Deriving Plant Functional Types for Amazonian forests for use in vegetation dynamics models

14 Pages·2011·1.44 MB·English

by  Nikolaos M. Fyllas

PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110

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Perspectives in Plant Ecology, Evolution and Systematics

journal homepage: www.elsevier.de/ppees

Researcharticle

Deriving Plant Functional Types for Amazonian forests for use in vegetation

dynamics models

NikolaosM.Fyllasa,∗,CarlosA.Quesadaa,b,JonLloyda,c

aEcologyandGlobalChange,SchoolofGeography,UniversityofLeeds,WoodhouseLane,LS29JTLeeds,UK
bInstitito Nac ionald ePesquis asdaA m azônia,Man aus,Brazil
cSchoolo fEarthan dE nvironme nta lSciences, JamesCoo kUniversity,Cairns,Australia

a r t i c l e i n f o a b s t r a c t

Articlehistory: Recentadvancesinourunderstandingofthelinkagesbetweenplantphysiologicalandmorphological
Receiv ed15February2011 traitss uggestan ew me ansbywhich to defi nePlant Functiona lTyp es(˚)foruse inc onceptualand
AReccceepivteedd i2n Nr eovviesmedb feor r2m0 111 November 2011 math ematical m odel s of veg eta tion dy na mics. In this study we u sed da ta fr om the RA INFOR-netw ork
database,aimingtonumericallyderive˚fortropicalforesttreesbyjointlyanalysinganAmazon-wide

datasetof(409)speciesabundance,speciesfunctionaltraits(10)andsiteedaphicandclimaticconditions
Keywords: across5 3 plots. Wefollo wedastepw isepro cedureofn umer ical˚ de finit ionwith incr easingc omplexity,
PlantFunctionalTypes
startingfromasimplePCAonspeciesfunctionaltraits.Wesubsequentlyappliedathree-table(RLQ)
RLQanalysis
multivariateordinationmethodintwoways:withandwithoutspatialautocorrelationbetweenplots
Amazonia
FTuronpcitcioaln faolrtersatists obueitn.Tgh tuakseonu rinrteos ualctcsoluinnkt.s Ipne aclile csa-sspese cthifiec e“ninvhireornemnte”ntrtaali tcvoanltureibsuwtiiothn atsos torcaiiatt veadrsiapteiocine shaadbu bnedeann pcaerstiaallolnedg
Vegetation dynamics envi ronm ent algradi ents .Ourfinalclassi ficationof Ama zonian tree speciesbas edonfo liardryleaf mass

(DDyGnVamMisc) global vegetation models dpeenr sairteya ( ((cid:3)MX)A a),n ldea mf caoxnimceunmtr attrieoen hs eoifg Ch,t N(H, Pm,a xC)a y, Kie,l dMegd, cfoaurbr odnis icsroettoep ˚ic. dTihscersiem ˚in wateioren f(o(cid:2)u)n, db rtaon rcehp rxeysleenmt
distinctlife-historystrategiesandcanbealignedwithpreviousempiricaldefinitionsoftropicaltree

guilds.Inparticular,twoecologicaldimensionsareidentified:(1)aleafdeploymentdimensionwhich

co-varieswithsoilfertilityand(2)astemdeploymentdimensionwhichco-varieswithsoiltexture.By

analysingdiametergrowthratesofthesametreesusedtodefinethefour˚wefoundeach˚tohavea

differentoverallgrowthpattern.Furthermore,fromaBasin-wideforestsurvey,differencesintherelative

abundanceofthefour˚wererelatedtostandlevelbasalareagrowthand/orturnoverratevariations.

Thesenewderived˚shouldenhanceourabilitytobetterunderstandandmodelthedynamicsofthe

Amazonforest,withthegeneralprocedureforplantfunctionaltraitdefinitiondescribedherepotentially

applicabletomanyotherecosystems.

© 2011 Elsevier GmbH. All rights reserved.
Introduction Rietz,1931;Raunkiær,1934),butitisonlyinthelasttwodecades

that the categorisation of plants as “Plant Functional Types” (˚)

Theclassificationofplantspeciesintodifferentphysiognomic hasassumedapivotalroleinecologicalresearch(Smithetal.,1997;

formsgoesallthewaybacktoTheophrastus(370–285BC)who, LavorelandGarnier,2002;Lavoreletal.,2007)havingplayedacen-

writingthefirstscientificbookabouthigherplants,HistoriaPlan- tralroleinvegetationandearth-systemmodellingsincesomeof

tarum, divided them into tree, shrub, under-shrub and herb. theearliestconceptions(Prenticeetal.,1992;Box,1996;Prentice

Theophrastusalsosubdividedthesecategoriesthroughtheuseof et al., 2007). There is, however, now a clear need to increase ˚

geneticallydependentcharactersofsystematicvaluesuchasleaf diversity or abandon their inelastic description to provide bet-

form,longevity,theannualversusperennialhabitandmanymore ter representations of ecosystem responses to both climatic and

(Morton, 1981; Pavord, 2009). More sophisticated classification disturbance gradients (Lavorel et al., 2007) as befits our rapidly

schemesemergedintheearly20thcentury(Warming,1909;Du increasingunderstandingoftheimportanceoftraitdiversityand

plasticity;withintheonevegetationtype–orevenwithinindi-

vidualspecies(Fyllasetal.,2009;Albertetal.,2010;Baralotoetal.,

∗ 2010;HulshofandSwenson,2010;Messieretal.,2010;Kattgeetal.,
Correspondingauthor.
E-mailaddress: [email protected](N.M.Fyllas). 2011).

1433-8319/$–seefrontmatter© 2011 Elsevier GmbH. All rights reserved.

doi:10.1016/j.ppees.2011.11.001
98 N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110

The concept of expressing the wealth of plant taxa with a chemical defence should also be related to this ecological axis

finitenumberofgroupsalreadyhasalonghistoryintropicalfor- withspeciesthatfollowaconservativelifestyleinvestingresources

est research with different tree species categorised into “guilds” (C,tannins,phenolics)forbetterdefenceoftheirphotosynthetic

in terms of their shade tolerance (gap dependent or indepen- tissue (Specht and Rundel, 1990; Fine et al., 2004; Read et al.,

dent) and/or pattern of resource allocation (growth vs. survival 2008).

trade-off); such classifications generally being based on empir- Focusing on the second ecological dimension of Hmax, taller

ical information of a species performance along temporal and plantshavetheadvantageofpotentiallyharvestingmorelightdur-

spatial gradients (Denslow, 1987; Swaine and Whitmore, 1988; ingtheirlifetimecomparedwithshorterones(Poorteretal.,2005)

Turner, 2001). Other studies have attempted to quantitatively and also to reduce light availability of their smaller competitors

define ˚ for tropical ecosystems using sets of morphologi- below(Poorteretal.,2008).Howeversmallerstaturedspeciestend

cal, demographic and/or growth data (Lieberman et al., 1985; tobemoreshadetolerantandpresenthighersurvivalratesunder

Vanclay, 1991; Condit et al., 1996; Nascimento et al., 2005). lowlightavailability(Poorter,2007).

In general ˚ definitions used in local/regional scale vegetation Intermsofthethirddimensionconsideredhere,wewereinter-

models (Huth and Ditzer, 2000; Kohler et al., 2000; Phillips estedtoseeifbranchxylemdensity,(cid:3) (Patin˜oetal.,2009),and
X

et al., 2002) include more “guilds” compared to global scale carbon isotope discrimination, (cid:2), integrate into a whole-plant

ones, but usually lack the generality required for larger scale hydraulics/water transport/water use efficiency spectrum, this
resea rch o bjective s (Pi card and Franc , 2003). F urth ermor e, the potentially aiding species classifi catio n. Photosy nthetic 13C /12C

way key processes are simulated in stand-level tropical forest discriminationisanindicatoroftherelativemagnitudesofpho-

models are inevitably empirical because the appropriate val- tosyntheticcapacityversusstomatalconductance(Farquharetal.,

ues for the process-based drivers are unknown. For example, 1989) with its genetic variation within tropical trees correlating

carbon gain is often estimated through a simple light satu- negatively with a range of physiological and structural charac-

ration type curve (Chave, 1999; Huth and Ditzer, 2000; Sato, teristics (Fyllas et al., 2009; Lloyd et al., 2010; Patin˜o et al.,

2009), or an empirical optimum growth curve (Gourlet-Fleury 2011).

