Assessing the multi-scale predictive ability of ecosystem functional attributes for species ...

31 Pages·2017·5.92 MB·English

RESEARCHARTICLE
Assessing the multi-scale predictive ability of
ecosystem functional attributes for species
distribution modelling
SalvadorArenas-Castro1*,JoãoGonc¸alves1,PauloAlves1,DomingoAlcaraz-Segura2,3,4,
JoãoP.Honrado1,5
1 CentrodeInvestigac¸ãoemBiodiversidadeeRecursosGene´ticos(InBIO/CIBIO-ICETA),Universidadedo
Porto,Vairão,Portugal,2 DepartamentodeBota´nica,FacultaddeCiencias,UniversidaddeGranada,
Granada,Spain,3 AndalusianCenterfortheAssessmentandMonitoringofGlobalChange(CAESCG),
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UniversidaddeAlmer´ıa,Almer´ıa,Spain,4 iecolab.InteruniversitaryInstituteforEarthSystemResearch
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(IISTA),UniversidaddeGranada,Granada,Spain,5 FaculdadedeCiências,UniversidadedoPorto,Porto,
a1111111111 Portugal
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a1111111111 *[email protected]
Abstract
OPENACCESS Globalenvironmentalchangesarerapidlyaffectingspecies’distributionsandhabitatsuitabil-
Citation:Arenas-CastroS,Gonc¸alvesJ,AlvesP, ityworldwide,requiringacontinuousupdateofbiodiversitystatustosupporteffectivedeci-
Alcaraz-SeguraD,HonradoJP(2018)Assessing sionsonconservationpolicyandmanagement.Inthisregard,satellite-derivedEcosystem
themulti-scalepredictiveabilityofecosystem
FunctionalAttributes(EFAs)offeramoreintegrativeandquickerevaluationofecosystem
functionalattributesforspeciesdistribution
responsestoenvironmentaldriversandchangesthanclimateandstructuralorcompositional
modelling.PLoSONE13(6):e0199292.https://doi.
org/10.1371/journal.pone.0199292 landscapeattributes.Thus,EFAsmayholdadvantagesaspredictorsinSpeciesDistribution
Models(SDMs)andforimplementingmulti-scalespeciesmonitoringprograms.Herewe
Editor:ShijoJoseph,KeralaForestResearch
Institute,INDIA describeamodellingframeworktoassessthepredictiveabilityofEFAsasEssentialBiodi-
versityVariables(EBVs)againsttraditionaldatasets(climate,land-cover)atseveralscales.
Received:November15,2017
Wetesttheframeworkwithamulti-scaleassessmentofhabitatsuitabilityfortwoplantspe-
Accepted:June5,2018
ciesofconservationconcern,bothprotectedundertheEUHabitatsDirective,differingin
Published:June18,2018
termsoflifehistory,rangeanddistributionpattern(IrisboissieriandTaxusbaccata).Wefitted
Copyright:©2018Arenas-Castroetal.Thisisan foursetsofSDMsforthetwotestspecies,calibratedwith:interpolatedclimatevariables;
openaccessarticledistributedunderthetermsof
landscapevariables;EFAs;andacombinationofclimateandlandscapevariables.EFA-
theCreativeCommonsAttributionLicense,which
basedmodelsperformedverywellattheseveralscales(AUC from0.881±0.072to
permitsunrestricteduse,distribution,and median
reproductioninanymedium,providedtheoriginal 0.983±0.125),andsimilarlytotraditionalclimate-basedmodels,individuallyorincombination
authorandsourcearecredited. withland-coverpredictors(AUC from0.882±0.059to0.995±0.083).Moreover,EFA-
median
DataAvailabilityStatement:Allrelevantdataare basedmodelsidentifiedadditionalsuitableareasandprovidedvaluableinformationonfunc-
withinthepaperanditsSupportingInformation tionalfeaturesofhabitatsuitabilityforbothtestspecies(narrowlyvs.widelydistributed),for
files.
bothcoarseandfinescales.Ourresultssuggestarelativelysmallscale-dependenceofthe
Funding:Thisresearchwasdevelopedaspartof predictiveabilityofsatellite-derivedEFAs,supportingtheiruseasmeaningfulEBVsinSDMs
theECOPOTENTIALprojectfinancedbyEuropean
fromregionalandbroaderscalestomorelocalandfinerscales.Sincetheevaluationofspe-
Union’sHorizon2020researchandinnovation
programundergrantagreementNo.641762.SAC, cies’conservationstatusandhabitatqualityshouldasfaraspossiblebeperformedbasedon
DASandJPHreceivedfundingfromthe scalableindicatorslinkingtomeaningfulprocesses,ourframeworkmayguideconservation
ECOPOTENTIALproject.JGwassupportedbyFCT
managersindecision-makingrelatedtobiodiversitymonitoringandreportingschemes.
