 35

2311 - 25.8

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l

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for a site driest the between terms budget carbon and productivity be Fig. and western(dashed) datasets. dataset. inventory forest wider a symbols grey above time residence biomass above to allocation ( (CUE); efficiency use carbon ( (MCWD): mean deficit water of climatological maximum function a as plotted variables stock carbon and allocation Productivity, 3. Fig. climatological (MCWD). waterdeficit two the indicating arrows with Amazonia, western and eastern in sites study of location The 2. Fig. This article isprotected rights by All copyright. reserved. productivity. ofnetFig. primaryVarious productivity components as 5.of afunction total netpr SE. Dotted Fig. 4.Relationship

Accepted indicate Bars forests. wet and dry tween Article the

6 - . Results from analysis framework exploring how above how exploring framework analysis from Results . rud od boas Bak ybl idct cmrhnie esrmn sites, measurement comprehensive indicate symbols Black biomass. woody ground

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Bars ±1 indicate ) coarse woody woody coarse ) imary ; ( ; . b f ) ) water deficit. S4. Figure deficit. S3. Figure Figure S2. Figure S1. S3. Table S2. Table S1. Table Appendix S1. This article isprotected rights by All copyright. reserved. Accepted carbon cycle Figure S5. Article Supporting Information

Measurements ofcarbon measuredcycle components in1haplots. Comparison Sampling period and interval in1haplots. collection for data Relationships amongc Relationship between Relationship betweenabove Relationship eainhp ewe rsdne ie n above and time residence between Relationship .

Detailed data collection 1ha methodsfor plots.

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- rud ims ad seasonal and biomass ground

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