and Houllier, 2000; Phillips et al., 2003). On the other hand, Existing dynamic global vegetation models (DGVMs) have

despite usually defining their ˚ in a way little more advanced alreadystartedtoincorporatesuchtraitlinkages.Forexamplein
than Th eophras tus, mos t glob al veg et ation mod els are of a rel- the“LPJ ”model ,Si tchetal.(20 03)u sedth eM −L rel ationship of
A L

atively sophisticated process based design, for example usually Reichetal.(1997)tocharacterisedeciduousversusevergreen˚.

usingthedetailedFarquharetal.(1980)photosynthesismodelwith Moorcroftetal.(2001)usedequationsfromtheleafeconomicspec-

therelevantparametersgenerallydependentonsimulatedfoliar trum,toderiveM andleaf[N]fromleaflifespan,alsospecifying
A

physiologicalpropertiessuchasnitrogenand/orleafmassperunit afunctionalrelationshipbetweenLL,andHmaxandwooddensity

area. ((cid:3) )fortheirfourdefined˚.Itshouldbenotedthattheaccuracyof
W

Ultimately, the way that tropical ˚ are defined will depend theseparameterisationscouldaffectthereliabilityofsimulations

on the research objectives and the ecological processes that are (Wrightetal.,2005),andthus“optimum”functionaldescriptions

required to be represented in the conceptual framework used. shouldbepursued.Treediversityintropicalforestsremains,how-

Nevertheless,withtheidentificationof“majoraxesofplantspe- ever, poorly represented: For example by one broadleaved ˚ in

cialization” (Westoby et al., 2002; Diaz et al., 2004) and with TRIFFID(Coxetal.,2000)andtwobroadleavedgroupsinLPJ(Sitch

datasetsofageographiccoveragesufficienttocapturetheenvi- etal.,2003).Refiningthewayfunctionaldiversityisrepresented

ronmental heterogeneity of tropical forest now available (Fyllas especiallyinthehighlysignificanttropicalforestecosystemmay

et al., 2009; Quesada et al., 2010) it should be possible to come beofcriticalimportanceinimprovingmodelpredictionsoffuture

up with ˚ definitions applicable for both local and global scale vegetationchange,especiallyinrelationtopotentialincreasesin

modelling schemes and here we present one procedure to do boththefrequencyandintensitiesofsoilwaterdeficitsincurrently

this. moisttropicalregions(Ostleetal.,2009).

Our approach is based on the notion that integrated dimen- With the implementation of a “continuum” approach of trait

sionsofplantphysiologicalvariationcanbeconsideredtoreflect variation within DGVMs still under development (Scheiter and

adaptations to environmental conditions (Westoby and Wright, Higgins, 2009) the development of new numerical ˚ definition

2006), but that for any set of environmental conditions there proceduresshouldconstituteanimportantstepforwardinglobal

is no one combination of traits that necessarily constitute the vegetation modelling. Here we derive plant functional groups

only viable approach. The study here relates to three integrated (LavorelandGarnier,2002)basedontraitsthataredirectlyrelated

dimensions, namely the leaf mass per area (M ) – leaf lifes- tophysiologicalprocesses,focussingonthewaygroupsofspecies
A
pan(M −L )continu um; thet rade-o ffbe twee nmax im umh eight (d efined by a sp ecific set o f function al trai ts) o rdinate alo ng the
A L

(Hmax) and shade tolerance and the interplay between xylem environmental gradients which exist within the Amazon Basin.
hydrau lics, woodd ensityand leaf size .TheM − L dimen sionor The basic idea was to id entify t he tra it grou ping s (with s pecies
A L

“leafeconomicspectrum”representsacontinuumofcarbonand taken as the inherent source of variability) that were best asso-

nutrientinvestmentstrategyattheleaflevel(Reichetal.,1997). ciated with the observed differences in species abundances as

At the fast return end, species are generally characterised by a affectedbyvariationsinsoilsandclimate.Differencesinthefunc-

short L , low M and high nutrient concentrations (dry weight tional characteristics of the derived ˚ are then considered in
L A

basis).Suchplantsexhibithighergasexchangerates(Reichetal., relation to, and as possible extensions of, previously published

1994;Raaimakersetal.,1995;Dominguesetal.,2010)andhigher classification schemes (Turner, 2001; Poorter et al., 2006). We

growth but lower survival rates (Reich et al., 1992; Poorter and also modelled the potential growth curve for each ˚ (Uriarte

Bongers, 2006) at least at the seedling/sapling stage; although a etal.,2004)inordertoexploretrait-associatedvariationsinlife-

recent study in mature tropical forests showed that M was an history strategies and ontogenetic growth changes, especially in
A

ambiguouspredictorofdiameterincrement(Poorteretal.,2008). relation to soil fertility. Finally, stand-level variations in the rel-

The other end of the spectrum, and characterised by a higher ativeabundancesofthefour˚,wererelatedtointegratedstand

M andlowernutrientconcentrations,arespeciesbeingconsid- levelpropertiessuchasmeanturnovertimeandbasalareagrowth
A

ered to follow a more conservative resource use strategy. Leaf rate.
N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110 99

Materialsandmethods witha=4.4‰,b=30‰anddtakenas0.2andisdiscussedfurther

inPatin˜oetal.(2011).

Weuseaneigenanalysisthatmaximisesthecovarianceofalin-

ear combination of the variables of an environmental table and Numericaltechniques

linearcombinationsofthevariablesofaspeciestraittableweighted

by a species abundance table (Ribera et al., 2001). This type of In a previous study we have shown that plot-level contri-

analysisiscalledRLQ(Dolédecetal.,1996),andithasbeenused butions to trait variation along a wide geographical gradient in

to link species traits with habitat characteristics (Ribera et al., Amazonia, are in most cases spatially patterned but with these

2001;Hausneretal.,2003;Choler,2005),tomatchforestdatasets patterns also predictable from a set of key soil and climate

fromdifferentspatialsamples(Drayetal.,2002),andtoexplore variables (Fyllas et al., 2009). Here, the plot-level contribu-

patterns of plant invasions based on a suite of functional traits tion to trait variation was excluded by using an estimate of

(Thuiller et al., 2006). Species scores on the RLQ axes are clus- the intrinsic value for each trait, given from the summation of

teredtoidentifyan“optimum”numberofgroups.ThederivedPlant intercept+family+genus+species terms of the multilevel mod-

FunctionalTypesaresubsequentlytestedagainstanindependent els(intercept+family/genus/species+plot)asfittedinFyllasetal.

pioneer index, tree-level diameter growth data and stand-level (2009) and Patin˜o et al. (2009). Thus, for the traits of interest, a
abovegr oundg rowthand turnoverr atesdat a. matrix (den otedQ+ )w as constru ctedw hic hth eoretic all yexclude s

thecontributionofclimaticandsoilpatternsontraitvariation.Vari-
able sinQ+were re tainedin alo g transform ed form at,apart from
10
Data
(cid:3) forwhichtherawdatawasalreadyapproximatelynormallydis-
X

tributed(Patin˜oetal.,2009).ItwasalsofoundthatrawHmaxwere
For the purpose of this study three data tables, viz. R (envi- similarly closeto n orm allydi st ribut ed(s eealso Pat in˜oe tal.,2011)

ronmental), L (link-species abundance) and Q (species traits)
andsosimilarlywithnotransformationappliedtothisestimate.

werecompiledusingdatafromtheRAINFORandForestPlots.net
WealsoassumedthatourHmaxestimates(fromBakeretal.,2009)
databases (Peacock et al., 2007; Lopez-Gonzalez et al., 2011).
reflectaspecies-specificheightpotentialandwithnopartialling

The R table combined climatic data (mean annual tempera-
outofenvironmentaleffectsbeingrequired.

ture[T ],totalannualprecipitation[P ]andprecipitationduring
A A TheRmatrixconsistsofenvironmentaldatain53RAINFORplots

the three driest months [P ]) from the Worldclim dataset
D distributedacrossAmazonia,overwhichbothclimatic(Sombroek,

(http://www.worldclim.org)withasetofsoilchemicalandphysi-
2000;MalhiandWright,2004)andsoilgradients(Sombroek,2001;

caldatafor0–0.3mdepth,collectedandanalysedasdescribedin
Quesada et al., 2011) have been documented. We attempted to

