Seasonal trends of Amazonian rainforest phenology, net primary productivity, and carbon allocation

16 Pages·2016·0.7 MB·English

PUBLICATIONS
Global Biogeochemical Cycles
RESEARCH ARTICLE Seasonal trends of Amazonian rainforest
10.1002/2015GB005270 phenology, net primary productivity,
and carbon allocation
SpecialSection:
GlobalLand-UseChangeand
Carbon/ClimateDynamics CécileA.J.Girardin1,YadvinderMalhi1,ChristopherE.Doughty1,DanielB.Metcalfe2,PatrickMeir3,4,
JhondelAguila-Pasquel5,6,AlejandroAraujo-Murakami7,AntonioC.L.daCosta8,
JavierE.Silva-Espejo9,FilioFarfánAmézquita9,andLucyRowland3
KeyPoints:
(cid:129) FielddataconfirmthatAmazon
forestsmaximizeleafproduction 1EnvironmentalChangeInstitute,SchoolofGeographyandtheEnvironment,UniversityofOxford,Oxford,UK,
duringdryseason 2DepartmentofPhysicalGeographyandEcosystemScience,LundUniversity,Lund,Sweden,3SchoolofGeosciences,
(cid:129) Trade-offsincarbonallocationdeter- EdinburghUniversity,Edinburgh,UK,4ResearchSchoolofBiology,AustralianNationalUniversity,Canberra,ACT,Australia,
mineseasonalityofNPPcomponents 5DepartmentofBiology,UniversidadNacionaldelaAmazoniaPeruana,Iquitos,Peru,6InstitutodeInvestigacionesdela
(cid:129) Consistentreproductiveseasonality
AmazoniaPeruana,Iquitos,Peru,7MuseodeHistoriaNaturalNoelKempffMercado,UniversidadAutónomaGabrielRené
acrosscontrastingprecipitation
regimes Moreno,SantaCruz,Bolivia,8CentrodeGeociencias,UniversidadeFederaldoPará,Belem,Brazil,9DepartmentofBiology,
UniversidadNacionalSanAntonioAbaddelCusco,Cusco,Peru
SupportingInformation:
(cid:129) SupportingInformationS1 AbstractTheseasonalityofsolarirradianceandprecipitationmayregulateseasonalvariationsintropical
forestscarboncycling.Controversyremainsovertheirimportanceasdriversofseasonaldynamicsofnet
Correspondenceto:
C.A.J.Girardin, primaryproductivityintropicalforests.WeusegrounddatafromninelowlandAmazonianforestplots
[email protected] collectedover3yearstoquantifythemonthlyprimaryproductivity(NPP)ofleaves,reproductivematerial,
woodymaterial,andfinerootsoveranannualcycle.Wedistinguishbetweenforeststhatdonotexperience
Citation: substantialseasonalmoisturestress(“humidsites”)andforeststhatexperienceastrongerdryseason
Girardin,C.A.J.,etal.(2016),Seasonal (“drysites”).WefindthatforestsfrombothprecipitationregimesmaximizeleafNPPoverthedrierseason,
trendsofAmazonianrainforestphenology, withapeakinproductioninAugustatbothhumid(mean0.39±0.03MgCha(cid:1)1month(cid:1)1inJuly,n=4)and
netprimaryproductivity,andcarbon
allocation,GlobalBiogeochem.Cycles,30, drysites(mean0.49±0.03MgCha(cid:1)1month(cid:1)1inSeptember,n=8).Weidentifytwodistinctseasonal
700–715,doi:10.1002/2015GB005270. carbonallocationpatterns(theallocationofNPPtoaspecificorgansuchaswoodleavesorfineroots
dividedbytotalNPP).Theforestsmonitoredinthepresentstudyshowevidenceofeither(i)constant
Received1SEP2015
Accepted16APR2016 allocationtorootsandaseasonaltrade-offbetweenleafandwoodymaterialor(ii)constantallocationto
Acceptedarticleonline21APR2016 woodandaseasonaltrade-offbetweenrootsandleaves.Finally,wefindstrongevidenceofsynchronized
Publishedonline14MAY2016 floweringattheendofthedryseasoninbothprecipitationregimes.Flowerproductionreachesamaximum
of0.047±0.013and0.031±0.004MgCha(cid:1)1month(cid:1)1inNovember,inhumidanddrysites,respectively.
Fruitfallproductionwasstaggeredthroughouttheyear,probablyreflectingthehighvariationinvarying
timestodevelopmentandlossoffruitamongspecies.
1. Introduction
Controversy remains overthe roles of solar irradiance and precipitation as the main drivers of seasonal
dynamicsofthenetprimaryproductivity(NPP)oftropicalforests.Whereasarelationshipbetweenseaso-
nal peaks in precipitation and wood productivity is relatively well established for Amazonian forests
[BrienenandZuidema,2005;Rowlandetal.,2014;Wagneretal.,2012;Hofhansletal.,2015],thepredomi-
nant drivers of the seasonal production of leaf and reproductive organs remain unclear. Systematic
ground-based monitoring of the major components of biomass productivity is needed to understand
thetrendsgoverningtheirseasonality.
Apparentseasonalityofleafphenologyhasbeenobservedfromopticalremotesensingdata[Hueteetal.,
2006;Myrenietal.,2007;Saleskaetal.,2007;Brandoetal.,2010;Wuetal.,2016]and,toalesserextent,from
ground-basedcollectionofleaflitterfromlitterfalltraps[Chaveetal.,2010].Theformerproduceddivergent
resultsandhasgeneratedcontroversiesaroundthevalidityandinterpretationofseasonalvariationinremo-
telysensedmetricsinmoisttropicalforests,leadingtoongoingcontroversyovertheproposedparadigmof
light-limited forests responding to a surge of solar irradiance during the dry season [Morton et al., 2014].
Litterfall studies provide evidence of positive correlations between litterfall and rainfall seasonality, but it
©2016.AmericanGeophysicalUnion.
