TECHNICAL REPORT TO ENVIRONMENTAL MONITORING COMMITTEE OF LOWER ATHABASCA:

OWL MONITORING USING WILDLIFE ACOUSTICS ARU RECORDERS  

VERSUS STANDARD PLAYBACK METHODS 

Prepared By: Dr. Erin Bayne –

Department of Biological Sciences – University of Alberta

TheEMCLAowlmonitoringprogramin2012hadmultipleobjectives:

1) Usestandardplaybackmethodsonroadstodeterminetheprobabilityofobservationofowlsinareaswithhighversuslowenergysectorfootprint.Playbackmethodswereintendedtoincreasetheprobabilityofobservationforowlsbutrequiredfieldpersonneltoworkatnightduringspringbreak‐upseason.

2) CompareplaybackresultswiththoseobtainedusingAutonomousRecordingUnitsthatpassivelyrecordowlcallsbutthatcanalsobeusedtorecordspeciesotherthanowls.Goalwastodetermineifpassiverecorderscouldbecomeareplacementsystemforallaudio‐basedmonitoringprotocolscurrentlyinuseinEMCLAregion,includingowls.

3) Determinewhatthedataindicatesaboutowlhabitatuseversusowlabundance.Comparevariousstatisticalapproachesfordealingwithaudiodetectiondatatodeterminetheefficacyandefficiencyofthedifferentstatisticaltechniquesforunderstandingabundance,occupancy,anduse.

4) Identifytypeofsampling,timingofsampling,andamountofsamplingrequiredforfuturemonitoring/researchprogramsforowlsandhowthismighttieintofuturemonitoringinitiativestotrackotherspeciesoforganismsusingacousticcues.

Inthisreport,wesummarizethedataandgeneratepreliminarymodelsfromtheowlmonitoringprojectto:

1) ProvidebaselineinformationontheprobabilityofobservationandcountsatdifferenttemporalandspatialscalesinLowerAthabasca.

2) UsethesedataalongwithotherinformationonowlsinAlbertatounderstandwhethertheassumptionsaboutdetectionprobability,occupancy,detection‐correctedcounts,anddistance‐baseddensityestimatesaremetandcanbeusedtocalculateabsoluteowlabundance.

3) Placeowlmonitoringinthecontextofmonitoringallotherorganismsthatgiveacousticcuesandidentifyhowsuchinformationshouldbeusedtocoordinatemonitoringeffortsamongvariousgroups.

Allresultsandrecommendationsinthisreportaresubjecttochangeconditionalonfurtheranalysis.

DESCRIPTIONOFFIELDMETHODSTwotypesofmonitoringweredonein2012byEMCLAforowls.

Playbacksurveysconsistedofadiurnal(Table1)andanocturnalsurvey(Table2)thatoccurredalongaroadorpipeline.Alongeachroadorpipeline,anobserverdroveanATVor4x4truck.Playbacksurveystookplaceat29sites.Every1.6kilometresalongtheroadorpipelinethroughasitetheobserverwouldstop.Eachroutewascomprisedofapproximately10stops.Ateachstop,aplaybacksequencewasbroadcastandtheowlsdetectedbefore,during,andaftereachplaybacksequencerecorded.Thedistanceandbearingfromtheobservertothedetectedowlwererecorded.Twovisitsabout3weeksapartoccurredtoapproximatelythesamelocation.Quiteregularlywecouldnotvisittheexactstopbecauseofroadconditions.Insomecases,wecouldnotrevisitthesameroutebecauseofdeterioratingroadconditionsduringspringmelt.

Table 1. Calling sequence for diurnal owl survey used by EMCLA Call

Interval Call Type Total Time Cumulative

Time

1 Northern Pygmy-Owl call 20 seconds 0:20 2 Silent listening 1 minute 1:20 3 Northern Pygmy-Owl call 20 seconds 1:40 4 Silent listening 1 minute 2:40 5 Northern Pygmy-Owl call 20 seconds 3:00 6 Silent listening 1 minute 4:00 7 Northern Hawk Owl 20 seconds 4:20 8 Silent listening 1 minute 5:20 9 Northern Hawk Owl 20 seconds 5:40 10 Silent listening 1 minute 6:40 11 Northern Hawk Owl 20 seconds 7:00 12 Final silent listening 1 minute 8:00

Table 2. Calling sequence for nocturnal owl survey used by EMCLA

Call Interval

Call Type Total Time Cumulative Time

1 Initial silent listening 2 minutes 2:00 2 Northern Saw-whet Owl call 20 seconds 2:20 3 Silent listening 1 minute 3:20 4 Northern Saw-whet Owl call 20 seconds 3:40 5 Silent listening 1 minute 4:40 6 Boreal Owl call 20 seconds 5:00 7 Silent listening 1 minute 6:00 8 Boreal Owl call 20 seconds 6:20 9 Silent listening 1 minute 7:20 10 Long-eared Owl call 20 seconds 7:40 11 Silent listening 1 minute 8:40 12 Long-eared Owl call 20 seconds 9:00 13 Silent listening 1 minute 10:00 14 Great Gray Owl call 20 seconds 10:20 15 Silent listening 1 minute 11:20 16 Great Gray Owl call 20 seconds 11:40 17 Silent listening 1 minute 12:40 18 Barred Owl call 20 seconds 13:00 19 Silent listening 1 minute 14:00 20 Barred Owl call 20 seconds 14:20 21 Silent listening 1 minute 15:20 22 Barred Owl call 20 seconds 15:40 23 Final silent listening 1 minute 16:40

TheotherapproachwastouseARUs(AutomatedRecordingUnits).ARUswerelocatedalongroadedges,forestinteriors,andwetlandedges.Theywereplacedfor10‐14daysandturnedoneveryhourfor10‐minutes.Noowlcallswereplayedandthesystemsimplypassivelistenedforowlsfortheseextendedperiodsoftime.Eachsitehad6recordersthatwereaminimumof1kmapart.Observersthenlistenedtotherecordingsinthelabforaminimumofthreedifferentnightsforeachstop,generallyatmidnight.Asubsetofrecordingswasprocessedmoreextensivelytogetdetaileddataonowlcallingbehavioratdifferenttimesofdayandtimesoftheyear.

PLAYBACKVS.ARUFOROWLS

ThemostcommonapproachtosurveyingforestowlsinAlbertahasbeentouseplayback(i.e.broadcastingthecallofanowlthroughaloudspeaker)toelicitterritorialindividualstocall.Therationaleofusingplaybackversuspassivelisteningisowlcallingratesarethoughttobequitelow,althoughtherehavebeenfewexplicitteststocompareplaybacktopassivelistening.Byplayingintraandinter‐specificcalls,owlsarethoughttoincreasecallingratetherebyincreasingtheprobabilityofobservation.Probabilityofobservationinfluencesthestatisticalpowertodetecttrendsanddifferencesinspatialpatternsofdensity,relativeabundance,andhabitatusebyowls.

Totestifplaybackmethodsincreasetheprobabilityofobservation,wecomparedroad‐basedplaybacksurveystopassiverecordingstakenbyARUsplacedinthesamesitesbutatdifferentstations/stops.Table3summarizestherawprobabilityofobservationforplayback‐elicitedsurveysversusARUrecordingsatthestation/stoplevel.

Comparingasingle10minutepassivelisteningperiodatmidnighttoa20minutelongplaybacksurvey,thereisevidencetheprobabilityofobservationishigherusingplaybackdependingontheplaybacksequenceused.Atfirstglance,thissuggeststhatplaybackincreasescallingrates,therebyincreasingprobabilityofobservationbasedonasinglesamplingevent.However,thetimeittakestoconductmanyoftheplaybacksurveytechniquesislongerthanasinglepassivesamplingevent(10versus20minutes).Fromthepassiverecordingslistenedtothusfar,whenwelistenforowlsontwoormoreseparatenightsandcalculatethecumulativeprobabilityofobservation,20minutesofARUlisteninggenerallyresultsinsimilarprobabilitiesofobservationtoasingle20minuteplaybacksequencedoneonasinglenight(Table3–ARU–Atleasttwo“visits”).Two20minuteplaybacksequenceshaveacumulativeprobabilityofobservationthatishigherthantwo10minutepassivelisteningsequences.However,thePB/ARUratioisdecreasingwithincreasedvisitssuggestingthebenefitsofconductingtwo20minuteplaybacksequencesondifferentnightsarenotasgreataslisteningtotwodifferentARUrecordings.

Determininghowmany10minuterecordingondifferentnightsoratdifferenttimesofthenightwillberequiredtomatchtheprobabilityofobservationfromasingleplaybacksessionwillrequirelisteningtomorerecordingsonmorenightstodevelopacumulativeprobabilityofobservationcurve.However,basedonthePlaybacktoARUratiofortheprobabilityofobservationfromasingleplaybackvisitorsingle10minutelisteningperiod,itwillrangefrom1.21to5.00dependingonspecies.GreatHornedOwls,whicharenotpartoftheEMCLAplaybackprotocol,weremorelikelytobeobservedusingARUsthanplayback.Atotalof1hourlisteningtopassiverecordingswillresultinasimilardetectionratetoa20‐minuteplaybacksequenceinthefieldataminimum.Wearecurrentlyworkingtodeterminehowtodecreasetheactualtimeittakestoprocess1‐houroffieldrecordingsforowls(i.e.byvisuallyscanningsonogramsforowlcalls).Assumingtheobjectivewastomatchthecumulativeprobabilityofobservationoftwoplaybacksequences(40minutesofplayback)thisratiowaslessformostspeciessuggestingthebenefitofplaybacktopassiverecordingsdeclinesasyoulistentomorerecordings.Threespecies(BarredOwl,GreatHornedOwl,NorthernSaw‐whetOwl)weremorelikelytobedetectedviaARUafterlisteningfor20minutesthantwoplaybacksequences.

Table3‐Probabilityofobservationforforestowlsbasedatsinglestationbasedonpassivelisteningusingautomatedrecordingunits(ARU)atmidnightinEMCLAstudyarearelativetoroad‐basedplayback‐elicitedcallsurveysalongroadsidesdoneinvariouslocationsacrossAlbertawithdifferentplaybacksequencesandatdifferenttimesofday.

