Ecology and Evolution 201991985ndash2003 emsp|emsp1985wwwecolevolorg

Received25September2018emsp |emsp Revised5November2018emsp |emsp Accepted7December2018DOI101002ece34890

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

Partitioning global change Assessing the relative importance of changes in climate and land cover for changes in avian distribution

Matthew J Clement12 emsp|emspJames D Nichols1 emsp|emspJaime A Collazo3emsp|emsp Adam J Terando4 emsp|emspJames E Hines1 emsp|emspSteven G Williams5

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicensewhichpermitsusedistributionandreproductioninanymediumprovidedtheoriginalworkisproperlycitedPublished2019ThisarticleisaUSGovernmentworkandisinthepublicdomainintheUSAEcology and Evolution

1UnitedStatesGeologicalSurveyPatuxentWildlifeResearchCenterLaurelMaryland2ArizonaGameandFishDepartmentResearchBranchPhoenixArizona3UnitedStatesGeologicalSurveyNorthCarolinaCooperativeFishandWildlifeResearchUnitDepartmentofAppliedEcologyNorthCarolinaStateUniversityRaleighNorthCarolina4SoutheastClimateAdaptationScienceCenterUSGeologicalSurveyRaleighNorthCarolina5NorthCarolinaCooperativeFishandWildlifeResearchUnitDepartmentofAppliedEcologyNorthCarolinaStateUniversityRaleighNorthCarolina

CorrespondenceMatthewJClementArizonaGameandFishDepartmentResearchBranchPhoenixAZEmailmclementgmailcom

Funding informationAssociationofFishandWildlifeAgenciesGrantAwardNumberNCM‐1‐HMUSGeologicalSurveyNorthCarolinaCooperativeFishandWildlifeResearchUnitResearchWorkOrder119

AbstractUnderstanding the relative impact of climate change and land cover change onchangesinaviandistributionhasimplicationsforthefuturecourseofaviandistribu‐tionsandappropriatemanagementstrategiesDuetothedynamicnatureofclimatechangeourgoalwastoinvestigatetheprocessesthatshapespeciesdistributionsratherthanthecurrentdistributionalpatternsTothisendweanalyzedchangesinthe distribution of EasternWood Pewees (Contopus virens) and Red‐eyed Vireos(Vireo olivaceus) from1997 to2012usingBreedingBirdSurveydataanddynamiccorrelated‐detectionoccupancymodelsWeestimatedthelocalcolonizationandex‐tinction ratesof thesespecies in relation tochanges inclimate (hoursofextremetemperature)andchangesinlandcover(amountofnestinghabitat)WefitsixnestedmodelstopartitionthedevianceexplainedbyspatialandtemporalcomponentsoflandcoverandclimateWeisolatedthetemporalcomponentsofenvironmentalvari‐ablesbecausethisistheessenceofglobalchangeForbothspeciesmodelfitwassignificantlyimprovedwhenwemodeledvitalratesasafunctionofspatialvariationinclimateandlandcoverModelfitimprovedonlymarginallywhenweaddedtempo‐ralvariation inclimateand landcover to themodelTemporalvariation inclimateexplainedmoredeviancethantemporalvariationinlandcoveralthoughbothcom‐binedonlyexplained20(EasternWoodPewee)and6(Red‐eyedVireo)oftempo‐ralvariationinvitalratesOurresultsshowingasignificantcorrelationbetweeninitialoccupancyandenvironmentalcovariatesareconsistentwithbiologicalexpectationandpreviousstudiesTheweakcorrelationbetweenvitalratesandtemporalchangesincovariatesindicatedthatwehaveyettoidentifythemostrelevantcomponentsofglobalchangeinfluencingthedistributionsofthesespeciesandmoreimportantlythatspatiallysignificantcovariatesarenotnecessarilydrivingtemporalshiftsinaviandistributions

K E Y W O R D S

dynamicoccupancymodelsEasternWoodPeweelocalcolonizationlocalextinctionNorthAmericanBreedingBirdSurveyRed‐eyedVireo

1986emsp |emsp emspensp CLEMENT ET aL

1emsp |emspINTRODUC TION

In recent decades the distributions of many bird species havechanged inBritain (Fuller et al 1995ThomasampLennon1999)continental Europe (Boumlhning‐Gaese amp Bauer 1996 BrommerLehikoinen amp Valkama 2012) South Africa (Hockey SiramiRidleyMidgleyampBabiker2011)andNorthAmerica(SauerLinkFallon Pardieck amp Ziolkowski 2013) These shifts are of bothecological and conservation interest and they have stimulateda great deal of researchWhile various factorsmay be contrib‐uting to distributional shifts including invasive species (Crowl CristParmenterBelovskyampLugo2008)pollution(Trathanetal2015) exploitation (Laursen amp Frikke 2008) and other causesclimatechangeandlandcoverchangesarelikelytobetwoofthestrongest drivers for many species (Barbet‐Massin Thuiller ampJiguet2012LemoineBauerPeintingerampBoumlhning‐Gaese2007Travis2003)Understandingtherelativeimpactofthesedriversof distributional change has implications for ecological under‐standingpredictionsoffuturechangesappropriatemanagementstrategiesandtheallocationofconservationresourcesHoweverspeciesdistributionmodelsfocusedontheeffectsofclimatearefarmorecommonthananalysesthatcomparethecontributionsofclimateandlandcover(Siramietal2017)

Ecologists have long debated the factors that affect speciesranges(Grinnell1917MacArthur1972)becauseoftherelevanceto ecology biogeography and community ecology In recent de‐cades global changehasgivenmoreurgency to the topic (Urban2015) and the expectation that both the global environment andspecies distributionswill continue to changehas stimulatedmuchresearchintopredictingthefuturecourseofchange(Huntleyetal2006)Questionsabout the relative impactof climatechangeandland cover change are important because of the implications forthesepredictions(Siramietal2017)Forexampleclimatechangeis expected tooccur alonga latitudinal gradientwhile changes inlandcovermayexhibitlessdirectionalityiffactorssuchasurbaniza‐tionorconversiontoorfromagriculturalusearesignificantdriversof landcoverchange (Lemoineetal2007Travis2003)Asare‐sultspeciesdistributionsthatrespondprimarilytoclimatearemorelikely to also exhibit directionality (Brommer et al 2012Hockeyetal2011ParmesanampYohe2003)Whetherspeciesdistributionsrespondtolandcoverorclimatechangehasimplicationsforappro‐priateresponsessuchasthedesignandlocationofreserves(ArauacutejoCabezaThuillerHannahampWilliams2004)habitatrestorationandassistedmigration (Hoegh‐Guldbergetal2008)Accordinglypo‐liticallyandeconomicallysignificantdecisionsmaybeinfluencedbytheseprojectionsTherefore there isaneed for research into therelativeeffectsofclimatechangeandlandcoverchangeonchangesinthedistributionofspecies

Whenthereisinterestinprojectingchangesinspeciesdistri‐butions a crucial insight is that environmental factors that cor‐relatewiththecurrentspeciesdistributionmaynotcorrelatewithfuture changes in distribution (Barbet‐Massin et al 2012) Onereason is that projections developed from current distribution

patterns assume that species are in equilibriumwith their envi‐ronment(ElithKearneyampPhillips2010)whichmaynotbetrue(ZhuWoodallampClark2012)A second reason is thatdispersallimitations may prevent species from occupying their preferredhabitat in the future (Devictor JulliardCouvetampJiguet2008)Therefore investigating theprocesses that shape species distri‐butionsratherthancurrentspeciesdistributionpatternsislikelytogenerategreaterecologicalunderstandingandbetterprojecteddistributions (YackulicNicholsReidampDer2015)ForexampletherecentdistributionofbreedingLouisianaWaterthrush(Parkesia motacilla) inNorthAmerica is best describedbymean tempera‐turebutchangesinthatdistributioncorrelatewithmeanprecip‐itation aswell asmean temperature indicating that theprocesscannotbefullydescribedbythepatternobservedatonepointintime(ClementHinesNicholsPardieckampZiolkowski2016)WefurthernotethatdataonenvironmentalvariablestypicallyincludebothspatialandtemporalcomponentsForexampletemperaturemayvaryalonganorthndashsouthgradient aswell as through timeWhentheprimaryresearchinterestisintemporalchangesindis‐tributions itmakessensetoisolatethetemporalcomponentsofenvironmentalvariables(whicharetheessenceofglobalchange)wheninvestigatingspeciesresponsetoglobalchange

HereweassesstherelativeimportanceofchangesinclimateandlandcovertochangesindistributionsfortwobirdspeciestheRed‐eyedVireo(Vireo olivaceus)andtheEasternWoodPewee(Contopus virens)Weselectedjusttwospecieswithsimilarbiologybutdiffer‐ingpopulationtrendssothatwecoulddevelopspecies‐specifichy‐pothesesaboutdistributionalshiftsWepartitionedthespatialandtemporal componentsofourenvironmentalpredictors to improveour inferencesabout theprocesses affecting speciesdistributionsandaboutthelikelyconsequencesoffutureglobalchangeWeuseddynamic occupancymodeling to explicitly estimate the vital ratesthatgovernchanges indistributionssoastoavoidtheequilibriumassumptionimplicitinstaticspeciesdistributionmodelsFinallyweusedananalysisofdevianceapproachtoassesstherelativeimpor‐tanceofclimateand landcovertothechanges indistributionsforthesetwospecies

2emsp |emspMATERIAL S AND METHODS

21emsp|emspHypotheses

Ourgoalwastoassesstherelativeimportanceofclimateandlandcovertochanges inthedistributionsoftwobirdspeciestheRed‐eyedVireoandtheEasternWoodPeweeWeselectedthesebirdsbecausetheyaremigratoryinsectivorousandinhabitsimilarplantcommunities(Hamel1992)butexhibitdivergenttrendsinrelativeabundancereportedbytheBBS(Saueretal2015)TheRed‐eyedVireobreedsintheeasternandnorthernUnitedStatesandmuchofsouthernCanadaandwintersinSouthAmericaItisacommonandwidespreadspeciesthatusesawidevarietyofforesthabitatstonestandgleaninsectsfromfoliageCountsofRed‐eyedVireoshavebeenincreasingby08annuallysince1966andby10annuallysince

emspensp emsp | emsp1987CLEMENT ET aL

2003withsomelocalizeddeclinesinFloridaTexasandthePacificNorthwest(Saueretal2015)TheEasternWoodPeweebreedsintheeasternUnitedStatesandpartsofsoutheasternCanadaIncon‐trasttoRed‐eyedVireoscountsoftheEasternWoodPeweehavebeendecliningby15since1966andby12since2003(Saueretal2015)DespitethegenerallynegativetrendtherehavebeenlocalizedincreasesincountsintheMidwest

Toestimatetherelativeimportanceofclimateandlandcovertochangesinbirddistributionswegeneratedtestablehypothesesex‐pressingrelationshipsbetweenbirdsandenvironmentalcovariatesAcommonapproachistogenerateanextensivesetofhypothesesfromasuiteofenvironmentalmetricsWecanthenfitthespecifiedmodelsandobservewhichmetricsyieldsignificantresultsHowevertestingmanystatisticalhypotheseshasapropensitytoidentifyspu‐riousrelationshipsandsuchanalysesshouldbeconsideredexplor‐atory(AndersonBurnhamGouldampCherry2001Ioannidis2005)Inthisstudywedevelopedtwoapriorihypothesesfromecologicalprinciples

1 Changes in the total annual periods of extreme temperaturedrive changes in bird distributionsWe focus on extreme tem‐perature because we expect these are the periods of greateststress due to cold or heat stress increased metabolic costsreduced resource availability or other mechanisms (Dawson ampWhittow 2000 Root 1988) We defined ldquoextremerdquo relativetothethermoneutralzone(TNZ)whichistherangeofambienttemperaturesunderwhichendothermscanmaintain theirbodytemperature without deviating from their basal metabolic rate(Calder amp King 1974) The TNZ varies among species but hasbeenestimatedas18ndash38degC fora ldquogenericrdquobird (CalderampKing1974) Several small temperate‐zone passerines (similar to ourstudy species) including the Northern Cardinal Verdin HouseSparrow and Red‐breasted Nuthatch have been documentedto have TNZs similar to that of this ldquogenericrdquo bird (Dawson1958 Goldstein 1974 Hinds amp Calder 1973 Khaliq HofPrinzinger Boumlhning‐Gaese amp Pfenninger 2014) Therefore weexpected temperatures below 18degC and above 38degC to beassociated with changes in bird distributions

2 Changes in the amount of appropriate habitat locally availableduringthebreedingseasondrivechangesinbirddistributionsbe‐cause inappropriatehabitatwillnotprovidesufficient foodandshelterforbreedingbirds(FriggensampFinch2015IgleciaCollazoampMcKerrow2012) For theEasternWoodPeweewedefinedappropriatehabitats asdeciduous forest evergreen forest andmixedforest(Hamel1992)FortheRed‐eyedVireowedefinedappropriate habitats as the same forest types and shrubndashscrub(Hamel1992)

Ofcoursethesehypothesesarenotexhaustivebutweselectedthem as logical general mechanisms that might underlie avian re‐sponsestoclimateandlandcoverchangeWeexpectedthepaucityand specificity of our hypotheseswould reduceour chancesof ob‐taining statistically significant resultsbychanceForexample anull

hypothesisthatbirddistributionswillnotexpandataspecifictempera‐tureishardertorejectthanthenullhypothesisthatbirddistributionswillnotexpandatanytemperatureWeviewamorespecifichypothe‐sisasamorepowerfuldiscriminatorytool(Platt1964Popper1959)moreinkeepingwiththehypothetico‐deductivemethod(Chamberlin1890Romesburg1981) andwith aType1error rate closer to thenominallevel(Andersonetal2001Ioannidis2005)

Westatedourhypothesizedeffectsintermsofchanges inbirddistribution(iebirdswillincreasewherehabitatisavailable)ratherthan static patterns of bird distribution (ie birds will be locatedwhere habitat is available)We prefer this formulation because itfocusesontheecologicalprocessesthatunderliespeciesdistribu‐tionsandtheirdynamics Inmetapopulationtheorythevitalratesdescribingtheseprocessesareoftenthelocalcolonizationandex‐tinction rates Models that account for ecological vital rates (iedynamicmodels)areusefulbecausetheyarerelativelymechanisticthereby strengthening the generality of findings and contributingmoretoecologicalunderstandingIncontrastinordertobeusefulforforecastingandprojectionrelationshipsbetweenenvironmentalcovariates andoccurrence require an assumption that the speciesinquestion is inequilibriumwith theenvironmental factorsunderstudy (Elith et al 2010) Themore phenomenological orientationofmodelsthatfocusonoccurrencecanbelimitingwhenprojectingmodelresultsintonovelsituationssuchasfutureclimateandlandcoverconditions(Cuddingtonetal2013Gustafson2013)Incon‐trast dynamicmodels avoid the equilibrium assumption enablingmore robust predictions of occurrence under future conditions(Clementetal2016Yackulicetal2015)

Bothclimateand landcovervary spatiallyand temporally andeachprocess could impactbirddistributions It is possible for thelevelofcorrelationwithbirdvital rates todifferacross those twodimensions For example consider an environmental variable thatiscompletelystaticthroughagiventimeperiodandaspeciesdistri‐butionthatshiftsduringthesametimeperiodInthiscaseitisnotpossibleforchangeinthestaticvariabletohavecausedthedistri‐butionalshiftbecausenosuchchangeoccurredHoweveritisstillpossibleforcolonizationandextinctiontocorrelatewiththeenvi‐ronmentalvariablebecauseofaspatialcorrelationbetweenthemHere our goalwas to incorporate temporal variation in covariatevalues intoouranalysisbecausemuchresearch intoglobalchangeismotivatedbyinterestintemporalchangesinspeciesdistributionsTherefore our hypotheses and models are structured to isolateandevaluatetheabilityofenvironmentalcovariatestoaccountforspatial and temporal variation in local colonization and extinctionprobabilities

22emsp|emspData

We obtained data on the detection and nondetection of breed‐ing Red‐eyed Vireos and Eastern Wood Pewees from the NorthAmerican Breeding Bird Survey (Pardieck Ziolkowski amp Hudson2015 wwwpwrcusgsgovBBSRawData) The Breeding BirdSurvey (BBS) is a joint project of the US Geological Survey and

emspensp emsp | emsp1987CLEMENT ET aL

2003withsomelocalizeddeclinesinFloridaTexasandthePacificNorthwest(Saueretal2015)TheEasternWoodPeweebreedsintheeasternUnitedStatesandpartsofsoutheasternCanadaIncon‐trasttoRed‐eyedVireoscountsoftheEasternWoodPeweehavebeendecliningby15since1966andby12since2003(Saueretal2015)DespitethegenerallynegativetrendtherehavebeenlocalizedincreasesincountsintheMidwest

Toestimatetherelativeimportanceofclimateandlandcovertochangesinbirddistributionswegeneratedtestablehypothesesex‐pressingrelationshipsbetweenbirdsandenvironmentalcovariatesAcommonapproachistogenerateanextensivesetofhypothesesfromasuiteofenvironmentalmetricsWecanthenfitthespecifiedmodelsandobservewhichmetricsyieldsignificantresultsHowevertestingmanystatisticalhypotheseshasapropensitytoidentifyspu‐riousrelationshipsandsuchanalysesshouldbeconsideredexplor‐atory(AndersonBurnhamGouldampCherry2001Ioannidis2005)Inthisstudywedevelopedtwoapriorihypothesesfromecologicalprinciples

