Analysing Amazonian forest productivity using a new individual and trait-based model

19 Pages·2014·2.62 MB·English

Geosci.ModelDev.,7,1251–1269,2014
www.geosci-model-dev.net/7/1251/2014/
doi:10.5194/gmd-7-1251-2014
©Author(s)2014.CCAttribution3.0License.
Analysing Amazonian forest productivity using a new individual
and trait-based model (TFS v.1)
N.M.Fyllas1,*,E.Gloor1,L.M.Mercado2,S.Sitch2,C.A.Quesada3,T.F.Domingues4,D.R.Galbraith1,
A.Torre-Lezama5,E.Vilanova5,H.Ramírez-Angulo5,N.Higuchi3,D.A.Neill6,M.Silveira7,L.Ferreira8,
G.A.AymardC.12,Y.Malhi9,O.L.Phillips1,andJ.Lloyd10,11
1EcologyandGlobalChange,SchoolofGeography,UniversityofLeeds,UK
2SchoolofGeography,UniversityofExeter,Exeter,UK
3InstitutoNacionaldePesquisasdaAmazônia,Manaus,Brazil
4SchoolofGeoSciences,UniversityofEdinburgh,Edinburgh,Scotland,UK
5InstitutodeInvestigacionesparaelDesarrollo,ForestalFacultaddeCienciasForestalesyAmbientales,
UniversidaddeLosAndes,Merida,Venezuela
6DepartmentofWildlifeConservationandManagement,UniversidadEstatalAmazónica,Puyo,Pastaza,Ecuador
7UniversidadeFederaldoAcre,RioBranco,Brazil
8MuseuParaenseEmílioGoeldi,Belém,Brazil
9EnvironmentalChangeInstitute,SchoolofGeographyandtheEnvironment,UniversityofOxford,Oxford,UK
10CentreforTropicalEnvironmentalandSustainabilityScience(TESS)andSchoolofMarineandTropicalBiology,
JamesCookUniversity,Cairns,Australia
11DepartmentofLifeSciences,ImperialCollegeLondon,SilwoodParkCampus,Ascot,UK
12UNELLEZ-Guanare,ProgramadeCienciasdelAgroyelMar,HerbarioUniversitario(PORT),
estadoPortuguesa,Venezuela
*currentlyat:DepartmentofEcology&Systematics,FacultyofBiology,UniversityofAthens,Athens,Greece
Correspondenceto:N.M.Fyllas([email protected])
Received:24January2014–PublishedinGeosci.ModelDev.Discuss.:20February2014
Revised:19May2014–Accepted:21May2014–Published:3July2014
Abstract.Repeatedlong-termcensuseshaverevealedlarge- nitrogen (N ) and phosphorus (P ) content and wood den-
L L
scale spatial patterns in Amazon basin forest structure and sity (D ) varying from tree to tree – in a way that repli-
W
dynamism,withsomeforestsinthewestofthebasinhaving catestheobservedcontinuafoundwithineachstand.Wefirst
uptoatwiceashighrateofabovegroundbiomassproduction applied the model to validate canopy-level water fluxes at
and tree recruitment as forests in the east. Possible causes three eddy covariance flux measurement sites. For all three
forthisvariationcouldbetheclimaticandedaphicgradients sites the canopy-level water fluxes were adequately simu-
acrossthebasinand/orthespatialdistributionoftreespecies lated.Wethenappliedthemodelatsevenplots,whereinten-
composition. To help understand causes of this variation a sive measurements of carbon allocation are available. Tree-
new individual-based model of tropical forest growth, de- by-treemulti-annualgrowthratesgenerallyagreedwellwith
signedtotakefulladvantageoftheforestcensusdataavail- observations for small trees, but with deviations identified
ablefromtheAmazonianForestInventoryNetwork(RAIN- for larger trees. At the stand level, simulations at 40 plots
FOR), has been developed. The model allows for within- were used to explore the influence of climate and soil nu-
stand variations in tree size distribution and key functional trient availability on the gross (5 ) and net (5 ) primary
G N
traitsandbetween-standdifferencesinclimateandsoilphys- production rates as well as the carbon use efficiency (C ).
U
ical and chemical properties. It runs at the stand level with Simulated 5 ,5 and C were not associated with tem-
G N U
four functional traits – leaf dry mass per area (M ), leaf perature.Ontheotherhand,allthreemeasuresofstandlevel
a
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
1252 N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel
productivitywerepositivelyrelatedtobothmeanannualpre- et al., 2004; Malhi et al., 2006). This macroecological vari-
cipitationandsoilnutrientstatus.Sensitivitystudiesshowed ationmaypossiblybeexplainedbythebasin-wideobserved
aclearimportanceofanaccurateparameterisationofwithin- climateandsoilnutrientavailabilitygradients(TerSteegeet
and between-stand trait variability on the fidelity of model al.,2006;Quesadaetal.,2012).Theclimaticgradientcom-
predictions.Forexample,whenfunctionaltreediversitywas prisesa southeastto northwestincreasein annualprecipita-
notincludedinthemodel(i.e.withjustasingleplantfunc- tion and decrease in dry season length (Sombroek, 2001),
tionaltypewithmeanbasin-widetraitvalues)thepredictive with aboveground wood productivity positively related to
ability of the model was reduced. This was also the case precipitation (Malhi and Wright, 2004). On the other hand,
when basin-wide (as opposed to site-specific) trait distribu- asoilage/nutritionalaxisspansfromthenortheasternpartof
tionswereappliedwithineachstand.Weconcludethatmod- thebasintosouthwesternAmazonia,withgenerallyyounger
elsoftropicalforestcarbon,energyandwatercyclingshould and richer soils in the west and highly weathered nutrient
strivetoaccuratelyrepresentobservedvariationsinfunction- poorsoilsintheeast(Sombroek,2000;Quesadaetal.,2011),
allyimportanttraitsacrosstherangeofrelevantscales. although at regional and local scales the patterns are of-
tenmorecomplicatedthanthismacro-gradientmightimply
(Higginsetal.,2011).Soilphysicalproperties(suchasroot-
ingdepth,drainageandwaterholdingcapacityandsoilstruc-
1 Introduction ture) are similarly related to soil age and parental material
(Quesadaetal.,2010).Poorphysical(forexamplesoildepth)
TheAmazonbasin,encompassingoneoftheplanet’slargest conditions (less weathered soils) are often associated with
forestareasandhostingonequarteroftheEarth’sbiodiver- highersoilnutrientavailability(WalkerandSyers,1976;Vi-
sity,constitutesalargereservoiroflivingbiomass(Malhiand tousek and Farrington, 1997), leading to increased nutrient
Phillips,2005).Amazonforestsalsohaveasubstantialinflu- concentrationsattheleaflevel(Fyllasetal.,2009)andthus
ence on regional and global climates (Shukla et al., 1990; apotentialforhigherphotosyntheticrates(Reichetal.,1994;
Spracklen et al., 2012). These forests are, however, under Raaimakersetal.,1995).Inaddition,increaseddisturbance-
stronghumanpressurethroughloggingandforest-to-pasture associated mortality rates in soils of poor physical proper-
conversion,andfaceatpresentawarmingandmorevariable tieslendtowardsmoredynamicstandswherefastergrowing
climateandchangingatmosphericcomposition(Lewisetal., species dominate (Chao et al., 2009; Quesada et al., 2012).
