Journal of Biogeography. 2019;00:1–15. wileyonlinelibrary.com/journal/jbi  | 1© 2019 John Wiley & Sons Ltd

Received:30November2018  |  Revised:1March2019  |  Accepted:5April2019DOI: 10.1111/jbi.13627

S P E C I A L I S S U E

Discovering floristic and geoecological gradients across Amazonia

Hanna Tuomisto1  | Jasper Van doninck1,2  | Kalle Ruokolainen1  | Gabriel M. Moulatlet1,3  | Fernando O. G. Figueiredo4  | Anders Sirén1  | Glenda Cárdenas1  | Samuli Lehtonen5  | Gabriela Zuquim1

1DepartmentofBiology,UniversityofTurku,Turku,Finland2DepartmentofGeographyandGeology,UniversityofTurku,Turku,Finland3UniversidadRegionalAmazónicaIkiam,Tena,Ecuador4CoordenaçãodePesquisasemBiodiversidade,InstitutoNacionaldePesquisasdaAmazônia—INPA,Manaus,Brazil5BiodiversityUnit,UniversityofTurku,Turku,Finland

CorrespondenceHannaTuomisto,DepartmentofBiology,UniversityofTurku,20014Turku,Finland.Email:hanna.tuomisto@utu.fi

Funding informationAcademyofFinland,Grant/AwardNumber:139959,273737and296406;SuomenKulttuurirahasto;FAPEAM;CNPq;UniversityofTurku

Editor:SimonScheiter

AbstractAim: TomapandinterpretfloristicandgeoecologicalpatternsacrosstheAmazonbasinbycombiningextensivefielddatawithbasin‐wideLandsatimageryandclimaticdata.Location: Amazonia.Taxon: Ground truthdataon ferns and lycophytes; remote sensing results reflectforestcanopyproperties.Methods: WeusedfieldplotdatatoassessmainecologicalgradientsacrossAmazoniaandtorelatefloristicordinationaxestosoilbasecationconcentration,ClimatologiesatHighResolution for theEarth'sLandSurfaceAreas (CHELSA)climaticvariablesandreflectancevaluesfromabasin‐wideLandsatimagecompositewithgeneralizedlinearmodels.OrdinationaxeswerethenpredictedacrossallAmazoniausingLandsatandCHELSA,andaregionalsubdivisionwasobtainedusingk‐medoidclassification.Results: Theprimaryfloristicgradientwasstronglyrelatedtobasecationconcen‐tration in the soil, and the secondary gradient to climatic variables. The Landsatimagecompositerevealedatapestryofbroad‐scalevariationincanopyreflectancecharacteristicsacrossAmazonia.OrdinationaxisscorespredictedusingLandsatandCHELSAvariablesproducedspatialpatternsconsistentwithexistingknowledgeonsoils,geologyandvegetation,butalsosuggestednewfloristicpatterns.TheclearestdichotomywasbetweencentralAmazoniaandtheperipheralareas,andtheavailabledatasupportedaclassificationintoatleasteightsubregions.Main conclusions: Landsatdataarecapableofpredictingsoil‐relatedspeciescom‐positional patterns of understorey ferns and lycophytes across theAmazon basinwithsurprisinglyhighaccuracy.Althoughtheexactfloristicrelationshipsmaydifferamongplantgroups, theobservedecologicalgradientsmustberelevant forotherplantsaswell,sincesurfacereflectancerecordedbysatellites ismostly influencedbythetreecanopy.Thisopensexcitingprospectsforspeciesdistributionmodelling,conservation planning, and biogeographical and ecological studies on Amazonianbiota.Ourmapsprovideapreliminarygeoecological subdivisionofAmazonia thatcannowbetestedandrefinedusingfielddataofotherplantgroupsandfromhith‐ertounsampledareas.

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1  | INTRODUC TION

Amazonianrainforestshavelongfascinatedbiologists,buttheirin‐ternalheterogeneity remainspoorlyunderstood.Oneof theopenquestionsishowtobestsubdividethemintofloristicallyandbiogeo‐graphicallymeaningfulunits.Suchsubdivisionwouldbeuseful forbiogeographical inferences,conservationplanning,andforecastingpotentialeffectsofclimatechangeordeforestationonspeciesdis‐tributionsandtheirviability.

Amazoniahasbeensubdividedintoareasofendemismusingin‐formationaboutspeciesandsubspeciesdistributionsespeciallyforbirds(Cracraft,1985;Haffer,1974;Ribas,Aleixo,Nogueira,Miyaki,&Cracraft,2012),but the lowcollectingdensitywithinAmazonianecessarily renders maps based on species occurrence recordsspeculative.ThemostdetailedsubdivisionofAmazoniaisprobablytheWWFecoregionmap,whichtakesintoaccountbothlandscapefeaturesandinformationaboutriversasdispersalbarriers(Olsonetal.,2001).Animaldistributionsandabundanceshavebeenfoundtovary between seasonally inundated versus non‐inundated forestsand even in response tomore subtle changes in soil productivityand floristic composition (Halme & Bodmer, 2007; Peres, 1999;Stevenson,2014).Therefore,knowledgeonfloristicvariabilityandrelatedenvironmentalheterogeneitycanprovidethekindofbase‐lineinformationneededtounderstandgeneralbiogeographicalrela‐tionshipsandecologicalpatternsacrossAmazonia.

An early geochemical subdivision of Amazonia (Fittkau, Junk,Klinge,&Sioli,1975)recognizedfourmajorregions:centralAmazoniawithpoorsedimentarysoils,westernAmazoniawithrichersoilsde‐rivedfromAndeanmaterial,andnorthernandsouthernperipheralregionsonthePrecambrianformationsoftheGuyananandBrazilianshields, respectively.AsomewhatmoredetailedclassificationwasusedbyterSteegeetal.(2013),andSombroek(2000)draftedaclas‐sificationfocusingonsoiltypes.Inthesecases,theauthorsrecog‐nizedtheimportanceofgeochemicaldifferencesforvegetationorspeciesrichnessbutdidnotelaborateonhowfloristiccompositionmighthavevariedamongtherecognizedregions.

Floristicstudies inthepastfewdecadeshaveshownthatspe‐cies compositional patterns of many plant groups in Amazonia,including both trees and understorey plants, are linked to soilproperties (Baldeck, Tupayachi, Sinca, Jaramillo, & Asner, 2016;Duivenvoorden, 1995; Higgins et al., 2012, 2011; Pansonato,Costa,deCastilho,Carvalho,&Zuquim,2013;Phillipsetal.,2003;Ruokolainen,Tuomisto,Macía,Higgins,&Yli‐Halla,2007;Tuomistoetal.,2016;Tuomisto,Poulsen,etal.,2003;Tuomisto,Ruokolainen,Aguilar, & Sarmiento, 2003; Tuomisto, Ruokolainen, & Yli‐Halla,2003).Soilproperties,inturn,dependtoalargedegreeonthesoilparentmaterial,especiallyitsmineralogy,sedimentationhistoryand

thetimeithasbeenexposedtoweathering.Therefore,consideringthecomplexgeologicalhistoryofAmazoniaisrelevantwhenstudy‐ing speciesdistributionsand floristicvariability.Geological forma‐tionsinAmazoniahavehadvariousorigins.Thereare,forexample,Precambriancratonicshields,Miocenesemimarinetolacustrinede‐posits,recentdepositsofmaterialfromAndeanvolcaniceruptionsandsedimentsconsistingofmaterialthathasbeenleachedtovar‐ious degrees during cycles of fluvial sedimentation and resuspen‐sion(Fittkauetal.,1975;Hoorn,1993;Hoornetal.,2010;Räsänen,Linna, Santos, & Negri, 1995; Räsänen, Neller, Salo, & Jungner,1992;Sombroek,2000).Asaresult,Amazoniansoilshavebecomehighlyheterogeneous,andtheconcentrationsofmajorplantnutri‐entsinthemvaryovermorethantwoordersofmagnitude(Hengletal.,2014;Sanchez&Buol,1974;Tuomistoetal.,2016;Tuomisto,Ruokolainen,&Yli‐Halla,2003).