AllRightsReserved. remainsunclearhowtimingofleaflitterfallisrelatedtothetimingofcanopyleafproduction.
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 700
Global Biogeochemical Cycles 10.1002/2015GB005270
MuchoftheliteratureanddebateontheseasonalityofAmazoncanopyleafproductionhasimplicitlyassumeda
positiverelationshipbetweencanopy“greening”andoverallecosystemproductivity.However,aquestionthat
remainsunresolvedistheextenttowhichaseasonalincreaseincanopyleafproductionreflectsanincreasein
overallproductivity,oralternativelysimplyashiftincarbonallocationamongroots,wood,thecanopy,andnon-
structuralcarbohydratestoragepools.ArecentstudyinBoliviahasdemonstratedthatallocationshiftsaremore
importantthanoverallchangesinproductivityinexplaininginterannualvariabilityincanopy,wood,andfineroot
growthrates[Doughtyetal.,2014a].Hereweinvestigatethephysicaldriversofcarbonallocationacrossawider
range of Amazonian sites, test if Amazon rainforests may be separated into radiation-controlled and
precipitation-controlledforests,andexploretheargumentsfromanevolutionaryperspective.
Athirdsetofrelevantquestionsrevolvearoundthenatureandtimingofreproductivephenologyintropicalfor-
ests, how this relates to the overall seasonality of climate and productivity, and the amount of productivity
investedinreproduction.Thelinksbetweenfloweringphenology,weatherparameters(solarirradiance,rainfall,
andtemperature),andanthepotentialroleofanintrinsicbiologicalclockdevelopedasanevolutionaryresponse
tobioticinteractions(pollinationandseeddispersal)remaintobedeciphered[Lieberman,1982;Wrightandvan
Schaik,1994;WrightandClarendon,1995;Leigh,1999;Borchertetal.,2002,2005;Calleetal.,2010;Pauetal.,2013].
All these interlinked questions can potentially be examined by a multiyear data set comprising measure-
ments of all the major components of forest NPP at seasonal resolution and across contrasting sites in
Amazonia.Herewepresentsuchadatasetasacontributiontothisdebate,presentingdataover3yearsfrom
anetworkof1hapermanentplots:theGlobalEcosystemsMonitoringnetwork(GEM,gem.tropicalforests.ox.
ac.uk).OuranalysisisfocusedonnineplotslocatedinoldgrowthintactAmazonianforests(Table1).Forest
plots were categorized into two precipitation regimes: sites that did not experience a strong dry season
(termed“humidsites”forthepurposeofthispaper)andthosethatexperiencedadryseasonoverseveral
months(termed“drysites”).
Inthisstudy,weaskthefollowingquestions:
1. Istherefield-basedevidenceofapeakinthenetprimaryproductivityofleaves(NPP )duringthedry
Leaves
season,assuggestedbyexistingopticalremotesensingobservations?
2. Howdoesthenetprimaryproductivityofflowers(NPP ),fruit(NPP ),abovegroundcoarsewoody
Flowers Fruit
material(NPP ),andfineroots(NPP )varyovertheseasonalcycleinhumidanddrysites?
ACW Fineroots
3. Is there evidence of clear trade-offs in carbon allocation driving seasonal variation of woody growth,
canopyproduction,andfinerootproduction?Ifso,howdothesetrendsvaryacrosssites?
2. Methods
2.1. SiteDescription
OurstudyregionisacrosstheAmazonforestbiome.Nine1halong-termintensivecarbonmonitoringplots
were established in lowland Amazonia as part of the Global Ecosystems Monitoring Network (GEM) of
intensive plots and nested with the RAINFOR Amazon Forest Inventory network (RAINFOR) of 1ha plots.
Two plots were in the Tambopata reserve, Tambopata Province, Department of Madre de Dios, Peru
(RAINFOR codes TAM-05 and TAM-06); two plots in the province of Maynas, on the Allpahuayo-Mishana
National Reserve (ALP-01 and ALP-30); two plots on private property at the Hacienda Kenia in Guarayos
Province, Santa Cruz, Bolivia (KEN-01 and KEN-02); two plots in the Fazenda Tanguro, Mato Grosso State,
Brazil (TAN-05, TAN-06);and three plots in Caxiuanã National Forest Reserve, Pará in the eastern Brazilian
Amazon(CAX-03,CAX-06,andCAX-08).Severalplotshavebeenestablishedforover8years.However,we
chosetoincludedatacollectedovertheperiod2009–2012forthisanalysistoensurethesamedatacollection
periodbetweenplots.Allplotswereselectedinareaswithrelativelyhomogeneousstandstructure.Allhad
closedcanopieswithoutanylargegaps.Forestcompositionvariedacrossthesites,bothacrosstheregion
andbetweenneighboringplotsbecauseofedaphicordisturbancehistoryfactors.
2.2. SiteClassification
Siteswereclassifiedintodryandhumidsites,accordingtoestimatesofmaximumclimatologicalwaterdeficit
(MCWD)andfollowingtheclassificationdescribedinMalhietal.[2015].Ourclassificationdiffersfromthatof
Doughtyetal.[2015]inoneway:whereasDoughtyetal.[2015]classifyKEN-01aswetforest,basedonspecies
composition(AmazoniantreespeciesversusChiquitanodryforestspecies)andedaphiccharacteristics(deep
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 701
Global Biogeochemical Cycles 10.1002/2015GB005270
TAN-06TAN-05 (cid:1)(cid:1)13.076513.076552.385852.38583853856.74251770(cid:1)48210.7±0.210.8±0.2FerralsolFerralsol1471470.160.162.552.5567.1282819.4719.4745.7345.7348.948.95.375.37 vftoedsohoenfifreirAcfelseemesudrtxesie,adtnpsrzbeechotanemhanstleiiilecaanodla.fywfBlseodpcocsnlretotiymhicinmliespeag)entls,oeaddwctnrasooeynimMfncdstlpCethaahoWaseressseDicoiKftl1yni,ieomkatnnwhmsaiaitaisKseasteseipdstinrtepareiireasvetuace—xitpsnpsrputdeohblrmrynieyy-.