Quesadaetal.(2010).ThesoilvariablesofinterestwerepH,total
accountforspatialpatternsinthematrixofenvironmentalvari-

reservebases(˙ :agoodindicationoftheextentofsoilweath-
RB ables (R) by constructing a new matrix (R*), where the spatial

ering), total [C] and [N], total extractable P obtained through a
autocorrelation had been removed using PCNM (principal coor-

sequentialextractionmethod(Quesadaetal.,2010),exchangeable
dinates of neighbour matrices) analysis (Borcard and Legendre,

concentrationsofsoilCa,Mg,K,Na,Al,aswellassandandclayfrac-
2002;Drayetal.,2006).ThePCNMmethodinitiallycreatesaset

tions.Foreachsite,maximumsoilavailablewaterwasestimated ofspa tialex pl ana toryva riab les(ф) obtaine dthroug haprin ci pal

usingdataobtainedduringsoilsamplingandrootdensityobserva-
coordinateanalysisbasedonsitecoordinates,afterwhichanappro-

tion(inincreasingincrementsto4mdepth:Quesadaetal.,2011)
priateselectionmethodisusedtoreducethosespatialvariablesto

withlaboratorymeasuredsoiltexturesfrompitsamplesusedto asmal lersetwh ereeach ф cont rib utessig nifican tlytot heexplan a-

estimatesoilwaterholdingcapacityusingthetropicalsoilpedo-
tionoftheresponsedata(Borcardetal.,2004).Herewederivedthe

transferfunctionsofHodnettandTomasella(2002).Calculations fulls et of1 8фfrom thec oordinat es oft heRAIN FOR plo ts(pack age

weredoneforeachsampledlayerandthenintegratedacrossthe
spacemakeR)usingaforwardselectionmethod(packagepackfor)

observedrootingdepth.
toidentifythesmallersetsignificantlycontributingtotheexpla-

TheLtablequantifiedtheperplotspeciesabundance,usingas
nationoftheoriginalenvironmentalvariablesmatrix.Thereduced

anindicatortheestablishedbasalareaperhectare,in53permanent setcon ta ined eightф .Finallyaredun dancyana lysis(R DA) ofRwas

RAINFORplotsacrosstheAmazonianbasin(Lopez-Gonzalezetal., con ducted,us ingth ee ightsel ec tedфasthe covarian cemat rix w ith

2011).TheQtablecontainedasetofstructuralandfoliartraits.The
residuals of this RDA providing an estimate of the environmen-

structuraltraitsofinterestwerebranchxylemdensity,(cid:3)X(Patin˜o tal variab ilit y no t acc ounted for by the ф. It is thus denoted the

et al., 2009), and species-specific maximum height, Hmax (Baker spa ce-freeenv iron mentalvar iab ility (R* )m at rix .

et al., 2009; Patin˜o et al., 2011). Foliar traits (Fyllas et al., 2009)
Usingacompletesetof409fullyfunctionallydescribedspecies,

included:leafmassperarea(M )andleaflevel[N],[C],[P],[Ca],
A PlantFunctionalTypeswerederivedforAmazonianforestsusing
[K]and[M g]c oncen trat ions( dry-w eigh tba sis).F olia r13C /12C dis-
(1)theclusteringofspeciesscoresfollowingaprincipalcomponent
crim ina tion(cid:2) ,wasestimat edfrommea surem entsoffoliar(cid:2) 13C ana lysi s(usingm atr ixQ+,P CA),(2) anormal th ree-table ordination

(Fyllasetal.,2009;Lloydetal.,2010)usinganassumedvaluefor
(usingmatrixR,denotedasRLQ-n)and,(3)athree-tableordination
theiso top ic compo sition of sou rceai requa lto −8.0‰( Farqu har
wherespatialautocorrelationhasbeenremoved(usingmatrixR*,

etal.,1989),andsubsequentlytransformedtoastomatallimitation
denotedasRLQ-s).Thestepwiseprocedurefollowedforthelatter

index,ø,accordingto:
twocasesisillustratedinFig.1.TheRLQprocedureweusedisa

(cid:2)
formofco-inertialanalysis(Drayetal.,2003)andrelatesspecies
∅=1− ((cid:2) − a)/(b − a) − d, (1) traits to the enviro nmental condit ion s w here t he sp ecies a re most
[ci/ca]max abundant. This is achieved through the joint analysis of the R (envi-
ronmental),L(link–speciesperformance)andQ(speciestraits)

which utilises the well-known relationship between (cid:2) and the tablesasdetailedelsewhere(Dolédecetal.,1996).Astepwisepro-

ratio of intercellular to ambient CO2concentrations, ci/ca(Farquhar cedure is usually followed when implementing any RLQ analysis.
etal.,1989).Eq.(1)assumesthatatcurrentdayca,photosynthesis ThisconsistsfirstofseparateordinationsoftheR,LandQtables,and

canbeconsideredaroughlylinearfunctionofc andwithamaxi- thenasimultaneousordinationofthesethreetables(Riberaetal.,
i
mum practical ci/ca(indicating minimal stomatal limitation) of 0.8, 2001). In our case we first conducted a correspondence analysis
100 N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110

Table1
Raw Traits Data Raw Environmental Data Correl ationbetweenthevariablesandthefirstthreeaxesofthePCAonthegenetic
Q table R table traitsmatri xQ*.The high estcorre latio ns( >0.60 )are indic ate dw ithb old fac e.

Eigenvalue 3.060 1.848 1.273

Proportionofvariance 0.306 0.185 0.127
(Fmyulllatsil eevt eall .m , 2o0d0 e9l ) smpaatpiapl ineigg,e PnC veNctMorss M[CA] −−00..556235 −00..352403 00..500429
(Borcard et al. [N] 0.452 −0.744 -0.089
2004) [P] 0.612 −0.642 0.027

[Ca] 0.719 0.404 0.176

[K] 0.802 0.134 0.029

Genetic Component RDA residuals [Mg] 0.740 0.397 0.050
(inter ce pt + gen etic term) (R x PCNMs) (cid:3)IX −00..232304 −00..131551 −00..266410
Hmax −0.151 −0.095 0.671
Q* R*
Species
R toidentifypotentialdifferencesintraitinterrelationshipsamong
perLfo tramblaen ce L RLQ a nalysis th e derived ˚ (pack age smatr). Fo r th e same trees from which

trait data had been obtained for the analysis above, diameter at

breastheight(D)incrementbetween1997and2001wasestimated

K-mea ns clu stering fromrepeatedcensusmeasurements(Bakeretal.,2009)andwith
of species scores anav eragedia meterg rowthrate,dD /dt,sub se que ntlyca lcul ated.

Witheachindividualalreadyassignedtooneofthederived˚,and

assumingthatpotentialdiametergrowth(g)ofeachtreeisaunique
Ф identifi cation functiono ftre esizeand ˚,theob served gro w thra tefo re a chtree

was then modelled using a log-normal function as proposed by

Zeide(1993);seealso(Uriarteetal.,2004)viz.
Fig.1. Schematicrepresentationoftheimplementednumericaltechnique.Forthe
sripgaht ti asl iaduet oofc othrree fila gtuiorne ,(wR htialbelteh pe abtrh owkea ny)l isnoelsidi nlidniecsa tie ntdhiceactaes tehwe hcearsee sopf arteiam loavu itnog- g = gme−0.5((log D/Do)/Db)2 (2)
correla tion is not taken into a cco unt (by passi ng the sp atia l eig envect ors and RDA wheregm isthemaximumpotentialgrowthrate(mma−1)atthe
procedures).
peakofthelog-normalgrowthcurve,D isthevalueofDassociated
0

withthemaximumgrowthrate,andD determinesthebreadth
b
(CA)ofthespeciesabundance(L)table,providingthemaximalcor- ofthefunction.Forthisanalysisallindividualsexhibitinggrowth
relat ion be tweens itesandspe cie sscor es.Thoser esu ltsweret hen >− 5m mand<5 0m mpe ryearw ere used.
compare dwithth eRLQ ord ination, whileth espec iesand sitesc ores F inall y,Ke nda ll’s(cid:4) (a robu stnon parametricmeasureofasso-
oftheCAse rved asl inks betweenth eRan dQ+ tables( Hau sne retal., ciation)wa susedto ex pl orethe relationshipbe tweensta nd level
20 03) .Th eRand Q +mat riceswer ean a lyse dwithaP CAonthe c or- ˚abund ance (exp re ssedast hep roportionof stemsfor each˚ per
relatio n ta bl e wi th row weig hts f rom the r espe ct ive C A site and pl ot)andtwo keystandle ve lva riables,viz. ab ovegro und basa la rea
species scores .Allm ulti variatea nalys esw ereimplem en tedw ith grow thra te( (cid:2)B) andtu rnov errate(ϕ ).Th eformer(m2 ha−1 a−1)
theade 4packa gea vailablewith inR(Dra yetal .,2007). isthesu mo fthe incre mentsin cros s-se ction alstem areafromall
P lant functiona lgroups( ˚)wer e define du sin gaK-meansclus- tr eesi neac hp lot ,measureda tb reastheight(1.3 m)o r0.5 mabo ve
teringal gorithm(p ackage clus ter).I nthePC Acase , speciess cores buttr ess esif prese nt.Turnov er ratesw erecal cula ted as the av erage
on the first three orthogo nal axes w ere used , whi le in bo th RLQ of stem re cr uitment and mor tality rates . All analy ses we re per-
cas es o nly t he fir st two axes were used . The best n um ber o f ˚ fo rmed withtheRsta tisti calplatform (RD eve lopmentC oreT eam,
ineac hcas ew asin form edby consi dering the Calin ski–Har aba sz 2010)e mplo ying t hepackag esmentio ne dabove.

