Habitat use of the ocelot (Leopardus pardalis) in

Ecology and Evolution 201995049ndash5062 emsp|emsp5049wwwecolevolorg

Received2November2018emsp |emsp Revised24January2019emsp |emsp Accepted1February2019DOI 101002ece35005

O R I G I N A L R E S E A R C H

Habitat use of the ocelot (Leopardus pardalis) in Brazilian Amazon

Bingxin Wang123 emsp| Daniel G Rocha45 emsp| Mark I Abrahams6 emsp| Andreacute P Antunes7 emsp| Hugo C M Costa8 emsp| Andreacute Luis Sousa Gonccedilalves9emsp| Wilson Roberto Spironello9emsp| Milton Joseacute de Paula1011 emsp| Carlos A Peres12emsp| Juarez Pezzuti10emsp| Emiliano Ramalho13emsp| Marcelo Lima Reis14emsp| Elildo Carvalho Jr1516emsp| Fabio Rohe1718emsp| David W Macdonald1emsp| Cedric Kai Wei Tan1

1WildlifeConservationResearchUnitDepartmentofZoologyTheRecanati‐KaplanCentreUniversityofOxfordTubneyOxonUK2StateKeyLaboratoryofVegetationandEnvironmentalChangeInstituteofBotanyTheChineseAcademyofSciencesBeijingChina3UniversityofChineseAcademyofSciencesBeijingChina4GraduateGroupinEcologyDepartmentofWildlifeFishandConservationBiologyUniversityofCaliforniaDavisDavisCalifornia5GrupodeEcologiaeConservaccedilatildeodeFelinosnaAmazocircniaInstitutodeDesenvolvimentoSustentaacutevelMamirauaacuteTefeacuteBrazil6FieldConservationandScienceDepartmentBristolZoologicalSocietyBristolUK7Redefauna‐RededePesquisaemBiodiversidadeConservaccedilatildeoeUsodaFaunadaAmazocircniaManausBrazil8ProgramadePoacutes‐graduaccedilatildeoemEcologiaeConservaccedilatildeodaBiodiversidadeUniversidadeEstadualdeSantaCruzIlheacuteusBrazil9GrupodePesquisadeMamiacuteferosAmazocircnicosInstitutoNacionaldePesquisasdaAmazocircniaManausBrazil10CentreforAdvancedAmazonStudiesUniversityofParaAltamiraBrazil11ProgramadePoacutes‐GraduaccedilatildeoemEcologiaUniversidadeFederaldoParaacuteeEMBRAPAAmazocircniaOrientalBeleacutemBrazil12SchoolofEnvironmentalScienceCetreforEcologyEvolutionandConservationUniversityofEastAngliaNorwichUK13InstitutodeDesenvolvimentoSustentaacutevelMamirauaacuteTefeacuteBrazil14ICMBioCNPqBrasiacuteliaBrazil15CentroNacionaldePesquisaeConservaccedilatildeodeMamiacuteferosCarniacutevorosInstitutoChicoMendesdeConservaccedilatildeodaBiodiversidadeAtibaiaBrazil16FacultyofEcologyandNaturalResourceManagementNorwegianUniversityofLifeSciencesAringsNorway17ProgramadePoacutes‐graduaccedilatildeoemGeneacuteticaConservaccedilatildeoeBiologiaEvolutivandashGCBEvInstitutoNacionaldePesquisasdaAmazocircniandashINPAManausBrazil18WildlifeConservationSocietyBrazilndashAmazonProgramManausBrazil

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicensewhichpermitsusedistributionandreproductioninanymediumprovidedtheoriginalworkisproperlycitedcopy2019TheAuthorsEcology and EvolutionpublishedbyJohnWileyampSonsLtd

CorrespondenceCedricKaiWeiTanWildlifeConservationResearchUnitDepartmentofZoologyTheRecanati‐KaplanCentreUniversityofOxfordTubneyOxonUKEmailcedrictanzoooxacuk

Funding informationCAPESDoutoradoPlenonoexteriorGrantAwardNumber888811281402016‐01Recanati‐KaplanFoundationTangFamilyFoundationGordonampBettyMooreFoundationInstitutoNacionaldePesquisasdaAmazocircniaProgramaAacutereasProtegidasdaAmazocircnia

AbstractAmazoniaforestplaysamajorroleinprovidingecosystemservicesforhumanandsanctuariesforwildlifeHoweverongoingdeforestationandhabitatfragmentationintheBrazilianAmazonhasthreatenedbothTheocelotisanecologicallyimportantmesopredatorandapotentialconservationambassadorspeciesyettherearenopre‐viousstudiesonitshabitatpreferenceandspatialpatternsinthisbiomeFrom2010to2017twelvesitesweresurveyedtotaling899cameratrapstationsthelargestknowndatasetforthisspeciesUsingoccupancymodelingincorporatingspatialau‐tocorrelationwe assessed habitat use for ocelot populations across the BrazilianAmazon Our results revealed a positive sigmoidal correlation between

5050emsp |emsp emspensp WANG et Al

1emsp |emspINTRODUC TION

SouthAmericasAmazonbasinharborsoverhalfofallthetropicalrain‐forestsleftonEarthspanningavastareaof67millionkm2(WittmannampJunk2016)andishometoroughlyhalfoftheworldsspecies(ShuklaNobreampSellers1990)Unfortunatelyhuman‐inducedchanges to itsecosystemforahostofsocial‐economicreasonsarecausingwidespreadbiodiversitydeclinesintheAmazon(Gibsonetal2011Newboldetal2015GuilhermedeAndradeVasconcelos2017)Over2000ndash2012theaveragerateoftropicaldenseforestslosswas74400km2year(MalhiGardnerGoldsmithSilmanampZelazowski2014)Deforestationisinten‐sifyingpressuresonforestvertebratesaswellasonindigenousandnon‐indigenousforestdwellersandtheirlivelihoodsInadditiontheprocessofdeforestationisnotrandomwithremainingforestsoftenbeingcon‐finedtosteepslopesandhilltopsunsuitableforbothlarge‐scaleagricul‐tureandcattleranchThisleadstohabitatfragmentationandpopulationisolation(Malhietal2014)especiallythroughouttheso‐calledarcofdeforestationregionwhichtogetherinfluencethenatureandfrequencyof species interactionswithunknowncascadingeffectson long‐termbiodiversitypersistence(Haddadetal2015)

Forestcarnivoresespeciallyapexpredatorsarethoughttobepar‐ticularlyvulnerableandsensitivetodeforestationandforestfragmen‐tation(NossQuigleyHornockerMerrillampPaquet1996)becauseoftheirrestrictedcarnivorousdiet(VetterHansbauerVeacutegvaacuteriampStorch2011)and largehomerangesTheyareessential formaintainingthecommunity structure within a foodweb and are vital to ecosystemfunctioning (Ripple etal 2014) Mesopredators can fill this role tosomedegreewhenapexpredatorsareeradicatedordepleted(Prughetal2009)Someomnivorousmesopredatorstypicallyopportunistswithbroaddiets suchas raccoons (Procyon lotor)mayrespondpos‐itively toanthropogenic resourceswithbehavioralchange (PrangeampGehrt 2004) In these casesmesopredatorswith good adaptabilitymight serve as a buffer to sustain ecosystem stability and integritywhenapexpredatorsareinadequateAlternativelymesopredatorsaresometimesassociatedwithunpredictablecascadeeffectssuchasdis‐easeoutbreaksandhumanndashwildlifeconflicts(Prughetal2009)Thesevariousandunpredictablepossibilitiesprovideabackgroundforanin‐terestinmedium‐sizedNeotropicalcatsinadditiontothefundamentalinterestintheirpoorlydocumentedautecology

TheocelotLeopardus pardalis(Linnaeus1758Figure1)isame‐dium‐sized(66ndash186kg)Neotropicalspottedcatwithabroadgeo‐graphicdistributionintheAmericasrangingfromtheextremesouthofTexas(USA)throughoutMesoamericaandtheAmazontoopenenvironmentsinnorthernArgentinaandfloodplainsdryconiferousforestsandrainforests(EmmonsampFeer1998MurrayampGardner1997)Ocelots are considered solitary nocturnalcrepuscular andsemi‐arboreal andareexcellent climbers (DiBitettiPavioloampDeAngelo 2006) Documented home ranges are average 125plusmnSE 34km2 (Gonzalez‐Borrajo Loacutepez‐Bao amp Palomares 2016) Theyhavebeenrecordedatelevationsupto1200m(NowellampJackson1996) and are classified as Least Concern on the IUCN Red List(Paviolo etal 2015) They were heavily exploited in Amazoniaby the international fur trade between the 1930s andmid‐1970s(Antunes etal 2016 Smith 1976)Currently ocelots suffer habi‐tatlossfragmentationandotheranthropogenicpressuressuchasoilexploration (KolowskiampAlonso2010)vehiclecollisions illegaltradeand retaliatorykillingdue todepredationonsmall livestock(Pavioloetal2015)

Nevertheless ocelot amesopredator has been studiedmuchless than largermore charismatic felids such as jaguar (Panthera onca)andpuma(Puma concolor)Since2000studiesofocelotusing

remote‐sensingderivedmetricsofforestcoverdisjunctcoreareadensityelevationdistancetoroadsdistancetosettlementsandhabitatuseandthathabitatusebyocelotswasnegativelyassociatedwithslopeanddistancetoriverlakeThesefind‐ings shed lighton the regional scalehabitatuseofocelotsand indicate importantspeciesndashhabitat relationships thusprovidingvaluable information for conservationmanagementandland‐useplanning

