pan‐tropical prediction of forest structure from the ... · bastin et al. | 3 36malaysia campus,...

2 | BASTIN et al.

Samir Gonçalves Rolim81 | Hugo Romero Saltos82 | Luiz Marcelo Brum Rossi81 | Wilson Roberto Spironello32 | Francesco Rovero76 | Philippe Saner83 | Denise Sasaki84 | Mark Schulze85 | Marcos Silveira86 | James Singh87 | Plinio Sist10,88 | Bonaventure Sonke60 | J. Daniel Soto21 | Cintia Rodrigues de Souza24 | Juliana Stropp89 | Martin J. P. Sullivan35 | Ben Swanepoel34 | Hans ter Steege25,90 | John Terborgh91,92 | Nicolas Texier93 | Takeshi Toma94 | Renato Valencia95 | Luis Valenzuela75 | Leandro Valle Ferreira96 | Fernando Cornejo Valverde97 | Tinde R. Van Andel25 | Rodolfo Vasque77 | Hans Verbeeck61 | Pandi Vivek22 | Jason Vleminckx98 | Vincent A. Vos78,99 | Fabien H. Wagner100 | Papi Puspa, Warsudi101 | Verginia Wortel102 | Roderick J. Zagt103 | Donatien Zebaze60

1DepartmentofEnvironmentalSystemsScience,InstituteofIntegrativeBiology,ETHZürich,Zürich,Switzerland2 CarbonCycleandEcosystemGroupVS,NASA,JetPropulsionLaboratory,CaliforniaInstituteofTechnology,Pasadena,California,USA3LandscapeEcologyandPlantProductionSystem,UniversitélibredeBruxelles.CP264‐2,Bruxelles,Belgium4Carboforexpert(carboforexpert.ch),Hermance,Switzerland5SmithsonianTropicalResearchInstitute,Balboa,Panama6DepartmentofEcologyandEvolutionaryBiology,BrownUniversity,Providence,RhodeIsland

7InstituteatBrownforEnvironmentandSociety,BrownUniversity,Providence,RhodeIsland8NASA,JetPropulsionLaboratory,CaliforniaInstituteofTechnology,Pasadena,California, USA9AMAPLab,IRD,CIRAD,CNRS,INRA,Univ.Montpellier,Montpellier,France10Cirad,URForest&Societies,Montpellier,France11INPHB(InstitutNationalPolytechniqueFélixHouphouetBoigny),Yamoussoukro,IvoryCoast12FacultyofScience,UniversitiBruneiDarusallam,Gadong,BruneiDarussalam13GemblouxAgro‐BioTech,UniversitédeLiège,Gembloux,Belgium14CIRCLE,EnvironmentDepartment,WentworthWay,UniversityofYork,York,UnitedKingdom15TropicalForestsandPeopleResearchCentre,UniversityoftheSunshineCoast,SunshineCoast,Queensland,Australia16FlamingoLandLtd,KirbyMisperton,UnitedKingdom17NicholasSchooloftheEnvironment,DukeUniversity,Durham,NorthCarolina,USA18FieldMuseumofNaturalHistory,Chicago,Illinois,USA19BiologicalDynamicsofForestFragmentProject(BDFFP–INPA/STRI),Manaus,Brazil20InstitutFacultairedesSciencesAgronomiquesdeYangambi,Yangambi,DemocraticRepublicoftheCongo21MuseodeHistoriaNaturalNoelKempffMercado,SantaCruz,Bolivia22DepartmentofEcologyandEnvironmentalSciences,PondicherryUniversity,Pondicherry,India23FrenchInstituteofPondicherry(IFP),Pondicherry,India24EmbrapaAmazôniaOcidental,Manaus,Brazil25NaturalisBiodiversityCentre,Leiden,TheNetherlands26UniversidadeFederaldoAcre,CampusFloresta,Acre,Brazil27ServiceofWoodBiology,RoyalMuseumforCentralAfrica,Tervuren,Belgium28InstituteofTropicalForestConservation,MbararaUniversityofScienceandTechnology,Mbarara,Uganda29IsotopeBioscienceLaboratory–ISOFYS,GhentUniversity,Ghent,Belgium30SenckenbergBiodiversityandClimateResearchCentre(BiK‐F),FrankfurtamMain,Germany31DepartmentofBiologicalSciences,GoetheUniversität,FrankfurtamMain,Germany32NationalInstituteforAmazonianResearch(INPA),Manaus,Brazil33SchoolofScienceandtheEnvironment,ManchesterMetropolitanUniversity,Manchester,UnitedKingdom34WildlifeConservationSociety,NewYork,NewYork, USA35SchoolofGeography,UniversityofLeeds,Leeds,UnitedKingdom

| 3BASTIN et al.

36MalaysiaCampus,JalanBroga,Semenyih,Malaysia37UCL‐ELI,EarthandLifeInstitute,UniversitécatholiquedeLouvain,Louvain‐la‐Neuve,Belgium38EcoleRégionalePost‐universitaired’AménagementetdeGestionIntégrésdesForêtsetTerritoiresTropicaux,Kinshasa,DemocraticRepublicoftheCongo39LaboratoireEvolutionetDiversitébiologique,CNRS&UniversitéPaulSabatier,Toulouse,France40DepartmentofEcologyandEvolutionaryBiology,UniversityofConnecticut,Storrs,Connecticut,USA41DepartmentofBotanyandPlantPhysiology,UniversityofBuea,Buea,Cameroon42DepartmentofBiology,UniversityofMissouri‐StLouis,StLouis,Missouri, USA43FieldMuseumofNaturalHistoryandMortonArboretum,Chicago,Illinois, USA44Coronado,Inst.deInvestigacionesdelaAmazoniaPeruana,Iquitos,Peru45ONFpôleR&D,Cayenne,France46Cirad,UMREcoFoG(AgroParisTech,CNRS,Inra,UniversitedesAntilles,UniversitedelaGuyane),Kourou,FrenchGuiana47DepartmentofBotanyandPlantPathology,OregonStateUniversity,Corvallis,Oregon48DepartamentodeBiología,UniversidaddeLaSerena,LaSerena,Chile49UniversidadAutónomadeSanLuisPotosí,SanLuisPotosí,México50DepartmentofConservationEcology,Philipps‐UniversitätMarburg,Marburg,Germany51Geography,CollegeofLifeandEnvironmentalSciences,UniversityofExeter,Exeter,UnitedKingdom52ConservationInternationalSuriname,Paramaribo,Suriname53FacultyofScience,DepartmentofPlantBiology,UniversityofYaoundeI,Yaoundé,Cameroon54SmithCollegeBotanicGarden,Northampton,Massachusetts55RoyalBotanicGardenEdinburgh,Edinburgh,UnitedKingdom56MuseodeHistoriaNaturalAlcided’Orbigny,Cochabamba,Bolivia57PlantEcology,UniversityofGoettingen,Goettingen,Germany58DepartmentofEcologyandEvolutionaryBiology,UniversityofCalifornia,LosAngeles,California, USA59OrganizationforTropicalStudies,LaSelva,CostaRica60PlantSystematicandEcologyLaboratory,HigherTeacher’sTrainingCollege,UniversityofYaoundéI,Yaoundé,Cameroon61CAVElab–ComputationalandAppliedVegetationEcology,GhentUniversity,Ghent,Belgium62CTFS‐ForestGEO,SmithsonianTropicalResearchInstitute,Washington,USA63DepartmentofSystematicandEvolutionaryBotany,UniversityofZurich,Zurich,Switzerland64AgroParisTech,DoctoralSchoolABIES,Paris,France65CenterforInternationalForestryResearch,Bogor,Indonesia66CentreforTropicalEnvironmentalandSustainabilityScience,CollegeofScienceandEngineering,JamesCookUniversity,Cairns,Queensland,Australia67DepartmentofLifeSciences,ImperialCollegeLondon,Ascot,UnitedKingdom68DepartmentofEnvironmentalScienceandPolicy,GeorgeMasonUniversity,Fairfax,Virginia69EnvironmentalChangeInstitute,SchoolofGeographyandtheEnvironment,UniversityofOxford,Oxford,UnitedKingdom70UniversidadedoEstadodeMatoGrosso,CampusdeNovaXavantina,NovaXavantina,Brazil71UdzungwaEcologicalMonitoringCentre,UdzungwaMountainsNationalPark,Mang’ula,Tanzania72SokoineUniversityofAgriculture,Morogoro,Tanzania73FacultyofForestry,MulawarmanUniversity,Samarinda,Indonesia74EscueladeCienciasForestales,UnidadAcadémicadelTrópico,UniversidadMayordeSanSimón,Sacta,Bolivia75JardínBotánicodeMissouri,Oxapampa,Peru76MUSE‐MuseodelleScienze,Trento,Italy77UniversidadEstatalAmazónica,Puyo,Pastaza,Ecuador78UniversidadAutónomadelBeni,Riberalta,Bolivia79ScienceandEducation,TheFieldMuseum,Chicago,Illinois80CentreValBio,Ranomafana,Madagascar81EmbrapaFlorestas,Colombo,Brazil82SchoolofBiologicalSciencesandEngineering,YachayTechUniversity,Urcuquí,Ecuador83DepartmentofEvolutionaryBiologyandEnvironmentalStudies,UniversityofZurich,Zurich,Switzerland84FundaçãoEcológicaCristalinoAltaFloresta,MatoGrosso,Brazil85H.J.AndrewsExperimentalForest,BlueRiver,Oregon86MuseuUniversitário,UniversidadeFederaldoAcre,RioBranco,Brazil