dHumidSite CAX-08 (cid:1)1.7160(cid:1)51.457047 22.4±0.1TerraPreta37.40.060.833567.1301.3483.6910.685.64 TbccoohynneDttdeeoxxisutttgioonhffctyetaixoepsnttlouianrdlit.nyog[2ohs0nue1ma5ds]riodownuaaagnlshdvtardereilrfaeyftveisaocinttnset.soiIfmnnNaotPdhuPeer,
n
dIntoDrya CAX-06 (cid:1)1.7369(cid:1)51.46194475.725.82311(cid:1)20327.1±0.3Ferralsol178.50.131.6851.93522.8232.5453.7613.7 bseoatshonKeannidasphlooutsldebxepecrliaesnsicfieedantoegxettrheemr,e dry
e 2.3. Weather
fi
e,andClassi CAX-04 (cid:1)1.7160(cid:1)51.457047 22.4±0.1eticAcrisol37.40.060.8335301.3483.6910.685.64 tpTeirmmecepipesirteaarttiuieorsenf((om°rCm)s,omrleaolra(cid:1)ti1irv)reawdhieaurnemcecidoil(tlWeycmt(e%(cid:1)d)2,)f,raonamdir
m V
o an Automatic Weather Stations (Campbell
nForestBi KEN-02 (cid:1)16.0158(cid:1)62.7301384 16.0±0.3Cambisol244.70.17267.15460.7455.4818.2526.27 Straeclmiesonitvteiefi.cTo)huleotlscieeartsde,daatn~ad1wkmemroenfrtqohumlyaleittiaymchceoesnextprrioeelrsliemwdeetnroe-
azo 5.9 gap-filled as described in da Costa et al.
osstheAm KEN-01 (cid:1)16.0158(cid:1)62.7301384 23.41310(cid:1)38619.7±0.4Cambisol447.10.222.474.83275.5858.0519.1322.82 [A[22r00a11u44jo]],,-MdDueorlaukgAahgmtuyiilaee-ttPaaals.lq[.2u0[e2l104e1]t.4ba]l,. M[2a0l1h4i],etanadl.
cr
dBrazil,A TAM-06 (cid:1)12.8385(cid:1)69.296215 35.5±0.4Alisol528.80.171.237.43756.824652 2A.l4l.sNiteestPwriemrearymPornoitdourecdtiviatcycording to the
n 8
u,Bolivia,a TAM-05 (cid:1)12.8309(cid:1)69.27052234.24.41900(cid:1)25921.8±0.2Cambisol256.30.161.5143.71344.8404417 GpthrloeobtoaficleolEdl.coMmsyeastntheuomadlss Maavroeaniliadtboelsreicnrgiobne(GdEthMine)dnweetetawbilsoitrinek
er http://gem.tropicalforests.ox.ac.uk, as well as
P
ocatedin ALP-30 (cid:1)3.9543(cid:1)73.4267150 10.8±0.2Arenosol37.60.081.1316.4104.982216 ie[n2t0a1al.4[]s2,e0rd1iee4sl],AoDgfouusilgiathe-Pt-ysapseeqtcuaiefill.c[2ept0a1ap4leb.r]s,[2.M0da1al4h]i,Ceoatsnatdal.
L
ntForestPlots ALP-11 (cid:1)3.95(cid:1)73.43331205.225.22689(cid:1)626.8±0.3Alisol/Gleysol125.60.11.1992.951230.4651520 Apceatrraerabhulo.jeon2n-0Msci1vuy5erc]al.sekiTsathemfeodirepesrttochrteaiopsl.cetoi[o2lsn0imts1ee4saa]n(sdTpuarrtboehlvseeidac1eno)ddm[Mscpuolaemmlthes-i
e all major components of NPP and autotrophic
n
a
m 1) respiration(Ra)onmonthlytimescalesineach
Per cm) (cid:1)kg 1haforestplot.Thepresentpaperfocuseson
of11 (°C) –030 molc the NPP components of these measurements.
Table1.SiteCharacteristics RAINFORSiteCode LatitudeLongitudeElevation(mabovesealevel)(cid:1)(cid:1)21yr)Solarradiation(GJmMeanannualairtemperature(cid:1)1Precipitation(mmyr)MeanMCWD(mm)Soilmoisture(%)Soiltype(cid:1)1Ptotal(mgkg)TotalN(%)TotalC(%)(cid:1)1fromSoilCstock(MgChaSoilorganiclayerdepth(cm)Cationexchangecapacity(mSand(%)Clay(%)Silt(%) sp(cmRhainCeiseartaUsenecvoirstEodgeyue)gn.prnapneEpabtcrdaieopozoacdtidelvnshecreuagunimnm.clthattiehaaptt(hialsalGoisetnnPusamgPyssres)erao,otimrgnearifeoymnestsrndoyoaootstftucatifptcesalhrsmreNebieutssaPreoenertP-nonicsc,mreptuRerueesptdacnaae,risiciofinngueepctfrrrtifiaoetehpagcmsseaiisa,nepeptntpinweriocerertnieysss--
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 702
Global Biogeochemical Cycles 10.1002/2015GB005270
The GEM methodology has shown close agreement with independent eddy covariance data on annual
timescalesforbothRaandGPP[Malhietal.,2009;Fennetal.,2014].Thefollowingparagraphsprovideabrief
descriptionofthefieldmethodsusedinthepresentstudy.Webrieflydiscussthepotentialbiasesaccruedby
eachmeasurement.