1 Changes in the total annual periods of extreme temperaturedrive changes in bird distributionsWe focus on extreme tem‐perature because we expect these are the periods of greateststress due to cold or heat stress increased metabolic costsreduced resource availability or other mechanisms (Dawson ampWhittow 2000 Root 1988) We defined ldquoextremerdquo relativetothethermoneutralzone(TNZ)whichistherangeofambienttemperaturesunderwhichendothermscanmaintain theirbodytemperature without deviating from their basal metabolic rate(Calder amp King 1974) The TNZ varies among species but hasbeenestimatedas18ndash38degC fora ldquogenericrdquobird (CalderampKing1974) Several small temperate‐zone passerines (similar to ourstudy species) including the Northern Cardinal Verdin HouseSparrow and Red‐breasted Nuthatch have been documentedto have TNZs similar to that of this ldquogenericrdquo bird (Dawson1958 Goldstein 1974 Hinds amp Calder 1973 Khaliq HofPrinzinger Boumlhning‐Gaese amp Pfenninger 2014) Therefore weexpected temperatures below 18degC and above 38degC to beassociated with changes in bird distributions

2 Changes in the amount of appropriate habitat locally availableduringthebreedingseasondrivechangesinbirddistributionsbe‐cause inappropriatehabitatwillnotprovidesufficient foodandshelterforbreedingbirds(FriggensampFinch2015IgleciaCollazoampMcKerrow2012) For theEasternWoodPeweewedefinedappropriatehabitats asdeciduous forest evergreen forest andmixedforest(Hamel1992)FortheRed‐eyedVireowedefinedappropriate habitats as the same forest types and shrubndashscrub(Hamel1992)

Ofcoursethesehypothesesarenotexhaustivebutweselectedthem as logical general mechanisms that might underlie avian re‐sponsestoclimateandlandcoverchangeWeexpectedthepaucityand specificity of our hypotheseswould reduceour chancesof ob‐taining statistically significant resultsbychanceForexample anull

hypothesisthatbirddistributionswillnotexpandataspecifictempera‐tureishardertorejectthanthenullhypothesisthatbirddistributionswillnotexpandatanytemperatureWeviewamorespecifichypothe‐sisasamorepowerfuldiscriminatorytool(Platt1964Popper1959)moreinkeepingwiththehypothetico‐deductivemethod(Chamberlin1890Romesburg1981) andwith aType1error rate closer to thenominallevel(Andersonetal2001Ioannidis2005)

Westatedourhypothesizedeffectsintermsofchanges inbirddistribution(iebirdswillincreasewherehabitatisavailable)ratherthan static patterns of bird distribution (ie birds will be locatedwhere habitat is available)We prefer this formulation because itfocusesontheecologicalprocessesthatunderliespeciesdistribu‐tionsandtheirdynamics Inmetapopulationtheorythevitalratesdescribingtheseprocessesareoftenthelocalcolonizationandex‐tinction rates Models that account for ecological vital rates (iedynamicmodels)areusefulbecausetheyarerelativelymechanisticthereby strengthening the generality of findings and contributingmoretoecologicalunderstandingIncontrastinordertobeusefulforforecastingandprojectionrelationshipsbetweenenvironmentalcovariates andoccurrence require an assumption that the speciesinquestion is inequilibriumwith theenvironmental factorsunderstudy (Elith et al 2010) Themore phenomenological orientationofmodelsthatfocusonoccurrencecanbelimitingwhenprojectingmodelresultsintonovelsituationssuchasfutureclimateandlandcoverconditions(Cuddingtonetal2013Gustafson2013)Incon‐trast dynamicmodels avoid the equilibrium assumption enablingmore robust predictions of occurrence under future conditions(Clementetal2016Yackulicetal2015)

Bothclimateand landcovervary spatiallyand temporally andeachprocess could impactbirddistributions It is possible for thelevelofcorrelationwithbirdvital rates todifferacross those twodimensions For example consider an environmental variable thatiscompletelystaticthroughagiventimeperiodandaspeciesdistri‐butionthatshiftsduringthesametimeperiodInthiscaseitisnotpossibleforchangeinthestaticvariabletohavecausedthedistri‐butionalshiftbecausenosuchchangeoccurredHoweveritisstillpossibleforcolonizationandextinctiontocorrelatewiththeenvi‐ronmentalvariablebecauseofaspatialcorrelationbetweenthemHere our goalwas to incorporate temporal variation in covariatevalues intoouranalysisbecausemuchresearch intoglobalchangeismotivatedbyinterestintemporalchangesinspeciesdistributionsTherefore our hypotheses and models are structured to isolateandevaluatetheabilityofenvironmentalcovariatestoaccountforspatial and temporal variation in local colonization and extinctionprobabilities

22emsp|emspData

We obtained data on the detection and nondetection of breed‐ing Red‐eyed Vireos and Eastern Wood Pewees from the NorthAmerican Breeding Bird Survey (Pardieck Ziolkowski amp Hudson2015 wwwpwrcusgsgovBBSRawData) The Breeding BirdSurvey (BBS) is a joint project of the US Geological Survey and

1988emsp |emsp emspensp CLEMENT ET aL

theCanadianWildlifeServicedesignedtomonitor thestatusandtrendsofbirdsbreedinginNorthAmerica(Saueretal2013)Sinceitsinceptionin1966theBBShasexpandedtoincludeover5000survey routes across theUnited States and southernCanada ap‐proximately3000ofwhichare surveyedeachyearEach route issurveyedonceperyearbyaskilledvolunteerThedateofthesurveyistimedtooccurduringpeakterritorialbehaviortypicallylateMaytoearlyJulydependingon latitudeSurveyorsfollowaprescribedroutealongsecondaryroadsstoppingatapproximately800‐min‐tervalsandperforming3‐minroadsidepointcountsgenerating50countsper394kmrouteWeselectedBBSdatabecausetheirgreatgeographicand temporalextent is suitable for investigatingdistri‐butionalchanges

We selected a subsetof the availableBBSdata for use inouranalysis Our modeling approach described below makes use ofstop‐specificsurveyresultsbutthesedetailedresultsarecurrentlyavailableindigitalformatsonlysince1997Thereforeweuseddatafrom1997to2012BecauseourstudyspeciesarefoundinforestedregionsofeasternNorthAmericawedelineatedthestudyareabyaggregatingthreeOmernikLevelIclassesrepresentingeasternfor‐estecoregionsNorthernForestsEasternTemperateForestsandTropicalWetForests (CommissionforEnvironmentalCooperation1997Omernik19952004OmernikampGriffith2014)ThisstudyareaincludestheUnitedStateseastoftheGreatPlains(29millionkm2) This region encompassed the entire breeding range of theEasternWoodPeweeexceptsomeareasinCanadanotcoveredbyourclimateandhabitatcovariatesWeselectedBBSroutesentirelywithinthisstudyareaforanalysisWeexcludedroutesthatwerenotclassifiedasldquoactiverdquobytheBBSandroutesthatdifferedfromtherecommendedlength(394km)bymorethan25Weonlyanalyzedsurveys conducted under acceptable conditions (ie acceptableweatherdatetimeofdaystopsreportedbyBBSasldquoruntype=1rdquo)OntherareoccasionsthataroutehadmultipleacceptablesurveysperyearweuseddatafromthefirstacceptablesurveyThisyielded1371routesforouranalysisFinallyweconvertedcountsofbirdsateachstopalongeachBBSroutetodetectionnondetectiondatasuitableforpresencendashabsencemodelingThisconversionsacrificeddata richness but allowed us to account for imperfect detectionof species Using the full count data typically requires additionalassumptions (Illaacuten et al 2014) or incorporation of additional datasources(HootenWikleDorazioampRoyle2007)

ClimatedatawereobtainedfromthePRISMproject(Dalyetal2008)ThePRISMoutputuseslocallyweightedregressionmodelstointerpolatelong‐termweatherstationobservationstoa4kmresolu‐tiongridDailyinterpolatedclimatedataareavailablefrom1981tothepresentFromthesedatawecalculatedtwotemperature‐basedindices based on our first hypothesis described above The firstindexwascalculatedasthetotalannualhoursabove38degCwhilethesecondindexwascalculatedasthetotalannualhoursbelow18degC

Becausethetemperature‐basedindiceswerebasedonhoursofexposureitwasnecessarytointerpolatethedailyPRISMdatatoes‐timatehourlyvaluesWeconsideredasimplelinearinterpolationtoimputevaluesbetweendailymaximumandminimumtemperatures

However for extreme temperatures thismethodwould likely re‐sult in estimated exposure durations that are biased low InsteadweadoptedthemethoddescribedbyCesaraccioSpanoDuceandSnyder(2001seeFigure3)andEccel (2010)usingtheRsoftwarepackageldquoInterpolTrdquo (EccelampCordano2013) Inthismethodcan‐didatefunctionsarefit toobservedhourlytemperaturedatafromnearby high‐quality ASOS (Automated Station Observing System)weather stations in the study regionusing a combinationof sinu‐soidalandquadratic functionsForeach4kmPRISMgridcell theresultingcalibrationfunctionfromthenearestASOSstationisthenappliedtoeachdaysmaximumandminimumtemperaturetopredicttheinterveninghourlyvalues

Usingtheinterpolatedhourlytemperatureestimateswecalcu‐latedthetotalannualhourswithinthezonesofheatstressorcoldstress for each year from 1981 to 2012 Annual valueswere cal‐culatedover theperiodJune1ndashMay31 to represent thebreedingcycleOncetheannualresidencetimeswereobtainedweapplieda15‐yearmovingaveragewindowtothedataresultingin16mov‐ingaverageperiodsstartingwiththe198182ndash199596periodandendingwiththe199697ndash201112periodApplyingamovingaver‐ageactsasahigh‐passfilterthatattenuatesinterannualvariabilitywhileamplifyinganylong‐termclimatechangesignalsOtherperiodlengths could have been chosen however we hypothesized that15years represented a reasonable tradeoff between (a) reducingthemovingaveragelengthsoasnottoexceedthelengthofthedailyPRISMdataserieswhichbeginsin1981(b)increasingthemovingaveragelengthsothatclimaticchanges(asopposedtobackgroundclimaticvariation)canbeidentifiedand(c)usingaperiodthatisbio‐logicallymeaningfulinthatwecanreasonablyexpectanylong‐termclimaticchangesinthemovingaverageswouldbeoutsidethehis‐torical rangeofvariabilityand thusshouldaffectcolonizationandextinctionratesofsensitivespecies

We obtained land cover data from the National Land CoverDatabase (NLCD) for 2001 (Homer et al 2007) 2006 (Fry et al2011)and2011(Homeretal2015)WereclassifiedthegivenlandcovertypesintohabitatornonhabitatforeachbirdspeciesbasedonoursecondhypothesisdescribedaboveWecalculatedtheper‐centof landareathatwasasuitablehabitatwithin400mofeachrouteandusedthisasacovariate inouranalysisWeused400mbecausethisishalfthedistancebetweenstopsonBBSroutesForyearswithoutlandcoverdataweusedthelandcovervaluesofthesubsequent NLCD survey Specifically we used 2001 values for1997ndash20012006valuesfor2002ndash2006and2011valuesfor2007ndash2012Weusedaz‐transformationtocenterandscaleallcovariatespriortoanalysis

23emsp|emspAnalysis

OurgoalwastoassesstherelativeimportanceofclimateandlandcovertochangesinthedistributionofRed‐eyedVireosandEasternWoodPeweesWhileourbirddataconsistedofdetectionnonde‐tection data from BBS surveys our parameters of interest weretheprobabilitiesoflocalcolonizationandlocalextinctionasthese

emspensp emsp | emsp1989CLEMENT ET aL

processes drive changes in distribution Our predictor variableswereourmeasuresof climate and land cover suitability for eachspeciesdescribedaboveWeuseddynamic correlated‐detectionoccupancymodels with detection heterogeneity to estimate therelationshipbetweenourpredictorsandbirddistributiondynam‐ics (Clement et al 2016HinesNichols amp Collazo 2014Hinesetal2010)Occupancymodelsareaclassofmodelsthatestimatetheprobabilityofpresencewhileaccountingforimperfectdetec‐tionofspeciesbyanalyzingreplicated(usuallytemporally)surveys(MacKenzieetal20022018)Dynamicoccupancymodelsmodelinterannual changes in occupancy by positing aMarkov processinwhich the probability of occurrence at time t is a function ofthe probability of occurrence at time tminus1 (MacKenzie NicholsHinesKnutsonampFranklin2003)IncontrasttostaticmodelsthisMarkovprocess recognizes that thedistributionof a species is afunctionofpreviousenvironmentalconditionsanddispersalcon‐straintsaswellascurrentenvironmentalconditions(Dormannetal2012KeacuteryGuillera‐ArroitaampLahoz‐Monfort2013)Dynamiccorrelated‐detectionoccupancymodels areamodelextension inwhichspatiallycorrelatedreplicatedsurveysareusedtoestimatedetectionprobabilities (Hinesetal20142010)Weselectedanoccupancymodelingapproachbecause failure toaccount for im‐perfect detection can bias estimates of colonization and extinc‐tionrates(Ruiz‐GutieacuterrezampZipkin2011)Weselectedadynamicapproachbecausedynamicmodelsoffermoreecological realismmoreaccurateprojectionsandgreatergeneralizabilitythanstaticmodels (Clementetal2016Yackulicetal2015)Weusedcor‐related‐detection models because the BBS generates spatiallyreplicatedsurveysandfailuretoaccountforspatialcorrelationofreplicatescanbiasoccupancyestimates(Hinesetal20142010)Finallywe used a finitemixturemodel to account for detectionheterogeneity because of the variation in habitat and focal spe‐ciesabundanceacrossthestudyarea (Clementetal2016)Afi‐nitemixturemodel approximates the heterogeneity of detectionprobabilitiesbypositingthatthepopulationconsistsofamixtureofrouteswhichhaveeitherarelativelyhighdetectionprobabilityorarelativelylowdetectionprobability

AsdetailedaboveBBSdata consistofnumerous routes eachcomposedofbirdobservationsat50 stopsA speciesmaybeab‐sentfromindividualstopswhenitispresentonaroutebutnotviceversaDetectionof a species is taken asproofof presence but anondetectionmayresultfromatrueabsenceorafalseabsenceForclarityweusethetermldquooccupiedrdquotoindicateaspeciesispresentonarouteandthetermldquoavailablerdquotoindicateaspeciesislocallypres‐entataspecificstopatthetimeofthesurvey(NicholsThomasampConn2009)Bythisterminologythedynamiccorrelated‐detectionoccupancymodelincludesthefollowingparameters

ψ=Pr(routeoccupiedduringthefirstseasonofsurveys)θ = Pr (species available at stop | route occupied and species unavailable

at previous stop)θprime= Pr (species available at stop | route occupied and species available

at previous stop)

p1 = Pr (detection at a stop for low‐detection routes | route occupied and species available at stop)

p2 = Pr (detection at a stop for high‐detection routes | route occupied and species available at stop)

π = Pr (probability that a route is a low‐detection route)εt = Pr (route is not occupied in season t + 1 | occupied in season t) andγt = Pr (route is occupied in season t + 1 | not occupied in season t)

Hinesetal(20142010)developedamodellikelihoodthatallowstheseparameterstobemodeledasfunctionsofroute‐andyear‐spe‐cificcovariateswhiledetectionparameterscanalsobeinfluencedbystop‐specificcovariatesParameterscanthenbeestimatedusingmax‐imum‐likelihoodestimationinprogramPRESENCE(Hines2006)

WedevelopedsixspecificmodelsofγandεforeachbirdspeciesWepartitionedspatialandtemporalvariationincovariatesbyparti‐tioningcovariatesintotheaveragevalueovertimeandtheannualdeviationfromthataverageInthiswaytheaveragedcovariatexiincludedspatialvariationonlywhilethedeviationcomponentΔxiyincludedtemporalvariationonlyUsingthesecovariatesourmodelsincluded(a)anullmodelinwhichγandεareconstantthroughtimeandspaceand(b)aglobal(mostgeneral)modelinwhichγandεvarythroughspaceaccordingtomeanclimateandlandcoversuitabilityandthroughtimeusingadummycovariate foreachyearWealsoconsideredintermediatemodelstoassesstheexplanatorypowerofourcovariates(c)γandεvaryspatiallywithmeanclimateandlandcover suitability but notwith time (d) γ and ε vary spatiallywithmeanclimateandlandcoversuitabilityandtemporallywithannualchangesinclimateonly(e)γandεvaryspatiallywithmeanclimateand land cover suitability and temporally with annual changes inlandcoveronlyand(f)γandεvaryspatiallywithmeanclimateandlandcoversuitabilityandtemporallywithannualchangesinclimateandlandcover

Prior to comparing these models we worked to achieve ade‐quatefitfortheothermodelparametersBecauseofthenumberofmodelparametersweconsideredthemsequentiallyInitiallywefittheglobalmodelforγandεandwemodeledθθprimeandψasfunctionsofclimateandlandcovercovariatesInthisinitialmodelweusedafinitemixturemodelonptoaccountforheterogeneitypresumablycausedbydifferencesamongroutesinhabitatobserversbirdabun‐danceandotherfeaturesthatwerenotexplicitlymodeled(Clementetal2016)Wealsomodeledpasa functionofclimateand landcover covariates and year Because bird activity often varieswithtimeofdaywealsomodeleddetectionasaquadratic functionofstopnumberwhichweconsidertobeaproxyfortimeofdayFromthis highly parameterizedmodelwe fit a reducedmodel by elim‐inating the covariatewith the smallest estimate‐to‐standard errorratioWecontinuedtoeliminatecovariatesinthiswayuntilAICin‐creasedandthenselectedthemodelwiththelowestAICWeusedthisparameterreductionprotocolwithψθθprimeandpinturnmain‐tainingthemodelstructureonγandεAfteridentifyingparsimoni‐ousmodelsforψθθprimeandpwefitthesetofsixmodelsforγandεdescribedaboveThesequentialapproachmaynotyieldidenticalresultsasasingleanalysisbutitisapragmaticapproachfordealing

1990emsp |emsp emspensp CLEMENT ET aL

with complexmodels (MacKenzie et al 2018) By beginningwithhighlyparameterizedmodelsandprogressingtowardreducedmod‐elswereducedtheriskofconfoundingtheoccupancyanddetectionprocesses (MacKenzieet al2018)Wealsochecked forpotentialmulti‐collinearityissuesbycalculatingPearsonscorrelationcoeffi‐cientforthedifferentcovariatesusedinthemodelsWetreatedanR2lt05asindicativeofalackofmulti‐collinearity

WealsoevaluatedthegoodnessoffitoftheglobalmodelforeachspeciesusingthenaiumlvecolonizationandextinctionratestocalculatetheteststatisticofaHosmerndashLemeshowtest(HosmerampLemeshow2000) Inthiscontext thenaiumlvecolonizationrate is thenumberofsitesthatchangedfromnondetectionsitesinyeart todetectionsitesinyear t+1while thenaiumlveextinction rate is thenumberof sitesthatchangedfromdetectionsitesinyeart tonondetectionsitesinyeart+1Wedividedtheroutesintodecilesbasedontheexpectedcolonizationandextinctionratesandcomparedthepredictedratesto theobservedrateswithchi‐squaretests (α=005) Ifnecessarywecombineddecilestoensurethatthepredictednumberofdetec‐tionsinadecilewasgt4AlthoughtheHosmerndashLemeshowteststatis‐ticisoftencalculatedfrompredictedpresencevitalratesseemedtobemoreappropriatemeasuresinthiscasebecausedynamicmodelsestimatechangesinpresenceratherthanpresence(Clementetal2016)Wenotethatthisuseofnaiumlveratesfocusesonproductsofmodelparameters(ieγεp ψ ϑ)andthereforewedonottestthefitofthefullyparameterizedmodelWefocusedonnaiumlveratesbe‐causethetruevital rates (ieγandε)cannotbedirectlyobservedwhendetectionisimperfectwhilethenaiumlveratescanbeobserved

We used an analysis of deviance approach (ANODEV egSkalski 1996) to evaluate the relative importance of climate andland cover to changes in bird distributions In this procedure wecompared thedeviance explainedbyour parameter of interest tothedevianceexplainedbythemostfullyparameterizedmodelinthemodelset

Dev(Mnull)isthedevianceofModel3whichaccountsforspatialbutnottemporalvariationincolonizationandextinctionprobabili‐tiesDev(Mfull)isthedevianceofModel2theglobalmodelwithbothspatialandtemporalvariationinγandεWethenevaluatedthedevi‐anceexplainedbyclimateandorlandcoverbyusingmodels45and6 forDev(Mcov)HighervaluesofR

2

Dev indicategreaterexplanatory

powerwith0indicatingnopowerand1indicatingpowerequaltothatofthefullyparameterizedmodelalthoughR2

Devcouldpotentially

exceed1becauseourcovariatemodelsarenotstrictlynestedwithinModel2ThereforewecalculatedandreportR2

Dev forbothclimate

and landcoverThis isnot theonlypossibleapproach toassessingrelativeimportanceofexplanatoryvariablesbutitissoundandhasbeenusedandrecommendedbyothers(Grosboisetal2008)

The general ANODEV approach to partitioning variation ledustofocusonhypothesistestingasameansofassessingtheneedforextramodelparameters (sourcesofvariation)Thesmallsetof

specificapriorihypothesesalsosets thisworkapart fromexplor‐atorystudiesinvestigatingtheadequacyofmanydifferentmodelsForthesereasonsweusedlikelihoodratiotestsasameansofcom‐paringdifferentmodelsalthoughwenotethatuseofmodelselec‐tioncriteria(egAICc)yieldedsimilarinferences

3emsp |emspRESULTS

Ourcovariatesindicatedasmalllossofhabitatandincreasedtem‐peraturesduringthestudyperioddespitetherelativelyshorttimescaleForEasternWoodPeweehabitatchangewasgt1on38ofrouteswiththemeanshareofsuitablehabitatnearroutesde‐cliningfrom416to409ForRed‐eyedVireohabitatchangewasgt1on23of routeswithmeansuitablehabitatdecliningfrom480to476Routetemperatureswererarelyabovetheheatstress thresholdbut theaveragetimeabovethis thresholdper route increased from 05hr per year from 1982 to 1997 to17hrperyearfrom1997to2012Wealsoobservedadeclineinperiodsofcoldstressfrom5450to5291hrperrouteperyearThePearsons correlation coefficient did not indicate anymulti‐collinearityproblemsamongourcovariates(R2lt02inallcases)

31emsp|emspEastern Wood Pewee

ForEasternWoodPeweethebest‐supportedmodelincludedbothclimateandhabitatmeasuresas covariatesofψθ andθprimeWeex‐cludedclimateandhabitatcovariatesfromthedetectionprobabilitymodelbecausesomeofthosemodelsdidnotconvergeThereforethedetectionmodelforeachspeciesincludedyearandstopnum‐ber as covariates As a result the global model included 85 pa‐rameters (Table1)TheHosmerndashLemeshowtest indicatedthattheglobalmodel fit thedataadequatelywithnoevidenceofdiscrep‐ancies between estimated and observed naiumlve colonization rates(χ2=441 df =8 p=082) and naiumlve extinction rates (χ2=1117df =8p=019)

EasternWoodPeweewerewidespreadinthestudyareawithanaverageprobabilityofoccupancyonBBSroutesin1997of090intheglobalmodel(Figure1)Occupancydeclinedslightlyto088by2012mirroringthedeclineincountsreportedbytheBBSInitialoccupancywaspositivelyrelatedtoavailablehabitatandhoursofcoldstress(Table1)Probabilityofdetectionatthefirstindividualstopduring1997waslowat013whileprobabilityofdetectionat the route level was much higher at 095 because birds weretypically available atmany stopswithina routeModel3whichincludedspatialvariationinclimateandhabitatinthecolonizationandextinctionmodelssignificantlyimprovedmodelfitrelativetoModel1(likelihoodratioχ2=4419df =6plt0001)Underthismodelestimatedcolonizationratesin1998rangedamongroutesfrom 008 to 028 (with amean of 018) while extinction ratesvariedfrom001to004(withameanof002Figure2)Model2differedfromModel3inthat itallowedcolonizationandextinc‐tionratestovaryeachyearModel2improvedmodelfitrelative

R2

Dev=Dev(Mnull)minusDev(Mcov)

Dev(Mnull)minusDev(Mfull)

emspensp emsp | emsp1991CLEMENT ET aL

TA B L E 1 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel2relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγεp1p2andπdefinedintext

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

Ψintercept 2367 0177 4136 0404

Ψhabitat 0556 0163 2315 0300

Ψheatstress minus0011 0198 minus0337 0281

Ψcoldstress 0598 0166 1020 0258

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0258 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0876 0019

γintercept minus0884 0353 0010 0424

γ 1999 minus0601 0585 minus0150 0532

γ 2000 minus1083 0633 0516 0464

γ 2001 minus0300 0484 0228 0483

γ 2002 minus1113 0577 0262 0511

γ 2003 minus1055 0564 minus0311 0551

γ2004 minus0656 0525 minus0179 0534

γ 2005 minus0677 0533 0468 0485

γ2006 minus1014 0620 minus0217 0579

γ2007 minus0162 0453 minus0093 0548

γ 2008 minus23545 70483373 minus0478 0607

γ 2009 minus0428 0455 minus0253 0566

γ 2010 minus0292 0478 minus0159 0517

γ 2011 minus1136 0607 0012 0490

γ 2012 minus0498 0474 0141 0497

γaveragehabitat 0095 0091 0801 0112

γaverageheatstress minus0024 0067 0005 0062

γaveragecoldstress 0227 0071 0358 0120

εintercept minus3957 0386 minus3767 0242

ε 1999 0365 0482 minus0189 0337

ε 2000 0049 0512 minus0921 0399

ε 2001 minus0266 0548 minus0532 0362

ε 2002 minus0576 0660 minus1156 0398

ε 2003 minus0298 0595 minus0692 0355

ε2004 0113 0509 minus0621 0343

ε 2005 0173 0482 minus1096 0429

ε2006 minus0145 0561 minus1173 0463

ε2007 0451 0458 minus0894 0366

ε 2008 0283 0480 minus0976 0424

ε 2009 0218 0496 minus0898 0381

(Continues)

1992emsp |emsp emspensp CLEMENT ET aL

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

ε 2010 minus0397 0605 minus0348 0304

ε 2011 0643 0455 minus0999 0406

ε 2012 0153 0644 minus0803 0410

εaveragehabitat 0238 0090 minus1670 0122

εaverageheatstress minus0071 0092 minus0075 0074

εaveragecoldstress minus0307 0141 minus0904 0104

p1intercept minus2172 0043 1352 0038

p1 1998 minus0118 0048 minus0127 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0016 0059

p1 2001 minus0156 0049 0031 0060

p1 2002 minus0179 0049 0076 0061

p1 2003 minus0216 0051 0161 0064

p12004 minus0242 0051 0066 0060

p1 2005 minus0124 0050 0149 0060

p12006 minus0182 0051 0124 0059

p12007 minus0149 0050 0184 0061

p1 2008 minus0202 0052 0192 0063

p1 2009 minus0221 0052 0135 0061

p1 2010 minus0195 0051 0062 0059

p1 2011 minus0122 0051 0203 0062

p1 2012 minus0227 0055 0194 0062

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0557 0036

p2 1998 minus0138 0033 minus0106 0050

p2 1999 minus0137 0032 0000 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0053 0053

p2 2002 minus0101 0032 minus0019 0052

p2 2003 minus0199 0033 0086 0053

p22004 minus0186 0032 0124 0057

p2 2005 minus0146 0032 0076 0051

p22006 minus0155 0032 0128 0054

p22007 minus0153 0032 0120 0054

p2 2008 minus0168 0032 0072 0056

p2 2009 minus0077 0033 0101 0055

p2 2010 minus0114 0033 0085 0060

p2 2011 minus0079 0032 0124 0054

p2 2012 minus0186 0033 0036 0061

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0183 0010

π 0533 0064 minus0505 0065

TA B L E 1 emsp (Continued)

emspensp emsp | emsp1993CLEMENT ET aL

toModel3althoughnotsignificantlyduetothenumberofaddi‐tional parameters (χ2=2679df =28p=053)UnderModel 2estimatedcolonizationratesvariedfrom000in2008to030in1998whileextinctionratesvariedfrom001 in2001ndash2003and2010to004in2011WeusedModels45and6toestimatethepercentof improvedmodel fit (measuredby thechange indevi‐ance)generatedbyModel2thatcouldbeaccountedforbytempo‐ralvariationinclimateandhabitatcovariates(Table2)Temporalvariationinhabitataccountedfor04ofthechangeindeviancewhile temporal variation in climate accounted for 196Model6whichaccountedfortemporalvariationinbothhabitatandcli‐mateindicatedthatin1998colonizationinthesouthwestportionofthestudyareawasabovethelocalaveragewhileextinctioninthesouthwestwasbelowthelocalaverageindicatingarelativelystrongoccupancy trend in thesouthwest in thatyear (Figure3)By2012thispatternhadreversedyieldingarelativelyweakoc‐cupancytrendinthesouthwest(Figure3)NoneofthesemodelsrepresentedasignificantimprovementoverModel3(pgt026)Insomecasesthesignoftheestimatedeffectoftemporalvariationinclimateandhabitatwasoppositeoftheexpectedeffectbutinnoinstancesweretheseestimatessignificant(Table3)

32emsp|emspRed‐eyed Vireo

The best‐supportedmodel for Red‐eyedVireo included the samecovariatesasforEasternWoodPeweeyieldingaglobalmodelwith85parameters (Table1)TheHosmerndashLemeshowtest indicatedfitwasadequate for theRed‐eyedVireoglobalmodel forbothnaiumlvecolonization rates (χ2=540df =4p=025) and naiumlve extinctionrates(χ2=100df =4p=091)

Red‐eyedVireowere similarlywidespread in the study areawith an average probability of occupancy of 091 in the global

model (Figure1)Occupancydecreasedslightlyto090by2012incontrasttothe increase incountsreportedbytheBBS Initialoccupancywas positively related to available habitat and hoursofcoldstress (Table1)Probabilityofdetectionat the first stopwasrelativelyhighat038whileprobabilityofdetectionattheroute levelnearlyperfectat098Model3which includedspa‐tialvariationinclimateandhabitattothecolonizationandextinc‐tionmodelsimprovedmodelfitdramatically(χ2=41884df =6plt0001)relativetothenullmodelInkeepingwiththegreaterchangeindevianceforRed‐eyedVireoModel3estimatedmorespatialvariationinvitalratescomparedtoEasternWoodPeweeIn 1998 estimated colonization rates varied across BBS routesfrom 010 to 088 (mean of 049)while extinction rates rangedfrom000to048(meanof004Figure4)Model2whichallowedcolonization and extinction rates to vary each year improvedmodelfitrelativetoModel3butnotsignificantlyduetothenum‐berofadditionalparameters(χ2=3427df =28p=019)UnderModel2estimatedcolonizationratesvariedfrom039in2008to060 in2000while estimatedextinction rates varied from003in2006to007in1998WeusedModels45and6toestimatethepercentofthedeviancereductioninModel2thatcouldbeac‐countedforbytemporalvariationinclimateandhabitatcovariates(Table2)Wefoundthattemporalvariation inhabitataccountedfor01of the change indeviancewhile climate accounted for56Model 6 which accounted for temporal variation in bothhabitatandclimateindicatedthatin1998bothcolonizationandextinctionwereaboveaverageindicatinghigherturnoverearlyinthestudyperiod(Figure5)By2012bothcolonizationandextinc‐tion rateshaddeclined indicatinggreater stability inoccupancy(Figure 5) None of these models represented a significant im‐provementoverModel3(pgt074)Someoftheestimatedeffects

F I G U R E 1 emspOccupancyprobabilityfor(a)EasternWoodPeweeand(b)Red‐eyedVireointheeasternUnitedStates1997underModel2Occupancyvariesbytheamountofsuitablehabitathoursofheatstressandhoursofcoldstressin1997

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp1989CLEMENT ET aL

processes drive changes in distribution Our predictor variableswereourmeasuresof climate and land cover suitability for eachspeciesdescribedaboveWeuseddynamic correlated‐detectionoccupancymodels with detection heterogeneity to estimate therelationshipbetweenourpredictorsandbirddistributiondynam‐ics (Clement et al 2016HinesNichols amp Collazo 2014Hinesetal2010)Occupancymodelsareaclassofmodelsthatestimatetheprobabilityofpresencewhileaccountingforimperfectdetec‐tionofspeciesbyanalyzingreplicated(usuallytemporally)surveys(MacKenzieetal20022018)Dynamicoccupancymodelsmodelinterannual changes in occupancy by positing aMarkov processinwhich the probability of occurrence at time t is a function ofthe probability of occurrence at time tminus1 (MacKenzie NicholsHinesKnutsonampFranklin2003)IncontrasttostaticmodelsthisMarkovprocess recognizes that thedistributionof a species is afunctionofpreviousenvironmentalconditionsanddispersalcon‐straintsaswellascurrentenvironmentalconditions(Dormannetal2012KeacuteryGuillera‐ArroitaampLahoz‐Monfort2013)Dynamiccorrelated‐detectionoccupancymodels areamodelextension inwhichspatiallycorrelatedreplicatedsurveysareusedtoestimatedetectionprobabilities (Hinesetal20142010)Weselectedanoccupancymodelingapproachbecause failure toaccount for im‐perfect detection can bias estimates of colonization and extinc‐tionrates(Ruiz‐GutieacuterrezampZipkin2011)Weselectedadynamicapproachbecausedynamicmodelsoffermoreecological realismmoreaccurateprojectionsandgreatergeneralizabilitythanstaticmodels (Clementetal2016Yackulicetal2015)Weusedcor‐related‐detection models because the BBS generates spatiallyreplicatedsurveysandfailuretoaccountforspatialcorrelationofreplicatescanbiasoccupancyestimates(Hinesetal20142010)Finallywe used a finitemixturemodel to account for detectionheterogeneity because of the variation in habitat and focal spe‐ciesabundanceacrossthestudyarea (Clementetal2016)Afi‐nitemixturemodel approximates the heterogeneity of detectionprobabilitiesbypositingthatthepopulationconsistsofamixtureofrouteswhichhaveeitherarelativelyhighdetectionprobabilityorarelativelylowdetectionprobability

AsdetailedaboveBBSdata consistofnumerous routes eachcomposedofbirdobservationsat50 stopsA speciesmaybeab‐sentfromindividualstopswhenitispresentonaroutebutnotviceversaDetectionof a species is taken asproofof presence but anondetectionmayresultfromatrueabsenceorafalseabsenceForclarityweusethetermldquooccupiedrdquotoindicateaspeciesispresentonarouteandthetermldquoavailablerdquotoindicateaspeciesislocallypres‐entataspecificstopatthetimeofthesurvey(NicholsThomasampConn2009)Bythisterminologythedynamiccorrelated‐detectionoccupancymodelincludesthefollowingparameters

ψ=Pr(routeoccupiedduringthefirstseasonofsurveys)θ = Pr (species available at stop | route occupied and species unavailable

at previous stop)θprime= Pr (species available at stop | route occupied and species available

at previous stop)

p1 = Pr (detection at a stop for low‐detection routes | route occupied and species available at stop)

p2 = Pr (detection at a stop for high‐detection routes | route occupied and species available at stop)

π = Pr (probability that a route is a low‐detection route)εt = Pr (route is not occupied in season t + 1 | occupied in season t) andγt = Pr (route is occupied in season t + 1 | not occupied in season t)

Hinesetal(20142010)developedamodellikelihoodthatallowstheseparameterstobemodeledasfunctionsofroute‐andyear‐spe‐cificcovariateswhiledetectionparameterscanalsobeinfluencedbystop‐specificcovariatesParameterscanthenbeestimatedusingmax‐imum‐likelihoodestimationinprogramPRESENCE(Hines2006)