2004; Gloor et al., 2013). Due to the enormous area of for- This positive feedback mechanism could explain the higher
estwithintheAmazonbasin,thesefactorshavethepotential aboveground productivity and turnover rates observed for
tomodifyglobalatmosphericgreenhouseconcentrations,re- westernforests(Quesadaetal.,2012).
gionalandglobalclimate,andtheoverallbiodiversityofthe The simplistic ways by which plant functional diversity
planet(Crameretal.,2004). is currently reflected in DGVMs is an important shortcom-
Traditionally, two approaches have been followed to un- inginpredictingecosystemresponsetoenvironmentalgradi-
derstandcurrentandfuturestateoftheAmazonforests.First, ents and their vulnerability to global change (Lavorel et al.,
dynamicglobalvegetationmodels(DGVMs)havebeenused 2007).SomeofthewidelyappliedDGVMsrepresentAma-
to simulate vegetation patterns and carbon fluxes across zonian plant diversity with only few plant functional types
Amazonia (Moorcroft et al., 2001; Galbraith et al., 2010) (PFTs), for example the LPJ model uses only two tropical-
withsomepredictingsubstantialcarbonlossesunderscenar- orientedPFTs(Sitchetal.,2003)andtheJULESmodelonly
ios of global change (White et al., 1999; Cox et al., 2004) one (Clark et al., 2011). The mean values of key model pa-
but with others less so (Cramer et al., 2004), or even gains rameterslikephotosyntheticcapacity,wooddensityandleaf
(Huntingfordetal.,2013).Asecondapproachtounderstand turnovertimesareselectedtodescribeanaprioriPFTdefini-
Amazonianforestsdynamicsisthroughtheanalysisoflong- tion(Fyllasetal.,2012).Thismeansthatmanyprocessesare
termfieldobservationsofpatternsoftreegrowthandmortal- controlled by a set of fixed parameters that describe viable
ityastheyrelatetoclimaticandedaphicvariationsacrossthe plant strategies within very limited boundaries. Such PFT
basin(e.g.Phillipsetal.,2004;Quesadaetal.,2012). implementationhasimportantdrawbacks.Itisusuallybased
Analyses of Amazon forest inventory data, and particu- ontheaveragevalueofaplanttraitrecordedfromdifferent
larlythoseoftheAmazonForestInventoryNetwork(RAIN- field studies and different species. But recent studies have
FOR)(Malhietal.,2002),haverevealedlarge-scaletemporal shown that key traits present a wide variation, dependent
trendsinbiomassandspeciescompositionaswellasintrigu- upon species identity and site growing conditions (Sultan,
ingspatialpatternsinmanystandproperties(Phillipsetal., 2000; Fyllas et al., 2009; Baraloto et al., 2010a). Thus any
1998; Baker et al., 2004; Phillips et al., 2009). Specifically, givenspecieshasthepotentialtoexhibitsite-dependentshifts
there is systematic spatial variation in species composition, in its trait value; this being in addition to the inter-specific
biomass,growthandturnoverrates,withwesternforestsex- traitvariabilityexpectedatanygivensite.Ignoringthisplas-
hibiting higher wood productivity, faster turnover time and ticity could potentially bias modelling through an underes-
lowerstandwooddensitycomparedtoeasternforests(Baker timation of the PFT’s resilience by projecting dramatic but
Geosci.ModelDev.,7,1251–1269,2014 www.geosci-model-dev.net/7/1251/2014/
N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel 1253
artificial switches in vegetation state caused by the limited respiration (Chave et al., 2005; Mori et al., 2010, although
anddiscrete(step-wise)natureofPFTdescriptions. see Larjavaara and Muller-Landau, 2012). These two di-
Such unaccounted variability could be particularly im- mensionscaptureessentiallyagrowthvs.survivaltrade-off.