GiventhehugespatialextentofAmazonia,anymappingeffortisfacedwithpracticaldifficulties,especiallydatascarcity.Somestud‐iesonforeststructuralpropertiesandtreespeciesrichnesshaveap‐pliedsimplespatialinterpolationtechniquestocoverareasbetweenfieldsamplingpoints(Stropp,terSteege,Malhi,ATDN,&RAINFOR,2009; terSteegeet al., 2006,2003).Manyothershave takenad‐vantage of the possibilities offered by remote sensing. For exam‐ple,datafromcoarse‐resolutionsensorshavebeenusedtoassessbiomass,productivityand seasonality in leafproductionacrossallofAmazonia(Mitchardetal.,2014;Saatchi,Houghton,DosSantosAlvalá,Soares,&Yu,2007;Saatchietal.,2009;Wagneretal.,2017).Vegetationmappinghastakenadvantageofradarimagesandaerialphotographstoidentifyforesttypesthatdifferinstructuralandter‐raincharacteristics(Duivenvoorden&Lips,1993;Huber&Alarcón,1988;Huber,Gharbarran,&Funk,1995;IBGE,2004).Severalstud‐ieshaveusedLandsatdatatopredictedaphicpropertiesordifferentaspectsofplantcommunities(speciescomposition,turnoverorrich‐ness)overlandscapeextents(Draperetal.,2019;Higginsetal.,2012,2011;Salovaara,Thessler,Malik,&Tuomisto,2005;Sirén,Tuomisto,& Navarrete, 2013; Thessler, Ruokolainen, Tuomisto, & Tomppo,2005; Tuomisto, Poulsen, et al., 2003; Tuomisto, Ruokolainen,Aguilar,etal.,2003).ThehighestlocalresolutionhasbeenobtainedbyairbornehyperspectralsensorsandLiDAR,whichhavebeenusedtomapforestpropertiessuchascanopyheight,above‐groundcar‐bonstocksandcanopychemistryat the regionalextent (Asneretal.,2015,2013,2014).However,floristicpatternsatthebasin‐wideextentremaintobeclarified.

Here we have two main objectives. Firstly, we document themain floristic gradients across the Amazon basin and their envi‐ronmental correlates. Secondly, we assess to what degree thesefloristicandenvironmentalpatternscanbe identifiedandmappedacrossthebasin.Weaddressthesequestionsusingacombination

K E Y W O R D S

Amazonia,biogeographicalregions,floristicgradients,Landsatsatellite,mediumresolutionmultispectralimagery,remotesensing,tropicalrainforest,vegetation

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ofanextensivefielddataset (1,572plotswithdataonferns, lyco‐phytesandsoils),climaticdatalayersandabasin‐widecompositeofLandsatimagery.Finally,wedrawsomeecologicalandbiogeograph‐icalconclusionsfromtheresultsandderiveafloristicsubdivisionofAmazoniatostimulatefurtherresearchonthistopic.

2  | MATERIAL AND METHODS

2.1 | Floristic, environmental and remotely sensed data

We combined floristic and soil data collected independently bytwoteams,theAmazonResearchTeamoftheUniversityofTurku(UTU) and theBrazilianProgram inBiodiversityResearch (PPBio).Whenputtogether,theseinventoriesspanacrossalargepartoftheAmazonbasinandcoveranelevationrangeofabout50–600m.

Thefieldsamplingprotocolsofthetwoteamswereslightlydiffer‐ent.ThePPBiodatasetconsistsof309plotsof2mby250m(500m2)thatfollowtheterraincontourstoavoidlocaltopographicalvariation(Magnussonetal.2005).Ofthese,102originatedfromfourperma‐nentPPBiogrids,wheretheplotswereplacedat1‐kmintervals,andtherestweremorescattered.TheUTUdatasetconsistsof388linetransectsthatwere2mor5mwideandeither500m,1,300mor9.8–43kmlong(Tuomisto,Poulsen,etal.,2003;Tuomisto,Ruokolainen,Aguilar,etal.,2003).Theyfollowedapredeterminedcompassbear‐ing,thuscrossingthelocaltopographicalvariation.Forthepurposesofthepresentpaper,wesubdividedtheUTUtransectsintocontig‐uousnon‐overlapping subunits that as closely aspossiblematchedtheplotsizeinthePPBiodataset,i.e.either5mby100m(500m2)or2mby200m (400m2).Weretainedthose1,263transectsub‐units(plots)inwhichatleastonesoilsamplehadbeencollected.The500‐m‐transectsyielded1–3plotseach(374transects,1,015plots),the1,300‐m‐transects1–13plotseach(8transects,68plots)andthelongertransectsabout1plotforeachkmoftransectlength(6tran‐sects,180plots). In total,1,572plots from697separate transectswereavailableforanalysis.Thevastmajorityoftheserepresentnon‐inundatedterrafirmeforests,butsomeweresituatedininterveningswampsorfloodplainsofsmallrivers.Allplotsweregeoreferencedusingcoordinatesobtainedinthefieldwithhand‐heldGPSdevices.

Within each of the 1,572 plots, terrestrial fern and lycophyteindividualsthathadatleastoneleaf(leafysteminthecaseoflyco‐phytes) longerthan10cmwererecorded.Speciesthataremostlyepiphyticorhemiepiphyticwereexcluded.Wefocusedonfernsandlycophytesbecausetheseareofmoderatesize,relativelyabundantandnot toospecies‐rich,whichmade itpossible toobtain floristi‐callyrepresentativesamplesfrommanysites.Atlocaltolandscapeextents,theirspeciesturnoverpatternshavebeenfoundtocloselymirrorbothspeciesturnoverpatternsinotherplants(includingtrees)and differences in soils (Duque et al., 2005; Higgins et al., 2011;Jonesetal.,2013;Pansonatoetal.,2013;Ruokolainenetal.,2007;Tuomisto et al., 2016; Vormisto, Phillips, Ruokolainen, Tuomisto,&Vásquez, 2000). Since ferns and lycophyteshavehighdispersalability, theymay indicategeoecologicalpatternsmore reliablybut

suggest less biogeographical differentiation acrossAmazonia thanmoredispersal‐limitedplantswould(Tuomisto,Ruokolainen,&Yli‐Halla,2003).

Voucherspecimensofeachspecieswerecollectedduringeachfieldcampaign forverificationof species identifications.Vouchersaredepositedinoneortwoherbariainthecountryoforigin(AMAZ,USM and/or CUZ in Peru, QCA and QCNE in Ecuador, COAH inColombia,SPand/orINPAinBrazil,CAYinFrenchGuiana)andfortheUTUdataalsoinTUR(herbariumacronymsaccordingtoThiers,continuouslyupdated).InitialidentificationsweredonefortheUTUandPPBiodatasetsseparately,butidentificationswereharmonizedbyH.T.andG.Z.onthebasisofeitherthevouchersthemselvesorphotographs of them. Several species pairs or species complexesweresosimilarthattheycouldnotalwaysbereliablyseparatedinthefield.Thesewerelumpedtoensureaconsistenttaxonomyovertheentiredataset(forsimplicity,bothtruespeciesandspeciescom‐plexeswillbereferredtoasspecies).