WedevelopedsixspecificmodelsofγandεforeachbirdspeciesWepartitionedspatialandtemporalvariationincovariatesbyparti‐tioningcovariatesintotheaveragevalueovertimeandtheannualdeviationfromthataverageInthiswaytheaveragedcovariatexiincludedspatialvariationonlywhilethedeviationcomponentΔxiyincludedtemporalvariationonlyUsingthesecovariatesourmodelsincluded(a)anullmodelinwhichγandεareconstantthroughtimeandspaceand(b)aglobal(mostgeneral)modelinwhichγandεvarythroughspaceaccordingtomeanclimateandlandcoversuitabilityandthroughtimeusingadummycovariate foreachyearWealsoconsideredintermediatemodelstoassesstheexplanatorypowerofourcovariates(c)γandεvaryspatiallywithmeanclimateandlandcover suitability but notwith time (d) γ and ε vary spatiallywithmeanclimateandlandcoversuitabilityandtemporallywithannualchangesinclimateonly(e)γandεvaryspatiallywithmeanclimateand land cover suitability and temporally with annual changes inlandcoveronlyand(f)γandεvaryspatiallywithmeanclimateandlandcoversuitabilityandtemporallywithannualchangesinclimateandlandcover

Prior to comparing these models we worked to achieve ade‐quatefitfortheothermodelparametersBecauseofthenumberofmodelparametersweconsideredthemsequentiallyInitiallywefittheglobalmodelforγandεandwemodeledθθprimeandψasfunctionsofclimateandlandcovercovariatesInthisinitialmodelweusedafinitemixturemodelonptoaccountforheterogeneitypresumablycausedbydifferencesamongroutesinhabitatobserversbirdabun‐danceandotherfeaturesthatwerenotexplicitlymodeled(Clementetal2016)Wealsomodeledpasa functionofclimateand landcover covariates and year Because bird activity often varieswithtimeofdaywealsomodeleddetectionasaquadratic functionofstopnumberwhichweconsidertobeaproxyfortimeofdayFromthis highly parameterizedmodelwe fit a reducedmodel by elim‐inating the covariatewith the smallest estimate‐to‐standard errorratioWecontinuedtoeliminatecovariatesinthiswayuntilAICin‐creasedandthenselectedthemodelwiththelowestAICWeusedthisparameterreductionprotocolwithψθθprimeandpinturnmain‐tainingthemodelstructureonγandεAfteridentifyingparsimoni‐ousmodelsforψθθprimeandpwefitthesetofsixmodelsforγandεdescribedaboveThesequentialapproachmaynotyieldidenticalresultsasasingleanalysisbutitisapragmaticapproachfordealing

1990emsp |emsp emspensp CLEMENT ET aL

with complexmodels (MacKenzie et al 2018) By beginningwithhighlyparameterizedmodelsandprogressingtowardreducedmod‐elswereducedtheriskofconfoundingtheoccupancyanddetectionprocesses (MacKenzieet al2018)Wealsochecked forpotentialmulti‐collinearityissuesbycalculatingPearsonscorrelationcoeffi‐cientforthedifferentcovariatesusedinthemodelsWetreatedanR2lt05asindicativeofalackofmulti‐collinearity

WealsoevaluatedthegoodnessoffitoftheglobalmodelforeachspeciesusingthenaiumlvecolonizationandextinctionratestocalculatetheteststatisticofaHosmerndashLemeshowtest(HosmerampLemeshow2000) Inthiscontext thenaiumlvecolonizationrate is thenumberofsitesthatchangedfromnondetectionsitesinyeart todetectionsitesinyear t+1while thenaiumlveextinction rate is thenumberof sitesthatchangedfromdetectionsitesinyeart tonondetectionsitesinyeart+1Wedividedtheroutesintodecilesbasedontheexpectedcolonizationandextinctionratesandcomparedthepredictedratesto theobservedrateswithchi‐squaretests (α=005) Ifnecessarywecombineddecilestoensurethatthepredictednumberofdetec‐tionsinadecilewasgt4AlthoughtheHosmerndashLemeshowteststatis‐ticisoftencalculatedfrompredictedpresencevitalratesseemedtobemoreappropriatemeasuresinthiscasebecausedynamicmodelsestimatechangesinpresenceratherthanpresence(Clementetal2016)Wenotethatthisuseofnaiumlveratesfocusesonproductsofmodelparameters(ieγεp ψ ϑ)andthereforewedonottestthefitofthefullyparameterizedmodelWefocusedonnaiumlveratesbe‐causethetruevital rates (ieγandε)cannotbedirectlyobservedwhendetectionisimperfectwhilethenaiumlveratescanbeobserved

We used an analysis of deviance approach (ANODEV egSkalski 1996) to evaluate the relative importance of climate andland cover to changes in bird distributions In this procedure wecompared thedeviance explainedbyour parameter of interest tothedevianceexplainedbythemostfullyparameterizedmodelinthemodelset

Dev(Mnull)isthedevianceofModel3whichaccountsforspatialbutnottemporalvariationincolonizationandextinctionprobabili‐tiesDev(Mfull)isthedevianceofModel2theglobalmodelwithbothspatialandtemporalvariationinγandεWethenevaluatedthedevi‐anceexplainedbyclimateandorlandcoverbyusingmodels45and6 forDev(Mcov)HighervaluesofR

2

Dev indicategreaterexplanatory

powerwith0indicatingnopowerand1indicatingpowerequaltothatofthefullyparameterizedmodelalthoughR2

Devcouldpotentially

exceed1becauseourcovariatemodelsarenotstrictlynestedwithinModel2ThereforewecalculatedandreportR2

Dev forbothclimate

and landcoverThis isnot theonlypossibleapproach toassessingrelativeimportanceofexplanatoryvariablesbutitissoundandhasbeenusedandrecommendedbyothers(Grosboisetal2008)

The general ANODEV approach to partitioning variation ledustofocusonhypothesistestingasameansofassessingtheneedforextramodelparameters (sourcesofvariation)Thesmallsetof

specificapriorihypothesesalsosets thisworkapart fromexplor‐atorystudiesinvestigatingtheadequacyofmanydifferentmodelsForthesereasonsweusedlikelihoodratiotestsasameansofcom‐paringdifferentmodelsalthoughwenotethatuseofmodelselec‐tioncriteria(egAICc)yieldedsimilarinferences

3emsp |emspRESULTS

Ourcovariatesindicatedasmalllossofhabitatandincreasedtem‐peraturesduringthestudyperioddespitetherelativelyshorttimescaleForEasternWoodPeweehabitatchangewasgt1on38ofrouteswiththemeanshareofsuitablehabitatnearroutesde‐cliningfrom416to409ForRed‐eyedVireohabitatchangewasgt1on23of routeswithmeansuitablehabitatdecliningfrom480to476Routetemperatureswererarelyabovetheheatstress thresholdbut theaveragetimeabovethis thresholdper route increased from 05hr per year from 1982 to 1997 to17hrperyearfrom1997to2012Wealsoobservedadeclineinperiodsofcoldstressfrom5450to5291hrperrouteperyearThePearsons correlation coefficient did not indicate anymulti‐collinearityproblemsamongourcovariates(R2lt02inallcases)

31emsp|emspEastern Wood Pewee

ForEasternWoodPeweethebest‐supportedmodelincludedbothclimateandhabitatmeasuresas covariatesofψθ andθprimeWeex‐cludedclimateandhabitatcovariatesfromthedetectionprobabilitymodelbecausesomeofthosemodelsdidnotconvergeThereforethedetectionmodelforeachspeciesincludedyearandstopnum‐ber as covariates As a result the global model included 85 pa‐rameters (Table1)TheHosmerndashLemeshowtest indicatedthattheglobalmodel fit thedataadequatelywithnoevidenceofdiscrep‐ancies between estimated and observed naiumlve colonization rates(χ2=441 df =8 p=082) and naiumlve extinction rates (χ2=1117df =8p=019)

EasternWoodPeweewerewidespreadinthestudyareawithanaverageprobabilityofoccupancyonBBSroutesin1997of090intheglobalmodel(Figure1)Occupancydeclinedslightlyto088by2012mirroringthedeclineincountsreportedbytheBBSInitialoccupancywaspositivelyrelatedtoavailablehabitatandhoursofcoldstress(Table1)Probabilityofdetectionatthefirstindividualstopduring1997waslowat013whileprobabilityofdetectionat the route level was much higher at 095 because birds weretypically available atmany stopswithina routeModel3whichincludedspatialvariationinclimateandhabitatinthecolonizationandextinctionmodelssignificantlyimprovedmodelfitrelativetoModel1(likelihoodratioχ2=4419df =6plt0001)Underthismodelestimatedcolonizationratesin1998rangedamongroutesfrom 008 to 028 (with amean of 018) while extinction ratesvariedfrom001to004(withameanof002Figure2)Model2differedfromModel3inthat itallowedcolonizationandextinc‐tionratestovaryeachyearModel2improvedmodelfitrelative

R2

Dev=Dev(Mnull)minusDev(Mcov)

Dev(Mnull)minusDev(Mfull)

emspensp emsp | emsp1991CLEMENT ET aL

TA B L E 1 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel2relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγεp1p2andπdefinedintext

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

Ψintercept 2367 0177 4136 0404

Ψhabitat 0556 0163 2315 0300

Ψheatstress minus0011 0198 minus0337 0281

Ψcoldstress 0598 0166 1020 0258

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0258 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0876 0019

γintercept minus0884 0353 0010 0424

γ 1999 minus0601 0585 minus0150 0532

γ 2000 minus1083 0633 0516 0464

γ 2001 minus0300 0484 0228 0483

γ 2002 minus1113 0577 0262 0511

γ 2003 minus1055 0564 minus0311 0551

γ2004 minus0656 0525 minus0179 0534

γ 2005 minus0677 0533 0468 0485

γ2006 minus1014 0620 minus0217 0579

γ2007 minus0162 0453 minus0093 0548

γ 2008 minus23545 70483373 minus0478 0607

γ 2009 minus0428 0455 minus0253 0566

γ 2010 minus0292 0478 minus0159 0517

γ 2011 minus1136 0607 0012 0490

γ 2012 minus0498 0474 0141 0497

γaveragehabitat 0095 0091 0801 0112

γaverageheatstress minus0024 0067 0005 0062

γaveragecoldstress 0227 0071 0358 0120

εintercept minus3957 0386 minus3767 0242

ε 1999 0365 0482 minus0189 0337

ε 2000 0049 0512 minus0921 0399

ε 2001 minus0266 0548 minus0532 0362

ε 2002 minus0576 0660 minus1156 0398

ε 2003 minus0298 0595 minus0692 0355

ε2004 0113 0509 minus0621 0343

ε 2005 0173 0482 minus1096 0429

ε2006 minus0145 0561 minus1173 0463

ε2007 0451 0458 minus0894 0366

ε 2008 0283 0480 minus0976 0424

ε 2009 0218 0496 minus0898 0381

(Continues)

1992emsp |emsp emspensp CLEMENT ET aL

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

ε 2010 minus0397 0605 minus0348 0304

ε 2011 0643 0455 minus0999 0406

ε 2012 0153 0644 minus0803 0410

εaveragehabitat 0238 0090 minus1670 0122

εaverageheatstress minus0071 0092 minus0075 0074

εaveragecoldstress minus0307 0141 minus0904 0104

p1intercept minus2172 0043 1352 0038

p1 1998 minus0118 0048 minus0127 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0016 0059

p1 2001 minus0156 0049 0031 0060

p1 2002 minus0179 0049 0076 0061

p1 2003 minus0216 0051 0161 0064

p12004 minus0242 0051 0066 0060

p1 2005 minus0124 0050 0149 0060

p12006 minus0182 0051 0124 0059

p12007 minus0149 0050 0184 0061

p1 2008 minus0202 0052 0192 0063

p1 2009 minus0221 0052 0135 0061

p1 2010 minus0195 0051 0062 0059

p1 2011 minus0122 0051 0203 0062

p1 2012 minus0227 0055 0194 0062

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0557 0036

p2 1998 minus0138 0033 minus0106 0050

p2 1999 minus0137 0032 0000 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0053 0053

p2 2002 minus0101 0032 minus0019 0052

p2 2003 minus0199 0033 0086 0053

p22004 minus0186 0032 0124 0057

p2 2005 minus0146 0032 0076 0051

p22006 minus0155 0032 0128 0054

p22007 minus0153 0032 0120 0054

p2 2008 minus0168 0032 0072 0056

p2 2009 minus0077 0033 0101 0055

p2 2010 minus0114 0033 0085 0060

p2 2011 minus0079 0032 0124 0054

p2 2012 minus0186 0033 0036 0061

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0183 0010

π 0533 0064 minus0505 0065

TA B L E 1 emsp (Continued)

emspensp emsp | emsp1993CLEMENT ET aL

toModel3althoughnotsignificantlyduetothenumberofaddi‐tional parameters (χ2=2679df =28p=053)UnderModel 2estimatedcolonizationratesvariedfrom000in2008to030in1998whileextinctionratesvariedfrom001 in2001ndash2003and2010to004in2011WeusedModels45and6toestimatethepercentof improvedmodel fit (measuredby thechange indevi‐ance)generatedbyModel2thatcouldbeaccountedforbytempo‐ralvariationinclimateandhabitatcovariates(Table2)Temporalvariationinhabitataccountedfor04ofthechangeindeviancewhile temporal variation in climate accounted for 196Model6whichaccountedfortemporalvariationinbothhabitatandcli‐mateindicatedthatin1998colonizationinthesouthwestportionofthestudyareawasabovethelocalaveragewhileextinctioninthesouthwestwasbelowthelocalaverageindicatingarelativelystrongoccupancy trend in thesouthwest in thatyear (Figure3)By2012thispatternhadreversedyieldingarelativelyweakoc‐cupancytrendinthesouthwest(Figure3)NoneofthesemodelsrepresentedasignificantimprovementoverModel3(pgt026)Insomecasesthesignoftheestimatedeffectoftemporalvariationinclimateandhabitatwasoppositeoftheexpectedeffectbutinnoinstancesweretheseestimatessignificant(Table3)

32emsp|emspRed‐eyed Vireo

The best‐supportedmodel for Red‐eyedVireo included the samecovariatesasforEasternWoodPeweeyieldingaglobalmodelwith85parameters (Table1)TheHosmerndashLemeshowtest indicatedfitwasadequate for theRed‐eyedVireoglobalmodel forbothnaiumlvecolonization rates (χ2=540df =4p=025) and naiumlve extinctionrates(χ2=100df =4p=091)

Red‐eyedVireowere similarlywidespread in the study areawith an average probability of occupancy of 091 in the global

model (Figure1)Occupancydecreasedslightlyto090by2012incontrasttothe increase incountsreportedbytheBBS Initialoccupancywas positively related to available habitat and hoursofcoldstress (Table1)Probabilityofdetectionat the first stopwasrelativelyhighat038whileprobabilityofdetectionattheroute levelnearlyperfectat098Model3which includedspa‐tialvariationinclimateandhabitattothecolonizationandextinc‐tionmodelsimprovedmodelfitdramatically(χ2=41884df =6plt0001)relativetothenullmodelInkeepingwiththegreaterchangeindevianceforRed‐eyedVireoModel3estimatedmorespatialvariationinvitalratescomparedtoEasternWoodPeweeIn 1998 estimated colonization rates varied across BBS routesfrom 010 to 088 (mean of 049)while extinction rates rangedfrom000to048(meanof004Figure4)Model2whichallowedcolonization and extinction rates to vary each year improvedmodelfitrelativetoModel3butnotsignificantlyduetothenum‐berofadditionalparameters(χ2=3427df =28p=019)UnderModel2estimatedcolonizationratesvariedfrom039in2008to060 in2000while estimatedextinction rates varied from003in2006to007in1998WeusedModels45and6toestimatethepercentofthedeviancereductioninModel2thatcouldbeac‐countedforbytemporalvariationinclimateandhabitatcovariates(Table2)Wefoundthattemporalvariation inhabitataccountedfor01of the change indeviancewhile climate accounted for56Model 6 which accounted for temporal variation in bothhabitatandclimateindicatedthatin1998bothcolonizationandextinctionwereaboveaverageindicatinghigherturnoverearlyinthestudyperiod(Figure5)By2012bothcolonizationandextinc‐tion rateshaddeclined indicatinggreater stability inoccupancy(Figure 5) None of these models represented a significant im‐provementoverModel3(pgt074)Someoftheestimatedeffects

F I G U R E 1 emspOccupancyprobabilityfor(a)EasternWoodPeweeand(b)Red‐eyedVireointheeasternUnitedStates1997underModel2Occupancyvariesbytheamountofsuitablehabitathoursofheatstressandhoursofcoldstressin1997

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

1990emsp |emsp emspensp CLEMENT ET aL

with complexmodels (MacKenzie et al 2018) By beginningwithhighlyparameterizedmodelsandprogressingtowardreducedmod‐elswereducedtheriskofconfoundingtheoccupancyanddetectionprocesses (MacKenzieet al2018)Wealsochecked forpotentialmulti‐collinearityissuesbycalculatingPearsonscorrelationcoeffi‐cientforthedifferentcovariatesusedinthemodelsWetreatedanR2lt05asindicativeofalackofmulti‐collinearity

WealsoevaluatedthegoodnessoffitoftheglobalmodelforeachspeciesusingthenaiumlvecolonizationandextinctionratestocalculatetheteststatisticofaHosmerndashLemeshowtest(HosmerampLemeshow2000) Inthiscontext thenaiumlvecolonizationrate is thenumberofsitesthatchangedfromnondetectionsitesinyeart todetectionsitesinyear t+1while thenaiumlveextinction rate is thenumberof sitesthatchangedfromdetectionsitesinyeart tonondetectionsitesinyeart+1Wedividedtheroutesintodecilesbasedontheexpectedcolonizationandextinctionratesandcomparedthepredictedratesto theobservedrateswithchi‐squaretests (α=005) Ifnecessarywecombineddecilestoensurethatthepredictednumberofdetec‐tionsinadecilewasgt4AlthoughtheHosmerndashLemeshowteststatis‐ticisoftencalculatedfrompredictedpresencevitalratesseemedtobemoreappropriatemeasuresinthiscasebecausedynamicmodelsestimatechangesinpresenceratherthanpresence(Clementetal2016)Wenotethatthisuseofnaiumlveratesfocusesonproductsofmodelparameters(ieγεp ψ ϑ)andthereforewedonottestthefitofthefullyparameterizedmodelWefocusedonnaiumlveratesbe‐causethetruevital rates (ieγandε)cannotbedirectlyobservedwhendetectionisimperfectwhilethenaiumlveratescanbeobserved