K E Y W O R D S

BrazilianAmazoncameratrapsmesopredatoroccupancyocelotrestrictedspatialregression

F I G U R E 1 emspOcelotwastakenbyonecameratrapin2013(photosprovidedbyDanielGRocha)

emspensp emsp | emsp5051WANG et Al

cameratrapshaveproliferated(Blakeetal2015deOliveiraetal2010 Paviolo etal 2015 Pratas‐Santiago Gonccedilalves da MaiaSoaresampSpironello 2016Wang2002) in particular thoseesti‐mating thespeciesrsquoabundanceanddensity (DiBitettiPavioloDeAngelo ampDi Blanco 2008 Di Bitetti etal 2006 Dillon amp Kelly2007 Penido etal 2016 Rocha Sollmann Ramalho Ilha amp Tan2016)Thesestudieshaverevealedvariousaspectsofocelotecol‐ogy(SupportingInformationTableS1)andthreeofthemusedtheoccupancymodelingframeworktwooftheminvestigatedtheinter‐actionsbetweenocelotsandsympatricspecies(MassaraPaschoalBailey Doherty amp Chiarello 2016Massara deOliveira Paschoaletal 2018Massara Paschoal etal 2018) the third investigatedhow an attractant affected detection (Cove Spinola Jackson ampSaenz 2014)Other studies report that ocelot densities correlatewithforestcover(Pavioloetal2015)precipitation(MaffeiNossCueacutellarampRumiz2005Rochaetal2016)and latitude(DiBitettietal 2008 Rocha etal 2016) in addition ocelotsmay have anaffinity for some specific matrices such as eucalyptus plantation(MassaradeOliveiraPaschoaletal2018MassaraPaschoaletal2018)Ocelots have been recorded in a great variety of habitatsfromheavily loggedandfragmentedforests toearlyand latesuc‐cessionalforeststheoutskirtsofmajorcitiesandtownsdisturbedscrubwoodlandSavannahandagriculturalareas(deOliveiraetal2010)Notwithstandingthesefragmentsofresearchstudiesonthehabitatpreferenceofocelotsonaregionalscalearelacking

Occupancymodelinghasbecomeapopulartoolfor investigat‐ingspeciesoccurrenceovertemporalandspatialscalesThistypeofmodelestimatestheprobabilityofasitebeingoccupiedbyaspeciestakingintoaccountimperfectdetectionprocesses(Mackenzieetal2002)

Weusecameratrapdetectionnondetectiondatafrom12sitesinBrazilianAmazoniatoexaminethehabitatuseoftheocelotThisisbyfarthelargestknowndatasetforthisspeciesOurkeyobjectiveis to reveal the influenceofdifferentenvironmental variablesandanthropogenicimpactsonocelotoccupancyatalandscapescaleandthuspredictitshabitatuseacrosstheBrazilianAmazon

2emsp |emspMETHODS

21emsp|emspStudy area

DatawerecollectedacrosstwelvesitesintheAmazonbasinBrazilfrom2010to2017(a)CaboFrioandKm37experimentalforestre‐servesfrompartoftheBiologicalandDynamicsofForestFragmentsProject(PBDFF)(LauranceFerreiraRankin‐deMeronaampLaurance1998)(b)CuieirasForestReserveandTropicalForestryExperimentalStation (ZF2) (c) Adolpho Ducke Forest Reserve (DUCKE) (d)AmanatildeSustainableDevelopmentReserve (RDSA) (e)MeacutedioJuruaacuteExtractive Reserve and Uacariacute Sustainable Development Reserve(REMJampRSUA)(f)UatumatildeBiologicalReserve(Uatuma)(g)CamposAmazocircnicos National Park (PNCA) (h) Mapinguari National Park(PNM)(i)JuruenaNationalPark(PNJU)(j)TerradoMeioEcologicalStation(TMES)(k)SatildeoBeneditoRiver(SBR)(l)NascentesdoLago

JariNationalParkIgapoacute‐AccediluSustainableDevelopmentReserveandTupanaSettlementProject(BRA319)ApartfromtheSatildeoBeneditoRiver(SerradoCachimbo)whichisaprivateareaandtheTupanaSettlementProject the sites are located inprotectedareasor re‐servesTheclimaticclassificationofthisregionaccordingtoKoumlppen(KottekGrieserBeckRudolfampRubel2006) istropicalmoistcli‐mateTheentiresurveyregionconsistedofasimilarbaselinemosaicoftropicalforestmostlyuplandnonfloodableterra firmeforests(drylandsolidground)andtoalesserextentseasonallyfloodedforests

22emsp|emspCamera trap survey

Datacollectionandsurveysatmostofourstudyareasweredesignedto study largemammals like jaguars so our data on ocelots repre‐sentby‐catch(exceptfortheREMJandRSUAdatasetseemethodsin Costa Peres amp Abrahams 2018) In Malaysia Tan etal (2017)usedthemtoestimatehabitatuseofcloudedleopardsasdidPenjorMacdonaldWangchuk Tandin and Tan (2018) in Bhutan Cameratrappingalthoughoriginallymotivatedbystudiesoflargemammalsyieldeddataonocelots(Figure2)Intotal899unbaitedcameratrapstationswereoperatedinvolvingatotalsurveyeffortof40347daysyielding334independentdetectionsofocelotsTheindependentde‐tection eventswere defined as the consecutive conspecific imageswithgt30minapartatthesamecameratrapstationStationsatRDSAhad two cameras facing each other 4ndash5m apart and stations at allothersurveyareashadonlysinglecamerasAllcameratrapstationswereplacedatapproximately30ndash50cmabovegroundalongrandomlyselectedtransectsindifferentsurveyedsitesperpendiculartoexist‐ingtrailsoranimaltracksusedforpreviouscensusesofprimatesandterrestrialvertebratestoenhancetheopportunitytodetectthefocalspecies(DiBitettietal2006)ThesensitivitysensorwassetatldquohighrdquoCameratrapswereoperationalfor24hradayduringthemonitoringperiodasidefrommalfunctionsdamageortheftDetailsofcameratrapdeployment(thenumbersofstationseffortmeantrapspacingandtotalnumbersofrecordsofocelot)areprovidedinTable1

23emsp|emspData analysis

Detection histories based on photographic records were con‐structedinatwodimensionalmatrixformatDatawereanalyzedusing(a)single‐speciessingle‐seasonoccupancymodelsinamaxi‐mum‐likelihood framework (Mackenzie etal 2002) which canhelptoselectthemostinformativecovariatesand(b)single‐sea‐sonspatialoccupancymodelsthataccountforspatialautocorrela‐tioninaBayesianframework(JohnsonConnHootenRayampPond2013)ThelattermethodwasusedasourstudycombinedmultipleprotectedareasatvaryingdistancesapartdistributedacrosstheBrazilianAmazonbasinTominimizethepossibilityofviolatingtheassumptionofpopulationclosure(RotaFletcherDorazioampBetts2009)onlythefirst120‐dayperiodofeachsurveywasincludedintheanalysisCollapsingsamplingperiodsminimizesthefailureofconvergence inmodelswhenoveralldetectionprobability is low(DillonampKelly2007OtisBurnhamWhiteampAnderson1978)It


canalsoincreasetemporalindependenceamongoccasions(Dillonamp Kelly 2007) The 120‐day data subsets were collapsed intomultiple‐day samplingoccasions (7101215daysofperiod) tomaximizetemporalindependenceofcapturesTheoptimumnum‐berofdaysperoccasionwasselectedbasedonachi‐squaregood‐ness‐of‐fit (MacKenzieampBailey 2004) test for the globalmodelperformedwith 1000 bootstraps A 12‐day period representedthe optimum number of days tomaximizemodel fit (SupportingInformationTableS2)

Building on previous studies of similar mesopredators suchasgoldencats (Pardofelis teminckii) andclouded leopards (Neofelis nebulosa Haidir Dinata Linkie amp Macdonald 2013 Tan etal2017)weinterpretedocelotoccupancyasaproxyforhabitatuseof ocelot Habitat use was modeled by occupancy models usingthreetypesofcovariates (a)habitatusecovariatesonnaturalen‐vironmentelevationslope(meanangleofslope)forestcover(VCFGFC30GFC50GFC75GFC90)distancetoriversanddistancetolakes (b)habitatusecovariatesonhumanactivityandfragmenta‐tiondistance to roadsdistance to settlements andmeasuresofforestfragmentation(CWEDContigDCAD)and(c)detectionco‐variatesthatdescribeeachofsurveyedsitessurveysite(the12dif‐ferentsurveyedsites)andeffort(numberofdaysthateachcameratrap stationwas activewithin occasions) The summary statisticsofeachof thesecovariatesare tabulated (Supporting InformationTableS3)Wehypothesizedthatocelotswouldhaveabiasforflatland dense forests areas near riverslakes and avoid approach‐ing roads settlements and fragmented forests For the detectioncovariates we hypothesized that the higher the camera trappingeffort thehigherprobabilityofdetecting focal speciesDifferentsurveyedsiteswouldhavedifferentdetectionprobabilitiesdueto