(PortugueseScienceFoundation)throughPhD
grantSFRH/BD/90112/2012.DASreceivedfunding
PLOSONE|https://doi.org/10.1371/journal.pone.0199292 June18,2018 1/31
Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
fromMinisteriodeEducacio´n,CulturayDeporte, Introduction
JC2015-00316grant,andMinisteriodeCienciae
Globalenvironmentalchangesareaffectingspeciesdistributionsandecosystemfunctioning
Innovacio´n,CGL2014-61610-EXPproject.The
fundershadnoroleinstudydesign,datacollection worldwide,withprofoundeffectsintermsoflossandrelocationofbiodiversity[1,2].Thus,a
andanalysis,decisiontopublish,orpreparationof continuousupdateofbiodiversitystatusandtheeffectivenessofconservationpoliciesare
themanuscript. internationalgoalsforthecomingyears[3,4].Differentapproachescombiningstatistical
Competinginterests:Theauthorshavedeclared modellingtoolsandbiodiversitymonitoringhaveallowedtoquantifyandassessbiodiversity
thatnocompetinginterestsexist. distributionandchangeacrossscales[5,6].Oneofthemostcommonapproachesforassessing
speciesdistributionanddynamicshasbeenthedevelopmentofSpeciesDistributionModels
(SDMs)[7–9](andreferencestherein).SpeciesDistributionModels(SDMs)canbedefinedas
associativemodelsforquantifyingspecies-environmentrelationships,andarethusbasedon
assessingthespecies’ecologicalniche[10,11].Fromatheoreticalperspective[12],thespecies
fundamentalnicheisaresultoflimitingabioticfactorsatabroadgeographicalscale,typically
relatedtoclimateoredaphicandgeologicalproperties[13];whereastherealizednicheis
definedatafinerscalebyhabitatandbioticfactors,mainlyrelatedtointerspecificcompetition
anddispersalability,amongothers[14].InthecontextofSDMs,theecologicalnicheisconsid-
eredasahypervolumeinmultivariateenvironmentalspacethatdepictsaspecies’environmen-
talrequirementsorlimitations[12,15].Oncetheecologicalnicheofaspecieshasbeendefined
throughstatisticalfunctions,thesecanbeappliedtoscenariosofclimateorlandscapecondi-
tionstoprojectthefuturevariationofthespecies’distribution.However,theapplicationof
SDMsinconservationandmanagementisstillhamperedbysignificantspatialandtemporal
biases(e.g.taxonomyerrors,samplingoverlapping,interpolationswithinsufficientdata,inac-
curaciesingeo-referencing,etc.),bothinspeciesoccurrencedata,andinthesetofpredictive
variablesthatrepresenttheenvironmentalvariability[16].
Oneofthemajordrawbacksofspeciesdistributionmodellingisthatspeciesoccurrences
areusuallyavailableatcoarseresolutions[17],whiletheirconservationandmanagement
withinprotectedareasareneededatfinerresolutions[18,19].Anotherdrawbackisthatmany
predictivevariablesarenotmeasurableoravailableattherequiredresolution,sosurrogates
andinterpolateddata(e.g.frommeteorologicalstations)havetobeusedinstead[20,21].Struc-
turalpredictorsderivedfromthematiccartography,suchasland-covervariables,alsohold
limitationssincetheymaynotrepresentrelevantlandscapefeaturesnortheecosystempro-
cessesrelevantforthetargetspecies.Furthermore,bothoccurrencedataandpredictorvari-
ablescanhaveinadequateordissimilarspatial,thematicand/ortemporalresolutions[22].
Earthobservationtechniquesarebecomingafundamentaltoolkittodealwithsomeof
thesemodellingbiasesanddrawbacks[23–26].Satelliteremotesensingofferscontinuousand
cost-effectivemeasuresofbothabioticandbioticfactorsacrossspaceandtime,hardlyquanti-
fiablebyothermeans[27,28].Recentproductsderivedfrommultispectralandhyperspectral
sensorsareplayingakeyroleinthequantification,assessmentandforecastingofbiodiversity
[29–31]byprovidingmeaningfulinformationtopredictspeciesdistributionsthroughclimatic
variables[32]aswellasstructural[33]orfunctionaldescriptorsofecosystems[34,35].