Singlevisit toastationSpecies ARU

Nightn=455

EMCLADayn=587

EMCLANightn=649

WEYCODay

n=1637

WEYCONightn=2491

ANOSCDP

n=3025

TAKATSCDB

n=1738

TAKATCDFn=390

BADO 0.042 0.002 0.059 0.000 0.068 0.002 0.056 0.054BOOW 0.020 0.002 0.062 0.001 0.111 0.002 0.114 0.100GGOW 0.004 0.000 0.020 0.004 0.029 0.003 0.012 0.008GHOW 0.073 0.005 0.063 0.000 0.044 0.162 0.055 0.054LEOW 0.004 0.002 0.014 0.000 0.005 0.020 0.000 0.000NHOW 0.000 0.007 0.002 0.009 0.000 <0.001 0.000 0.000NSOW 0.037 0.002 0.046 <0.001 0.149 0.103 0.068 0.069NPOW 0.000 0.007 0.000 0.106 0.002 0.000 0.007 0.000SEOW 0.000 0.000 0.000 0.000 0.000 <0.001 0 0.000AnyOwl 0.156 0.025 0.231 0.119 0.334 0.253 0.280 0.267

2visits tothesamestation ontwoseparatedayswithinthesameyear n=156 n=332 N=342 N=1569 N=1340 n=2146 n=680 n=279BADO 0.115 0.003 0.111 0.000 0.127 0.002 0.143 0.075BOOW 0.058 0.003 0.117 0.001 0.207 0.003 0.293 0.140GGOW 0.013 0.000 0.038 0.004 0.054 0.004 0.029 0.011GHOW 0.167 0.009 0.120 0.000 0.082 0.228 0.141 0.075LEOW 0.013 0.003 0.026 0.000 0.009 0.028 0.000 0.000NHOW 0.000 0.012 0.003 0.01 0.000 <0.001 0.000 0.000NSOW 0.090 0.003 0.089 <0.001 0.278 0.145 0.173 0.097NPOW 0.000 0.012 0.000 0.110 0.004 0.000 0.019 0.000SEOW 0.000 0.000 0.000 0.000 0.000 <0.001 0.000 0.000AnyOwl 0.353 0.045 0.439 0.124 0.621 0.357 0.716 0.372

Asowlshavelargehomerangesandmovewidely,theymaynotalwaysbepresentatthesamestation/stopondifferentdays.Thus,forthelargestspecies(i.e.BarredOwl),manyanalystsusetherouteorsiteastheunitofmeasurement(presenceofspeciesalongxstopsoratxARUs).Atthesite/routelevelusingtwo10‐minuteARUperiodsandtwo‐20minuteplaybacksequenceswefoundthattheprobabilityofobservationusingplaybackwashigherformostspeciesthanARUs.Inthisscenario,wecomparedsixstopsonaroutethatwereclosesttothesixARUswithineachsite.Wefoundhigherprobabilityofobservationforplayback‐basedroutesthanARU‐basedsites.Thereareseveralexplanationsforthispattern.First,theARUsitesmayhavebeenlesslikelytocrossasmanyowlterritoriesastheplaybackroutes.ThemainaxisoftheminimumconvexpolygonfortheARUswasapproximately4kmlongwhiletheaverageplaybackroutewas8km.Thesecondisthatplaybackactivelymovestheowltowardstheobserver,accomplishingtheobjectiveofincreasingprobabilityofobservationBUTreducingthe“accuracy”oftheobservationbyalteringtheareawheretheowlisactivelycallingtobelocated“neararoadinhabitatthattheowlmaynotregularlyuse”.ThethirdisthatlisteningtomoreARUperiodswewillbeabletodetecttheowlwithequalfrequency.ARUrecordingsandplaybackwillresultinsimilardetectionratesifsufficienttimeisspentlisteningandtherecordersareplacedinoptimallocationstodetecttheowls.TheonlywayARUswillnotachievethesameprobabilityofobservationwouldbeiftheowlsdoNOTcallnaturallyandonlycallinresponsetothepresenceofplayback.Thisisnotlikelygivenowlcallsarenotsolelyforterritorialdefensebutarealsousedforcommunicationbetweenmalesandfemalesofthesamespecies.Table4–Probabilityofobservationforforestowlsatsitelevelbasedonpassivelisteningusingautomatedrecordingunits(ARUatmidnightinEMCLAstudyarearelativetoroad‐basedplayback‐elicitedcallsurveysalongroadsidesdoneinvariouslocationsacrossAlbertawithdifferentmethodsandatdifferenttimesofday.Asitewasdefinedasroad‐sideroutewith6stopsversusanARUsitewhichwasasitewithin6ARUrecorders.

Species ARUNight(n=27

EMCLADay(n=31

EMCLANight(n=31

WEYCODay

(n=151

WEYCONight(n=122

ANOSCDP

(n=217

TAKATSCDB

(n=72

TAKATCDF(n=29

BADO 0.333 0.032 0.548 0.000 0.590 0.018 0.555 0.379BOOW 0.296 0.032 0.581 0.013 0.680 0.014 0.708 0.689GGOW 0.074 0.000 0.290 0.046 0.320 0.032 0.194 0.103GHOW 0.518 0.065 0.581 0.000 0.492 0.820 0.514 0.517LEOW 0.074 0.032 0.194 0.000 0.090 0.180 0.000 0.000NHOW 0.000 0.065 0.032 0.100 0.000 0.005 0.000 0.000NSOW 0.259 0.032 0.419 0.007 0.754 0.618 0.667 0.586NPOW 0.000 0.097 0.000 0.569 0.041 0.000 0.139 0.000SEOW 0.000 0.000 0.000 0.000 0.000 0.005 0.000 0.000AnyOwl 0.778 0.323 0.968 0.649 0.959 0.912 0.944 0.931

DOWENEEDTOCOUNTOWLSATSTATIONLEVEL?Anadditionalreasonsuggestedforusingplaybacktechniquestosurveyowls,ishumanobservershavetheabilitytocountthenumberofowlsheardatapointlocation(i.e.therewas1versus2owlsofaspeciescalling).Humanslisteningtoaudiorecorderssometimesfindthatdeterminingthenumberofindividualscallingisdifficult,especiallyiftheowlsarealongdistancefromtherecorder.WehavebeenabletodistinguishmultipleowlsonthesameARUrecordinghowever.

Countingthenumberofowlsateachstationmattersifthenumberofowlscommonlyrecordedduringasurveyisgreaterthanone.Fieldbasedsurveytechniquesshouldprovidebetterresolutiontodetectchangesinrelativeorabsoluteabundanceovertimeifmultipleowlsaredetectedperpoint.However,ifthemaximumnumberofindividualsofaspeciesthatarecountedpersurvey(whenowlsareobservedistypically1thenobtainingthisresolutionmaynotbenecessary).Theprobabilityanowlwasobservedversusthenumbersofowlsobservedduringasurveyatastopwasverysimilar(Table5).Duringdaytimesurveys,wealmostneverdetectedmorethanoneindividualofaspecies.Duringnighttimesurveys,weoccasionallyobservedmorethanoneindividualofaspeciesbutthiswasrarewithonlyfouroftheeightspecieshavinghighercountsthanprobabilityofobservation.Forthesefour,themeancountwasonly1.1to1.25timeshigherthanprobabilityofobservation.Poolingdatafromtwovisitstoastation,wefoundthemaximumnumberofowlsobservedwasgreaterthantheprobabilityofobservation.

Atthesitelevel,thenumberofowlsobservedwasconsiderablylargerthantheprobabilityofobservationforalmostallspecies(1to3timeshigher).Countinformationatthesitelevel(10stopspooledor6ARUspooledisamoreprecisemetricfortrackingchangeinowls).Understandingwhythenumberofowlsobservedatthesitelevelishigherthanprobabilityofobservationisimportantforinterpretingpatternsinabundanceandtrendhowever.Thefirstpossibilityisthatthereismorethanoneowlperspeciesatthesitelevel(10stops),resultinginahighercountthantheprobabilityofobservation.Iftrue,thenumberofowlsobservedisrecommendedasthemetrictotrackbecausecountsaremorelikelyreflectiveoftrueabundance.Alternatively,individualowlsmaybemovingbothwithinandbetweenvisitsandthusareheardatmorethanonestationwithinthesamesite.Inthiscase,usingprobabilityofobservationismoreconservativeasitdoesnotdoublecountthesameindividualwhichoverestimatesthenumberofowlsoccurringinanarea.Furtherworkwithradio‐markedindividualswouldbeusefulindetermininghowoftenthesameowlisdetectedatdifferentstationswithinthesamesite.

Thenumberoftimeswheremorethanoneowlisdetectedatastationisquitelow.Thus,recordingthepresenceofowlsatthestationlevelandsummarizingthenumberofstationswithinasitewhereowlswererecordedasthe“count”couldbeareasonablemetricfortrackingowlabundance.Thecaveattothisisthatowlscanbedouble‐countedatthesitelevelandrulesneedtobedevelopedthatminimizethispossiblebias.

Table5–Meancount±1standarddeviationforsinglevisittoastationandforasinglevisittoasite(6stationsforARUandplaybackcounts.Themeancount±1standarddeviationforthemaximumcountfromallvisitstoastationorsitearealsoshown.