2.4.1. AbovegroundCoarseWoodyNetPrimaryProductivity
Wedetermined plot-level NPP using multiple censuses(2009, 2010, and 2011)of theforest plots and
ACW
three-monthlydendrometerbandmeasurements(2009–2011).Wecompletedtreecensusestodetermine
the growth rate of existing surviving trees and the rate of recruitment of new trees. Data on diameter at
breastheight(dbh,usingdiametertape)andtreeheight(usingclinometerwhenpossible,oravisualesti-
mate) were recorded forall trees≥10cmdbh. Aboveground coarse woody biomass was calculated using
theallometricequationofChaveetal.[2005]fortropicalmoistforests,employingdataondiameter,height,
andwooddensity.
AGB ¼ 0:0509(cid:3)ðρ(cid:3)dbh(cid:3)HÞ (1)
whereAGBisabovegroundbiomass(kg),ρisdensityofwood(gcm(cid:1)3),dbhisdiameteratbreastheight(cm),
andHisheight(m).Wooddensitywasestimatedforeachspeciesfromaglobaldatabaseoftropicalforest
wooddensity[Chaveetal.,2006;Zanneetal.,2009],ideallyassignedtospecies,buttogenusorfamilylevel
wherespeciesidentityorspecies-levelwood-densitydatawerenotavailable.Toconvertbiomassvalues
intocarbon,weassumedthatdryACWbiomassis47.3%carbon,basedonrecentstudiesinlowlandforests
inPanamathatincludedvolatilecarboncompoundsnotrecordedbyconventionaldryassessment[Martin
andThomas,2011].Forthefewtreeswhereheightdatawerenotavailableweestimatedheightbyemployinga
plot-specificpolynomialregressionbetweendbhandheight[Feldspauchetal.,2011].AnnualNPP wascal-
ACW
culatedasthesumofbiomassincreaseofindividualsurvivingtreesbetweencensusintervals.Todetermine
seasonalvariationinwoodygrowthrates,weinstalleddendrometerbandsin2009onapproximately200ran-
domlyselectedtreesineachplot(alltreesinTAM-05andTAM-06).Thedendrometerbandsweremeasured
every3monthswithcallipers.Thedendrometerdatawerescaledupto1habyusingtheannualfullcensusdata
todeterminetheratioofwoodnetprimaryproductivity(NPP )ofalltreesovertheNPP ofdendrometer
ACW ACW
trees.ThisratioprovidedascalingfactorthatweappliedtothedendrometerdatatoestimateseasonalNPP
ACW
for the entire plot (i.e., to include trees that had no dendrometers), with the implicit assumption that the
productivityofthedendrometertreesisrepresentativeofthewiderpopulation.
Oneconcernistheeffectofseasonalchangesinhydraulics,thepossiblebiasintroducedbymoistureexpan-
sion,andshrinkageofthebarkorxylemontreegrowthseasonality.Toestimatetheeffectofmoistureexpan-
siononapparenttreegrowthduringthewetseason,Doughtyetal.[2015]separatedthetreeswithalmostno
annual tree growth (woody NPP <1kgCha(cid:1)1yr(cid:1)1) and quantified their apparent seasonal trends in dia-
meter.Fortheseslowornongrowingtrees,wefoundameanseasonalamplitudeofapparentgrowthpeak-
inginAprilanddecreasinguntilOctober.Forexample,forourtwoBoliviansitesweestimatedtheseasonal
effectofmoistureexpansionbetweenMarchandNovember(themaximumandminimum)tobeanapparent
change of 0.08MgCha(cid:1)1month(cid:1)1 and 0.19MgCha(cid:1)1month(cid:1)1, although this may underestimate the
effect,sincefastergrowingtreestendtoshrinkmoreinthedryseason,becausetheypossesslargervessels
[Rowlandetal.,2014].Asthecensusintervalwassmall(1year)wedidnotapplyacorrectionfortreesthat
growanddiebetweencensusintervalswithoutbeingrecorded[Malhietal.,2004].Wedidnotaccountfor
thewoodproductivityoflianas,althoughtheirleafproductivityisrecordedbylitterfalltraps.
2.4.2. Litterfall
Canopylitterfallwascollectedin250.25m2(50×50cm)littertrapsinstalled1mabovethegroundoneach
plot.Litterfallwascollectedevery15daysoveraperiodof18or24monthsbetween2009and2011,splitinto
differentcomponents(leaves,fruits,flowers,seeds,woodytissue,bromeliads,otherepiphytes—nonvascular
epiphytes,mosses,andliverworts—andunidentifiedfinedebris),ovendriedat80°C,andweighed.Annual
estimatesofleaflosstoherbivorywereobtainedfromscansoflitterfall,accordingtoMetcalfeetal.[2014].
Seasonalvariationoflargepalmleaflitterwasnotaccountedforinthelowlandplots,implyinganunderes-
timationoflitterfallattheTAM-06siteinparticular[Malhietal.,2014].
Therearetwopotentialseasonalbiasesinlitterfall.First,decompositionoflittermayincreaseduringthewet
season;thiswouldimplythatourmeasurementsunderestimatewetseasonlitterfall,althoughcollectinglit-
terfallevery15daysensures minimumlossbydecomposition.Second, wedonotcorrectlitterfalldatato
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 703
Global Biogeochemical Cycles 10.1002/2015GB005270
account for seasonal leaf herbivory. On an annual time scale, Metcalfe et al. [2014] estimated an average
canopy loss of 7%±0.5% in nine Andean and Amazonian plots, including Tambopata. Although we did
notfindclearseasonaltrendsforherbivoryinourlowlandAmazoniansites,Lieberman[1982],Aide[1988],
andGivnish[1999]estimatedthatinsectactivityincreasesinthewetseason.