We used an analysis of deviance approach (ANODEV egSkalski 1996) to evaluate the relative importance of climate andland cover to changes in bird distributions In this procedure wecompared thedeviance explainedbyour parameter of interest tothedevianceexplainedbythemostfullyparameterizedmodelinthemodelset

Dev(Mnull)isthedevianceofModel3whichaccountsforspatialbutnottemporalvariationincolonizationandextinctionprobabili‐tiesDev(Mfull)isthedevianceofModel2theglobalmodelwithbothspatialandtemporalvariationinγandεWethenevaluatedthedevi‐anceexplainedbyclimateandorlandcoverbyusingmodels45and6 forDev(Mcov)HighervaluesofR

2

Dev indicategreaterexplanatory

powerwith0indicatingnopowerand1indicatingpowerequaltothatofthefullyparameterizedmodelalthoughR2

Devcouldpotentially

exceed1becauseourcovariatemodelsarenotstrictlynestedwithinModel2ThereforewecalculatedandreportR2

Dev forbothclimate

and landcoverThis isnot theonlypossibleapproach toassessingrelativeimportanceofexplanatoryvariablesbutitissoundandhasbeenusedandrecommendedbyothers(Grosboisetal2008)

The general ANODEV approach to partitioning variation ledustofocusonhypothesistestingasameansofassessingtheneedforextramodelparameters (sourcesofvariation)Thesmallsetof

specificapriorihypothesesalsosets thisworkapart fromexplor‐atorystudiesinvestigatingtheadequacyofmanydifferentmodelsForthesereasonsweusedlikelihoodratiotestsasameansofcom‐paringdifferentmodelsalthoughwenotethatuseofmodelselec‐tioncriteria(egAICc)yieldedsimilarinferences

3emsp |emspRESULTS

Ourcovariatesindicatedasmalllossofhabitatandincreasedtem‐peraturesduringthestudyperioddespitetherelativelyshorttimescaleForEasternWoodPeweehabitatchangewasgt1on38ofrouteswiththemeanshareofsuitablehabitatnearroutesde‐cliningfrom416to409ForRed‐eyedVireohabitatchangewasgt1on23of routeswithmeansuitablehabitatdecliningfrom480to476Routetemperatureswererarelyabovetheheatstress thresholdbut theaveragetimeabovethis thresholdper route increased from 05hr per year from 1982 to 1997 to17hrperyearfrom1997to2012Wealsoobservedadeclineinperiodsofcoldstressfrom5450to5291hrperrouteperyearThePearsons correlation coefficient did not indicate anymulti‐collinearityproblemsamongourcovariates(R2lt02inallcases)

31emsp|emspEastern Wood Pewee

ForEasternWoodPeweethebest‐supportedmodelincludedbothclimateandhabitatmeasuresas covariatesofψθ andθprimeWeex‐cludedclimateandhabitatcovariatesfromthedetectionprobabilitymodelbecausesomeofthosemodelsdidnotconvergeThereforethedetectionmodelforeachspeciesincludedyearandstopnum‐ber as covariates As a result the global model included 85 pa‐rameters (Table1)TheHosmerndashLemeshowtest indicatedthattheglobalmodel fit thedataadequatelywithnoevidenceofdiscrep‐ancies between estimated and observed naiumlve colonization rates(χ2=441 df =8 p=082) and naiumlve extinction rates (χ2=1117df =8p=019)

EasternWoodPeweewerewidespreadinthestudyareawithanaverageprobabilityofoccupancyonBBSroutesin1997of090intheglobalmodel(Figure1)Occupancydeclinedslightlyto088by2012mirroringthedeclineincountsreportedbytheBBSInitialoccupancywaspositivelyrelatedtoavailablehabitatandhoursofcoldstress(Table1)Probabilityofdetectionatthefirstindividualstopduring1997waslowat013whileprobabilityofdetectionat the route level was much higher at 095 because birds weretypically available atmany stopswithina routeModel3whichincludedspatialvariationinclimateandhabitatinthecolonizationandextinctionmodelssignificantlyimprovedmodelfitrelativetoModel1(likelihoodratioχ2=4419df =6plt0001)Underthismodelestimatedcolonizationratesin1998rangedamongroutesfrom 008 to 028 (with amean of 018) while extinction ratesvariedfrom001to004(withameanof002Figure2)Model2differedfromModel3inthat itallowedcolonizationandextinc‐tionratestovaryeachyearModel2improvedmodelfitrelative

R2

Dev=Dev(Mnull)minusDev(Mcov)

Dev(Mnull)minusDev(Mfull)

emspensp emsp | emsp1991CLEMENT ET aL

TA B L E 1 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel2relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγεp1p2andπdefinedintext

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

Ψintercept 2367 0177 4136 0404

Ψhabitat 0556 0163 2315 0300

Ψheatstress minus0011 0198 minus0337 0281

Ψcoldstress 0598 0166 1020 0258

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0258 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0876 0019

γintercept minus0884 0353 0010 0424

γ 1999 minus0601 0585 minus0150 0532

γ 2000 minus1083 0633 0516 0464

γ 2001 minus0300 0484 0228 0483

γ 2002 minus1113 0577 0262 0511

γ 2003 minus1055 0564 minus0311 0551

γ2004 minus0656 0525 minus0179 0534

γ 2005 minus0677 0533 0468 0485

γ2006 minus1014 0620 minus0217 0579

γ2007 minus0162 0453 minus0093 0548

γ 2008 minus23545 70483373 minus0478 0607

γ 2009 minus0428 0455 minus0253 0566

γ 2010 minus0292 0478 minus0159 0517

γ 2011 minus1136 0607 0012 0490

γ 2012 minus0498 0474 0141 0497

γaveragehabitat 0095 0091 0801 0112

γaverageheatstress minus0024 0067 0005 0062

γaveragecoldstress 0227 0071 0358 0120

εintercept minus3957 0386 minus3767 0242

ε 1999 0365 0482 minus0189 0337

ε 2000 0049 0512 minus0921 0399

ε 2001 minus0266 0548 minus0532 0362

ε 2002 minus0576 0660 minus1156 0398

ε 2003 minus0298 0595 minus0692 0355

ε2004 0113 0509 minus0621 0343

ε 2005 0173 0482 minus1096 0429

ε2006 minus0145 0561 minus1173 0463

ε2007 0451 0458 minus0894 0366

ε 2008 0283 0480 minus0976 0424

ε 2009 0218 0496 minus0898 0381

(Continues)

1992emsp |emsp emspensp CLEMENT ET aL

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

ε 2010 minus0397 0605 minus0348 0304

ε 2011 0643 0455 minus0999 0406

ε 2012 0153 0644 minus0803 0410

εaveragehabitat 0238 0090 minus1670 0122

εaverageheatstress minus0071 0092 minus0075 0074

εaveragecoldstress minus0307 0141 minus0904 0104

p1intercept minus2172 0043 1352 0038

p1 1998 minus0118 0048 minus0127 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0016 0059

p1 2001 minus0156 0049 0031 0060

p1 2002 minus0179 0049 0076 0061

p1 2003 minus0216 0051 0161 0064

p12004 minus0242 0051 0066 0060

p1 2005 minus0124 0050 0149 0060

p12006 minus0182 0051 0124 0059

p12007 minus0149 0050 0184 0061

p1 2008 minus0202 0052 0192 0063

p1 2009 minus0221 0052 0135 0061

p1 2010 minus0195 0051 0062 0059

p1 2011 minus0122 0051 0203 0062

p1 2012 minus0227 0055 0194 0062

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0557 0036

p2 1998 minus0138 0033 minus0106 0050

p2 1999 minus0137 0032 0000 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0053 0053

p2 2002 minus0101 0032 minus0019 0052

p2 2003 minus0199 0033 0086 0053

p22004 minus0186 0032 0124 0057

p2 2005 minus0146 0032 0076 0051

p22006 minus0155 0032 0128 0054

p22007 minus0153 0032 0120 0054

p2 2008 minus0168 0032 0072 0056

p2 2009 minus0077 0033 0101 0055

p2 2010 minus0114 0033 0085 0060

p2 2011 minus0079 0032 0124 0054

p2 2012 minus0186 0033 0036 0061

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0183 0010

π 0533 0064 minus0505 0065

TA B L E 1 emsp (Continued)

emspensp emsp | emsp1993CLEMENT ET aL

toModel3althoughnotsignificantlyduetothenumberofaddi‐tional parameters (χ2=2679df =28p=053)UnderModel 2estimatedcolonizationratesvariedfrom000in2008to030in1998whileextinctionratesvariedfrom001 in2001ndash2003and2010to004in2011WeusedModels45and6toestimatethepercentof improvedmodel fit (measuredby thechange indevi‐ance)generatedbyModel2thatcouldbeaccountedforbytempo‐ralvariationinclimateandhabitatcovariates(Table2)Temporalvariationinhabitataccountedfor04ofthechangeindeviancewhile temporal variation in climate accounted for 196Model6whichaccountedfortemporalvariationinbothhabitatandcli‐mateindicatedthatin1998colonizationinthesouthwestportionofthestudyareawasabovethelocalaveragewhileextinctioninthesouthwestwasbelowthelocalaverageindicatingarelativelystrongoccupancy trend in thesouthwest in thatyear (Figure3)By2012thispatternhadreversedyieldingarelativelyweakoc‐cupancytrendinthesouthwest(Figure3)NoneofthesemodelsrepresentedasignificantimprovementoverModel3(pgt026)Insomecasesthesignoftheestimatedeffectoftemporalvariationinclimateandhabitatwasoppositeoftheexpectedeffectbutinnoinstancesweretheseestimatessignificant(Table3)

32emsp|emspRed‐eyed Vireo

The best‐supportedmodel for Red‐eyedVireo included the samecovariatesasforEasternWoodPeweeyieldingaglobalmodelwith85parameters (Table1)TheHosmerndashLemeshowtest indicatedfitwasadequate for theRed‐eyedVireoglobalmodel forbothnaiumlvecolonization rates (χ2=540df =4p=025) and naiumlve extinctionrates(χ2=100df =4p=091)

Red‐eyedVireowere similarlywidespread in the study areawith an average probability of occupancy of 091 in the global

model (Figure1)Occupancydecreasedslightlyto090by2012incontrasttothe increase incountsreportedbytheBBS Initialoccupancywas positively related to available habitat and hoursofcoldstress (Table1)Probabilityofdetectionat the first stopwasrelativelyhighat038whileprobabilityofdetectionattheroute levelnearlyperfectat098Model3which includedspa‐tialvariationinclimateandhabitattothecolonizationandextinc‐tionmodelsimprovedmodelfitdramatically(χ2=41884df =6plt0001)relativetothenullmodelInkeepingwiththegreaterchangeindevianceforRed‐eyedVireoModel3estimatedmorespatialvariationinvitalratescomparedtoEasternWoodPeweeIn 1998 estimated colonization rates varied across BBS routesfrom 010 to 088 (mean of 049)while extinction rates rangedfrom000to048(meanof004Figure4)Model2whichallowedcolonization and extinction rates to vary each year improvedmodelfitrelativetoModel3butnotsignificantlyduetothenum‐berofadditionalparameters(χ2=3427df =28p=019)UnderModel2estimatedcolonizationratesvariedfrom039in2008to060 in2000while estimatedextinction rates varied from003in2006to007in1998WeusedModels45and6toestimatethepercentofthedeviancereductioninModel2thatcouldbeac‐countedforbytemporalvariationinclimateandhabitatcovariates(Table2)Wefoundthattemporalvariation inhabitataccountedfor01of the change indeviancewhile climate accounted for56Model 6 which accounted for temporal variation in bothhabitatandclimateindicatedthatin1998bothcolonizationandextinctionwereaboveaverageindicatinghigherturnoverearlyinthestudyperiod(Figure5)By2012bothcolonizationandextinc‐tion rateshaddeclined indicatinggreater stability inoccupancy(Figure 5) None of these models represented a significant im‐provementoverModel3(pgt074)Someoftheestimatedeffects

F I G U R E 1 emspOccupancyprobabilityfor(a)EasternWoodPeweeand(b)Red‐eyedVireointheeasternUnitedStates1997underModel2Occupancyvariesbytheamountofsuitablehabitathoursofheatstressandhoursofcoldstressin1997

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp1991CLEMENT ET aL

TA B L E 1 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel2relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγεp1p2andπdefinedintext

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

Ψintercept 2367 0177 4136 0404

Ψhabitat 0556 0163 2315 0300

Ψheatstress minus0011 0198 minus0337 0281

Ψcoldstress 0598 0166 1020 0258

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0258 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0876 0019

γintercept minus0884 0353 0010 0424

γ 1999 minus0601 0585 minus0150 0532

γ 2000 minus1083 0633 0516 0464

γ 2001 minus0300 0484 0228 0483

γ 2002 minus1113 0577 0262 0511

γ 2003 minus1055 0564 minus0311 0551

γ2004 minus0656 0525 minus0179 0534

γ 2005 minus0677 0533 0468 0485

γ2006 minus1014 0620 minus0217 0579

γ2007 minus0162 0453 minus0093 0548

γ 2008 minus23545 70483373 minus0478 0607

γ 2009 minus0428 0455 minus0253 0566

γ 2010 minus0292 0478 minus0159 0517

γ 2011 minus1136 0607 0012 0490

γ 2012 minus0498 0474 0141 0497

γaveragehabitat 0095 0091 0801 0112

γaverageheatstress minus0024 0067 0005 0062

γaveragecoldstress 0227 0071 0358 0120

εintercept minus3957 0386 minus3767 0242

ε 1999 0365 0482 minus0189 0337

ε 2000 0049 0512 minus0921 0399

ε 2001 minus0266 0548 minus0532 0362

ε 2002 minus0576 0660 minus1156 0398

ε 2003 minus0298 0595 minus0692 0355

ε2004 0113 0509 minus0621 0343

ε 2005 0173 0482 minus1096 0429

ε2006 minus0145 0561 minus1173 0463

ε2007 0451 0458 minus0894 0366

ε 2008 0283 0480 minus0976 0424

ε 2009 0218 0496 minus0898 0381

(Continues)

1992emsp |emsp emspensp CLEMENT ET aL

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

ε 2010 minus0397 0605 minus0348 0304

ε 2011 0643 0455 minus0999 0406

ε 2012 0153 0644 minus0803 0410

εaveragehabitat 0238 0090 minus1670 0122

εaverageheatstress minus0071 0092 minus0075 0074

εaveragecoldstress minus0307 0141 minus0904 0104

p1intercept minus2172 0043 1352 0038

p1 1998 minus0118 0048 minus0127 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0016 0059

p1 2001 minus0156 0049 0031 0060

p1 2002 minus0179 0049 0076 0061

p1 2003 minus0216 0051 0161 0064

p12004 minus0242 0051 0066 0060

p1 2005 minus0124 0050 0149 0060

p12006 minus0182 0051 0124 0059

p12007 minus0149 0050 0184 0061

p1 2008 minus0202 0052 0192 0063

p1 2009 minus0221 0052 0135 0061

p1 2010 minus0195 0051 0062 0059

p1 2011 minus0122 0051 0203 0062

p1 2012 minus0227 0055 0194 0062

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0557 0036

p2 1998 minus0138 0033 minus0106 0050

p2 1999 minus0137 0032 0000 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0053 0053

p2 2002 minus0101 0032 minus0019 0052

p2 2003 minus0199 0033 0086 0053

p22004 minus0186 0032 0124 0057

p2 2005 minus0146 0032 0076 0051

p22006 minus0155 0032 0128 0054

p22007 minus0153 0032 0120 0054

p2 2008 minus0168 0032 0072 0056

p2 2009 minus0077 0033 0101 0055

p2 2010 minus0114 0033 0085 0060

p2 2011 minus0079 0032 0124 0054

p2 2012 minus0186 0033 0036 0061

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0183 0010

π 0533 0064 minus0505 0065

TA B L E 1 emsp (Continued)

emspensp emsp | emsp1993CLEMENT ET aL

toModel3althoughnotsignificantlyduetothenumberofaddi‐tional parameters (χ2=2679df =28p=053)UnderModel 2estimatedcolonizationratesvariedfrom000in2008to030in1998whileextinctionratesvariedfrom001 in2001ndash2003and2010to004in2011WeusedModels45and6toestimatethepercentof improvedmodel fit (measuredby thechange indevi‐ance)generatedbyModel2thatcouldbeaccountedforbytempo‐ralvariationinclimateandhabitatcovariates(Table2)Temporalvariationinhabitataccountedfor04ofthechangeindeviancewhile temporal variation in climate accounted for 196Model6whichaccountedfortemporalvariationinbothhabitatandcli‐mateindicatedthatin1998colonizationinthesouthwestportionofthestudyareawasabovethelocalaveragewhileextinctioninthesouthwestwasbelowthelocalaverageindicatingarelativelystrongoccupancy trend in thesouthwest in thatyear (Figure3)By2012thispatternhadreversedyieldingarelativelyweakoc‐cupancytrendinthesouthwest(Figure3)NoneofthesemodelsrepresentedasignificantimprovementoverModel3(pgt026)Insomecasesthesignoftheestimatedeffectoftemporalvariationinclimateandhabitatwasoppositeoftheexpectedeffectbutinnoinstancesweretheseestimatessignificant(Table3)