Theuseofsatellite-derivedecosystemfunctionalattributes(EFAs)aspredictorsinSDMs
canhavesomeadvantages[35].EFAsaredescriptorsoftheoverallecosystemfunctioning
[36,37],i.e.theexchangesofmatterandenergybetweenthebiotaandthephysicalenviron-
ment,including,amongothers,indicatorsofproductivity,seasonalityandphenologyofcar-
bongains[38–41].Attheregionalscaleovernaturalvegetation,EFAsaremainlydrivenby
climatewhiletheyaremorelinkedtoland-coverandland-useatthelocalscaleandwith
increasinghumaninfluence[39].Thisway,EFAsofferanintegrativeandquickerviewofeco-
systemresponsestoenvironmentaldriversandchangesthanstructuralorcompositionalattri-
butes[42],linkingspeciesresponsestopressuresonecosystemfunctioningandstate[35],
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
whichisanadvantageforimplementingspeciesmonitoringprograms[9,37,43].Mostimpor-
tantly,EFAscanbemonitoredthroughremotesensingandderivedgloballyundercommon
protocolsatrelativelyhightemporalandspatialresolutions,whichisparticularlyinteresting
fortrackingandforecastingbiodiversitychangeswhenappliedinSDMs[35].Inaddition,
EFAscanalsohelptoovercomedrawbacksofcurrentclimateandland-coverdatasets,suchas
theirdifficultytobeupdated[41]andtheinterpolationeffects[32].Alcaraz-Seguraetal.[35]
alreadyprovidedafirstillustrationofthepotentialadded-valueofEFAsasmeaningfulspe-
cies-levelEssentialBiodiversityVariables(EBVs)[30,44,45]toguidemonitoringschemesfor
multipleprotectedspecies,duetotheirgoodpredictivepowerinSDMs.
Severalstudiesindicatethatclimateimpactsonspeciesdistributionsaremostapparentat
macro-scales[6,46],whereasland-covermaybeamoreimportantconstraintthanclimatefor
speciespresenceatthelocalscale[25,47].ThepredictiveroleofEFAsinSDMsmaythusbe
scale-dependent,asdemonstratedforotherabioticandbioticpredictors[48–50].Inaddition,
therelevanceofEFAsforrangeshiftspredictionisknowntovaryacrossgroupsofspecies
[35].AccountingforthesepotentialcaveatsoftheapplicationofEFAsinSDMsisofhigh
importanceforreal-worldconservationchallenges[51],particularlytoassessthestatusand
trendsofspeciesofconservationconcern[52].Buildingonthisrationaleandonthemodel-
assistedbiodiversitymonitoringapproach[9,35],themaingoalofthisstudyistoassessthe
predictiveabilityofremotely-sensedecosystemfunctionalattributes(EFAs)inSpeciesDistri-
butionModels(SDMs),andtherebytotesttheirpotentialasEssentialBiodiversityVariables
(EBVs)forbiodiversitymonitoringandreporting.Forthis,wedevelopedandappliedamulti-
scalemodellingframeworkundertwospecificobjectives:1)tocomparetheperformanceand
scale-dependence(intermsofspatialextentandresolution)ofEFAsaspredictorsinSDMs,
againsttraditionalclimateandland-coverpredictors;and2)tocomparethespatialprojections
ofhabitatsuitabilityderivedfromSDMsbasedonEFAsandontraditionalpredictorsatvari-
ousscales,andthecorrespondingimplicationsforreportingtheconservationstatusofpro-
tectedspecies(e.g.underArt.17oftheHabitatsDirective[53]),andforguidinglocal
conservationstrategies.Ourtestablehypothesesandtheirunderlyingrationalearedetailed
below(‘Modellingframework’section).
Materialsandmethods
Studyareas
Wetestedourmulti-scaleapproachusingthreenestedstudyareas(Fig1):TheIberianPenin-
sula(IP),theIberianNorthwest(GaliciainSpain,andnorthernPortugal;NW),andthe
Peneda-GerêsNationalPark,inPortugal(NP).WeestablishedtheIP(581000km2)asthebio-
geographiccontextofreferencetofitsub-continentalmodels,sinceitscombinationofnatural
history,geologicandtopographicheterogeneity,andstrongclimaticgradientsoffersawide
rangeofenvironmentalconditionsforhostingabroadvarietyofendemicandrareplants[54].
WithintheIP,weconsideredthenorthwestcorner(NW),andwithinitthePeneda-Gerês
NationalPark(NP),tofitregionalandlocalmodels,respectively.TheNW(48000km2)isa
diversephytogeographicareawithadiversifiedflora(ca.2300nativespecies)dominatedby
EurosiberianandMediterraneanelements,andwithalargenumberofnarrowendemicsand
biogeographicdisjunctions[55].Peneda-Gerês(700km2)isamountainrangehostingmore
than800plantspecies,includingvariousnarrowlydistributedendemicsandotherregionally
rarespecies.
TheIberianPeninsula(Fig1Aand1B)ischaracterizedbyastrongclimaticgradient,from
therainiestandcoldestareaswithtemperateclimateinthenorthandnorthwest(Euro-Sibe-
rianregion),tothedriestandwarmestareasinthesouthandsoutheast(Mediterranean
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
Fig1.Studyareasandspeciesoccurrencedata.(a)Thethreenestedstudyareasandoccurrencedataoftargetspecies(IrisboissieriandTaxusbaccata)in
(b)theIberianPeninsulaat5km2cellsize,(c)theIberianNorthwestat5km2(emptysquares)and1km2(filledsquares),and(d)thePeneda-Gerês
NationalParkat1km2cellsize.