2.4.3. CanopyProductivity
Canopylitterfallisagoodestimatorofcanopyproductivityonannualorlargertimescales.However,itrepre-
sentsthetimingofcanopybiomassloss,notbiomassgain,andhencecannotrecordseasonalvariationin
canopy productivity. To overcome this issue, we combined data sets of canopy litterfall, canopy leaf area
index (LAI) [da Costa et al., 2014; Doughty et al., 2014b; Malhi et al., 2014; del Aguila-Pasquel et al., 2014;
Araujo-Murakamietal.,2014],andspecificleafarea(SLA)[Salinasetal.,2011]toestimatetheseasonalcycle
ofcanopynetprimaryproductivity(NPP )ineachplot[DoughtyandGoulden,2008]:
Canopy
NPP ¼ ðΔLAI=SLAÞ þ litterfall (2)
Canopy
whereΔLAIisthechangeinLAIbetweenmonths(m2m(cid:1)2),SLAisthemeanspecificleafarea(m2g(cid:1)1),andlitterfall
isthetotallitterfall(gm(cid:1)2).WeassumenodifferenceinSLAinbetweenmatureandsenescentleavesanddonot
attempttoaccountforresorptionofcarboninsenescentleaves.Boththeseuncertaintiesarelikelytobesmall.
WeestimatedLAIusingcanopyimagesrecordedwithadigitalcameraandhemisphericallensandanalyzed
withCAN-EYEsoftware(http://www6.paca.inra.fr/can-eye).WeestimateSLAbytakingasubsampleoffresh
leaflitterfall,scanningeachleaftodeterminefreshleafarea,andthendryingat80°Candweighingtheleaf
todeterminedrymass.SLAisfreshleafareadividedbydryleafmass.Leafareawascalculatedusingimage
analysissoftware,ImageJfreeware(http://rsb.info.nih.gov/ij/).NPP isestimatedingm(cid:1)2.Thismethod
Canopy
ispurelybasedonamassbalanceapproach.Weareprovidingestimatesofchangingstocksoflitterfallover
time,withoutmakingassumptionsonleafage.
2.4.4. FineRootProductivity
We collected fine roots (<5mm diameter) from 16 ingrowth cores (12cm diameter, 30cm depth) every
3monthsfromSeptember2009toMarch2011.Rootsweremanuallyremovedfromtheingrowthcoresin
four10mintimesteps,andthetrendofcumulativeextractionovertimevasusedtopredictrootextraction
beyond40min,asdescribedinMetcalfeetal.[2006].Root-freesoilwasthenreplacedineachingrowthcore,
keepingthesamebulkdensityoftheundisturbedsoil.Collectedrootswerethoroughlyrinsed,ovendriedat
80°Ctoconstantmass,andweighed.MonthlyNPP wasestimatedfromthequantityoffinerootmass
Fineroots
producedinthe3monthintervalsincethelastcollection,allowingustoestimateseasonalfinerootgrowth.
Whilemostecologicalstudiesdefinefinerootsas<2mm,wenotedthatafewrootsbetween2and5mm
alsogrowintoouringrowthcores.Forpragmaticreasonswedidnotseparatetheseout;thesemayaccount
forafewpercentofrootbiomassinouringrowthcores.Thisdefinitionoffinerootsalsomaintainsconsis-
tencywithourotherrecentpapers[Doughtyetal.,2014a;Malhietal.,2015;Doughtyetal.,2015].
As in all carbon budget measurement protocols, the measurement of roots proved to be the most
challenging.Wereportresultsfromingrowthcoresastheyhavepreviouslyprovidedrobustestimatesofroot
productivity over seasonal and annual time scales. For example, Girardin et al. [2013] provide detailed
comparisonsandshowexcellentagreementofresultsobtainedfromingrowthcoresandrhizotronsinsix
GEMplotslocatedinAndeanandAmazonianforests,includingtheTambopataplotspresentedhere.
2.4.5. TotalNetPrimaryProductivity
NPPcanbeestimatedasthesumofNPP ,NPP ,branchturnover(NPP ),NPP ,
ACW Canopy BranchTurnover Fine roots
coarseroots(NPP ),andvolatileorganiccompoundproduction(NPP ).
Coarseroots VOC
NPP ¼NPP þNPP þNPP þNPP þNPP þNPP (3)
AG ACW Canopy BranchTurnover Fineroots CoarseRoots VOC
WeobtainedseasonalNPP estimatesfromthedendrometerbandgrowth,andNPP valuesthrough
ACW Canopy
theseasonallitterfallmeasurementscoupledwithseasonalvariationinleafareaindex.Branchturnoverand
coarserootproductivityaredifficulttoestimateatseasonalresolution[daCostaetal.,2014;Doughtyetal.,
2014b;Malhietal.,2014;delAguila-Pasqueletal.,2014;Araujo-Murakamietal.,2014];hence,forthepurpose
of this paper, we concentrate on NPP , NPP , and NPP seasonality. Finally, we did not
ACW Canopy Fine roots
estimatethecontributionofvolatileorganiccarbonemissionfromvegetation,whichwasfoundtobeavery
minorcontributioninlowlandtropicalforestecosystems[Malhietal.,2009].
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TheGEMprotocolmethodologiesareusedbymanyresearchgroupsgloballyandinnumerouspublished
scientificstudies.Eachmeasurementincorporatedinthispaperispresentedindetailinaseriesoffivecom-
panionpapers[daCostaetal.,2014;Doughtyetal.,2014b;Malhietal.,2014;delAguila-Pasqueletal.,2014;
Araujo-Murakamietal.,2014].