Compositesamplesofthesurfacesoil(top5or10cmafterre‐movingthelitterlayer,5–6subsamples)werecollectedwithineachplotforchemicalandtexturalanalyses.InthePPBioplots,thesub‐samplesweretakenat50‐mintervalsalongthelongaxisoftheplot.IntheUTUplots,eachsoilsamplewastakenwithinanareaofabout5mby5m,butthesamplescollectivelyrepresenteddifferenttopo‐graphicalpositionswithinatransect.Furtherdetailsonthefieldandlaboratorymethodology are available in earlier studies (Tuomisto,Poulsen,et al.,2003;Tuomisto,Ruokolainen,Aguilar, et al.,2003;Tuomistoetal.,2017;Zuquimetal.,2014).WhentwosoilsampleswereavailableforthesameUTUplot,weaveragedtheirmeasure‐mentvaluesfortheanalyses.Here,wefocusonthesumofthecon‐centrations of exchangeable Ca,Mg and K (measured in cmol(+)/kg),whichwewillrefertoasbasecationconcentration.Allanalyseswerecarriedoutusinglogarithmicallytransformedvalues(logCat).Thisvariablewaschosenbecauseearlierstudieshavefounditsre‐lationshipwithplantspeciescompositionandchangesthereintobeconsistentlystrong,evenwhentherelationshipswiththeindividualcationshavevariedamongregions(Higginsetal.,2011;Ruokolainenetal.,2007;Tuomistoetal.,2016;Tuomisto,Poulsen,etal.,2003;Zuquimetal.,2014).

Althoughdigitalsoilmapsexist(Hengletal.,2017),theydonotprovidelayersofsoilbasecationconcentration.Thedigitallyavail‐ablevariables,suchassoiltypeandcationexchangecapacityhavehadonlyweak relationshipswith field‐measuredbase cation con‐centration(Moulatletetal.,2017).Therefore,weusedLandsatimag‐eryasasurrogate,sinceearlierstudiesatthelandscapeextenthavefoundthemusefulforidentifyingspatialheterogeneityinsoilsandsoil‐related floristicpatterns (Higginsetal.,2012,2011;Salovaaraetal.,2005;Tuomisto,Poulsen,etal.,2003;Tuomisto,Ruokolainen,Aguilar,etal.,2003).

Landsat image analyses across all Amazoniaweremade possi‐blebyarecentbasin‐widecompositeofLandsatTM/ETM+images.Thecompositeisbasedonmorethan16,000sufficientlycloud‐freeacquisitionsfromthe10‐yearperiod2000–2009,andithasalreadybeen shown to predict soil base cation concentration relatively

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well(Vandoninck&Tuomisto,2018).Technicaldetailsonhowtheimage composite was produced have been described elsewhere(Van doninck & Tuomisto, 2017a,2017b,2018). Climatic data (19bioclimaticvariables)at30arcsec resolution (approximately1km)were obtained from the Climatologies at High Resolution for theEarth's Land Surface Areas (CHELSA, Karger et al., 2017) (http://chelsa‐climate.org/).

2.2 | Data analyses

Toidentifyfloristicgradients,wefirstcalculatedcompositionaldis‐similaritiesamongtheplotsusingtheSørensenindex,whichisthepresence‐absenceversionoftheBray‐Curtisindex.Almost30%ofthe dissimilarity values were saturated to the maximum value ofunity, soweused theextended (step across) versionof the indexinorder toobtain ecologically realistic dissimilaritiesbetween theplotsthatsharednospecies(De'ath,1999;Tuomisto,Ruokolainen,&Ruokolainen,2012).ThenweperformedanordinationbasedonPrincipal CoordinatesAnalysis (PCoA) to visualize themain floris‐ticgradients.WeusedunivariatelinearregressionanalysistoassessthedegreetowhicheachofthefirstthreePCoAaxes(PCoA1–3)wererelatedtotheexplanatoryvariables(log‐transformedsoilbasecationconcentration,bioclimaticvariablesandreflectancevaluesintheLandsatTM/ETM+composite).

Landsatbands1(blue)and2(green)werenotusedbecausetheywereverynoisyduetoresidualatmosphericcontamination.Fortheremaining visible band3 (red) and thenear to shortwave infraredbands4,5and7,weextractedthereflectancevaluescorrespond‐ingtothecoordinatesofeachplotinthreedifferentways:(a)full‐resolutiondataat1arcsec(approximately30m)resolution,(b)dataretainingthefullresolutionbutfilteredbypassingamovingwidowof15by15pixelsover the imageandassigning toeachpixel themedianvaluefromthewindowcentredonit,and(c)lowresolutiondata obtained by coarsening the resolution to pixels of 15 arcsec(approximately450m) by assigning to eachnewpixel themedianvaluefromthecorresponding15by15originalpixels.Non‐forestedpixelsweremaskedusinganunsupervisedk‐meansclusteringwithpost‐classificationinterpretationbasedonvisualinspectionofspec‐tral signatureand spatialdistribution.For (a) and (b),maskingwasbasedontheoriginalfull‐resolutiondata,for(c)itwasbasedonthecoarsenedpixels.

ManyoftheCHELSAvariablesweremutuallyhighlycorrelated.To keep a reduced but representative set of climate variables formodelling,wecalculatedthevarianceinflationfactor(VIF)forall19variables.ThenweiterativelyexcludedthevariablewiththehighestVIFandrecalculatedVIFfortheremainingvariablesuntilnoneoftheVIFvaluesexceeded50.Allanalyseswerethenbasedontheremain‐ingelevenCHELSAvariables.Theserepresentedtemperaturevari‐ability(Bio2–4),meantemperature(Bio8,9,11),meanprecipitation(Bio13,14,18,19)andprecipitationvariability(Bio15).

ToformallymodelfloristicandsoilgradientsacrosstheAmazonbasin,weconstructedgeneralized linearmodels (GLMs)witheachofthePCoAaxes1–3andlogCatastheresponsevariable inturn.

GLMs using different combinations of predictor variables weretestedbyrandomlydividingthedataintotenfoldsandusingeachfoldasanindependenttestsetinturn.Adjacentplotsthatwerepartofthesamefieldtransectwereallowedtogotoseparatefoldsonlyifthedistancebetweenthemwasatleast1km.WealsoevaluatedelevationfromSRTMdigitalelevationmodelandtexturaldatalay‐ersobtainedfromstandarddeviationswithinthewindowofLandsatpixelsaspredictivevariables.GLMsusingtheLandsatmedianval‐ues,CHELSAvariablesandbothtogetherwereappliedovertheen‐tireAmazonbasininordertoproducepredictivemapsofthemainfloristicgradients(PCoAaxes1–3).Thepredictivemapswerethenclassifiedusinganimplementationofk‐medoidsclusteringforlargeapplications(CLARA).

AllanalyseswerecarriedoutinRversion3.4.1(RCoreTeam,2017)usingthepackages“raster”forrasterimageanalysis(Hijmans,2017),“vegan”forordinations(Oksanenetal.,2017),“cluster”forunsupervisedclustering(Maechler,Rousseeuw,Struyf,Hubert,&Hornik,2016),and“stats”forGLM(RCoreTeam,2017).

3  | RESULTS

3.1 | Amazonian heterogeneity as seen from space

The Landsat TM/ETM+ colour composite reveals a tapestry ofbroad‐scalevariationincanopyreflectancecharacteristicsacrossAmazonia(Figure1;unannotatedversioncanbefoundinAppendixS1: Figure S1a in Supporting Information). The parallel Landsatflightpaths remain tosomedegreevisible in the redband3 (as‐signedtothebluecolourchannelinFigure1andFigureS1a).Thisisbecausecorrectionofatmosphericeffectsintheimagecompos‐itewasnotperfect,andtheshortervisiblewavelengthsaremoreaffectedbyscatteringcausedbyaerosolsthanthelongerinfraredones.The falsecolourcompositebasedon the infraredbands4,5and7 isvirtuallyseamless (FigureS1b).Nevertheless,addingavisiblewavelengthimprovestheseparabilityofnuances,asthein‐fraredbandsarehighlycorrelatedwitheachother (AppendixS1:FigureS2).

Several known geo‐ecological entities of floristic relevancecan be recognized in the raw Landsat composite. Some of themost notable ones correspond to traditionally recognized vege‐tation formations, including the heterogeneous floodplain for‐ests alongmajor rivers, swamp forests like those in thePastazafan (“Pas” inFigure1;Räsänenet al., 1992), bamboo‐dominatedforests like those in Acre (“Bam”; Carvalho et al., 2013), andwhite‐sandforestslikethoseintheupperRioNegrobasin(“WS”;Adeney,Christensen,Vicentini,&Cohn‐Haft,2016).Otherrecog‐nizablefeaturesincludethefloristicturnoverzonethathasbeensuggested to correspond to the limit between theSolimões andIçá formations (“S” and “I”, respectively; Higgins et al., 2011;Schobbenhaus et al., 2004; Tuomisto et al., 2016), the BrazilianandGuyananPrecambrian shields (“Bra” and “Gui”, respectively)andsandymegafanformationsinRoraima(“MF”;Rossetti,CassolaMolina,&Cremon,2016).

