J Anim Ecol. 2019;00:1–16. wileyonlinelibrary.com/journal/jane | 1© 2019 The Authors. Journal of Animal Ecology © 2019 British Ecological Society
Received:29January2019 | Accepted:30March2019DOI:10.1111/1365-2656.13036
B I O L O G G I N G : ' H O W T O . . . '
Light‐level geolocator analyses: A user's guide
Simeon Lisovski1 | Silke Bauer1 | Martins Briedis1 | Sarah C. Davidson2,3,4,5 | Kiran L. Dhanjal‐Adams1 | Michael T. Hallworth6 | Julia Karagicheva7 | Christoph M. Meier1 | Benjamin Merkel8 | Janne Ouwehand9 | Lykke Pedersen10 | Eldar Rakhimberdiev9,11 | Amélie Roberto‐Charron12 | Nathaniel E. Seavy13 | Michael D. Sumner14 | Caz M. Taylor15 | Simon J. Wotherspoon16 | Eli S. Bridge17
1DepartmentofBirdMigration,SwissOrnithologicalInstitute,Sempach,Switzerland;2DepartmentofMigration,MaxPlanckInstituteforAnimalBehavior,Radolfzell,Germany;3DepartmentofCivil,EnvironmentalandGeodeticEngineering,TheOhioStateUniversity,Columbus,Ohio,USA;4CentrefortheAdvancedStudyofCollectiveBehaviour,UniversityofKonstanz,Konstanz,Germany;5DepartmentofBiology,UniversityofKonstanz,Konstanz,Germany;6MigratoryBirdCenter,SmithsonianConservationBiologyInstitute,Washington,DistrictofColumbia,USA;7DepartmentofCoastalSystems,NIOZ,RoyalNetherlandsInstituteforSeaResearch,UtrechtUniversity,Texel,TheNetherlands;8NorwegianPolarInstitute,FramCentre,Tromsø,Norway,DepartmentofArcticandMarineBiology,UniversityofTromsø–TheArcticUniversityofNorway,Tromsø,Norway;9ConservationEcologyGroup,GroningenInstituteforEvolutionaryLifeSciences,UniversityofGroningen,Groningen,TheNetherlands;10CenterforMacroecology,EvolutionandClimate,NaturalHistoryMuseumofDenmark,UniversityofCopenhagen,Copenhagen,Denmark;11DepartmentofVertebrateZoology,LomonosovMoscowStateUniversity,Moscow,Russia;12UniversityofManitoba,Winnipeg,Manitoba,Canada;13PointBlueConservationScience,Petaluma,California,USA;14AustralianAntarcticDivision,Kingston,Tasmania,Australia;15EcologyandEvolutionaryBiology,TulaneUniversity,NewOrleans,Louisiana,USA;16InstituteforMarineandAntarcticStudies,UniversityofTasmania,Hobart,Tasmania,Australiaand17OklahomaBiologicalSurvey,UniversityofOklahoma,Norman,Oklahoma,USA
CorrespondenceSimeonLisovskiEmail:[email protected]
Funding informationDanishNationalResearchFoundation,Grant/AwardNumber:DNRF96;BundesamtfürUmwelt,Grant/AwardNumber:UTF254.08.08andUTF400.34.11;SchweizerischerNationalfondszurFörderungderWissenschaftlichenForschung,Grant/AwardNumber:31003A_160265andIZ32Z0_135914⁄1;USNationalScienceFoundation,Grant/AwardNumber:#EF-0553768,EAGER0946685,IDBR-1152356andDBI-0963
HandlingEditor:GarrettStreet
Abstract1. Light-levelgeolocatortagsuseambientlightrecordingstoestimatethewherea-boutsofanindividualoverthetimeitcarriedthedevice.Overthepastdecade,thesetagshaveemergedasanimportanttoolandhavebeenusedextensivelyfortrackinganimalmigrations,mostcommonlysmallbirds.
2. Analysing geolocator data can be daunting to new and experienced scientistsalike.Over thepastdecades, severalmethodswith fundamentaldifferences intheanalyticalapproachhavebeendevelopedtocopewiththevariouscaveatsandtheoftencomplicateddata.
3. Here,weexplaintheconceptsbehindtheanalysesofgeolocatordataandprovideapracticalguideforthecommonstepsencompassingmostanalyses–annotationoftwilights,calibration,estimatingandrefininglocations,andextractionofmove-mentpatterns–describinggoodpracticesandcommonpitfallsforeachstep.
4. Wediscusscriteriafordecidingwhetherornotgeolocatorscananswerproposedresearchquestions,provideguidanceinchoosinganappropriateanalysismethodandintroducekeyfeaturesofthenewestopen-sourceanalysistools.
5. Weprovideadviceforhowtointerpretandreportresults,highlightingparametersthatshouldbereportedinpublicationsandincludedindataarchiving.
6. Finally,weintroduceacomprehensivesupplementaryonlinemanualthatappliestheconceptstoseveraldatasets,demonstratestheuseofopen-sourceanalysis
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1 | INTRODUC TION
Light-level data loggers, or geolocators, have been used to trackanimals since the early 1990s (Wilson, Ducamp, Rees, Culik, &Niekamp,1992)but it hasbeen theadventof lightweightdevicessome10yearslaterthatmadethistechnologyatremendoussuccessstory.Theirweight(<0.5g)andrelativelylowcostcompared,forex-ample,tosatellitetransmittershavemadethemapplicableinlargenumbers toanimalsweighingas littleas10g (Bridgeetal.,2011).Althoughtheuseofgeolocatorscomeswithobviousethicalandlo-gisticalchallenges,suchasthepotentialimpactontheanimal(Brlíketal.,2019)andtheneedtoretrievethedevice,thesedeviceshavebeenappliedtothousandsofanimalstoansweradiverserangeofquestionsrelatingtomigrationpatterns(Briedisetal.,2019;Finch,Butler,Franco,&Cresswell,2017;Stutchburyetal.,2009),species–habitatrelationships(e.g.MacPhersonetal.,2018),identificationofbreedinglocations(Johnsonetal.,2010;Lisovski,Gosbell,Hassell,&Minton,2016),delineationofwinteringdistributions(Braceyetal.,2018;Egevangetal.,2010),migrationstopoverbehaviour(Salewskietal.,2013;Yamauraetal.,2017)andgeneralmigrationstrategies(Briedisetal.,2019;Knightetal.,2018).Light-levelgeolocationhasalreadyprovidedunprecedented insights intomigratorybehaviourandannualmovementsofanimals(McKinnon&Love,2018)andwillcontinuetodosointhefuture.