pseudo-Findex(packagevegan)andtheneedtopursuerelatively

simpleandusableinterpretationsofourresults.InbothRLQ-based Results

classificationsthefirstordinationaxiscontainedmostoftheinfor-

mation with a relatively small species regrouping taking place Defining˚forAmazoniantreespecies

whenconsideringthesecondaxis.Therewas,however,anappre-

ciable effect of spatial autocorrelation in the allocation of some PrincipalcomponentsanalysisoftheQ*matrix(PCA)
specie stoth ev arious˚ .Wethusonlyr eta ined forthefin ala naly- The fi rst three P CA axes o f th e functio nal trait matrix Q+

sisthosespecieswhichpartitionedintothesamegroupsforboth accountedfor0.62ofthetotalvariance(Table1).Inbrief,thefirst

theRLQ-nandRLQ-sclassification. axiswaspositivelyrelatedtoleafcationconcentrations(Ca,Kand

Asanadditionalmetricforexploringour˚definitionsweused Mg)andnegativelytoM andleaf[C].Thesecondaxiswasstrongly
A
thein dep endentpi oneeri nde x(П)ofP hilli ps ,Vasquez-M ar tınez nega tive lyassociat ed withfo liar [N] and [P],this bei ngsi milarto

andMonteagudo(Malhadoetal.,2009)torankgeneraaccording the“leafeconomicspectrum”ofWrightetal.(2004).Thethirdaxis

totheextenttowhichtheyareperceivedtobepioneers.Tocalcu- was mainly associated with Hmax, the stomatal limitation index

latetheindexeachtaxonhadbeenjudgedindependentlybyeach (ø)andM .Thesefirstthreeaxesareverysimilarinstructureand
A

botanistandgivenavaluebetweenzero(“non-pioneer”)andone weightingstothoseidentifiedthroughacommonprincipalcompo-
(“pionee r”).A mean s pecies Пcloset ozer oindicatesanon -pion eer nentsanaly sis onas ubsetofth esame d atamatrix (fewerp lotsbut
specieswhi le amea nПclo se rto1 in dicat esaspec ie swithmore witha dditiona lst ru cturalt ra its) byPat in˜oe tal.(20 11)and towh ich

“pioneer”typecharacteristics. thereaderisreferredforanextensivediscussionofthevarioustrait

Differencesbetween˚meantraitvalueswereexploredwith contributionsaboveaxesofvariationandtheirsignificance.

analysesofvariancefollowedbyaTukeyposthoctest.Additionally Using these first three PCA axes, application of the

standardisedmajoraxis(SMA)regressionswereappliedinorder Calinski–Harabasz pseudo-F index identified an optimum of
N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110 101

characteristics,RLQ2wasalsostronglyrelatedtoHmaxsuggesting

thatpotentiallytallerspeciesaremorelikelytooccurinthewarmer

and/ormoisterareasoftheBasin.

TheCalinskipseudo-Fpeakedequivocallyatbothfourandeight

groups for the RLQ-n case (Fig. 2) and consistent with the RLQ-s

describedlater(andforwhichasixgroupcaseismoreextensively

considered)weinvestigatehereonlythelessercaseoffour˚.This

divisiongivesour˚ asrelativelysmallstaturedtreespecieswitha
1

potentialforhighphotosyntheticrates(lowestM –highleafnutri-
A

ents),thesealsotendingtobefoundonfertilerelativelydrysites

(FigSI2.1)with˚ thetaller,fastlifestyle“equivalent”of˚ mainly
3 1

abundantinmoisterlocations.Attheotherendofthespectrum˚
4

groupstogetherwithtallertreespecieswithlowphotosynthetic

rates(lowestM –lowleafnutrients)thesewithatendencytobe
A

foundonrelativelyinfertileandwetsites;with˚ beingthecon-
2

servativecounterpartof˚ viz.smallerstaturedspeciesofalow
1

photosyntheticpotential.Theytendtobefoundoninfertileplots

inrelativelydryareas.

“SpatialRLQ”ordinationoftheR*,L,Q+matrices(RLQ-s)

AdetailedpresentationoftheRLQ-sbasedclassificationcanbe

foundinSI3withdifferencestotheRLQ-ncaseabovearisingfrom

thecontributionofthe“space-free”R*matrixtotheordinationof
Fig.2. Calisnky–HarabaszpseudoFforanumberofclustersrangingfrom2to10 the R*, L, Q+ trip let . Th is separate o rd ination of tab le R* yield ed
usin gt hespeciesscoresof thePCA ,R LQ n andRLQ so rdination . abe tte rd ifferentiati ono ftheclim aticandth ee daphi cv ariables

thanforR,withthefirstPCAaxisreflectingasoilfertilitygradi-

onlytwo˚(Fig.2).Thefirstgroupbringstogetherspecieswith entmainlycontrolledbythesoilchemicalproperties,thesecond
pote ntiall yh igh gas exch ange rates (asind icatedby lowM and PCA axisrefl ectingapr eci pita tion gradient, andthethir dax issum-
highleaf[N ]and [P]: Domingue setal .,20 10)withl ow inve stmAents mar ising a soil tex tu re gradient. The simu ltan eou s ord inati on of
inca rbon ric hco mp ounds,viz. al ow leaf[C ],a low (cid:3) andhigh tablesR*, L ,Q+a gainind icatedas ignifi cantlinkbetw eenenviro n-
lea f cation con centrations ( Fig. S 1.1). All of th e se ch arXacter istics menta lva ri ablesand functiona l traits,with the firsttwo RLQaxes
may becon sideredindicati veof specie sw ith anear lysuccessional again a ccounting for 0.89 and 0 .05 of the tota l var ianc e res pec-
(fast gr owth)strate gy(Turne r, 2001)a ndth es econd groupthus tively (TableSI3.3 ).As forth e“n on-sp at ial”c ase,t hefirstax is,RLQ1,
have the gen erally op posing c haract erist ics o f late s uccess ional wasp ositive lyasso cia ted wit hsoilpHande xcha nge able [Ca] ,[Mg]
type spec ies.Consis tentwith thisnotion,the me anp ioneerindex and [K]andtot alextracta blep hop sho rus, indicatingaso ilfer tility
(П)o fthefir stgroup±s tand arde rror(S. E.)w as0. 18±0.02 with grad ien t (Ta ble S I3.4). The im portance of climatic v a riab les was,
П= 0.1 1± 0.01 forthe se condgro up. however ,notm aintain edin RLQ2ascom pa redwith theRLQ-n case.
Here,ase con dtexturegr ad ientrun ningfrom sand ys oilsof high