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3.2 | Floristic gradients and their environmental relationships

ToobtainageneralideaoftheecologicaldriversoffloristicvariationacrosstheAmazonbasin(Figure2a),wefirstidentifiedthemaingra‐dientsinfernandlycophytespeciescompositionusingPCoAordi‐nation(Figure2b).Themainspatialpatternatthebasin‐wideextentwasthatwesternAmazoniawasveryheterogeneousinbothfloristiccompositionandsoilbasecationconcentrationwithmanyplotshav‐inghighvalues,whereasmostplotsfromcentralAmazoniahadlowvaluesforbothPCoAaxis1andsoilbasecationconcentration.

Indeed, the strongest floristic gradient was tightly related tobasecationconcentrationinthesoil:asimplelinearregressionwithlogCatasthepredictorvariableexplained75%ofthevariationintheordinationscoresalongPCoAaxis1(Figure2c;Table1).Addingei‐therfilteredLandsatspectraldata,CHELSAclimaticdataorbothtoGLMsthatalreadyincludedsoilbasecationconcentrationincreasedmeancross‐validatedpredictivepoweronlyby4–7%(R2=79–82%).Without soil cation concentration in the model, mean predictivepower in cross‐validation was 47% with filtered Landsat spectralvalues, 38%with CHELSA climatic values and 58%with both to‐gether(Table1).SoilbasecationconcentrationwasalmostaswellpredictedasthefirstfloristicgradientbytheLandsatdata,butthecontributionoftheclimaticdatawaslessinthecaseofsoilsinboth

absoluteandrelativeterms.ThissupportstheuseofLandsatdataasasurrogateformostlysoil‐relatedenvironmentalgradients(Table1andTableS1,FiguresS3andS4).ElevationasderivedfromSRTMandtexturaldatafromLandsathadlittlepredictivepower(TableS1).

TheoriginalLandsat resolution (30m)gives finedetail thatal‐lowsvisualinterpretationofthelandscape.Forexample,Figure3aclearlyshowsridge‐swalestructures intheriverfloodplain,creeksinnon‐inundatedterrainandtheextentsoftheIçáFormation(darkgreen–brown),SolimõesFormation(palebluishgray)andtheirtran‐sitionzone(pink–orange).Thesepatternsgetblurredwhenpixelsareaggregatedby factor15 (Figure3c),with the filtered imagebeingintermediate (Figure3b).However,at the30‐mresolutionthere issomuchlocalvariationinpixelvaluesthatthepredictivepowerofGLMsforPCoAaxis1wasclearlylowerwhenbasedonLandsatdatawithoriginal30‐mpixels(R2=21%)thanwhenbasedonthefiltereddata (R2=47%;Table1andTableS1).Whenprojectedonamap,thepredictionsobtainedusingtheoriginalpixelswereclearlynoisierandprovidedapoorercontrastamongthefloristicallyandedaph‐ically different kinds of forest than predictions obtainedwith thefilteredorcoarseneddatadid(Figure3d–i).

Thesecondfloristicgradientformedawest‐to‐eastspatialtrend.Correspondingly, PCoA axis 2was related tomany bioclimatic vari‐ables,with themaximum temperatureof thewarmestmonth (Bio5)providingthebestexplanatorypower(R2=42%)inaunivariatelinear

F I G U R E 1  Pixel‐basedLandsatTM/ETM+compositeoverAmazonia,basedonallLandsatimagesacquiredinJuly–Septemberduringthe10‐yearperiod2000–2009.Forthisquickviewimage,theoriginal30‐mimageresolutionhasbeencoarsenedbygriddingto450m,witheachgridcellgiventhemedianreflectancevalueofeachbandfromthe15by15pixelscorrespondingtothegridcell.Red,greenandbluehavebeenassigned,respectively,tobands4,7and3.Areasthatwereeitherclassifiedasnon‐forestonthebasisofthereflectancedataorareabove600melevationhavebeenmaskedoutandappearwhite.Pas:Pastazafan;Bam:bambooforest,S:SolimõesFormation;I:IçáFormation;WS:whitesands;MF:megafans;Gui:Guianashield;Bra:Brazilianshield.Versionswithouttheelevationlimitandfreeoftheannotationofboththiscolourcompositeandacolourcompositebasedonbands4,5and7areavailableinFigureS1

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regression(Figure2d;FigureS4). Indeed,GLMmodelswithCHELSAclimatevariableshadmeanpredictivepowerof48%incross‐validation.AddingLandsatvariablestothemodelonlyincreasedpredictivepowermarginally(to49%).ThisindicatesthatPCoAaxis2ismainlyaclimatic

gradient.Landsatvariablesontheirownexplainedlessthan10%ofthevariationalongPCoAaxis2,andsoilbasecationconcentrationnoneatall(Table1andTableS1).ThethirdPCoAaxiswasmostlypredictedbytheclimaticvariables,althoughnotverywell(R2=16%).

F I G U R E 2   (a)Mapof1,572Amazonianplantinventoryplotsusedintheanalyses.(b)PrincipalCoordinatesAnalysis(PCoA)ordinationbasedonfloristicdissimilaritiesbetweenthe1,572plots(presence‐absencedataofterrestrialfernsandlycophytes,extendedSørensendissimilarity).(c)RelationshipbetweenthefirstfloristicPCoAaxisandsoilbasecationconcentrationasmeasuredfromsoilsamplestakenineachplot.(d)RelationshipbetweenthesecondPCoAaxisandmaximumtemperatureofthewarmestmonthasmodelledbytheCHELSAvariableBio5.(e–h)Sameas(a–d),butwithallanalysesbasedonasubsetoftworegions,togethercontaining398plots,toshowgraphswithlesscluttering.Theseregionswereselectedbecausetheyarefarapartbuthaveanalogousgeologicalsettings.Inallpanels,coloursindicatethedifferentgeographicalregionsandsymbolsizeisproportionaltolog‐transformedconcentrationofexchangeablebasecations(Ca,Mg,K)inthesoil.Thefirstthreeaxesintheordinationofthefulldatasetaccountfor38%,13%and8%ofthetotalvariationandintheordinationofthetworegionsfor51%,11%and7%

Longitude

Latit

ude

−80 −75 −70 −65 −60 −55

−15

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−1.0 −0.5 0.0 0.5 1.0

−1.0

−0.5

0.0

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1.0

PCoA Axis1P

CoA

Axi

s2

(b)

−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5

−1.0

−0.5

0.0

0.5

1.0

log10(Soil base cations)

PC

oA A

xis1

(c)

R2 = 0.75 ***

270 280 290 300 310 320 330

−0.6

−0.4

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0.0

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Bio5 (Max temperature warmest month)

PC

oA A

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R2 = 0.42 ***

Longitude

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ude

−76 −71

−8−3

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−0.5 0.0 0.5

−0.5

0.0

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PC

oA A

xis2

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−1.5 −1.0 −0.5 0.0 0.5 1.0 1.5

−0.5

0.0

0.5

log10(Soil base cations)P

CoA

Axi

s1

(g)

R2 = 0.82 ***

274 276 278 280 282 284 286

−0.4

−0.2

0.0

0.2

0.4

Bio5 (Max temperature warmest month)

PC

oA A

xis2

(h)