The fundamental underlying principle of light-level geoloca-tors is to estimate geographic locations frompatterns of ambientlight,allowing researchers to infer thegeneral locationandmove-mentpatternsofan individualover the time it carried thedevice.Afundamentalbutoftenunrecognizedchallenge isthetranslationof light levels into geographic locations. The simplest geolocatoranalysisderives latitude fromday lengthand longitude fromsolarnoon,which is relatively straightforward (Hill,1994).Lessobviousishowtominimizeandestimateerrors in locationdatacausedbyvariabilityandambiguityinthelightdatathatoriginatefromavari-etyofcauses,includingweather,habitat,behaviourandtimeofyear(fordetaileddescriptionontheaccuracyandprecisionofgeolocatorestimatessee:Fudickar,Wikelski,&Partecke,2012;Lisovskietal.,2012;Merkeletal.,2016;Phillips,Silk,Croxall,Afanasyev,&Briggs,2004;Rakhimberdievetal.,2016;Shafferetal.,2005).Researchersregularlystrugglewiththeanalyticalstepstoobtain,interpretandpresentlocationestimates.
Overthe lastdecade,arangeoftoolshavebeendevelopedtoanalysegeolocatordata.Choosingtherighttools,performingasci-entificallysoundanalysisandpresentingtheresultsinatransparent
mannerrequiresathoroughunderstandingoftheconceptsbehindgeolocationandthemethodologicaldifferencesbetweentheavail-abletools.Weaimto(a)describetheconceptsandprovidepracticalguidelinesforthegeneralprocedureofanalysinggeolocatordata,(b)outlinekeycriteriaformatchingresearchquestionstoappropriateanalysis and tools and (c) highlight themost important limitationsandpitfallsofgeolocatoranalysesanddiscusshowtoaddressthem.As an additional aid, we provide a comprehensive supplementaryonlinemanualthatincludesexampledataandcodeforstateoftheartanalyses,examplesofcommonproblemsandtheirsolutions,anddetailsourguidelinesforinterpretation,reportingandarchiving.
2 | FROM LIGHT‐LE VEL RECORDINGS TO LOC ATION ESTIMATES: GENER AL METHODS
Methodsof light-levelgeolocationcanbeclassified into thresholdmethods and curve-fitting methods (also known as “template-fitmethods”;Figure1).
Threshold methodsproduceasingle locationbasedontheesti-mated timesof two successive twilights events, that is, the timesatwhich the recorded light level crosses a predetermined thresh-old.Standardastronomicalformulaethendeterminethepositionofterminatoratthesetwotimes.Inastronomicalterms,the terminator isthetransitionbetweentheilluminated(day)andnon-illuminated(night)facesoftheplanet.Assumingthetaghasbeenstationaryintheinterveningperiod,itmustbelocatedattheintersectionofthesetwocurves(Figure1a).Byconsideringthealternatingsequenceofsunrise/sunsetandsunset/sunrisepairs,twolocationscanbeesti-matedper24hr.
Curve‐fitting methodsproducea locationestimatefromasingletwilight. The location of the terminator is derived from the esti-matedtimeoftwilight,andthelocationofthetagontheterminatorisdeterminedbytherateofchangeinlightlevelduringthetwilightperiod, that is, the interval of rapidly increasing/decreasing light(Figure1b).AlthoughtheEarthitselfrevolvesataconstantrate,sim-plegeometryrequiresthatthespeedatwhichtheterminatorpassesovertheEarth'ssurfaceisgreateratlowlatitudesthanathighlati-tudes.Consequently,variationintherateofilluminationchangecanbeusedtolocatethetagalongtheterminator.
Independentofmethod, twilight timesandperiodsarea focalpoint of geolocator analyses. Defining discrete twilight events– sunrise and sunset times – from raw light recordings (“twilight
toolswithstep-by-step instructionsandcodeanddetailsour recommendationsforinterpreting,reportingandarchiving.
K E Y W O R D S
animaltracking,archivaltags,FLightR,GeoLight,Movebank,probGLS,SGAT,solargeolocation
| 3Journal of Animal EcologyLISOVSKI et aL.
annotation”) is, therefore, the first importantstep forall analyses.Oncetwilighttimesaredefined,onecanobtainquickbutcrudeloca-tionestimatesandapplymoresophisticatedmodellingapproachestorefinetheseinitiallocationestimates.Particularlyforthelatter,adiverserangeofanalyticaltoolshasbeendevelopedinrecentyears.
3 | WHAT DETERMINES THE CHOICE OF TOOL S FOR MY ANALYSIS?
Wehererecommendasuiteofsixfrequentlyusedopen-sourcetools,forgeolocatoranalysesanddiscusstheirrequirements intermsofdataquality,userexperienceandcomputingpower:theonlineplat-form TAGS (https://tags.shinyapps.io/tags_shiny), the R packagesTwGeos(Lisovski,Sumner,&Wotherspoon,2015),GeoLight(Lisovski&Hahn,2012),probGLS(Merkeletal.,2016),SGAT(Wotherspoon,Sumner, & Lisovski, 2013a) and FlightR (Rakhimberdiev, Saveliev,Piersma,&Karagicheva, 2017).Weomit three other open-sourceRpackagesUkfsst(Nielsen,Bigelow,Musyl,&Sibert,2006),Trackit (Nielsen&Sibert,2007)andPolarGeolocation(Lisovski,2018),asthefirsttwoarespecializedtoolsforanalysisoffishmovementsandthelastoneaddressesarelativelyspecificproblemofestimatingloca-tionsduringpolarsummerunder24-hrdaylight.
Notoolormethodshouldbeconsideredasilverbullet for theanalysisofgeolocatordata.Thechoiceofanalysismethodwillul-timatelydependon theoverall researchquestion, the typeof tagused,thelifehistoryandbehaviouroftheanimalsstudied,andre-searchers' available time andexperience. For instance, is theuserlookingforcoarseestimatesofbreedingandnon-breedinglocationsorfiner-scalestopoverlocationsanddurations?Istheanimallivingindenseforestswhereshadingisexpectedtobehighduringsomeperiodsoftheyear?
Inthefollowingsection,wediscussthesetrade-offsbasedonthedifferent analysis stepsneededand theavailability and limitationsofmethodsforeachstep(Figure2andTable1).Inaddition,thereareim-portantconsiderationstotakeintoaccountbeforeusinggeolocatorsinananimalmovement study.Forexample, light-level geolocators can-notbeusedtoanswerresearchquestionsaboutmovementslessthan~200km,becausethesemovementsarelessthanthetypicalerrorofallmethods(Lisovskietal.,2012).Similarly,ifananimalmainlymoveslati-tudinallyduringtheperiodsaroundtheequinoxes,whenlocationerrorislargest,estimatingmigrationroutesandstopoversitesfortheseani-malsishardlyornotpossible,thoughbreedingornon-breedingrangescanstillbeestimated(Briedisetal.,2016).Thus,therearelimitationstotheuseofgeolocatorstoanswerquestionsaboutmigratoryanimalsthatcannotbeovercomebyeventhemostsophisticatedanalysisandshould,therefore,beconsideredpriortothebeginningofanystudy.