https://doi.org/10.1371/journal.pone.0199292.g001
region).Thisenvironmentalcontextgrantsahighdiversityofhabitatsandbioticcommuni-
ties.TheEuro-Siberian(Atlantic)regionholdsvegetationtypessuchasalpinenaturaland
seminaturalgrasslandsandheathlands,andforestecosystemswithalpineneedleleafconifer-
ousandtemperatebroadleafdeciduousandsemideciduousspecies;whereastheMediterra-
neanregionisprimarilyrepresentedbyevergreenbroadleafandconifercanopyspeciesin
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
forestecosystems,andahugerepresentationofshrubandherbaceousspeciesabletoresistthe
adverseconditionsoflongsummer-droughtperiods.Landscapesinbothregionshavebeen
largelytransformedbyhumanmanagement,firstthroughacombinationoffarming,grazing,
fires,andfirewoodcollection,morerecentlyintospecializedagricultural,agro-forestryorfor-
estryproductionlandscapes.ProtectedareasacrosstheIPnowholdmostofitsremaining
mosaicsofnaturalandseminaturalvegetation.
TheNWoftheIberianPeninsula(Fig1C)isamountainousterritorywithaltitudesranging
fromsealevelto2650m,withslopesoftenover25%.Theclimateisquitevaried,butfollowsa
generaloceanicpatternwithannualrainfallfrom600to2000mm(3000mminmountain
areas)[56].Theaverageannualtemperaturerangesfrom5˚Cinthehighestmountainsto
15˚Cinthelowlandsouthernterritories.Inspiteofthegreatinfluenceofhumanactivities,
andtheexpansionofalienspeciesfortimberproductionandornamentaluse,thecurrentveg-
etationstillholdssimilaritieswiththeEuropeanAtlanticflora[57,58].Deciduousforestdomi-
natedmainlybyQuercusroburandFagussylvaticausuallyappearrestrictedtothetop-halfof
theregion,namelyinsideprotectedareas.MixedforestsofUlmussp.,Acersp.,andSalixsp.,
amongothers,aremostlyfrequentatthesouthwest.Replacingforests,seralscrubofthorny
bushes(Cytisussp.,Ulexsp.andother)arecommonacrosstheregion,asaremeadowsand
othergrasslandswhichplayarelevantroleintraditionalagriculturalsystems.Towardssouth,
thetransitionintotheMediterraneanregionisrevealedbytheoccurrence(anddominance)of
evergreenvegetationsuchasforestsofQuercusrotundifolia, Q.suber,Q.fagineaandscrub
dominatedbyArbutusunedoandotherevergreenshrubs.
ThePeneda-GerêsNationalPark(NP;Fig1D)isamountainousprotectedarealocatedat
thecoreofNWIberia.Theaverageannualtemperaturesrangefrom5˚Cinthehighlandsto
20˚Cinthevalleys;whereasthetotalmeanrainfallreaches2000mmperyear(withmorethan
130rainydaysperyear),andsnowfallisfrequentinthemountaintops.Thesteeptopographic
andclimaticvariationshaveproducedamosaicofvegetationtypescharacteristicofMediterra-
nean,Euro-SiberianandAlpineenvironments.DeciduousoakforestsofQ.roburandQ.pyre-
naicaarecommonthroughouttheParkinareasabove700m.Still,long-termgrazingandthe
useoffirehavefacilitatedthereplacementofforestsbypastures,heathandscrub(74%ofthe
Park’sarea).ForestplantationsmainlyofPinuspinasteralsooccupysubstantialareas.The
highestrockyoutcropsandremoteareasarethemainhabitatforseveralendemicspecies,
manyofwhichareconsideredendangeredandareundernationalandEuropeanprotection
programs.
Testspeciesandoccurrencedata
Astestspecies,wefocusedontwovascularplantscoveredbytheHabitatsDirective(hereafter
HD)andforwhichEUmember-statesholdregularreportingobligations(underArticle17of
theHD):the‘Gerêslily’(Iris[Xiphion]boissieriHenriq.;AnnexIV),anendemic,narrow-ran-
gedbulbousplantholdinga‘criticallyendangered’conservationstatus(http://www.
iucnredlist.org/details/162312/0),andthe‘Europeanyew’(TaxusbaccataL.),indicatorand
dominantspeciesofHDAnnexIpriorityhabitattype9580(cid:3)(‘MediterraneanTaxusbaccata
woods’).Thesetwospeciessharplydifferintermsoftheirdistributionrange(narrowlyvs.
widelydistributed)andlife-form(bulbousgeophytevs.tree),representingcontrastingrarity
typesandthusdifferentchallengesforpredictivenichemodelling.