R2 = 0.25 ***

TA B L E 1  Thedegreetowhichfloristicordinationaxes(PCoAshowninFigure2b)andsoilbasecationconcentrationcanbemodelledwithdataderivedfromaLandsatTM/ETM+imagecomposite,the11CHELSAclimatevariablesthatweresufficientlyuncorrelatedwitheachothertohaveVIF<50,andlogarithmicallytransformedsoilbasecationconcentrationasmeasuredfromsoilsamplestakenineachfieldplot(logCat).AdjustedR2(in%)isgivenforthelinearregressionbetweenGLM‐predictedandmeasuredvalues.“AllR2”referstothefitofaGLMmodelthatusesallfieldplots,“TestR2”givestheaverageofR 2valuesobtainedusingcross‐validation.Here,theplotswererandomlydividedintotenfolds,eachofwhichwasusedasanindependentvalidationdatasetinturn.Plotsfromthesametransectwerekeptinthesamefoldiftheirdistancewaslessthan1km.PCoA,PrincipalCoordinatesAnalysis;Landsat30,singlepixelvaluesatthefieldplotlocality;Landsat450,medianof15by15pixelwindowscentredonthefieldplot;VIF,VarianceInflationFactor;GLM,GeneralizedLinearModel

Predictors

logCat PCoA axis 1 PCoA axis 2 PCoA axis 3

All R2 Test R2 All R2 Test R2 All R2 Test R2 All R2 Test R2

Landsat30 15 15 21 21 6 7 3 4

Landsat450 42 41 48 47 10 11 5 6

CHELSA 30 28 40 38 48 48 17 16

Landsat450+CHELSA 49 46 60 58 50 49 18 16

logCat 75 74 0 1 0 2

Landsat450+logCat 79 78 10 11 9 9

CHELSA+logCat 80 79 50 50 20 18

Landsat450+CHELSA+logCat 83 82 51 50 22 20

     |  7TUOMISTO eT al.

Theenvironmentalrelationshipsofthefirsttwofloristicgradi‐entsbecomeevenclearerwhentwosubsetsofthedataarecom‐pared,onefromtheTigreriverbasininthewestandtheotherfromtheJuruáriverbasinabout1,000kmfurthersouth‐east(Figure2e).Ordinationoftheplotsfromthetworegionsrevealedthattheyspanabout the same rangealongPCoAaxis1but are to somedegreeshiftedinrelationtoeachotheralongPCoAaxis2(Figure2f).Theedaphicgradientscoveredbytheplotswererathersimilarinbothregions, and the relationship between the main floristic gradient(PCoA1)andsoilbasecationconcentrationwaseventighterthaninthefulldataset(compareFigure2gwithFigure2c).Thegeograph‐icaldistancebetweentheTigreandJuruáregionsisrelatedtodif‐ferencesinseveraloftheCHELSAclimaticvariables.ComparedtoallofAmazonia,theclimaticvariationwithinandbetweentheTigreandJuruáareasissmall,andthesecondPCoAaxiswasmoreweaklyexplainedbythemaximumtemperatureofthewarmestmonththaninthecaseofthefulldataset(compareFigure2hwithFigure2d).

3.3 | Predicting species composition

ThemapbasedonthecombinedLandsat+CHELSAGLMsuggestsa general compositional difference between central–north‐west‐ern Amazonia (blue–green in Figure 4a) and the peripheral areas

(red–orangeinFigure4a).MuchofthispatternisduetothestrongspatialstructureinpredictedPCoAaxis1,whichisassignedtoredinFigure4aand ishighlycorrelatedwithsoilbasecationconcen‐tration. Indeed, the samepattern is evenmore clear inFigure4b,whichshowspredictionsforaxis1usingLandsatdataonly.Manyofthe knowngeological and vegetation characteristics alreadymen‐tionedabove(Pastazafan,bambooforests,Içá‐Solimõesboundary)wererecoveredinthePCoAaxis1scorespredictedbyLandsatdata(Figure4b). Since these formations are not related to climatic dif‐ferences,theybecamesomewhatblurredwhenCHELSAdatawereincludedinthemodel(Figure4a)eventhoughtheoverallpredictivepowerincreased(Table1).

AsecondarygeneralpatterncanbeidentifiedasaNW–SEtrendincoreAmazonia.Thisemergedespeciallyfromvariationinthepre‐dictedscoresalongPCoAaxes2and3,whichinturnweremostlyexplainablebytheclimaticCHELSAdata.

Zooming in to the regional scale highlights the inherent dif‐ferences between the Landsat and CHELSA data. Patterns in theLandsatpredictionscanbeeasily relatedto local to regional land‐scapefeaturesthatarealsoidentifiableintheoriginalLandsatimage.Forexample, inthefalse‐colourLandsatcomposite(Figure5a)theswamp forestsof thePastaza fanaredistinguishable asdark red‐brownpatches, and the limitbetween the forests growingon the

F I G U R E 3  TerraincharacteristicsfromasitealongthemiddleJuruáRiverinAmazonia(midpointat68.764°W,6.400°S).(a–c)Falsecolourcomposite(bands4,5and7)oftheLandsatTM/ETM+imageryatfullresolution(a),fullresolutionfilteredbyassigningeachpixelthemedianvalueofthe15‐by‐15‐pixelwindowcentredonit(b)andcoarsenedto450‐mresolution(15‐by‐15‐pixelgrid)(c).Inthenon‐inundatedareasnorthoftheJuruáfloodplain,darkgreen–browncorrespondstotheIçáFormation,palebluishgraytotheSolimõesFormationandpink–orangetotheirtransitionzone.(d–f)ValuespredictedforPCoAaxes1–3onthebasisofGLMstrainedwiththeAmazon‐widefernandlycophyteordinationdatashowninFigure2b.Axis1isassignedtored,axis2togreenandaxis3toblue.Predictionsineachpanelarebasedonthereflectancevaluesfromthecolourcompositeaboveit.(g–h)Sameas(d–f),butonlyvaluespredictedforPCoAaxis1areshownwithbluecorrespondingtolowvaluesandredtohighvalues.Eachblackcirclerepresentsonefieldplotwithdiameterproportionaltolog‐transformedsoilbasecationconcentration.Colourschemesarethesameforallpanelsonthesamerow.Panel(c)isadetailofFigureS1bandpanel(i)ofFigure4b.GLM,generalizedlinearmodel;PCoA,PrincipalCoordinatesAnalysis

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

8  |     TUOMISTO eT al.

NautaandPebasFormations(darkgreenvs.lightergreenwithpink‐ish tint) isclearlyvisible.ThesamepatternsappearascontrastingpredictionsforPCoAaxis1scoreswhenonlyLandsatdataareused(Figure5c). Incontrast,climatevariesmuchmoregraduallyacrossspace,sothepatternsproducedbyclimaticmodelshaveaweakerrelationshipwiththelocallandscape(Figure5b,d).

Onthebasisof theGLM‐predicted floristicPCoAaxes,wedi‐vided Amazonia into different numbers of geoecological classesusing k‐medoid clustering (CLARA). The number of classes that is

chosenforuse isofcoursearbitrary,butsince largeareasareen‐tirelydevoidoffielddata,wechosetofocusonthebroad‐scalepat‐ternsandarelativelysmallnumberofclasses.Wefoundthesolutionwitheightclassestoprovideadecentcompromisebetweenrecog‐nizingthemostdistinctiveknownmacrounitsandavoidingthecre‐ationofclassesthatcannotbesubstantiatedwiththedataavailabletous(Figure6).Furthersubdivisionnotonlyresultedinspatiallyveryfragmentedclasses,butalsoproducedboundariesthatlookedarti‐factual(e.g.,byexactlyfollowingalineseparatingtwotemperaturevaluesinCHELSA).Theresultingmapisherepresentedasahypoth‐esisofageoecologicalsubdivisionofAmazoniathatcanbefurthertestedandrefinedwithadditionalfieldandremotesensingdata.

