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ContentslistsavailableatSciVerseScienceDirect

Ecological

Modelling

j ourna l h o me pa g e: w w w . e l s e v i e r . c o m / l o c a t e / e c o l m o d e l

Occurrence

and

abundance

models

of

threatened

plant

species:

Applications

to

mitigate

the

impact

of

hydroelectric

power

dams

Ernestino

de

Souza

Gomes

Guarino

a,∗

,

Ana

Márcia

Barbosa

b,c

,

Jorge

Luiz

Waechter

a

aProgramadePós-Graduac¸ãoemBotânica,UniversidadeFederaldoRioGrandedoSul.AvenidaBentoGonc¸alves,9500,Prédio43432.2,Bloco4,Sala203,CampusdoValeBairro

Agronomia,CEP91501-970,PortoAlegre,Brazil

bCátedra“RuiNabeiro”Biodiversidade,CIBIOUniversidadedeÉvora,7004-516,Évora,Portugal

cDepartamentofLifeSciences,ImperialCollegeLondon,SilwoodParkCampus,Ascot(Berkshire)SL57PY,UnitedKingdom

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received14June2011 Receivedinrevisedform 25December2011 Accepted6January2012 Available online 17 February 2012 Keywords:

Speciesreintroduction

Insituandexsituplantconservation Environmentalassessment Modelvalidation

a

b

s

t

r

a

c

t

Speciesoccurrenceandabundancemodelsareimportanttoolsthatcanbeusedinbiodiversity conser-vation,andcanbeappliedtopredictorplanactionsneededtomitigatetheenvironmentalimpactsof hydropowerdams.Inthisstudyourobjectiveswere:(i)tomodeltheoccurrenceandabundanceof threat-enedplantspecies,(ii)toverifytherelationshipbetweenpredictedoccurrenceandtrueabundance,and (iii)toassesswhethermodelsbasedonabundancearemoreeffectiveinpredictingspeciesoccurrence thanthosebasedonpresence–absencedata.Individualrepresentativesofninespecieswerecounted within388randomlygeoreferencedplots(10m×50m)aroundtheBarraGrandehydropowerdam reser-voirinsouthernBrazil.Wemodelledtheirrelationshipwith15environmentalvariablesusingboth occurrence(GeneralisedLinearModels)andabundancedata(HurdleandZero-Inflatedmodels).Overall, occurrencemodelsweremoreaccuratethanabundancemodels.Forallspecies,observedabundancewas significantly,althoughnotstrongly,correlatedwiththeprobabilityofoccurrence.Thiscorrelationlost sig-nificancewhenzero-abundance(absence)siteswereexcludedfromanalysis,butonlywhenthisentailed asubstantialdropinsamplesize.Thesameoccurredwhenanalysingrelationshipsbetweenabundance andprobabilityofoccurrencefrompreviouslypublishedstudiesonarangeofdifferentspecies, sug-gestingthatfuturestudiescouldpotentiallyuseprobabilityofoccurrenceasanapproximateindicator ofabundancewhenthelatterisnotpossibletoobtain.Thispossibilitymight,however,dependonlife historytraitsofthespeciesinquestion,withsometraitsfavouringarelationshipbetweenoccurrence andabundance.Reconstructingspeciesabundancepatternsfromoccurrencecouldbeanimportanttool forconservationplanningandthemanagementofthreatenedspecies,allowingscientiststoindicatethe bestareasforcollectionandreintroductionofplantgermplasmorchooseconservationareasmostlikely tomaintainviablepopulations.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

AccordingtostatisticsfromtheBrazilianElectricityRegulatory

Agency(ANEEL,2008),morethan150hydropowerplantswerein

operationinBrazilasof2008,amountingtoanoperatingcapacityin

excessof74GW.Hydroelectricenergyaccountsformorethan70%

oftheelectricityconsumedinBrazil,andthispercentageislikelyto

increasebecauseBrazilpossessestheworld’slargesthydroelectric

potential,withmorethan260GWavailable.Someestimates

indi-catethatbytheendofthetwentiethcentury,morethan30,000km2

of land were flooded by hydropowerdam reservoirs in Brazil.

∗ Correspondingauthor.Currentaddress:EmbrapaAcre,RodoviaBR364,km14, P.O.Box321,CEP69908-970,RioBranco,Brazil.Tel.:+555192273184.

E-mailaddress:esguarino@cpafac.embrapa.br(E.d.S.G.Guarino).

However,fewinitiativeshavebeenimplementedtoimprove

pro-ceduresforthemitigationandmanagementoflandscapesaffected

bydamreservoirs.

DuetoBrazil’shighbiodiversity,itisalmostimpossibletodefine

samplingproceduresthattakeintoconsiderationeveryorganism

affectedbythedevelopmentofanartificialreservoir.An

insuffi-cientnumber offield experts,time andmoney constraints,and

thelargeareascoveredbyhydropowerdamreservoirsaddtothis

challenge.Thus,toovercometheseobstacles,speciesdistribution

models(SDMs)constituteaviablealternativethatiseffectivein

predictingtheoccurrenceofdifferentorganismsaffectedbydam

construction.Theuseofthesemodelshasgreatlyincreasedover

thelastthreedecadesduetothedevelopmentanddisseminationof

GeographicInformationSystems(GuisanandZimmermann,2000).

Today,thesemodelsareessentialtoolswithinconservationbiology

(Elithetal.,2006).Usingdifferentalgorithms,speciesdistribution

0304-3800/$–seefrontmatter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2012.01.007

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modelscorrelatespeciesoccurrencewithenvironmentaldata(e.g.

climate,soil, topography)topredictspeciespresenceona map

(SoberónandPeterson,2005).

Duetotheneedtofindthehighestqualityareasforthe

con-servationofbiodiversity(PearceandFerrier,2001)andtoimprove

thestatisticaltechniquesusedtopredictspeciesabundance,

abun-dancemodelsareundergoinganexpansionsimilartothatobserved

inthebeginningofthelastdecade.Asearchforpapersinindexed

journalsontheISIWebofScience(performedonMarch5,2010;

http://www.isiknowledge.com),usingthetermsspeciesabundance

modelsandspeciesdistributionmodels,identifiedover3700papers

onthis topicpublishedinthelast 30 years (1979–2009).More

than 50% ofthese articleswere publishedin the last six years

(2004–2009),indicatingan increasinginterestin understanding

patternsofspeciesabundance.Informationabouttheabundance

ofaspeciesprovidesthefollowingintuitiveidea:thegreaterthe

numberofindividualsofacertainspeciesinonearea,thegreater

theprobabilityofmaintainingaviablepopulationforthatspecies

(AraújoandWilliams,2000).However,thisintuitiveassumption

isatargetofabroaddiscussion(i.e.,SkagenandYackelAdams,

2011;VanHorne,1983).AccordingtoVanHorne(1983),

popula-tiondensityisnotagooddirectmeasureofhabitatquality.This

authorsuggeststhathabitat-qualityassessmentisbasedonlyon

simpleestimationsoftotaldensityandforgetstotakeintoaccount

thedemographyofthespeciesandofthefactorsinfluencing

pop-ulationlevelsthroughtheirinfluenceonsurvivalandproduction.