4 | BASTIN et al.

87GuyanaForestryCommission,Georgetown,Guyana88ForestsandSocieties,Univ.Montpellier,CIRAD,Montpellier,France89InstituteofBiologicalandHealthSciences,FederalUniversityofAlagoas,Maceió,Brazil90SystemsEcology,FreeUniversity,Amsterdam,TheNetherlands91FloridaMuseumofNaturalHistoryandDepartmentofBiology,UniversityofFlorida–Gainesville,Gainesville,Florida92DepartmentofBiology,JamesCookUniversity,Cairns,Queensland,Australia93Laboratoired’EvolutionBiologiqueetEcologie,FacultédesSciences,UniversitélibredeBruxelles,Bruxelles,Belgium94ForestryandForestProductsResearchInstitute,Tsukuba,Japan95EscueladeCienciasBiológicas,PontificiaUniversidadCatólicadelEcuador,Quito,Ecuador96CoordenaçãodeBotânica,MuseuParaenseEmilioGoeldi,Belém,Brazil97AndestoAmazonBiodiversityProgram,MadredeDios,Peru98DepartmentofIntegrativeBiology,UniversityofCalifornia,Berkeley,California99CentrodeInvestigaciónyPromocióndelCampesinado–NorteAmazónico,Riberalta,Bolivia100RemoteSensingDivision,NationalInstituteforSpaceResearch–INPE,SãoJosédosCampos,Brazil101TheCenterforReforestationStudiesintheTropicalRainForest(PUSREHUT),MulawarmanUniversity,EastKalimantan,Indonesia102CenterforAgriculturalResearchinSuriname(CELOS),Paramaribo,Suriname103TropenbosInternational,Wageningen,TheNetherlands

CorrespondenceJean‐FrançoisBastin,InstituteofIntegrativeBiology,DepartmentofEnvironmentalSystemsScience,ETHZürich,8092Zürich,Switzerland.Email:[email protected]

AbstractAim:Largetropical trees formthe interfacebetweengroundandairborneobserva‐tions,offeringauniqueopportunitytocaptureforestpropertiesremotelyandtoinves‐tigatetheirvariationsonbroadscales.However,despiterapiddevelopmentofmetricstocharacterizetheforestcanopyfromremotelysenseddata,agapremainsbetweenaerialandfieldinventories.Toclosethisgap,weproposeanewpan‐tropicalmodeltopredictplot‐levelforeststructurepropertiesandbiomassfromonlythelargesttrees.Location:Pan‐tropical.Time period:Early21stcentury.Major taxa studied:Woodyplants.Methods:Usingadatasetof867plotsdistributedamong118sitesacrossthetropics,wetestedthepredictionofthequadraticmeandiameter,basalarea,Lorey’sheight,communitywooddensityandabovegroundbiomass(AGB)fromtheithlargesttrees.Results:Measuringthelargesttreesintropicalforestsenablesunbiasedpredictionsofplot‐andsite‐levelforeststructure.The20largesttreesperhectarepredictedquad‐raticmeandiameter,basal area, Lorey’sheight, communitywooddensity andAGBwith12,16,4,4and17.7%ofrelativeerror,respectively.Mostoftheremainingerrorinbiomasspredictionisdrivenbydifferencesintheproportionoftotalbiomassheldinmedium‐sizedtrees(50–70cmdiameteratbreastheight),whichshowssomeconti‐nentaldependency,withAmericantropicalforestspresentingthehighestproportionoftotalbiomassintheseintermediate‐diameterclassesrelativetoothercontinents.Main conclusions:Ourapproachprovidesnewinformationontropicalforeststruc‐tureandcanbeusedtogenerateaccuratefieldestimatesoftropicalforestcarbonstocks to support the calibration and validationof current and forthcoming spacemissions.Itwillreducethecostoffieldinventoriesandcontributetoscientificunder‐standingoftropicalforestecosystemsandresponsetoclimatechange.

K E Y W O R D S

carbon,climatechange,foreststructure,largetrees,pan‐tropical,REDD+,tropicalforestecology

mailto:[email protected]

| 5BASTIN et al.

1 | INTRODUC TION

The fundamental ecological function of large trees is well estab‐lished for tropical forests. They offer shelter to many organisms(Lindenmayer,Laurance,&Franklin,2012;Remm&Lõhmus,2011),regulate forest dynamics, regeneration (Harms,Wright, Calderón,Hernández,&Herre,2000;Rutishauser,Wagner,Herault,Nicolini,&Blanc,2010)andtotalbiomass(Stegenetal.,2011),andareimport‐antcontributors to theglobal carboncycle (Meakemetal.,2018).Beingmajorcomponentsofthecanopy,thelargesttreesmayalsosuffermore than sub‐canopy and understorey trees from climatechange,becausetheyaredirectlyexposedtovariationsinsolarradi‐ation,windstrength,temperatureseasonalityandrelativeairhumid‐ity(Bennett,McDowell,Allen,&Anderson‐Teixeira,2015;Laurance,Delamônica,Laurance,Vasconcelos,&Lovejoy,2000;Lindenmayeretal.,2012;Meakemetal.,2018;Nepstad,Tohver,Ray,Moutinho,&Cardinot,2007;Thomas,Kellner,Clark,&Peart,2013).Giventhattheyarevisiblefromthesky,largetreesareidealformonitoringfor‐estresponsestoclimatechangeviaremotesensing(RS;Asneretal.,2017;Bennettetal.,2015).