geographicalandbiological featuresTheoccupancycovariatesateachcameratraplocationweregeneratedusingQGISversion2189(QGISDevelopmentTeam2017)Elevationandslopevalueswereextracted from a 30times30m of resolution digital elevation model(DEM)theShuttleRadarTopographyMission(USGS2003)down‐loadedfromUS Geological Survey(httpsearthexplorerusgsgov)Thedistance to riverslakesandpaved roadswasproducedusingCartographic IntegratedBasisDigitalCIM IBGE (IBGE2011)Thedistancetosettlementswasfromanopensource(OpenStreetMapContributors 2015 httpsplanetopenstreetmaporg) includingtowns villages and isolated settlements Vegetation ContinuousForestof250‐m resolution (DiMiceli etal 2011) and30‐m reso‐lutionGlobalForestChange(Hansenetal2013)wasusedasmea‐suresof forest cover Specifically theGlobalForestChange layer(Hansenetal2013)allowsuserstosetathresholdofpercentageoftreecoverthatistobeconsideredasforestfortheareaofinterestOnaccountof this andaprevious similar study (Tanetal 2017)weset fourdifferent thresholdvalues (305075and90)Forest fragmentationvariablessuchasCWED(Contrast‐weightededgedensityisameasureofedgedensitystandardizedtoaperunitarea)Contig(Contiguityindexisanindexofspatialconnectednessofforest)andDCAD(Disjunctcoreareadensity isthenumberofdisconnectedpatchesofsuitableinteriorhabitatperunitarea)werechosentoexaminetheeffectsofedgeandforestfragmentationonocelot habitat use Themeasures of forest fragmentationdatasetwereproducedbyFRAGSTATS4(McGarigalCushmanNeelampEne2002)Forallabovecontinuouscovariatesvalueswereextractedfromthemeanofallrastercellsincludedina500‐mradiusaroundeachcameratrapstationandwerederivedusingtheldquozonalstatis‐ticsrdquotoolinQGISThisradiuswaschosentorepresentanoverview

TA B L E 1 emspDetailsofcameratrapsurveyforocelotsinBrazilianAmazon

Year Site Area (km2) Stations Effort

No of camera traps per station Spacing (SD) in m

Records of ocelots

2010 PDBFF(Manaus) 350 30 946 1 136508(7190) 10

2010 ZF2(Manaus) 380 30 1050 1 138933(1932) 8

2010ndash2011 BRA319 81274518 196 9647 1 31279(32194) 8

2012 DUCKE(Manaus) 100 30 1877 1 135125(8799) 4

2013ndash2014 RDSA 23500 64 2682 2 124576(26250) 45

2013ndash2014 REMJampRSUA 88622 183 6169 1 45770(26584) 48

2014 Uatuma 1601704 95 2867 1 115332(105538) 5

2015 REMJampRSUA 88622 25 1112 1 737160(436787) 14

2016 PNCA 9613 86 5537 1 287218(104853) 28

2016 PNM 1722852 58 1939 1 374717(181393) 57

2016 PNJU 1958203 18 1276 1 98764(1328) 16

2016 TMES 3373111 61 3652 1 134078(6059) 86

2017 SBR 831 23 1593 1 1380649(13588) 5

Total 899 40347 334

NotesEffortisinnumberofcameratraptimesdaysthespacingistheaveragedistancebetweencameratrapsandtheirnearestneighbor


of the environmental setting and habitat type surrounding eachcameratrapstationDuetothelimitedavailabilityofVCFandGFCmaps(thelatestmapsareforyears2010and2014respectively)weusedthetemporallyclosestone

StatisticalanalyseswereundertakenintwopartsThefirstse‐lectedthemost informativecovariatesFirstPearsonscorrelationtest was conducted to examine collinearity between continuouscovariates Covariates with rgt|06| were considered correlatedSecondunivariateoccupancymodelswereconductedwithRpack‐age ldquounmarkedrdquo (Fiskeamp Rochard 2011) andwe selected the co‐variate(ofthecorrelatedpair)basedonthemodelwithlowerΔAICvalueWeused the ldquoAICcmodavgrdquo package (Mazerolle 2017) inR(RDevelopmentCoreTeam2017)forthissecondstepInordertoavoidbiasfromcorrelateddetectionsduetospatialreplicatesthatarenotsampledrandomlyweconductedoccupancymodelsinpro‐gram PRESENCE (Hines 2006) account for correlated detections(Hinesetal2010)tocheckingfortheeffectofcorrelateddetections(SupportingInformationTableS6)Thirdthebestcandidatemodelincluding the most informative covariates was selected by AICc (corrected Akaikes information criterion used due to small‐sam‐plecorrection)Modelswithallpossiblecombinationsofremainingcovariateswerecomparedand themodelswithinΔAICc lt 2 were considered to the best‐performingmodels (BurnhamampAnderson2004)Thedredgingcommand in themulti‐model inferencepack‐age ldquoMuMInrdquo (Bartoń 2013)was used to average the parametersinR(TeamRC2017)Finallybasedonthesummedmodelweights(importanceBarbieriampBerger2004KaliesDicksonChambersampCovington2012)themostinfluentialcovariates(importancegt05)wereretainedforthesubsequenceanalysis

The secondpart of the statistical process used theRpackageldquostoccrdquo to account for spatial autocorrelation (Johnson 2015) Arestrictedspatialregressionmodel(RSR)wasusedtogeneratethespatialautocorrelationparameterRSRmodelsuseanefficientGibbssamplerMarkovchainMonteCarlomethodtomakeBayesianinfer‐enceaboutthedetectionandoccupancyprocessesandmodelswerefittedusingaprobitlinkfunction(probitlinkusestheinverseofthecumulativedistributionfunctionofthestandardnormaldistributiontotransformprobabilitiestothestandardnormalvariableRazzaghi2013) insteadof the logit link functionused in the firstpartThisincreasedcomputationalefficiency(Johnsonetal2013)IntheRSRmodel the thresholdwasset to199kmaccording to theaverageocelot home range (1246plusmnSE 339km2 which corresponded to199km radius Gonzalez‐Borrajo etal 2016) andmorancut 899(01numberofcameratrapstations)asrecommendedbyHughesandHaran(2013)ForeachBayesianmodeltheGibbssamplerwasrun for 50000 iterations following a burn‐in of 10000 iterationsthatwerediscardedandathinningrateof5(Tanetal2017)Weappliedan improvedoccupancy‐basedmodelingapproach that in‐corporatesspatialautocorrelationThisimprovedmodelincludedaspatialcomponentwhichcanhelptomitigatebiasfromnonindepen‐dentenvironmentalcovariates (Johnsonetal2013)AllstatisticalanalysesforthisstudywereconductedintheRsoftwareenviron‐mentv333(RDevelopmentCoreTeam2017)

3emsp |emspRESULTS

31emsp|emspSelection of contributing covariates

311emsp|emspDetection covariates

Bothsiteandeffortstronglycontributedtovariationinthedetec‐tion probability of ocelot PNM had the highest detection prob‐abilityfollowedbyTMESPNJUandRDSAPNCAhadthelowestdetection probability (Table3) Effortwas positively correlated todetectionprobability(beta=0175SE=0029Table3)

312emsp|emspOccupancy covariates

Therewas correlation amongall forest cover covariates (VCFandGFC30507590)andamongallmeasuresofforestfragmentation(CWED Contig and DCAD) Based on these correlations and theperformanceofeachcovariateintheunivariatehabitatusemodels(Supporting Information Table S4) GFC30 DROADRIV DLAKDSETELESLOandDCADwereselectedforthefurtheranalysis

32emsp|emspSelection of the best model

Among themodels that incorporated all possible combinations ofthe eight occupancy covariates sixteen models (out of 256) hadΔAIClt2fromthetoprankedmodel (Table2)Thebestcandidatemodelwasp(site+effort)ψ[forest cover (GFC30)]with a highestweightof011Basedon thesummedmodelweight (importance)all of the covariates had some degree of influence on the habitatuseofocelot(importancefrom03to1Table3)Specificallyhabitatusebyocelotwas stronglypositivelyassociatedwith forest cover(GFC30 importance=10 Table3 Figure3a)withDCAD (impor‐tance=051 Table3 Figure3d) and strongly negatively relatedto slope (SLO importance=058Table3Figure3c)Therewasaweakerpositivesigmoidalcorrelationbetweenhabitatuseanddis‐tancetoroadswhichthenleveledoffathighervaluesofdistancetoroads(DROAimportance=046Table3Figure3f)andtherewasa weaker negative relationship between habitat use and distanceto river (DRIV importance=042 Table3 Figure3e) The rest ofcovariateshadimportancelt04(seedetailsinTable3andFigure3)Ourresultsindicatedthatthecovariatesforestcover(GFC30)slope(SLO) and disjunct core area density (DCAD) attained a summedmodelweight(importance)ofgt05(Table3)whichwereusedinthesubsequentphasetotestforspatialautocorrelation

33emsp|emspBest model accounting for spatial autocorrelation

Theposteriorpredictivelosscriteriawereslightlydifferentforthemodel with the spatial correlation parameter (D=4851454) andwithout that parameter (D=4853477) In addition the posteriorvariationwas larger for the nonspatialmodel Further the poste‐rior distribution of the spatial variance parameter (=1∕

radic

) was


farfromzero(95credibleintervalof84975ndash5903902) implyingthatadditionalspatialcorrelationintheoccupancyprocessstronglycontributedtothevariation inthehabitatuseprobabilitiesBasedonthe95credibleintervalsofthecovariatestherewasstrongevi‐dencetosuggestthatforbothnonspatialmodelsandspatialmodelsGlobalForestChangeThreshold30(GFC30)wassignificantlyas‐sociatedwithhabitatuseasthe95CIdidnotoverlapzerowhileslope(SLO)andDCADwerenotsignificantlycorrelatedwithhabitatuse(SupportingInformationTableS5)