Althoughimportant,thisinformationisexpensivetocollectand

timedemanding,undesirablefactorswhenitcomestoplanning

conservationactions.Insomecircumstancesthereisnochoicebut

toassumethathabitatqualityiscorrelatedwithabundance(Pearce

andFerrier,2001).

Thepositiverelationshipbetweenoccupancyandabundanceis

oneofthemostinterestingtopicswithinecology(Brown,1984;

Gastonetal.,2000; HeandGaston,2007)andis thesubjectof

constantdiscussion(BlackburnandGaston,2009;Komonenetal.,

2009;Kotiahoetal.,2009).Thismacroecologicalpatternsuggests

thatlocallyabundantspeciestendtobemorewidelydistributed

than locally rare species,which tend to beof restricted

occur-rence(HeandGaston,2000).Thispatterncanbeattributedtothe

relationshipbetweenthepopulationdensityofaspecieswiththe

spatialdistributionofenvironmentalfactorswhichdetermineits

distribution.Bothdensity(orabundance)andprobabilityof

occur-renceareintuitiveindicatorsofhabitatquality,sowecanexpecta

positiverelationshipbetweenthem(e.g.PearceandFerrier,2001).

However,thisrelationshipisnotalwaysobserved.Thereisalarge

number ofnon-environmentalcontrolsof abundance,including

bioticinteractionssuchaspredationorinterspecificcompetition

(Holtetal.,2002;VanHorne,1983),dispersallimitation(Holtetal.,

2002; Pulliam,2006; Verbeket al.,2010)and different species

detectabilityamonghabitats,seasons,weatherconditions(Guand

Swihart,2004;PearceandFerrier,2001)andobservers(Chenetal.,

2009).Thiscanleadtothepopulationofaspeciesreachinghigh

valuesofabundancewithinlowprobabilityofoccurrenceareas.

Fewstudieshaveattemptedtoreproduceabundancepatterns

usingprobabilityofoccurrencedatageneratedbyspecies

distribu-tionmodels,withdifferentstatisticalapproachesandinconsistent

results(Jiménez-Valverdeetal.,2009;Nielsenetal.,2005;Pearce

andFerrier,2001;Realetal.,2009;VanDerWaletal.,2009).

Work-ingwithalargenumberofspeciesfromdifferentgroups(arboreal

marsupials,smallreptiles,diurnalbirds,vascularplantsand

arthro-pods),PearceandFerrier(2001)andJiménez-Valverdeetal.(2009)

showedthatforasmallnumberofspeciestheprobabilityof

occur-rencemayalsoserveasaproxyofabundance.Jiménez-Valverde

etal.(2009)suggestthatthisrelationshiptendstobemore

com-monforspecieswithhighdispersalability.VanDerWaletal.(2009)

showedthatprobabilityofoccurrencegeneratedbypresence-only

species distributionmodels could predict upper limits of local

abundanceforrainforestvertebratesintheAustralianwet

trop-ics.Realetal.(2009)showedthatabundanceofIberianlynxand

Europeanrabbitcorrelatespositively,althoughnotstrongly,with

probabilityofoccurrence.Contrastingpatternsweredescribedby

Nielsenetal.(2005)forbrackenfernandmoose.

Regardlessof geographicalrange ororganism,species

distri-butionmodelshave beensuccessfullyusedina largevarietyof

conservationbiologystudies(Cayuelaetal.,2009;Rodríguezetal.,

2007).Goodexamplesofapplicationofspeciesdistributionmodels

inconservationbiologyproblemsaregivenbyAlvesandFontoura

(2009)withfishinahydrographicbasininsouthernBrazil;Barbosa etal.(2003,2010)andRealetal.(2009)withotters,lynxand

rab-bits,and desmans, respectively,in theIberian Peninsula;Willis

etal.(2009)withbirdsinsub-SaharanAfrica;Zhuetal.(2007)

withinvasiveplantsinChina;andParoloetal.(2008)withplant

reintroductioninAlps.However,examplesofspeciesabundance

modelsappliedtosolveconservationproblemsarerare,probably

duetothedifficultytoobtainandtoanalyseabundanceordensity

data.

Inthisstudy,wemodelledboththeoccurrenceandabundance

ofasetofplantspeciesinthesurroundingsofapowerdamand

testedthefollowinghypotheses:(i)thereisapositiverelationship

betweenpredictedprobabilityofoccurrenceandtrueabundance;

(ii)thereisapositiverelationshipbetweenamodel’s

discrimina-tioncapacityanditsrelationshipwithtrueabundance;and(iii)

modelsbasedonabundancearemoreeffectiveinpredictingspecies

occurrencethanthosebasedonpresence–absencedata.We

dis-cusstheapplicationofourmethodsandtheimplicationsofour

resultsfortheconservationofspeciesaffectedbytheconstruction

ofhydroelectricpowerdams.

2. Materialsandmethods

2.1. Studyarea

ThefieldsurveywasconductedaroundtheBarraGrandedam

reservoirinsouthernBrazil(Fig.1).LocatedinthePelotasRiver

Basin betweenthe states of SantaCatarina and Rio Grande do

Sul, this reservoir encompasses approximately 90km2, and its

surroundings,delimitedbywatershedboundaries,coveranarea

ofroughly4600km2.Elevationrangesbetween500and1200m

abovesealevel,andtheclimatetypesrangefromCfa(humid

sub-tropical)toCfb(oceanicormarinetemperate),dependingonthe

elevationquota (Köppenclimateclassification).Annual

precipi-tationis1.412mm,andthemeantemperatureis15.2◦C (Maluf,

2000).Topographyvariesfromrollinghighlands(Southern

Brazil-ianPlateau),wherecambisolandalfisolarethemostcommonsoil

classes,tosteepslopesnearthePelotasRiver,wherelithosolis

themostfrequentclass(Potteretal.,2004;sensuBraziliansoil

classificationsystem–SiBCS).

Thevegetationischaracterisedbycontinuousareasof

semi-deciduousforestspredominantlylocatedclosetothePelotasRiver.

Inthehighlands(≥800m),Araucariaforestscoveralargeareathat

isnaturallyfragmentedbygrasslands(Jolyetal.,1999;Klein,1975).

2.2. Targetspecies

Thetime and resourcesavailableallowedthefield sampling

of nine selected species: Araucaria angustifolia (Bertol.) Kuntze

(Araucariaceae), Butia eriospatha (Mart. ex Drude) Becc.

(Are-caceae), Clethra scabra Pers. (Clethraceae), Dicksonia sellowiana

(Presl.)Hook.(Dicksoniaceae),ErythrinafalcataBenth.(Fabaceae),

Maytenus ilicifolia (Schrad.) Planch. (Celastraceae), Myrocarpus

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Fig.1. Locationmapofthestudyareaandsamplingsites(UniversalTransverseMercatorcoordinatesystem,Zone22J,southernBrazil).

Endl.(Podocarpaceae)andTrithrinaxbrasiliensisMart(Arecaceae).