TheGerês-lily(Xiphionboissieri(Henriq.)Rodion=IrisboissieriHenriq.)isanarrow
endemicbulbousplantoftheIridaceaefamily.ItislistedinAnnexIVoftheHDandis
restrictedtomountainousareasofNWIberianPeninsula.Portugalconcentratesthelargest
populationsofthespecies,especiallyinthePeneda-GerêsNationalPark,whereasinSpainit
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
occursassmallpopulationsintheneighbouringSierrasofBaixaLimia-Xure´sandSantaEufe´-
miaandinafewothermountains(https://eunis.eea.europa.eu/species/186604).Itmainlycolo-
nizessmalldepressionswithaccumulationofcoarsedeposits,butitalsooccursinlowscrub
andincrevicesofgraniteoutcrops,atelevationsbetween500and1500meters[55].Theaban-
donmentofpastoralsystemsistriggeringvegetationsuccessionandpotentiallyreducingits
areaofsuitablehabitat[59].
TheEuropeanyew(Taxusbaccata)isalong-livingtreenativeinmostofEurope,withthe
southernlimitofitsdistributionrangeinmountainousareasoftheMediterraneanbasin
[60,61].ThereisevidenceofstrongregressioninsouthwestEurope,whereT.baccatanow
occursassmall,isolatedpopulations,makingitavulnerablespecies[62].T.baccatawoodlands
mayoriginateasasenescentordisturbedphaseofdeciduouswoodlands,inwhichthespecies
occurredasanunderstorytree.Yewstandscanalsobefoundalongmountainstreamswhere
thetreescanshelterfromfiredisturbanceandfromexpansionoftallcanopybroadleaved
trees.EvenifthishabitattypeisprotectedundertheHD(https://eunis.eea.europa.eu/habitats/
10239),thereisalackofknowledgeaboutthedistributionandconservationstatusofT.bac-
catawoodlandsinthissouthernedgeoftheirrange[63,64].
WeusedoccurrencerecordsforI.boissieriandT.baccata(presence-onlydataset)tocom-
putetheresponsevariablesforSDMcalibration.Allgeoreferencedrecordswereobtained
fromtheGlobalBiodiversityInformationFacility(http://www.gbif.org;accessedSeptember
2016)withgeographicaccuracyequalto,orbetterthan,1km2spatialresolution.Anadditional
setof72recordsresultingfromalocal(Peneda-Gerês)surveyofI.boissieriperformedin2007
wasaddedtothisdataset.Subsequently,recordswerecheckedusingGIStodetectgeoreferen-
cingandspeciesnomenclatureerrors.Thefinaloccurrencedatasetwasassumedtorepresent
thewhole(ormostofthe)geographicandenvironmentalrangeofbothspecies(cf.Fig1).The
finaldatasetforI.boissieriincludedrecordsthatrangedfrom1992to2007,whileforT.baccata
theyrangedfrom1971to2016(Table1)(S1andS2Datasets).
Totestourmodellingapproachatthethreefocalscales,theavailablerecordswerethen
aggregatedintwospatialgridswithdistinctcellsize–5km2forthesub-continental(IP)and
theregional(NW)scales,and1km2fortheregional(NW)andthelocal(NP)scales.Thetest
wasthusconductedconsideringtwodimensionsofspatialscale:theresolutionofthespecies
records(1x1kmvs.5x5kmgridcellsize)andthespatialextentofthetestarea(IPvs.NWvs.
NP).
Modellingframework
Thereisaccumulatedevidenceoftheimportanceofclimaticandland-coverpredictorsin
SDMs,theirperformanceandscale-dependence[20,65].Remotesensingcanalsoprovidea
broaddiversityofenvironmentaldescriptors[26]toSDMs,butthereisstilllittleknowledgeof
Table1. Occurrencedata(numberofgridcells)availableperspeciesateachcombinationofspatialresolution
andspatialextent.
Spatialresolution Spatialextent Speciesdistributionrecords
Irisboissieri Taxusbaccata
5km2 IP 30 440
5km2 NW 30 37
1km2 NW 91 139
1km2 NP 62 50
IP=IberianPeninsula;NW=North-westernIberianPeninsula;NP=Peneda-GerêsNationalPark
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
Table2. Specifictestablehypothesesforcomparisonoftheperformanceandscale-dependence(intermsofspatialextentandresolution)ofecosystemfunctional
attributes(EFAs)againsttraditionalclimateandland-coverdatasetsinSpeciesDistributionModels(SDMs).
Hypotheses Rationale
H GiventhatEFAscapturetheoverallintegrativeresponseofthesystemtoallenvironmentalfactors[35,38],remotely-sensedEFAsshouldperformas
1
predictorsinSDMssimilarlyorbetterthanthecombinationofinterpolatedclimatologygridsplusland-coverdata.
H Beingclimatethemainspeciesdriverattheregionalscaleandland-coverrelativelymoreimportantatthelocalscale(e.g.[6]),theadded-valueofEFAs
2
willalsobescale-dependent.
H Asobservedinpreviousstudies[35],suchscale-dependence H Foranarrowlydistributedspecies,climateshouldperformbetterthanEFAsat
3 3.1
shoulddifferaccordingtothespeciesdistributionrange. macro-scalesandcoarseresolutions,whiletheyshouldsimilarlyperformatlocal
scalesandfineresolutions.