The open-source tools that are currently available implementthestepsof light-levelgeolocationanalysis inslightlydifferentways(Figure2b,Table1).Forexample,thefirststepforanalysinglight-leveldata is toannotate the twilightevents (step 1) that canbedone inTAGS,TwGeos and GeoLight.Forcurve-fittingmethodsimplementedinTwGeos and FlightR,oneshouldusethetwilighteventspredefinedinoneoftheabove-mentionedpackagestoextracttwilightperiodsfromrawlight-leveldatausingfunctionsinFLigthR or TwGeos.Calibrationfunctions (step 2)are included inall toolsexceptSGAT.Methodstoestimatelocations(step 3),refinetheseestimates(step 4)andextractmovement patterns (step 5) are implemented inGeoLight, probGLS,SGAT and FLightR.Insum,thesetoolsareasfollows:
• TAGSisanonlineplatformthatallowsuserstouploadrawlight-leveldata,annotatetwilights,performcalibrationandperformasimplethreshold-basedanalysis.Althoughmoststepsarebasedon GeoLight,theadvantageofthistoolisagraphicaluserinterface
F I G U R E 1 Theprinciplebehindlocationestimatesfromlightrecordings.Twobroadmethodsexist,thethresholdmethodandthecurvemethod.Anyobserverexperiencingatwilightmustbelocatedontheterminator–theboundarybetweentheilluminatedandthenon-illuminatedsurfaceoftheEarth.Thethresholdmethod(a)producesasinglelocationfortwosuccessivetwilightsestimatedviathetimesatwhichtherecordedlightlevelcrossesapredeterminedthreshold.Thecurve-fittingmethod(b)producesalocationforasingletwilightbasedonthepositionoftheterminatorandtherateofchangeinlightlevels.Thisrateofchangevarieswithlatitude,asindicatedbythearrows(arrowsizeisroughlyproportionaltorateofchange)
Threshold method Curve method(a) (b)
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tovisualizetherawlight-levelrecordingswithdifferentzoomop-tions,fastprocessingandauser-friendlyinterfacethatdoesnotrequireuserstowritecode.
• TheRpackageTwGeosprovidestoolstoimportrawlight-levelrecordings from various tags, a set of functions to plot andinvestigate the rawdata,andan interactive interface for twi-light annotation and extraction of twilight times and twilightperiods.
• TheRpackageGeoLightprovidessimplethreshold-basedlocationestimatesandmovementanalysis.Groupedmethods,whichpro-videsinglelocationestimateswithuncertaintybasedonaseriesoftwilighttimesduringstationarybehaviour,haverecentlybeenincluded(Hiemer,Salewski,Fiedler,Hahn,&Lisovski,2018;andsupplementaryonlinemanualChapter5“Movementanalysis”).
• TheRpackageprobGLSusesiterativeprobabilitysamplingtoes-timatenumerous trajectoriesbasedon threshold-based twilight
eventsandadditionalinputssuchasthetwilighterror,movementandspatialmasks.Thesecanbeusedtoderiveamostlikelytrackand location-specific uncertainties (see supplementary onlinemanualChapter6forexampleofaBrünnich'sguillemot(Uria lom‐via)track).
• TheRpackageSGATusesametropolissamplingprocesstocreateMarkovchainMonteCarlosimulationofmovementtrajectories(Sumner, Wotherspoon, & Hindell, 2009). Both threshold andcurvemethodscanbe implemented,and locationestimatesarerefinedusingatwilightandmovementmodelaswellasaspatialmask.
• TheRpackageFLightRusesthecurvemethodandaparticlefiltertoestimateactualmovement.Itsstartingpointisthespatiallyex-plicitlikelihoodforeachtwilight,whichiscalculatedbasedonthevalueandthevariationoftheslopeofobservedversusexpectedlightintensitiesduringthecalibrationperiod,whengeographiclo-cationofatagisknown.Usingamovementmodelandaspatialmask,theparticlefiltergraduallyprogressesthroughthespatiallyexplicit likelihoods in time,weighing possiblemovement trajec-tories to derive a probability distribution, which can ultimatelybeusedtocalculatethemediantrackanditscredibleintervalss(Rakhimberdievetal.,2015).
4 | ANALYSING LIGHT‐LE VEL GEOLOC ATOR DATA
4.1 | Step 1: Twilight annotation
Definingtwilighteventstypicallyrequiresalightintensitythreshold.Thetimewhenlightlevelsexceedthisthresholdinthemorningand
FIGURE 2 Exampleofhigh-andlow-qualitydata(a).Thequalitycanbeaffectedbymanyfactorssuchasthesensitivityofthesensor,thetagsettings(recordingfrequency),theindividual’sbehaviorandthehabitat(grounddwellingvs.openlandscapespecies),aswellasbyweatherandtopography.Thelefthigh-qualitydatawasrecordedonablack-tailedgodwitusingaloggingintervalof5minutesandasensorthatisabletoresolvetheentirelightrange(datafromRakhimberdievetal.,2016).Therightlow-qualitydatahasbeenrecordedonaCommonRosefinch(Carpodacus erythrinus)witharecordingintervalof10minutesandasensorthatonlyresolvesdimlightvalues(early/latesunrise/sunsettimes).Themajordifferenceisthatthereareclearsunriseandsunsettimesinthehigh-qualitydata,whichrecordmorethan5readingsduringthetwilightperiods(unpublisheddata).Theseperiodsarenotresolvedinthelow-qualitydataduetothelongerintervalbetweenrecordingsandpresenceoflightvaluesofzeroevenduringtheday,probablyduetoheavyshadingcausedbytreesandshrubsatthenon-breedinglocation.Thequalityofthedataisamajordeterminantforthemethodandthetoolthatcanbeusedfortheanalysis(b).Whilecurvemethodsrequiregoodqualitydataaroundtwilightandatleastacoupleofrecordingsduringtheincreasinganddecreasinglight,thresholdmethodscandealwith(almost)alldatawithacleardifferencebetweennightandday.Theflowchartshowssubsequentstepsandavailableanalysistoolsforeach
High quality data Low quality dataLi
ght
DateLi
ght
Date
Tim
e
Day Day
extract twilight periodsTAGS, TwGeos, FLightR
2. CalibrationTAGS, TwGeos, GeoLight
3. Simple Threshold Estimates
TAGS, GeoLight, SGAT
1. Twilight annotationTAGS, TwGeos, GeoLight
Movement analysisGeoLight
Curve methods Threshold methods
2. CalibrationSGAT, FLightR
FLightR SGAT probGLS GeoLight
(a)
(b)
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fallbelowitintheeveningaredefinedassunriseandsunset,respec-tively.Althoughthesetwilight eventsarethesoleparametersusedforthethresholdmethod,theyarealsousedasanintermediatesteptocalculatetwilight periodstoapplycurve-fittingmethods.Duringthisfirststepoftheanalysis,westronglyrecommendtakingthetimetovisuallyreviewtherawlightdatatoidentifypartsofthetimese-ries thatmightbestronglyaffectedbyshading (Figure2a;dueto,forexample,feathergrowth,monsoonsorbreedingbehaviour)andto identifydata gapsor light recordingerrors associatedwithde-vicemalfunction.Becomingfamiliarwiththedataprovidesaroughideaoftheoveralldataqualityandwhichmethodsarefeasible(seeFigure2andTable1).TheonlineplatformTAGSandtheRpackageTwGeosprovidethemostconvenientprocedurestodefinetwilight
events. BothTAGS and TwGeos allow interactive visualization andeditingoftwilightevents.Inaddition,theykeeptrackofuseractionsandthusdocumentthechoiceoftwilightevents(ortheirexclusion)forrepeatability.