ExceptforE.falcata,allofthesespeciespossessthreatcategories,

bothglobally(IUCNRedList)andlocally(RioGrandedoSulStatelist

ofthreatenedspecies).Duetotheintensecommercialexploitation

ofD.sellowiana,thisfernhaditsmarketregulatedbythe

Conven-tiononInternationalTradeinEndangeredSpeciesofWildFauna

andFlora(CITES,AppendixII).Allofthesespeciesareeasily

iden-tifiedinthefield,minimisingsamplingerrors(falseabsences).

2.3. Sampling

Past studies have used standardised abundance estimates

obtainedfromdifferentsurveysorestimatesofabundancefrom

indirectmethods (F-igueiredoandGrelle, 2009;HeandGaston,

2007).Thesemethods,accordingtoAustinandMeyers(1996),can

createanunwantedbias.Incontrast,ourworkwasbasedon

reli-ableoccurrenceandabundancedata.Thesedatawereobtainedby

conductingadetailedfieldsurveyandgermplasmcollection

expe-ditionsforthepurposeofexsituconservationofthetargetspecies.

We randomly sampled 388 georeferenced plots (10m×50m)

aroundtheBarraGrandehydropowerdamreservoir.Ineachplot

wecountedthenumberofindividualsofeachtargetspecieswith

heightgreaterthan1.5m.Topreventorreducetheinfluenceof

topographyinsampling,plotswereallocatedalongdifferent

topo-graphiccontourlevels,similartothosedescribedbyMagnusson

etal.(2005)forrapidsurveysofbiodiversity.

Toavoid anyeffectsofspatialautocorrelation,theminimum

distancebetweenplotswas50m.Thevalueofthisprecautionwas

latervalidatedbyMoran’sIcorrelograms.Allcorrelogramswere

calculatedusingthesoftwareSAMv3.1(Rangeletal.,2010).Forall

targetspecies,thevariationfollowsarandompatternwithasmall

oscillationaroundthezerovalue,whichrepresentstheabsence

ofsignificantautocorrelation(AppendixA;FortinandDale,2005;

LegendreandLegendre,1998).

2.4. Environmentalvariables

Weused15environmentalvariables(directandindirect,sensu

AustinandMeyers,1996)topredictthespatialdistributionofthe

targetspecies(Table1).Allenvironmentalmapshadaspatial

res-olutionof30m.Allspatialdatawerestoredandanalysedusingthe

Table1

Environmentalvariablesusedtopredictthespatialdistributionofthetargetspecies.

Group Variable Soil pHH2O Ca+2+Mg2+* H++Al3−* K+ S2− P+ Totalnitrogen Silt:clayratio* Bulkdensity Topography Elevation(m)* Northness Eastness

Topographicalwetnessindex(TWI) Slope(◦)

Currentvegetation NormalisedDifferenceVegetationIndex(NDVI)

*VariablesexcludedfromanalysisduetohighPearsoncorrelationcoefficient(r)

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softwareQuantumGISv.1.5.0(QuantumGISDevelopmentTeam,

2009)anditsinterfacetoGRASS(GRASSDevelopmentTeam,2010).

2.4.1. Topographicvariables

ElevationvalueswereobtainedfromaDigitalElevationModel

(DEM)generatedbytheASTERsensor(ASTERGDEM).Fromthis

DEM,fournewtopographicvariablesweregenerated(northness,

eastness,topographicwetnessindexandslope).(i)Northnessand

eastness(Roberts,1986):theoccurrenceof differentvegetation

physiognomiesisintimatelyconnectedtotheamountofavailable

solarradiation.Inthesouthernhemisphere,placeswitha

north-eastsolarorientationhavegreaterSunexposureandconsequently

ahigherrateofevapotranspiration,resultingintheoccurrenceof

vegetationwithxerophyticcharacteristics.Conversely,slopes

fac-ingsouth–southeast,especiallyduringthewinter,areexposedto

lesssunlight(Kirkpatricketal.,1988;KirkpatrickandNunez,1980).

Toexplorethisrelationship,aspect-modifiedmapswerecreatedin

thisstudy.Themapsindicateatrendtothenorth(northness=cos

[aspect])andeast(eastness=sin[aspect]).Afterthis

transforma-tion,bothvariablesreachedvaluesbetween1and−1,indicating

agradientnorthtosouthandwesttoeast.(ii)Topographic

wet-nessindex(TWI)describesthespatialpatternofsoilwetnessand

isdefinedasafunctionofslope.TWIisobtainedusingthefunction

ln(A/tanˇ),whereAistheupslopedrainingthroughadetermined

pointxgridcellsizeandˇisthepointslope(NetelerandMitasova,

2008;Sørensenetal.,2005).(iii)Slopewasusedtoindicatesoil

depth.A higherdegreeofslopecorresponds toa shallowersoil

depth(PeníˇzekandBor ˚uvka,2006;Tsaietal.,2001).

2.4.2. Soilvariables

Duetothelackofsoilmapsatthespatialresolutionadopted,we

collectedsoilsamplesat381ofoursamplingplots(depth:0–20cm)

and analysed theirchemical and physical properties (Table1).

Owingtologisticalproblemswewerenotabletoanalysesoil

sam-plesfromtheremainingsevensites.Toovercomethis problem,

weperformedaspatialinterpolationofthepropertiesofour381

soilsamples,usingtheregularisedsplinewithtensionalgorithm

(RST;MitasovaandHofierka,1993;MitasovaandMitas,1993),to

coverallthesitesstudied.RSTisarobustandflexiblemethodused

toselectparametersthatcontrolthepropertiesofthe

interpola-tion(tensionandsmoothing).Inaddition,estimatesgeneratedby

RSThaveaccuracysimilartotraditionalgeostatisticalmethods(e.g.

ordinaryanduniversalkriging;seeChaplotetal.,2006formore

details).Thebestinterpolationcontrolparametercombinationwas

selectediterativelyusingcross-validation(leave-one-outmethod;

Tomczak,1998).Toensureaccuracy,weexaminedthedecreasein

rootmeansquareerror(RMSE)andmeandifferencesbetweenthe

observedandpredictedvalues(theclosertozero,thebetterthe

estimate).Inaddition,oursoilmapswerevalidatedusingother,

coarser-scalesoilmapsavailableforthisregion.

2.4.3. Currentvegetationcover

Toestimatecurrentvegetationcover,weusedtheNormalised

DifferenceVegetationIndex(NDVI),obtainedbydividingbands3

(visiblered)and4(nearinfrared)ofLandsat5TM.NDVIvalues

varybetween−1.0and+1.0,andhighpixelvaluesrepresent

plen-tifulvegetation.Toavoidanyeffectsofleafphenologyduringthe

year(forestdeciduousness),weusedLandsat5TMimagescollected

duringsummerinthesouthernhemisphere.

2.5. Dataanalysis

ExceptfortheMoran’sIcorrelogramsdescribedabove,allother

statisticalanalyseswerecarriedoutwithRsoftware(v.2.10.0;R

DevelopmentCoreTeam,2009).

2.5.1. Presence–absencemodels

Generalised Linear Models (GLMs) are the most common

regression methodusedtopredictspecies’spatialdistributions.