“Normal”RLQordinationoftheR,L,Q*matrices(RLQ-n) plantavailablephosphorusand[C]toclaysoilsofhighexchange-
A deta iled presentat ion of th e RL Q-n base d classification can able[ K]emerge dasthemaj oren vir on men talco rre lateo fthisaxis.
befo undinSI 2andresults are sum maris edonly brieflyhere. The Inte rms ofspecie str aits ,RLQ1 wasagainrelate dwithle afn utr ients
firs tfive axe so fthe CAon spe ciesabundan ceac counte dfor 0.27 an dcatio n sconcen tratio nswithh igher elemen talco nce ntrations
ofth eto talva ria nce ,wi tha maxim alcorrelati onof0.82b etw een foun d on ri cher soils (Tabl e SI3. 5) and RLQ2 relat ed mainly to a
sit es a nd sp ecies sco res. T h e PCA of the R table id entifi ed three maxim um heigh t–xy lemde nsityd imen sion.
envir onm entalgr adients; the firstr ela ted w ithsoi lfertilityan dthe Againu singth eC alinsk ipseudo -Ftohelpidentifytheoptimum
secondandthi rdrelated wit hclim atica ndso ilte xturalp aram e- number o f clus ters (Fig. 2) we chos e t o sel ect only fou r groups.
ters(Ta bleS I2.1). Similar tothe simple PCA ofTa ble1,th eQ+-PCA Thiswas ev enthoug hasl igh tly higher Fv aluew asobt ained forthe
(we ighted here w ith CA sp ecie s score s) yi eld ed th ree ec ological more com plex caseof s ix˚and forwh i chthe inte restedre ade ris
dimension s, aga in re late d with l eaf cons truction , leaf economics referr edtoade scrip tio na nd dis cuss ionin SI6.
and plant w ater u se stra tegies (Ta ble SI2.2). Th e sim ultaneous Each of t he four ˚ i n th e final RLQ -n analysis had a similar
ordi nation of the R, L , Q+ triplet then i ndicate d a s ignificant link species com po sition as co mp ared to the RLQ-n ca se, s om ething
between e nvi ronm e nt al variable s and functiona l traits, with the thatwa sexpectedas m ostofthev ari ation wase xplain edinboth
firsttwo RLQaxesaccou ntingfor 0.89 and0.05o fthet otalv ari- case sby thefirstRL Q axis.T hu s,˚ 1againyi elded asmallH max and
ganracde i reenstp oefcstoiv ilefleyr (t Tilaitbylew SiIt2h.3s)o. i Tlh˙e fi,rtsot tRaLlQe xatxriasc (tRaLb Qle1)p, hsousgpghe ostrsu as ablesion gw mitho stthlye floouw nedsti n MfAeratn ilde ehnigv hireosnt mleeanf t[sNw] aitnhdh [iP g]h osfo ainl pyH g,rtooutpa;l
andexch an gea bleCa,M gan dK allRpBo sitive lyrelated( TableSI2.4). extrac tableP, andex ch angea blecationscon centr ation s(F ig.S I3.1).
Tdhriee rsescitoensd( daexfiisn, eR dLQin2 , treer flmesct oedf iann c oemnvinirgonpmr eecniptiatla tg iroand)iebnu tt walistho Tabhue nlodwan fteor tnils itayn cd oyusnotielsr.pOarftt hoef ˚ fo1uwrgarso augpasinid ˚en2twifiheidc,h ˚ w3afso lmloowres
corre spond ingwith so ilswhi ch haveahig hsandconten tan dan theleastc on servati vestr ate gyp rese ntingt hegroup-w idelowest
associatedlow erwa terho ldingc apaci ty .Ofno te,Ta bleSI2.5 sho ws xyle mde nsity((cid:3)X)wit hhigher leafcation conc entrationsa ndalso
thatRLQ a lsoag gregat edthel eafconstr uc tiona ndlea feco nomic charac terised bya high ø.This grou pwas alsomoreabun dan ton
dime nsio1nsfo undintheQ +-P CA( Table1).This sug gest sacorre- fertileclayso ils. Fi nally ,˚ 4 br ingsto geth erta lltree speciesw ith
spondenceo fspec ies dep endent leafph ysio logic alcharac te ristics lowle afnu trient concen trationsan dspecific leaf area ,againb eing
and soil nu tr ient sta tus along A maz onia. In term s of tree trait mos tlya bundant onrelativelyin ferti lesoils.
102 N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110

Fig. 3. Bivariate relationships for important trait pairs. Red squares for the genetic trait component of species classified at ˚1, green circles for ˚2species, blue triangles for
˚3species and cyan triangles for ˚4species. Lines illustrate the standardised major axis (SMA) regression. Please see SI5 for intercept and slope estimates of the SMA line
fits.Separatelinesindicatecaseswheresignificantdifferencesineithertheslopeortheinterceptbetween˚-specificSMAlineshavebeenidentified.Asinglelineindicates

caseswhereacommonlinecanbeusedforallgroups.(Forinterpretationofthereferencestocolorinthisfigurelegend,thereaderisreferredtothewebversionofthe

article.)
Anoptimum˚definitioncombiningtheRLQ-nandtheRLQ-s commonlypartitionedspeciestocharacteriseourfourPlantFunc-

basedclassifications tionalTypesandtoprovidethemeantraitvaluesforourbest˚

Theprimarystrategiesidentifiedbythe“non-spatial”andthe definition.ThesearesummarisedinTable2withthespecieslisted

“spatial” RLQ ordinations were similar, with most of the varia- by˚-classificationinTableS1.1.Thisshows˚ tohavethegroup-
1

tionexplainedthroughthefirstaxis,summarisingsimilarpatterns wide lowest M and high [N] and [P] suggesting a potential to
A

in soil fertility and species dependent leaf physiological prop- achievehighphotosyntheticrates.Both˚1and˚2hadalowHmax

erties. In the case of the species dependent properties, even with˚ havingthegroupwidehighestleaf[C]andhighest(cid:3) .It
2 X

though the second RLQ axis could be similarly related through thushassubstantialstructuralcarboninvestmentssuggestiveofa

variations in tree hydraulic architecture for both analyses, the conservativelifestyle.Bycontrast,˚ hasthelowestleaf[C]and
3

environmental correlates of this second axis varied. In the RLQ- (cid:3) andwithhigh[N]and[P]andcations,alsoyieldingthelowest
X

n case variation was mostly driven by annual and dry season ø.Finally˚ hadthegroup-widehighestM andlow[N]and[P]
4 A

precipitation,butfortheRLQ-scaseitwasdrivenmostlybysoil suggestingalowphotosyntheticpotential.Thus,inmanyways,˚
1

texture. Despite this difference, when comparing the two cases and˚ canbeconsideredthesmallerstatureequivalentsof˚ and
2 3

therewasasimilarrangeoftraitvaluesforeachgroupwith0.68of ˚ ,respectively.
4

thestudyspecieshavingthesameclassificationforboththeRLQ- Differencesinwithingrouptraitinterrelationshipswerethen

nandtheRLQ-scases.Wethususedthetraitvaluesofthese276 assessed with standardised major axis regression (Fig. 3 and
N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110 103

Table2

Meantraitintrinsicvaluesfortheoptimum˚definition.InallcasesANOVAsweresignificantfortheoveralldifferencebetweenthefourgroups.Differentlettersdenote

significancedifferenceinthemeantraitvaluebasedonaTukeyHSDposthoctest.

˚1 ˚2 ˚3 ˚4 Min. Max. F274,3 p
MA(g/m2) 85.4a 93.5b 95.9b 103.2c 67.9 140.1 38.8 <0.001
[C](mg/g) 471.4b 483.2c 462.8a 488.5c 398.7 521.2 25.4 <0.001

[N](mg/g) 22.89c 20.80b 21.34b 18.80a 14.91 28.31 40.1 <0.001

[P](mg/g) 1.02b 0.88a 1.01b 0.84a 0.58 1.40 50.8 <0.001

[Ca](mg/g) 6.42b 4.80a 7.25b 4.95a 2.66 13.43 48.6 <0.001

[K](mg/g) 6.33b 4.89a 6.96c 5.09a 2.78 10.05 69.0 <0.001

[Mg](mg/g) 2.69c 1.83a 2.89c 2.04b 1.29 4.31 77.6 <0.001
(cid:3)X(g /cm3) 0.611 b 0.642 c 0.582 a 0.618 b 0.47 9 0.79 4 16.9 <0.001
ø 0.178a 0.174a 0.187b 0.174a 0.144 0.219 15.1 <0.001

Hmax(m) 22.4a 20.5a 38.5b 35.9b 8.0 50.0 139.5 <0.001
TableSI5.1).Slightlydifferentscalingrelationshipsweredetected thehighestD,thehigherHmaxgroupingof˚3and˚4havehigher
for th e [N]↔ M sca ling with the in tercept of th e SMA line fit gro wthrate st han theirm oreverticallyc on straine dcounter parts.
A
bei ng s tatis tic ally differe nt for the [P]↔M , [ca tion s]↔M , a nd
A A

pairs.Specifically,althoughslopesweresimilarforallfourgroups, Functionalcompositionandstand-levelproperties