portantwhenmodellingAmazonianforestdynamics,where Thereismixedevidenceforacoordinationbetweenleafand
environmental heterogeneity and plant functional diversity stemtraits,i.e.acorrelationbetweenslowreturnrelatedleaf
comprise key components of the ecosystem (Townsend et traits and denser wood (Chave et al., 2009), with Baraloto
al., 2008). For example, the variation in leaf mass per area et al. (2010b) suggesting that these two axes are indepen-
(M ) recorded within Amazon forests covers an approxi- dent, but with Patiño et al. (2012) showing some important
a
matelysimilarrangetotheoneidentifiedinglobaldatasets, correlations with foliar traits such as P . For the purpose
Lm
rangingfrom30to300gm−2(Fyllasetal.,2009).Similarly, ofthisstudyweconsiderleafandstemdimensionsasinde-
there are large contrasts in soil physical and chemical con- pendentaxesoftreefunctionalvariation,withnopredefined
ditions (Quesada et al., 2010). These important ecosystem interrelationshipbetweentherepresentativetraits.However,
flux drivers have now been better quantified, with Amazon- the observed among-stand variability of these four charac-
wideclimate(MalhiandWright,2004),soil(Quesadaetal., tersisusedtoexpresshowgrowingconditionscontrolplant
2011)andfunctionaltraitdatasetsalsohavingbeenobtained processes,whilethewithin-standtraitvariationrepresentsa
(Baker et al., 2009; Fyllas et al., 2009; Patiño et al., 2009; rangeofecologicalstrategiesfoundunderthesamegrowing
Patiñoetal.,2012).Thisisinadditiontocontinuallyexpand- conditions.
ing long-term forest inventory data in which tree growth, Themodelisinitialisedwithsite-specifictreediameterand
mortality and species composition data are regularly being functionaltraitsdata,andforcedwithdailyclimatedata.We
recorded(Keelingetal.,2008;Chaoetal.,2009). firsttesttheabilityofthemodeltoestimatestand-levelwater
We here introduce a vegetation dynamics model devel- fluxes at three eddy-flux tower sites. For a subset of seven
oped as a tool to better analyse these observed Amazonian RAINFORplotswheresite-specificcarbonallocationcoeffi-
large-scale productivity patterns. This is achieved through cientsareknown,atree-leveltestofstemgrowthratesisap-
specificincorporationsofobservedenvironmentalandthebi- plied.Wefurthervalidatetheabilityofthemodeltosimulate
otic variations into the model formulation. Specifically we the spatial patterns of aboveground biomass productivity at
focus (a) on the architectural variability, expressed through 40 RAINFOR plots, and subsequently explore the variation
thesize-classdistributionofastand,and(b)onthefunctional ofGrossPrimaryProductivity(5 ),NetPrimaryProductiv-
G
variability,expressedthroughsimulateddistributionsoffour ity(5 )andCarbonUseEfficiency(C )alongestablished
N U
important functional traits which are allowed to vary from Amazonianclimaticandedaphicgradients.
tree to tree within individual plots. Following a continuum
approach, we replace the use of a discrete number of PFTs,
2 Materialsandmethods
with distributions of a functional trait “quartet”, the within-
stand distributions of which also vary from plot to plot in
2.1 Modeldescription
accordancewithobservation.
Two axes of functional variation/strategy are represented
“Traits-basedForestSimulator”(TFS)isanindividual-based
in the model: the leaf economic and the tree architecture
forest model, i.e. it simulates water and carbon fluxes for
spectra.Thefourfunctionaltraitsincludeleafmassperarea
each tree in a stand. In the current version of the model,
(M ), leaf nitrogen and phosphorous dry mass concentra-
a standstructureisprescribedintermsofthenumberoftrees
tion (N and P respectively) and wood density (D ).
Lm Lm W andtheirdiameteratbreastheight(d).Thisisthusa“snap-
Thefirstthreetraitsexpressonecomponentoftheleafeco-
shot”versionofthemodel,whichdoesnottakeintoaccount
nomic spectrum (Reich et al., 1997; Wright et al., 2004),
tree recruitment and mortality. In this version of TFS, each
i.e. a global photosynthetic tissue trade-off between inex-
individualisfullydescribedthroughd,withallometricequa-
pensive,short-livedandfastpaybackleavesvs.costly,long-
tions used to estimate other attributes of interest like tree
lived and slow payback leaves, although we emphasise that
height (H), crown area (C ), total leaf area (L ) and tree-
a a
other factors such as leaf cation concentrations may be im-
level leaf area index (L). Whole tree biomass is then parti-
portantinthisrespect(Fyllasetal.,2012;Patiñoetal.,2012).
tionedtoleaf(B ),stem(B ),coarseroot(B )andfineroot
L S CR
LowM andhighnutrientcontentleavesareassociatedwith
a (B )biomassusingestablishedallometricequations.Allo-
FR
comparably short longevity and usually have high (mass-
cation of assimilated carbon to different plant components
based) gas exchange rates (Reich et al., 1994; Raaimakers
is static, i.e. it does not change with size or resource avail-
et al., 1995). Lately the role of P has been highlighted,
Lm ability,butratherimplementsfield-derivedallocationcoeffi-
as it expresses alternative limitations of the photosynthetic
cients (Aragão et al., 2009). The general architecture of the
efficiency of tropical tree species (Domingues et al., 2010).
modelispresentedinFig.1.
Thefourthtrait,D ,isusedtorepresentatreearchitectural
W
axis with denser wood species supporting an overall higher
abovegroundbiomassandthushavingahighermaintenance
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1254 N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel
Figure1.Thefivebasiccomponentsofthemodelandinformationflowamongthem.Treebytreetraitsandsizeinitialisationtakesplaceat
thebeginningofeachsimulation.Carbonandwaterfluxes,aswellasgrossandnetprimaryproductivityareestimateddaily.