Largetreesencompassadisproportionatefractionoftotalabo‐vegroundbiomass (AGB) in tropical forests (Chave,Riera,Dubois,&Riéra,2001;Lutzetal.,2018),withsomevariations intheir rel‐ative contribution to the total AGB among the tropical regions(Feldpauschetal.,2012).InCentralAfrica,thelargest5%oftreesinaforestsampleplot(i.e.,the5%oftreeswiththelargestdiameterat130cm)store50%of forestplotAGBonaverage (Bastinetal.,2015).Consequently,thedensityoflargetreeslargelyexplainsvari‐ationinforestAGBatlocal(Clark&Clark,1996),regional(Malhietal.,2006;Saatchi,Houghton,DosSantosAlvalá,Soares,&Yu,2007)andcontinentalscales(Sliketal.,2013;Stegenetal.,2011).Detailingthecontributionofeach single tree to thediameter structure,weshowedpreviouslythatplot‐levelAGBcanbepredictedfromafewlargetrees(Bastinetal.,2015),withthemeasurementofthe20larg‐esttreesperhectarebeingsufficienttoestimateplot‐levelbiomasswith<15%error inreferencetogroundestimates.Thesefindingssuggested that a substantial gain of cost‐effectiveness might beachievedbyfocusingforest inventoriesonthe largesttreesratherthanallsizeclasses.Likewise,itsuggestedthatRSapproachescouldfocusonthemeasurementofthelargesttrees,insteadofpropertiesoftheentireforeststand.

SeveraleffortsareunderwaytoclosethegapbetweenRSofforestbiomassandfieldsurveys(Coomesetal.,2017;Juckeretal.,2017).However,existingRSapproachestypicallyrequiregroundmeasurement of all trees ≥10cm in diameter (D) for calibration(Asner&Mascaro,2014;Asneretal.,2012).Collectingsuchdatain the field is costly and time consuming,which therefore limitsthespatialrepresentativenessofavailableplotnetworks.Besides,extrapolationmethods of ground‐based biomass estimations onRSdatastill face important limits.For instance,usingmeancan‐opy height extracted from active sensors (HoTongMinh et al..,2016;Mascaro,Detto,Asner,&Muller‐Landau,2011)orcanopygrain derived fromoptical images (Bastin et al., 2014; Ploton et

al., 2017; Ploton, Pélissier, & Proisy, 2012; Proisy, Couteron, &Fromard,2007),thebiomassispredictedwithanerrorofonly10–20%comparedwithground‐basedestimates.However,thisgoodlevel of accuracy is limited to the extent of the RS scene used,whichdecreasesconsiderablyintheupscalingstepnecessaryfornationalorglobalmaps(Xuetal.,2017).Apromisingdevelopmenttoalleviatethisspatialrestrictionliesinthe‘universalapproach’,proposed by Asner et al. (2012) and further adapted by AsnerandMascaro (2014), inwhich plot‐level biomass is predicted bya linear combinationofground‐basedand remotely sensedmet‐rics. The ‘universal approach’ relies upon canopy heightmetricsderivedfromradarorLiDAR(topofcanopyheight,TCH),andbasalarea(BA;i.e.,thecumulatedcross‐sectionalareaofthestems)andcommunitywooddensity(i.e.,weightedbybasalarea,WDBA)de‐rivedfromfieldinventories.PlotAGBisthenpredictedasfollows(Asneretal.,2012):

Although generally performing better than approaches basedsolelyonRSoftreeheight(Coomesetal.,2017),thismodelreliesonexhaustivegroundmeasurements(i.e.,wooddensityandthebasalareaofalltrees>10cmindiameterat130cm,neitherofwhichismeasuredusinganyexistingremotelysenseddata).

Recent advances in RS allow the identification of single treesin the canopy (Ferraz, Saatchi,Mallet,&Meyer, 2016), estimationofadultmortalityratesforcanopytreespecies(Kellner&Hubbell,2017), description of the forest diameter structure (Stark et al.,2015),depictionofcrownandgapshapes(Coomesetal.,2017)andeventheidentificationofsomefunctionaltraitsofcanopyspecies(Asneretal.,2017).Giventhatroutineretrievalofsomecanopytreemetricsiswithinreach,inthepresentstudywetestthecapacityofthelargesttrees(i.e.,treesthatcanpotentiallybederivedusingRS),topredictplot‐levelbiomass.Tothisend,weadaptedEquation(1)asfollows:

wherefortheithlargesttrees,DgLTisthequadraticmeandiameter,HLTthemeanheight,andWDLTthemeanwooddensityamongtheithlargesttrees.

Using a large database of forest inventories gathered acrossthetropics(Figure1), includingsecondaryandold‐growthforestplots, we test the ability of the largest trees to predict variousmetrics estimatedat1‐haplot level, namely themeanquadraticdiameter, the basal area, the Lorey’s height (i.e., plot‐averageheightweightedbybasalarea),thecommunitywooddensity(i.e.,plot‐average wood density weighted by basal area) and meanaboveground live biomass (Supporting Information Figure 1). Bytesting different numbers of largest trees as predictors,we aimto propose a threshold for theminimal number of largest treesrequiredtopredictforestplotmetricsatapan‐tropicallevelwithnobiasandlowuncertainty(i.e.,error<20%).Althoughpreviouswork focused on estimating biomass in Central African forests(Bastin et al., 2015), the present study aims at generalizing the

(1)AGB= aTCHb1BA

b2WD

b3

BA

(2)AGB= a(DgLTiHLTiWDLTi)b1

6 | BASTIN et al.

potentialof largetrees topredict thesedifferentplotmetricsatcontinentalandpan‐tropicalscales.Takingadvantageofauniquedatasetgatheredacrossthetropics(8671‐haplots),wealsoinves‐tigatemajordifferencesinforeststructureacrossthethreemaintropicalregions:theAmericas,AfricaandAsia.WefurtherdiscusshowthisapproachcanbeusedtoguideinnovativeRStechniquesandincreasethefrequencyandrepresentativenessofgrounddatatosupportglobalcalibrationandvalidationofcurrentandplannedspace missions. These include the NASA Global EcosystemDynamics Investigation (GEDI), NASA‐ISRO Synthetic ApertureRadar(NISAR)andESAP‐bandradar(BIOMASS)(Dubayahetal.,2014;LeToanetal.,2011).Thisstudyisastepforwardsinbring‐ingtogetherRSandfieldsamplingtechniquesforquantificationofterrestrialcarbonstocksintropicalforests.

2 | MATERIAL S AND METHODS

2.1 | Database

Forthisstudy,wecompiledstandardforestinventoriesconductedin8671‐haplotsfrom118sitesacrossthethreetropicalregions(Figure1),includingmatureandsecondaryforests.Eachsitecom‐prisesall theplots inagivengeographical location (i.e.,withina10‐km radius and collected by a principal investigator and theirteam). These consisted of 389 plots in America (69 sites), 302plotsinAfrica(35sites)and176plotsinAsia(14sites).Datawereprovided by principal investigators (see Supporting InformationTable 1) and through datasets available on the following net‐works:TEAM,CTFS(www.forestgeo.si.edu/;Conditetal.,2012)and ForestPlots (https://www.forestplots.net/) for AfriTRON(the African Tropical Rainforest Observation Network; www.af‐ritron.org)andRAINFOR(theAmazonforest inventorynetwork;networks.