OurestimatesoftotalNPPisbasedonthesummationofindependentmeasurements(litterfall,treegrowth,fine
rootproduction,andbranch fall).Whilesomeofthesetermscancarrysubstantialmeasurementandscaling
uncertainties,thesamplinguncertaintiesareindependentforeachmeasurement.Hence,someoftheseuncer-
taintiespotentiallycanceleachotherout,reducingtheoveralluncertaintyoftheNPP,GPP,andCUEestimates
obtainedusingtheGEMprotocol.Bycontrast,aneddycovariance-basedestimateofGPPisbasedonasingle
typeofmeasurement(ofnetecosystemexchange);hence,anyuncertaintiesinthemethod,suchasunderesti-
mationofnighttimerespirationinstableatmosphericconditions,canresultinanequivalentuncertaintyinthe
finalestimateofGPP.Malhietal.[2015]andDoughtyetal.[2015]explorethemeasurementandscalinguncer-
taintiesassociatedwitheachmeasurementtakenbytheGEMcarbonbudgetandarguethatacarbonsumma-
tion measurement composed of multiple independent measurements may be more accurate and less error
prone than an eddy covariance-based estimate composed of one type of measurement. One key difference
betweenatopdown(eddycovariance)andbottomup(GEMsummationmethod)approachisthatthelatter
givesanestimateonanannualandseasonalscale,whiletheformerprovideinsightatthehourly(orless)scale.
TheonlyuncertaintiesweconsideredforNPPvaluesarethoseassociatedwithsampling.Wedonotattempt
toestimatesystematicuncertainties,butnotethatinthecontextofthispaperthekeysystematicbiasesof
concerntouswouldbethosewhichshowastrongseasonalvariation.
2.5. AnalyticalTechniques
WeusedthesedatatodescribetheseasonalvariationofNPP ,NPP ,andNPP .Alluncertainty
Canopy ACW Fineroots
estimatesaregivenasstandarddeviation(±SD).Thepropagateduncertaintiesforeachvariabledescribedin
theGEMprotocol,includingNPP,grossprimaryproductivity(GPP),andcarbonuseefficiency(CUE),arepro-
videdinthefivecompanionpaperscitedabove.Inthepresentpaper,wefocusoninterannual(temporal)and
interplot(i.e.,spatial)variabilityofeachNPPcomponent.Toestimateinterannualvariation,weestimatethe
interannualvariationover3or(forTambopata)4yearsforeachplotandprovidetheaverageofthesevaria-
tionsforallplots:thisisthetypicalinterannualvariationofahumidordryplot.Weestimateintersitevariation
bycalculatingtheSDofeachNPPcomponentforaggregatedhumidordryplots.Alltemporalvariabilityis
presentedasgreyribbons,andthevariabilityacrossplotsispresentedaserrorbarscenteredonthemean.
Ternarydiagramswereusedtoexploretrendsinallocationofproductivityoverseasonalcyclesinhumidand
dryforestsites.Weexploretheseasonalvarianceinallocationtoeachcomponentusingaternarydiagramof
thevarianceofNPPallocation.Anormalizedallocationvarianceparameter,NVar(x)wascalculatedas
NVarðxÞ ¼varðxÞ=ðvarðxÞ þvarðyÞ þvarðzÞÞ (4)
wherevar(x)isthevarianceoftheallocationcoefficientofavariablexandyandzaretheothertwoallocationterms.
Weexploredtheseasonalamplitudeofeachabioticandbioticparameters.Seasonalamplitudewasestimatedby
subtractingtheminimum3monthsmovingaveragefromthemaximummovingaveragerecorded.Repeated-
measuresANOVAwasusedtoassessthesignificanceofseasonalincreasesinproductivity.Allstatisticalanalyses
wereperformedwiththeRversion2.9.0statisticalpackage[Chambers,2008].TheRscriptsusedtoanalyzethedata
areavailableongithub.com/oxfordecosystemslab.Aswearecontinuallyimprovingthesescripts,thevalueswepre-
sentmaydifferslightlybetweencompanionpapers.AllchangesinthecodesinceMay2014areavailableonline.
3. Results
3.1. SeasonalTrendsinWeather
3.1.1. SolarIrradiance
Atallsites,solarirradianceincreasedtowardtheendofthedryseason,reachingapeakduringthedryto
wettransitionseason,betweenAugustandOctober(Figure1a).Inmostsites,lightavailabilitywasabove
150Wm(cid:1)2 throughout the year.Allpahuayo,the cloudiestandwettestsite, in NE Amazonia, experienceda
more pronounced seasonality of solar irradiance, with values only increasing above 150Wm(cid:1)2 between
AugustandDecember(Figure1a).
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 705
Global Biogeochemical Cycles 10.1002/2015GB005270
Figure1.SeasonalityofweathervariablesinthreelowlandAmazonianforestssitesinPeru,Bolivia,andBrazil.Weather
stationswerelocatedatTambopata(TAM-05),Kenya(KEN-01),Caxiuanã(CAX-06),Allpahuayo(ALP-01),andTanguro
(TAN-05),andsolarirradiance(Wm(cid:1)2),precipitation(mmmonth(cid:1)1),airtemperature(°C),andrelativehumidity(%)are
presentedonamonthlytimescale,asameanover3years(2009–2011).Thesolidlinesrepresenttheaverageofhumid
sites(Caxiuanã,Tambopata,andAllpahuayo),andthedottedlinesrepresentthedrysites(KenyaandTanguro).The
KEN-01weatherstationisusedinbothdryandhumidsiteanalyses,asitisthelocalweatherstationforKEN-01and
KEN-02.Interannualvariabilitiesareprovidedasgreyribbon(±SD);intersitevariabilitiesarepresentedaserrorbars
centeredonthemean(±SD).
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 706
Global Biogeochemical Cycles 10.1002/2015GB005270
3.1.2. Precipitation
Meanmonthlyrainfalloverthestudy
period showed consistent seasonal
trends at all sites, peaking between
DecemberandMarch.Thedryseason
isdefinedastheperiodwhenpoten-
tial evapotranspiration exceeds rain-
fall, typically when rainfall reaches
values below 100mmmonth(cid:1)1.
Humidsitesdonotexperienceapro-
longed or intense dry season. The
monthly precipitation of Allpahuayo
exceeds 100mmmonth(cid:1)1 through-
out the year. Caxiuanã in NE
Amazonia experiences a long but
mild dry season from August to
November, when precipitation
averages 59mmmonth(cid:1)1. On aver-
age,drysitesexperienceasignificant
(t=5.73, df=4, p-value<0.0005)
prolonged dry season from May to
September (Figure 1b), although
Caxiuanã experiences a dry season
later in the year, from June to
November (Figure S1 in the
supportinginformation).