Tree functional diversity is expressed through four traits dailyradiationincorrespondencetothesun–shademodelof
(M ,N ,P ,D ),whicharerandomlyassignedtoeach dePuryandFarquhar(1997).Thedirectanddiffusefraction
a Lm Lm W
tree:thesepseudo-databeinggeneratedfromlocalobserva- ofsolarradiationisestimatedusingtheSpittersetal.(1986)
tionsusingarandomvectorgenerationalgorithm.Leafpho- approximation.Thefunctionalconfigurationofatree(i.e.the
tosynthesisiscalculatedusingamodifiedversionoftheFar- values of the quartet of traits) does not affect its light com-
quharbiochemicalmodel(Farquharetal.,1980),thatincor- petitivestatus,astreeheightandcrownareaarenotdirectly
porates leaf chemical and soil moisture effects. The maxi- associatedwithanyofthefourtraits.Futureversionsofthe
mum photosynthetic rate is regulated by N or P through modelwillincorporatesucheffects.
L L
theco-limitationmodelofDominguesetal.(2010).Incon- Soil water balance is approximated through a simple
trasttomostecosystemfluxesmodels,wherephotosynthetic bucketmodel,withsoilwatercontentaffectingleafconduc-
ratesaredirectlyregulatedbywateravailability(Scheiterand tanceandthusphotosyntheticrates.Competitionforsoilwa-
Higgins,2009;Clarketal.,2011),wecouplewater“stress” terisapproximatedthroughasizehierarchy,i.e.biggertrees,
to reduction of canopy conductance by estimating a daily with a more extensive root system are assumed to have ac-
fractional available soil water content for each tree in the cesstodeeperwater(S1,WaterBalanceAlgorithm).Byas-
stand. Carbon fluxes are simulated on an hourly and water suming that a tree with a higher leaf biomass (B ) requires
L
fluxesonadailytimestep. a higher fine root biomass (B ), we indirectly implement
FR
Lightcompetitionisbasedontheassumptionofaperfect aM effectonwatercompetition(S1,Definition,Allometry
a
canopytessellation.Theflat-topversionoftheperfectplas- andStoichiometryofIndividualTreesinTFS).Inparticular,
ticitymodel(Purvesetal.,2007)hasbeenusedinthecurrent between two trees of the same size, the higher M tree will
a
versionofTFStocharacterisecanopyandsub-canopytrees, bemorecompetitiveintermsofacquiringsoilwater.
by assuming that all of a tree’s foliage is found at the top TFS is coded in Java and it is fully described in S1. The
ofitsstem(S1,CanopyArchitectureandRadiationEnviron- main effects of including functional diversity are realised
ment).AcanopyheightZ*isestimatedforeachforeststand, throughtrait-driveneffectsonphotosynthesisandrespiration
defining canopy and sub-canopy trees. By summing up the (Reichetal.,2008,2009).Modelcomponentsthatarelinked
crownarea(C )ofalltreesinthestand, Z*isestimatedas with any of the four base traits are described in the follow-
a
the height of the last tree that enters to the sum before the ingparagraphs.Allstatisticalanalysesandgraphsweremade
cumulativecrownareaisequaltotheplotarea.Canopytrees withR(RDevelopmentCoreTeam,2013).
areabsorbingameandailyamountofshort-wavesolarradi-
ation equal to the sum of mean beam, diffuse and scattered
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N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel 1255
2.1.1 Within-standfunctionaldiversity higherN andP comparedtoinfertileplots(Fyllasetal.,
Lm Lm
2009),withthisbeingreflectedinthephotosyntheticcapac-
ityofindividualtrees,asdescribedinthenextparagraph.
As noted above, TFS employs neither species nor PFT de-
scriptions,butratheradifferentdiscretecombinationofeach 2.1.2 Photosynthesis
the four key functional traits M , N , P and D is as-
a Lm Lm W
signed to each individual tree along with a diameter-based A tree-level leaf area index (L), estimated as the ratio of
allometry.Toachievethis,thefourfunctionalcharactersas- La to Ca, is used to compute the energy, carbon and water
signed are generated using a procedure based on the ac- fluxes for each tree in a stand. The net photosynthetic rate
tualvaluesrecordedwithineachplot.Thisisachievedusing (µmolm−2s−1)isgivenby
a random vector generation algorithm (Taylor and Thomp- A =g (C −C ) (1)
N S α c
son, 1986). This algorithm, appropriate for generating non-
withC theatmosphericCO mixingratio(µmolmol−1),C
repeated pseudo-observations from a relatively small sam- α 2 c
theCO mixingratioinsidethechloroplastandg theCO
pleofobservations,wasoriginallydevelopedtoprovidefor 2 S 2
stomatal conductance (molm−2s−1) calculated from Med-
a realistic probabilistic representation of shrapnel projectile
lynetal.(2011)andmodulatedbyasoilmoistureterm.The
distributionsinmilitarybattlefieldsimulationsinthefaceof
leaf-levelphotosyntheticrateA isscaleduptothetree-level
onlyalimitedamountofavailabledata(duetothecostand N
bymultiplyingwiththeC ofthetree.
difficulty of undertaking the appropriate experiments). This a
The co-limitation equation suggested by Domingues et
“ballistic method” is notable in that it was specifically de-
al. (2010), whereby the leaf level photosynthetic capacity
signed to short-circuit the usual step of multivariate density
(areabasis)ispotentiallylimitedbyeithernitrogenorphos-
in the generation a pseudorandom population with approxi-
phorus, is used in TFS to estimate the leaf maximum car-
mately the same moments as the original sample. The bal-
boxylationandelectrontransportrates:
listic method is readily programmable as follows (with the
underlying rationale as discussed in Taylor and Thompson, V =M (min{a +v N ,a +v P }) (2)
max a NV NV Lm PV PV Lm
1986andThompson,1989)andwiththefollowingdescrip-
J =M (min{a +v N ,a +v P }) (3)
max a NJ NJ Lm PJ PJ Lm
tionbasedonVisualNumerics(2014).