AccordingtoMcCarthyandElith(2002),GLMsprovidearigorous

andstatisticallyrobustmethodtopredicttheoccurrence(or

abun-dance) ofspecies.We modelledtheobservedpresence–absence

of each targetspecies withGLM(binomialdistribution,logistic

linkfunction)usingthe15environmentalvariablesdescribedin

Table 1 as predictors. The predictors were selected through a

forward–backward stepwiseprocedure based on

small-sample-size-correctedAkaike’sInformationCriterion(AICc;Burnhamand

Anderson,2002),usingamodifiedversionofthestepAICfunction

oftheRMASSpackage.Variableswerethusaddedtoorremoved

fromthemodelaccordingtohowtheychangeditsAICc;thebest

(orminimaladequate)modelforeach speciesisachievedwhen

novariablecanbeaddedorremovedwithoutanincreaseinAICc.

Stepwiseselectionisausefulandeffectivetooltoinferdistribution

patternsinductivelyfromobserveddata,whennotheoryor

previ-oushypothesesexistabouttheimportanceofeachvariable(Guisan

andZimmermann,2000;Realetal.,2009).

We evaluatedthe models’discriminationcapacity (i.e.,their

abilitytodistinguishpresencefromabsencecases)usingtheArea

under the ReceiverOperating Characteristic (ROC) curve (AUC;

FieldingandBell,1997).AUCvaluesmayvarybetween0and1.

Val-uescloseto0.5indicatethatmodelpredictionsarenobetterthan

random,andAUCvaluesequalto1indicatea100%chanceforthe

modeltocorrectlyclassifyanevent(inourcase,speciespresence

orabsence).AUCvalueslowerthan0.5indicatethatthemodelis

discriminatingpresencesfromabsences,butusingtheinformation

inareversedway(Fawcett,2006).Thenullhypothesisthatthearea

underROCcurveis≤0.5wastestedusingaMann–WhitneyU-test

(MasonandGraham,2002).

Model predictions were alsocompared withthe abundance

datacollectedinthesamesites.UsingSpearman’srankcorrelation

(rho),wetestedtherelationshipbetweenobservedspecies

abun-danceandthepredictedprobabilityofoccurrenceforeachspecies.

Wealsotestedthehypothesis thatifamodelcorrectlypredicts

abundance,ithasahighdiscriminationcapacity(Jiménez-Valverde

etal.,2007).Werepeatedthisanalysisusingonlylocationswith

observedabundance>0assuggestedbyPearceandFerrier(2001).

Wealsoverifiedtherelationshipbetweenmeannumberof

occu-piedsitesperspecies(whichisthesamplesizeforabundance>0)

andabundanceusingaWilcoxonranksumtest(Zar,1999).

2.5.2. Abundancemodels

RegressionmodelsbasedonPoissondistributionsareoftenused

toanalysecountdata(suchasabundance).However,thesedata

areoftenZero-inflated,i.e.,theincidenceofzeros islargerthan

expectedbychance(Ridoutetal.,1998;Welshetal.,1996).Zuur

etal.(2009)describedfivesourcesofzeros,tworelatedtospecies

occupancypatterns(i.e.,thehabitatisnotsuitableandthespecies

is notpresent, orthehabitatis suitablebut is notusedby the

species)andthreerelatedtosamplingerrors(i.e.,designerror,low

speciesdetectability,andsamplingoutsidespecies’habitatrange

–“naughtynaughts”sensuAustinandMeyers(1996)).The

rigor-ousnessofourfieldsurveyandthehighdetectabilityofourtarget

specieswarrantthatoursamplesdonotcontainfalsenegatives

(samplingerrors),sothezeros thatdooccurareonlyrelatedto

patternsofspeciesoccupancy.

Tomodelabundanceweusedthesame15environmental

pre-dictorsdescribedinTable1andtestedthreedifferentalgorithms:

(i) GLM withPoisson (P) and negative binomial (NB)

distribu-tions(GLMPandGLMNB),(ii)Hurdlemodels(HPandHNB,Zeileis

et al., 2008) and (iii) Zero-Inflated Count Data Regression (ZIP

andZINB,respectively).GLMP (standardPoisson)isthesimplest

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(variance=mean),butthisassumptionisnotalwaysmetin

prac-ticeduetozero-inflation(variance>mean).Onealternativetodeal

withoverdispersionistheuseofnegative-binomialregressions,

wherevarianceisestimatedasaquadraticfunctionofthemean

(VerHoefandBoveng,2007;LindénandMäntyniemi,2011).

Hurdle models are two-componentclass models capable of

accountingforoverdispersion,orunderdispersion,usingPoisson

(HurdleP)ornegative binomial(HurdleNB)distributions.Hurdle

modelsslacktheassumptionthatzerosandvalues>0comefrom

thesame process (Cameronand Trivedi, 1998).As a first step,

Hurdlemodelsusea truncatedcountcomponent forvalues >0,

assumingthatthesevaluesarisefromtheeffectofconditionsthat

resultinpassingaprobabilitythresholdorzero-hurdle(Cameron

andTrivedi,1998; Gray,2005;Pottsand Elith,2006).Asa

sec-ondstep,aHurdlecomponentmodelszerosvs.non-zerovalues

usingabinomialGLM(Zeileisetal.,2008).Zero-inflatedmodels

aretwo-componentmixturemodelsinwhichzerosaremodelled

asoriginatingfromtwo stochasticprocesses, thebinomial

pro-cessandthecountprocess.SimilartoHurdlemodels,Zero-Inflated

modelsusebinomialGLMtomodeltheprobabilitiesof

measur-ingzeros,andthecountprocessismodelledbyaPoisson(ZIP)or

negativebinomial(ZINB)GLM(Zuuretal.,2009).Thepredictorsof

allabundancemodels(GLM,HurdleandZIPmodels)wereselected

usingthesameAICc-basedapproachappliedtopresence–absence

models(seeSection2.5.1).

Weselectedthebestabundancemodelforeachtargetspecies

usingAICc and evaluatedit withthefollowing fourcriteria:(i)

Pearsoncorrelationcoefficient(r),whichvariesfrom−1to+1and

providesanindicationofagreementbetweenobservedand

pre-dictedabundancevalues(notethataperfectadjustment(r=1)does

notimplyanexact prediction);(ii)Spearman’srankcorrelation

(rho),whichalsovariesfrom−1to+1andprovidesanindication

ofsimilarityinrankbetweenobservedandpredictedabundance

values;(iii)linearregressioncoefficients,whichareobtainedby

fittingasimple linearregression (observedvalues=m(predicted

values)+b;inaperfectlycalibratedmodel,mshouldequal1andb

shouldequal0);and(iv)rootmeansquareerror(RMSE)and

aver-ageerror(AVEerror),bothofwhicharedependentonsamplesize

andmeasuredivergencesbetweenobservedandpredicted

abun-dancevalues(PottsandElith,2006).Confidenceintervals (95%)

foreachevaluatedparameterwerecalculated usingabootstrap

procedure(1000replicates).