Species ARUAvg.Count1Visit

ARUMax.Count2Visits

PlaybackAvg.Count1Visit

PlaybackMax.Count2Visits

StationBADO 0.048±0.244 0.126±0.383 0.071±0.306 0.129±0.406BOOW 0.020±0.139 0.058±0.226 0.062±0.241 0.117±0.322GGOW 0.004±0.066 0.012±0.109 0.022±0.156 0.038±0.206GHOW 0.084±0.314 0.186±0.448 0.079±0.336 0.143±0.446LEOW 0.004±0.066 0.012±0.109 0.014±0.117 0.023±0.151NHOW 0.000±0.000 0.000±0.000 0.002±0.039 0.003±0.054NSOW 0.042±0.221 0.096±0.334 0.051±0.240 0.088±0.313NPOW 0.000±0.000 0.000±0.000 0.000±0.000 0.000±0.000SEOW 0.000±0.000 0.000±0.000 0.000±0.000 0.000±0.000AnyOwl 0.202±0.521 0.443±0.716 0.299±0.631 0.509±0.780

SiteBADO 0.200±0.739 0.704±1.353 0.692±1.322 1.194±1.662BOOW 0.082±0.307 0.333±0.555 0.585±0.900 0.903±1.012GGOW 0.018±0.134 0.074±0.267 0.215±0.515 0.355±0.608GHOW 0.345±0.851 0.926±1.357 0.723±1.463 1.226±1.927LEOW 0.018±0.134 0.074±0.267 0.138±0.390 0.226±0.497NHOW 0.000±0.000 0.000±0.000 0.015±0.124 0.032±0.180NSOW 0.173±0.572 0.444±0.974 0.369±0.821 0.645±1.082NPOW 0.000±0.000 0.000±0.000 0.000±0.000 0.000±0.000SEOW 0.000±0.000 0.000±0.000 0.000±0.000 0.000±0.000AnyOwl 0.836±1.645 2.259±2.551 2.738±2.906 3.903±3.534

COST‐BENEFITASSESSMENTWhatismeantbyeffortdifferssubstantiallybetweentheARUandplaybackapproaches.Choosingonemethodovertheothermustweighthefollowingissues:

1) UsingtheEMLCAplaybackapproachforowls,anobservercanvisitapproximately10stationspernight(usingatruckorATVonaroadpernightwhenweatherconditionsareappropriate.Tenstationstakeapproximately3.5hourstosurveyplus1hourofdriving/settingupbetweensamplepointsforacombinedsurveyfieldeffortof~4.5hourspersitepernight.Eachvisitrequiresanequalamountofeffort.Whiledataacquisitionis“instantaneous”,dataentryfromfieldsheetstakesabout2hourspersite.Combined,thenumberofhourstocompleteasinglesitefortwovisitsaswasrecommendedinlastyearsEMCLAreportisapproximately12‐14hours.

2) Placing10ARUunitsinasite,assumingtheyareplacedalongaroad,takesapproximately100minutes(10minutesperstationplus60minutesofdrive/setuptime.CollectionofARUstakesaslightlyshorteramountoftime.Ifaproject’sobjectivewastonotspendmoretimeusingARUtechnologythanittakestodoaplaybackassessment,thisleavesapproximately6‐7hoursoftimeforequipmentmaintenance,audiolistening,andfileprocessing.

3) SetuptimeoftheARUsintheofficeisdifficulttocalculatebecauseitdependsonwhethertheunitisstraightoutoftheboxorifyouareusingexistingunits.Forthiscost‐benefitassessmentweassumethatallequipmenthasbeenlabeled,SDcardsprepared,andlockingbracketsconstructed.Oncethisisdonetheusercansimplydrivethemtothefield,whichrequiretheamountoftimedescribedin2.Thisisasignificantupfronttimecosthoweverandshouldnotbeignored.Aguestimateisthatitwilltake15‐20minutestosetupeachARUintheofficeforatotalofabout2hours.

4) Wehavebeen“learning”howtocreateasystemfordownloadingrecordingsfromARU,uploading/backupaudiofilestoservers,andmanagementofaudiofiles.Assuchourtimebudgetsonthisaspectareverypoorlyestablished.AlargepartofwhatneedstobedoneifamovetoARUsismadeistodevelopefficientinternetbaseduploadsystemsforsoundfiles.Thisstepisdoneinbulk,sowithanefficientsystemuploadsystemitshouldnottaketheusermuchtimetodoso.However,uploadtimesaremeasuredinhoursbecauseofthelargefilessizesofaudiorecordings.Each10minuterecordingisstoredasaWACfilewhichisabout50MB.

5) Gettingafileoffthecurrentserversystem,settingituptolistento,listeningtothefile,andenteringthedatatakesbetween15‐20minutesper10minuterecording.Assumingtheuserlistensto40minutesworthofrecordingsperstation,itwilltakeabout10‐13hourstoprocess10stations.Whether40minutesofobservationisrequiredneedsmoreinvestigation.

6) Combiningtheseveryroughnumberstogether,theARUsystemtakesmoretimetogetthesametotallisteninglengthasowlplayback.Addingapassivelisteningperiodformarshbirdsandamphibiansnearwetlandsaddsanadditional12minutestypicallytoanowlsurvey.However,timingofowlsurveysduringtheyeardoesnotusuallyoverlaptimingrequiredbyotherspeciessothisisnotrecommended.WithARUshoweverthesespecieswillbedetectedneartheendofowlsurveys.AswellARUsareveryeffectivelyformonitoringwoodpeckersandotherresidentsongbirdsthatcallmoreintheearlyspringaspartofowlmonitoring.Addingwoodpeckersetctoowlsurveysrequiressuboptimalsamplingforwoodpeckersastherecallingpeaksinearlymorningwhilediurnalowlsurveystakeplaceinlateafternoon/earlyevening

7) Atfacevalue,thiswouldsuggestthatplaybackwouldbeamoretimeandcost‐efficientoptionthanARU.However,manyotherfactorsmustbeconsidered.

a. Poorweatherconditionsmeansthatplaybacksurveysonenergysectorleasesoftenhavetobecancelled.Thiscostsalargeamountoftimeinrearrangingsafetyvisitsandpermissions.Relationshipswithcompanypersonnelwerestrainedoccasionallyin2012becausewehadtocomebacktoachieveoptimalweatherforplayback.

b. Alternatively,playbackdatacanbecollectedduringpoorweatherbutwillbeofpoorqualitybecauseitisdoneinpoorsamplingconditions.ARUscanbeputoutinanyconditions.ARUScanstayoutforextendedperiodsoftimeandthuscanbeoptimizedtoachievethebestsurveyconditionspossiblefortheperiodofobservation.SchedulingconflictsareminimizedwithARUs.WehavedevelopedprotocolsfordecidingwhichrecordingshavesuitableweatherconditionsandrarelydowenotgetagoodwindowforrecordingwhentheARUsareinplace.

c. Placingrecordersoutdoesnothavetobedonebyexternalstaff,resultinginareducedneedfortravelcosts,accomodations,etc.Employeesofenergycompaniescouldbetrainedtodistributerecordersanduploadfilestoaserver.Externalstaffwouldthenberequiredtoprocessandlistentorecordings.WearepilotingthisapproachwithatleastafewSAGDandminecompaniesin2013.

d. ARUsmustbepurchased,alongwithSDcards,batteries,andfilestorage.Thesecostsaresignificant.TheupfrontpurchasecostoftheARUisapproximately$700withSDcards,brackets,andlocks.BatteriesmustbepurchasedandreplacedeverymonthorsoforARU.Theonlyadditionequipmentrequiredbyplaybacksurveys

thatisnotalsorequiredforARUlayoutistheplaybackdevicewhichtypicallyisaonetime$200‐300expensebutonlyoneisrequiredperobserver.Batteriesareoftenrequiredandarereplacedweekly.Cheapertechnologicaloptionsarepossiblebutmustbecustombuiltandrequireconsiderabletestingbeforewidespreadimplementation.Discussionswouldneedtooccurwithcomputerscientistsandengineers.

e. Thisyearwerequired9TBofharddrivespacetostorealloftherecordingswecollected.Thisisdoubledbecausewebackeditallup(18TBofdata.Alargeinvestmentincomputerinfrastructureisrequiredtostoreaudiorecordings,althoughhowmuchrecording,howlongitisstored,etc.,canchangecomputinginfrastructureneedsconsiderably.WewouldliketopartnerwiththeDepartmentofComputingSciencesattheUniversityofAlbertatodiscusshowtocreatesuchasystembutbuildingsuchasystemrequiresalong‐termcommitmentoutsidecurrentEMCLAfundingenvelope.

f. ARUscanbeplacedoutduringdaylighthours.Playbackmustbedoneatnightbypeople.Safetyissuesareparamount.Whilewesufferednoinjuriesthereclearlyisgreaterriskcausedbyworkingatnight.

g. Beingabletodistributerecordersoff‐roadduringdaylighthoursiseasierthanmovingtosuchsitesatnightandisconsiderablysaferthanhavingpeoplewalkingintotheforestinterioratnighttodoplayback.Road‐sidesurveysarebiasedtosomedegreeassomeowlsmayselectandothersavoidsuchareas.Playbacksurveysdoneonfootwillallowtwotothreesurveyspernightatbestbasedondistancesbetweenstationsusedinroad‐basedplaybacksurveys.Whenthisistakenintoaccountthecostofhuman‐basedplaybackoff‐roadsurveysaremuchlessviablerelativetoARUs.Thus,ARUsprovidemoreflexibilityinlayoutandsamplingofhabitat.

h. AllspeciesthatgiveacousticcuescanberecordedbyARUsaslongastheyareactivatedattherighttimeoftheyear.Duringplaybacksurveysforowlsorrails,incidentalsightingsofotherspeciescanoccurbutoftenisnotdonebecauseofthefocusonrecordingspeciesspecificdataanddealingwithplaybackequipment.ThisincludesrailsandamphibiansasmandatedbyEMCLA.PasserinebirdswhicharenotpartoftheEMCLAmandatebutareahighpriorityfor

monitoringbyJOSMcanalsoberecorded.

i. ARUsandplaybackapproachesbothsampleanimalstoanunknowndistance.ItisassumedthatpeoplecanhearfurtherthantheARUusedinthisstudybutthismustbetestedtodeterminetheareasampledbythetwotechniques.ThisisahighpriorityfortestingthisyearandtheexperimentsaredescribedindetailinNextStepssection.

j. ARUrecordingscanbelistenedtorepeatedlytoensureproperidentification.MorestatisticalapproachestoanalyzingdetectionerrorarepossibleusingARUrecordings.Theabilitytopausearecordingwhenprocessingdatareducesdatatranscriptionerrors.OurdataentrysheetforARUdataentryandapproachallowsforall“currentstatisticalapproaches”forestimatingabundance,occupancy,singingrate,etc.,withtheexceptionofdistancesampling.Aprojectisproposedtodeterminewhetherdistancecanbeestimatedfromrecordings.