Two frequentquestions troubling researchers are (a)what is agood threshold? and (b) should one edit potentially false twilighttimes?Regardingthethreshold,oneshouldchoosethelowestvaluethatisconsistentlyabovenoiseinthenight-timelightlevels.Itisim-portanttorealizethatanythresholdistag-andspecies-specific.Theprospectofeditingtwilight isafarmoresensitiveissue.Thereareprocedures inTAGS and TwGeos thatautomaticallydetermine twi-lightevents(seeBox1andChapter4inthesupplementaryonlinemanual).However,shadingduringthetwilightperiodsmayleadto
TA B L E 1 Abriefcharacterizationofsixopen-sourcetoolsfortheanalysisofgeolocatordata
TAGS TwGeos* GeoLight probGLS SGAT** FLightR
Softwarecapability
1.Twilightannotation
Definetwilightevents ✓ ✓ ✓
Extracttwilightperiods ✓ ✓ ✓
2.Calibration
Thresholdmethod ✓ ✓ ✓ ✓
Curvemethod ✓ ✓ ✓
3.Locationestimation
Method(Threshold/Curve) T T T T/C C
Uncertaintyestimates (✓) ✓ ✓ ✓
4.Refinementoflocations
GroupedAnalysis*** ✓ ✓
Trackoptimization Probabilitysampling
MCMC Particlefilter
Inputs:Twilighterror ✓ ✓ ✓
Movementmodel ✓ ✓ ✓
Spatialmask ✓ ✓ ✓ ✓
5.Movementanalysis
MovementversusResidency
✓ ✓
Requirements
Dataquality
Tolerancetoshading ••• •• •• •
Minimumrecordingfrequency
1/10 min 1/10 min 1/10 min 1/5min****
Fulllightrangerequired ✓
Codingexperience •• •• ••• •
Calculationtime(trackestimation)
Minutes Minutes Minutes Hours
Note:Thetoolsdifferinwhichanalyticalstepstheycover,complexityandrequirementsastodataqualityandresearcherexperience(seetextfordetail).*basedonBAStag(Wotherspoon,Sumner,&Lisovski,2013b)withextrafunctionalities.**furtherdevelopmentoftheRpackagetripEstimation(Sumneretal.,2009).***Singlelocationestimatesforstationaryperiods(seriesoftwilightevents).****Insomecases,notablyunderlowshading,FLightRcandealwith1/10minfrequency.
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atimeserieswhereinthe lightvaluescross thethresholdmultipletimesduringashortperiodoftime,andautomatedprocessesmaypickthewrongsunrise/sunsettime.Onehastheoptionhereofad-justingordeletingsuchtwilightevents.Werecommendaconserva-tiveapproachtoediting,meaningthatitisbettertohavesomenoiseinthedatasetthantoerroneouslyremovegooddatapointsorinfor-mativedatathatmightconveyrapidlong-distancemovements.Also,the criteria for editing or removing poorly classified twilightswilldependonthemethodthatisfurtherusedtoinferlocations.Duringperiodsoflimitedmovement,forcurve-fittingmethods,day-to-dayconsistency in the shapeof the curve around sunriseor sunset is
mostimportant,whileforthresholdmethodstheday-to-dayconsis-tencyinthetwilighteventsisimportant(Figure3).
4.2 | Step 2: Calibration
Geolocatorsvary in theway theymeasure light and other informa-tion.Most importantly, tags can have different data logging sched-ulesand(arbitrary)unitsfor light-leveldata.Therefore,calibration isneededtorelateobservedandexpectedlightlevels.Thisprocedureisthemostimportantstepinanygeolocatoranalysisasitaccountsfortwotypesoferrors:(a)thedevice-specificaccuracytomeasuretrue
BOX 1 Annotating twilight times using the R package TwGeos
ThefunctionlightImageprovidesanoverviewplotoflightdatarecorded(data:Europeanbee-eater,seesupplementaryonlinemanualChapter1).
library(TwGeos)
raw <- readMTlux(̀ LightRecordings.lux̀ )
offset = 12 # the offset for the vertical axis in hours.
lightImage(raw, offset = offset, zlim = 12)
Inthisplot,eachdayisrepresentedbyathinhorizontallinethatplotslightvaluesasgrayscalepixels(dark=lowlightandwhite= maximumlight).
threshold <- 2.5 # slightly above night recordings
twl <- preprocessLight(raw, threshold=threshold, offset=offset)
Tovisualizethequalityofthedata(e.g.cleanlighttransitionsduringthetwilighttimes)andtodefinealight-levelthresholdwecanplotpartsofthelightrecordings.
with(raw[2000:5000,], plot(Date, Light))
abline(h=threshold, col="grey80", lty = 2, lwd = 2)
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lightintensitiesand(b)thelevelofshadingananimalexperiencedinits typical habitat andperforming its typical behaviour.To this end,aperiodisoftenselectedwhentheanimalwasataknownlocationin its preferred habitat for at least a couple ofweeks, for example,at the site of deployment, and light levels recorded during this pe-riodarecomparedtothoseexpectedfortheknownlocation.Forthe“thresholdmethod”,thecalibrationprocessdeterminesasinglebestreferencesunelevationangle,whichisthepositionofthesun(e.g.5degreesbelowthehorizon)duringthetwilighteventsacrossallcali-brationrecordings.Notethatsomeanalyses(e.g.SGAT)requirezenithangle,definedastheanglefromthezenith, insteadofsunelevationanglewhichistheangleofthesuninrelationtothehorizon.Forthe“curve-fittingmethod”,thecalibrationprovidestheslopeofrecordedversusexpectedchangeinlightduringthetwilightperiods.
Giventheimportanceofcalibration,itiscrucialtoplanthestudy,forexampledeploymenttiming,toensurethatgeolocatorrecordingsincludeanappropriatecalibrationperiodataknownlocation.Suchacalibration isbestperformedas “in-habitat calibration”,wherein thedeviceisattachedtotheanimaltobetrackedwhileitisinitstypicalenvironment.Thisformofcalibrationaccountsfortheinfluenceofbe-haviourandhabitatondataqualityandmightbeparticularlyusefulforspecies that live inshadedhabitatsor thosewhosebehaviour leadstofrequentshading.Inreality,however,manybirdsaretaggedduringthebreedingperiod,wherebehaviourandhabitatmaydifferfromtherestof theannual cycle.Forexample,birdsmayusenestboxes re-sultinginmisdetectionsofthetruetimeofsunriseorsunset,causingthelightpatternrecordedinthebreedingseasontobedifferentfromthelightpatternrecordedthroughouttherestoftheyear,whenthe
birdisnotusinganestbox(Meieretal.,2018).Thoughthebreedinglocationmaythereforenotbequalifiedas“typical”behaviour,inmanycasesitwillbetheonlyknownlocationforthetaggedbirdthatcanbeusedtocalibratethedata.Ifcavityorburrownestingisapparentinthetaggedbird,itisnotrecommendedtousethisnestingperiodforcalibration.Similarly,whengeolocatorsaremountedonthe leg, thismayalso introduceshadingeventsduring thecalibrationperiod, forexamplewhenthelegsarecoveredorsubmerged.