32emsp|emspRed‐eyed Vireo

The best‐supportedmodel for Red‐eyedVireo included the samecovariatesasforEasternWoodPeweeyieldingaglobalmodelwith85parameters (Table1)TheHosmerndashLemeshowtest indicatedfitwasadequate for theRed‐eyedVireoglobalmodel forbothnaiumlvecolonization rates (χ2=540df =4p=025) and naiumlve extinctionrates(χ2=100df =4p=091)

Red‐eyedVireowere similarlywidespread in the study areawith an average probability of occupancy of 091 in the global

model (Figure1)Occupancydecreasedslightlyto090by2012incontrasttothe increase incountsreportedbytheBBS Initialoccupancywas positively related to available habitat and hoursofcoldstress (Table1)Probabilityofdetectionat the first stopwasrelativelyhighat038whileprobabilityofdetectionattheroute levelnearlyperfectat098Model3which includedspa‐tialvariationinclimateandhabitattothecolonizationandextinc‐tionmodelsimprovedmodelfitdramatically(χ2=41884df =6plt0001)relativetothenullmodelInkeepingwiththegreaterchangeindevianceforRed‐eyedVireoModel3estimatedmorespatialvariationinvitalratescomparedtoEasternWoodPeweeIn 1998 estimated colonization rates varied across BBS routesfrom 010 to 088 (mean of 049)while extinction rates rangedfrom000to048(meanof004Figure4)Model2whichallowedcolonization and extinction rates to vary each year improvedmodelfitrelativetoModel3butnotsignificantlyduetothenum‐berofadditionalparameters(χ2=3427df =28p=019)UnderModel2estimatedcolonizationratesvariedfrom039in2008to060 in2000while estimatedextinction rates varied from003in2006to007in1998WeusedModels45and6toestimatethepercentofthedeviancereductioninModel2thatcouldbeac‐countedforbytemporalvariationinclimateandhabitatcovariates(Table2)Wefoundthattemporalvariation inhabitataccountedfor01of the change indeviancewhile climate accounted for56Model 6 which accounted for temporal variation in bothhabitatandclimateindicatedthatin1998bothcolonizationandextinctionwereaboveaverageindicatinghigherturnoverearlyinthestudyperiod(Figure5)By2012bothcolonizationandextinc‐tion rateshaddeclined indicatinggreater stability inoccupancy(Figure 5) None of these models represented a significant im‐provementoverModel3(pgt074)Someoftheestimatedeffects

F I G U R E 1 emspOccupancyprobabilityfor(a)EasternWoodPeweeand(b)Red‐eyedVireointheeasternUnitedStates1997underModel2Occupancyvariesbytheamountofsuitablehabitathoursofheatstressandhoursofcoldstressin1997

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

1992emsp |emsp emspensp CLEMENT ET aL

Parameters

Eastern Wood Pewee Red‐eyed Vireo

Estimate SE Estimate SE

ε 2010 minus0397 0605 minus0348 0304

ε 2011 0643 0455 minus0999 0406

ε 2012 0153 0644 minus0803 0410

εaveragehabitat 0238 0090 minus1670 0122

εaverageheatstress minus0071 0092 minus0075 0074

εaveragecoldstress minus0307 0141 minus0904 0104

p1intercept minus2172 0043 1352 0038

p1 1998 minus0118 0048 minus0127 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0016 0059

p1 2001 minus0156 0049 0031 0060

p1 2002 minus0179 0049 0076 0061

p1 2003 minus0216 0051 0161 0064

p12004 minus0242 0051 0066 0060

p1 2005 minus0124 0050 0149 0060

p12006 minus0182 0051 0124 0059

p12007 minus0149 0050 0184 0061

p1 2008 minus0202 0052 0192 0063

p1 2009 minus0221 0052 0135 0061

p1 2010 minus0195 0051 0062 0059

p1 2011 minus0122 0051 0203 0062

p1 2012 minus0227 0055 0194 0062

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0557 0036

p2 1998 minus0138 0033 minus0106 0050

p2 1999 minus0137 0032 0000 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0053 0053

p2 2002 minus0101 0032 minus0019 0052

p2 2003 minus0199 0033 0086 0053

p22004 minus0186 0032 0124 0057

p2 2005 minus0146 0032 0076 0051

p22006 minus0155 0032 0128 0054

p22007 minus0153 0032 0120 0054

p2 2008 minus0168 0032 0072 0056

p2 2009 minus0077 0033 0101 0055

p2 2010 minus0114 0033 0085 0060

p2 2011 minus0079 0032 0124 0054

p2 2012 minus0186 0033 0036 0061

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0183 0010

π 0533 0064 minus0505 0065

TA B L E 1 emsp (Continued)

emspensp emsp | emsp1993CLEMENT ET aL

toModel3althoughnotsignificantlyduetothenumberofaddi‐tional parameters (χ2=2679df =28p=053)UnderModel 2estimatedcolonizationratesvariedfrom000in2008to030in1998whileextinctionratesvariedfrom001 in2001ndash2003and2010to004in2011WeusedModels45and6toestimatethepercentof improvedmodel fit (measuredby thechange indevi‐ance)generatedbyModel2thatcouldbeaccountedforbytempo‐ralvariationinclimateandhabitatcovariates(Table2)Temporalvariationinhabitataccountedfor04ofthechangeindeviancewhile temporal variation in climate accounted for 196Model6whichaccountedfortemporalvariationinbothhabitatandcli‐mateindicatedthatin1998colonizationinthesouthwestportionofthestudyareawasabovethelocalaveragewhileextinctioninthesouthwestwasbelowthelocalaverageindicatingarelativelystrongoccupancy trend in thesouthwest in thatyear (Figure3)By2012thispatternhadreversedyieldingarelativelyweakoc‐cupancytrendinthesouthwest(Figure3)NoneofthesemodelsrepresentedasignificantimprovementoverModel3(pgt026)Insomecasesthesignoftheestimatedeffectoftemporalvariationinclimateandhabitatwasoppositeoftheexpectedeffectbutinnoinstancesweretheseestimatessignificant(Table3)

32emsp|emspRed‐eyed Vireo

The best‐supportedmodel for Red‐eyedVireo included the samecovariatesasforEasternWoodPeweeyieldingaglobalmodelwith85parameters (Table1)TheHosmerndashLemeshowtest indicatedfitwasadequate for theRed‐eyedVireoglobalmodel forbothnaiumlvecolonization rates (χ2=540df =4p=025) and naiumlve extinctionrates(χ2=100df =4p=091)

Red‐eyedVireowere similarlywidespread in the study areawith an average probability of occupancy of 091 in the global

model (Figure1)Occupancydecreasedslightlyto090by2012incontrasttothe increase incountsreportedbytheBBS Initialoccupancywas positively related to available habitat and hoursofcoldstress (Table1)Probabilityofdetectionat the first stopwasrelativelyhighat038whileprobabilityofdetectionattheroute levelnearlyperfectat098Model3which includedspa‐tialvariationinclimateandhabitattothecolonizationandextinc‐tionmodelsimprovedmodelfitdramatically(χ2=41884df =6plt0001)relativetothenullmodelInkeepingwiththegreaterchangeindevianceforRed‐eyedVireoModel3estimatedmorespatialvariationinvitalratescomparedtoEasternWoodPeweeIn 1998 estimated colonization rates varied across BBS routesfrom 010 to 088 (mean of 049)while extinction rates rangedfrom000to048(meanof004Figure4)Model2whichallowedcolonization and extinction rates to vary each year improvedmodelfitrelativetoModel3butnotsignificantlyduetothenum‐berofadditionalparameters(χ2=3427df =28p=019)UnderModel2estimatedcolonizationratesvariedfrom039in2008to060 in2000while estimatedextinction rates varied from003in2006to007in1998WeusedModels45and6toestimatethepercentofthedeviancereductioninModel2thatcouldbeac‐countedforbytemporalvariationinclimateandhabitatcovariates(Table2)Wefoundthattemporalvariation inhabitataccountedfor01of the change indeviancewhile climate accounted for56Model 6 which accounted for temporal variation in bothhabitatandclimateindicatedthatin1998bothcolonizationandextinctionwereaboveaverageindicatinghigherturnoverearlyinthestudyperiod(Figure5)By2012bothcolonizationandextinc‐tion rateshaddeclined indicatinggreater stability inoccupancy(Figure 5) None of these models represented a significant im‐provementoverModel3(pgt074)Someoftheestimatedeffects

F I G U R E 1 emspOccupancyprobabilityfor(a)EasternWoodPeweeand(b)Red‐eyedVireointheeasternUnitedStates1997underModel2Occupancyvariesbytheamountofsuitablehabitathoursofheatstressandhoursofcoldstressin1997

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp1993CLEMENT ET aL

toModel3althoughnotsignificantlyduetothenumberofaddi‐tional parameters (χ2=2679df =28p=053)UnderModel 2estimatedcolonizationratesvariedfrom000in2008to030in1998whileextinctionratesvariedfrom001 in2001ndash2003and2010to004in2011WeusedModels45and6toestimatethepercentof improvedmodel fit (measuredby thechange indevi‐ance)generatedbyModel2thatcouldbeaccountedforbytempo‐ralvariationinclimateandhabitatcovariates(Table2)Temporalvariationinhabitataccountedfor04ofthechangeindeviancewhile temporal variation in climate accounted for 196Model6whichaccountedfortemporalvariationinbothhabitatandcli‐mateindicatedthatin1998colonizationinthesouthwestportionofthestudyareawasabovethelocalaveragewhileextinctioninthesouthwestwasbelowthelocalaverageindicatingarelativelystrongoccupancy trend in thesouthwest in thatyear (Figure3)By2012thispatternhadreversedyieldingarelativelyweakoc‐cupancytrendinthesouthwest(Figure3)NoneofthesemodelsrepresentedasignificantimprovementoverModel3(pgt026)Insomecasesthesignoftheestimatedeffectoftemporalvariationinclimateandhabitatwasoppositeoftheexpectedeffectbutinnoinstancesweretheseestimatessignificant(Table3)

32emsp|emspRed‐eyed Vireo

The best‐supportedmodel for Red‐eyedVireo included the samecovariatesasforEasternWoodPeweeyieldingaglobalmodelwith85parameters (Table1)TheHosmerndashLemeshowtest indicatedfitwasadequate for theRed‐eyedVireoglobalmodel forbothnaiumlvecolonization rates (χ2=540df =4p=025) and naiumlve extinctionrates(χ2=100df =4p=091)

Red‐eyedVireowere similarlywidespread in the study areawith an average probability of occupancy of 091 in the global

model (Figure1)Occupancydecreasedslightlyto090by2012incontrasttothe increase incountsreportedbytheBBS Initialoccupancywas positively related to available habitat and hoursofcoldstress (Table1)Probabilityofdetectionat the first stopwasrelativelyhighat038whileprobabilityofdetectionattheroute levelnearlyperfectat098Model3which includedspa‐tialvariationinclimateandhabitattothecolonizationandextinc‐tionmodelsimprovedmodelfitdramatically(χ2=41884df =6plt0001)relativetothenullmodelInkeepingwiththegreaterchangeindevianceforRed‐eyedVireoModel3estimatedmorespatialvariationinvitalratescomparedtoEasternWoodPeweeIn 1998 estimated colonization rates varied across BBS routesfrom 010 to 088 (mean of 049)while extinction rates rangedfrom000to048(meanof004Figure4)Model2whichallowedcolonization and extinction rates to vary each year improvedmodelfitrelativetoModel3butnotsignificantlyduetothenum‐berofadditionalparameters(χ2=3427df =28p=019)UnderModel2estimatedcolonizationratesvariedfrom039in2008to060 in2000while estimatedextinction rates varied from003in2006to007in1998WeusedModels45and6toestimatethepercentofthedeviancereductioninModel2thatcouldbeac‐countedforbytemporalvariationinclimateandhabitatcovariates(Table2)Wefoundthattemporalvariation inhabitataccountedfor01of the change indeviancewhile climate accounted for56Model 6 which accounted for temporal variation in bothhabitatandclimateindicatedthatin1998bothcolonizationandextinctionwereaboveaverageindicatinghigherturnoverearlyinthestudyperiod(Figure5)By2012bothcolonizationandextinc‐tion rateshaddeclined indicatinggreater stability inoccupancy(Figure 5) None of these models represented a significant im‐provementoverModel3(pgt074)Someoftheestimatedeffects

F I G U R E 1 emspOccupancyprobabilityfor(a)EasternWoodPeweeand(b)Red‐eyedVireointheeasternUnitedStates1997underModel2Occupancyvariesbytheamountofsuitablehabitathoursofheatstressandhoursofcoldstressin1997

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

1994emsp |emsp emspensp CLEMENT ET aL

oftemporalvariationinclimateandhabitatdifferedfromexpecta‐tionsbutnotsignificantly(Table3)

4emsp |emspDISCUSSION

Wefound that forbothbirdspecies initialoccupancycoloniza‐tionratesandextinctionratescorrelatedwithspatialvariationinclimateandhabitatcovariatesHowevercolonizationandextinc‐tionrateswerenotcorrelatedwithtemporalvariation inclimateand habitat covariates Our results correlating initial occupancywith environmental covariates are broadly speaking consistentwithbiologicalexpectationandaraftofpreviousstudiesthathaveindicatedasignificantrelationshipbetweenspeciesdistributionsandhabitat(RobinsonWilsonampCrick2001ThogmartinSauerampKnutson 2007) climate (Barbet‐Massinamp Jetz 2014BellardBertelsmeier Leadley Thuiller amp Courchamp 2012) or both(Seoane Bustamante amp Diacuteaz‐Delgado 2004 Sohl 2014)Howeverwesuggestthatestimatedrelationshipsbetweenspatialvariationinenvironmentalcovariatesandcolonizationandextinc‐tion rates are of greater ecological interest than relatively phe‐nomenological species distribution models or climate envelopemodels(Clementetal2016)Theestimatedvitalratesofcoloni‐zationandextinctionbringusclosertounderstandingthedynamicprocessunderlying thedistributionof these speciesGivencon‐stantvital rateswecanalsoproject theequilibrium levelofoc‐cupancyatsites ( i

i+i

atsite i)Wecancomparetheseprojected

equilibria tocurrentoccupancytoproject trends for thespeciesunder current environmental conditions We can also use

projectedchangesinkeyenvironmentalvariablestodeveloppre‐dicted rangechanges in the futureWeargue that theseprojec‐tions are more robust than those from static climate envelopemodels which assume that species are currently in equilibrium(Yackulicetal2015)Incontraststaticmodelscanoverestimaterange changes when the equilibrium does not hold (Morin ampThuiller2009)

Given the importanceofglobal changeweweremotivated toinvestigate factors affecting temporal aswell as spatial variationin vital rates In this case spatial variation must be incorporatedintosuchanalysesinordertoaccommodatethedifferentdynamicsexpectedindifferentportionsofaspeciesrangeWeviewthere‐lationshipbetweentemporalvariationincovariatevaluesandvitalrates as the most relevant to understanding the effect of globalchangeontherecentandfuturedistributionsofourstudyspeciesFurthermore a correlation between vital rates and spatially vary‐ingcovariatesmaynot indicateasimilarcorrelationwithtemporalchangesinthosesamecovariatesForexampleaspecieswithstrongdispersal constraintsmight have a strong spatial relationshipwithacovariatebutaweaktemporalrelationshipduetoits immobility(Devictor et al 2008) Similarly an estimated spatial relationshipmaybepurelyphenomenologicalduetothenatureofspatialvari‐ablesWhen spatial variables followgradients it is relatively easytoobtainstatisticallysignificantcorrelationsabsentanycausalre‐lationship(Grosboisetal2008)ForexampleifcolonizationratesandtemperaturesbothfollownorthndashsouthgradientstheymaybespatiallycorrelatedevenifcolonizationisgovernedbysomeotherunidentifiedfactorInthiscasewewouldexpecttheweakcorrela‐tion between temporal changes in temperature and colonization

F I G U R E 2 emspProbabilityof(a)colonizationand(b)extinctionforEasternWoodPeweeintheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp1995CLEMENT ET aL

ratestobemoreindicativeofthetruerelationshipthanthestrongcorrelationwithspatialvariationintemperature

BBS surveys indicate different abundance trajectories for ourstudyspecieswithcounts increasingforRed‐eyedVireosandde‐creasing for EasternWood Pewees (Sauer et al 2015) Becauseabundanceandoccupancytendtobelinked(Gastonetal2000)weexpectedsomedivergenceinoccupancydynamicsWefoundthatestimatedoccupancywashighandstableforbothspeciesalthoughtherewasgreaterturnoverforRed‐eyedVireosForexampleunderModel3 (spatialvariationonly incovariates)bothaveragecoloni‐zationrates(049)andaverageextinctionrates(004)wereatleasttwiceashighforRed‐eyedVireoscomparedtoEasternWoodPewee(018and002respectively)Red‐eyedVireosexhibitedgreaterspa‐tialvariationincolonizationandextinctionrates(Figure4)althoughour selected covariates explained little temporal variation for thisspeciesEasternWoodPeweecolonizationandextinctionratesex‐hibitedmoreofalatitudinalgradientreflectingaspatialcorrelationwith extreme temperature (Figure 2)Our results also hinted at agreatercorrelationbetweentemporalchanges in temperatureandoccupancyvitalratesforEasternWoodPeweewhichcouldplayaroleinthedeclineofthisspeciesalthoughtheresultswerenotsig‐nificant(Table3)Alternatively ithasbeensuggestedthatEasternWoodPeweemaybedecliningduetoa lossofforestedwinteringhabitat(RobbinsSauerGreenbergampDroege1989)orduetodeer‐mediatedchanges in foreststructureonbreedinggrounds (deCal‐esta 1994) illustrating the complexity of ascertaining cause andeffectusingobservationalstudiesinthesenaturalsystems