k. Owlcallsareoftenquitefaintonmanyoftherecordings.Hearingtheserequiressignalboostingandhighvolumesonlisteningequipment.Careneedstobetakentoidentifyoptimalvolumelevelofrecordingstoensureobserverislisteningatasafelevel.Protocolsarenowavailableuponrequest.

l. Overlapwithotherspeciescallslaterintheseason(i.e.amphibiansseemstoreducetheabilityofobserverstodetectowls.Furtherinvestigationisrequiredtoseewhetherthisbiascanberemovedstatistically,throughsignalprocessing,orthroughdifferentsamplinglayouts.ThisissomethingwewilllookataswelistentomorerecordingsfromtheMay/Juneperiod.

m. Humannoiseinfluencestheabilityofobserverstohearowlsonrecordings.Presumablythesameeffecthappensduringplayback.However,duringplaybackowlstendtomovetowardsplaybackaspartofterritorialdefense,whichmayincreasethechangeofdetectinganowlinanoisyenvironmenteithervisuallyoracoustically.However,humannoisepresumablyalsoinfluencestheabilityoftheowltohearplayback,decreasingtheefficacyofthistechnique.Understandinghowhumannoiseinfluencesthedistancesampledisakey

aspectofthisyear’sprotocoldevelopmentandisdescribedinNextSteps.

n. Asseeninthisobjectiveandbelowinobjective2,ARUrecordingsseemtohavelowerprobabilityofobservationthanplayback.ThismaycreateconcernsamongsomeaboutobservationratesofARUs.IfanARUprotocolwasusedasthemonitoringtoolbyEMCLAandfoundnottohaveachievedthedesiredprecisionatsometimeinthefuture,playbackunitscouldbeaddedtotheARUsystemtomakedirectcomparisonsofowlobservationbeforeandafterplayback.Suchasystemwouldallowdatastandardizationovertimeevenwhichmethodologicalshiftsbecausebothtypesofdatacouldbecollected.WorkistakingplacetobuildacheapautonomousplaybackunittolinkwiththeARUs.

o. Basedontheseideas,theEMCLAdecidedtofurthertesttheARUprotocolforowlsin2013.Playbacksurveyscouldbeimplementedbypeopleonroadsquiteeasilyandcost‐effectively.Buthowthiswouldcost‐effectivelybeaddedtoaprogramthatisoff‐roadlikeABMIiscurrentlyunknown.Itisthegeneralopinionthatanon‐roadbasedplaybackprogramforowlsalonewouldbecost‐prohibitive.FinalrecommendationsontheuseofARUsforowlrecordingaspartoflong‐termmonitoringwilloccurin2014.

WHATISPROBABILITYOFOBSERVATIONANDWHATDOESITTELLUS?

Probabilityofobservationisaninstantaneousmeasureofhowlikelyanobserveristoobserveananimalusingaparticulararea.Whileacommonmetricinbiodiversitymonitoringandwildliferesearch,interpretationofthismetricmeansintermsofhabitatuseand/ortemporaltrendisnotwellunderstood.Itisoftenassumedthatadifferenceinthismetricbetweendifferentenvironmentalconditionsortimeperiodsmeansachangeinsizeofthepopulationoratleastachangeintheimportanceofthequalityofanareaforanindividualanimal.However,numerousissuesmustberesolvedbeforeitcanbeproventhatachangeinprobabilityofobservationisreflectiveofachangeinapopulationorbehavioralprocess.

1) Withplaybackandpassiverecordings,theareasampledisill‐defined.Surveysbasedonsoundthatdonotestimatethedistancetoacallingorganismareeffectivelyunboundedmeaningthatthereisnodefinitionoftheareasampled.Thismattersforseveralreasons:

a. Soundtravelsdifferentdistancesindifferentvegetationandenvironmentalconditions.Forexample,soundwilltravelconsiderablyfurtherandbemoreaudibleoverastilllakethaninanaspenforestwhenitiswindy.Thus,thechanceofdetectingaspeciesormultipleindividualsofaspecieswillbehigherinmoreopenvegetationincalmconditionsthaninclosedvegetationduringsuboptimalsampling.Aswell,speciesthatsingatdifferentfrequenciescanbeheardatverydifferentdistancesmeaningthereisadifferenteffectivesamplingareaforeachspecies.

b. Anthropogenicnoisecanmaskthecallsofanimalsmakingthemdetectableovershorterdistancesthaninnon‐noisyareas.Thus,changesinprobabilityofobservationintheoil‐sandsregionmayhavemoretodowithdifferentialsoundpropagationthanactualchangesinabundanceorindividualhabitatselection.

c. Changesintechnology,observerability,etcinfluencetheareaoverwhichanimalsaresampled.Lastyearweconductedanexperimentthatcomparedthedistanceoverwhichdifferentrecordingtechnologiescoulddetectknownfrequencysounds.ThiscomparisonneedstobemadewithinandbetweenthedifferentSongmetreunitsthatEMCLAisusing,aswelltounderstandhowwecancompareresultsfromdifferenttechnologies(i.e.ABMIrecorders).

d. Putsimply,anyandallstatisticalcorrectionsthattrytoconvertbasicobservationalorcountdatatosomemeasureof“importance”or“abundance”requireanunderstandingoftheareaoverwhichacousticsignalstraveltounderstandwhatthedatameans.ThisisapriorityforEMCLAin2013andwillinvolveexperimentsusingsoundplayback.Thisisintendedtoprovidedatastandardsrequiredfordistancecorrectionindifferentenvironments.

2) Probabilityofobservationisabiasedestimateofthetrueuseofanareabyananimal.Acrucialconsiderationiswhetherornotananimalcanbepresentinthesamplingareabutnotbedetectedbyanobserverbecause:

a. Theacousticcuegivenbytheanimalismissedbytheobserverand/ortheobservermisidentifiesthecue.Thisisknownasobservererror.Observererrorcanbeminimizedbytrainingintermsofmisidentification.ARUtechnologyallowsthesamerecordingtobelistenedtomultipletimesbydifferentpeople,allowingdouble‐observerapproachestobeusedtocorrectformisidentification.Resourcessuchassonogramsandexemplarrecordingsallowobserverstomakecomparisonstowhattheyarehearingandseeing.Thishasbeendevelopedaspartofouronlinedataentrysystemwhichcanbeseenwithpermissionat:https://sharepoint.ualberta.ca/bayne/abmit/emcla/EMCLA%20Database/default.aspE‐mailErinBayneatbayne@ualberta.catoobtainpermissiontovisitthesite.Aswelistentomorerecordings,wearedevelopingamorecompleteexemplarsetofrecordingsforcomparisonpurposesthatwillbeavailabletothegeneralpublic.

b. TheotheraspectofobservererrorthatisfundamentaltobothplaybackandARUdataisthatdifferentpeoplehavesensitivitiestodifferentsoundfrequenciesatdifferentdistances.Thismeansthatcalibrationshouldbedoneforeachperson.Thisisverycostlytocalculateinthefield.Differencesbetweenobserverscanbemoreeasilytestedinthelabandcorrectionfactorscomputedforeachindividual.AspartofourprocessingofARUfileswearedevelopingaprotocoltotesteachpersonagainsttheotherandtodeterminehoweachindividual’sabilitytodetectasignalisinfluencedbythesignalstrengthasmeasuredbysonogramsoftware(decibels).Webelievewecanusethistocomparedifferentobserver’sabilitytoheardifferentdistancesinarecording.Thisprotocolisindevelopmentandwillbediscussedinforthcomingreports.

3) Theanimalispresentwithanareathatcouldbesampledbyanobserverifananimalgaveanacousticalcuebutnocuewasgiventhatcouldbedetected.Thisoccursbecausethesingingorcallingrate(#ofcallsperunittimeofanindividualand/orspeciesvaries.Ifthevariationisconstantamongenvironmentalconditionsorvegetationtypesthenprobabilityofobservationremainsareasonablemetricformakingrelativecomparisons.However,aconcerninthescientificliteraturecurrentlyisthatvariationinsinging/callingrateisnotconstantamongvegetationtypessuchthatprobabilityofobservationprovidesabiasedestimateofhabitatuseandpossiblytrendsovertime.Thisproblemisknownasdetectionerror.Understandingwhat“detectionerror”meansandhowwemeasureitiscrucialforinterpretationofthiseffect.Iftheindividualwaspresentwithinthesamplingduringthetimeofobservationandcouldhavebeendetected,thisisdetectionerror.Toproperlyestimatedetectionerrorratesrequirestheanimalbe

presentintheareasampledwhenasurveywasdone.Thisisknownastheclosureassumption.Iftheclosureassumptionismet,onewaytoestimatedetectionerroristocomputedetectionprobabilities.Detectionprobability,whenalltheothersourcesoferror(i.e.observererroranddistancesareunderstood),isafunctionofsinging/callingrate.Adjustingprobabilityofobservationtoanewmetriccalledoccupancycanbedonebycomputingsinging/callingrates.Therearemanywaystocomputesinging/callingrate.Oneapproachbeingusedextensivelyintheliteratureistorepeatedlyvisitthesamestationatdifferenttimes.ThisisknownasthemultiplevisitapproachandwasrecommendedbyFisheretal.(2011)intheowlproposaltotheEMCLA,albeitwithcaveatshighlightedbyBayne.Muchoftheliteratureonmultiplevisitapproachessamplesthesamestationonxdifferentdays.Visitingonaseparatedayensurestheassumptionofindependenceismet.Independencemeanstheobserverisnotbiasedtohearingthesamespeciestheyheardinthepreviousvisit.Independenceislessofaconcernthanassumptionsaboutclosure,properidentificationofspecies,andareasampled.Thus,visitsdonothavetooccurondifferentdays.Recommendationsbythedevelopersofthemultiplevisitapproachsuggestalargenumberofvisitstothesamestationarerequiredtogetaccurateestimatesofdetectionprobabilityforuncommonspecies.Thisisfinanciallyandlogisticallychallengingandeffectivelytradesoffgoingtodifferentstations(akeydeterminantofstatisticalpowerfortrendandhabitatmodeling)versusaccuratelyestimatingdetectionprobability.Theresourcesrequiredtoaddmultiplevisitstostandardplaybackmethodsaresubstantive.Thus,theefficacyofmultiplevisitsandtheirinterpretationmustbecriticallyevaluatedtominimizecostsandmaximizedataprecision.Inthenextsection,wegothroughanevaluationofthedifferentwaysrepeatvisitsurveyscanbeusedanddiscusstheprosandconsofeachmethodandwhattheytellusaboutowlhabitatuseversusabundance.