3. Results

3.1. Presence–absencemodels

AUCvaluesrangedfrom0.71to0.96andinallcaseswere

sig-nificantlydifferentfrom0.5(Mann–WhitneyU-test,P<0.01;for

detailsaboutenvironmentalvariablesincludedintheGLM

mod-elsseeAppendixB).Overall,therelationshipbetweenobserved

abundanceand occurrence probability waspositive and

statis-ticallysignificant (Table 2 and Fig. 2).When we truncatedthe

abundancedatatoeliminatezeros,theresultsweredifferent.Only

A.angustifolia (whichhadthehighestprevalence) andC.scabra

(witharelativelylowprevalence)retainedsignificantcorrelations

betweenobservedabundanceandprobabilityofoccurrencevalues

(Table2andFig.2).Fortheremainingsevenspecies,the

relation-shipbetweenabundanceand probabilityofoccurrencewasnot

significantwhenunoccupiedsiteswereexcluded.Note,however,

thatthisexclusionofunoccupiedsitesimpliedthatthemean

sam-plesizeforthesespecieswassignificantlysmaller(Mann–Whitney

U-test,P<0.001).

ThecorrelationbetweenAUC(i.e.,modeldiscrimination

capac-ity) and rho (i.e., the rank correlation between occurrence

Table2

Presence–absence model evaluation. Abundance: median and range, P (%): (Prevalence=[truepositives+falsenegatives]/samplesize)×100;rho:Spearman’s rank correlation;AUC: areaunder the receiver operator characteristic curve (Mann–WhitneyU-test).

Targetspecies Abundance P(%) rho rho(Abd>0) AUC

A.angustifolia 4(0–49) 72.42 0.42* 0.17* 0.77** B.eriospatha 0(0–24) 1.55 0.21* 0.39 0.95** C.scabra 0(0–27) 9.79 0.30* 0.32* 0.79** D.sellowiana 0(0–153) 13.40 0.25* 0.02 0.71** E.falcata 0(0–4) 4.38 0.23* 0.16 0.83** M.ilicifolia 0(0–14) 21.13 0.47* 0.08 0.83** M.frondosus 0(0–29) 5.15 0.32* 0.12 0.92** P.lambertii 0(0–24) 5.67 0.18* 0.00 0.73** T.brasiliensis 0(0–21) 4.12 0.32* 0.21 0.96** *P<0.05. **P<0.001.

probability and abundance) was 0.18 (Spearman coefficient,

P=0.63).Therefore,wefoundnoproofthatmodelswithbetter

dis-criminationcapacitywerebetteratpredictingspeciesabundance.

3.2. Abundancemodels

Duetothehighprevalenceofzerocounts(98%),wewerenot

abletomodeltheabundanceofB.eriospatha.BasedonAICcvalues,

HurdleandZero-Inflatedmodels(Poissonornegativebinomial

dis-tributions)werebetterthanGLMwithPoissonornegativebinomial

distributions(Table3).

We could clearly distinguish three groups of species using

abundanceevaluationparameters.(i)C.scabra,D.sellowiana,M.

frondosusandP.lambertiishowedtheworst-fitmodelswiththe

highest values of RMSEand AVEerror (Fig.3).D. sellowiana had

astrongandinconsistentbias(b=1.461andm=0.151),whileC.

scabra,M.frondosusand P.lambertii showedsimilarvalues ofb

(0.230,0.203and0.304,respectively)andm(0.01,0.05and0.00,

respectively).(ii)E.falcata,M.ilicifoliaandT.brasiliensisshowed

accurateabundanceestimates,withbandmequalorclosertozero

andone,respectively(Fig.3).Thesespecieshadrelativelyhigh

val-uesofr(≥0.55)andrho(≥0.33),indicatingthatbothobservedand

predictedabundancemeasuresweresimilarinmagnitude,butnot

similarlyordered(PottsandElith,2006).Also,thesespeciesgave

thesmallestRMSEandAVEerror(Fig.3).(iii)Finally,agroup

com-posedbyonlyA.angustifoliawhichhadconsistentbias(b=1.06and

m=0.83;Fig.3).

3.3. Environmentalcorrelatesofoccurrenceandabundance

SoilpHwasnegatively relatedtotheabundanceofA.

angus-tifolia, C. scabra, D. sellowiana and M. frondosus, and positively

relatedtotheabundanceofT.brasiliensis(Table4).SoilpH

pro-videsanindirectnutrientgradientinthesoil,anditsvaluesdirectly

affect theuptake of K+,S2− and P+ (low or negative ˇ values,

Table3

SelectedabundancemodelsbasedonAICc.ZIP,Zero-InflatedCountDataRegression

withPoissondistribution;ZINB,Zero-inflatedCountDataRegressionwithNegative

Binomialdistribution;HurdleNB,HurdleModelRegressionwithNegativeBinomial

distribution.

Targetspecies Model AICc

A.angustifolia ZINB 2148.84 C.scabra ZINB 347.28 D.sellowiana ZINB 617.37 E.falcata ZINB 144.35 M.ilicifolia HurdleNB 569.96 M.frondosus ZIP 173.67 P.lambertii ZIP 241.48 T.brasiliensis ZINB 158.12

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Table4

Variablesincludedintheabundancemodels(countandzerocomponents)obtainedforthestudiedspecies.ˇ,coefficient;S.E.,standarderror;P(z),significanceofthe z-statistictest.

Targetspecies Modelcomponent Variable ˇ S.E. P(z)

A.angustifolia Count pHH2O −0.72 0.14 <0.001 S2− −0.07 0.03 0.03 Eastness 1.82 0.40 <0.001 Slope 0.03 0.01 <0.001 Zero pHH2O 1.33 0.58 0.02 S2− −0.18 0.05 <0.001 Ntotal −11.44 3.75 0.002 Northness 10.44 3.22 <0.001 Eastness 5.95 2.39 0.01 NDVI −5.13 1.76 0.005 Slope −0.18 0.07 0.01 C.scabra Count Constant 58.82 12.63 <0.001 pHH2O −1.27 0.61 0.03 S2− −0.07 0.02 0.003 P+ −0.56 0.16 <0.001 Bulkdensity −17.54 4.23 <0.001 NDVI −20.18 4.00 <0.001 Slope 0.27 0.05 <0.001 S2− −0.13 0.05 0.01 P+ −1.03 0.51 0.04 NDVI −20.63 8.55 0.01 D.sellowiana Count Constant 7.94 3.38 0.02 pHH2O −1.30 0.61 0.03 K+ 1.68 0.61 0.006 Ntotal 6.22 2.81 0.02 TWI −0.05 0.19 0.009 Slope 0.14 0.06 0.01 E.falcata Count Constant −31.89 3.59 <0.001 pHH2O 2.67 0.73 <0.001 K+ −3.46 0.69 <0.001 Ntotal 19.75 4.36 <0.001 Bulkdensity 6.27 1.52 <0.001 Eastness −8.38 2.13 <0.001 Zero K+ −55.17 27.09 0.04 M.ilicifolia Count P + 0.69 0.26 0.007 Northness 2.03 0.88 0.02 Zero Constant −17.60 2.07 <0.001 pHH2O 2.66 0.35 <0.001 S2− 0.08 0.01 <0.001 M.frondosus Count Constant 76.65 13.24 <0.001 K+ 1.09 0.23 <0.001 S2− −0.36 0.06 <0.001 P+ −1.02 0.17 <0.001 Bulkdensity −23.20 3.54 <0.001 Eastness 8.09 0.91 <0.001 Slope 0.13 0.02 <0.001 Zero Constant 673.77 288.92 0.02 pHH2O −42.25 18.68 0.02 K+ −6.83 3.35 0.04 S2− −3.38 1.47 0.02 P+ −2.82 1.30 0.03 Ntotal 89.08 43.27 0.04 Bulkdensity −137.98 59.40 0.02 Eastness 38.07 16.31 0.02 Slope −1.25 0.57 0.03 P.lambertii Count Constant −3.75 1.46 0.01 K+ −2.16 0.47 <0.001 P+ 0.53 0.11 <0.001 Ntotal 4.68 2.31 0.04 Northness 8.88 2.36 <0.001 Eastness −6.21 1.65 <0.001 TWI 0.52 0.08 <0.001 NDVI 9.48 2.00 <0.001 Slope −0.56 0.08 <0.001 Zero P+ 0.91 0.30 <0.001 Northness 12.57 5.23 0.01 Slope −0.55 0.21 0.09 T.brasiliensis Count pHH2O 7.86 2.56 0.002 K+ 5.70 1.20 <0.001 S2− −0.25 0.09 0.06 TWI 0.92 0.19 <0.001 NDVI 6.13 1.60 <0.001 Slope 0.52 0.10 <0.001