We selected plots located between 23°N and 23°S, in‐cluding tropical islands,with an area of 1ha to ensure stableintra‐samplevarianceinbasalarea(Clark&Clark,2000).Plotsinwhich≥90%of thestemswere identified tospeciesand inwhich all stems with the diameter at 130cm of ≥10cm had

beenmeasuredwereincluded.Wooddensity,hererecordedasthewooddrymassdividedbyitsgreenvolume,wasassignedtoeachtreeusingthe lowestavailabletaxonomic levelofbotan‐ical identifications (i.e., speciesorgenus)and thecorrespond‐ingaveragewooddensityrecordedintheGlobalWoodDensityDatabase (GWDD; Chave et al., 2009; Zanne et al., 2009).BotanicalidentificationwasharmonizedthroughtheTaxonomicNamesResolutionService(https://tnrs.iplantcollaborative.org),forbothplot inventoriesandtheGWDD.Fortreesnot identi‐fied to species or genus (ca.5%), we used plot‐average wooddensity.Weestimatedheights of all trees usingChave et al.’s(2014)pan‐tropicaldiameter–heightmodel,whichaccountsforheterogeneity in theD–H relationship using an environmentalproxy:

whereDisthediameterat130cmandEisameasureofenvironmen‐talstress(Chaveetal.,2014).Forsiteswithtreeheightmeasurements(n=20),wedeveloped localD–Hmodels, using aMichaelis–Mentenfunction(Moltoetal.,2014).WeusedtheselocalmodelstovalidatethepredictedLorey’sheight(i.e.,plot‐averageheightweightedbyBA)fromthe largest trees,ofwhichheighthasbeenestimatedwithagenericH–Dmodel[Equation(3),Chaveetal.,2014].

Weestimatedplotbiomassasthesumofthebiomassoflivetreewithdiameterat130cmof≥10cm,usingthefollowingpan‐trop‐ical allometric model (Réjou‐Méchain, Tanguy, Piponiot, Chave, &Hérault,2017):

2.2 | Plot‐level metric estimation from the largest trees

Therelationshipbetweeneachplotmetric,namelybasalarea(BA),the quadratic mean diameter (Dg), Lorey’s height (HBA; the meanheightweightedbythebasalarea)andthecommunitywoodden‐sity(WDBA;themeanwooddensityweightedbythebasalarea),and

(3)Ln(H)=0.893−E+0.760ln(

D)

−0.0340ln(

D)2

(4)AGB=exp

{

−2.024−0.896E+0.920ln(

WD)

+2.795ln(

D)

−0.0461[

ln(

D2)]

}

F I G U R E 1 Geographicaldistributionoftheplotdatabase.Weused867plotsof1hafrom118sites.Dotsarecolouredaccordingtofloristicaffinities(Sliketal.,2015),withAmerica,AfricaandAsiainorange,greenandblue,respectively.Theyarealsosizedaccordingthetotalareasurveyedineachsite.Inthebackground,moistforestsaredisplayedindarkgreenanddryforestinlightgreen

https://www.forestgeo.si.edu/

https://www.forestplots.net/

www.afritron.org

www.afritron.org

https://tnrs.iplantcollaborative.org

| 7BASTIN et al.

thosederivedfromlargesttreeswasdeterminedusinganiterativeprocedurefollowingBastinetal.(2015).Treeswerefirstrankedbydecreasingdiameterineachplot.Anincrementalprocedure(i.e.,in‐cludinganewtreeateachstep)wasusedtosumoraverageinforma‐tionoftheilargesttreesforeachplotmetric.Eachplot‐levelmetricwaspredictedbytherespectivemetricderivedfromtheithlargesttrees.Foreachincrement,theability(goodnessoffit)ofthei larg‐esttreestopredictagivenplotmetricwastestedthroughalinearregression.Toavoidoverfitting,aleave‐one‐outprocedurewasusedtodevelop independentsite‐specificmodels (n=118).Specifically,themodel tobe testedatasitewasdevelopedwithdata fromallother sites. Errorswere then estimated as the relative rootmeansquare error (rRMSE) computed between observed and predictedvalues(X):

Theformoftheregressionmodel(ie,linear,exponential)wasse‐lectedtoensureanormaldistributionoftheresiduals.

To estimate plot basal area,we used a simple power‐law con‐strainedontheorigin,aslinearmodelresultedinnon‐normalresid‐uals.Plot‐levelbasalarea(BA)wasrelatedtothebasalareaforthei largesttrees(BAi)using:

Toestimatethequadraticmeandiameter,Lorey’sheightandthewooddensityof thecommunity,weusedsimple linearmodelsre‐lating theplot‐levelmetrics and thevalueof themetrics for the i largesttrees:

Both Lorey’s height (HBA) and the average height (Hi ) of theith largest trees depend on the sameD–H allometry, which al‐wayscontainsuncertaintywhetherweusea local,acontinentalorapan‐tropicalmodel.Totestthedependenceoftheprediction of HBA on the allometric model, we used measurement fromMalebointheDemocraticRepublicoftheCongo,whereallheights were measured on the ground (see Supporting InformationFigure2).

The quality of the predictions of plot‐level metrics from thelargesttreesisquantifiedusingtherRMSEbetweenmeasuredandpredictedvaluesanddisplayedalongthecumulatednumberoflarg‐esttrees.Modelcoefficientsareestimatedforeachmetricderivedfromthelargesttrees(NLT)andaveragedacrossthe118models(seeSupportingInformationTable2).

MeanrRMSEisplottedasacontinuousvariable,anditsvariationispresentedasacontinuousareabetweenthe5thandthe95thper‐centilesofobservedrRMSE.

2.3 | The optimal number of largest trees for plot‐level biomass estimation

Theoptimalnumberoflargesttrees,NLT,wasdeterminedfromthepre‐dictionofeachplot‐levelmetricconsideredabove(i.e.,keepingasmallnumberoftreeswhileensuringalowleveloferrorforeachstructuralparameter).Wethenpredictedplot‐levelbiomassfromtheNLT model [Equation(2)].ThefinalerrorwascalculatedbypropagatingtheentiresetoferrorsrelatedtoEquation(4)(Réjou‐Méchainetal.,2017)intheNLTmodel(i.e.,errorassociatedwitheachallometricmodelused).Themodelwasthencross‐validatedacrossallplots(n=867).

2.4 | Investigating residuals: What the largest trees do not explain

TounderstandthelimitsofpredictingAGBthroughNLT,wealsoin‐vestigatedtherelationshipbetweenAGBresidualsandkeystructuralandenvironmentalvariablesusing linearmodelling.Foreststructurewas investigated through the total stem density (N), the quadraticmeandiameter(Dg),Lorey’sheight(HBA)andcommunitywoodden‐sity(WBBA).Asenvironmentaldata,weusedthemeanannualrainfallandthemeantemperaturecomputedoverthe last10yearsateachsite using the Climate Research Unit data (New et al., 1999; New,Lister,Hulme,&Makin,2002),alongwithrough informationonsoiltypes(Carré,Hiederer,Blujdea,&Koeble,2010).Majorsoiltypeswerecomputed fromthesoilclassificationof theHarmonizedWorldSoilDatabaseintoIPCC(IntergovernmentalPanelonClimateChange)soilclasses.Inaddition,consideringobserveddifferencesinforeststruc‐ture across tropical continents (Feldpausch et al., 2011, 2012) andrecentresultsonpan‐tropicalfloristicaffinities(Sliketal.,2015),wetestedforaneffectofcontinent(America,AfricaandAsia)ontheAGBresiduals.DifferencesinforeststructureandAGBamongcontinentswerealsoillustratedthroughtheanalysisoftheirdistribution.