H Forawidelydistributedspecies,climateandEFAsshouldperformsimilarlybothat
3.2
macro-scalesandcoarseresolutions,andatlocalscalesandfineresolutions.
https://doi.org/10.1371/journal.pone.0199292.t002
thepredictiveabilityofthedifferentvariables,andoftheeffectofspatialextentanddatareso-
lution(butsee[66,67]).Toassessthepredictiveabilityofremotely-sensedEFAsinSDMs,we
establishedtestableguidinghypotheses(Table2)basedonliteraturereviewandexpertknowl-
edge.Totestthesehypotheses,wedevelopedamodellingsetup(Fig2)thatinvolvedtwospe-
cies,twospatialresolutions,threespatialextents(Fig2),andfourgroupsofpredictors
(Table3):1)interpolatedclimatedata,2)landscapecomposition,structureanddiversitymet-
rics,3)remotelysensedproxiesofvegetationfunctioning(EFAs),and4)acombinationofcli-
mateandlandscape(land-cover)metrics.Thismodellingsetupwasalsodesignedtofacilitate
theinterpretationofEFApredictorsintermsofclimaticandland-coverconstraints,andthe
identificationofthemostrelevantpredictorsateachofthefocalscales.
Environmentalpredictors. ThesetofpredictorsusedforSDMcalibrationincluded
(Table3):
Fig2.Multi-scalemodellingframework.Generalframeworktotestthescale-dependenceoftheperformanceof
satellite-derivedEcosystemFunctionalAttributes(EFAs)aspredictorsinSpeciesDistributionModels(SDMs).
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
Table3. Finalsetsofpredictorsusedtocalibratemodels. Thedescriptionandattributesoftheoriginaldatasetsarealsoprovided.
Set Predictors Code Description Units Spatial Source
resolution
CLI MeanTemperatureofWettest TmWQ Theaveragetemperatureofthethreeconsecutive ˚C(cid:3)10 0.0083˚ http://www.worldclim.org
Quarter monthswiththehighestcumulativeprecipitationtotal. (~1km)
MeanTemperatureofDriest TmDQ Theaveragetemperatureofthethreeconsecutive
Quarter monthswiththelowestcumulativeprecipitationtotal.
TemperatureAnnualRange TAR Themeandifferencebetweenthemonth’smaximum
andminimumtemperatureoverthetwelvemonthsof
theyear.
PrecipitationofWettest PpWM Thetotalprecipitationthatprevailsduringthewettest mm
Month month(withthehighestcumulativeprecipitationtotal).
PrecipitationofDriestMonth PpDM Thetotalprecipitationthatprevailsduringthedriest
month(withthelowestcumulativeprecipitationtotal).
PrecipitationSeasonality PS Theratioofthestandarddeviationofthemonthlytotal
(CoefficientofVariation) precipitationtothemeanmonthlytotalprecipitation
overthecourseoftheyear.
LC Composition Agriculture agric Areascharacterizedbyherbaceousvegetationthathas % 0.00083˚ http://land.copernicus.eu/
beenplantedorisintensivelymanagedforthe area (~100m)
productionoffood,feed,orfiber.
Forest forest Areascharacterizedbytreecover,naturalorsemi-
naturalwoodyvegetation,generallygreaterthan6
meterstall.
Scrubs scrubs Areascharacterizedbynaturalorsemi-naturalwoody
vegetationwithaerialstems,generallylessthan6meters
tall,withindividualsorclumpsnottouchingto
interlocking.
Baresoil bs Areascharacterizedbybarerock,gravel,sand,silt,clay,
orotherearthenmaterial,withlittle(widelyspacedand
scrubby)orno"green"vegetationpresentregardlessof
itsinherentabilitytosupportlife.
Diversity Shannon’s SHDI Proportionofthelandscapeoccupiedbyagivenpatch
DiversityIndex class.
Structure MeanPatch AREAmean Theaveragemeansurfaceofpatches.
Area
EFAs ALBAnnualMaximum ALBmx TheaveragebetweenDaytimeBlack-Sky(Direct - 0.002˚ https://lpdaac.usgs.gov/
radiation)ShortwaveAlbedoandWhite-Sky(Diffuse (~250m) dataset_discovery/modis/
radiation)ShortwaveAlbedo. modis_products_table/
EVIAnnualMaximum EVImx TheinterannualmeanoftheEVImaximum. mod13q1
EVIAnnualMinimum EVImn TheinterannualmeanoftheEVIminimum.
EVIsineofthemomentumof EVIdmxs Themomentumofthemaximumgreen-updaysofyear
maximum decomposingitintothesineorthogonalvectorrelated
tospringinessandautumnessaxis.
LSTStandardDeviation LSTsd TheinterannualstandarddeviationoftheLandSurface ˚C
Temperaturemean.
LSTAnnualMinimum LSTmn TheinterannualmeanoftheLandSurfaceTemperature
minimum.