Toaccountforsuchsourcesoferror,thecalibrationcanpoten-tiallybedoneusing recordingsofa shortperiodafter thenestingwhile individualsremainatthebreeding location.However, ifcali-brationofgeolocatorsonanimals isnotpossible,asanalternativeor in addition to an “in-habitat” calibration, it is recommended toperforma“rooftopcalibration”,whereeachdeviceissimplyplacedin a stationary location (e.g. a rooftop), for one or 2 weeks priorto, or after, the deployment. This form of calibration helps to es-timate thedevice-specific inaccuracy.FlightR provides anoptionalprocess inwhich calibration canbe calculated for stationaryperi-odsinunknownlocations(supplementaryonlinemanualChapter8“Calibration”).Forthresholdmethods,thiscanalsobedoneusinga“Hill-Ekstromcalibration”(Box3andsupplementaryonlinemanualChapter5“Hill-Ekstromcalibration”).
4.3 | Step 3: Simple threshold estimates
Afteridentifyingtwilighteventsandperformingcalibration,thenextstep is to estimate geographic locations.We recommend startingwithasimpleprocedurebasedonthethreshold-basedanalysis(see
WecancalltheinteractiveprocesstodefinetwilighttimesusingthefunctionpreprocessLight(seesupplementarymaterialfordetails).Finally,wecanplotthesunriseandsunsettimesintothelightimage:
lightImage( tagdata = raw, offset = offset, zlim = 12)
tsimagePoints(twl$Twilight, offset = offset, pch = 16, cex = 1.2,
col = ifelse(twl$Deleted, "grey20", ifelse(twl$Rise, "firebrick", "cornflowerblue")))
AndextractthetwilightperiodsusingthefunctionextractCrepuscular.
Detailedexplanationonhowtoannotatetwilight,commonpitfallsandbestpracticerecommendationsareavailableinsupplementaryonlinemanualChapter4.
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BOX 1 (Continued)
8 | Journal of Animal Ecology LISOVSKI et aL.
Box2andFigure2).Evenifamoresophisticatedanalysisisplanned,aquickthreshold-basedanalysisprovidesafirst impressionofthetrackandcanrevealdata-qualityissuesthatmayhaveescapedear-lierscrutiny.Aninitialthresholdanalysiscanalsoserveasasanitycheckforfurtheranalysis. It isunlikelythatanysuccessivemodel-lingapproachwill dramatically change thegeneralmovementpat-ternrevealedbyasimplethresholdanalysis.Infact,ifthemodellingdoes produce dramatically different results, then somethingwentwrongwiththeanalysis(e.g.erroneouscalibration,poormovement/behavioural model, inappropriate spatial mask). Finally, a simplethreshold-basedanalysisprovidesanopportunitytoevaluatethein-fluenceofthecalibrationparameters,particularlythereferencesunangle.Althoughexperimentingwithparametersettingssoundsverysubjective,we recommendperforming several preliminary thresh-oldanalysestoevaluatetheeffectsofchangesinthereferencesunelevation angle on the results/track. For example, the amount ofshadingexperiencedbyananimaloftenvariesacross the trackingperiodasthehabitatsusedforbreeding,stopoverandnon-breedingmaydiffer in topography, foliage and localweather that canhavedifferentially influence on location estimates during distinct peri-ods. Insuchcases,goingbacktothecalibrationstepandapplyingalternativemethodssuchasthe“Hill-Ekstromcalibration”(Lisovskietal.,2012)mightberequired.SeeBox3andsupplementaryonlinemanualChapter5(“Hill-Ekstromcalibration”)forexamples investi-gatingsuchissuesaswellasoptionsthatmayallowaccountingforandevencorrectingthem.Theresultsofasimplethresholdanalysisprovideapilotoverview,butoftenshowlargeandunrealisticmovements(Box2)overshorttimeperiodsduetovariableshading.Furthermore,aroundtheequi-noxesdaylengthissimilaracrosstheglobe,andevensmalldeviationsinsunriseorsunsettimesorslopewillresultinlargespatialerrors,particularlyinlatitude.Therefore,itisoftendesirabletorefinethesecrude location estimates by incorporating knowledge about thetrackedanimal,suchasitsmovementcapabilitiesandwhathabitat(e.g.oceanvs.land)itislikelytouse.
Sep/04 Sep/05
Ligh
t (a
rbitr
ary
units
)
Jul Sep Nov Jan Mar May
24 h
rRaw Geolocator Data
1. Twilight annotation
Light intensity threshold
2. CalibrationTwilight events (sunrise/sunset)
Twilight period
Freq
uenc
y
– 6° –5° –4° –3° –2° –1°Sun elevation angle at sunrise/sunset
(degrees below horizon)
Threshold method(twilight events)
Curve method(twilight periods)
Mea
sure
d lig
ht
Expected light–3 –2 –1 0 1 2
2
4
8
3. Location estimation
Calibrationperiod
Median
Reference sun elevation angle Reference slope parameter
F I G U R E 3 Aschematicoverviewoftheanalyticalstepsrequiredtotranslaterawlight-levelgeolocatorrecordingstosimplelocationestimates.Geolocatorrecordambientlightlevelsovertime(toppanel)resultinginatimeseriesofdaylightpatterns.Inaninitialstep(1),discretesunriseandsunsettimes(twilighttimes)aredefinedusingalightintensitythreshold.Whilethesetwilighteventsareusedtoobtainlocationestimateswiththe“thresholdmethod”,twilightperiodsthatfollowasunriseandprecedeasunsetareusedin“curvemethods”.Usinglightrecordingsfromaknownlocation(e.g.afterthedeploymentofthelogger),thecalibrationprocess(2)establishestherelationshipbetweenobservedandexpectedlightlevels.Thiscalibrationprovidesareferencesunelevationangleforthe“thresholdmethod”andareferenceslopeparameterforthe“curvemethods”thatcanthenbeusedtoestimatetwodiscretelocationsperday.TheexemplarydatashownhavebeenrecordedonaBar-tailedgodwit(Limosa limosa)andweredescribedinRakhimberdievetal.(2016).SimplelocationestimateswerecalculatedusingtheRpackageGeoLight
| 9Journal of Animal EcologyLISOVSKI et aL.
Box 2 Simple location estimation using GeoLight
Startingwiththetablethatcontainstheannotatedtwilighttimes(seeBox1),wecanestimatethereferencesunelevationangle.Here,weuseaspecificrangeoftherecordings,aftermakingsurethatthebirdhasbeenstationaryatthedeploymentsite(51.32°N,11.96°E)duringthisperiod(e.g.breedingtime).