˚ had a noticeably higher intercept than the other groups for
3
both th e P↔MA and [cation s]↔MA l inkag es a nd wi th ˚2 h av- The relationship between the proportions of ˚ in each plot
ingt helo w es tinterce pt.Inter ms of[P]↔[N] ,˚3 again hadthe (stemb asis)withbo thabove grou ndbasalare ag row th ((cid:2)B) and
hig hest interce pt and ˚ 2 t he low es t an d with , as for [ P]↔ MA stem turnov er ra tes (ϕ ) is shown i n Fig. 5. S everal s ignifi cant
and [ca tions]↔M A, w ith ˚1 and ˚4 inte rmedi ate and te nd ing corre lations w ere ap pare nt . For ex am ple, alt hough t he relative
to fa ll on the sa me line, b ut with ˚4 shifted toward s hig her MA, abundance o f Ф1d id not sho w an y strong r elationshi p wi th (cid:2)B, it
lower[cations]andlower[P].Differencesbetweenthefourgroups waspositivelycorrelatedwithtostand-levelϕ.Ontheotherhand,
wapepraer me notr.eF aomr [b ciagtuioo nuss] f↔or [[CN]], ↔alt MhoAu, tghhous ogmh ewittrha dt he-eo ˚ff1i/s˚ a4psphairf-t hbuigthn eort Фϕ3.Tshteem p raobpuonrdtiaonnc oef wФa s sa tsesmocsiawteads nwe igtah t aivne ilnycr reelaasteed inw (cid:2)ithB
4
entfor˚1and˚3,norelationshipisevidentfor˚2and˚4with bothstand-levelproperties,withthisfunctionalgroupbeingmost

thesetwogroupslimitedtoadomainofrelativelylow[cations] abundantinstandswithlowstem(cid:2)Baswellaslowϕ.Different
andre lativ elyhigh [C].Les sw e lldefine d, butstillst atisti callysig- totheoth er threegr oupi ngs, there lativ ea bund an ceo fФ didnot
nific ant relatio nshi ps w ere obse rved for the MA −Hmax, MA− (cid:3)X sh ow anysig nifica ntrelation ship stoeith er(cid:2)Borϕ. 2
andthe [P]−(cid:3) pairs with nodiffere nces bet ween thev arious ˚
X

discernable. Discussion
Our ˚ classification does not allocate species randomly in

terms of the pioneer index estimates of Phillips and colleagues PlantFunctionalTypescanbeusedatarangeofscalesrang-
(Malh ado et al., 200 9). Th e potentia lly fast re sour ce capture ing from the stand level ( ∼1h a) to the p la net, w ith data-d riven
˚1 strate gy has a mea n (± S.E.) П of 0.17 ±0.02 co mpared ˚d efinit ions based onpl ant phy sio logic alands truct uralproper-
with the da taba se mean of 0.15 w ith the po tent ially taller

˚ (but otherwise with more or less the same trait charac-
3
teristics as ˚ ) ha ving the hi ghe st o vera ll П o f 0.2 5±0.03.
1
These v alu es co ntrast with П=0.05 ±0.01 for ˚ , wi th the
2

taller but similarly low [N] and [P] grouping of ˚ having
4
П=0. 10± 0.02.

Growthpatterns

UsingthemodelofEq.(2),˚-specificpotentialgrowthcurves

arepresentedinFig.4,withfittedparameterslistedinTableSI4.1.
Ove rall,Ф1 yi eld ed ah ighe rpote ntialgrowth gm≈ 5.4 mma −1 at
adiame terofDo≈2 7 cm,com paredto itstheo retica lly mor econ-
se rvative˚ 2 count erp art withagm ≈4 .4m ma−1atad iamet erof
Do≈34cm .SimilarlyФ3s howe d acon tinu ous lyincre a singgrow th
up to a siz e of Do≈ 36cm, ach ie ving a higher gm≈6.3m ma−1.
Thi s c om par es with the slo wer growi ng Ф w hich a chi eved its
4
gm≈ 4.3mma− 1atth ebi ggestsi zeofDo≈ 50cm.Thu sFig.4su g-

geststhat˚ and˚ ,bothofwhichhavetraitsthatareexpected
1 3

tobeassociatedwitharapidgrowthrate,indeedhaveagreater

gmthanthemoreconservativelyaligned˚2and˚4.Nevertheless,

soonafterthismaximumgrowthrateisreachedboththesegroups

showarapiddeclineingrowth,especiallytheshorter˚ .Thiscon-
1 Fig.4. PotentialdiametergrowthperФasinferredfromthenon-linearmodel
trasts the more conservatively aligned ˚2 and ˚4 who, although desc rib edinEq. (2)(seete xtforde tail s)and paramet erest ima tessummar isedin
showing less of an increase in growth rate, as D increases from
10cmto 30cm ,a lso showno sig nificant decli ne in gathigher D.At bTaebtwlee SeIn4.d1i.a Imndeitveirdautabl rleevaestl ahneniguhatl bgertowwetehn r2at0e0s1 easntidm1a9te9d7 acse nthsue smese.an difference
104 N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110

Fig.5. CorrelationsbetweenstandfunctionalcompositionasportionoftotalstemsperФandtwostandlevelproperties,abovegroundbasalareagrowth((cid:2)B)ontheleft

andturnoverrate(ϕ)ontheright.Kendall’s(cid:4)andsignificancelevelareprovidedwithineachfigure.

ties,asdevelopedhere,withapotentialtoyieldbettersimulations traits, and its relative abundance as influenced by edaphic and

oftherelevantvegetationprocesses.Inaddition,thecharacteris- environmentalvariables.TheRLQanalysisappliedheredoes,how-

ticvariabilityoftraitswithinanyone˚arealsoquantifiedbyour ever,identifysuchrelationshipsandfurthertesttheirsignificance

procedure,alongwiththerelevantcovariances.Thisshouldallow by the use of the linkage table L (Dolédec et al., 1996; Choler,

futurevegetationmodelstoincorporatevariabilitywithinagiven 2005);thisinourcasebeingabasin-widespecies/plotbasalarea
˚;con sistentwit htheco nce ptofatraits continua ashasr ec ently table. T he d er ived Ф t herefo re provides n ot only a cla ssifica tion

beenimplementedindynamicglobalvegetationmodels(Scheiter ofAmazonforesttreeswithsimilarcharacteristics,butalsobrings

andHiggins,2009). togethergroupsofspecieswithsimilarpatternsofabundancealong

environmental gradients. This has an advantage for many mod-

Theproposednumericalframework ellingapplications,assimulatingchangesinplantdistributionin

responsetospatialand/ortemporalvariabilityintheenvironment
Numericalclassificationframeworkshavepreviouslybeenused is often t he resear ch que stion to w hich Ф ar e bein g applied in

toidentifygroupsofspecieswithsimilarbehaviour(Lavoreland thefirstplace.Wealsonotethatourresultsindicatethatinher-

Garnier, 2002) at both the global and the regional scale (see for entdifferencesintreephysiologicalandstructuralcharacteristics

example,PillarandSosinski,2003;Diazetal.,2004).Ashasbeen arecurrentlyinteractingwithsoilsandclimatetoinfluencetree

donehere,mostoftheseschemeshavegenerallyinvolvedamul- species distributions across the Amazon Basin. This is because,

tivariate analysis of the functional traits of interest followed by aswellasbeingasimpleordinationtechnique,RLQanalysispro-

agroupingofspeciesbasedontheirtraitsscoresorperformance videsonemeanstosolvethe4th-cornerproblem(Legendreetal.,

alongenvironmentalanddisturbancegradients(Conditetal.,1996; 1997; Dray and Legendre, 2008) with the potential to answer

DiazandCabido,1997;Nascimentoetal.,2005).Nevertheless,with questionsdirectlyapplicabletonichetheoryviz.aquantification

onlyafewexceptionssuchasThuilleretal.(2006)andBernhardt- of the extent to which the biological characteristics of a species
Röm er man netal.(20 08),t he senume ric al Фdefin ition shavenot de term ines it s r elative loca tions in an ecosystem (L ege n dre and

explicitlyprovidedforadirectlinkbetweenataxon’sfunctional Legendre,1998).
N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110 105

EcologicaldimensionsandPlantFunctionalTypesforAmazonian precipitation.Althoughaslightlydifferentresult,thisagainhigh-

trees lights water availability dimension linked with a higher species

dependent maximum height, but with a lower xylem density.