TheimportanceofeachvariablewasevaluatedbycalculatingthetypeIIsumofsquares,whichmeasuresthedecreaseinresidualsumofsquaresowingtoanaddedvariableoncealltheothervariableshavebeenintroducedintothemodel(Langsrud,2003).Residualswerein‐vestigatedatbothplotandsitelevels,thelatteranalysedtotestforany influenceofthediameterstructure,which isusuallyunstableattheplotlevelowingtothedominanceoflargetreesonforestmetricsatsmallscales(Clark&Clark,2000).Here,weuseaprincipalcompo‐nentsanalysis(PCA)tosummarizetheinformationheldinthediameterstructurebyordinatingthesitesalongtheabundanceoftreesineachdiameterclass(from10to+100cmin10cmbins).

3 | RESULTS

3.1 | Plot‐level metrics

Plotmetricsaveragedat thesite level (867plots,118sites)pre‐sent importantvariationswithinandbetweencontinents. Inour

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database,thequadraticmeandiametervariesfrom15to42cm2/ha,thebasalareafrom2to58m2/ha,Lorey’sheightfrom11to33m,andthewooddensityweightedbythebasalareafrom0.48to0.84g/cm3 (Supporting InformationFigure1).Such importantdifferences between minimal and maximal values are observedbecauseourdatabasecoverssiteswithvariousforesttypes,fromyoungforestcolonizingsavannastoold‐growthforest.However,

mostofoursitesarefoundinmatureforests,asshownbytherela‐tivelyhighaverageandmedianvalueofeachplotmetric(averageAGB=302Mg/ha;Supporting InformationFigure1). Ingeneral,highestvaluesofAGBarefound inAfrica,drivenbythehighestvaluesofbasalareaandhighestestimationsofLorey’sheight.ThehighestvaluesofwooddensityweightedbybasalareaarefoundinAmerica.

F I G U R E 2 Qualityofthepredictionofplotmetricsfromlargesttrees.Variationoftherelativerootmeansquareerror(rRMSE)ofthepredictionofplotmetricfromilargesttreesversusthecumulativenumberoflargesttreesfor:(a)basalarea,(b)quadraticmeandiameter,(c)Lorey’sheight,and(d)wooddensityweightedbythebasalarea.Resultsaredisplayedatthepan‐tropicallevel(mainplotingrey)andatthecontinentallevel(subplots;orange=America;green=Africa;blue=Asia).ThecontinuouslineandshadingshowsthemeanrRMSEandthe5thand95thpercentiles.DashedlinesrepresentthemeanrRMSEobservedforeachmodel,whenconsideringthe20largesttrees

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3.2 | Plot‐level estimation from the i largest trees

Overall,plotmetricsatthe1‐hascalewerewellpredictedbythelargest trees,withqualitative agreement amongglobal and con‐tinental models (Figure 2). When using the 20 largest trees topredict basal area (BA) and quadratic mean diameter (Dg), themeanrRMSEwas<16and12%,respectively(Figure3a,b).Lorey’sheight (HBA) and wood density weighted by basal area (WDBA)wereevenbetterpredicted(Figure3c,d),withmeanrRMSEof4%forthe20largesttrees.ThepredictionofLorey’sheightfromthelargest trees using the local diameter–heightmodel (SupportingInformation Figure 2a) yielded results similar to those obtainedusingEquation(3)ofChaveetal.(2014).Moreimportantly,italso

yieldedsimilarresultstothepredictionofLorey’sheightfromthelargesttreesusingplotswhereallthetreesweremeasuredontheground(SupportingInformationFigure2b).Thissuggeststhatourconclusionsarerobusttotheuncertaintyintroducedbyheight–di‐ameterallometricmodels.

3.3 | Aboveground biomass prediction from the largest trees

Weselected20asthenumberoflargesttreestopredictplotmet‐rics.TheresultingmodelpredictingAGB(inmegagramsperhectare)basedonthe20largesttreesis:

F I G U R E 3 Predictionofplotmetrics(yaxis)fromthe20largesttrees(xaxis).Resultsareshownfor:(a)basalarea,(b)quadraticmeandiameter,(c)Lorey’sheight,and(d)wooddensityweightedbythebasalarea.Eachdotcorrespondstoasingleplot,colouredinorange,greenandblueforAmerica,AfricaandAsia,respectively.Bothpan‐tropical(blackdashedlines)andcontinental(colouredlines)regressionmodelsaredisplayed.Theseresultsshowthatasubstantialpartoftheremainingvariance(i.e.,notexplainedbythelargesttrees)isfoundwhenpredictingthebasalareaandthequadraticmeandiameter,withslightbutsignificantdifferencesbetweencontinents

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Given that theexponentwas close toone,wealsodevelopedanalternativeandmoreoperationalmodelwiththeexponentcon‐strainedtoone,givenby:

Groundmeasurements of plot AGBwere predicted by ourNLT modelwith the exponent constrained to one,with a total error of17.9%(Figure4),avaluewhichencompassestheerroroftheNLT model andtheerrorrelatedtotheallometricmodelchosen.Theleave‐one‐outcross‐validationprocedureyieldedsimilarresults(rRMSE=0.19;R2=0.81),validatingtheuseofthemodelonindependentsites.

3.4 | Determining the cause of residual variations

Theexplanatoryvariablesalltogetherexplainca.37%ofthevari‐anceinAGBatbothplotandsitelevelswhenomittingthediam‐eterstructure,andca.63%atsitelevelwhenincluded(Figure5).Ingeneral,foreststructureandparticularlythestemdensityex‐plainedmostoftheresiduals(Table1;weights:79and54%atplotandsitelevel,respectively).Thestemdensitywasfollowedbyacontinental effect (weights: 18, 28 and 1% for Africa, AmericaandAsia, respectively) andby theeffectofHBAandWDBA (re‐spectiveweights:1and0%attheplotlevel,0and11%atthesite

level,and23and0%whenaccountingforthediameterstructureatthesitelevel).Inclusionofthediameterstructureprovidedthebest explanation of residuals, with 63% of variance explained,and a weight of 69% for the first axis of the PCA (SupportingInformationFigure3). This first axis of thePCAwas related tothe general abundanceof trees at a site, and in particular,me‐dium‐sized trees (40–60cm). Among environmental variables,onlyrainfallwassignificantlyrelatedtotheresidualsatthesitelevelwhenthediameterstructurewasconsidered(2%).

3.5 | Differences among continents

Althoughdiameterstructureexplainedalargefractionofthere‐sidualvarianceofourglobalmodel,therewasamarkeddifferencein forest structure across continents (Figure 6). Consequently,we investigated differences between continents in the distri‐bution of residuals of the pan‐tropicalmodel (Figure 6a), in therelativecontributionofthe20largesttreestoplottotalbiomass(Figure6b)andinthecontributiontothetotalAGBperdiameterclass(Figure6c–f).Tothisend,weconsideredthefollowingfourclassesofdiameterat130cm:10–30,30–50,50–70and>70cm.Theresultsshowthatthepredictionofbiomassfromthe20larg‐esttreesusingthepan‐tropicalmodeltendstobeslightlyoveres‐timatedinAfrica(+3%)andunderestimatedinAmerica(−3%)andinAsia(−5%)(Figure6a).Theproportionofbiomassishigherinthehigh‐diameter class (>70cm) inAfrica, in intermediate‐diameterclasses(between30and70cm)inAmerica,andisequallydistrib‐utedamongthedifferentdiameterclassesinAsia(Figure6c,d).