Althoughrelativelyrare(Siramietal2017)otherstudieshaveinvestigated both climate and land cover effects on bird distribu‐tions Inagreementwithour finding that temporalvariation incli‐mate explainedmuchmore of themodel deviance than temporalvariation in habitat climate‐only species distribution models hadbetter cross‐validation support than habitat‐only models for 409Europeanbirdspecies(Barbet‐Massinetal2012)SimilarlymodelsestimatingthenumberofnewlyoccupiedareasbyHoodedWarblers(Wilsonia citrina) inOntariousingclimatedatahadbetter informa‐tioncriterionsupportthanmodelsusingforestdata(MellesFortinLindsay amp Badzinski 2011) In contrast land‐use models outper‐formedclimatemodels for18 farmlandbird species in theUnitedKingdom(EglingtonampPearce‐Higgins2012)Similarlyvegetation‐based species distributionmodels had superior performance thanclimate‐based models for 79 Spanish bird species (Seoane et al2004)andforthreebirdspeciesinNewMexico(FriggensampFinch2015) Two studies focused on colonization and extinction ratesfoundmixed results ForGardenWarblers (Sylvia borin) inBritainvegetationexplainedmorevariationincolonizationwhiletempera‐turesexplainedmorevariationinlocalextinction(MustinAmarampRedpath2014)For122birdspeciesinOntariothebest‐supportedcovariates (land cover or climate change) varied among species(YalcinampLeroux2018)

Thedivergentresultsamongstudiesregardingtheimportanceofclimateandlandcovermaypartlybeduetothebiologyofindividualspecies (Yalcinamp Leroux 2018) For example farmland birdsmayTA

BLE

2emspR2 DevfordifferentmodelsofcolonizationandextinctionprobabilitiesforbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012

Mod

el

Cova

riate

s on

colo

niza

tion

and

extin

ctio

nEa

ster

n W

ood

Pew

eeRe

d‐ey

ed V

ireo

Inte

rcep

tAv

erag

e H

abita

tAv

erag

e Cl

imat

eH

abita

t Dev

iatio

nCl

imat

e D

evia

tion

Year

Eff

ect

Δ minus

2LL

R2 Dev

Δ minus

2LL

R2 Dev

2X

XX

X0

0010

00

0010

0

6X

XX

XX

minus2138

202

minus3230

57

4X

XX

Xminus2154

196

minus3234

56

5X

XX

Xminus2667

04

minus3422

01

3X

XX

minus2679

0minus3427

0

1X

minus7098

NA

minus45311

NA

Not

eNAnotapplicable

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

1996emsp |emsp emspensp CLEMENT ET aL

beparticularlyaffectedbychangesinagriculturalintensitybecauseoftheirextensiveuseoffarms(EglingtonampPearce‐Higgins2012)Alternativelyithasbeenarguedthatspatialscalemayplayaroleintherelativeimportanceofglobalchangefactorswithclimatemoreimportantatlargescales(PearsonampDawson2003)Wenotethattwostudiesthatfoundalargerroleforclimatebothreliedonlower‐resolution bird surveys (05deg atlas in Barbet‐Massin et al 2012

10km2atlasinMellesetal2011)IntheBBSdatathatweanalyzedsurveyswereconductedevery800mbutwecalculatedoccupancyacross each 394km transect In addition the cited studies useddifferentdata sources anddiverse analysismethods complicatinginterpretationofthedifferingresults

While we estimated that climate explained more model de‐viance than habitat these estimated relationships were not

F I G U R E 3 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionrates((t)minus (t)minus )forEasternWoodPeweeintheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp1997CLEMENT ET aL

TA B L E 3 emspEstimatedparametersforcorrelated‐detectiondynamicoccupancyModel6relatinghabitatandclimatecovariatestooccupancydynamicsofbreedingEasternWoodPeweeandRed‐eyedVireo1997ndash2012Parameterestimatesindicatechangeinlogoddsofparametersinresponsetoz‐transformedcovariatesParametersψϑϑprimeγε p1p2andπdefinedintextForkeyparameters(boldtext)expectedsignsincluded

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

Ψintercept 2367 0177 4087 0381

Ψhabitat 0556 0163 2323 0288

Ψheatstress minus0011 0198 minus0322 0275

Ψcoldstress 0598 0166 1054 0251

ϑintercept minus1821 0026 minus1385 0010

ϑhabitat minus0055 0026 0516 0013

ϑheatstress minus0094 0030 0017 0008

ϑcoldstress 0172 0040 0256 0013

ϑprimeintercept 2292 0045 0426 0020

ϑprimehabitat 0316 0036 1210 0018

ϑprimeheatstress minus0006 0038 0054 0013

ϑprimecoldstress minus0622 0064 0875 0019

γintercept minus0884 0353 0023 0162

γhabitatdeviation + minus0292 0478 minus0240 1768

γheatstressdeviation minus minus1136 0607 0064 0194

γcoldstressdeviation minus minus0498 0474 2398 2253

γaveragehabitat 0095 0091 0788 0111

γaverageheatstress minus0024 0067 0004 0062

γaveragecoldstress 0227 0071 0333 0119

εintercept minus3957 0386 minus4471 0141

εhabitatdeviation minus minus0397 0605 0121 1492

εheatstressdeviation + 0643 0455 minus0034 0182

εcoldstressdeviation + 0153 0644 2042 2072

εaveragehabitat 0238 0090 minus1664 0123

εaverageheatstress minus0071 0092 minus0080 0074

εaveragecoldstress minus0307 0141 minus0939 0104

p1intercept minus2172 0043 1351 0039

p1 1998 minus0118 0048 minus0128 0058

p1 1999 minus0112 0049 minus0043 0058

p1 2000 minus0166 0050 minus0017 0059

p1 2001 minus0156 0049 0031 0061

p1 2002 minus0179 0049 0076 0062

p1 2003 minus0216 0051 0160 0064

p12004 minus0242 0051 0066 0061

p1 2005 minus0124 0050 0149 0061

p12006 minus0182 0051 0124 0060

p12007 minus0149 0050 0183 0062

p1 2008 minus0202 0052 0191 0064

p1 2009 minus0221 0052 0134 0062

p1 2010 minus0195 0051 0062 0060

p1 2011 minus0122 0051 0202 0063

(Continues)

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

1998emsp |emsp emspensp CLEMENT ET aL

Parameters

Expected Eastern Wood Pewee Red‐eyed Vireo

Effect Estimate SE Estimate SE

p1 2012 minus0227 0055 0193 0063

p1stopnumber 0033 0009 minus0055 0011

p1stopnumbersquared minus0069 0010 minus0247 0013

p2intercept minus0921 0018 minus0555 0037

p2 1998 minus0138 0033 minus0121 0050

p2 1999 minus0137 0032 minus0025 0052

p2 2000 minus0164 0032 minus0027 0051

p2 2001 minus0077 0032 minus0059 0053

p2 2002 minus0101 0032 minus0012 0052

p2 2003 minus0199 0033 0085 0053

p22004 minus0186 0032 0113 0059

p2 2005 minus0146 0032 0082 0052

p22006 minus0155 0032 0135 0054

p22007 minus0153 0032 0124 0055

p2 2008 minus0168 0032 0073 0057

p2 2009 minus0077 0033 0100 0056

p2 2010 minus0114 0033 0071 0062

p2 2011 minus0079 0032 0120 0055

p2 2012 minus0186 0033 0030 0063

p2stopnumber 0045 0007 0165 0010

p2stopnumbersquared minus0105 0008 minus0182 0011

π 0533 0064 minus0505 0065

TA B L E 3 emsp (Continued)

F I G U R E 4 emspProbabilityof(a)colonizationand(b)extinctionforRed‐eyedVireointheeasternUnitedStatesfollowing1997underModel3Colonizationandextinctionvarybytheamountofsuitablehabitathoursofheatstresshoursofcoldstressallaveragedoverthe1997ndash2012studyperiod

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp1999CLEMENT ET aL

significantanddidnotexplainalargefractionofvariationThereareseveralpotentialexplanationsforwhywedidnotfindasub‐stantialresponsetochangesinhabitatandclimateFirstourdatawerelackingindynamiceventstoinformourmodelsThecovari‐ateschangedrelativelylittleoverthetimeperiodmakingitdiffi‐culttodetectaresponseThelackofchangewaspartlybecauseoftherelativelyshort(16‐year)timeperiodstudiedAlsoweusedamovingaverageforclimatedatawhichtendstoreducedifferences

overshorttimeperiods Inadditionthestudyregionhasexperi‐encedlesswarmingoverthepastcenturythanotherpartsoftheplanet (Walshetal2014)Thebirddistributionsalso lackeddy‐namismBecauseextinctionrateswerelowtherewerefewlocalextinctioneventstoinformthemodelAlthoughcolonizationrateswerehighthefactthatinitialoccupancywashighandextinctionrateswerelowmeanttherewerefewopportunitiesforcoloniza‐tionevents

F I G U R E 5 emspAnnualdeviationofcolonizationandextinctionratesfromaveragecolonizationandextinctionratesforRed‐eyedVireointheeasternUnitedStatesfollowing1997and2011underModel6Deviationsincolonizationandextinctionratesvarywithdeviationsintheamountofsuitablehabitathoursofheatstresshoursofcoldstressrelativetoaveragelevelsoverthe1997ndash2012studyperiod(a)Colonizationdeviationfollowing1997(b)extinctiondeviationfollowing1997(c)colonizationdeviationfollowing2011and(d)extinctiondeviationfollowing2011

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

2000emsp |emsp emspensp CLEMENT ET aL

Second measurement error may have obscured responses toglobalchangeThePRISMclimatedataareestimatedbyinterpola‐tionratherthandirectmeasurementacrossthestudyarea(Dalyetal2008)AssuchtheremaybeartifactsoranomaliesthatimpededourestimationandincreaseduncertaintySimilarlyNLCDdataareremotelysensedandmay includesomemeasurementerrorwhichcan increase uncertainty in our estimates (Homer et al 2015)Additionallylandcoverwasonlymeasuredinthreeyearsrequiringus to imputevalues in13yearsAsa result all landcover changethatoccurredbetween2002and2006wasrecordedin2002whilechangesbetween2007and2011wererecorded in2007Thisap‐proximation isnot idealbutwasnecessarygiventhe5‐yearreso‐lutionofthedataThislowerresolutionofthelandcoverdatamayhavereducedthesensitivityofthemodel

Third wemay havemisspecified ourmodelWe tried to de‐velopbiologicallymeaningfulcovariatesbutwemightnotunder‐standthesystemaswellaswehopedItispossiblethatbirdvitalrates respond to climateon adifferent time scale (ie not a15‐yearmoving average) or to climateduringdifferent timesof theyearortodifferenttemperaturethresholdsortodifferentclimatecomponents (egprecipitation Illaacutenetal2014) It isalsopossi‐ble that thebirds respond to finerhabitat features than the landcovertypesweusedoratadifferentspatialscale(ienot400mPearsonampDawson2003)Alternatively theremaybe importantregionaldifferencesor interactionsamongcovariatesthatwedidnotcaptureOritispossiblethatchangesinclimateandhabitatintheirbreedingareassimplyhavelittleeffectonvitalratesforthesespecies relative to other factors such as competitionwith otherbirdspeciesorglobalchangeoccurringontheirwinteringgroundsinSouthAmerica(Dormann2007)Ofcoursethesemisspecifica‐tionissuesareinherenttoallobservationalstudiesandnotuniquetothisstudy

Itwould be possible for us to explore some of themisspec‐ification issues just mentioned For example we could rerunthe samemodelsusingnewcovariates representing a varietyoftemperature thresholds and habitat buffer sizes to search for astrongercorrelationExperiencesuggeststhatpersistencewouldeventuallyberewardedwithap‐valuelt005ora lowmodelse‐lection valueHowever one reason to avoid such exploration isthat it tends to lead toType Ierrorsoverfitmodels andbiasedparameterestimates(FiebergampJohnson2015)Furthermorewefind thecontrastbetweensignificant correlations for spatial co‐variatesandnonsignificantcorrelationsfortemporalcovariatesil‐luminatingIntypicalclimateenvelopestudieswewouldestimatethe relationshipbetweencovariatesand thecurrentdistributionofaspeciesmuchlikeourestimateofinitialoccupancy(Dormann2007)BecauseweobtainedsignificantparameterestimateswemightconcludethatwehaveidentifiedcovariatesthatdeterminetherangeofthisspeciesThenwemightusetheseresultstoproj‐ectthefuturerangeofthespeciesundervariousclimatescenariosbased on the (questionable) assumption that the covariatendashspe‐cies relationship will not change as climate and habitat change(Cuddington et al 2013 Elith et al 2010Gustafson 2013) In

this case the insignificant correlationwith the temporal covari‐atessuggeststhatouroccupancycovariatesmerelydescribetherangeofthesebirdswithoutdeterminingtherangeFurthermoreour results suggest that despite significant results in the staticportionof themodelwehaveyet to identify themost relevantcomponents of climate change shaping thedistributionof thesebirds although itmay be difficult to identify these componentsgiven the lackofobservedchangeduringa relatively short timeperiodFinallyourresultssuggestthatprojectionsbasedonthestaticportionofourmodelareunlikelytobereliabledespitetheirstatisticalsignificance

Ifourinterpretationiscorrectitsuggeststhatsomeprojectionsof futurerangeshiftsbasedonstaticmodelsrelyonunsupportedassumptionsandarenot reliableWehavedemonstratedadiffer‐entapproachthatdoesnotrequirethestrongassumptionsofstaticmodelingapproachesOurapproachwouldbemoreapttoidentifyfactorsthatguidespeciesrsquoresponsetoglobalchangeifboththespe‐ciesandthecovariatesexhibitedmoredynamismandifweanalyzedalongertimeperiodthatencompassedgreaterchange

ACKNOWLEDG MENTS

WethankKeithPardieckDaveZiolkowskiJrandthethousandsofhighlyskilledvolunteerswhohavecoordinatedandperformedtheNorthAmericanBreedingBirdSurvey for half a centuryWealsothanktheClimateResearchUnitattheUniversityofEastAngliaformakingvaluableclimatedataeasilyaccessibleMJCwasfinanciallysupported by the BBS and the USGS Northeast Climate ScienceCenterThecontentsofthisdocumentaretheresponsibilityoftheauthorsanddonotnecessarilyrepresenttheviewsoftheUSGSAnyuseoftradeproductorfirmnamesisfordescriptivepurposesonlyanddoesnotimplyendorsementbytheUSGovernment

DEDIC ATION

We dedicate this paper to the late Chandler S Robbins ldquoFatherrdquooftheNorthAmericanBreedingBirdSurvey (BBS)DatafromtheBBSformthebasisforthisspecificpaperandforhundredsofothersfocusedon topics ranging from local trends tocontinentalmacro‐ecologyChanwasauniquecombinationofbird‐loverandscientistwhosevisionandinquisitiveenthusiasmwereaninspirationtoallofusfortunateenoughtohaveknownhim

CONFLIC T OF INTERE S T

Nonedeclared

AUTHORSrsquo CONTRIBUTION

JACandJDNconceivedthestudyAJTandSWanalyzedhabitatandclimatedataMJCJEHandJDNperformedtheoccupancyanaly‐sisMJCledthewritingofthemanuscriptAllauthorsgavefinalap‐provalforpublication

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

emspensp emsp | emsp2001CLEMENT ET aL

DATA ACCE SSIBILIT Y

Raw data are available from the following sources Breeding BirdSurvey httpswwwpwrcusgsgovbbsrawdata PRISM climatedata httpwwwprismoregonstateedu NLCD land cover datahttpswwwmrlcgov Processed data will be archived with theDryadDigitalRepository

ORCID

Matthew J Clement httpsorcidorg0000‐0003‐4231‐7949

James D Nichols httpsorcidorg0000‐0002‐7631‐2890

Adam J Terando httpsorcidorg0000‐0002‐9280‐043X

James E Hines httpsorcidorg0000‐0001‐5478‐7230

Steven G Williams httpsorcidorg0000‐0003‐3760‐6818

R E FE R E N C E S

Anderson D R Burnham K P Gould W R amp Cherry S (2001)Concerns about finding effects that are actually spuriousWildlife Society Bulletin29311ndash316

Arauacutejo M B Cabeza M Thuiller W Hannah L amp WilliamsP H (2004) Would climate change drive species out of re‐serves An assessment of existing reserve‐selection meth‐ods Global Change Biology 10 1618ndash1626 httpsdoiorg101111j1365‐2486200400828x

Barbet‐MassinMampJetzW(2014)A40‐yearcontinent‐widemulti‐speciesassessmentofrelevantclimatepredictorsforspeciesdistri‐butionmodellingDiversity and Distributions201285ndash1295httpsdoiorg101111ddi12229

Barbet‐Massin M Thuiller W amp Jiguet F (2012) The fate ofEuropean breeding birds under climate land‐use and disper‐sal scenarios Global Change Biology 18 881ndash890 httpsdoiorg101111j1365‐2486201102552x

Bellard C Bertelsmeier C Leadley P Thuiller W ampCourchamp F (2012) Impacts of climate change on the fu‐ture of biodiversity Ecology Letters 15 365ndash377 httpsdoiorg101111j1461‐0248201101736x

Boumlhning‐Gaese K amp Bauer H G (1996) Changes in species abun‐dance distribution and diversity in a central European birdcommunity Conservation Biology 10 175ndash187 httpsdoiorg101046j1523‐1739199610010175x

BrommerJELehikoinenAampValkamaJ(2012)Thebreedingrangesof central European and Arctic bird speciesmove poleward PLoS ONE7e43648httpsdoiorg101371journalpone0043648

CalderWAampKingJR(1974)ThermalandcaloricrelationsofbirdsInDSFarnerampJRKing(Eds)Avian biology(Vol4pp259ndash413)NewYorkNYAcademicPress

CesaraccioCSpanoDDucePampSnyderRL(2001)Animprovedmodel for determining degree‐day values from daily temperaturedataInternational Journal of Biometeorology45161ndash169httpsdoiorg101007s004840100104

ChamberlinTC (1890)ThemethodofmultipleworkinghypothesesScience1592ndash96

ClementMJHinesJENicholsJDPardieckKLampZiolkowskiDJJr (2016)Estimating indicesofrangeshifts inbirdsusingdy‐namicmodelswhendetectionisimperfectGlobal Change Biology223273ndash3285httpsdoiorg101111gcb13283