Principal components ordination of the traits table (Table 1) Our analysis does not, however, include many plots in the for-

revealed three main axes of variation for Amazonian trees and est/savanna transition zone at the drier southern Amazon forest

usingasimilardatasetbutaslightlydifferentapproachtodifferen- periphery,manyofwhicharedominatedbyseasonallydeciduous

tiatebetweenspeciesassociatedwithlowversushighfertilitysoils, species not found in the contiguous forest region (Killeen et al.,

Patin˜oetal.(2011)obtainedsimilarresultswhichtheyalsodiscuss 2005)andwhosestructuralandphysiologicalcharacteristicsawait

insomedetail.Inbrief,thefirstaxisreflectsanunder-appreciated quantification.

dimension of the leaf economic spectrum (Poorter and de Jong, Evenso,asmostofthevariationinouranalysiswasfoundalong

1999),thisbeingacontinuumofstrategiesforleafconstruction, asoilfertilityaxis,ouroptimum˚identificationcombiningboth

ranging from the use of relatively cheap components (minerals) RLQ analyses should provide robust plant functional definitions

tomoreexpensivecarbonbasedconstituentssuchaslignin;the applicabletomuchoftheAmazonBasinwiththefour˚identified

latterstrategyalsobeingassociatedwithincreasedcarbonbased showingcontrastingpatternsintermsofresourceallocationand

defencesandotherfactorsassociatedwithlongleafdurabilitysuch lifehistorystrategy(Table2).Ingeneral˚ encompassesrelatively
1

asahighM (seealsoFyllasetal.,2009).Speciesatthepositiveend smallstaturedtreespecieswithapotentialforhighphotosynthetic
A

ofthisaxisprobablygambleoncheapandquickfoliagedevelop- rates(Reichetal.,1992),mainlybeingfoundonfertilesoilsand

ment,thisgivingthemadvantagesovershorttimeperiodsbutwith investingtheirresourcestowardsrapidgrowthratestoprovidethe

a high risk of large-scale foliar damage due to herbivory and/or bestpositioninadevelopingcanopyorgap.Thisrapidinvestment

otherformsofstructuraldamage.Theseconddimensionreflects return strategy is also suggested by the early peak in maximum

theclassicleafeconomicspectrumasidentifiedbyWrightetal. growthratesobservedforthisgroup(Fig.4).˚ illustratesanother
2

(2004); identifying a trade-off between M and leaf concentra- relativelysmall-staturedgroup,butherewithmid-rangeM ,low
A A

tionsofthetwobasicnutrientsdirectlyrelatedtophotosynthesis, leaf[N]and[P],italsobeingthegroupwiththehighest(cid:3) (Table2),
X

namelyNandP(FieldandMooney,1986;RaoandPessarakli,1997; withmemberspeciesapparentlyconfiguredformediumrategas

Dominguesetal.,2010).AsshownbyPatin˜oetal.(2011)thethird exchangebutwithastrongselectivepressuretoprotecttheirleaves

PCA axis may be related more to whole-plant hydraulics and/or byinvestinggreater(high[C])amountsofcarbonbasedcompo-

lightacquisition,withHmax,MA andøhavingstrongloadingson nents(Bryantetal.,1983;Fineetal.,2006).Wethereforeinterpret

this dimension and suggesting that taller trees are, on average, ˚ torepresentagroupingofspecieswitharelativelyconserva-
2

more constrained in terms of water use compared with smaller tivelifehistorystrategy,thisalsobeingreflectedinthegrowthrate

ones(NabeshimaandHiura,2004;Lloydetal.,2010;Patin˜oetal., curve(Fig.4).Specifically,˚ havearelativelyconstantpotential
2

2011).Lesssignificantecologicaldimensionsidentifiedbytheanal- growthratethatonlyoutperforms˚ afteradiameterofapprox-
1

ysisofourdatasetanddetailsofhowallphysiologicalandstructural imately 40cm. The ˚ illustrate the least conservative resource
3

traitsstudiedherevaryinasamplebivariatemannerareillustrated allocationstrategyintermsofbothfoliageandarchitecture,hav-

inFyllasetal.(2009)andPatin˜oetal.(2011). ingthelowestleaf[C]andlowest(cid:3) .Wethusinterpretthisgroup
X

In both the “spatial” and “non-spatial” RLQ analyses the first ascontainingspeciesthatgambleoncheapfoliagedevelopment,

twoleafdimensionsofthePCAwereaggregatedontothefirstRLQ using minerals of cheap construction cost (Poorter and de Jong,

axis; this then summarising most of the variation in the dataset 1999)andavoidinginvestmentintoexpensivecarbonrichcom-

(Table SI2.3 and 3.3). This RLQ axis aggregated the leaf devel- ponents for protecting their photosynthetic tissue (Coley et al.,
1

opmentandleafeconomicsdimension,discussedabove,aligning 1985).Itdiffersfrom˚ mostobviouslyintermsofbeingchar-
1

speciesalongabasinwidefertilitygradient(Quesadaetal.,2010) acterised by a greater Hmax. The acquisition of resources by this

runningfrominfertilesoilsdominatedbyspeciesofahighM and pioneergroupshouldbeallocatedonthebasisofarapidfoliarand
A

intrinsicallylownutrientconcentrationstofertilesoilswherethe stemexpansion,thisprovidingacompetitiveadvantageinterms

speciespresenttendtohavelowM andhigherintrinsicnutrient ofverticalpositioningforlightresourcesatleastintheshortterm.
A

concentrations. Thus, inherently higher nutrient leaf concentra- Thisstrategyseemstoagreewiththehighestpotentialdiameter

tions (accompanied with lower M and [C]) are characteristic of growthratesachievedby˚ comparedtotheotherthreegroups.
A 3

specieswhicharemoreabundantonthemorefertilesoilscharac- Finally,˚ withthehighestgeneticallyconstrainedM inaddition
4 A

terisedbyahighpH,highplantphosphorusavailabilityandhigh tolowleaf[N]and[P],mightbeexpectedtoachievethelowestgas

exchangeable cations concentrations. This is consistent with the exchangerates(PoorteranddeJong,1999).Thislongtermresource

results of Fyllas et al. (2009) who found that species located on turnoverstrategyclearlyyieldsamaximumgrowthatrelatively

morefertilesoilstendtohaveinherentlyhighernutrientconcen- biggersizeclasses(Fig.4).

trationsandalowerM thanthosegenerallyfoundonlessfertile Althoughnotspecificallytakenintoaccountinthisanalysisa
A

soils,asindicatedbyashiftalongthesameaxesofseveralsimple set of additional characters should co-vary with our plant func-

bivariaterelationships. tionalclassification.Forexample,leaflongevityhasbeenshownto

In both RLQ ordinations the second axis was more related bepositivelyrelatedwithM inanumberofstudies(Reichetal.,
A

to traits associated with variations in tree hydraulic properties, 1992;SantiagoandWright,2007;Shioderaetal.,2008).Theextent

also being associated with variations in precipitation and/or the towhichotherplantdimensionssuchasseedsize,leafareaand

soilwaterholdingcapacity.Althoughaccountingforonlyasmall leafarea;sapwoodarearatiointegratewiththetraitsusedinour

amount of the total variance the way in which functional traits classificationisconsideredinPatin˜oetal.(2011).

andenvironmentalvariablesarerelatedalongthisaxispointsto

an important water availability dimension involving both stem Comparisonwithotherclassificationschemes

andleaftraits.IntheRLQ-ncase,moistersiteswerealsoassoci-

atedwithfinertexturedsoilswhichingeneralalsosupporthigher Functional classifications of tropical tree species have tradi-

water availability and taller trees. When spatial autocorrelation tionally used two plant dimensions, with growth potential and

betweenthe53plotswasremoved(i.e.theRLQ-scase),RLQ was adultsizebeingtheassumedmainaxesofecologicalspecializa-
2

mainlyrelatedtosoiltextureandtoasmallerextenttodryseason tion(Daviesetal.,1998;Turner,2001;Nascimentoetal.,2005).
106 N.M.Fyllasetal./PerspectivesinPlantEcology,EvolutionandSystematics14 (2012) 97–110

potential gas exchangerate ofxylemcavitation(lowest(cid:3)X).Thelatter,alongwiththecharac-

high mid low taelsroisbtiec tthaell rHeamsaoxnimfoproresilnatgi vaedldyihtiiognhaøl ohbysderravueldic( Lcloonysdtreatinalt.s, 2m0i1g0h)t.