CUMULATIVEPROBABILITYOFOBSERVATION,OCCUPANCY,&DETECTIONERROR:

WHYMOVEMENTINFLUENCESINTERPRETATION

CANSPACEREPLACETIMETOESTIMATEPROBABILITYOFOBSERVATION?Visitingthesamestationondifferentdayswithplaybackisintendedto:A)ensurethatvariationwithinandbetweenspeciesinsinging/callingrateasafunctionofweather,timeofyearetcaresufficientlyrandomizedtoreducethechancesofmissinganowlobservation;andB)toincreasethechancethattheobserverandtheowlinteractinthesamespacetherebyallowingtheowltoheartheplaybackandgivearesponsethattheobservercandetect.

Theassumptionthatvisitingondifferentdaysachievestheseobjectiveshasnotbeenwelltested.InourpreviousreportonowlstoEMCLA(Fisheretal.2011)wefoundlittleevidencethattheprobabilityofobservationusingplaybackwasinfluencedbytheweekoftheyearwhendatawerecollected(withexceptionofNorthernSaw‐whetOwl).Timeofnightwhensurveywasdonehadlargereffects,butwasnotparticularlyimportantaslongassurveysweredonebetweenabout9PMand3AMforthenocturnallyactiveowls.

Thus,iftheobjectiveofamonitoringprogramissimplytodeterminetheprobabilityofobservationofowlsinanarea,spendingresourcestocomebackonanothernightmaybelessdesirablethansimplyputtingmoreeffortintosamplingmorestationswithinanareaonasinglevisit.

Table6showsthecumulativeprobabilityofobservationofowlsusingplaybacktechniquesonroadsfromacrossAlberta(usesdatadescribedinTable3).Cumulativeprobabilityofobservationiswhetherornotaspecieswasdetectedwithxamountofeffort,notthenumberoftimesitwasdetectedwiththateffort.Thistablecomparesthecumulativeprobabilityofobservationachievedbygoingtoasinglestationtwiceversusspendingthesameeffortgoingtotwostationswithinthesamevisit.Thisisalsodoneforcomparisonsof3visitsvs.3stationsand4visitsvs.4stations.Ifthegoalistomaximizetheprobabilityofobservationforanarea,thistablesuggeststhatgoingtomorepointswithinanareaisequallyeffectiveasvisitingthesamestationmultipletimesondifferentdays.

Thereasonthisrelationshipexistsisbecauseowlsmovewidelythroughoutthelandscapeandhaverelativelylargehomerangescomparedtothedistanceoverwhichowlcallscanbeheard.Thus,whenvisitingastationonanygivennightthereisacertainprobabilitythattheowlwillbepresenttoheartheplaybackandgiveacuethattheobservercanhear.Alternatively,theowlmaysimplybeinanareaofitshomerangeoutsidetheareaoverwhichtheplaybackcanbeheard.Movingthroughoutanareaandtherebytryingtogettoallareasofthehomerangeinanefforttoessentiallyfindtheowl,worksaseffectivelyasgoingbacktothesameplaceoverseveralnightsandwaitingfortheowltomoveintotheareawheretheplaybackcanbeheard.

Basedonthisanalysis,extraeffortinvestedinplaybacksurveysonasinglenightcouldbeequallyeffectiveinincreasingtheprobabilityofobservationforanareathancomingbacktothesamestation.Themainassumptionofthisanalysisisthattheowlbeingdetectedisthesameindividual.Asspatialextentincreasesthereisahigherprobabilityofcountingtwodifferentindividualswhichalterswhatisbeingmeasured.

Table6‐Cumulativeprobabilityofobservationformultiplevisitstothesamestopcomparedtothesamesubsetofdatawherethecumulativeprobabilityofobservationforanequivalenteffortplacedtosamplingagreaterspatialextentduringasinglevisit.Samplesizechangesbecausetheamountofdataavailablewithmultiplevisitsvaried.Comparingestimatesof1stop:xvisitsorxstops:1visitshouldbedonecautiouslybecausethespatiallocationandplaybackmethodswherethexvisitdatawerecollectedvary.Comparisonsbetweentemporalversusspatialeffort(i.e.1stop:2visitsvs.2stops:1visitaredirectlycomparablebecausetheyuseidenticaldatasetstocomputeestimates.ThesedatacomefromsurveysdoneacrossallofborealAlberta.

Species 1stop:1visit(n=8418

1stop:2visits(n=2816

2stops:1visit(n=2748

1stop:3visits(n=488

3stops:1visit(n=444

1stop:4visits(n=102

4stops:1visit(n=108

BADO 0.039 0.084 0.085 0.100 0.124 0.147 0.185BOOW 0.067 0.133 0.139 0.227 0.259 0.314 0.351GGOW 0.014 0.028 0.030 0.047 0.056 0.039 0.046GHOW 0.090 0.151 0.150 0.156 0.189 0.196 0.241LEOW 0.010 0.018 0.019 0.002 0.002 0.000 0.000NHOW <0.001 0.001 0.001 0.000 0.000 0.000 0.000NPOW 0.002 0.005 0.004 0.004 0.007 0.029 0.046NSWO 0.103 0.180 0.186 0.225 0.216 0.156 0.194SEOW <0.001 <0.001 <0.001 0.000 0.000 0.000 0.000AnyOwl 0.280 0.462 0.472 0.564 0.622 0.647 0.713

OCCUPANCY&DETECTIONERROR:SEPARATEVISITSONDIFFERENTDAYS

Therationaleforcomingbackondifferentdaysforowlsisthatseasonalvariationincallingratemayinfluencewhetherornotanowlrespondstoplayback.Callingbyowlsisaformofterritorialand/ormatedefensethatmaychangewiththestageofbreeding.Assumingthattheassumptionofclosurehasbeenmet(i.e.theowlispresentintheareasampledduringtwodifferentdays)thendetectionrateisameasureofhowlikelythespeciesistogiveacuethattheobservercanrecord.TheoriginallogicofthetwovisitstothesamestationduringplaybacksurveysasenvisionedbyFisheretal.(2011),wasthisapproachwouldprovidesuchanestimatewhichthencouldbeusedtocorrectnaïveprobabilityofobservationtoanoccupancyestimate.UsingplaybackdatafromacrossAlberta(Table7),wefounddetectionprobabilityacrossspecieswasabout0.25formostspecies.Thismeansthatreturningtothesamestationandusingplaybackonadifferentdayresultsinanowlofasinglespeciesbeingdetectedonly1outofevery4times.

Assumingtheassumptionofclosureismet,thismeansthatowlsdonotalwaysrespondtoplayback.Iftheassumptionofclosureisnotmetandtheowlhasleftthesamplingarea,thenthisresultsuggeststhat75%ofthetimewhenanobserverisconductingaplaybacksurvey,theowlisinanotherlocationwheretheowlcan’theartheplaybackandtherebygiveacuethattheobservercandetect.

Incontrast,ARUshaveamuchlowerprobabilityofdetection(between0.05to0.1).Admittedly,morerecordingsanddataareneededtoproperlyestimatethisvalue.ThisdifferencesuggeststhatplaybackDOESincreasedetectionrateforowlsconsiderably.However,whythisoccursisnotentirelyclear.First,playbackmayelicitowlstocallmorefrequentlythanwhatisrecordedpassively.Second,playbackmaycausetheowlstomovetowardstheobservertherebyincreasingthechancethattheobservercandetectanacousticcuegivenbyanowl.Owlscanhearconsiderablyfurtherthanhumans,soplaybacklikelydrawsowlstowardstheobserver.Thus,thedistanceandareathatyousamplechangesbetweenpassiveandplaybackmethods.

Whatisimportanttorealizefromthisanalysis,isthatdetectionrateasestimatedbyreturningtothesamestationondifferentdayshasalargeeffectonoccupancyestimatesdependingonwhetheryouuseplaybackversuspassivesampling.Naïveestimatesofprobabilityofobservationarerelativelysimilarbetweenplaybackandpassivesampling.However,whendetectionerroriscorrectedfor,occupancyestimatesdifferwidelybetweenthetwoapproaches.Ifoccupancyestimatesaretobeviewedasameasureofabundance,thetwotechniqueswouldimplya4‐5folddifferenceinthenumberofowlspresentinthelandscape.

Thismayinpartbeasamplesizeissueforthepassivelisteningdonethusfar.However,webelieveitisafundamentaldifferenceininterpretationcausedbydifferentialviolationsoftheclosureassumptionofplaybackversuspassivelistening.

1) Theareasampledbypassivelisteningisprobablysmallerthanthatsampledbyplaybackbecauseowlsmovetowardsplaybackandthisoccursbecauseowlscanhear

furtherthanahumanobserverlisteningtoanARU.

2) BothplaybackandARUrecordingassumethattheowlisalwayspresentintheill‐definedsamplingareawhichisveryunlikely.Itismorelikelythattheowlwillmoveintothesamplingareaduringaplaybacksurveythanduringapassivesurveytherebyreducingtheamountthatoccupancyestimatesarecorrected.

3) AtthistimeourconclusionisthatreturningtothesamestationondifferentdayswhetherbyARUorplaybackwillNOTprovidevalidinformationaboutowlabundanceunlessadditionalinformationiscollected.Occupancyinthiscontextisprobablybestviewedastheprobabilityanowlwilluseaparticularareaduringtheentiresamplingseason(i.e.theprobabilityanowlwoulduseanareasometimeduringMarchthroughendofApril).ThelackofinformationabouttheareasampledbyplaybackandARUpassivelisteninglimitsinterpretationofthismetrictosomeill‐definedmeasureofrelativeuseofhabitatbyowlsnotowlabundance.