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Fig.2.Relationshipbetweenoccurrenceprobabilityandtrueabundancevalues(abundance≥0)foreachstudiedspecies.Blackdots,speciesabsence;whitedots,species presence.

Table4)for each species. The occurrence of M. ilicifolia

corre-latedwithsoilpH.However,neitherabundancenoroccurrence

ofP.lambertiiwasaffectedbysoil pH.The abundanceofE.

fal-cata, was positively related to the amount of nitrogen in soil,

whiletheabundanceofP.lambertiiwaspositivelyrelatedto

nitro-gen (Table 4). Northness, eastness, slope and TWI each had a

differentrelationship withthevalues of abundanceand

occur-rence,occasionallyaffectingboth simultaneously(Table4).The

valuesofNDVI,anindirectindicatorofthestageofvegetation

suc-cession(currentvegetationbiomass),werenegativelyrelatedto

theabundanceofC.scabra.OccurrenceofC.scabrawasalso

asso-ciatedwithlowvaluesofNDVI,whileP.lambertiiandT.brasiliensis

werepositivelyrelatedtoNDVI(Table4).

4. Discussion

4.1. Presence–absencemodelsandtheirrelationshipwith

abundance

Becauseoftheneedforreliablespeciesdistributionmodelsto

aidindevelopingconservationstrategies,methodsusedtoassess

modelaccuracyareoneofthemostimportantissues inspecies

distributionmodelling(seee.g.Elithetal.,2006;FieldingandBell,

1997;Maneletal.,2001).Loboetal.(2008)conductedadetailed

descriptionofthedifferentissuesinvolvedwiththemisuseofthe

AUC,which iswidelyusedasameasureofaccuracy ofspecies’

distributionmodels.Onealternativeproposedbytheauthorsisthe

useofabundancedatatovalidatethesemodels.Thisideaisbased

onaninductiverelationship,whereprobabilitiesofoccurrenceare

functionallyrelatedtospeciesabundance(Nielsenetal.,2005).

Pearceand Ferrier(2001)attemptedtousepredicted

proba-bilities of occurrence asa surrogatemethod topredict species

abundance.However,accordingtotheseauthors,therelationship

betweentheprobabilitiesgeneratedbylinearmodelsandobserved

abundanceisweakandrestrictedtoafewspecies.Similarresults

havebeenreportedbyNielsenetal.(2005)andJiménez-Valverde

etal.(2009).Inbothstudies,theprobabilityofoccurrenceisnot

correlatedwithabundancewhenpointswithzeroabundanceare

excludedfromanalysis.PearceandFerrier(2001)suggestthatthe

overallcorrelationbetweenabundanceandprobabilityof

occur-renceisduemostlytothedifferenceinmeanpredictedprobability

betweenoccupiedandunoccupiedsites.However,thefactthatthis

correlationoftendisappearswhenunoccupiedsitesareexcluded

mightalsobedue,atleastinpart,toareductioninsamplesizeand

theconsequentlossofanalyticalpower.Indeed,inourdata,

sam-plesizewashighlysignificantlyreducedwhenunoccupiedsites

wereexcluded (mean occupiedsample size=59.33±86.32; see

“prevalence”inTable2),whichmayhaveimpededthedetection

of significant relationships between abundance and

probabil-ityof occurrence inoccupiedsites. AlsoinPearce andFerrier’s

(2001)study,for59species,therelationshipbetweenabundance

and probability of occurrence (mean sample size=55.49,

stan-darddeviation=47.50)wasnolongersignificantwhenunoccupied

siteswere excluded(meansample size=42.98,standard

devia-tion=38.43).Wefoundthatthemeansamplesizeconsideringonly

occupiedsiteswasalsosignificantlylowerintheirstudy(Wilcoxon

(8)

Fig.3.Abundanceevaluationparameters(95%CI).r,Pearsoncorrelationcoefficient;rho,Spearman’srankcorrelation;b,intercept;andm,gradientofthefittedline (observed=b+m(predicted));RMSE,rootmeansquareerror;AVEerror,averageerror.

correlationremainedsignificant(meansamplesize=111.38,

stan-dard deviation=47.31). These results suggest that sample size

mightbeinfluencing thedetectionof anoccupancy–abundance

relationship.

Realetal.(2009)alsoshowedthatpredictionsofGLMsbased

onthepresence–absencedataoftheIberianlynx(Lynxpardinus)

andwildrabbit (Oryctolaguscuniculus)inSpainaresignificantly

correlated with independent abundance data. Even when we

repeatedtheiranalysisexcluding unoccupiedsites,the

correla-tionsremainedsignificantfortheabundanceofrabbit(Kendall’s

Tau-b=0.100,P=0.036,n=300)andlynxin1950(therewereno

zero-abundancedataforthisyear),1965(Tau-b=0.108,P=0.005,

n=355),and1975(Tau-b=0.086,P=0.035,n=315).Onlyinone

case(lynxdatafrom1985)wasthecorrelationnolongersignificant

betweentheirpredictedfavourability and observed abundance,

possibly due to the lower sample size (n=215) when

zero-abundancecellswereexcludedforthisyear.Thisprovidesfurther

supporttotheidea that,givensufficient samplesize,predicted

probabilityofoccurrencemaybearoughindicatorofactualspecies

abundance.

Reconstructing or inferring abundance from species

occur-rencedatacanpotentiallybeanimportanttoolforconservation

planning efforts and species management, especially given the

difficulty of obtaining and analysing abundance data. Current

techniques are oftennot reliable becausethey generate

unsta-ble,low-qualityresults(spuriousestimates;Josephetal.,2009).