Ф2: small statured Ф4: canopy Despitethisapparenttradeoff,themainadvantagefor˚3species

optimumdiameter growth lowhigh Фlh1o:ih wgsihg m M hNa cAl,,al ht mpiiogiiondhns CP,e ,,e r Фmmmmhih3lidio:ldiidi odg gwt M whaM hNN l c AclA dH, a,, a,p eht lthmlioniiiooioogws gawnh nnihxt s s eyP P,C,C e, , , r , hliogmlhow hiiwg d MNh cd A,aHe ,tl n oimh oswiingat xyhs P,, ,C infertile soils fertile soils more abundant bcwsgswcmspehairyaoiaorteo tttevoywhcuehda di bdleaett d ieh (ii snhh vrh e t iaieaarihgxggral blhahhyehtoit i e lr pn cibi(cid:3)tsantohhgi Xtytvna-eoe ) emtttfi.yst eos Oo gochrit smdenumafiy g din rratnn hep a hiritsdn tnaaehoDe typ felowsa obait t(idotrianehFci lfclois ye slgpckln r uoap.ao s hscwirr4ttntbhoaer)aetaonnit.ei eretnndtT revcige c hagaettaedhelesy sa p eds e.g sletinT rampifmxhoaoorocbwginilatslhilea lea aitnla ntngahetnio gderto argti u nataiy(seohhltl,to tlenarewiy usagst mi tnu htthsoaeag r i wvttcls[e oulaCe hdeet grrs] hrl ei )eult ˚sa Ф h swas cttu2teno h4iaapidmterscirhpn nepaedb o a n etneacirshcnotonltslieipeenooseriddyees---r
lifecycle(Fig.4).Thus,thetwo-wayclassificationschemeofTurner
mid density, low dens ity,
(2001)iswellsupportedbyouranalysis.Andourfouridentified
low Hmax high Hmax
setsofleafandstructuralcharacterscouldbereadilyincorporated

into it, providing a physiological characterisation of the distinct
Fig.6. SchematicrepresentationofthederivedФ,byextendingthefunctional
life-historystrategiesidentified(Fig.6).
classification of Turner (2001). Poorter et al. (20 06) also e nhan ced Turner’s two-way clas-

sification, by considering a set of architectural and shade

For example, in his concluding synthesis chapter, Turner (2001) tolerancecharacteristicswiththeir“short-livedpioneers”,“long-

arguesonthebasisofaccumulatedevidencefortherebeingfour lived pioneers”, “shade-tolerants” and “partial shade-tolerants”,

discretegroupsoftropicaltreespecies;viz.smallpioneers,large descriptions partially coinciding with our ˚ , ˚ , ˚ and ˚ ,
1 3 3 4

pioneers,understoreyspeciesandcanopyspecies.Inthisscheme respectively.Inagreementwiththeir54Bolivianspeciesdataset,

smallandlargepioneersareconsideredtoexhibitfastgrowthrates short-lived pioneers and shade tolerants illustrate lower adult

whileunderstoreyandcanopyspecieshavelowergrowthpoten- stature compared to long-lived pioneers and canopy species

tialsAdditionallythefourgroupsareidentifiedonthebasisoftheir (Table2).However,theirshadetolerantgrouphadamuchlower

adultsizewithsmallpioneersandunderstoreyspeciesofalesser Hmax compared to those derived here (˚2). This may be a con-

stature and also with a shorter life cycle (see also Poorter et al., sequenceofthedesignofourstudy,whereonlyindividualswith

2006). leavesexposedtosunlightforatleastsomeofthedayweresampled

Ourfour˚canbeconsideredpartiallycoincidentwithTurner’s Thusourdatasetcouldcontainabiastowardslargerindividuals.

classification(Fig.6).Here˚ equateswithsmallstatured(under- Alternatively,thiscouldbeduetothecontrastsinthegeographic
2

story)species,˚ withlargepioneersand˚ withTurner’scanopy rangebetweenthetwodatasetswiththeBolivianstudysampling
3 4

species. Less clear is ˚ , though they may equate in part with onlyfromonesiteonthesouthernfringeoftheAmazonbasin.Leaf
1

Turner’ssmallpioneerspecies.Itshouldbenoted,however,that life-spanswerefoundtobehigherforPoorteretal.(2006)shade

˚ maynotbeasolidgroupofpioneerspecies.Butratheraguild tolerantgroupscomparedwithbothpioneertypes,thisalsobeing
1

ofsmallstaturedtreesusuallyfoundonhighfertilitysoilswithhigh inaccordancewithourdescendingmeanM valuesfrom˚ to˚ .
A 4 1

initialpotentialgrowthratesbutwithalimitedabilitytomaintain ThehigherM displayedbyourcanopyfunctionalgroup(˚ )is
A 4

thesegrowthrates(Fig.4).Thisgroup,whichbearssomesimilari- alsoinaccordancewithpositiveheighteffectsonM bothwithin
A

tiestothe“shortandshortlived”pioneergroupofAckerly(1996) andbetweenAmazonforesttrees(Lloydetal.,2010).

is relatively rare in low-fertility forests of Amazonia which sug- Althoughwechosetoundertakeourmainanalysiswithagroup-

gests they may be at a distinct growth or survival disadvantage ing of four ˚, the Calinski grouping of six ˚ also provided a

whenoccurringonsuchsoils.Consideringtheirphysiologicaland reasonablefunctionalgroupderivationfortheRLQ-scase(Fig.2).

structuralproperties(Table2)alongwiththeirmeangrowthrate The ˚ derived in this case “divide” the canopy to three levels

trajectories (Fig. 4), this group might be anticipated to perform (low,midandtallstaturedgroups),beinglocatedatthesametime

bestinmoredynamicforestswherethereareampleopportunities alongasoiltextureaxis(SI6).Thisaddedverticaldimensionpro-

for gap colonisation and rapid growth to and beyond reproduc- ducesa˚partitioningwithstrongsimilaritiestothe“guild”type

tivematurity.Afterwhichtheywouldbepronetobeingshaded approachofindividualbasedmodelsasappliedtotropicalforests.

out by longer lived and eventually taller competitors. Occasion- For example, for a simulation of lowland Malaysian dipterocarp

allymembersofthisgrouparefoundinappreciableamountson forestdynamics,HuthandDitzer(2000)classifiedallspeciesinto

poorersoils,butwhenso,thesearethemostinfertilestandswith fivefunctionalgroupsbasedsolelyonpotentialheight,thenassign-

“white sand” ˚1 species such as members of the genus Eperua ingshade-tolerancecharacteristicstoeachHmaxgrouping.Taking

sometimes present in large numbers in these low stature slow datafromFrenchGuiana,Chave(1999)delineatesfourHmax cat-

growingforests.Perhapssmallertreesareatanadvantagewhere egoriesandwithineachoftheseheightgroupingstherearethree

soilresourcesareseverelyortherearesomeothercharacteristics shadetolerancegroups.ForanEastKalimantanforestPhillipsetal.

ofthesespeciesthatputsthematarelativeadvantageinsuchsit- (2002)usedHmaxandphylogeneticinformationtodefinetheirten

uations:Forexampleanabilitytoobtainnitrogenthrougheither groups.Ingeneral,boththenumberof˚andthecriteriausedto

nodulationornon-nodulatingbacterialinteractions(Villadasetal., definethemwilldependontheresearchobjectivesforwhichthe

2007) or the ability to produce heavy and toxic seeds on these groupingisused.Forexample,stand-levelgrowthmodelsrequire

infertilesoils(terSteegeetal.,2010). ahighnumberof˚inordertorepresentimportantecologicalpro-

Relatively high assimilation rates might also reasonably be cesseslikeforestsuccessionorresponsestopartiallogging(Chave,

anticipatedforthelargepioneergroup,˚ .Thisfunctionalgroup 1999;HuthandDitzer,2000;Phillipsetal.,2003),withspecies-
3

seemstofollowtheleastconservativestrategy,thisbeingreflected dependentcharacteristicssuchasseedsizeanddispersalmodefor

inboththedevelopmentofunprotectedleaves(lowest[C])andrisk realisticsimulations.Ontheotherhand,continental-to-globalscale