(10)AGB=0.0735× (Dg20H20WD20)

1.1332(

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F I G U R E 4 Qualityofthepredictionofabovegroundbiomass(AGB)fromlargesttreesplotmetrics.Variationoftherelativerootmeansquareerror(rRMSE)ofthepredictionofAGBfromilargesttreesversusthecumulativenumberoflargesttrees(a)anddetailedpredictionofAGBfromplotmetricsofthe20largesttrees(b).Resultsareshownforthe867plotsinthethreecontinents,colouredorange,greenandblueforAmerica,AfricaandAsia,respectively.Theregressionlineofthemodelisshownasacontinuousblackline,andthedashedblacklineshowsa1:1relationship.ThefigureshowsanunbiasedpredictionofAGBacrossthe867plots,withslightbutsignificantdifferencesbetweenthethreecontinents

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| 11BASTIN et al.

4 | DISCUSSION

4.1 | The largest trees, convergences and divergences between continents

Samplingafewlargesttreesperhectaregenerallyallowsanunbi‐asedpredictionoffourkeydescriptorsofforeststructuresacross

the tropics.There isgenerallyno improvement inpredictingbio‐mass,quadraticmeandiameter,Lorey’sheight(HBA)orcommunitywooddensitybeyondthefirst10–20largesttrees(Figures2,3and3a).Butwhenaforestplotpresentsanabundantnumberoflargetrees(Figure5d),increasingthenumberoftreessampleddoesim‐provetheaccuracyofthemodel.Thisisattributabletothefactthat

F I G U R E 5 Predictedversusobservedresidualsofabovegroundbiomasspredictedfromthe20largesttrees.Residualsareexploredatthreedifferentlevels:(a)plot,(b)site(withoutconsideringthediameterstructureasanexplanatoryvariable),(c)site(consideringthediameterstructure),and(d)alongthestemdensityofmedium‐sizedtrees.America,AfricaandAsiaarecolouredinorange,greenandblue,respectively.Thepanelsshowagoodpredictionofresidualsin(a)and(b),drivenbystemdensity,andalessbiasedpredictionin(c),drivenbythediameterstructure.Variancesofobservedresidualsarealsowellexplainedbythestemdensityofmedium‐sizedtrees(d),whichmainlydrivethefirstaxisoftheprincipalcomponentsanalysis

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12 | BASTIN et al.

thehigherthetotalAGBinaplot,thelowertheproportionoftotalAGB encompassed by the largest trees. This is particularly trueforBA,forwhichrRMSEcontinuestodecreaseupto100largesttrees(Figure2a).Incontrast,Lorey’sheightpredictionsarealteredwhen a large number of trees are included (Figure 2c; i.e.,whensmaller, often suppressed, trees draw the average down; Farrior,Bohlman,Hubbell,&Pacala,2016).Thismightexplainwhythepre‐dictionofAGBdoesnotmirrorthatofbasalarea(Figures2,3band3a) and suggests that the number of largest trees should be setindependently from each predictor considered. Interestingly, theevolutionof relativeerror inAGBpredictionasa functionof thenumberoflargesttreesconsidereddoesnotfollowthesamepathbetweencontinents.Forinstance,theerrorofpredictionsaturatesmorequicklyinAfricaandAsiathaninAmerica.Investigationofre‐sidualsshowedthatthediameterstructure(Figure5c;SupportingInformationFigure3b),and inparticular, thenumberofmedium‐sized trees (Figure5d), drives variability inAGBpredictions. It istherefore not surprising to see that in our dataset the site with

higherlevelsofunderestimationsistheonewiththehighestnum‐berofmedium‐sizedtrees,which isfoundinAsia intheWesternGhatsofIndia.

Thegoodperformanceofmodelsbasedonthe20largesttreesinpredictingLorey’sheightandcommunitywooddensityatthesitelevelwasnotsurprising.Bothmetricswereweightedbybasalarea,driven de factobythelargesttrees.Nonetheless,theirconsistencyacrosssitesandcontinentswasnotexpected,whichemphasizethegeneralityofourapproach.

Thepredictabilityofplot‐levelforeststructuremetricsfromthelargesttreesimpliesthatcharacteristicsofsmallertreesdonotvaryinacompletelyindependentmannerfromthoseofthelargertrees.Forexample,plotswherethelargesttreeshavealowbasalareatendtohavelowplot‐levelbasalarea(Figure3a),meaningthatthetotalsizeofthesmallertreesissufficientlyconstrainedthatitdoesnotcompensateforthesmallsizeofthelargesttrees.Suchconstraintscould arise through size–frequency distributions being set by al‐lometric scaling rules (Enquist,West,&Brown,2009)or couldbe

TA B L E 1 Weightofeachvariableretainedfortheexplanationofabovegroundbiomassresiduals

Level of residual Parameter Weight

Plot

Stemdensity 79

Continent 18

Lorey’sheight 1

Majorsoiltypes 1

Temperature 1

Wooddensityweightedbythebasalarea 0

Rainfall 0

Sitewithoutdiametricstructure

Stemdensity 54

Continent 28


Rainfall 3

Majorsoiltypes 3

Temperature 2

Lorey’sheight 0

Sitewithdiametricstructure

PCAaxis1 69

Lorey’sheight 23

Rainfall 3

Majorsoiltypes 3

Continent 1

Temperature 1


PCAaxis2 0

Note.Weightsarecalculatedasatypellsumofsquares,whichmeasuresthedecreasedresidualsumofsquaresattributabletoanaddedvariableoncealltheothervariableshavebeenintroducedintothemodel.Resultsareshownfortheexplorationofresidualsattheplotandatthesitelevel,withandwithoutconsiderationofthediameterstructure.Weightsaredominatedbystructuralvariables,andinparticular,thestemdensityandthediameterstructure.Height,wooddensityandcontinenthavealsoanon‐negligibleinfluenceonresiduals.PCA=principalcomponentsanalysis.

| 13BASTIN et al.

attributabletothelargesttreesrespondinginthesamewayastheremainingsmallertreestoenvironmentaldrivers.

Despite the general consistency of these relationships acrosscontinents,slightdifferencesareevidentwhencomparingthepan‐tropical model residuals across continents (Figure 6; Supporting

Information Figure 4). These differences indicate biogeographicalvariation in forest structure. In America, our pan‐tropical modeltendsslightlytounderestimatebasalarea(mean:−5%)andoveresti‐mateLorey’sheight(mean:+3%)(SupportingInformationFigure4).Thissuggeststhatlargetreesmakeupasmallerproportionofbasal

F I G U R E 6 Comparisonacrosscontinentsofabovegroundbiomass(AGB)predictionpersiteandtheircontributiontodifferentsharesofthediameterstructure.Africa,AsiaandAmericaarecolouredingreen,blueandorange,respectively.(a)Thedistributionoftheresidualsofpan‐tropicalAGBpredictionfromthe20largesttreesshowsthatpredictionsareslightlyoverestimatedinAfrica(+3%),andslightlyunderestimatedinAsia(−3%)andAmerica(−5%).(b)TheproportionofAGBinthe20largesttreesishighestinAfrica(48%),followedbyAsia(40%)andAmerica(35%).(c–f)Thedecompositionacrossfourdiameterclasses[i.e.,10–30,30–50,50–70and>70cmdiameteratbreastheight(DBH)]oftheirrelativeshareofthetotalbiomassshowsthatmostofthebiomassisfoundinthelargetreesinAfricaandinthesmalltomediumtreesinAmerica.Asiapresentsamorebalanceddistributionofbiomassacrossthediameterstructure

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14 | BASTIN et al.

areainAmericaandthatforagivendiameterwefindhighertrees(SupportingInformationFigure2),whichconfirmsthattheshapeofheight–diameterallometriesvariesbetweencontinents(Baninetal.,2012; Sullivan et al., 2018). InAfrica, large trees (i.e., diameter atbreastheight>70cm) aremoreabundant andaccount for a largefractionofplotbiomass (Figure6f).This supportspreviousobser‐vations thatAfrican forests are characterized by fewer but largerstems(Feldpauschetal.,2012;Lewisetal.,2013),whereasforestsintheAmericashavemorestemsbutgenerallyhavelowerbiomass(Sullivanetal.,2017).InAsia,thedistributionofthebiomassacrossdiameterclassesappearsmorebalanced(Figure6c–f).Suchdiffer‐encesinforeststructure,albeitlimited,suggestthattropicalforestsdifferbetweencontinentsintermsofdynamics,carboncycling,re‐sponseandfeedbacktoclimateandresiliencetoexternal forcings(e.g.,climatechange,forestdegradationanddeforestation).