First take a vector X with n multivariate observations (both in µmolm−2s−1), with a , a , a , a (in
NV NJ PV PJ
(x ,...,x ). To generate a pseudo data set from x, one µmolg−1s−1) and ν , ν ,ν , ν (in µmolmg−1s−1)
1 N NV NJ PV PJ
observation (x ) is first chosen at random and its near- empirical coefficients (see Table of symbols in Sup-
j
estmneighbours,x ,x ,x arethendeterminedandwith plement S1). The canopy-level photosynthetic capacity
j1 j2 jm
the mean x of those nearest neighbours subsequently cal- V (µmolm−2s−1) is estimated using the tree-level leaf
j Cmax
culated. Next, a random sample u ,u ,...,u is generated area index L, taking into account within-canopy gradients
1 2 m
q
fromauniformdistributionwithlowerbound 1 − 3(m−1), in light and photosynthetic capacity based on Lloyd et
m m2 al.(2010).Nutrientoptimisationisapproximatedusingequa-
q
and upper bound 1 + 3(m−1). The random variate is then tions in Lloyd et al. (2010), with M also dependent on the
m m2 a
m heightofeachtree(H )andthemeancanopyheight(H ):
estimated as Pu (x −x )+x and the process then re- i S
l=1 1 jl j j Ma∗=Ma·exp(cid:2)aH ·(Hi−HS)(cid:3), (4)
peatedasrequired.Somewhatsubjectivehereistheselection
witha anempiricalcoefficient.
oftheappropriatevalueofthenumberofnearestneighbours H
(m)althoughthenatureofthesimulationsisnotstronglyde-
2.1.3 Respiration
pendentuponthatvalue(TaylorandThompson,1986).Thus,
followingtheirrecommendationandasintheVisualNumer-
Treerespirationincludesagrowthandamaintenancecompo-
ics(2014)default,wehavetakenherem=5.
nent,bothcomputeddaily.Growthrespirationisconsidered
In our case, applying this procedure resulted in a coordi-
asaconstantfraction(0.25)ofdailyphotosynthesis(Cannell
natedtraitquartetforeachtreeinastandbeinggeneratedon
andThornley,2000).Threedifferentmaintenancerespiration
thebasisonthesmallerobservationaltraitquartetssampled
formulationsareallowedinTFS(S1,Respiration),butinthis
from trees in the same stand (Baker et al., 2009; Fyllas et
studyweusetheonedescribedbelow.Leafmaintenanceres-
al., 2009; Patiño et al., 2012) and without any assumptions
pirationR isestimatedasafractionofV (Scheiterand
mL Cmax
havingtobemadeabouttheirunderlyingstatisticaldistribu-
Higgins,2009):
tions.Thusnosinglefunctionaltrait“averagestand”valueis
used (or even required). Further, between-stand differences RmL=0.015VCmax. (5)
in the trait distributions and their covariances are also in- Stem maintenance respiration is estimated from the sap-
trinsically taken into account. This is because each stand is woodvolume(V )ofatree:
S
characterised by its own multivariate trait sample and size
R =δV (6)
distribution.MorefertileplotshaveanoveralllowerM and mS S
a
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1256 N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel
with δ=39.6 (µmolm−3s−1) as reported in Ryan et maximum stomatal conductance is subsequently reduced to
al.(1994)fortropicaltrees. theactualg bymultiplyingthesecondtermofEq.(8)with
S
Sapwood volume is estimated by inversion of the pipe awaterstresscoefficient.
model and assuming that the ratio of leaf area to sapwood In contrast to most ecosystem flux models, where pho-
area (8 ) increases with the height and the wood density tosynthetic rates are directly regulated by water availability
LS
for tropical trees (following Calvo-Alvarado et al., 2008; (Scheiter and Higgins, 2009; Clark et al., 2011), we couple
Meinzeretal.,2008): soilwaterdeficittocanopyconductancebyestimatingadaily
fractionalavailablesoilwatercontentϑ foreachitreeinthe
i
8LS=0.5×(λ1+λ2·H+δ1+δ2DW), (7) stand (S1, Water Balance and Soil Water Stress). This term
is then used to estimate the water stress γ that has a direct
i
with λ1=0.066m2cm−2, λ2= 0.017mcm−2, δ1= effectonstomatalconductance,asalsodescribedinKeenan
−0.18m2cm−2andδ2=1.6cm3g−1. etal.(2010).
Sapwood area (m2) and volume (m3) are then calculated
from 2.2 Studysitesandsimulationssetup
S =L /8 (8) Threesetsofsitedatawereusedtoexplorethebehaviourof
A A LS
the model. These include a set of three eddy flux measure-
withLathetotalleafareaofthetree(m2)and ment(EFM)sites,sevenplotswithintensivecarbonbalance
andallocationmeasurements(IMs),and40permanentmea-
SV=SA·(H−CD) (9) surement(PM)plots.
withCDthecrowndepth(m). 2.2.1 Eddyfluxsites
Coarse-rootmaintenancerespirationR isestimatedas
mCR
inScheiterandHiggins(2009): Daily climate and energy flux data from three EFM sites
(Caxiuanã[1.72◦S,51.46◦W],Manaus[2.61◦S,60.21◦W]
R =0.218β BCR, (10) and Tapajós [2.86◦S, 54.96◦W]) were used to assess
mCR R
8CN the ability of the model to estimate canopy-level wa-
ter fluxes. Data were obtained from the Large Scale
where 8 is the root C:N ratio estimated on the basis of
CN
Biosphere-Atmosphere Experiment in Amazonia (LBA)
the simulated N assuming a dry weight carbon fraction of
R
project (http://daac.ornl.gov/LBA/lba.shtml). In particular
0.5.
meandailyclimateparametersincludingincomingradiation,
Fine-rootmaintenancerespirationR isassumedtobe
mFR
temperature,precipitation,relativehumidityandwindspeed
equaltoleafrespiration.