Commission for Environmental Cooperation (1997) Ecological regionsofNorthAmericaTowardacommonperspectiveCommission for

EnvironmentalCooperationMontrealRetrievedfromftpftpepagovwedecoregionscec_naCEC_NAecopdf

Crowl TA Crist TO Parmenter R R BelovskyGamp LugoA E(2008)Thespreadofinvasivespeciesandinfectiousdiseaseasdriv‐ersofecosystemchangeFrontiers in Ecology and the Environment6238ndash246httpsdoiorg101890070151

Cuddington K FortinM J Gerber L RHastings A Liebhold AOConnorMampRayC(2013)Process‐basedmodelsarerequiredtomanageecologicalsystemsinachangingworldEcosphere41ndash12httpsdoiorg101890ES12‐001781

DalyCHalbleibMSmithJIGibsonWPDoggettMKTaylorGHhellipPasterisPP(2008)Physiographicallysensitivemappingofcli‐matologicaltemperatureandprecipitationacrosstheconterminousUnited States International Journal of Climatology 28 2031ndash2064httpsdoiorg101002joc1688

DawsonW (1958) Relation of oxygen consumption and evaporativewater losstotemperatureintheCardinalPhysiological Zoology3137ndash48httpsdoiorg101086physzool31130155377

DawsonWRampWhittowGC (2000)Regulationofbodytempera‐ture InG CWhittow (Ed)Sturkiersquos avian physiology (5th ed pp343ndash390)SanDiegoCAAcademicPress

DeCalestaDS(1994)Effectofwhite‐taileddeeronsongbirdswithinmanagedforestsinPennsylvaniaJournal of Wildlife Management58711ndash718httpsdoiorg1023073809685

DevictorVJulliardRCouvetDampJiguetF(2008)Birdsaretrack‐ing climatewarming but not fast enoughProceedings of the Royal Society of London B Biological Sciences2752743ndash2748httpsdoiorg101098rspb20080878

Dormann C F (2007) Promising the future Global change projec‐tionsofspeciesdistributionsBasic and Applied Ecology8387ndash397httpsdoiorg101016jbaae200611001

Dormann C F Schymanski S J Cabral J Chuine I Graham CHartigFhellipSingerA(2012)Correlationandprocessinspeciesdis‐tributionmodelsBridgingadichotomyJournal of Biogeography392119ndash2131httpsdoiorg101111j1365‐2699201102659x

EccelE(2010)WhatwecanasktohourlytemperaturerecordingPartIIHourlyinterpolationoftemperaturesforclimatologyandmodel‐lingItalian Journal of Agrometeorology1545ndash50

EccelEampCordanoE(2013)InterpolTHourlyinterpolationofmulti‐pletemperaturedailyseriesRpackageversion211RetrievedfromhttpsCRANR‐projectorgpackage=InterpolT

EglingtonSMampPearce‐HigginsJW(2012)Disentanglingtherela‐tiveimportanceofchangesinclimateandland‐useintensityindriv‐ingrecentbirdpopulationtrendsPLoS ONE7e30407httpsdoiorg101371journalpone0030407

Elith JKearneyMampPhillipsS (2010)Theartofmodelling range‐shifting species Methods in Ecology and Evolution 1 330ndash342httpsdoiorg101111j2041‐210X201000036x

FiebergJampJohnsonDH(2015)MMIMultimodelinferenceormod‐elswithmanagement implications Journal of Wildlife Management79708ndash718

FriggensMMampFinchDM (2015) Implicationsof climatechangeforbirdconservationinthesouthwesternUSunderthreealternativefuturesPLoS ONE10e0144089

FryJXianGZJinSDewitzJHomerCGYangLhellipWickhamJD (2011)Completionof the2006national landcoverdatabasefor theconterminousUnitedStatesPhotogrammetric Engineering amp Remote Sensing77858ndash864

Fuller R J Gregory R D Gibbons D W Marchant J HWilson J D Baillie S R amp Carter N (1995) Population de‐clines and range contractions among lowland farmland birdsin Britain Conservation Biology 9 1425ndash1441 httpsdoiorg101046j1523‐1739199509061425x

Gaston K J Blackburn T M Greenwood J J Gregory R DQuinn R M amp Lawton J H (2000) Abundancendashoccupancy

2002emsp |emsp emspensp CLEMENT ET aL

relationships Journal of Applied Ecology 37 39ndash59 httpsdoiorg101046j1365‐2664200000485x

GoldsteinRB(1974)RelationofmetabolismtoambienttemperatureintheverdinCondor76116ndash119httpsdoiorg1023071365995

GrinnellJ(1917)FieldtestsoftheoriesconcerningdistributionalcontrolAmerican Naturalist51115ndash128httpsdoiorg101086279591

GrosboisVGimenezOGaillardJMPradelRBarbraudCClobertJhellipWeimerskirchH(2008)Assessingtheimpactofclimatevari‐ation on survival in vertebrate populationsBiological Reviews 83357ndash399httpsdoiorg101111j1469‐185X200800047x

GustafsonEJ(2013)WhenrelationshipsestimatedinthepastcannotbeusedtopredictthefutureUsingmechanisticmodelstopredictlandscapeecologicaldynamicsinachangingworldLandscape Ecology281429ndash1437httpsdoiorg101007s10980‐013‐9927‐4

Hamel P B (1992) LandManagers guide to the birds of the SouthGeneralTechnicalReportSE‐22USDAForestServiceSoutheasternForestExperimentStationandTheNatureConservancyChapelHillNC

Hinds D S amp Calder W A (1973) Temperature regulation of thePyrrhuloxiaandtheArizonaCardinalPhysiological Zoology4655ndash71httpsdoiorg101086physzool46130152517

HinesJE(2006)PRESENCE Software to estimate patch occupancy and related parameters Laurel MD US Geological survey PatuxentWildlife Research Center Retrieved from httpwwwmbr‐pwrcusgsgovsoftwarepresencehtml

HinesJENicholsJDampCollazoJA(2014)Multiseasonoccupancymodels for correlated replicate surveys Methods in Ecology and Evolution5583ndash591httpsdoiorg1011112041‐210X12186

HinesJENicholsJDRoyleJAMacKenzieDIGopalaswamyAMKumarNampKaranthKU(2010)TigersontrailsOccupancymodeling for cluster sampling Ecological Applications 20 1456ndash1466httpsdoiorg10189009‐03211

HockeyPA SiramiCRidleyARMidgleyGFampBabikerHA(2011) Interrogating recent range changes in South African birdsConfounding signals from land use and climate change present achallenge for attribution Diversity and Distributions 17 254ndash261httpsdoiorg101111j1472‐4642201000741x

Hoegh‐Guldberg O Hughes L McIntyre S Lindenmayer D BParmesanCPossinghamHPampThomasCD (2008)AssistedcolonizationandrapidclimatechangeScience321345ndash346

Homer C Dewitz J Fry J Coan M Hossain N Larson C hellipWickham J (2007) Completion of the 2001 national land coverdatabase for the conterminous United States Photogrammetric Engineering amp Remote Sensing73337

HomerCDewitzJYangLJinSDanielsonPXianGhellipMegownK(2015)Completionofthe2011NationalLandCoverDatabasefortheconterminousUnitedStatesndashrepresentingadecadeoflandcoverchange informationPhotogrammetric Engineering amp Remote Sensing81345ndash354

HootenMBWikleCKDorazioRMampRoyleJA(2007)HierarchicalspatiotemporalmatrixmodelsforcharacterizinginvasionsBiometrics63558ndash567httpsdoiorg101111j1541‐0420200600725x

HosmerDWJrampLemeshowS(2000)Applied logistic regression(2nded)NewYorkNYJohnWileyampSons

Huntley B Collingham Y CGreen R EHiltonGM RahbekCamp Willis S G (2006) Potential impacts of climatic change upongeographical distributions of birds Ibis 148 8ndash28 httpsdoiorg101111j1474‐919X200600523x

IgleciaMNCollazoJAampMcKerrowAJ(2012)Useofoccupancymodels to evaluate expert knowledge‐based species‐habitat rela‐tionshipsAvian Conservation and Ecology7(2)13pRetrievedfromhttpwwwace‐ecoorgvol7iss2art5

IllaacutenJGThomasCDJonesJAWongWKShirleySMampBettsMG(2014)Precipitationandwintertemperaturepredictlong‐termrange‐scale abundancechanges inWesternNorthAmericanbirds

Global Change Biology 20 3351ndash3364 httpsdoiorg101111gcb12642

IoannidisJP (2005)WhymostpublishedresearchfindingsarefalsePLoS Medicine2e124

KeacuteryMGuillera‐ArroitaGampLahoz‐Monfort J J (2013)Analysingand mapping species range dynamics using occupancy modelsJournal of Biogeography 40 1463ndash1474 httpsdoiorg101111jbi12087

Khaliq I Hof C Prinzinger R Boumlhning‐Gaese K amp PfenningerM(2014) Global variation in thermal tolerances and vulnerability ofendotherms to climate change Proceedings of the Royal Society B Biological Sciences 281(1789) 20141097 httpsdoiorg101098rspb20141097

Laursen K amp Frikke J (2008) Hunting from motorboats displacesWadden Sea eiders Somateria mollissima from their favouredfeeding distribution Wildlife Biology 14 423ndash433 httpsdoiorg1029810909‐6396‐144423

LemoineNBauerHGPeintingerMampBoumlhning‐GaeseK (2007)Effectsof climate and land‐use changeon species abundance in acentralEuropeanbirdcommunityConservation Biology21495ndash503httpsdoiorg101111j1523‐1739200600633x

MacArthurRH(1972)Geographical ecology Patterns in the distribution of speciesNewYorkNYHarperampRow

MacKenzieDINicholsJDHinesJEKnutsonMGampFranklinAB(2003)Estimatingsiteoccupancycolonizationandlocalextinc‐tionwhenaspeciesisdetectedimperfectlyEcology842200ndash2207httpsdoiorg10189002‐3090

MacKenzieDINicholsJDLachmanGBDroegeSRoyleJAampLangtimmCA(2002)Estimatingsiteoccupancyrateswhendetec‐tionprobabilitiesarelessthanoneEcology832248ndash2255httpsdoiorg1018900012‐9658(2002)083[2248ESORWD]20CO2

MacKenzieDINicholsJDRoyleJAPollockKHBaileyLampHinesJE(2018)Occupancy estimation and modeling Inferring pat‐terns and dynamics of species occurrence (2nd ed) BurlingtonMAAcademicPress

MellesSJFortinMJLindsayKampBadzinskiD(2011)ExpandingnorthwardInfluenceofclimatechangeforestconnectivityandpop‐ulationprocessesonathreatenedspeciesrangeshiftGlobal Change Biology1717ndash31httpsdoiorg101111j1365‐2486201002214x

Morin X amp Thuiller W (2009) Comparing niche‐and process‐based models to reduce prediction uncertainty in species rangeshifts under climate change Ecology 90 1301ndash1313 httpsdoiorg10189008‐01341

MustinKAmarAampRedpathSM(2014)Colonizationandextinctiondynamicsofadecliningmigratorybirdareinfluencedbyclimateandhabitat degradation Ibis 156 788ndash798 httpsdoiorg101111ibi12173

NicholsJDThomasLampConnPB(2009)InferencesaboutlandbirdabundancefromcountdataRecentadvancesandfuturedirectionsInDLThomsonEJCoochampMJConroy(Eds)Modeling demo‐graphic processes in marked populations(pp201ndash235)NewYorkNYSpringer

OmernikJM(1995)EcoregionsAspatialframeworkforenvironmen‐talmanagement InWSDavisampTSimon(Eds)Biological assess‐ment and criteria Tools for water resource planning and decision making (pp49ndash62)BocaRatonFLLewisPublishers

OmernikJM(2004)Perspectivesonthenatureanddefinitionofeco‐logicalregionsEnvironmental Management34(Supplement1)S27ndashS38httpsdoiorg101007s00267‐003‐5197‐2

Omernik J M amp Griffith G E (2014) Ecoregions of the contermi‐nousUnited States Evolution of a hierarchical spatial frameworkEnvironmental Management541249ndash1266httpsdoiorg101007s00267‐014‐0364‐1

Pardieck K L ZiolkowskiD J JrampHudsonM‐A R (2015)NorthAmericanBreedingBirdSurveyDataset1966ndash2014version20140

emspensp emsp | emsp2003CLEMENT ET aL

USGeologicalSurveyPatuxentWildlifeResearchCenterRetrievedfromhttpswwwpwrcusgsgovbbsrawdata

ParmesanCampYoheG (2003)Agloballycoherentfingerprintofcli‐mate change impacts across natural systemsNature 421 37ndash42httpsdoiorg101038nature01286

PearsonRGampDawsonTP(2003)PredictingtheimpactsofclimatechangeonthedistributionofspeciesArebioclimateenvelopemod‐elsusefulGlobal Ecology and Biogeography12361ndash371

PlattJR(1964)StronginferenceScience146347ndash353PopperK(1959)The logic of scientific discoveryLondonUKHutchinsonRobbins C S Sauer J R Greenberg R S amp Droege S (1989)

Population declines in North American birds that migrate to theNeotropics Proceedings of the National Academy of Sciences 867658ndash7662httpsdoiorg101073pnas86197658

Robinson R A Wilson J D amp Crick H Q (2001) The impor‐tance of arable habitat for farmland birds in grassland land‐scapes Journal of Applied Ecology 38 1059ndash1069 httpsdoiorg101046j1365‐2664200100654x

Romesburg H C (1981) Wildlife science Gaining reliable knowl‐edge Journal of Wildlife Management 45 293ndash313 httpsdoiorg1023073807913

Root T (1988) Energy constraints on avian distributions and abun‐dancesEcology69330ndash339httpsdoiorg1023071940431

Ruiz‐GutieacuterrezVampZipkinEF (2011)Detectionbiasesyieldmis‐leadingpatternsof speciespersistence and colonization in frag‐mented landscapesEcosphere2 1ndash14 httpsdoiorg101890ES10‐002071

SauerJRHinesJEFallonJEPardieckKLZiolkowskiDJJrampLinkWA(2015)The North American breeding bird survey results and analysis 1966ndash2013 Version 01302015LaurelMDUSGSPatuxentWildlifeResearchCenterhttpswwwmbr‐pwrcusgsgovbbsbbshtml

Sauer J R LinkWA Fallon J E Pardieck K L amp ZiolkowskiDJJr(2013)TheNorthAmericanbreedingbirdsurvey1966ndash2011SummaryanalysisandspeciesaccountsNorth American Fauna791ndash32httpsdoiorg103996nafa790001

SeoaneJBustamanteJampDiacuteaz‐DelgadoR(2004)Competingrolesforlandscapevegetationtopographyandclimateinpredictivemod‐elsofbirddistributionEcological Modelling171209ndash222httpsdoiorg101016jecolmodel200308006

Sirami C Caplat P Popy S Clamens A Arlettaz R Jiguet F hellipMartin J‐L (2017) Impacts of global change on species distri‐butions Obstacles and solutions to integrate climate and landuse Global Ecology and Biogeography 26 385ndash394 httpsdoiorg101111geb12555

SkalskiJR(1996)Regressionofabundanceestimatesfrommarkrecap‐ture surveys against environmental covariatesCanadian Journal of

Fisheries and Aquatic Sciences53196ndash204httpsdoiorg101139f95‐169

SohlTL(2014)Therelativeimpactsofclimateandland‐usechangeonconterminousUnitedStatesbird species from2001 to2075PLoS ONE9e112251httpsdoiorg101371journalpone0112251

ThogmartinWESauerJRampKnutsonMG(2007)ModelingandmappingabundanceofAmericanwoodcockacrosstheMidwesternandNortheasternUnitedStatesJournal of Wildlife Management71376ndash382httpsdoiorg1021932005‐680

ThomasCDampLennonJJ (1999)Birdsextendtheirrangesnorth‐wardsNature399213httpsdoiorg10103820335

Trathan P N Garciacutea‐Borboroglu P Boersma D Bost C ACrawford R J CrossinG ThellipWienecke B (2015) Pollutionhabitatlossfishingandclimatechangeascriticalthreatstopen‐guins Conservation Biology 29 31ndash41 httpsdoiorg101111cobi12349

TravisJMJ(2003)Climatechangeandhabitatdestructionadeadlyanthropogenic cocktail Proceedings of the Royal Society of London Series B Biological Sciences 270(1514) 467ndash473 httpsdoiorg101098rspb20022246

UrbanMC (2015)Acceleratingextinction risk fromclimatechangeScience348571ndash573httpsdoiorg101126scienceaaa4984

WalshJWuebblesDHayhoeKKossinJKunkelKStephensGhellipSomervilleR(2014)OurchangingclimateInJMMelilloTCRichmondampGWYohe(Eds)Climate change impacts in the United States The Third National climate assessment(pp19ndash67)WashingtonDCUSGovernmentPrintingOffice

YackulicCBNichols JDReid JampDerR (2015)Topredict thenichemodelcolonizationandextinctionEcology9616ndash23httpsdoiorg10189014‐13611

Yalcin S amp Leroux S J (2018) An empirical test of the relative andcombinedeffectsofland‐coverandclimatechangeonlocalcoloni‐zationandextinctionGlobal Change Biology243849ndash3861httpsdoiorg101111gcb14169

ZhuKWoodallCWampClarkJS(2012)FailuretomigrateLackoftreerangeexpansioninresponsetoclimatechangeGlobal Change Biology181042ndash1052httpsdoiorg101111j1365‐2486201102571x

How to cite this articleClementMJNicholsJDCollazoJATerandoAJHinesJEWilliamsSGPartitioningglobalchangeAssessingtherelativeimportanceofchangesinclimateandlandcoverforchangesinaviandistributionEcol Evol 201991985ndash2003 httpsdoiorg101002ece34890