Table7–Naïveestimatesofprobabilityofobservation,occupancy,anddetectionratefordifferentspeciesofowlsasestimatedbyplaybackmethodsversuspassivelisteningtoARUrecordingsondifferentdateswithinEMCLAarea.Inthisexample,thestationistheunitofreplicationforestimatingoccupancy.Eachstationwasvisited2to9timesondifferentdaysintheseanalyses.

2‐5playbackvisitsondifferentdates(n=3148stations

Species Naïve Occupancy DetectionBADO 0.082 0.175 0.251BOOW 0.129 0.263 0.269GGOW 0.029 0.085 0.175GHOW 0.144 0.316 0.242LEOW 0.017 0.038 0.238NHOW 0.001 N/A N/ANPOW 0.009 0.023 0.198NSWO 0.179 0.385 0.249SEOW <0.001 N/A N/AAnyOwl 0.447 0.664 0.408

2‐9ARUvisitsondifferentdates(n=156stations

BADO 0.115 0.660 0.063BOOW 0.057 0.992 0.021GGOW 0.013 0.961 0.005GHOW 0.167 0.401 0.183LEOW 0.012 0.066 0.075NHOW N/A N/A N/ANPOW N/A N/A N/ANSWO 0.090 0.262 0.146SEOW N/A N/A N/AAnyOwl 0.353 0.743 0.215

OCCUPANCY&DETECTIONERROR:SITELEVELOCCUPANCYUSINGMULTIPLETEMPROALVISITSTOASITE

Whileitmayseemobviousthatclosureisviolatedatthestation/stoplevel,itislesscertainwhetheritwouldbeviolatedatthesiteorroutelevel.Thisassumptionispredicatedonthenotionthatthehomerangeoftheowlisentirelywithintheareathatissampled.Oftentimes,onlyasmallportionofthehomerangemayoverlaptheareasampledsuchthatclosureisviolated.IdentifyingthenumberofstopsornumberofARUsandtheareaoverwhichtheymustbeplacedtoensurethattheentirehomerangeisencompassedwithinthesamplingareaisafundamentalunknown.AspartofourproposedworkplanwithinEMCLAwewanttopartnerwithAlbertaEnvironmentandSustainableResourceDevelopmenttodeterminewhatasamplinggridwouldhavetolookliketoachievethisfortheBarredOwl.

Usingrepeatvisitsurveystothesamesite,wefoundthatthenaïveprobabilityofobservationwassomewhathigherwhenusingplaybackoverARUs.Importantlyhowever,detectabilitywasmuchhigherusingplayback.Whenyouadjustnaïveprobabilityofobservationfordetectionerroryouestimateoccupancyrate.Ourfindingwastheoccupancyestimatesweremoresimilartonaïveestimatesofprobabilityatthesitelevelthanatthestationlevelsuggestingclosureismorelikelytohavebeenachieved.Incontrast,detectabilityatthesitelevelusingARUswasmuchlowerwhichresultedinmuchhigherestimateofoccupancy.

ThisfindingrequiresmoreinvestigationandisinfluencedbythenumberofminutesARUsarelistenedto.Identifyingasitescalethatworksforallowlshoweverisproblematic.Thus,amajorfocusoftheEMCLAprogramisidentifyingthescaleofanalysisthatisappropriateforthespeciesofinterestbasedonthesizeoftheirhomerange.

Table8‐Naïveestimatesofprobabilityofobservation,occupancy,anddetectionratefordifferentspeciesofowlsasestimatedbyplaybackmethodsfromroad‐sidesurveysversuspassivelisteningtoARUrecordings.Inthisanalysis,thesiteistheunitofreplication.Repeatvisitsondifferentdaysoftheyeararethereplicatesusedtocreatedetectionhistories.Asitewasdefinedasroad‐sideroutewith6stopsversusanARUsitewhichwasasitewith6ARUrecorders.

Species NaïveProb.Obs.Playbackn=293

PlaybackOccupancyTemporal

PlaybackDetectionTemporal

NaïveProb.Obs.ARUn=27

ARUOccupancyTemporal

ARUDetectionTemporal

BADO 0.334 0.427 0.509 0.259 0.593 0.250BOOW 0.461 0.560 0.558 0.296 0.998 0.148GGOW 0.174 0.357 0.261 N/A N/A N/AGHOW 0.553 0.703 0.511 0.444 0.907 0.285LEOW 0.092 0.169 0.297 N/A N/A N/ANHOW 0.010 0.012 0.534 N/A N/A N/ANPOW 0.078 0.226 0.174 N/A N/A N/ANSWO 0.621 0.725 0.594 0.185 0.197 0.749SEOW N/A N/A N/A N/A N/A N/AAnyOwl 0.928 0.947 0.855 0.667 0.750 0.667

OCCUPANCY&DETECTIONERROR:STATIONSASSPATIALREPLICATESUSINGASINGLEVISITTOASITE

Giventhatowlsmoverelativelylargedistanceswithintheirhomerangeseachnight,analternativeapproachtomeasuringoccupancyanddetectionerroristoreplacetemporalreplicationwithspatialreplication(Hinesetal.2010.Specifically,multiplesurveystationscanbeselectedfromeachsiterandomlyandwithreplacementandarethensurveyedasingletime,usuallyonthesameday(MacKenzieetal.2006).Thisallowsestimationofoccupancyatthelevelofthesampleunit(notatthelevelofthespecificsitesorlocationswithineachunit).Whenthespeciesoccupiesasamplingunit,butisnotpresentatallstationswithinthesamplingunit,detectionprobabilityconsistsoftwocomponents:1)theprobabilitythatthespeciesispresentwithinthesiteand2)probabilityofdetectiongiventhatthespeciesispresentatthesurveysite.Thestopsalongaroad‐sideplaybacksurveyortheindividualARUunitswithinasitesampledonasinglenightcouldbeviewedasreplicatesurveyswithinthesitewhichtherebyistheunitofanalysis.

Therearetwowaystomodelsuchdata.Thefirstistoassumethatdetectionhistoriesinthesitearerandom,wherebytheanimalrandomlymovesthroughoutthesitetogenerateadetectionhistory.TheotherapproachistoassumethatthereissomedegreeofspatialdependencybetweenstationsthatareclosertogetherwhichrequiresaMarkovianspatialprocessmodel.Hereweshowtheresultsfromtherandommovementmodel.

UsingthestopsasspatialreplicateswefoundthattheoccupancyanddetectionratesforowlswererelativelysimilarbetweenARUandplaybackmethodsformostspecies(Table9).Admittedlythiscomparisonisnotperfectastheareacoveredbystopsonaplaybackrouteisdifferentthantheareasampledby6ARUsbecauseoflayoutandthedistanceoverwhichacousticcuescanbedetected.

Achallengewithusingthespacefortimeapproachtomeasureoccupancyisidentifying:A)whatthesizeofthesitesshouldbe;andB)howmanystationshavetobeplacedinasitetoensuresufficientprecision;andC)knowingifthismetricaccuratelyreflectsabundancegiventhatmultipleindividualscouldbedetectedwithinasite.Thekeytousingthisanalyticalapproachisensuringthatthesiteisscaledappropriatelytotheorganismofinterestandthescaleatwhichsoundtransmits.Inotherwords,thesizeofthesamplingareaforasiteneedstobeproportionaltothehomerangeoftheanimalofinterestandhowfarthatanimalcanbeheardover.Ifthesiteistoobig,thentheestimateofabundanceisaminimalestimatorofabundancebecausemorethanoneindividualmightbepresent.Ifthesiteistoosmall,thentheassumptionofclosurewillnotbemet.Thisresultsinadetectionerrorestimatethatismorelikelytobecausedbymovement.Correctingoccupancyestimatesfordetectionerrorwhenmovementoccurswillleadtooverestimatesofanimalabundance.Alsoyouneedtounderstandthedistanceoverwhichsoundscanbeheard.

Forthistobethemodelingapproachusedinowlmonitoring,severalissueshavetobeaddressed.Thismodelassumesthatobservationsofowlsatdifferentplaybackstationswouldbeindependent.Itishighlyplausiblethatowlsmoveinresponsetoplaybackandcouldintheoryfollowtheobserverfrompointtopointcreatingadetectionhistorythatwouldresultinaseverebiasinoccupancyestimation.

Withpassivelistening,thetimingofsurveyswouldhavetobevaried.IfyouuseddatafromalloftheARUrecordingsfromthesamedateandtimeacrossasiteyoushouldgetadetectionhistorywithasingle1andtherestzeros.Thiswouldbecausedbythefactthatifonlyoneispresentitshouldonlybedetectedatonerecorder.Todeterminethespatialmovementofananimalacrossthesiteandgetasuitablecapturehistory,itwouldbebettertolistentodifferentARUsatdifferenttimesofdayanddate.

Inbiodiversitymonitoringalloftheseissuescreateasignificantchallengebecausethehomerangesizeofdifferentorganismsvarieswidely,meaningthatthesiteneedstodifferinsizefordifferentspeciestoestimateabundance.Soundtransmissionalsovariesamongstspeciessothattheareasampleddiffersbasedonthefrequencyandamplitudeatwhichaspeciescalls.HowtooptimizethelayoutofplaybackstationsorARUlocationsatanappropriatescaleforthelargestnumberofspeciespossibleisasignificantchallengethattheEMCLAneedstoaddressifthegoalistoobtainametricthatreflectsthetrueabundanceofowls.Table9‐Naïveestimatesofprobabilityofoccurrence,occupancy,anddetectionratefordifferentspeciesofowlsasestimatedbyplaybackmethodsfromroad‐sidesurveysversuspassivelisteningtoARUrecordings.Inthisanalysis,thesitewastheunitforwhichoccupancywasestimatedandthepresenceofaspeciesatastationwasthereplicatesamplesusedtocreateadetectionhistory.Asitewasdefinedasroad‐sideroutewith6stopsversusanARUsitewhichwasasitewithin6ARUrecorders.