wereusedtoforcethemodel.Latentheatflux(λEinWm−2)
Allrespiratorycomponentsarecorrectedwiththetemper-
was used to estimate a daily mean canopy conductance de-
aturedependencefunctionofTjoelkeretal.(2001).Thetotal
finedasG = λE.TheEFMdatacoveraperiodfrom2001
maintenancerespirationRmisthen C DC
to 2008 for Caxiuanã, from 2000 to 2005 for Manaus and
Rm=RmL+RmS+RmCR+RmFR. (11) from 2002 to 2004 for Tapajós. GC was only estimated for
dayswithacompletediurnalrecordofλE.Ateachoneofthe
2.1.4 Stomatalconductance EFMsitesthemeandailyGC (molm−2s−1)wascompared
between observations and simulations. The model was ini-
Initially,amaximum(nowaterstress)stomatalconductance, tialisedwithsize-classdistributionandfunctionaltraitsdata
gs,maxiscalculatedfollowingMedlynetal.(2011,2012): fromRAINFORpermanentplotslocatedneartheeddyflux
towers. Specifically, CAX-06 inventory data were used for
g A
g =g +1.6·(1+√1 )× n (12) Caxiuanã,BNT-04forManaus,andTAP-55forTapajós.We
s,max 0
DC Ca notethattheEFMsitesaremainlyfoundattheeasternpart
ofAmazonia(Fig.2)growingonlownutrientstatussoils.
with g (molm−2s−1) the minimum stomatal conductance,
0 Themodelwasinitiallycalibratedtothesite-specificval-
g (−) an empirical coefficient that represents the water use
1 uesforg andg ofEq.(8)thatgavethebestperformance.
0 1
efficiencyoftheplant,andD theleaf-to-atmospherevapour
C Astandardisedmajoraxis(SMA)regression,forcedthrough
pressuredifference.Valuesofg andg thatleadtothebest
0 1 zero,wasusedtoverifytheabilityofthemodeltosimulate
modelperformanceweredifferentbetweensites,asindicated
G , with a regression slope close to one indicating a good
C
bythemodelcalibrationprocedure.Forthebasin-widesim-
modelperformance.
ulations constant values of g =0.020 (molm−2s−1) and
0
g =5.0(−)wereused,closetotheestimatesofDomingues
1
et al. (2014). In future versions of the model, we anticipate
that g and g will be related to other functional traits. The
0 1
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N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel 1257
agivenstandstructure,agivenclimaticandsoilprofileand
functionaltraitsconfigurationoftheestablishedtrees.Aver-
ageobservedstemgrowthrate(per10cmd bins),expressed
in carbon units (i.e. kgCyr−1), is compared with simulated
5 usingtheYorkmethodofbeststraightline,whichholds
N,s
whenbothx andy observationsaresubjecttocorrelateder-
rorsthatvaryfrompointtopoint(Yorketal.,2004).
2.2.3 Permanentmeasurement(PM)sites
Inventorydatafrom40RAINFORpermanentmeasurement
plots (Fig. 2), including tree diameter and multi-annual
growth for all trees greater than 10cm curated/managed in
ForestPlots.net (Lopez-Gonzalez et al., 2011), are used to
(a) validate the ability of the model to accurately simulate
stand-levelcarbonfluxesand(b)explorepatternsof5 ,5
G N
andC alongtheAmazonianclimaticandsoilnutrientavail-
U
ability gradient. The size-class distribution within each PM
Figure2.Geographicdistributionofstudysites.Darkgreytriangles
siteisusedtoinitialisethestandstructureofthemodeland
indicatethethreeeddyfluxtowersites(withlocalnames),lightgrey
simulate patterns of productivity for the 2000–2006 period.
circles indicate the seven intensive measurement plots (with plot
codes), and crosses indicate the coordinates of the 40 RAINFOR Climatedataforthesameperiodwereusedherewiththefirst
permanentmeasurementplots. year again used as a spin-up period (Sheffield et al., 2006).
Forthose40PMplots,sampledistributionsofthetraitsquar-
tet are available (Fyllas et al., 2009) as well as a descrip-
2.2.2 Intensivemeasurement(IM)sites tionofsoilchemicalandphysicalproperties(Quesadaetal.,
2011).
The ability of the model to realistically simulate carbon AtthePMsitesthesimulatedstand-levelaboveground5
N
fluxesatthetreelevelisevaluatedusingdatafromtheseven was compared with observed rates of aboveground growth
intensive measurement plots (Aragão et al., 2009; Malhi et 1B [kgCm−2yr−1] for trees that survived during the
ABG
al., 2009). These sites are amongst the intensively surveyed 2000–2006 time period using a SMA regression. A second
plotswithintheRAINFORnetwork(Fig.2),wheremeasure- stepwastoexplorethewaythat5 ,5 andC varyacross
G N U
ments of all major components of the C cycle are recorded an Amazon climatic and soil nutrient availability gradient
(Malhietal.,2009).Attheseplots,adetailedassessmentof (Quesada et al., 2010). The site scores of a principal com-
the carbon stocks is applied, and 5 allocation coefficients ponents analysis (PCA) on the soil properties of the 40 PM
N
to different plant components are estimated (Aragão et al., plots (see Fyllas et al., 2009) are used to categorise plots
2009; Malhi et al., 2011; Doughty et al., 2014). These site- alonganutrientavailabilitygradient(8 ),whilethekeycli-
1
specificcoefficientsareusedtocalculatetheamountofsim- matic variables used were the annual mean temperature T
a
ulated5 thatisallocatedtostems5 (kgCyr−1). andannualtotalprecipitationP .AKendallcorrelationcoef-
N N,s a
The IM sites of interest include two plots at Agua Pudre ficient(τ)wasusedtoidentifypotentialrelationshipsof5 ,
G
in Colombia (AGP-01 and AGP-02), one (ALP-30) at All- 5 andC withT ,P and8 ,asinmostcasesnon-linear
N U a a 1
pahuayo/Peru,one(BNT-04)atManaus/Brazil,oneinCax- associationswereobserved.