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1. Bioclimaticvariables(CLI)mainlyrelatedtotemperatureandprecipitationregimes,from
theWorldClimversion2(1970–2000period)databasewithaspatialresolutionof30arc-
seconds(~1km)(http://www.worldclim.org;[68]).CLIpredictorswereselectedfortheir
predictiveabilityaswell-knowndriversofspeciesdistributionsatbroaderscales[69].The
completeCLIdatasetincluded19candidatepredictors.
2. Severalstudieshaveshowntheimportanceoflandscapecompositionandconfiguration
forpredictingthedistributionofspecies[70].Wethereforecomputedseverallandscape
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
variablesfromtheCORINELandCover2006(Label3)databasewithaspatialresolutionof
100meters(http://land.copernicus.eu/pan-european/corine-land-cover).Composition
metricswerecomputedas%ofgridcellareacoveredbyeachLCclass.Wealsocomputed
spatialconfigurationmetricsusingtheFRAGSTATSsoftware(version4.2)[71].Thecom-
pletelandscapedataset(LC)included61compositionandconfigurationcandidate
predictors.
3. EcosystemFunctionalAttributes(EFAs).ThreeMODIS(ModerateResolutionImaging
Spectroradiometer)satellite-productswereselectedtodescribethreedimensionsofecosys-
temfunctioning:theEnhancedVegetationIndex(EVI)(MOD13Q1.006)asasurrogatefor
thecarboncycledynamics,theLandSurfaceTemperature(LST)(MOD11A2.005)asasur-
rogateofsensibleheatdynamics,andAlbedo(MCD43B3.005)asasurrogatefortheradia-
tivebalance[37],allforthe2001–2016periodattheoriginalspatialresolutionof230m.We
selectedEVIinsteadofanyothervegetationindex(suchasSAVI,ARVI,orNDVI)asan
indicatorofcarbongainssinceitisknowntobemorereliableinbothlowandhighvegeta-
tioncoversituations,andresistanttobothsoilinfluencesandcanopybackgroundsignals,
andatmosphericeffectsonvegetationindexvalues[72,73].Forthesethreedimensionsof
ecosystemfunctioning(EVI,LSTandAlbedo),weusedGoogleEarthEngine[74]toderive
theinter-annualmeanofthefollowingeightsummarymetricsoftheirseasonaldynamics:
annualmean(surrogateofannualtotalamount),annualmaximumandminimum(indica-
torsoftheannualextremes),seasonalstandarddeviation(descriptorofseasonality),and
sineandcosineofthedatesofmaximumandminimum(indicatorsofphenology)
[35,39,75].EVIvaluesrangedfrom-1to1,withhealthyvegetationgenerallyholdingvalues
between0.20and0.80.Temperatures(LST)rangedfrom-25˚Cto45˚C,andAlbedovalues
rangedfrom0to1(freshsnowandbaresoilusuallyfallaround0.9).Sineandcosineofthe
maximumandminimumgreen-updaysoftheyeararerelatedtospringiness/autumness
andwinterness/summerness,respectively[39,76].Thus,valuesnear+1onthesinearein
March/April,near–1areinSeptember/October,whilevaluesnear+1forthecosinearein
December/January,–1areinJune/July.ThecompleteEFAsdatasetincluded24ecosystem
functionalattributes(8metricsx3dimensions)ascandidatepredictors.
Alldatasetswerere-projectedtocoordinatesystemWGS84/UTMzone30(http://
spatialreference.org/ref/epsg/wgs-84)andresampledfromtheiroriginalspatialresolutionsto
theresolutionofspeciesoccurrencerecords.Resamplingwasfirstdonetoa1km2forusagein
theregional(NW)andlocal(NP)scalemodels,andtoa5km2cellsizefortheregional(NW)
andsub-continental(IP)scalemodels.Ashighlycorrelatedvariablesmayhamperthefitting
andvalidationofmodels[77],weconductedamulticollinearityanalysisofdatasetsusingthe
Spearman’scorrelationcoefficientandtheVarianceInflationFactor(VIF)[78,79].Consider-
ingfurtherthatthenumberofexplanatoryvariablesinthemodelsinfluencebothaccuracy
andpredictivepower[80],andthatthespecies’prevalencehasastrongimpactonmodelper-
formance[81],weestablishedthatnomorethanm/5predictorsshouldbeincludedineach
competingmodelforboththenarrow-rangedandthewide-rangedspecies,wheremisthe
numberofoccurrencerecords[82].Therefore,sincetheminimumnumberofrecordswas30
(cf.Table1),onlysixindependentpredictorswithSpearman’spairwisecorrelation<0.8[83]
andVIF<4(S1–S4FigsandS5andS6Figs,respectively),andwiththehighestrelativecontri-
butionpermodelextentandspatialscalecombinationinpreliminarytests,wereconsideredin
modelcalibration(i.e.thoselistedinTable3).Thefinalsetofpredictorswasdefinedconsider-
ingboththeresultsofthestatisticaltestsand,whentwoormorepredictorswerehighlycorre-
lated,onlytheonerepresentingamoredirectdeterminantoftheecologyanddistributionof
thespecieswaskept,basedonexpertjudgementandscientificliterature.