First,weexportthetableintotherequiredGeoLightformat:library(GeoLight)
twl.gl <- export2GeoLight(twl) # export the table into the required GeoLight format
Next,weperformcalibration:lon.calib <- 11.96
lat.calib <- 51.32
tm.calib <- as.POSIXct(c("2015-07-20", "2015-08-29"), tz = "GMT")
twl.calib <- subset(twl.gl, tFirst>=tm.calib[1] & tSecond<=tm.calib[2])
gE <- getElevation(twl = twl.calib,
known.coord = c(lon.calib, lat.calib))
Usingthereferencezenithangle(93.50°),wecanestimateandplotlocations:crds <- coord(twl.gl, degElevation = 90 - gE[1])
tripMap(crds, ylim = c(-35, 60),
xlim = c(0, 20), equinox = FALSE)
Longitude
Latitude
0 5 10 15 20
−20
0
20
40
60
10 | Journal of Animal Ecology LISOVSKI et aL.
4.4 | Step 4: Refinement of locations
Location estimates are typically refined via a statisticalmodellingeffort that incorporates at least three components: (a) a twilightmodel, (b) amovementmodel and (c) a spatialmask. The twilight model isbasicallyanextensionofthecalibrationthatnotonlyde-terminesoneoverallbestsunelevationangleorsingleslopeacrossthewholecalibrationperiod,butalsoconsidersvariationofthedailylightpatternwithinthecalibrationperiod.Incorporatingthetwilight
errordistributionallowscalculationofaspatial likelihoodforeachtwilight(Figure4)viatheknowndistributionofthetwilighteventsorfromthevariationintheslopeparameterofthetwilightperiods(e.g.standarddeviationofslope).
Amovement modelaimsto limittheoccurrenceofunrealistic lo-cationsbyconstraininglengthandfrequencyoflong-distancemove-mentsinaccordancewithwhatisknown(orassumed)aboutaspecies'propensity for movement. Various possibilities for specific move-mentmodelsexistthatdifferinlevelofcomplexityandassumptions;
Box 3 Movement analysis and “Hill‐Ekstrom” calibration
GeoLightofferstoolstodistinguishbetweenperiodsofmovementandperiodsofresidency.ThechangeLightfunctionusesthetwi-lighttimes(notthelocationestimates)thatareunaffectedby,forexample,theequinoxandsearchesforsuddenchangesthatindicatechangesinthelocationoftheanimal.
library(GeoLight)
cL <- changeLight(twl = twl.gl, quantile = 0.85)2.
53.
03.
54.
04.
55.
05.
5S
unris
e (r
ed)
16.5
17.0
17.5
18.0
18.5
19.0
19.5
Sun
set (
blue
)
0.00
0.04
0.08
0.12
0.00
0.04
0.08
0.12
Pro
babi
lity
of c
hang
e
Sep Nov Jan Mar May
| 11Journal of Animal EcologyLISOVSKI et aL.
for example, models can assume a constant velocity or a bimodalspeed distribution to distinguish resident andmovement behaviour(Schmaljohann,Lisovski,&Bairlein,2017;Yamauraetal.,2017).
Aspatial maskdefinesspatial likelihoodswithintheanalysis toreduceorevenpreventunrealisticlocationestimates,thatis,wherethe specific animal is very unlikely to occur. Spatialmasks can be
Theoutputplotshowsthetwilighttimes(sunriseinredandsunsetinblue)inthecenterplot,theprobabilitiesofchangeinthelowerplotandtheresultingsequenceofmovementsandperiodsofresidency.
Withthisinformation,wecanperformacalibrationforaperiodwithunknownlocation(e.g.thelongeststationaryperiodnr.5).ThefunctionfindHEZenithfromtheRpackageTwGeosanalysesthisperiodandcalculatesthezenithangle(sunelevationangle)thatresultsinthelowestvariationintheestimatesoflatitudes(fordetailssee:Lisovskietal.,2012andsupplementaryonlinematerialChapter5,“Hill-Ekstromcalibration”).
library(TwGeos)
StartEnd <-range(which(twl$Twilight>=min(twl.gl$tFirst[cL$site==8]) &
twl$Twilight<=max(twl.gl$tFirst[cL$site==8])))
HE <- findHEZenith(twl, range = StartEnd)
−60
−40
−20
0
20
40
60
Latit
ude
15−Jul 01−Oct 18−Dec 05−Mar 22−May
90 92 94 96 98
4
6
8
10
12
14
zenith angle
sd in
latit
ude
(with
in ra
nge)
zenith = 94.25
BOX 3 (Continued)
12 | Journal of Animal Ecology LISOVSKI et aL.
as simple (binary) as restricting land animals to land by excludingoceans or large waterbodies, or more complex when consideringspecies-specific habitat, altitudes or other preferences. Theymayalso include measurements of other variables, such as tempera-ture, that are sometimes recorded by multi-sensor loggers (Lam,Nielsen,&Sibert,2010;Lavers,Lisovski,&Bond,2019;Merkeletal.,2016;Shafferetal.,2005;Sumneretal.,2009).Thefullmodel-basedanalysiscombinesthesesourcesofinformationusingtheprior
distributionsofprobabilitiesfromthetwilightmodel,themovementmodeland the spatialmask togeneratemultiple (often thousandsof) simulated locations for each tracking interval (see Figure 4).Thesesimulatedpointsarethenregardedasaposteriorprobabilitydistributionfromwhichonecanderivethe“best”path(mostprob-able the path that is usually themean ormedian of all estimatedlocationswithineachtimeinterval)andtheassociatedcredible in-tervals. “Best” in thiscasemeans that theresult is themost likely
F I G U R E 4 Theprinciplesbehindtherefininglocationprocess.Usingknowledgeonthevariation(errordistribution)inthedetectionofsunriseandsunsettimesforthe“thresholdmethod”andthevariationintheslopeparameterforthe“curvemethod”allowscalculationofspatiallyexplicitlikelihoodsforeachtwilight(thetwilightmodel).Furtherassumptionsinthemovementbehaviourofthespecies(themovementmodel)andthespatialoccurrencecanthenbeincorporatedinamodellingapproachtoestimatethemostlikelymovementpathandtheassociatedcredibleintervals.ThedatausedareagainfromtheBar-tailedgodwitandthefinalresultscomefromananalysisusingtheRpackageFLightR(Rakhimberdievetal.2016)
Input 1: Twilight model Input 2: Movement model
Input 3: Spatial mask
Binary Continuousor
0 km/h 100 0 km/h 100
Residency
ore.g.
e.g.
e.g. probability sampling, MCMC, particle �lter
Median track95% credible interval
5
0
–5
50
45
40
35
March/01 May/01
Model:
Movement
Threshold method Curve method
Twilight error (min)
Sunrise (N = 1)
Likelihood maps
Sunset (N = 1) Sunrise (N = 1)Sunset (N = 1)
Sunset & Sunrise Sunset & Sunrise
0 50 –3 2Expected light
Den
sity
Long
itude
Latit
ude
Den
sity
Mea
sure
d lig
fht
| 13Journal of Animal EcologyLISOVSKI et aL.
representation of the recorded geolocator data and the specifiedpriorassumptionsontwilighterrordistribution,movementcapaci-tiesandspatialrestrictions.