iuanã/Brazil (CAX-06), one in Tambopata/Peru (TAM-05)
and one in (TAP-55) Tapajós/Brazil. Based on data from 2.2.4 Randomisationexercise
Quesada et al. (2011), AGP-01, AGP-02, TAM-05 can be
considered to be located on fertile soils, with the other four Inordertoexplore(a)theimportanceofincludingtraitvari-
plotsoninfertileones.Availablesoildepthdata(Quesadaet ability and thus functional diversity in our simulations and
al.,2011)andfunctionaltraitsdata(Fyllasetal.,2009)were (b)theimportanceofincludingconstraintsthatareknownto
usedforsite-specificsimulations.Forallsevensitesweesti- controlthelarge-scalepatternsofAmazonianforestdynam-
matedtheobservedaveragemulti-annualgrowthrate(2000– ics, we conducted a randomisation exercise with the model
2006)ofeachtreefromforestcensusdata,inordertocom- being run under four alternative setups at the 40 perma-
pareitwiththesimulated5 . nent RAINFOR plots. The first setup denoted as var-tr is
N,s
ThedailyclimatewasextractedfromthePrincetonGlobal thevariable-traitsimulationwithtraitinitialisationbasedon
Meteorological Forcing Data Set (Sheffield et al., 2006). theobservedstand-leveltraitdistributionasdescribedinthe
These simulations are used to validate the ability of the previous paragraphs (default setup). The second setup, de-
model to accurately estimate tree-level stem growth, under notedasfix-tr,isafixed-traitsimulationwithalltreeshaving
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1258 N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel
Figure3.SimulatedagainstobservedmeandailycanopyconductanceGCforthethreesiteswitheddyfluxdata.Thebrokenlinerepresents
a1:1relationshipandthecontinuouslineillustratesastandardisedmajoraxis(SMA)regression.
thesame(datasetmean)valuesforeachtrait:thisthusrep- 3.2 StemgrowthratesimulationsattheIMsites
resenting a single PFT case. The third setup (rand-tr) is a
variable-traitsimulationwithtraitinitialisationbasedonran- The mean simulated stem growth rate 5 of each tree in
N,s
domvaluesofthetraitquartetasrecordedinanyindividual the seven IM plots was compared with the observed above-
alongthe40permanentplots.Thissetupthusignoresanypo- ground biomass gains (1B ) for the 2000–2006 period.
ABG
tentialpatternsoffunctionaltraitbiogeography,i.e.traitsare An accurate simulation of 5 can be seen for small size
N,s
notrelatedtotheenvironmentaloredaphicconditionsunder classes, but with greater differences between the observed
which a tree is growing. The fourth setup (rand-tr-N) is a andthesimulatedmulti-annualgrowthfoundforbiggertrees
variable-trait simulation in which the photosynthetic capac- (Fig.4).AtinfertileALP-30,theestimateslopeoftheYork
ityofanindividualisonlydefinedbyitsleafNcontentand model indicated an overestimation of aboveground produc-
thustheNPco-limitationconstraintisremoved.Thesealter- tion(α=1.18±0.06),drivenmainlybyanoverestimationof
native setups were compared by considering both the slope the mid-size classes. At BNT-04 the model underestimated
and the R2 of SMA regressions between the predicted and the overall growth (α=0.82±0.03). Aboveground growth
theobserved5N,S. wasoverestimatedinCAX-06(1.11±0.07).AtTAP-55(α=
1.44±0.15)themodelunderestimatedabovegroundproduc-
tion (0.90±0.06). At fertile AGP-01 (α=1.36±0.08) and
3 Results
AGP-02(α=1.25±0.05)anoverestimationofaboveground
productivitywasobservedalthoughwithsimulationsofmost
3.1 CanopyconductancesimulationsattheEFMsites
size classes falling within the observed ranges. At TAM-05
Valuesofbestmodelperformanceforg andg werediffer- (α=0.79±0.07)though,thesimulatedabovegroundgrowth
0 1
ent between sites, with g =0.035 (molm−2s−1) and g = was underestimated with the overall slope driven by diver-
0 1
7.5 at Caxiuanã, g =0.035 and g =7.0 at Manaus with gencesinsmallersizeclasses.Therangeanddistributionof
0 1
g0=0.01andg1=2.5thesebeingsomewhatlowerthanthe 5NallocationtostemgrowthisadequatelycapturedbyTFS
estimates of Domingues et al. (2013) at Tapajós. Simulated assummarisedinFig.S2.1intheSupplement.
G wasunderestimatedforCaxiuana(α=0.85±0.05)and
C
Manaus (α=0.90 ± 0.02), with the model overestimating 3.3 5G,5NandCUsimulationsatthePMsites
G in Tapajós (α=1.28±0.04), but exhibiting an overall
C
adequateperformance(Fig.3).ForsimulationsattheIMand Simulated stand-level aboveground net primary productiv-
thePMsites,constantvaluesofg0=0.02(molm−2s−1)and ity5N,A waspositivelyassociatedwithobservedchangesin
g1=5(−) were used, which are found within the range of aboveground biomassof trees thatsurvived in thePM plots
values in the EFM sites and reported estimates (Medlyn et overthe2000–2006period1B ,withanR2=0.42,sug-
ABG
al.,2012;Dominguesetal.,2013). gesting an adequate model behaviour (Fig. 5). A summary
ofsimulatedstand-level5 ,5 andC relationshiptokey
G N U
environmentaldriversisgiveninTable1(seealsoFig.S2.2
intheSupplement).5 and5 andC werenotassociated
G N U
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N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel 1259
Figure4. Simulatedstemgrowthrate5N,sagainstobservedabovegroundbiomasschange1BABGfordifferentsizeclassesforthe2000–
2006 period. Upper panel: infertile plots. Lower panel: fertile plots. The broken line represents a 1:1 relationship. The continuous line
illustratesthestraightlinefitusingtheYorkmethod(seetextfordetails).
withtemperature.However,allthreemeasuresofstand-level
productivity were positively related to annual precipitation
andsoilnutrientavailability.