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Ecosystemfunctionalattributesaspredictorsinspeciesdistributionmodels
Table4. Rationaleforthefourgroupsofmodelsincludedinthemodellingsetup.
Datasets Rationale References
CLI Climaticgradients(CLI)usuallygovernspeciesdistributionsatglobaltoregionalscales. [20]
However,climatemaynotaffectequallythedistributionofnarrow-rangedandwide- [84]
rangedspecies. [69]
LC Land-cover(LC)mainlyaffectsspeciesoccupancypatternsatthelandscapeandlocalscales. [85]
Landscapecompositionandstructurehavebeenusedforpredictingspeciesdiversityaswell [25]
asthedistributionandabundanceofindividualspecies. [86]
CLI Climate(CLI)andland-cover(LC)areknowntoinfluencespeciesdistributionsatvarious [87]
+LC scales,thereforemodelscombiningclimateandland-coverpredictorsareduetoprovide [88]
robustpredictionsofthosedistributions.Climateandland-coverarealsodriversofEFAs, [89]
andsoCLI+LCmodelsareassumedtoapproach,fromastructuralperspective,the
potentialeffectsofEFAsonthosedistributions.
EFAs TheannualmetricsderivedfromEVItime-seriesarecloselyrelatedtothedynamicsof [38]
ecosystemcarbongains,andtherefore,tonetprimaryproductivity.Thesefunctional [90]
attributes(EFAs)allowcapturingmostofthevariabilityinphenology,seasonalityand [40]
productivity,holdinghighpredictivepoweroverspeciesdistributionsatthelocaland
regionalscales.
https://doi.org/10.1371/journal.pone.0199292.t004
Modellingsetup. Weestablishedthreegroupsofmodelsrepresentingtheeffectsofthe
threesetsofpredictorsdescribedabove(climate,land-cover,andEFAs)onthedistributionof
thetwotestspecies(Table4).Additionally,acombinationofpredictorsrelatedtoclimateand
landscapecomposition/configurationwasusedinafourthsetofmodels,usingthesamepre-
dictorsusedfortheCLIandLCmodels.
Toanalyseandrankmodelperformance,weconductedamodellingworkflowtestingthe
effectofthethreefocaldriversofmodelperformance(predictorset,spatialextent,andspatial
resolution),andforthenarrow-rangedandthewide-rangespecies.Thus,wecomparedmodel
performanceinthreesetsoftests:1)differentpredictorsets(CLI,LC,CLI+LC,EFAs),atsame
pixelsizeandspatialextent;2)differentpixelsizes(1km2vs.5km2),forthesamespatialextent
andwiththesamepredictorset;and3)differentspatialextents(IPvs.NWvs.NP),atthesame
pixelsizeandforthesamepredictorset.
Modelfittingandevaluation. WefittedSpeciesDistributionModels(SDMs)and
obtainedspatialprojectionsunderanensemble-forecastingframeworkimplementedonbio-
mod2package([91];availableathttp://cran.r-project.org/web/packages/biomod2/index.html).
Theensemble-forecastingframeworkhasbeenestablishedasapowerfultoolforanalysingspe-
cies-environmentalrelationships[92].Modelswerefittedusingall10modellingtechniques
availableinbiomod2,foreachsetofmodelsandusingdefaultparameters.Sincealgorithms
requiretheinputof(pseudo)absences[93],andsincetrue-absencedatawerenotavailablefor
thetargetspecies,pseudo-absencesweregeneratedbyrandomlyassigningunoccupiedgrid
cellseachthestudyregion,withthefollowingconstraints:1)generatingthesamenumberof
pseudo-absencesasofpresencestoavoidpotentialbiascausedbydifferentlevelsofprevalence
inthepresence/absencedatasets[94];and2)definingaminimumdistancebetweenpseudo-
absences,correspondingwitheachgrainsize(5kmand1km),andwithoutoverlappingwith
presences[95],toavoidspatialautocorrelationandinordertocoverthedifferentecological
conditionsineachstudyarea.Uncertaintywascontrolledbygenerating30differentsetsof
pseudo-absencesforeachspeciesandrunningthewholeprocess30times,resultingin9300
individualmodelsproducedforeachcombinationofspecies,spatialextentandgrainsize.
ModelaccuracywasweremeasuredastheAreaUndertheCurve(AUC)ofreceiveropera-
torcharacteristic(ROC)curves.AUCisarobustthreshold-independentmeasureofamodel’s
abilitytodiscriminatepresencefromabsence[79,96],rangingbetween0and1(measures
below0.7wereconsideredpoor,0.7–0.9moderate,and>0.9good).Theresultingmodels
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