4.5 | Step 5: Movement analysis
Inadditiontoderivinglocationdata,werecommendaformalanaly-sisofmovementbehaviour todelineateperiodsofmovementandresidency.Thisanalysiscanbeafinalstep,basedonposteriorprob-abilities,oran intermediatestepbasedontwilight times. Ineithersituation, determiningmovement dates is not trivial, given the in-herent limitsontheaccuracyandtheprecisionofthe locationes-timates.Differentmethodshavebeenproposedand implementedinthedifferenttools,someofwhichallowrefiningearliersteps inthe analysis (e.g. calibration). An advantage to determiningmove-mentandstationaryperiodsasanintermediatestepisthatitallowsforgroupingandclassifyingsegmentsofthelight-leveltimeseries.Withthesedatacategorizedinthismanner,wecancombinethedatafromseveral successivedays toderivea single location for apre-sumedstationaryperiod(seesupplementaryonlinemanualChapter5“MovementAnalysis”andChapter7“TheGroupModel”fordetailsandexamples).
5 | AF TER THE ANALYSIS
5.1 | Interpretation
Alwaysconsider the limitationsof theresults.Regardlessof theinitial research aims, the geolocation data quality andmigrationpathoftheanimaloftenrequireresearcherstoadjustthescaleatwhichtheydrawconclusions.Ageneralawarenessoftheprecisionandaccuracyofgeolocatortracksisalwayskey,anditisimportanttorealizethatprecisionandaccuracyfrequentlychangethrough-out the trackingperiod.Thehighprecisionexpectedduringonelongshade-freeperiodwilllikelynotapplytoashortshadyperiod.Generally, inferencebecomesmorechallengingoreven impossi-blewhenmigrationpathsaremoregradual rather thandiscrete,whenstopsareofshortdurationanddistancesaresmall,andforlatitudinalmovementsclosetoequinoxperiods.Inpractice,withnoisygeolocationdatayoumaynotalwaysbeabletoaccuratelyand reliably recover stopover sites and timingduring themigra-tionperiod, and it is important to communicate thisuncertaintythroughtheinclusionoferrorrangesintheresults.Notably,suchlimitationsarenotperseuniformacrossanalyses,sincedataqual-ityandmigrationpathscanvary, forexample,betweenseasons,devices,sexes,populationsandspecies.Thelargerthevariabilityindataquality,devicesandmigrationpaths(timingandlocations),themorethecautioniswarrantedtopreventoverestimatingpre-cision and accuracy and over-interpreting data. Caution is par-ticularlywarranted in studies investigatingmultiple specieswithdifferenttypesoftagsandspeciesthatoccupyareaswithhighlyvariablehabitats.
5.2 | Presentation
Geolocator data are often presented inmaps,which are an intui-tiveway to show locations andmigrationpaths.Unfortunately, inmany publications,maps of the results of geolocator analysis ne-glect the uncertainty and assumptions in the underlying analysis,yettheyareimportantforthevalidation,perceptionandinterpreta-tionofresults.Therefore,westronglyencouragevisualizationsthatshowtheprecisionandaccuracy (uncertainty)associatedwith thedata(e.g.Rakhimberdievetal.,2016).Forinstance,summarizedlo-cationsshouldalsoindicatetheirerror.Providingseparateplotsoflatitude versus time and longitude versus time is a simpleway tomakethispresentation(seesupplementaryonlinemanualChapter8“Explorationofresults”).
5.3 | Reporting
While conducting analyses, it is crucial to clearly and unambigu-ouslydocumentallstepstakensothatyourworkwillbereproduc-ible.Althoughinmanycasesthestandardproceduresandpathwaysoutlinedaboveand in thesupplementarymaterialwill suffice, thewealth of options in each analysis requires that methodologicalchoicessuchasparametersettings,filteringorsimilaranalyticalde-cisionsbedocumented.Thesameappliestodeviationsfromstand-ard procedures. The R packages described in this paper facilitatethe creation of annotated and clearly documented scripts, whichwerecommendarchivingassupplementstoanypublishedresearch.Werecommendcompleteandseparateanalysisforeachtag,ratherthansettingupgeneral analysesand looping throughseveral tagsusingtheverysameparametersettings.Thisrecommendationespe-ciallyappliestomoresophisticatedanalysesthatareoftensubjecttotag-specificassumptions.Thesupplementaryonlinemanualgivesanextensiveoverviewofhowtoreportmethodologicalchoicesandpresentresults.
5.4 | Archiving
Archiving geolocator data is not only good scientific practice,it is a precondition for publication in a growing number of jour-nals.Properarchivingretainsthevalueofthedataforfuture(re-)analyses,allowing for re-analysiswithnewanalyticalmethodsoranoverarchingsyntheses(Finchetal.,2017).Asthereareseveraltoolsthatcouldpotentiallybeused,andeachrequiresspecificval-uesforasuiteofparameters,itiscrucialtoarchivebothrawdataandlocationestimatesinconjunctionwiththecodethatledtothespecificresults.
Although several databasesexist for sharing andarchivingan-imalmovementdata, the foremost as a general researchplatformfor animal movement data isMovebank (https://www.movebank.org/).Movebank isparticularlywellsuitedforgeolocatordataas itincludes features tailored to tracking based on ambient light lev-els,supportingstorageofraw lightrecordings, twilightannotation
14 | Journal of Animal Ecology LISOVSKI et aL.
tables,locationestimateswithcredibilityintervalsandcustomfilessuch as R scripts or model output, along with deployment- andstudy-levelmetadata.Researchersareencouragedtopublishtheirdata inthepublicdomain,whichcanbedonebypublishing intheMovebank Data Repository,wherein a dataset can acquire a digitalobjectidentifier(DOI),persistentlink,licenceandcitationafterun-dergoingreview.Forin-progresswork,Movebankprovidesarangeofsharingoptions,forexampleallowingownerstostoreandman-agedataprivately,giveaccesstospecificresearchersandallowthepublictodiscoverbutnotaccessthedataortodownloaddataafteracceptingowner-definedtermsofuse.Werecommendstoringdatain Movebankorsimilarrepositoriesevenduringpreliminaryanalysisstageswhentheresultsandanalysesremainunpublished.Inthesesituations,theonlinearchiveprovidesasecurebackupandcanbediscoveredbyotherresearcherswhocancontacttheowneraboutpotentialcollaborationusingthedata.