4  | DISCUSSION

4.1 | Determinants of plant species composition in Amazonia

In general, it is assumed that climate determines species distribu‐tionpatternsatbroad scales, and theeffectof soilsbecomesno‐ticeableatregionalto localscales.However,wefoundthatacrossentire Amazonia, the strongest floristic gradient in our field datacorresponds toanedaphicgradient:asinglesoilvariable (concen‐tration of exchangeable base cations) explained as much as 75%of the variation in PCoA axis 1 values of ferns and lycophytes. Itis likely that if other important soil properties could be included,such as phosphorus and nitrogen concentration or hydrology, thepercentage of variation explained by soils would be even higher.Thisconsiderablyexpandstheconclusionsfromearlierstudiesthathavedocumentedsoilstobeimportantforplantspeciesturnoveratregional extents inAmazonia (Baldeck et al., 2016;Cámara‐Leret,Tuomisto,Ruokolainen,Balslev,&MunchKristiansen,2017;Higginsetal.,2011;Pansonatoetal.,2013;Phillipsetal.,2003;Ruokolainenetal.,2007;Tuomisto,Poulsen,etal.,2003;Tuomisto,Ruokolainen,Aguilar, et al., 2003; Tuomisto, Ruokolainen, & Yli‐Halla, 2003;Zuquimetal.,2012,2014).

Although soils explained the strongest floristic gradient in ourdata,climaticvariableswerealsoimportant.Theiruniquecontribu‐tionwasmostlyinexplainingthesecondaryfloristicgradients(PCoAaxes2and3),forwhichsoilbasecationconcentrationprovidednoexplanatory power at all. The relative importance of explanatoryvariablespartlyreflectthedegreetowhicheachofthemvaries inrelationtothetolerancesofthespeciesofinterest.Ourentirestudyareaiswithintheclimaticspaceofmoisttropicallowlandforestsandthismaybeashortergradientforplantsthantheobservededaphicone.Inaddition,soilbasecationconcentrationsweremeasuredfromsoilsamplescollectedinthesameplotsasthefloristicdata.Thismaygiveamoreaccurateestimateoftheconditionsexperiencedbytheplants than is the casewith theCHELSAclimatic variables,whichhavebeenderivedfromglobalclimatemodels.

Becauseearlierstudieshavefoundstrongedaphicrelationshipsin many plant groups (ranging from canopy trees to understoreyherbs),wesuggestthatsoilvariationneedstobetakenintoaccount

F I G U R E 4  ColourcompositesoverAmazoniashowingGLM‐predictedvaluesforthePCoAaxisscoresofthefernandlycophyteordinationshowninFigure2b.(a)PredictionsbasedonbothLandsatTM/ETM+surfacereflectanceandCHELSAclimatevariablesforthescoresofPCoAaxes1,2and3(assignedtored,greenandblue,respectively).(b)PredictionsbasedonLandsatreflectancedataonlyforthescoresofPCoAaxis1(showninacolourgradientrangingfromblueforlowaxisscoresthroughyellowtoredforhighaxisscores).The1,572fieldstudyplotsusedtoparameterizetheGLMmodelsareshownascirclesin(b),withdiameterproportionaltolog‐transformedbasecationconcentrationinthesoil.PixelspredictedtohaveextremePCoAaxisvalues(below−1.2orabove1.2)weremaskedandappearinwhite(inadditiontotheareasmaskedalreadyinFigure1).GLM,generalizedlinearmodel;PCoA,PrincipalCoordinatesAnalysis

     |  9TUOMISTO eT al.

asapotentiallyimportantfactorinalldiscussionsaboutthedistri‐butional patterns ofAmazonian plants. In particular, it can be ex‐pectedthatareaswithcontrastingsoilpropertieshavecontrastingfloristiccompositions,andthattheeffectsofdispersallimitationwillaccentuateiftwoareaswithsimilarsoilsareseparatedbylargeex‐pansesofdifferentsoils.Theseissuesareespeciallyimportantwhenconsideringhowspeciesdistributionsmaybeaffectedbydeforesta‐tionorclimatechange:naturalheterogeneityinsoilsmayreducetheavailabilityofsuitablehabitatsevenmorethanwouldbeexpectedfromdeforestationorclimatechangescenariosalone(Figueiredoetal.,2018;Zuquim,Costa,Tuomisto,Moulatlet,&Figueiredo,2019).

4.2 | Modelling species composition

TheLandsatcompositerevealedcleargeographicalpatterns,manyofwhich obviously correspond tomajor geological formations.Atthesametime, thereflectancevaluesprovidedreasonablepredic‐tionsofbothsoilbasecationconcentrationandthemostimportantfloristicgradient(PCoAaxis1)ofunderstoreyfernsandlycophytes.Thisisnoteworthy,becausesurfacereflectanceoverdenseforests

ismainlydeterminedbythetreecanopy,notbysoilsorunderstoreyplants.Therefore,suchastrongrelationship isonlypossible if thefloristicpatterns in theunderstoreyarecausally linkedwith thosevegetation properties that determine reflectance (including floris‐ticcomposition,structureandchemicalpropertiesof thecanopy).Itseemsclearthatinourstudyareathelinkismediatedmostlybysoils.Ofcourse,withoutfurtherstudieswecannotestablishtowhatdegreethereflectancepatternsmirrorspecies‐leveldifferences intreecompositionandtowhatdegreesimilarstructuralorfunctionalcanopy properties on similar soils irrespective of species identity.Nevertheless,thecausalchainfromsoilsthroughvegetationtore‐flectanceseemsrobustenoughtomake itpossibletouseLandsatdatatoidentifyecologicallyrelevantgeologicallimitsindenselyveg‐etatedareas.

TheGLMsthatusedLandsatreflectancedatapredictedthemainfloristicgradient(fernandlycophytePCoAAxis1)betterthantheypredictedsoilbasecationconcentration.LandsatdataalsohadsomepredictivepowerforPCoAaxis2, incontrastwithsoilbasecationconcentration,whichhadnone.Bothoftheseresultsareconsistentwiththeideathatallplantgroupsreacttobothsoilsandclimatein

F I G U R E 5  Landscapepatternsinnorth‐westernAmazoniaasmodelledwitheitherreflectancevaluesfromaLandsatTM/ETM+imagecompositeorbioclimaticvariablesfromCHELSA.(a)Landsatcolourcompositebasedonbands4,5and7assignedtored,greenandblue,respectively.(b)CHELSAcolourcompositeofthreebioclimaticvariablesusedtomodelPCoAaxis1ofthefernandlycophyteordinationshowninFigure2b(Bio8,Bio11andBio2assignedtored,greenandblue,respectively).(c,d)Resultsofmodellingthemainfloristicgradientofthearea(PCoAaxis1ofFigure2b)withLandsatdataonly(c)orCHELSAdataonly(d).Themodelledvaluesareshowninacolourgradientrangingfromblue(forlowscoresalongPCoAaxis1)throughyellowtored(forhighvalues).Eachblackcirclerepresentsonefieldplotwithdiameterproportionaltolog‐transformedbasecationconcentrationinthesoil.Panel(a)isadetailofFigureS1bandpanel(c)ofFigure4b.PCoA,PrincipalCoordinatesAnalysis

(a) (b)

(c) (d)

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waysthataffecttheirfloristiccomposition,structureand/orchem‐icalproperties,andthatLandsatreflectanceprovidesanintegratedviewofsuchenvironmentaleffects.WithinAmazonia,Landsatre‐flectancemostlymirrorssoilvariationbecausethesoilgradientsarelongerthantheclimaticgradientswhencomparedtothephysiolog‐icaltolerancesofAmazonianplants.