Figure2.Seasonalityofnetprimaryproductivitycomponentsin11lowland 3.1.3. RelativeHumidity
AmazonianforestplotsinPeru,Bolivia,andBrazil.Datawereaveragedover The seasonal amplitude of mean
humidsites(solidline,ALP-01,ALP-30,CAX-03,CAX-06,CAX-08,TAM-05,and monthly relative humidity over the
TAM-06)anddrysites(dottedline,KEN-02,KEN-01,TAN-05,andTAN-06).Net
study period was strongest in
primaryproductivityofleaf(NPPLeaf),abovegroundcoarsewood(NPPACW),
andfineroots(NPPFineroots)ispresentedonamonthlytimescale,asamean Tanguro and Kenia in the southern
over3years(2009–2011)or4years(2009–2012,Tambopata).Thestartand limit of Amazonia. All other sites
endofthedryseasonarerepresentedbyverticalgreylines.Interannualvari- experienced little (Tambopata) to
abilitiesareprovidedasgreyribbon(±SD);intersitevariabilitiesarepresented no (Allpahuayo, Caxiuanã) seasonal
aserrorbarscenteredonthemean(±SD).Dataforeachplotareprovidedin
variation in relative humidity
thesupportinginformation.
(Figure 1d).
3.1.4. AirTemperature
Meanmonthlytemperatureoverthestudyperiodshowednoconsistentseasonaltrendsacrosstheprecipi-
tationgradient(Figure1c).
3.2. SeasonalTrendsinNetPrimaryProductivity
3.2.1. CanopyProductivity
Themeanannualvalueforcanopynetprimaryproductivity(NPP )is3.89±0.10MgCha(cid:1)1yr(cid:1)1(n=8)for
Canopy
humidsitesand4.50±0.12MgCha(cid:1)1yr(cid:1)1(n=4)fordrysites.Totallitterfallincludesallcanopycomponents,as
describedabove(Figure2).
ThereisaconsistentsurgeinNPP duringthedryseasoninbothprecipitationregimes.NPP
Canopy Canopy
reaches a maximum at the start of the dry season (May) in dry sites and at the height of the dry
season(August)inhumidanddrysites.TheseasonalincreaseinNPP issignificantinbothhumid
Canopy
(p-value<0.005)anddry(p-value<0.0005)sites.Meanmonthlyvaluesoverthestudyperiodrangefrom
0.48±0.17MgCha(cid:1)1month(cid:1)1 to 0.20±0.07MgCha(cid:1)1month(cid:1)1 in humid sites and from 0.56
±0.23MgCha(cid:1)1month(cid:1)1to0.22±0.05MgCha(cid:1)1month(cid:1)1indrysites(Figure3b).
Canopy litterfall is produced throughout the year and increases at the onset of the dry season (August),
coincidingwiththepeakinNPP .SeasonalityinLAIwasnotdetectedinthesesites(notshown).LAI
Canopy
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 707
Global Biogeochemical Cycles 10.1002/2015GB005270
above or close to 5 will have some
degreeofsaturationinbothLAI2000
andthephotos;thus,itispossiblethat
wearenotabletodetecttheseasonal
trends in LAI in the less seasonal for-
ests. As canopy production, which
comprisesleaf,flower,fruit,twig,and
unidentified material, that is domi-
natedbyleafproductivity,weassume
that the seasonal trends of canopy
productivity are also those of
leafproductivity.
3.2.2. FlowerandFruitProductivity
3.2.2.1. Flower
Annualestimatesofflowernetprimary
productivity(NPP )showednosig-
Flower
nificant differences between humid
Figure 3.Seasonality of net primary productivity of reproductive organs anddrysites(p-value>0.5).Themean
in11lowlandAmazonianforestplots,Peru,Bolivia,andBrazil.Datawere annual value for NPP is 0.14
Flower
averagedoverhumidsites(solidline,ALP-01,ALP-30,CAX-03,CAX-06, ±0.01MgCha(cid:1)1yr(cid:1)1(n=8)forhumid
CTAANX--0085,,TaAnMd-T0A5N,a-0n6d).TNAeMt-p0r6i)maanrdydprryodsiutectsiv(ditoyttoefdflloinwee,rK(ENNP-P0F2l,oKwEeNr)-0a1n,d sites and 0.09±0.007MgCha(cid:1)1yr(cid:1)1
fruit(NPPFruit)ispresentedonamonthlytimescale,asameanover3years (n=4)fordrysites.
(2009–2011)or4years(2009–2012,Tambopata).Thestartandendofthe
NPP showstrongseasonalityin
dryseasonarerepresentedbyverticalgreylines.Interannualvariabilities Flower
areprovidedasgreyribbon(±SD);intersitevariabilitiesarepresentedas both humid and dry sites. In humid
errorbarscenteredonthemean(±SD).Dataforeachplotareprovided sites, production increased from a
inthesupportinginformation. negligible amount in July (middry
season) to a maximum of 0.04
±0.03MgCha(cid:1)1mo(cid:1)1inNovember(earlywetseason)anddecreasedsharplyoverthefollowingmonth.
AssumingthatthepeakinflowerlitterfallcapturesasurgeinNPP overthepreviousmonth[Girardin
Flower
etal.,2014],thepeakinNPP (dominatedbyleafNPP)precedesthesynchronizedfloweringeventby
Canopy
twomonths.Indrysites,flowerswerealsoproducedinnegligibleamountsthoughttheyear,increasing
significantly(p-value<0.0005)attheendofthedryseason(September).Thepeakinflowerproduction
coincideswiththepeakinNPP (Figure3b).