TheprotectedareaPNMhadthehighestestimatedhabitatuseprobability followed by TMES and PNJU (Supporting InformationTableS5)Forallprotectedareasthenaiumlvehabitatuseprobabilitywasmuch lowerthantheestimatedhabitatuseprobabilityshow‐ing evidence of ocelot imperfect detection (Figure4) Comparedtomodels not taking spatial autocorrelation into accountmodelsincorporating spatial autocorrelation resulted in slightly lower oc‐cupancy estimates for themajority of surveyed areas (expect forDUCKEPBDFFPNJUandZF2Table4)

Model AICc ΔAICc AICcwt K Log likelihood

ψ (GFC30)p(site+effort) 176778 0 011 15 minus86862

ψ (GFC30+DROA+DLAK+DCAD+ELE+SLO)p(site+effort)

176809 032 009 20 minus86357

ψ (GFC30+SLO)p(site+effort)

176818 041 009 16 minus86778

ψ (GFC30+DROA+DCAD+ELE+SLO)p(site+effort)

176825 048 008 19 minus86469

ψ (GFC30+DROA+DCAD+SLO)p(site+effort)

176852 075 007 18 minus86587

ψ (GFC30+DRIV+SLO)p(site+effort)

17688 102 006 17 minus86705

ψ (GFC30+DCAD)p(site+effort)

176885 108 006 16 minus86812

ψ (GFC30+DCAD+SLO)p(site+effort)

176891 113 006 17 minus86711

ψ (GFC30+DRIV+DROA+DCAD+SLO)p(site+effort)

176904 126 006 19 minus86509

ψ (GFC30+DRIV+DCAD)p(site+effort)

176923 145 005 17 minus86727

ψ (GFC30+DRIV+DLAK)p(site+effort)

176932 154 005 17 minus86731

ψ (GFC30+DRIV+DCAD+SLO)p(site+effort)

176934 156 005 18 minus86628

ψ (GFC30+DLAK)p(site+effort)

176955 177 004 16 minus86847

ψ (GFC30+DSET)p(site+effort)

176966 188 004 16 minus86852

ψ (GFC30+DRIV+DROA+DLAK+DCAD+ELE+SLO)p(site+effort)

176971 194 004 21 minus86333

ψ (GFC30+DROA+SLO)p(site+effort)

176974 196 004 17 minus86752

NotesAICcAkaikesinformationcriterioncorrectedforfinitesamplesizesΔAICcrelativedifferenceinAICcvaluescomparedwiththetoprankedmodelAICcwtweightKnumberofparametersSitecovariates tested were elevation (ELE) slope (SLO) distance to river (DRIV) distance to lakes(DLAK) distance to roads (DROA) distance to settlements (DSET)Global ForestChangewiththresholdvalues30 (GFC30)anddisjunctcoreareadensity (DCAD)Detectioncovariates testedwereeffortandsite

TA B L E 2 emspMultivariatemodelselectionresultsofocelotwithAICc lt 2


4emsp |emspDISCUSSION

Wefoundthathabitatusebyocelotsispositivelyassociatedwithforestcoverdisjunctcoreareadensitydistancetoroadssettle‐mentselevationandnegativelyrelatedtoslopedistancetoriv‐erslakesNeverthelessocelotsalsoemergeasratheradaptableandtheirhabitatuseisnotmuchinfluencedbyotherenvironmen‐talvariablesThissuggestsaswewouldhavepredictedfromtheirsizeandanatomy that theyareadaptablepredators toacertainextent and are able to thrive wherever there are forests popu‐latedwithsuitablepreymdashacharacterizationthatinformsthinkingabout both their role as Neotropical carnivore guilds and theirconservation

OurresultsimpliedthattheprobabilityofhabitatusebyocelotsisvariousindifferentsurveyedareasTheattributeofsurveyedareamightbeoneofthereasonstoexplainthevariationofprobabilityofhabitatuseindifferentstudysitesTheestimatedprobabilityofhabitatusebyocelotsinSBRwaslowbecauseitwasaprivateareawhileotherareaswereprotectedareaThismeantthattheocelotstatusisbetterinprotectedareathaninprivateareaAnotherrea‐sonmightbethehumandisturbanceatafewoftheprotectedareasDUCKEPBDFF andZF2protectedareasare fringedbycity sub‐urbsdue to rapidurbanexpansion (Gonccedilalves2013)OurhabitatuseanalysisrevealedthattheGlobalForestChangethreshold30(GFC30)hadanimportantinfluenceonocelotoccurrenceincreasedforestcoverwasassociatedwithincreasedestimatedprobabilityofhabitatuse(sigmoidalrelationship)ThisaccordswithfindingsfromPeruandTexaswhereocelotspreferreddenseandclosedcanopyforest(Emmons1988HainesGrassmanTewesampJanečka2006)Thiswasnotunexpectedinsofarasgreaterforestcoverwasproba‐blyassociatedwithhigherpreyavailability(DrozampPȩkalski2001butseeHearnetal2017)Additionallyithasbeensuggestedthatthestrongpreferenceofocelotsfordensecovermightalsobere‐latedtotheavoidanceofpotentialcompetitorssuchasthebobcat(Lynx rufus)inSouthTexas(deOliveiraetal2010)Itremainspos‐siblehowever thatapositive relationshipbetweenocelothabitatuseandGFC30arisesbecauseocelotsuselessforestedareaswithlowerprobabilityAlthoughno longerstatisticallysignificantwhenspatial autocorrelationwas taken into account slope and disjunctcoreareasdensity(DCAD)werealsoinfluentialcovariatesforoce‐lothabitatuseThereareprevioushintsthattheocelotmightavoidsteeper slopes due to lower availability of prey there (deOliveiraetal2010)Thepositive relationshipbetweenDCADandhabitatuse suggests that forest fragmentation process in somedegree isfavorable for ocelots concerning higher density of disconnectedpatchesofsuitableinteriorforesthabitatwhichsupportedbyprevi‐ousstudyaboutcloudedleopard(Neofelis nebulosiTanetal2017)

All other covariates (importance lt05) were not included insubsequentspatialautocorrelationanalysishowevertheycannotignoretheinfluenceonhabitatusebyocelotOurfindingssuggestthatdistancetoroad(DROA)emergedasimportantOcelotshavebeenrecordedkilledonroadsintheTariquiacuteaminusBarituacutecorridorbe‐tweenBoliviaandArgentina(CuyckensFalkeampPetracca2014)Similarly we found that distance to settlements had a negativeeffect although thiswasweak (importance=030) Distance toroads and settlementsmaybe to dowith persecution byavoid‐anceofhumansorindirectanthropogenicimpactslikeoverhuntingofpreyTemporalavoidanceofocelotinthepresenceofhumans(Massara de Oliveira Paschoal etal 2018 Massara Paschoaletal 2018 Pardo Vargas Cove Spinola de la Cruz amp Saenz2016)andothercompetitorpuma(MassaradeOliveiraPaschoaletal 2018Massara Paschoal etal 2018) has been observedwhichalsosuggeststhatocelotsmightavoidhumanactivitiesorother larger speciesAs predicted elevationwas also influentialcovariate for ocelot habitat use Previous studies indicated thattheprobabilityofhabitatusebyocelotsdecreasedwithelevation

TA B L E 3 emspSummedmodelweightsforcovariatesusedtomodeltheprobabilitiesofoccupancyanddetectionofocelots

Covariate

Summed model weights

β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304

DROA 046 minus2426 0921 minus26355

DRIV 042 minus0169 0247 minus06838

DLAK 038 minus0959 0624 minus15372

ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050

PNCA 100 minus4563 03909 minus11671

PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982

Uatuma 090 minus1303 05024 minus2594

BRA319 089 minus1973 04003 minus4929

DUCKE 083 minus1143 05523 minus2070

PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586

Notes AICc Akaikes information criterion corrected for finite samplesizesΔAICc relative difference inAICc values comparedwith the toprankedmodelAICcwtweightKnumberofparametersSitecovariatestestedwereelevation(ELE)slope(SLO)distancetorivers(DRIV)dis‐tance to lakes (DLAK) distance to roads (DROA) distance to settle‐ments(DSET)GlobalForestChangewiththresholdvalues30(GFC30)anddisjunctcoreareadensity(DCAD)DetectioncovariatestestedwereasfollowseffortandsiteEstimatesandstandarderror(SE)ofuntrans‐formedcovariateeffects(βparameters)aregivenforthemostparsimo‐niousmodelthatincludedthecovariate