Species NaïveProb.Obs.Playback

PlaybackOccupancy

PlaybackDetection

NaïveProb.Obs.ARU

ARUOccupancy

ARUDetection

BADO 0.159 0.299 0.124 0.109 0.197 0.205BOOW 0.235 0.339 0.188 0.072 0.404 0.048GGOW 0.074 0.281 0.051 0.018 N/A N/AGHOW 0.353 0.548 0.166 0.209 0.455 0.156LEOW 0.047 0.181 0.051 0.018 N/A N/ANHOW 0.019 N/A N/A N/A N/A N/ANPOW 0.101 0.163 0.156 N/A N/A N/ANSWO 0.387 0.541 0.199 0.118 0.347 0.105SEOW N/A N/A N/A N/A N/A N/AAnyOwl 0.775 0.867 0.337 0.372 0.633 0.239

OCCUPANCY&DETECTIONERROR:“SUBVISIT”DETECTIONERROR

Asthepreviousanalysesinthissectiondemonstrate,theassumptionofclosureisveryimportantforinterpretingwhataparticularmethodtellsusaboutowlabundanceversusowluseofanarea.Ensuringthattheareasampledislargeenoughtocovertheentirehomerangeofananimalisonewaytoensureclosure.Theother,istochangethetemporalextentoverwhichrepeatsurveysareconductedtominimizethechangeoftheanimalmovingduringtheperiodofobservation.ARUanalysisisideallysuitedtochangingthetemporaldefinitionofa“visit”.DuringtheARUrecordingswerecordedfor10minuteseachhour.WithARUswecanstoprecordings,writedowninformation,andthencontinuewithlistening.Thismakesitquiteeasytodetermineexactlywhenaspeciesgivesanaudiocueontherecording.Inourdatabase,werecordthetimewithineach1minuteintervaloftheentire10minuteperiodwhenanindividualofaspecieswasfirstheard.Ifweassumethatduringthe10minuteswhenthelisteningwasdonethatorganismsdonotmoveoutsidetheareathattheARUcanhear,ourapproachallowsestimationoftheminutebyminutedetectionprobability.Detectionprobabilityinthiscasemostlyreflectsanestimateofsinging/callingrateandisamoreaccuratemeasureoftruedetectionrateusingpassivelisteningmethodsthanlisteningtorecordingsondifferentdaysorpotentiallyevendifferenthours.

Table10showstheminutebyminutedetectionrateofowlsattheARUlocations.Differentdefinitionsofrepeatsamplesareshown(10–1minuterepeatsamples,5–2minuterepeatsamples,and2–5minuterepeatsamples.Occupancyestimationinthiscaseisverysimilartothenaïveprobabilityofobservation.Thisisbecausethesinging/callingrateofowlswithina10‐minuteperiodisinfactquitehigh.Formostspeciesdetected,anacousticcuewasgivenevery2ndminute.Ifanowlgaveanacousticcueinthefirst5minutesofapassivelisteningsession,therewasabouta75%chancetheywouldgiveacuethatwasdetectedinthe2ndfiveminuteperiod.

Becausethedetectionratewithina10‐minuteperiodissohigh,occupancyestimatesfromthisapproachdonotchangemuchrelativetothenaïveestimateofprobabilityofobservation.Thisdoesnotmeanthatthisisagoodmeasureoftrueabundancehowever.Occupancyinthisparticularcaseisaninstantaneousmeasureofhabitatusebyowls.Inotherwords,itistheprobabilitythatanowlaspresentatastationatthesametimethatanobserverwasthere(probabilityofusewithinthe10minuteperiodofobservation).Asstatedearlier,mostowlshavehomerangesthatarelikelylargerthantheareaofobservationforanARUrecordersuchthatonlyaportionoftheowl’shomerangeiswithinthesamplingareaoftheARU.Estimatingdensityfromsuchdatarequiresinformationaboutthedistanceatwhichtheowlwaslocatedand/ortheproportionoftheterritorywithinthesamplingareaoftherecorder.

Anissuethatshouldbeconsideredwhenusingthismetricisthatthepresenceofasecondowlofthesameordifferentspeciesmayalterthesingingrateofthefocalowl.Intra‐specificcompetitionmaycauseeachowltocallathigherratesthanwhentheyarealone.Inter‐specificcompetitioncouldcauseareverseeffectifsmallerowlschoosenottocallwhenalargerandperhapspredatoryowlcalls.Theseeffectscanbiasoccupancyestimatesifnottakenintoaccount.Forexample,if

GreatHornedOwlsmoveintomorehumandisturbedareas,smallerowlsmaybepresentbutlesslikelytocall.

Table10‐Naïveestimatesofprobabilityofoccurrence,occupancy,anddetectionratefordifferentspeciesofowlsasestimatedbyARUrecordings.Heredetectionismeasuredusing1‐minute,2‐minute,or5‐minutesubintervalswithina10‐minuterecordingasthetemporalreplicates.Detectionratemeasuredinthiswayisameasureofcallingratebyowlsasclosureislikelyachieved.

10ARU“subvisits”(n=464pointcountsSpecies Naïve Occupancy DetectionBADO 0.047 0.047 0.435BOOW 0.019 0.020 0.327GGOW N/A N/A N/AGHOW 0.081 0.081 0.470LEOW N/A N/A N/ANHOW N/A N/A N/ANPOW N/A N/A N/ANSWO 0.041 0.041 0.500SEOW N/A N/A N/AAnyOwl 0.159 0.160 0.494

5ARU“subvisits”(n=464pointcountsBADO 0.047 0.048 0.544BOOW 0.019 0.021 0.385GGOW N/A N/A N/AGHOW 0.081 0.082 0.570LEOW N/A N/A N/ANHOW N/A N/A N/ANPOW N/A N/A N/ANSWO 0.041 0.041 0.627SEOW N/A N/A N/AAnyOwl 0.159 0.161 0.602

2ARU“subvisits”(n=464pointcountsBADO 0.047 0.052 0.706BOOW 0.019 0.033 0.364GGOW N/A N/A N/AGHOW 0.081 0.089 0.712LEOW N/A N/A N/ANHOW N/A N/A N/ANPOW N/A N/A N/ANSWO 0.041 0.043 0.774SEOW N/A N/A N/AAnyOwl 0.159 0.170 0.756

OCCUPANCY&DETECTIONERROR:ROBUSTMODELSOFMOVEMENT&SINGINGRATEATSTATIONLEVEL

Apracticalwaytoaddresstheclosureassumptionistosamplepopulationsusingtherobustdesign,whichwasoriginallydevelopedforcapture–recapturesampling(Rotaetal.2009.Intherobustdesign,samplingconsistsofsecondarysamplingperiodsnestedwithinprimarysamplingperiods.Populationsareassumedtobeclosedtodemographicchangesormovementbetweensecondarysamplingperiodsandopentodemographicchangesbetweenprimarysamplingperiods.UsingthismodelatthestoplevelfortheARUdata,weestimatedtheprobabilityofoccurrenceduringthetwovisitstoasiteondifferentdaysandthedetectionratewithinthe10‐minutesamplingperiodwheretherecorderwasmonitoringowlcalls.

Thismodelislikelythemostrealisticwayofmeasuringoccupancyforowlsatthestationlevelasitensuresthattheassumptionofclosureismet.Theresultsfromthismodel(Table11)suggest:

A) Whenpresent,owlscallrelativelyfrequentlywithinthesamplingareaoftheARUwhenmeasuredonaminutebyminutebasis.Inotherwords,owlsdogivecuesthatcanbedetectedpassively.

B) OwlsmoveextensivelyandquiteoftenarenotpresentinthesamplingareaofanARUoranobserverusingplayback.70‐75%ofthetimeowlsarelikelytohavemovedfromastationwheretheyhavepreviouslybeenrecordedbasedontherobustmodel.

C) StationlevelsurveysprovideanindexofowluseofhabitatNOTmeasuresofowlabundance.ThesmallerthehomerangeofanowlspeciesthemorelikelystationsurveyswilltellussomethingaboutabundancebutwhetherasingleARUcanbeusedtoestimateabundanceforanyowlspeciesisnotclear.

D) Onlybypoolingstationstogethercananestimateofowlabundancebegeneratedthatcorrelateswithpopulationsize.Thus,thesiteneedstobetheunitofreplicationforallfuturemonitoring.Morethanonestationislikelyneededformostspecies.Howlargeasiteneedstobetoestimateabundancedependsonthespecieslifehistory,theirhomerangesize,andhowfardifferentspecies’callstransmitindifferentconditions.

Table11‐Naïveestimatesofprobabilityofoccurrence,occupancyduringthefirstandsecondvisitsondifferentdaystothesamestationwithARUrecorders.DetectionratefordifferentspeciesofowlsasestimatedbyARUrecordingsaredonewithina10‐minuteperiod.Theprobabilitythatanowlmovesintoorawayfromthatstationonthesecondvisitarealsoshown.

ARUvisitedon2separatedayswithinsameyearwith10‐1minutesubsamplesmeasuredduringeachvisit(n=156stations

Species Naïve1stvisit

Naïve2ndVisit

Occupancyon1stVisit

Occupancyon2ndVisit

“Arrive”on2ndVisit

“Leave”by2ndVisit

SubvisitDetectionRate

BADO 0.026 0.071 0.026 0.071 0.066 0.749 0.425BOOW 0.019 0.038 0.020 0.039 0.040 1.000 0.327GGOW N/A N/A N/A N/A N/A N/A N/AGHOW 0.064 0.071 0.064 0.071 0.061 0.800 0.456LEOW N/A N/A N/A N/A N/A N/A N/ANHOW N/A N/A N/A N/A N/A N/A N/ANPOW N/A N/A N/A N/A N/A N/A N/ANSWO 0.038 0.019 0.045 0.019 0.007 0.571 0.469SEOW N/A N/A N/A N/A N/A N/A N/AAnyOwl 0.132 0.168 0.132 0.168 0.152 0.727 0.467

ARUvisitedon2separatedayswithinsameyearwith2‐5minutesubsamplesmeasuredduringeachvisit(n=156stations

BADO 0.026 0.071 0.031 0.086 0.079 0.694 0.571BOOW 0.019 0.038 0.032 0.065 0.067 0.999 0.363GGOW N/A N/A N/A N/A N/A N/A N/AGHOW 0.064 0.071 0.069 0.076 0.066 0.783 0.727LEOW N/A N/A N/A N/A N/A N/A N/ANHOW N/A N/A N/A N/A N/A N/A N/ANPOW N/A N/A N/A N/A N/A N/A N/ANSWO 0.038 0.019 0.040 0.020 0.006 0.652 0.800SEOW N/A N/A N/A N/A N/A N/A N/AAnyOwl 0.132 0.168 0.143 0.182 0.163 0.704 0.717

OCCUPANCY&DETECTIONERROR:ROBUSTMODELSOFMOVEMENT&SINGINGRATEATSITELEVEL

Basedonourpreviousanalyses,therobustmodelislikelytoprovidethebestmeasureofhabitatuseatthestationlevelbasedonARUs.Wealsobelievethatthismodelwillbeofutilityinestimatingabundanceatthesitelevelbyincorporatingthevarioustypesoferrorassociatedwithlisteningsurveys.However,tothebestofourknowledgetherehasnotbeenanyevaluationofhowtherobustmodelshouldbescaledfromthestationtoasitelevel.Weproposethefollowingmodelbetested:

1) VisitxnumberofstationswithARUslevelwithinasite.Thesiteshouldbescaledappropriatelyforthespeciesofinterestbasedonliteraturereviewaboutowlhomerangesize.