Interestingly,althougharecentglobalphylogeneticclassificationoftropicalforestsgroupsAmericanwithAfricanforestsversusAsianforests (Slik et al., 2018), our study of forest structure propertiestendsmoretosingleoutAmericanforests,andinparticular,tohigh‐light thecontrastbetweenAfricanandAmerican forests.Althoughthisdeservesfurtherinvestigation,itmightrevealthelackofacloserelationshipbetweenforeststructurepropertiesandphylogenicsim‐ilarity,whichechoesrecentresultsontheabsenceofarelationshipbetweentropicalforestdiversityandbiomass(Sullivanetal.,2017).

4.2 | Largest trees: A gateway to global monitoring of tropical forests

Revealingthepredictivecapacityheldbythelargesttrees,ourresultsconstitute a major step forwards tomonitor forest structures andbiomassstocks.Thelargesttreesintropicalforestscanthereforebeusedtomakeaccuratepredictionsofvariousground‐measuredprop‐erties(i.e.,thequadraticmeandiameter,thebasalarea,Lorey’sheightandcommunitywooddensity),whereaspreviousworkhaspredictedonlybiomass‘estimates’(e.g.,Bastinetal.,2015;Sliketal.,2013).Theadvantagesofourapproachareasfollows:(a)itallowsustodescribeforeststructure independentlyofanybiomassallometricmodel; (b)itallowsustointegrateenvironmentallybasedvariationsintheD–H relationship,knowntovarylocally(Feldpauschetal.,2011;Kearsleyetal.,2013);and(c)itisalsorelativelyinsensitivetodifferencesinflo‐risticcompositionandcommunitywooddensity(Poorteretal.,2015).

Furthermore,the‘largesttrees’modelsweredevelopedforeachplot‐levelmetricandforanynumberoflargesttrees.Thus,theydonotrelyonanyarbitrarythresholdoftreediameter.Notethattheoptimalnumberoflargesttreestobemeasured(i.e.,20)wassetfordemonstrationandcanvarydependingontheneedsandcapacitiesofeachcountryorproject(seeSupportingInformationTable2).Inthe sameway, localmodels could integrate locallydevelopedbio‐massmodels,whenavailable.Consequentlyourapproach:(a)canbeusedinyoungorregeneratingunmanagedforestswithalow‘largesttree’ diameter threshold; and (b) is compatiblewith recentRS ap‐proachesabletosingleoutcanopytreesanddescribetheircrownandheightmetrics(Coomesetal.,2017;Ferrazetal.,2016).

4.3 | Aboveground biomass model from the largest trees: A multiple opportunity

Globally,theNLTmodelforthe20largesttreesallowsplotbiomasstobepredictedwith17.9%error.This result isapan‐tropicalvali‐dationof resultsobtained inCentralAfrica (Bastinetal.,2015). Itopensnewperspectivesforcost‐effectivemethodstomonitorfor‐eststructuresandcarbonstocksthroughlargesttreesmetrics(i.e.,metricsofobjectsdirectlyinterceptedbyRSproducts).

DevelopingcountrieswillingtoimplementReductionofEmissionsfromDeforestation and ForestDegradation (REDD+) activitieswillalso reporton theircarbonemissionsanddevelopanational refer‐encelevel(IPCC,2006;Maniatis&Mollicone,2010).However,mosttropical countries lack the capacity to assume multiple, exhaus‐tive and costly forest carbon inventories (Romijn,Herold,Kooistra,Murdiyarso,&Verchot,2012).Bymeasuringonlya few large treesperhectare,our results show that it ispossible toobtainunbiasedestimatesofabovegroundcarbonstocksinatime‐andcost‐efficientmanner.Assumingthat400–600treeswithD>10cmaremeasuredinatypical1‐hasampleplot,monitoringonly20treesisasignificantimprovement.Althoughfindingthe20largesttreesinaplotofseveralhundredindividualsrequiresevaluating>20trees,inpractice,acon‐servativediameterthresholdcouldbedefinedtoensurethatthe20largesttreesaresampled.Analternativecouldalsobefoundinthede‐velopmentofarelascope‐basedapproachadaptedtodetectionofthelargesttreesintropicalforests.Usingsuchapproachwouldfacilitaterapidfieldsamplinginextensiveareastoproducelarge‐scaleAGBes‐timates.ThosecouldfulfiltheneedsforcalibrationandvalidationofcurrentandforthcomingspacemissionsfocusedonAGB.

Our findings also point towards the potential effectiveness ofusingRS techniques tocharacterizecanopy trees for inferring theattributesofentireforeststands.Remotesensingdatacouldbeusedfordirectmeasurement(e.g.,tree‐levelmetrics,suchasheight,crownwidthandcrownheight)ofthelargesttreesasapotentialalterna‐tivetoindirectdevelopmentofcomplexmetrics(e.g.,meancanopyheight,texture)usedtoextrapolateforestproperties.Althoughtheuseofasingle‐treeapproachhasshownsomelimitationstoextrap‐olateplotmetrics(Coomes,Šafka,Shepherd,Dalponte,&Holdaway,2018), we have yet to investigate its potential to identify largesttrees.Somefurtherrefinementsareneeded,butmostofthetoolsrequired to develop ‘largest trees’models are readily available. Inparticular,Ferrazetal.(2016)developedanautomatedproceduretolocatesingletreesbasedonairborneLiDARdata,tomeasuretheirheight and crown area. Crown area could then be linked to basalarea,becausethelogarithmofcrownareaisconsistentlycorrelated,withaslopeof1.2–1.3,tothelogarithmoftreediameteracrossthetropics(Blanchardetal.,2016).Regardingwooddensity,hyperspec‐tralsignatureandhigh‐resolutiontopographyoffersapromisingwaytoassessfunctionaltraitsremotely(e.g.,Asneretal.,2017;Juckeretal.,2018),whichcouldpotentiallyprovideproxiesofwooddensity.Alternativeapproachescouldfocusonthedevelopmentofplot‐levelAGBpredictionbyreplacingthebasalareaofthelargesttreeswiththeircrownmetrics.Althoughthemeasurementofcrownareashas

| 15BASTIN et al.

yettobegeneralizedwheninventoryingplots,severalbiomassallo‐metricmodelsalreadypartitiontrunkandcrownmass (Coomesetal.,2017;Juckeretal.,2017;Plotonetal.,2016).