(AhumadaHurtadoampLizcano2013DiBitettiAlbanesiFoguetDeAngeloampBrown2013)Perhapsthisisbecauselowlandfor‐estshavehighernetprimaryproductivity(Robertsonetal2010)whichmay increase resources (Peres 1994) to sustain a greaterabundanceofocelotpreyThesepreymayinaseasonalwayuselowland forests to take advantage of the abundant trophic re‐source in this forest type following the receding waters (Costaetal 2018) However it is important to note that variability inelevation throughout central and southern Brazilian Amazonextends over a limited range (2256ndash24134m asl average9692m)whichmightbeonereasonwhytheeffectofelevationwas weak (importance=037) Distance to riverlakes was alsoomittedfromourfinalmodelbutapreviousstudyrevealedthatocelotstendtoaggregatenearmajorrivers(Emmons1987)Inourclassificationwaterbodiesincludedonlymajorriversandlakessofurtheranalysismightneedtofocusonsmallerstreamsandriversdeeperwithinprotectedareabecauseinthecaseofmanyareasin theAmazon that have great extensions of nonfloodable terra firme (dry landsolid ground) densityof small streamsmayhave

influenceInaddition inourcasethecameratrapstationsweremainlyconcentratedatcloseproximitytoriverssofurtheranalysisshouldinvestigatewhethertheeffectofriveronocelotoccupancystillexistwhenconsideringfurtherdistancesfromrivers

Thepresenceofsympatricspeciescaninfluenceocelotshabi‐tatuseinAtlanticForestremnantsThepresenceofatoppredator(jaguarsP oncaandpumasP concolor)waspositivelyassociatedwithocelothabitatuse(MassaradeOliveiraPaschoaletal2018Massara Paschoal etal 2018 Supporting Information Table S1)Therewas also aweakernegative relationship reportedbetweennumbers of domestic dogs (Canis familiaris) detected and ocelotoccupancy (Massara de Oliveira Paschoal etal 2018 MassaraPaschoal etal 2018 Supporting Information Table S1) This fac‐torandtheavailabilityofpreyorpresenceofapexpredatorswerenot included in our analysis The prey of ocelots is mainly com‐prisedofsmallandmedium‐sizedmammalssuchasthethree‐toedsloth (Bradypus variegatus) and nine‐banded long‐nosed armadillo(Dasypus novemcinctus) but also includes birds fish and snakes(Emmons1987Wang2002)Thepresencendashabsenceofpreymight

F I G U R E 2 emspMapwiththecameratrapsurveyedareasusedtomodelocelothabitatuseinCentralAmazonBrazilProtectedareasAmanatilde Sustainable Development Reserve(RDSA)Meacutedio Juruaacute Extractive ReserveandUacariacute Sustainable Development Reserve(REMJampRSUA)Campos Amazocircnicos National Park(PNCA)Mapinguari National Park(PNM)Adolpho Ducke Forest Reserve(DUCKE)Cabo Frio and Km 37 experimental forest reserves(PBDFF)Cuieiras Forest Reserve and Tropical Forestry Experimental Station(ZF2)The Juruena National Park(PNJU)Terra do Meio Ecological Station(TMES)Satildeo Benedito River(SBR)Uatumatilde(Uatuma)Nasentes do Lago Jari National Park and IGAP‐AU Sustainable Development(BRA319)ProjectionWGS84DatumWGS1984(EPSG4326)


F I G U R E 3 emspRelationshipbetweenocelotestimatedhabitatuseprobabilityandoccupancycovariateswithsummedmodelweightsgt03(a)GlobalForestChangeThreshold30(b)elevation(c)slope(d)disjunctcoreareadensity(e)distancetoriver(f)distancetoroads(g)distancetosettlements(h)distancetolakes


be a key and more immediate factor than forest cover or wateravailabilityinexplainingocelothabitatusepatternTherearesomestudiesfocusedonthesympatricspeciesorpreyofocelot(Massarade Oliveira Paschoal etal 2018 Massara etal 2016 MassaraPaschoal etal 2018 Pratas‐Santiago etal 2016 SupportingInformation Table S1) in the future they could be studied usingmultispecies occupancymodels (Rota etal 2016) and piecewisestructuralequationmodeling(SEMGearyRitchieLawtonHealeyampNimmo2018Graceetal2012)

Ourresultspromptcomparisonswithothersimilarmesopred‐ators suchas theclouded leopardwhichwouldappear tobeanecological analog of the ocelot They have some commonalitiessuchassimilarsize(11ndash23kgforcloudedleopard)activitypattern(DiBitettietal2006GrassmanTewesSilvyampKreetiyutanont2005) and similar functional role in the ecosystem A study inPeninsularMalaysiaindicatedthatcloudedleopardhabitatusein‐creasedwith increasing distance to rivers or streams and higherelevation Our findings for ocelotmirrored this elevation effectbutnottheeffectofdistancetoriversFurthermoreDCADwasastrongcontributoryfactorforocelotsandsimilarlyitwasaposi‐tiveinfluenceonhabitatuseoftheMalaysiancloudedleopard(Tanetal2017)Tanetal(2017)alsofoundthathabitatusebycloudedleopardswaspositivelyassociatedwithforestcovermirroringourresults for ocelots Findings like these start to resolve the nichedifferentiation of these seemingly similar felids which co‐occurandshareanevolutionaryhistoryNeverthelessingeneralforestcover topographical factor (elevationorslope)distancetowater(riverorlakes)anddistancetoroadsandsettlementsallemergeasimportanttothesemedium‐sizedfelids

Unsurprisinglytheresultsindicatethatdetectionprobabilitywaspositivelycorrelatedwithcameratrappingeffortsandwasnotcon‐stantacrossall surveyareasThiswas tobeexpectedbecause thelongeracameratrapsurveythehighertheprobabilityofdetectingaspeciesInfacttheincreaseinthesampleeffortstoobtainmorerobustdatashouldbeencouraged leavingcamerasforat least90ndash120daysinthefieldandhavingseveralyearsofsamplingMeanwhilethereareotherfactorsthatmightleadtodifferentdetectionproba‐bilitiessuchasseasonality(periodoftheyearthatthesurveyswerecarriedoutegdryorrainyseason)andthenumberofcameratrapsatastation(pairedorsinglecameras)Thetwosites(REMJampRSUAandRDSA)withdifferentdetectionprobabilitiesthataresituatedinthefarwestofBrazilianAmazoniaillustratestronginfluencesofriversandseasonalflooding(seealsoCostaetal2018)Previousreportsrevealedthatpositionofcameratrapstations(ontrailsornot)mightalsoaffect thedetectionprobabilityThedetectionprobabilitywashigher for camera trap stations located on roads than on trails (DiBitettietal2006)Additionallyvariationinocelotdensityatdiffer‐entsurveyedareaswillalsoaffectdetectabilitywithahigherocelotdensity associated with higher detectability (Massara De OliveiraPaschoalDohertyHirschampChiarello2015)Manysourcesofevi‐dencepointtoagradient inproductivityandbiomassbeinghigherinthewesternsouthwesternAmazonandlower inthecentralandeasternpartsof thebasin (HoughtonLawrenceHacklerampBrown2001)Thisgradientcould influencedensityandabundance there‐fore detection probability In our study we accounted for spatialautocorrelation(Johnsonetal2013)toobtainamoreaccuratees‐timateforocelothabitatuseThiscorrectionisbiologicallyimportant(Poleyetal2014)butoftenneglected(HodgesampReich2010)

RSR models Nonspatial models

Occupancy ()Occupancy () SE Occupancy () SE

BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739

NotesDetectioncovariatesweredifferentsurveyedarea(site)andnumberofdaysacameratrapstationwasactive forduringeachsamplingoccasion (effort)OccupancycovariateswereGlobalForest Change Threshold 30 (GFC30) disjunct core area density (DCAD) and slope (SLO)Restricted spatial regression (RSR)models incorporated spatial autocorrelationwhile nonspatialmodelsdidnotNaiumlveoccupancyestimaterepresentedtheestimateofoccupancyobtainedwithoutincorporatingvariationsindetectionprobabilityoccupancycovariatesorspatialautocorrelation

TA B L E 4 emspAverageprobabilityofoccupancyandstandarderror(SE)fromspatialandnonspatialoccupancymodelsbasedonthemodelp(site+effort)ψ(GFC30+DCAD+SLO)


Howeveralimitationofourstudyisthatalloursurveyswerecon‐ducted inprimehabitat (exceptSBRandpartofBRA319)Within therangeofvariationwestudiedocelotswereubiquitousAfurtherlimita‐tionisthatwedidnotconsiderpreyandsympatricpredatorsOurfindingsthatocelotswereubiquitousandseeminglyabundantinprotectedareasdonotjustifycomplacencyregardingtheirconservationDeforestationisdestroyingtheirhabitatOcelotsarestrongcandidatesforconservationambassadorspecies(Macdonaldetal2017)sotheirconservationtran‐scendsbenefitstotheirownpopulationsbutextendstothespecieswithwhichtheyaresympatricandthehabitatstheyoccupy

ACKNOWLEDG MENTS

Asincere thanks to JoannaRoss LisannePetracca andDrEgilDrogeforadvisingonQGISandLaraLSousaforadvisingonRDGR received a scholarship from the CAPESDoutorado Plenono exteriornordm 888811281402016‐01 We thank CamposAmazonicosNPandMapinguariNPforlogisticandfinancialsup‐portThisworkwassupportedbyWildCRUthroughagift fromtheRecanati‐Kaplan Foundation and by aWildCRU scholarship

fromtheTangFamilyFoundationBRA‐319datawerecollectedwithfundingfromGordonampBettyMooreFoundationtoWildlifeConservationSocietyBrazilWeareverygratefultoWCS‐Braziland its support on BRA‐319 project We warmly acknowledgeall thosewho helpedwith the fieldwork Data from TMES andPNJUwerecollectedundertheauspicesofProgramaNacionaldeMonitoramentodaBiodiversidade (Monitora)do InstitutoChicoMendes de Conservaccedilatildeo da Biodiversidade with funding fromtheProgramaAacutereasProtegidasdaAmazocircnia(ARPA)PartofthedatawasprovidedbytheTEAMNetworkapartnershipbetweenConservation InternationalTheMissouriBotanicalGardenTheSmithsonian Institution and TheWildlife Conservation Societyand partially funded by these institutions and the Gordonand BettyMoore FoundationWewould also like to thank theInstitutoNacionaldePesquisasdaAmazocircniaforitsfinancialandlogisticsupportofthesitesofManaus(BDFFPZF2andDUCKE)