Theoccupancy–abundancerelationshipmayadditionallybe

con-ditionedbyecologicalorlife-historytraitsofthemodelledspecies,

asweexposeinthenextsection.Furtherresearchmayprovide

usefulinsightsintothismatter.Inthemeanwhile,andgiventhat

urgentmeasuresareneededtomitigatetheimpactsof

hydroelec-tric powerdamsonthreatenedspecies,we advocatetheuseof

occurrencemodelsassurrogatesforspeciesabundancewhenthe

lattercannotbeobtained.Wedefendthat,givensufficientsample

sizes,probabilityofoccurrencemeasurescannotonlybean

(9)

andLoboetal.(2008),butalsoprovideasimpleandinexpensive

alternativefortheabundancemeasureformostspecies.This

alter-nativemethodalsohasgreatpotentialtobeusedinenvironmental

assessment(seealsoAraújoandWilliams,2000;Realetal.,2009).

4.2. Ecologicalconstraintsontheoccupancy–abundance

relationship

Jiménez-Valverdeetal.(2009)suggestthatgeneralistspecies

and those with a high degree of dispersal show positive and

significantrelationshipsbetweenabundance(>0)andthe

proba-bilityofoccurrence.Nielsenetal.(2005)recommendanapproach

basedonorganismlifehistorytoexplaintherelationshipbetween

occurrenceandabundancepatterns,whichisdifferentfromthe

traditionalapproach based onextensiveand exploratoryfitting

exercises. In our study, A. angustifolia and C. scabra were the

only species that exhibited a positive and significant

correla-tionbetweenprobabilitiesofoccurrenceandobservedabundance

whenzero-abundanceplotswereexcludedfromtheanalysis.Both

speciesaretiedtoearlysuccessionalstagesinthestudiedregion

(Duarteetal.,2006;SampaioandGuarino,2007),andseveral

stud-iesindicatethatA.angustifoliaisabletoadvanceforestexpansion

overnaturalgrasslandsandcolonisenewareasofgrasslandsinthe

highlandsofsouthernBrazil(Duarteetal.,2006).Furthermore,the

seedsofA.angustifoliaaredispersedbybirdsandsmallmammals

(Anjos,1991;IobandVieira,2008),whereasC.scabra’sseedsare

wind-dispersedandcanthustravelgreaterdistances.

Another interesting case is that of subtropical palms, B.

eriospathaand T.brasiliensis,which are gregarious specieswith

densebutsporadicpopulationsinthestudyarea.Becauseofthe

importanceoffibreand thehighnutritionalvalueof thefruits,

bothspecieswereinfluencedbyancienthumaninhabitantsofthe

region.Thepopulationofthesespecieswaspurposefullydispersed

bynativesandEuropeansettlers,whograzedlivestockforcenturies

intheareawestudied(Reitzetal.,1974).Pasthuman-mediated

dispersioncanaffectthecurrentpatternsofplantdistribution,

cre-atingartificiallyclusteredpopulationsthatareoftenunnoticedin

extensiveexploratorymodel-fittingexercises.Althoughthis

his-toryofplantmanipulationisimportant,ithasrarelybeenincluded

instudiesofspeciesdistributions(Lutolfetal.,2009).

Therelativelylowstrengthofthecorrelationbetweenpredicted

occurrenceprobabilitiesandobservedabundance(<0.5inallcases)

couldalsobeduetobioticinteractionsnotincludedinthe

occur-rencemodels.Austinetal.(1990)andGuisanandThuiller(2005),

forexample,suggestedthattheresponsecurveofaspeciesalong

anenvironmentalgradientcanbeseriouslyconstrainedby

inter-actionwithbioticfactors.Thishypothesiswasrecentlytestedby

Heikkinenetal.(2007)and Ritchieetal.(2009),usingdifferent

organisms,andtheybothfoundsimilarresults.Whendatarelated

tointerspecificcompetitorswereincorporatedintomodels,species

predictionsweresignificantly improved. In addition, this effect

likely plays a role in the relationship betweenoccurrence and

abundance,obscuringthetruerelationshipbetweenoccupiedarea

andabundance.Ritchieetal.(2009)confirmedthisidea

demon-stratingthatthepredictedabundanceofwallaroosandkangaroos

wasimprovedwiththeadditionoftheoccurrenceandabundance

ofinterspecificcompetitorsintothemodels.However,obtaining

thesedataisdifficultandmodelpractitionersmustcontinuetouse

mostlyabioticfactors(Barbosaetal.,2009;ElithandLeathwick,

2009).

4.3. Abundancemodels

Ingeneral,Zero-InflatedmodelsperformedbetterthanHurdle

models.Thisresultiscontrarytootherempiricalstudiesonboth

realpopulations(PottsandElith,2006)andtheoreticalstudieswith

pseudo-populations(MillerandMiller,2008),suggestingthat

Hur-dlemodelsgenerallyperform betterthanZero-Inflatedmodels.

Becausethetechniquesusedinthisstudytomodelabundancedeal

differentlywithzeros,theresultscanbeinterpretedinseveralways

(PottsandElith,2006;Zuuretal.,2009).Two-partmodelling

tech-niques,suchasGLMHurdlemodels,analyseabundancedatainthe

followingtwosteps:(i)zerovs.non-zerovaluesaremodelledwitha

logisticregression(binomialdistribution),and(ii)non-zero

obser-vationsaremodelledwithatruncatedPoissonornegative-binomial

regression.Mixedtechniques,suchasZero-Inflatedmodels,

clas-sifyzerosasoriginatingfromtwodifferentprocesses,binomialand

countprocesses(Zuuretal.,2009).InterpretingHurdlemodelsis

simplerthaninterpreting Zero-Inflatedmodels(Pottsand Elith,

2006),but,accordingtoWelshetal.(2000),mixedmodels

pro-videabettertoolwhenthereisoverdispersionandalargenumber

oftruezeros,aswasthecasewithourdata.

4.4. Variablesrelatedtospeciesoccurrenceandabundance

Ourmainobjectivewiththefittedmodelswastopredictspecies’

distributionandabundanceratherthantotesttheeffectsof

dif-ferent ecological drivers onspecies occurrence and abundance.

However, we can draw some conclusions about the ecological

factorsthatareassociatedtotheanalysedspecies’distributions.

AccordingtoBarrowsetal.(2005),environmentalmanagersmust

understand the anthropogenic and environmental factors that

influencetheoccurrenceandabundanceofspecies.Withthis

infor-mation,managerscanemployadaptivemanagementstrategiesto

maintainviablepopulationsofdesiredspecies.Overall,occurrence

andabundanceofstudiedspeciesaredetermined,tosomeextent,

bydifferentsuitesofenvironmentalvariables.Regardlessof

organ-ismtype,thispatternhasbeenwidelyreported(Heinanenetal.,

2008;Illánetal.,2010;Truscottetal.,2008),suggestingthatbiotic

and abioticeventsassociated withplantestablishment maybe

differentthanthoseinfluencingtheirabundance(Truscottetal.,

2008).