6 | SUPPLEMENTARY MATERIAL: THE MANUAL
Thispublicationisaccompaniedbyacomprehensiveonlinemanualthatapplies theconceptstoseveraldatasets,analysesthemusingthe open-source tools discussed above and provides step-by-stepcode as well as recommendations for addressing common issues(https://geolocationmanual.vogelwarte.ch/).We consider this sup-plementaryonlinemanualtobeacommunityendeavourforwhichwegivehereastartingpoint.Userscanaddtheirownexperiencesandanalysispathwayssimplybycontributingtothesourcecodeofthemanualvia itsGitHubrepository (https://github.com/slisovski/TheGeolocationManual). In addition, we recommend the onlineforum “Geolocator Discussion & Support” hosted by “ornithologyexchange” (http://ornithologyexchange.org/forums/forum/259-geolocator-discussion-support/)asaplatformfordiscussionandtheopportunitytoexchangequestionsandanswerswiththegeolocatorusercommunity.
7 | OUTLOOK
Overthenextdecadeorso,geolocatorswilllikelyremainanimpor-tant tool inmigration research, thanks to their low cost and lightweight,whichareasyetunmatchedbyanyofthemorepreciseGPStagsorothertrackingdevices.
There are several recent technological advancements thatwillperhaps even increase their appeal. The most important amongthem isprobably thecombinationof light recordingwithotheren routedata, suchasairpressure,acceleration,magnetismandtem-perature(Bäckmanetal.,2017;Dhanjal-Adamsetal.,2018;Sjöbergetal.,2018).Theseadditionaldatacanfacilitatetherefinementoflocationestimates (seestep4 in theanalysis).For instance,accel-eration data or changes in air pressure can distinguishmovementandresidencyperiodsrelativelyeasily,andsuchpriorknowledgecan
thenbefed intothestationary locationestimation(seeBox3andChapter7;“TheGroupModel”inthesupplementaryonlinemanual).
Theseadditional sensordataalsoprovideawealthof comple-mentary information on fine-scale behaviour: air pressure andtemperature canbeused to infer flight altitude and initiation andterminationofmigratoryor foraging flights; accelerationdata canbeusedtodeterminedailyactivitybudgetsand,givensupplemen-tarymeasurements, be related to energy expenditure and energybudgets;magneticfielddatacanbeusedtoinferstrategiesinorien-tationandnavigationduringmigration.Combiningsuchbehaviouralinformationwith refined, reliable and accurate location estimatescan assist in identifying habitat associations of migrants or theirresponses to evolutionarily novel factors such as artificial light atnight,sensorypollutionorwindfarms.Thisknowledgewillimproveourunderstandingofthefatesofmigrantsandthebottleneckstheymightexperienceatsensitivetimesandplaces,whichwillallowustoimproveourconservationandmanagementstrategiesformigratorypopulations.
ACKNOWLEDG EMENTS
Wewant to acknowledge allwhohavebeen involved in thede-velopment of geolocator tools as well as all participants of themany international geolocator workshops. Furthermore, we liketo acknowledge Steffen Hahn and Felix Liechti for organizing afirstworkshopontheanalysisofgeolocatordatafromsongbirdsback in 2011, financially supported by the Swiss OrnithologicalInstitute and the Swiss National Science Foundation (Grant#IZ32Z0_135914⁄1). Initial collaboration of the authorswas alsofacilitatedby theMIGRATE research coordinationnetwork (NSFIOS-541740). The National Centre for Ecological Analysis andSynthesis, a centre funded by NSF (Grant #EF-0553768), theUniversityofCalifornia,SantaBarbara,andtheStateofCalifornia,supportedtwomeetingsin2012and2013andmanyofthetoolsthat are discussed in the supplementary online manual werekick started at these meetings. We want to thank James FoxfromMigrate Technology Ltd., the Swiss Federal Office for theEnvironment(UTF254.08.08,UTF400.34.11),theSwissNationalScience Foundation (31003A_160265), the SEATRACK project,aswellastheUSNationalScienceFoundation(EAGER0946685,IDBR-1152356, DBI-0963969 and IDR-1014891, EPSCoR-0919466)andtheDanishNationalResearchFoundationforsup-portingtheCenterforMacroecology,EvolutionandClimate(Grantno.DNRF96)forcontinuingfinancialsupportinthedevelopmentoftoolsandorganizationofworkshops.
AUTHOR ' S CONTRIBUTIONS
S.L.andS.B.wrotethefirstdraftwithsignificantcontributionsfromK.L.D.-A.,J.O.andE.S.B.Allauthorscontributedtorevisionsandap-provedthefinalversion.S.L.,K.L.D.-A.,M.T.H.,B.M.,J.K.,E.R.andE.S.B.wrote the initial draft of the supplementary onlinemanual,andallauthorsaddedandcommentedonthecurrentversion.
| 15Journal of Animal EcologyLISOVSKI et aL.
DATA ACCE SSIBILIT Y
Code and data of online supplementarymaterial are available viaZenodo (Lisovski et al., 2019) and viaGitHub: https://github.com/slisovski/TheGeolocationManual.Theonlinesupplementarymanualisreleasedonhttps://geolocationmanual.vogelwarte.ch.
ORCID
Simeon Lisovski https://orcid.org/0000-0002-6399-0035
Silke Bauer https://orcid.org/0000-0002-0844-164X
Martins Briedis https://orcid.org/0000-0002-9434-9056
Sarah C. Davidson https://orcid.org/0000-0002-2766-9201
Kiran L. Dhanjal‐Adams https://orcid.org/0000-0002-0496-8428
Michael T. Hallworth https://orcid.org/0000-0002-6385-3815
Julia Karagicheva https://orcid.org/0000-0002-2404-6826
Christoph M. Meier https://orcid.org/0000-0001-9584-2339
Benjamin Merkel https://orcid.org/0000-0002-8637-7655
Janne Ouwehand https://orcid.org/0000-0003-2573-6287
Lykke Pedersen https://orcid.org/0000-0001-7359-6288
Eldar Rakhimberdiev https://orcid.org/0000-0001-6357-6187
Amélie Roberto‐Charron https://orcid.org/0000-0001-5917-4919
Nathaniel E. Seavy https://orcid.org/0000-0003-0292-3987
Michael D. Sumner https://orcid.org/0000-0002-2471-7511
Caz M. Taylor https://orcid.org/0000-0002-8293-1014
Simon J. Wotherspoon https://orcid.org/0000-0002-6947-4445
Eli S. Bridge https://orcid.org/0000-0003-3453-2008
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How to cite this article:LisovskiS,BauerS,BriedisM,etal.Light-levelgeolocatoranalyses:Auser'sguide.J Anim Ecol. 2019;00:1–16. https://doi.org/10.1111/1365-2656.13036
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly.Itwillnotbepublishedaspartofmainarticle.
Overthepastdecade,light-levelgeolocatortagshaveemergedasanimportanttoolfortrackinganimals.However,duetovariouscaveats,thetranslationoflightrecordingsintolocationestimatescanbedaunting.Inthis'Howto...'paper,weprovideapracticalguidefortheanalysisofgeolocatordatabydescribinggoodpracticesanddiscussingcommonpitfalls.
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