CONFLIC T OF INTERE S T

Nonedeclared

F I G U R E 4 emspBoxplotshowsestimatedocelotsoccupancyincorporatingspatialautocorrelationineachsurveyedsiteandthereddotswerethenaiumlveoccupancyineachsurveyedsite


AUTHORSrsquo CONTRIBUTIONS

CKWTandDGR conceived the idea for this articleData acquisi‐tionwasperformedbyDGRMIAAPAHCMCALSGMJDP JPERMLRandECJDataanalysiswasprimarilyconductedbyBXWwithhelpfromCKWTandDGRThearticlewasprimarilywrittenbyBXWAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication

DATA ACCE SSIBILIT Y

DataandsamplinglocationsavailablethroughtheDryadhttpsdoiorg105061dryadp7410jm

ORCID

Bingxin Wang httpsorcidorg0000‐0002‐7021‐8625

Daniel G Rocha httpsorcidorg0000‐0002‐0100‐3102

Mark I Abrahams httpsorcidorg0000‐0002‐6424‐187X

Andreacute P Antunes httpsorcidorg0000‐0003‐4404‐3486

Hugo C M Costa httpsorcidorg0000‐0003‐0139‐637X

Milton Joseacute de Paula httpsorcidorg0000‐0001‐9316‐1222

Cedric Kai Wei Tan httpsorcidorg0000‐0001‐6505‐2467

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DiBitettiMSPavioloAampDeAngeloC(2006)Densityhabitatuseand activity patternsof ocelots (Leopardus pardalis) in theAtlanticForest of Misiones Argentina Journal of Zoology 270 153ndash163httpsdoiorg101111j1469‐7998200600102x

DiBitettiMSPavioloADeAngeloCDampDiBlancoYE(2008)Localandcontinentalcorrelatesoftheabundanceofaneotropicalcat the ocelot (Leopardus pardalis) Journal of Tropical Ecology 24189ndash200httpsdoiorg101017S0266467408004847

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Geary W L Ritchie E G Lawton J A Healey T R amp NimmoD G (2018) Incorporating disturbance into trophic ecologyFire history shapes mesopredator suppression by an apex pred‐ator Journal of Applied Ecology 55 1594ndash1603 httpsdoiorg1011111365‐266413125

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Gonzalez‐BorrajoN Loacutepez‐Bao J V amp Palomares F (2016) Spatialecology of jaguars pumas and ocelots A review of the state ofknowledge Mammal Review 47 62ndash75 httpsdoiorg101111mam12081

Grace J B Schoolmaster D R Guntenspergen G R Little AMMitchellBRMillerKMampSchweigerEW(2012)Guidelinesforagraph‐theoretic implementationof structural equationmodelingEcosphere3art73httpsdoiorg101890es12‐000481

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Guilherme de Andrade Vasconcelos P Angelo H Nascimento deAlmeidaAAparecidoTrondoliMatricardiEPereiraMiguelENogueiradeSouzaAacutehellipSantosJoaquimM(2017)Determinantsof the Brazilian Amazon deforestation African Journal of Agricultural Research 12 169ndash176 httpsdoiorg105897ajar201611966

HaddadNMBrudvigLAClobertJDaviesKFGonzalezAHoltRDhellipTownshendJR(2015)Habitatfragmentationanditslast‐ing impact on Earths ecosystemsScience Advances1 e1500052httpsdoiorg101126sciadv1500052

Haidir IADinataY LinkieMampMacdonaldDW (2013)Asiaticgolden cat and Sunda clouded leopard occupancy in the KerinciSeblatlandscapeWest‐CentralSumatraCat News597ndash10

HainesAMGrassmanLITewesMEampJanečkaJE(2006)Firstocelot(Leopardus pardalis)monitoredwithGPStelemetryEuropean Journal of Wildlife Research 52 216ndash218 httpsdoiorg101007s10344‐006‐0043‐5

HansenMCPotapovPVMooreRHancherMTurubanovaSATyukavinaAhellipTownshendJRG(2013)High‐resolutionglobalmapsof 21st‐century forest cover changeScience342 850ndash853httpsdoiorg101126science1244693

HearnAJRossJBernardHBakarSAGoossensBHunterLTBBampMacdonaldDW(2017)ResponsesofSundacloudedleop‐ardNeofelis diardipopulationdensitytoanthropogenicdisturbanceRefiningestimatesof its conservation status inSabahOryx 1ndash11httpsdoiorg101017s0030605317001065

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HoughtonRALawrenceKTHacklerJLampBrownS(2001)ThespatialdistributionofforestbiomassintheBrazilianAmazonAcom‐parisonofestimatesGlobal Change Biology7731ndash746httpsdoiorg101046j1365‐2486200100426x

HughesJampHaranM(2013)Dimensionreductionandalleviationofconfounding forspatialgeneralized linearmixedmodelsJournal of the Royal Statistical Society Series B (Statistical Methodology)75139ndash159httpsdoiorg101111j1467‐9868201201041x

IBGEIBDEGEE(2011)Sinopse do censo demografico 2010(p261)RiodeJaneiroBrazilInstitutoBrasileirodeGeografiaeEstatistica

JohnsonDS(2015)stoccARpackageforfittingaspatialoccupancymodelviaGibbssampling

JohnsonDSConnPBHootenMBRayJCampPondBA(2013)SpatialoccupancymodelsforlargedatasetsEcology94801ndash808httpsdoiorg10189012‐05641

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Kolowski JM ampAlonso A (2010) Density and activity patterns ofocelots (Leopardus pardalis) in northernPeru and the impact of oilexplorationactivitiesBiological Conservation143917ndash925httpsdoiorg101016jbiocon200912039

Kottek M Grieser J Beck C Rudolf B amp Rubel F (2006)World map of the Koumlppen‐Geiger climate classification up‐dated Meteorologische Zeitschrift 15 259ndash263 httpsdoiorg1011270941‐294820060130

LauranceW F Ferreira L V Rankin‐deMerona JM amp LauranceS G (1998) Rain forest fragmentation and the dynamics ofAmazonian tree communitiesEcology79 2032ndash2040 httpsdoiorg102307176707

Macdonald E A Hinks A Weiss D J Dickman A Burnham DSandomCJhellipMacdonaldDW (2017) IdentifyingambassadorspeciesforconservationmarketingGlobal Ecology and Conservation12204ndash214httpsdoiorg101016jgecco201711006

MacKenzieD IampBailey L L (2004)Assessing the fit of site‐occu‐pancy models Journal of Agricultural Biological and Environmental Statistics9300ndash318httpsdoiorg101198108571104X3361

MackenzieDINicholsJDLachmanGBDroegeSAndrewJampLangtimmCA(2002)Estimatingsiteoccupancyrateswhendetec‐tionprobabilitiesarelessthanoneEcology832248ndash2255httpsdoiorg1018900012‐9658(2002)083[2248esorwd]20co2

Maffei L Noss A J Cueacutellar E amp Rumiz D I (2005) Ocelot (Felispardalis) population densities activity and ranging behaviour inthe dry forests of eastern Bolivia Data from camera trappingJournal of Tropical Ecology 21 349ndash353 httpsdoiorg101017S0266467405002397

MalhiYGardnerTAGoldsmithGRSilmanMRampZelazowskiP (2014) Tropical forests in the Anthropocene Annual Review of Environment and Resources 39 125ndash159 httpsdoiorg101146annurev‐environ‐030713‐155141

MassaraRLdeOliveiraPaschoalAMBaileyLLDohertyPFde Frias Barreto M amp Chiarello A G (2018) Effect of humansand pumas on the temporal activity of ocelots in protected areasof Atlantic Forest Mammalian Biology 92 86ndash93 httpsdoiorg101016jmambio201804009

Massara R LDeOliveira Paschoal AMDoherty P FHirschAamp Chiarello A G (2015) Ocelot population status in protectedBrazilian Atlantic Forest PLoS One 10 e0141333 httpsdoiorg101371journalpone0141333

MassaraRLPaschoalAMOBaileyLLDohertyPFampChiarelloAG (2016) Ecological interactions betweenocelots and sympat‐ricmesocarnivoresinprotectedareasoftheAtlanticForestsouth‐eastern Brazil Journal of Mammalogy 97 1634ndash1644 httpsdoiorg101093jmammalgyw129

MassaraR L PaschoalAMOBailey L LDoherty P FHirschAampChiarelloAG(2018)FactorsinfluencingocelotoccupancyinBrazilianAtlanticForest reservesBiotropica50125ndash134httpsdoiorg101111btp12481

MazerolleM J (2017)AICcmodavgmodel selection andmultimodelinferencebasedon(Q)AIC(c)RPackagversion21‐1

McGarigal K Cushman S A Neel M C and Ene E (2002)FRAGSTATS v3 Spatial Pattern Analysis Program for CategoricalMapsComputersoftwareprogramproducedbytheauthorsattheUniversityofMassachusettsAmherstRetrievedfromhttpwwwumassedulandecoresearchfragstatsfragstatshtml