Herewehighlighttherelationshipbetweentheabundanceof

E.falcataandtotalnitrogenavailability.Abundance(countmodel

component)ofE.falcatawasnegativelyrelatedtolowsoil

nitro-genvalues.Thisspecies,alongwithothersofthesamegenus,are

generallyabletoproducenitrogen-fixingrootnodules(Fariaetal.,

1984,1989;Schimannetal.,2008).However,itsrootshaveaunique

strategytoacquiresoilnitrogen.Theroots donotexploit

avail-ablenitrogeninthesoil,andinsteadusenitrogenintheleaflitter

(ChesneyandVasquez,2007;Payanetal.,2009onE.poeppigianain

agroforestrysystems),anenvironmentalvariablethatwasanalysed

inourstudy.Conversely,M.frondosus,anotherFabaceaespecies,

wasnot reportedtoproducenitrogen-fixingnodules (Allenand

Allen,1981;Fariaetal.,1984).

5. Conclusionsandfutureapplications

Ourstudyisthefirsttoapplyamacroecologicalapproachto

supportinsituandexsituplantconservationpracticesinaregion

affectedbyahydropowerdamreservoir.CurrentlyinBrazil,

Envi-ronmentalImpactAssessment(EIA)andStrategicEnvironmental

Assessment (SEA) methods require costly and time-consuming

fieldsurveys(floristicandphytosociologicalstudies), often

con-ductedbyprofessionalswithanincompleteunderstandingofthe

extreme diversity of Brazilian flora. As a result, these surveys

oftenproducelow-quality,inapplicabledatathataresusceptibleto

environmentalerror.Despitemethodologicallimitations,i.e.,low

(althoughsignificant)correlationbetweenpredictedprobabilityof

occurrenceandobservedabundance,andapossibledependenceon

(10)

Brazilcouldprofitfromusingspeciesdistributionmodelstopredict

futureimpactsandplanlandscapemanagementstrategiesand

mit-igationactions.Thisapproachisquickerandlessexpensivethanthe

currentapproach,althoughthismethoddoesrequirestafftraining

indatacollectionandanalysis.

Aneffectivewaytoorganisedatacollectionistocombinethe

gradsect methodproposed by Austin and Heyligers (1989) and

Wesselsetal. (1998)withtherapidassessmentsurveymethod

usedinthisstudyanddescribedbyMagnussonetal.(2005).For

example,insteadofmeasuringtheheightand diameterof each

treespecies within plots, we record onlythe presence of each

speciesingeoreferencedplots.Austinand Heyligers(1989)and

AustinandMeyers(1996)supporttheviewthat formost

envi-ronmentalgoals,includingspeciesnichemodelling,representative

samplesaremoreimportantthanaccuratebasalareaestimations,

whichisthemethodcurrentlyusedinthepreparationofEIA/SEA

inBrazil.Eveninasituationwherefieldsurveysarenotperformed,

presence-onlydataobtainedfromherbariumormuseum

collec-tionsareviablealternatives.Evenwithacoarseresolution,they

canproducepertinentinformation forthedevelopmentof

fine-scalespeciesconservationplanning(Araújo,2004;Barbosaetal.,

2003,2010).

Evenweakly,theoccupancy–abundancerelationshipcan

opti-mise ex situ conservation actions in similar situations. Based

onspeciesdistributionmodelsbuiltwithpresence–absencefield

dataobtainedin rapidand systematicbiodiversityassessments,

we can be able to, a priori, define the rare species set using

occupancy–abundance relationships (Flather and Sieg, 2006).

Regardlessof threatlevels,we canconcentrategermplasm

col-lectionefforts,atafirstglance,onlocallyrarespecieswithlow

abundanceandnarrowareaof occurrence,suchasB.eriospatha

andT. brasiliensis.In asecond step,speciesdistributionmodels

canbeusedasaguidetodefinethemostsuitableareasto

col-lectgermplasmsamplesoftheselectedspecies.Thisapproachis

lessmoneyandtime-consumingandcanaccelerateconservation

actionsinlandscapessubmittedtostronganthropogenicimpacts,

likehydroelectricpowerdamreservoirs.Practicalexamplesofthis

applicationof distributionmodels asa guideto definesuitable

areastocollectspecies germplasmsampleswerefirstdescribed

byJonesetal.(1997)andfollowedbyJarvisetal.(2003,2005a,b),

Villordonetal.(2006)andRamírez-Villegasetal.(2010).However,

inallthesepreviousexamplesauthorsusedpresence-onlymodels

toguidegermplasmcollectingexpeditionsofcropwildrelatives

(i.e.,peanut,potato,pepper)onalargescale.

Themereoccurrenceofonespeciesisnotsufficienttoensure

thepersistenceofaviablepopulationwithinasystemofprotected

areas(Barrowsetal.,2005).Asystemofprotectedareasshould

containsamplesofthelargestpossiblenumberoflocalecosystems,

therebymaintainingtheecologicalprocessesoftheseecosystems

(Australia,1997).Analternativetoaccomplishthisgoalwouldbeto

integrateinformationaboutspeciesoccurrence(orabundance)and

regionallanduseinaninteractivedecision-makingsystem.This

methodwouldensuretheconservationofviablesamplesin

dif-ferentecosystemsoccurringintheregionofinterest(Ferrieretal.,

2002).

Acknowledgments

Thefield workwassupportedby EmbrapaRecursos

Genéti-coseBiotecnologia.E.S.G.G.wassupportedbyBrazilianNational

CouncilforScientificandTechnologicalDevelopment(CNPq;proc.

140712/2007-0)andCoordenac¸ãodeAperfeic¸oamentodePessoal

deNívelSuperior(CAPES;proc.2298/09-0)scholarships.A.M.B.is

supportedbyaPost-DoctoralFellowship(SFRH/BPD/40387/2007)

fromFundac¸ãoparaaCiênciaeaTecnologia(Portugal).The“Rui

Nabeiro”BiodiversityChairisfundedbyDeltaCafésandanFCT

project (PTDC/AAC-AMB/098163/2008). We are grateful to A.A.

Santos,M.B.Sampaio,J.A.Santosandotherswhohelpedwithfield

work.M.B.Araújo,D.L.M.Vieira,A.B.Sampaio,F.S.Rocha,P.R.Vieira

andR.L.L.Orihuelahelpedwithimportantsuggestions.Wearealso

gratefultoC.Scherberwhokindlyprovidedthescriptforstepwise

modelselectionwithAICc.

AppendixA. Supplementarydata

Supplementarydataassociatedwiththisarticlecanbefound,in

theonlineversion,atdoi:10.1016/j.ecolmodel.2012.01.007.

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Imagem

Fig. 1. Location map of the study area and sampling sites (Universal Transverse Mercator coordinate system, Zone 22J, southern Brazil).
Fig. 2. Relationship between occurrence probability and true abundance values (abundance ≥ 0) for each studied species
Fig. 3. Abundance evaluation parameters (95% CI). r, Pearson correlation coefficient; rho, Spearman’s rank correlation; b, intercept; and m, gradient of the fitted line (observed = b + m(predicted)); RMSE, root mean square error; AVE error , average error.

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