In two areas, there is an apparent discrepancy between thepredictionsof theGLMsandtheactualpositionof thesitesalongPCoAaxis1 and the soil base cation concentrationgradient.Oneis thePastazafanarea,which is fedbyriversthatoriginate intheEcuadorianvolcanoesandcarryahighloadofcation‐richsediments.Alloursoilsamplesfromthefanhavehighbasecationconcentration(Figure4b),buttheareastandsoutashavinglowpredictedPCoAaxis1values,correspondingtoforestsoncation‐poorsoils.ThePastazafanisveryheterogeneous(Figure5a,c),andoursoilsamplescomefromthenarrowstripsalongtheriversthatwere,infact,predictedtohavehighvalues.Soilsfurtherawayfromtheriversareprobablybothmore cation‐poor andmorewaterlogged, as thePastaza fanisdominatedbyswampforests.Thesemayhavelowerreflectancebothbecauseinfraredwavelengthsareabsorbedbywaterandbe‐causewaterloggingmakes swamps stressful environments, whichcan give them structural and chemical characteristics resemblingthoseofforestsoncation‐poorsoils.Someoftheswampshaveevenevolvedintoombrotrophicpeatbogs,whicharenutrient‐limitedinthesamewayasforestsonwhitesandsoilsare,andhavealsobeenfoundtosharestructuralcharacteristicsandplantspecies(Draperetal.,2018;Lähteenoja&Page,2011).

The second example is the opposite: the bamboo‐dominatedforests in the border zone between southern Peru and adjacentBrazil have high infrared reflectance,which is generally indicative

ofrelativelyrapidgrowthandforestsonhigh‐cationsoils.Here,theestimatesareprobablyexaggeratedbecauseofthebamboointhecanopy.Bambooisarapidlygrowinggrass,andthereforecanbeex‐pectedtohavelesssclerophyllousleavesandhigherinfraredreflec‐tancethancanopytreesdo.Althoughthefernandlycophyteplotswehave fromthisareahavecation‐richsoilsandacorrespondingflora,thePCoAaxis1predictionsaremoreextremethanthefloristiccompositionoftheunderstoreywouldsuggest.

4.3 | Basin‐wide floristic mapping and the role of medium resolution multispectral imagery

Biodiversity studies covering all Amazonia have not used me‐dium‐resolution multispectral imagery, which is likely due to twomain problems. Firstly, atmospheric contamination and persistentcloudcoverhampercombiningscenesacquiredatdifferent times.Secondly, directional scattering of sunlight by the canopy surfacecauses an artifactual along‐scan (east‐west) gradient in pixel val‐ues,whichcancausespectraldifferencesaslargeasthosebetweencompositionallydifferentforesttypes(Muroetal.,2016;Toivonen,Kalliola, Ruokolainen, & Naseem Malik, 2006). However, recentadvances in open data access policies (Woodcock et al., 2008),cloud screening and atmospheric correction algorithms (Masek etal.,2006), anddirectionalnormalization (Vandoninck&Tuomisto,2017a)nowmakeitpossibletoconstructseamlessmediumresolu‐tion imagecompositeswithareasonablyhighsignal‐to‐noiseratiooverlargeareas(Vandoninck&Tuomisto,2018).

IntheLandsatcomposite(Figure1),reflectancevariationmostlycorrespondstoidentifiablesurfacefeatures.Ourmodellingresults(Table 1) confirmed that multispectral surface reflectance data

F I G U R E 6  ClassificationofAmazoniaintoeightgeoecologicalclassesbasedonpixel‐wisepredictionsoffloristicordinationscores(PCoAaxes1–3obtainedwithfernsandlycophytes;Figure2b).PredictionswerebasedonthesameGLMasFigure4a,whichincludesbothLandsatreflectancesandCHELSAbioclimaticvariablesaspredictors.Blackdotsindicatethelocationsofthefieldplotsthatwereusedintheordination.GLM,generalizedlinearmodel;PCoA,PrincipalCoordinatesAnalysis

     |  11TUOMISTO eT al.

layers make a useful contribution to modelling understorey fernand lycophytespeciescompositionat thebasin‐wideextent,com‐plementinginformationthatcanbeobtainedfromclimatevariables.Thisisinaccordancewithresultsofseveralstudiescarriedoutatrel‐ativelysmallextents(Buermannetal.,2008;Chaves,Ruokolainen,&Tuomisto,2018;Figueiredo,Venticinque,Figueiredo,&Ferreira,2015;Higginsetal.,2012,2011;Salovaaraetal.,2005;Thessleretal., 2005; Tuomisto, Poulsen, et al., 2003; Tuomisto, Ruokolainen,Aguilar, et al., 2003). New generation medium‐resolution multi‐spectral instrumentswith improvedspectralandradiometricreso‐lution(Landsat8OLI,Sentinel‐2)canbeexpectedtobeevenmorevaluablefortheseapplications.Consequently,wedisagreewiththesuggestion that the spatial and spectral resolution of this type ofsensorswould be inappropriate for studies on spatial distributionofbiodiversityor traitvariations (Lauschetal.,2016;Nagendra&Rocchini,2008).

Thechoiceofspatialresolutioninremotesensingstudiesdeter‐mineswhat information on biodiversity can be gained (Anderson,2018;Rocchinietal.,2016).Ourresultsshowthatwhentheaimistomapbroad‐scalepatternsinfloristiccomposition,valuableinfor‐mationcanbeextractedwithouthigh‐resolution imagery.Evenatmediumresolution,asingleimagepixel isaboutthesizeofa largetreecrownoratreefallgap inatropicalforest.Thiscausesahighdegree of local variability, because adjacent pixels can representdifferent phases of gap dynamics in different proportions, whichmakestheidentificationofgeneralpatternsmoredifficult.Filteringis the classicalmethod for eliminating local noise and it has beenused inearlierstudies inAmazonia (Chavesetal.,2018;Salovaaraetal.,2005).Weindeedfoundthatapplyinga15‐pixelmedianfilterconsiderablyimprovedmodelperformance,indicatingthatwhentheaimisbroad‐scalefloristicmapping,thehighheterogeneitybetweenadjacent30‐mpixelsismostlynoise.

Nevertheless,itisanadvantagetohaveaccesstothemedium‐resolutiondataandnotonlycoarse‐resolutiondata.Firstly,thisal‐lowsspectralvaluestobeextractedsuchthattheyarecentredontheexactlocalityoffieldsamplingpoints,ratherthanthefielddatabeingpotentiallymarginallyplacedinrelationtoalargepre‐definedpixel.Secondly, theremaybenon‐forest landcover types suchasroads,riversorcultivatedfieldsclosetothefieldsamplinglocations.Withcoarse‐resolution imagery, thesewould lead tomixedpixels,butwithmediumresolutionimagery,onecanmaskouttheirrelevantpixelsbeforeextractingthereflectancevalues.Finally,mediumres‐olutiondataallowsgeneratingentropyorvariabilitymetrics,whichmaybeindicativeoflocaltaxonomicdiversity(Rocchinietal.,2018),eventhoughherewefoundthesimplestandarddeviationmetrictobeuninformative.

4.4 | Biogeographical inferences and practical applications

Our results show that Landsat reflectance can be used to gener‐alize field data and to predict soil‐related floristic variation at thebasin‐wideextent.Thereby,Landsatprovidesinformationthatgoes

beyond and complements climatic data. Although our maps arebasedonmodellingfernandlycophytespeciescompositionalgradi‐ents,patternsidentifiedbyLandsatarehardlyspecifictotheseun‐derstoreyplants.Therefore,weexpecttheinformationinourmapstoberelevantforAmazonianbiotamoregenerally, includingotherplantgroupsandthoseanimalgroupsthatreacttospatialvariationinsoil‐relatedforestproperties.Furtherwork isneededto test towhatdegree thepatterns identifiedhereapply forother taxaandinareasforwhichwehadnofielddata.Nevertheless,thismappingapproachisbasedonsolidecologicalprinciplesandopensexcitingpossibilitiesforfutureecologicalandbiogeographicalresearchwithimplicationsforhowweviewAmazoniaandthethreatsitisfacing.

In general terms, the classical division of Amazonia into fourgeochemicallydefinedregionsasproposedbyFittkauetal.(1975)is discernible in our results. In the geoecological classificationofFigure 6, the red, pink and pale blue classes roughly correspondto western Amazonia, dark blue and green classes to centralAmazonia, and orange and yellow classes to southern AmazoniaandtheGuianaShield.However,thesouthernpartofthewesternperipheryappearsbothintheLandsatcomposite(Figure1)andinthegeoecologicalclassification(Figure6)asmorerelatedtosouth‐ern Amazonia than to the northern parts of western Amazonia,thus supporting the subdivision of the latter as proposed by terSteegeetal.(2013).