2) HavetheARUsallcomeonatexactlythesametimeandsamedate.Aslongastherecordersarefarenoughaparttonothearthesameowl,youwillhaveanindependentseriesofreplicatesforthatowl.ThenumberofunitswilldependonsizeofhomerangeandtheareaoverwhichtheARUcanhearowls.

3) IfthereisonlyoneowlinthesitethenitshouldbedetectedatonerecorderifitisgivinganacousticcuewithinthesamplingareaofanARU,assumingspacingiscorrect.IfitisnotwithinrangeofanARUthennoowlsshouldbedetected.

4) Alternatively,anowlmaybeinrangeofanARUbutnotprovideanacousticcue.

5) Toestimatethefrequencyofanowlgivingacousticcues,thesubvisitapproachwithineach10minuteintervalcanbeusedtoestimatecallingrate.Callingrateismeasuredbyestimatingthedetectionerrorwhichwouldbecausedbyananimalnotgivingacuewithinaparticularminuteofthe10minutesequence.Themostappropriatesub‐visitlengthmustbedetermined.

6) Callingratewithinthesiteiscomputedbydeterminingwhetherany1‐minuteinterval,anywhereinthesite,hadanowldetected.Inotherwords,theoccurrenceofanyowlatanystationwithineachofthe1‐minuteintervalsofthe10‐minuterecordingsmadebyeachARUbecomesthedetectionhistoryforthesite.

7) Theextinctionrateparameteris,assumingtheanimaldoesnotdieorpermanentlyemigrate,anestimateofthemovementratewithinthesitebytheowl.WhentheowlisnotdetectedatanystationthiscouldbecausedbytheowlnotbeinginthedetectionareaORtheowlnotgivinganaudiblecue.Thismodelshouldallowseparationofthoseeffectsbecausetheentiresamplingareaisbeingsampledinstantanteously.Assuchthepositionof

theowlisfixedduringthe10‐minuteintervalofsampling.

8) Withthisinformationtheoccupancyratefortheentiresite(whichbydesignislargeenoughtoincludethehomerangeofoneowl)canbeestimated.Thenumberofsitesoccupiedcanthenbeusedasanestimatorofpopulationsize.

9) Ifmorethanoneowlispresentwithinthearea,thisapproachcanbeestimatedbecausedetectionwouldoccurontworecorderssimultaneously.Ann‐mixturemodificationofrobustoccupancymodelsforcountscouldalsobeemployed.

10) Currentlywecan’ttestthismodelbecausewechosetorandomlypickwhichowltimesanddaystolistentowithineachsite.Weareintheprocessofre‐listeningtocreateamatchedsetofobservationsthatwillallowustotestthisideausingARUdata.

11) Anadditionalcomponentthatcouldbeadded,conditionalonbudgets,isestimationofdistancefromtheowltotherecorder.Thismodificationtothemodelrequiresthedevelopmentoftwotechnologieshowever.

12) THISANALYTICALAPPROACHISNOTPOSSIBLEWITHPLAYBACKMETHODS.

WHATDOWENEEDTODOTOTRACK

“POPULATONSIZE”INOWLS?

Whichassumptionaboutclosureiscorrecthassignificantimplicationsforinterpretingtheresultsfrommultiple‐visitanalysisandhowsuchastrategyshouldbeusedinamonitoringdesign.Whenclosureismetanddetectabilityislow,estimatesofoccupancyrelativetoprobabilityofobservationchangedramatically.Manyresearchersviewoccupancyasabettermeasureofrelativeabundanceorabsolutedensitythanprobabilityofobservation.Asdetectionrateisoftenconsiderablylessthanonethenthenumberofanimalsestimatedtobeinanareawilloftenbeconsiderablyhigherusingmultiplevisitanalysesthannaïveapproachesthatdonotcorrectfordetectionerror.However,iftheassumptionofclosureisnotmetthentheadjustmenttooccupancyratecanartificiallyinflatepopulationsizeestimatesbecausethedetectionerrorisconfoundedwithmovementwhichartificiallyaltersdetectionrates.

Table12showspopulationestimatesofowlsintheLowerAthabascaregionifweassume:1)stationsandsiteswererandomsamplesfromtheregion;2)fornaïveestimatesbasedonprobabilityofobservation,detectionrateassumedtobe100%;3)forestimatesderivedatthestationscale,owlswerealwayspresentwithina800metreradiusofthestationcentroid;

4)forestimatesderivedatthesitescale,owlswerealwayspresentwith800metresoftheroad;5)onlyowlswithin800metresofsurveylocationsweredetected;Table12demonstratesthattherearesignificantdifferencesintheabsolutenumbersofbirdsassumedtobeintheLowerAthabascausingdifferenttechniquesandsurveytimes.Daytimesurveysresultinverylowpopulationestimateswhenusingnaïvemethodsbecausetheprobabilityofobservinganowlissolow.ThisispartiallybecausewedonotuseplaybackofthenocturnalowlsatthistimeofthedayandviceversafordiurnalspeciesliketheNorthernHawkOwl.Adjustingoccupancyatthestationleveltoadjustfordetectionerrorduringtheday,resultsinanestimateofpopulationsizethatis126timeslargerthanusingprobabilityofobservation.Atthesitelevel,thisdifferenceis15.4times.Atnight,thedifferencebetweennaïveandcorrectedpopulationsizeestimatesisat7.6timeslargeratthestationlevel.Atthesitelevel,thedifferenceisonly1.8times.

WESTRESSTHEASSUMPTIONSREQUIREDTOESTIMATEPOPULATIONSIZEOFOWLSAREVIOLATEDTOUNKNOWNDEGREESINALLDATAEVERCOLLECTEDONOWLS.POPULATIONESTIMATESSHOWNHEREAREINCORRECTANDONLYPROVIDEDASANEXAMPLE.

Ourobjectiveinshowingthisresultistodemonstratethatnaïveoccupancyestimatesarelikelytotoolowbecauseplaybackdoesnotincreasedetectionrateto1.Incontrast,multiplevisitestimatesareoverestimatedbecausetheassumptionofclosureislikelyviolatedaswellasdetectionerrorbeingaproblem.Atthestationlevel,thepopulationestimatesaretoolargetobetakenasevenreasonable.DetectionratecannotbeproperlyestimatedatthestationlevelovertwovisitsusingplaybackORARUswhensurveyaredoneondifferentdaysbecauseviolationsofclosureareensured.Otherapproachestocalculatingdetectionrateatthestationlevelthataccountformovementarepossiblebutnotusingahumanobserverandplaybacktechniques.

Atthesitelevel,particularlyduringthenighttime,multiplevisitmethodsholdmorepromiseasasamplingstrategy.Occupancyanddetectionrateparameterswerestableandcouldbeestimatedformostspeciesatthisscaleandatthistimeoftheday.Atthisspatialscale,thereisagreaterlikelihoodtheassumptionofclosureismetbecauseeventhoughaspeciesmaymovebetweenstations,itisstilldetectedwithinthesitewhenvisitedonadifferentday.Importantly,thismaynotrequirevisitingexactlythesamestationsondifferentvisits.Aswell,whiletheassumptionthatclosureismetincreasesasthesizeofthesamplingincreasesrelativetothesizeofthehomerangeoftheanimalsurveyed,itdoesnotguaranteeit.BothARUsandplaybackmayallowsuchestimatestobecalculated.ARUsprovidegreaterflexibilitytogetmorerefinedestimatesofdetectionerrorandmovementrates.FurtherworkisneededhowevertodetermineoptimalARUspacingandthenumberofrecordingsthatmustbelistenedtohowever.

Table12–Examplethatshowsthemagnitudeofvariationinpopulationsizeestimatesofowlsderivedfromusingstationandsiteleveldatafromplaybackfordaytimeandnighttimesurveysdoneusingnaïveprobabilityofobservationandoccupancyratecorrectedfordetection.

Species Daytime Nighttime Populationestimatebasedonstationleveldensity Naïve Occupancy Naïve OccupancyAnyOwl 2,197  7,742  18,108  38,929 

BarredOwl 185  46,360  4,682  37,459 

BorealOwl 185  46,360  6,245  46,360 

GreatGrayOwl 0  0  1,405  3,904 

GreatHornedOwl 366  46,360  5,619  28,173 

Long‐earedOwl 185  46,360  1,247  3,162 

N.HawkOwl 366  413  158  46,360 

N.PygmyOwl 732  46,360  0  0 

N.Saw‐whetOwl 185  46,360  3,755  9,504 

SumofOwlSpecies 2,207  278,573  23,110  174,921 

PopulationestimatebasedonsiteleveldensityAnyOwl 1,656  2,980  4,491  4,565 

BarredOwl 166  4,636  2,318  3,621 

BorealOwl 166  4,636  2,753  3,769 

GreatGrayOwl 0  0  1,014  1,467 

GreatHornedOwl 331  4,636  2,608  3,477 

Long‐earedOwl 166  4,636  869  1,159 

N.HawkOwl 166  166  145  4,636 

N.PygmyOwl 662  4,636  0  0 

N.Saw‐whetOwl 166  4,636  2,318  3,621 

SumOfOwlSpecies 1,821  27,982  12,025  21,749