Canopy
3.2.2.2. Fruit
Annualestimatesoffruitnetprimaryproductivity(NPP )showednosignificantdifferencebetweenthetwo
Fruit
precipitationregimes(p-value=0.20).ThemeanannualvalueforNPP is0.23±0.007MgCha(cid:1)1yr(cid:1)1(n=8)
Fruit
forhumidsitesand0.18±0.01MgCha(cid:1)1yr(cid:1)1(n=4)fordrysites.
Wefoundastaggeredfruitingphenologyinbothforesttypes.Assumingthattheseasonalityoffruitlitterfall
isagoodproxyfortheseasonalityofNPP ,weobserveabimodalpeakinfruitproductioninhumidand
Fruit
drysites.Valuesrangefrom0.03±0.05MgCha(cid:1)1yr(cid:1)1to0.01±0.01MgCha(cid:1)1yr(cid:1)1inhumidsitesandfrom
0.03±0.06MgCha(cid:1)1yr(cid:1)1to0.003±0.004MgCha(cid:1)1yr(cid:1)1indrysites(Figure3c).TheseasonalityofNPP
Fruit
wassurprisinglysmallatallsites.
3.2.3. AbovegroundCoarseWoodyNetPrimaryProductivity
The mean annual value of aboveground coarse wood net primary productivity (NPP ) is 2.08
ACW
±0.04MgCha(cid:1)1yr(cid:1)1(n=8)forhumidsitesand2.40±0.09MgCha(cid:1)1yr(cid:1)1(n=4)fordrysites.
TheseasonaltrendofNPP isremarkablyconsistentbetweenbothprecipitationregimes,withgreaterseasonal
ACW
amplitudeindrysites.Atallsites,woodproductiongraduallyincreasesfromitslowestvalueinthedryseason
(June–August)toitshighestvalueinthehumidseason(December–January).Thisgradualdeclinetakesplaceover
aperiodof5months.Then,NPP beginstodeclinebeforetheendofthehumidseason(March),reaching
ACW
values close to its lowest value as early as April. Values range from 0.23±0.03MgCha(cid:1)1yr(cid:1)1 to 0.12
±0.04MgCha(cid:1)1yr(cid:1)1 (n=8) in humid sites and from 0.38±0.10MgCha(cid:1)1yr(cid:1)1 to 0.09±0.03MgCha(cid:1)1yr(cid:1)1
(n=4)indrysites(Figure2a).
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3.2.4. FineRootNet
PrimaryProductivity
Mean annual values of fine root net
primary productivity (NPP )
Fine roots
differ significantly between humid
and dry sites (p-value<0.0005). The
mean annual value for NPP
Fine roots
is 3.81±0.08MgCha(cid:1)1yr(cid:1)1 (n=8)
for humid sites and 2.24
±0.04MgCha(cid:1)1yr(cid:1)1 (n=4) for
drysites.
NPP seasonality is remark-
Fine rootss
ably low at all sites. We recorded a
nonsignificant increase in root pro-
ductionatthestartofthewetseason
in humid (p-value=0.35) and dry
sites (p-value=0.15) (Figures 2
andS2).
3.3. SeasonalTrendsin
NPPAllocation
We explore the seasonal carbon
allocation trends of each forest
using ternary diagrams (Figures 4
Figure 4.Seasonality of net primary productivity allocation to canopy and 5). A ternary diagram graphi-
(NPPCanopy),abovegroundcoarsewood(NPPACW),andfineroots cally depicts the ratios of the three
(NPPFine roots)in11lowlandAmazonianforestplots,Peru,Bolivia,and variables as positions in an equilat-
Brazil.Datawereaveragedoverhumidsites(solidline,ALP-01,ALP-30,
eral triangle. In Figure 5, each of
CAX-03,CAX-06,CAX-08,TAM-05,andTAM-06)anddrysites(dottedline,
the three apexes of the triangle
KEN-02,KEN-01,TAN-05,andTAN-06).Allocationdataarepresentedas
percentageoftotalNPPallocatedtoeachcomponenteachmonth,asa represent an NPP component, and
meanover3years(2009–2011)or4years(2009–2012,Tambopata). each side represents a percent
Interannualvariabilitiesareprovidedasgreyribbon(±SD);intersite
abundance scale. Each point
variabilitiesarepresentedaserrorbarscenteredonthemean(±SD).Data
plotted in the triangle represents
foreachplotareprovidedinthesupportinginformation.
the variance of monthly carbon
allocation to NPP , NPP ,
ACW Canopy
and NPP , as a percentage.
Fine roots
It shows two distinct seasonal NPP allocation patterns. Dry sites (KEN-02, KEN-01, TAN-05, and TAN-06)
have low variance in NPP allocation and high variance in NPP and NPP allocation. This
Root ACW Canopy
may suggest a constant allocation to fine roots, while there is a allocation trade-off between wood
production and canopy production. Humid sites (CAX-03, CAX-06, CAX-08, TAM-05, and TAM-06) show
little variance in allocation to NPP , but high variance in allocation between NPP and
ACW Fine roots
NPP (Figure S4).
Canopy
The data used in Figure 5 are presented in the supporting information as ternary diagrams of mean
monthly NPP allocation for each plot (Figure S5). We interpret seasonal movements along an axis as
an indication of a trade-off between two components. In the dry sites, we observe constant allocation
toroots(e.g.,~30%allocationtoNPP throughouttheyearinKEN-01andKEN-02)andaseasonal
Fineroots
trade-off between NPP and NPP . We find higher allocation to NPP during the wet season
ACW Canopy ACW
and higher allocation to NPP during the dry season. In the humid sites, we observe constant
Canopy
allocation to NPP and a seasonal trade-off between NPP and NPP . We find higher
ACW Fine roots Canopy
allocation to NPP during the wet season and higher allocation to NPP during the dry
Fine roots Canopy
season (Figure S5).
GIRARDINETAL. SEASONALTRENDSOFAMAZONIANFORESTS 709