Murray J L amp Gardner G L (1997) Leopardus pardalis Mammalian Species5481ndash10httpsdoiorg1023073504082

NewboldTHudsonLNHillSLLContuSLysenkoISeniorRAhellipPurvisA(2015)Globaleffectsoflanduseonlocalterrestrialbio‐diversityNature52045ndash50httpsdoiorg101038nature14324

Noss R F Quigley H B HornockerM GMerrill T amp Paquet PC (1996) Conservation biology and carnivore conservation in theRocky Mountains Conservation Biology 10 949ndash963 httpsdoiorg101046j1523‐1739199610040949x

NowellKampJacksonP(1996)Wild cats Status survey and conservation action planGlandSwitzerlandIUCN

Otis D L Burnham K P White G C amp Anderson D R (1978)StatisticalinferencefromcapturedataonclosedanimalpopulationsWildlife Monographs623ndash135httpsdoiorg1023073830650

PardoVargasLECoveMVSpinolaRMdelaCruzJCampSaenzJC(2016)Assessingspeciestraitsandlandscaperelationshipsofthe


mammaliancarnivorecommunityinaneotropicalbiologicalcorridorBiodiversity and Conservation25739ndash752httpsdoiorg101007s10531‐016‐1089‐7

PavioloACrawshawPCasoAdeOliveiraTLopez‐GonzalezCAKellyMhellipPayanE (2015)Leoparduspardalis(errataversionpublishedin2016)TheIUCNRedListofThreatenedSpecies2015eT11509A97212355

PenidoGAsteteSFurtadoMMJaacutecomoATdeASollmannRTorresNhellipMarinhoFilhoJ(2016)DensityofocelotsinasemiaridenvironmentinnortheasternBrazilBiota Neotropica161ndash4

PenjorUMacdonaldDWWangchukSTandinTampTanCKW(2018) Identifying important conservation areas for the cloudedleopard Neofelis nebulosa in a mountainous landscape Inferencefrom spatial modeling techniques Ecology and Evolution 8 4278ndash4291httpsdoiorg101002ece33970

PeresCA(1994)Compositiondensityandfruitingphenologyofar‐borescentpalms inanAmazonianterrafirmeforestBiotropica26285ndash294httpsdoiorg1023072388849

PoleyLGPondBASchaeferJABrownGSRayJCampJohnsonDS(2014)OccupancypatternsoflargemammalsintheFarNorthofOntariounderimperfectdetectionandspatialautocorrelationJournal of Biogeography41122ndash132httpsdoiorg101111jbi12200

PrangeSampGehrtSD(2004)Changesinmesopredator‐communitystructure in response to urbanizationCanadian Journal of Zoology821804ndash1817httpsdoiorg101139z04‐179

Pratas‐Santiago LPGonccedilalvesA L S daMaiaSoaresAMVampSpironelloWR(2016)Themooncycleeffectontheactivitypat‐terns of ocelots and their prey Journal of Zoology 299 275ndash283httpsdoiorg101111jzo12359

PrughLRStonerCJEppsCWBeanWTRippleWJLaliberteA S amp Brashares J S (2009) The rise of the mesopredatorBioScience59779ndash791httpsdoiorg101525bio20095999

QGISDevelopmentTeam(2017)QGISGeographicInformationSystemOpenSourceGeospatialFoundationProjectRetrievedfromhttpqgisosgeoorg

Razzaghi M (2013) The probit link function in generalized linearmodels for data mining applications Journal of Modern Applied Statistical Methods 12 164ndash169 httpsdoiorg1022237jmasm1367381880

RippleW JEstes JABeschtaRLWilmersCCRitchieEGHebblewhiteMhellipWirsingA J (2014)Statusandecologicalef‐fects of the worlds largest carnivores Science 343 1241484httpsdoiorg101126science1241484

Robertson A L Malhi Y Farfan‐Amezquita F Aragatildeo L E OC Silva Espejo J E amp Robertson M A (2010) Stem respira‐tion in tropical forests along an elevation gradient in theAmazonand Andes Global Change Biology 16 3193ndash3204 httpsdoiorg101111j1365‐2486201002314x

RochaDGSollmannRRamalhoEEIlhaRampTanCKW(2016)Ocelot(Leopardus pardalis)densityincentralAmazoniaPLoS One11e0154624httpsdoiorg101371journalpone0154624

RotaCTFerreiraMARKaysRWForresterTDKaliesELMcSheaWJhellipMillspaughJJ(2016)Amultispeciesoccupancymodel for twoormore interactingspeciesMethods in Ecology and Evolution71164ndash1173httpsdoiorg1011112041‐210X12587

RotaCTFletcherRJJrDorazioRMampBettsMG(2009)OccupancyestimationandtheclosureassumptionJournal of Applied Ecology461173ndash1181httpsdoiorg101111j1365‐2664200901734x

Shukla J Nobre C amp Sellers P (1990) Amazon deforestation andclimate change Science 247 1322ndash1325 httpsdoiorg101126science24749481322

SmithNJH(1976)SpottedcatsandtheAmazonskintradeOryx13362ndash371httpsdoiorg101017S0030605300014095

TanCKWRochaDGClementsGRBrenes‐MoraEHedgesLKawanishiKhellipMacdonaldDW(2017)Habitatuseandpre‐dicted range for themainlandclouded leopardNeofelis nebulosa inPeninsularMalaysiaBiological Conservation20665ndash74httpsdoiorg101016jbiocon201612012

TeamRC(2017)R A language and environment for statistical computing ViennaAustriaRFoundationforStatisticalComputing

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Wittmann F amp Junk W J (2016) The Amazon River basin In CM Finlayson G R Milton R C Prentice amp N C Davidson(Eds) The Wetland book II Distribution description and conser‐vation (pp 1ndash16) Dordecht Netherlands Springer httpsdoiorg101007978‐94‐007‐6173‐5_83‐1

SUPPORTING INFORMATION

Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle

How to cite this articleWangBRochaDGAbrahamsMIetalHabitatuseoftheocelot(Leopardus pardalis)inBrazilianAmazonEcol Evol 201995049ndash5062 httpsdoiorg101002ece35005


1emsp |emspINTRODUC TION

SouthAmericasAmazonbasinharborsoverhalfofallthetropicalrain‐forestsleftonEarthspanningavastareaof67millionkm2(WittmannampJunk2016)andishometoroughlyhalfoftheworldsspecies(ShuklaNobreampSellers1990)Unfortunatelyhuman‐inducedchanges to itsecosystemforahostofsocial‐economicreasonsarecausingwidespreadbiodiversitydeclinesintheAmazon(Gibsonetal2011Newboldetal2015GuilhermedeAndradeVasconcelos2017)Over2000ndash2012theaveragerateoftropicaldenseforestslosswas74400km2year(MalhiGardnerGoldsmithSilmanampZelazowski2014)Deforestationisinten‐sifyingpressuresonforestvertebratesaswellasonindigenousandnon‐indigenousforestdwellersandtheirlivelihoodsInadditiontheprocessofdeforestationisnotrandomwithremainingforestsoftenbeingcon‐finedtosteepslopesandhilltopsunsuitableforbothlarge‐scaleagricul‐tureandcattleranchThisleadstohabitatfragmentationandpopulationisolation(Malhietal2014)especiallythroughouttheso‐calledarcofdeforestationregionwhichtogetherinfluencethenatureandfrequencyof species interactionswithunknowncascadingeffectson long‐termbiodiversitypersistence(Haddadetal2015)

Forestcarnivoresespeciallyapexpredatorsarethoughttobepar‐ticularlyvulnerableandsensitivetodeforestationandforestfragmen‐tation(NossQuigleyHornockerMerrillampPaquet1996)becauseoftheirrestrictedcarnivorousdiet(VetterHansbauerVeacutegvaacuteriampStorch2011)and largehomerangesTheyareessential formaintainingthecommunity structure within a foodweb and are vital to ecosystemfunctioning (Ripple etal 2014) Mesopredators can fill this role tosomedegreewhenapexpredatorsareeradicatedordepleted(Prughetal2009)Someomnivorousmesopredatorstypicallyopportunistswithbroaddiets suchas raccoons (Procyon lotor)mayrespondpos‐itively toanthropogenic resourceswithbehavioralchange (PrangeampGehrt 2004) In these casesmesopredatorswith good adaptabilitymight serve as a buffer to sustain ecosystem stability and integritywhenapexpredatorsareinadequateAlternativelymesopredatorsaresometimesassociatedwithunpredictablecascadeeffectssuchasdis‐easeoutbreaksandhumanndashwildlifeconflicts(Prughetal2009)Thesevariousandunpredictablepossibilitiesprovideabackgroundforanin‐terestinmedium‐sizedNeotropicalcatsinadditiontothefundamentalinterestintheirpoorlydocumentedautecology