The western limit of central Amazonia in our results coin‐cides with a previously identified geological and floristic bound‐ary (Higgins et al., 2011; IBGE, 2004; Schobbenhaus et al., 2004;Tuomistoet al., 2016).CentralAmazoniaasdefinedby terSteegeet al. (2013)extends furtherwest than this,whereas in thenorththeirboundaryissouthoftheextensivewhitesandareas(Adeneyetal.,2016;Quesadaetal.,2011)thatinboththeLandsatcompositeandthegeoecologicalclassificationassociatewithcentralAmazonia.TheeasternAmazonianregionrecognizedbyterSteegeetal.(2013)aroundthemouthoftheAmazonriverstandsoutalsoinourLandsatcomposite(Figure1),andwasallocatedtoadifferentclassthanmostof southernAmazonia (Figure 6). Itmust be noted, however, thatlackoffielddatarendersourresultsforthisarea(andforsouthernAmazoniaingeneral)ratherspeculative.

Thelimitsbetweengeoecologicalsubregionsmostlydidnotfol‐lowmajorrivers,withtheexceptionoftheupperRioNegroand,tosomedegree,thelowerAmazonitself(Figures1and6).Traditionally,theAmazonRiveranditsmaintributarieshavebeenrecognizedasdistributional limits formany animal species, and it has been sug‐gested that the rivers functionasdispersalbarriers (Aleixo,2006;Godinho&daSilva,2018;Haffer,1974;Nazareno,Dick,&Lohmann,2017; Pomara, Ruokolainen, & Young, 2014; Ribas et al., 2012;Wallace,1852).Wherearivercoincideswithhabitatdifferences,itisdifficulttodisentanglethepossibleeffectsofariverbarrierfromthoseofhabitatselection,butinareaswhereriversandhabitatlim‐itsdonotcoincide,thetwohypothesesmakedifferentpredictionsdependingonspeciesvagilityanddegreeofhabitatspecificity.Thishasan importantpracticalconsequence:anyregionalmapswherecategory limits are drawn using rivers as boundaries (such as the

12  |     TUOMISTO eT al.

WWFecoregionmap;Olsonetal.,2001)mayonlyberelevantforriver‐limitedorganisms.

AfewrecentstudieshaveusedLandsatdataasenvironmentallay‐ersinspeciesdistributionmodels(SDMs)overrelativelysmallextents(Chavesetal.,2018;Figueiredoetal.,2015).Ourresultssuggestthatthesameapproachisfeasibleevenatthebasin‐wideextent(seealsoVandoninck&Tuomisto,2018).Forexample,themodelledfloristicgradient shown in Figure 4a allows making rather specific predic‐tionsaboutthepotentialdistributionsofplantspecies:onlyspeciestolerantofcation‐poorsoilsareexpectedtogrowintheblue‐greenareasincentralAmazonia,whileincreasingrednessinthemapindi‐catesincreasingprobabilityofoccurrenceforspeciesrequiringhighcationavailability.Untilnow,SDMhasoftenbeendoneusingclimaticvariablesonly,and the fewstudies thathaveuseddigital soilmaps(Figueiredoetal.,2018;Levisetal.,2017)mayhaveunderestimatedtheimportanceofsoilsduetotheproblemswiththematicandspatialaccuracyintheavailablesoilmaps(Moulatletetal.,2017).

Because ourmodels focus on the dominant floristic gradientsonly,andarebasedonplantswithrelativelygooddispersalability,theyhavebeentrainedtoemphasizeenvironmentalsiteconditions.Thedegreeof floristic regionalization thatemerges through isola‐tionbydistanceisprobablyunderestimatedevenforfernsandlyco‐phytes,andmoresoforplantgroupsthataremoredispersal‐limited.Thiswillneedtobetakenintoaccountwhenassessingtheecologi‐calandbiogeographicalsignificanceofthesubdivisionsofAmazoniathatemergefromourresults(especiallyFigure6).Ourresultspro‐videonegeoecologicalviewoverAmazonia,andcomparablestudiesusingotherplantgroupsarenowneededtotestthisview.

ACKNOWLEDG EMENTS

Wearegratefultothenumerouspeoplewhohavecontributedtothedataused in thispaperbyparticipating in fieldwork,helpingwith practical arrangements for the field expeditions or sharingtheirexpertiseforspeciesidentificationorsoilanalysis.Wethankthenationalauthoritiesineachcountryforgrantingthepermitstocarryoutfieldworkandcollectvoucherspecimens.WethankHansterSteege,EthanHouseholderandDilceRossettiforconstructivecommentsthathelped improvethemanuscript.Fundingthathasmadethisworkpossiblehasbeenprovidedbynumerousfundingagenciesovertheyears,mostrecentlybytheAcademyofFinland(grants139959and273737toH.T.,grant296406toRistoKalliola),FinnishCulturalFoundation(granttoG.Z.)andseveralgrantsasso‐ciatedwiththeBrazilianPrograminBiodiversityResearch(PPBio)fromBrazilianagencieslikeFAPEAMandCNPq.Thisisapublica‐tionoftheAmazonResearchTeamoftheUniversityofTurku,andalsocontributesaspublication759 to the technical seriesof theBiologicalDynamicsofForestFragmentsProject.

DATA AVAIL ABILIT Y S TATEMENT

Theplot dataused in this paper havebeendeposited in theDryadDigitalRepositoryat:https://doi.org/10.5061/dryad.v7fp8msandwill

bereleasedin2026.TheLandsatTM/ETM+compositehasbeende‐positedinIDAResearchdatastorageservice(www.fairdata.fi/en/ida/).ResearchersinterestedinthedatashouldcontactHannaTuomisto(foreitherdataset)orJasperVandoninck(fortheLandsatdataset).

Title:Data from:Discovering floristic and geoecological gradi‐entsacrossAmazoniaDOI:doi:10.5061/dryad.v7fp8ms

Journal: Journal of BiogeographyJournal manuscript number:JBI‐18‐0631

ORCID

Hanna Tuomisto https://orcid.org/0000‐0003‐1640‐490X

Jasper Van doninck https://orcid.org/0000‐0003‐2177‐7882

Kalle Ruokolainen https://orcid.org/0000‐0002‐7494‐9417

Gabriel M. Moulatlet https://orcid.org/0000‐0003‐2571‐1207

Fernando O. G. Figueiredo https://orcid.org/0000‐0002‐9333‐0708

Anders Sirén https://orcid.org/0000‐0003‐4159‐4506

Glenda Cárdenas https://orcid.org/0000‐0002‐3441‐4602

Samuli Lehtonen https://orcid.org/0000‐0001‐6235‐2026

Gabriela Zuquim https://orcid.org/0000‐0003‐0932‐2308

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BIOSKE TCH

Hanna Tuomisto,Kalle Ruokolainen,Gabriela ZuquimandmostoftheotherauthorsarebiologistsinterestedinhowspeciesaredistributedinAmazonia,whatthedeterminantsofspeciesoccur‐rencesareandhowtheserelatetothebroadercontextofgeo‐logicalhistoryandevolutionaryprocesses.Jasper Van doninckisageographerspecializedonusingremotesensingforbiodiversitymapping.

Authorcontributions:H.T.andK.R.conceivedtheoriginalidea,whichwasthenrefinedbyH.T,J.V.D.,G.Z.,K.R.andA.S.;H.T.,K.R.,G.Z.,G.M.M., F.O.G.F., A.S.,G.C. and S.L. collected thefielddata; J.V.D. andH.T. carriedout the analyses;H.T.,G.Z.and J.V.D. led thewriting and all authors commented on themanuscript.

SUPPORTING INFORMATION

Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle.

How to cite this article:TuomistoH,VandoninckJ,RuokolainenK,etal.DiscoveringfloristicandgeoecologicalgradientsacrossAmazonia.J Biogeogr. 2019;00:1–15. https://doi.org/10.1111/jbi.13627