Themainlimitationofourapproachliesinthelimitedinferencethat can be made on the understorey and sub‐canopy trees.Weshowthatmostoftheremainingvarianceisexplainedbyvariationsindiameter structures,and inparticular, in the total stemdensity.Interestingly,stemdensitywasgenerallyidentifiedasapoorpredic‐torofplotbiomassintropicalforests(Lewisetal.,2013;Sliketal.,2010).However,ourresultsshowthatstemdensityexplainsmostoftheremainingvariance(SupportingInformationTableS1).Thissug‐geststhat,inadditiontotryingtounderstandlarge‐scalevariationsinlargetreesandotherplotmetricsthatcanbequantifieddirectlyfromRS,weshouldalsoputmoreeffortintounderstandingvariationinsmallertrees,whichmainlydrivestotalstemdensityandthetotalfloristicdiversity.Smallertreesarealsoessentialtocharacterizefor‐estdynamicsandunderstandchangesincarbonstocks.Severalop‐tionsarenonethelesspossiblefromRS,consideringthevariationinLiDARpointdensitybelowthecanopylayer(D’Oliveira,Reutebuch,McGaughey,&Andersen,2012),thedistributionofleafareadensity(Starketal.,2015,2012;Tang&Dubayah,2017;Vincentetal.,2017)ortheuseofmultitemporalLiDARdatatoobtaininformationonfor‐estgapgenerationdynamicsand,consequently,onforestdiameterstructure(Farrioretal.,2016;Kellner,Clark,&Hubbell,2009).

4.4 | Large trees in degraded forests

Iflargetreesareakeyfeatureofunmanagedforests,theyarecon‐spicuouslyabsentfrommanagedordegradedforests.Indeed,largetreesaretargetedbyselectiveorillegalloggingandarethefirsttodisappearortosufferfromincidentaldamagewhentropicalforestsareexploitedfor timber (Sist,Mazzei,Blanc,&Rutishauser,2014).The loss of the largest trees drastically changes forest structuresanddiameterdistributions,andtheirlossislikelytocounteracttheconsistency in forest structures observed throughout this study.Understandinghow,orwhether,managedforestsdeviatefromourmodel predictions could help to characterize forest degradation,whichaccountsforalargefractionofcarbonlossworldwide(Baccinietal.,2017),acknowledgingthatrapidpost‐disturbancebiomassre‐covery(Rutishauseretal.,2015)willremainhardtocapture.

4.5 | Conclusion: Towards improved estimates of tropical forest biomass

Theacquisition,accessibilityandprocessingcapabilitiesofRSdatawith a very high spatial, spectral and temporal resolution has in‐creasedexponentiallyinrecentyears(Bastinetal.,2017).However,todevelopaccurateglobalmapswewillhavetoobtainalargernum‐beroffieldplotsanddevelopnewwaystouseRSdata.Ourresultsprovidea step forwards forbothby: (a) drasticallydecreasing thenumberof individual treemeasurementsrequiredtoobtainanac‐curate, yet less precise, estimateof plot biomass; and (b) opening

thewaytodirectmeasurementofplotmetricsmeasuredfromRStoestimateplotbiomass.

AshighlightedbyClarkandKellner(2012),newbiomassallo‐metricmodelsrelatingplot‐levelbiomassmeasuredfromdestruc‐tive samplingandplot‐levelmetricsmeasured fromRSproductsshouldbedeveloped,asanalternativetocurrenttree‐levelallo‐metricmodels.Suchaneffortwillgreatlyreduceoperationalcostsand uncertainties surrounding terrestrial carbon estimates and,consequently,willhelpdevelopingcountries in thedevelopmentof national forest inventories and aid the scientific communityin better understanding the effect of climate change on forestecosystems.

ACKNOWLEDG MENTS

J.‐F.B.wassupportedfordatacollectionbytheFRIA‐FNRS(FondNationalpourlaRechercheScientifique),ERAIFT(EcoleRégionalePost‐Universitaire d’Aménagement et de Gestion Intégrés desForêtsTropicales),WorldWideFundforNature(WWF)(...)WWFand by the CoForTips project (ANR‐12‐EBID‐0002); T.d.H. wassupportedbytheCOBIMFOproject(CongoBasinintegratedmon‐itoring for forest carbonmitigation and biodiversity) funded bytheBelgianSciencePolicyOffice(Belspo);C.H.G.wassupportedby the ‘Sud Expert Plantes’ project of French Foreign Affairs,CIRADandSCAC.SomeofthedatainthispaperwereprovidedbytheRAINFORNetwork,theAfriTRONnetwork,TEAMNetwork,the partnership between Conservation International, TheMissouriBotanicalGarden,TheSmithsonian InstitutionandTheWildlifeConservationSocietyandtheGordonandBettyMooreFoundation.WeacknowledgedatacontributionsfromtheTEAMnetwork not listed as co‐authors (upon a voluntary basis). WethankJean‐PhillipePuyravaud,EstaçãoCientíficaFerreiraPenna(MPEG)andtheAndrewMellonFoundationandNationalScienceFoundation (DEB 0742830). The forest plots inNova Xavantinaand Southern Amazonia, Brazil were funded by grants fromProjectPELD‐CNPq/FAPEMAT(403725/2012‐7;441244/2016‐5;164131/2013); CNPq‐PPBio (457602/2012‐0); productivitygrants(CNPq/PQ‐2)toB.H.Marimon‐JuniorandB.S.Marimon;ProjectUSA‐NAS/PEER (#PGA‐2000005316)andProjectReFlorFAPEMAT0589267/2016.Finally,wethankHelenMuller‐Landauforhercarefulrevisionandcommentsonthemanuscript.

CONFLIC T OF INTERE S T

Theauthorsdeclarethereisnoconflictofinterestassociatedwiththisstudy.

AUTHOR CONTRIBUTIONS

J.F.Bastin and E.Rutishauser conceived the study, gathered thedata,performedtheanalysisandwrotethemanuscript.Alltheco‐authors contributed by sharing data and reviewing themain text.A.R.Marshall,J.PoulsenandJ.KellnerrevisedtheEnglish.

16 | BASTIN et al.

DATA ACCE SSIBILIT Y

DataforplotsintheCTFSnetworkareavailablethroughtheonlineportalathttps://www.forestgeo.si.edu;intheForestplotnetworkathttps://www.forestplots.net/;andintheTEAMnetworkathttps://www.teamnetwork.org/.

ORCID

Koen Hufkens https://orcid.org/0000‐0002‐5070‐8109

Jean‐François Bastin https://orcid.org/0000‐0003‐2602‐7247

Ervan Rutishauser https://orcid.org/0000‐0003‐1182‐4032

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SUPPORTING INFORMATION

Additional Supporting Information may be found online in the supportinginformationtabforthisarticle.

How to cite this article:BastinJ‐F,RutishauserE,KellnerJR,etal.Pan‐tropicalpredictionofforeststructurefromthelargesttrees.Global Ecol Biogeogr. 2018;00:1–18. https://doi.org/10.1111/geb.12803

BIOSKETCHJean‐Francois Bastin is a postdoctoral Fellow of the CrowtherLaboratoryattheInstituteofIntegrativeBiology,DepartmentofEnvironmentalSystemsScience,ETH‐Zurich.Heisanecologistand a geographer using remote sensing to study the effect ofglobalchangeonterrestrialecosystems.Ervan Rutishauser is a postdoctoral Fellow at the SmithsonianTropical Research Institute, broadly interested in understand‐ing environmental resilience to natural or human‐induced dis‐turbances.Heaimsatprovidingrigorousandpracticalevidenceto trigger a shift towards better ressource management andconservation.

https://doi.org/10.1111/geb.12803

https://doi.org/10.1111/geb.12803

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