TheocelotLeopardus pardalis(Linnaeus1758Figure1)isame‐dium‐sized(66ndash186kg)Neotropicalspottedcatwithabroadgeo‐graphicdistributionintheAmericasrangingfromtheextremesouthofTexas(USA)throughoutMesoamericaandtheAmazontoopenenvironmentsinnorthernArgentinaandfloodplainsdryconiferousforestsandrainforests(EmmonsampFeer1998MurrayampGardner1997)Ocelots are considered solitary nocturnalcrepuscular andsemi‐arboreal andareexcellent climbers (DiBitettiPavioloampDeAngelo 2006) Documented home ranges are average 125plusmnSE 34km2 (Gonzalez‐Borrajo Loacutepez‐Bao amp Palomares 2016) Theyhavebeenrecordedatelevationsupto1200m(NowellampJackson1996) and are classified as Least Concern on the IUCN Red List(Paviolo etal 2015) They were heavily exploited in Amazoniaby the international fur trade between the 1930s andmid‐1970s(Antunes etal 2016 Smith 1976)Currently ocelots suffer habi‐tatlossfragmentationandotheranthropogenicpressuressuchasoilexploration (KolowskiampAlonso2010)vehiclecollisions illegaltradeand retaliatorykillingdue todepredationonsmall livestock(Pavioloetal2015)

Nevertheless ocelot amesopredator has been studiedmuchless than largermore charismatic felids such as jaguar (Panthera onca)andpuma(Puma concolor)Since2000studiesofocelotusing

remote‐sensingderivedmetricsofforestcoverdisjunctcoreareadensityelevationdistancetoroadsdistancetosettlementsandhabitatuseandthathabitatusebyocelotswasnegativelyassociatedwithslopeanddistancetoriverlakeThesefind‐ings shed lighton the regional scalehabitatuseofocelotsand indicate importantspeciesndashhabitat relationships thusprovidingvaluable information for conservationmanagementandland‐useplanning

K E Y W O R D S

BrazilianAmazoncameratrapsmesopredatoroccupancyocelotrestrictedspatialregression

F I G U R E 1 emspOcelotwastakenbyonecameratrapin2013(photosprovidedbyDanielGRocha)





2emsp |emspMETHODS















Records of ocelots

2010 PDBFF(Manaus) 350 30 946 1 136508(7190) 10

2010 ZF2(Manaus) 380 30 1050 1 138933(1932) 8

2010ndash2011 BRA319 81274518 196 9647 1 31279(32194) 8

2012 DUCKE(Manaus) 100 30 1877 1 135125(8799) 4

2013ndash2014 RDSA 23500 64 2682 2 124576(26250) 45


2014 Uatuma 1601704 95 2867 1 115332(105538) 5

2015 REMJampRSUA 88622 25 1112 1 737160(436787) 14

2016 PNCA 9613 86 5537 1 287218(104853) 28

2016 PNM 1722852 58 1939 1 374717(181393) 57

2016 PNJU 1958203 18 1276 1 98764(1328) 16

2016 TMES 3373111 61 3652 1 134078(6059) 86

2017 SBR 831 23 1593 1 1380649(13588) 5

Total 899 40347 334






3emsp |emspRESULTS










radic

) was







176809 032 009 20 minus86357


176818 041 009 16 minus86778


176825 048 008 19 minus86469


176852 075 007 18 minus86587


17688 102 006 17 minus86705


176885 108 006 16 minus86812


176891 113 006 17 minus86711


176904 126 006 19 minus86509


176923 145 005 17 minus86727


176932 154 005 17 minus86731


176934 156 005 18 minus86628


176955 177 004 16 minus86847


176966 188 004 16 minus86852


176971 194 004 21 minus86333


176974 196 004 17 minus86752









Covariate


β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304




ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050


PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982


BRA319 089 minus1973 04003 minus4929


PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586















BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S































































































2emsp |emspMETHODS















Records of ocelots

2010 PDBFF(Manaus) 350 30 946 1 136508(7190) 10

2010 ZF2(Manaus) 380 30 1050 1 138933(1932) 8

2010ndash2011 BRA319 81274518 196 9647 1 31279(32194) 8

2012 DUCKE(Manaus) 100 30 1877 1 135125(8799) 4

2013ndash2014 RDSA 23500 64 2682 2 124576(26250) 45


2014 Uatuma 1601704 95 2867 1 115332(105538) 5

2015 REMJampRSUA 88622 25 1112 1 737160(436787) 14

2016 PNCA 9613 86 5537 1 287218(104853) 28

2016 PNM 1722852 58 1939 1 374717(181393) 57

2016 PNJU 1958203 18 1276 1 98764(1328) 16

2016 TMES 3373111 61 3652 1 134078(6059) 86

2017 SBR 831 23 1593 1 1380649(13588) 5

Total 899 40347 334






3emsp |emspRESULTS










radic

) was







176809 032 009 20 minus86357


176818 041 009 16 minus86778


176825 048 008 19 minus86469


176852 075 007 18 minus86587


17688 102 006 17 minus86705


176885 108 006 16 minus86812


176891 113 006 17 minus86711


176904 126 006 19 minus86509


176923 145 005 17 minus86727


176932 154 005 17 minus86731


176934 156 005 18 minus86628


176955 177 004 16 minus86847


176966 188 004 16 minus86852


176971 194 004 21 minus86333


176974 196 004 17 minus86752









Covariate


β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304




ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050


PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982


BRA319 089 minus1973 04003 minus4929


PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586















BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S


































































































Records of ocelots

2010 PDBFF(Manaus) 350 30 946 1 136508(7190) 10

2010 ZF2(Manaus) 380 30 1050 1 138933(1932) 8

2010ndash2011 BRA319 81274518 196 9647 1 31279(32194) 8

2012 DUCKE(Manaus) 100 30 1877 1 135125(8799) 4

2013ndash2014 RDSA 23500 64 2682 2 124576(26250) 45


2014 Uatuma 1601704 95 2867 1 115332(105538) 5

2015 REMJampRSUA 88622 25 1112 1 737160(436787) 14

2016 PNCA 9613 86 5537 1 287218(104853) 28

2016 PNM 1722852 58 1939 1 374717(181393) 57

2016 PNJU 1958203 18 1276 1 98764(1328) 16

2016 TMES 3373111 61 3652 1 134078(6059) 86

2017 SBR 831 23 1593 1 1380649(13588) 5

Total 899 40347 334






3emsp |emspRESULTS










radic

) was







176809 032 009 20 minus86357


176818 041 009 16 minus86778


176825 048 008 19 minus86469


176852 075 007 18 minus86587


17688 102 006 17 minus86705


176885 108 006 16 minus86812


176891 113 006 17 minus86711


176904 126 006 19 minus86509


176923 145 005 17 minus86727


176932 154 005 17 minus86731


176934 156 005 18 minus86628


176955 177 004 16 minus86847


176966 188 004 16 minus86852


176971 194 004 21 minus86333


176974 196 004 17 minus86752









Covariate


β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304




ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050


PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982


BRA319 089 minus1973 04003 minus4929


PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586















BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S































































































3emsp |emspRESULTS










radic

) was







176809 032 009 20 minus86357


176818 041 009 16 minus86778


176825 048 008 19 minus86469


176852 075 007 18 minus86587


17688 102 006 17 minus86705


176885 108 006 16 minus86812


176891 113 006 17 minus86711


176904 126 006 19 minus86509


176923 145 005 17 minus86727


176932 154 005 17 minus86731


176934 156 005 18 minus86628


176955 177 004 16 minus86847


176966 188 004 16 minus86852


176971 194 004 21 minus86333


176974 196 004 17 minus86752









Covariate


β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304




ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050


PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982


BRA319 089 minus1973 04003 minus4929


PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586















BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S

































































































176809 032 009 20 minus86357


176818 041 009 16 minus86778


176825 048 008 19 minus86469


176852 075 007 18 minus86587


17688 102 006 17 minus86705


176885 108 006 16 minus86812


176891 113 006 17 minus86711


176904 126 006 19 minus86509


176923 145 005 17 minus86727


176932 154 005 17 minus86731


176934 156 005 18 minus86628


176955 177 004 16 minus86847


176966 188 004 16 minus86852


176971 194 004 21 minus86333


176974 196 004 17 minus86752









Covariate


β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304




ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050


PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982


BRA319 089 minus1973 04003 minus4929


PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586















BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S

































































































Covariate


β‐parameters

Estimate SE z

Ocelotoccupancy(ψ)

GFC30 100 1303 0441 29566

SLO 058 minus0839 0366 minus22934

DCAD 051 0542 0332 16304




ELE 037 minus1161 0638 minus18177

DSET 030 0013 0416 00312

Ocelotdetection(p)

Effort 100 0175 00289 6050


PNM 100 1620 02880 5623

TMES 100 1482 02924 5067

RDSA 096 1205 03027 3982


BRA319 089 minus1973 04003 minus4929


PNJU 080 1254 04668 2687

REMJampRSUA 074 1032 03444 2997

PBDFF 064 0822 04718 1743

SBR 046 minus0229 06086 minus0377

ZF2 036 0252 04306 0586















BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S








































































































BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S



































































































BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S

































































































BRA319 7771 04021 7788 04021 459

PNCA 6279 03043 6297 03043 2442

PNM 5999 02060 6042 02060 4138

RDSA 7959 02498 7977 02498 4844

REMJampRUSA 7661 03481 7782 03481 2212

DUCKE 6382 04262 6298 04262 1333

PBDFF 6396 03645 6337 03645 2667

ZF2 6842 03633 6780 03633 2667

TMES 7767 01848 7794 01848 6230

PNJU 6839 02263 6825 02263 5000

Uatuma 6796 04266 7019 04266 421

SBR 6943 03820 7010 03820 1739





ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S





























































































ACKNOWLEDG MENTS




Nonedeclared







ORCID








R E FE R E N C E S
































































































ORCID








R E FE R E N C E S























































































































































































Documents

Habitat use of the ocelot (Leopardus pardalis) in