3.4 Randomisationexercisesimulations
Results from the randomisation exercise (Fig. 6) found the
fully constrained default setup (var-tr) to have the best pre-
dictiveperformance(R2=0.42withaSMAslopea=0.92).
Thisisascomparedtothefixed-traitsimulation(fix-tr)sin-
glePFTparameterisationwithadecreasedpredictiveability
of TFS (R2=0.29, a=0.82) and an overall higher mean
predicted aboveground productivity. Not accounting for the
site-specificdistributionofthetraitquartet,i.e.bypassingpo-
tential biogeographic patterns of functional diversity and/or
environmental trait interactions (rand-tr), also reduced the
predictive ability of the model (R2=0.29, a=0.74). Fi-
nally,therandomtraitno-NPco-limitationsetup(rand-tr-N)
similarly led to an inferior model performance (R2=0.33,
a=0.88)andwiththehighestmeansimulatedaboveground
productivity.
Figure5.Simulatedstand-levelabovegroundnetprimaryproduc-
4 Discussion
tivity (5AN) against observed stand-level aboveground biomass
growth(1BABG)ofsurvivingtrees,atthe40PMplots.Theline
We report here on the core components of an individual- illustratesaSMAregressionofα=0.92(0.72,...,1.18)andR2=
basedmodelthathasbeendevelopedinordertohelpbetter 0.42.Reddotsindicatehighnutrientavailabilityandbluedotsindi-
understand the patterns revealed by recent integrated mea- catelownutrientavailabilityplots.
surementsofclimate,soils,functionaldiversityandstanddy-
namicsforawiderangeofforestsacrosstheAmazonbasin.
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1260 N.M.Fyllasetal.:AnalysingAmazonianforestproductivityusinganewindividualandtrait-basedmodel
Table1.Kendallcorrelationcoefficients(τ)andassociatedsignificancelevels(p)betweensimulatedgrossprimaryproductivity(5G),net
primaryproductivity(5N),carbonuseefficiency(CU)andkeyenvironmentalfactors.Significantassociationsareindicatedwithbold.
5G 5N CU
(kgCm−2yr−1) (kgCm−2yr−1) (–)
MeanAnnualTemperature–Ta(◦C) τ =−0.17 τ =−0.21 τ =−0.11
p=0.131 p=0.065 p=0.33
AnnualPrecipitationPa(mm) τ=0.54 τ=0.60 τ=0.36
p<0.001 p<0.001 p=0.002
Soilnutrientavailability81(PCAAxis1) τ =0.48 τ =0.50 τ =0.39
p<0.001 p<0.001 p<0.001
Figure 6. Summary of the randomisation exercise simulations. (a) Simulated stand-level aboveground net primary productivity (5AN)
againstobservedstand-levelabovegroundbiomassgrowth(1BABG)forthefourdifferentsetups.TheslopeoftheSMA(a)andtheadjusted
R2aregiveninparenthesesforeachsetup.Differentcoloursindicatedifferentsetups.(b)SimulatedAmazon-wideabovegroundnetprimary
productivity(5AN)forthefourdifferentsetups.
In its current setup the model does not explicitly simulate entirerangeobservedinthe40PMplots.Theassociationof
regenerationandmortalitydynamicsbutratherusestheob- 5 with the nutrient availability axis is in agreement with
G
servedsizedistributionoftreesatthestudysites,thustaking fertilisation experiments showing an increase with nutrient
into account stand structure and functional trait variability supply(Giardinaetal.,2003).Inourbasin-wideexamination
asobservedalongthemainclimaticandedaphicaxesofthe of5 thesoilnutrientavailabilityandstandstructuregradi-
G
Amazon basin. With the current setup we were able to re- entsarenot,however,independent(Quesadaetal.,2012),as
produce the tree- and stand-level 5 patterns found across in the RAINFOR network permanent plots it has been ob-
N
Amazonia and to explore for potential environmental con- served that bigger/older trees are more abundant on eastern
trolsoverstand-level5 ,5 andC . infertile forests, where soil physical conditions can support
G N U
a bigger tree size (Baker et al., 2009) with a lower risk of
4.1 Scientificoutcomes trees being uprooted (Chao et al., 2009). Bigger trees gen-
erally support a greater foliage area and thus could signif-
icantly contribute to the overall carbon assimilation of the
Our simulations found no association of stand-level gross
stand. However, bigger trees on infertile plots are generally
primaryproductivity(5 )withtemperature,probablydueto
G
characterisedbylowerleafnutrientconcentrations(Fyllaset
therelativelysmallrangeofvariationoftemperatureacross
al., 2009) and thus slower assimilation rates (Reich et al.,
our plots. 5 decreased until an annual temperature of ap-
G
proximately26◦Cbutremainedrelativeconstantabovethis 1994; Domingues et al., 2010). On the other hand a higher
abundance of smaller trees with higher gas exchange rates
point (Table 1, Fig. S2.2 in the Supplement). However, our
is observed on more dynamic, fertile plots. Ultimately this
simulationssuggestthatastrongassociationof5 withthe
G
indicates that stand structure should be specifically taken
annualprecipitationandsoilnutrientavailabilityoftheplots.
into account when simulating 5 in tropical forests, and
5 was positively related to annual precipitation over the G
G
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