search and foraging behaviors from movement data: a ...received: 12 april 2017 ... research,...
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Ecology and Evolution 2018813ndash24 emsp|emsp13wwwecolevolorg
Received12April2017emsp |emsp Revised6October2017emsp |emsp Accepted11October2017DOI101002ece33593
O R I G I N A L R E S E A R C H
Search and foraging behaviors from movement data A comparison of methods
Ashley Bennison12 emsp|emspStuart Bearhop3emsp|emspThomas W Bodey3 emsp|emspStephen C Votier4emsp|emsp W James Grecian5 emsp|emspEwan D Wakefield56emsp|emspKeith C Hamer7emsp|emspMark Jessopp1
ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicensewhichpermitsusedistributionandreproductioninanymediumprovidedtheoriginalworkisproperlycitedcopy2017TheAuthorsEcology and EvolutionpublishedbyJohnWileyampSonsLtd
Co-seniorauthorship
1MaREICentreforMarineandRenewableEnergyEnvironmentalResearchInstituteUniversityCollegeCorkCorkIreland2SchoolofBiologicalEarthandEnvironmentalSciences(BEES)UniversityCollegeCorkCorkIreland3CentreforEcologyampConservationUniversityofExeterPenrynUK4EnvironmentampSustainabilityInstituteUniversityofExeterPenrynUK5SeaMammalResearchUnitScottishOceansInstituteUniversityofStAndrewsStAndrewsFifeScotland6InstituteofBiodiversityAnimalHealthandComparativeMedicineCollegeofMedicalVeterinaryandLifeSciencesUniversityofGlasgowGlasgowScotland7FacultyofBiologicalSciencesSchoolofBiologyUniversityofLeedsLeedsUK
CorrespondenceAshleyBennisonEnvironmentalResearchInstituteMaREICentreforMarineandRenewableEnergyUniversityCollegeCorkCorkIrelandEmailAshleybennisonuccie
Funding informationNaturalEnvironmentResearchCouncilGrantAwardNumberIRFNEM0179901andNEH0074661IrishResearchCouncilGrantAwardNumberGOIPG2016503MarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearchGrantAwardNumber12RC2302
AbstractSearchbehaviorisoftenusedasaproxyforforagingeffortwithinstudiesofanimalmovementdespiteitbeingonlyonepartoftheforagingprocesswhichalsoincludespreycaptureWhilemethodsforvalidatingpreycaptureexistmanystudiesrelysolelyonbehavioralannotationofanimalmovementdatatoidentifysearchandinferpreycaptureattemptsHoweverthedegreetowhichsearchcorrelateswithpreycaptureislargelyuntestedThisstudyappliedsevenbehavioralannotationmethodstoidentifysearchbehaviorfromGPStracksofnortherngannets(Morus bassanus)andcomparedoutputstotheoccurrenceofdivesrecordedbysimultaneouslydeployedtimendashdepthrecordersWetestedhowbehavioralannotationmethodsvaryintheirabilitytoiden-tifysearchbehavior leadingtodiveeventsTherewasconsiderablevariation inthenumberofdivesoccurringwithinsearchareasacrossmethodsHiddenMarkovmod-els proved tobe themost successfulwith81of all divesoccurringwithin areasidentifiedassearchk-Meansclusteringandfirstpassagetimehadthehighestratesofdives occurring outside identified search behavior First passage time and hiddenMarkovmodelshadthelowestratesoffalsepositivesidentifyingfewersearchareaswithnodivesAllbehavioralannotationmethodshadadvantagesanddrawbacks intermsof thecomplexityofanalysisandability to reflectpreycaptureeventswhileminimizingthenumberoffalsepositivesandfalsenegativesWeusedtheseresultswithconsiderationofanalyticaldifficultytoprovideadviceonthemostappropriatemethodsforusewherepreycapturebehaviorisnotavailableThisstudyhighlightsaneedtocriticallyassessandcarefullychooseabehavioralannotationmethodsuitablefortheresearchquestionbeingaddressedorresultingspeciesmanagementframe-worksestablished
K E Y W O R D S
behaviorfirstpassagetimehiddenMarkovmodelskerneldensityk-meansmachinelearningmovementstate-spacemodelstelemetry
14emsp |emsp emspensp BENNISON Et al
1emsp |emspINTRODUCTION
Movement ismajor part of a speciesrsquo ecologyThe underlying pro-cessesdrivingthemovementofindividualsandpopulationsarestud-iedwidelyhoweveritisoftenunfeasibletodirectlyobserveanimalsthroughconstanteffortAsaresultmovementstudieshavefocussedonremotedetectionofanimalsthroughtechnologiessuchasGPSandsatellite tracking The development miniaturization and reductionofcostinremotetrackingtechnologieshaveenableditswidespreaduse inecological studies (CagnacciBoitaniPowellampBoyce2010)Remote tracking enables behaviors to be inferred from an animalsrsquotrajectory (BuchinDriemelKreveldampSacristaacuten2010)andhas ledto rapid advances in theunderstandingof speciesrsquo ecology (Nathanetal2008)
While movement patterns are often used to distinguish activephasesfromrestorsearchbehaviorfromtraveling(vanBeestampMilner2013 Dzialak OlsonWebb Harju ampWinstead 2015) identifyingthesebehavioralstatestypicallyreliesonmorecomplicatedmodelingprocedurestodetectpotentialunderlyingmechanismswithinbehav-ior identification (JonsenMyersampJames 2006Kerk etal 2015)Considerableprogresshasbeenmadeindevelopingmethodsthatcancategorize behaviors based on simple movement metrics (EdelhoffSignerampBalkenhol 2016)Thesemethods commonly identifymul-tiplestatesandascribethesetopredefinedbehaviorssuchassearchrest or travel (Evans Dall Bolton Owen ampVotier 2015 Guilfordetal 2008 Hamer PhillipsWanless Harris ampWood 2000 KingGlahnampAndrews1995PalmerampWoinarski1999ShepardRossampPortugal2016Weimerskirchetal2006)HoweverGurarieetal(2016) argued for closer and more detailed exploratory analysis ofmovementdata topreventmis-specificationofbehavior suggestingthat the strengths of particularmethods need to bemore carefullyconsideredsotheyaresuitablyattunedtothespecificquestionsbeingaskedbyresearchers
Withinconservationmanagementthereisanincreasingrelianceon identifying space use by species of conservation concern (AllenampSingh2016)Forexamplewithin themarineenvironment forag-ingareascouldbeconsideredfortheprotectionandmanagementofseabirdspecies (Lascellesetal2016)Theuseof theseapproachesmaycontributetotheestablishmentofconservationmeasuresinclud-ingdesignationofmarineprotectedareas (GruumlssKaplanGueacutenetteRobertsampBotsford2011)Foragingactivity is akeycomponent inananimalrsquostimeandenergybudgetanditiswellestablishedthatan-imals inenvironmentswithpatchy resourcesmustengage in searchbehaviortooptimizetheirforagingeffortintermsofmaximizingpreyencounters (MacArthurampPianka1966)Therefore foraging canbeconsideredatwo-partsystemcontainingbothsearchandpreycap-tureattempts(Charnov1976)Understandingtheinteractionbetweensearchandpreycaptureisakeycomponentinoptimalforagingtheory(Pyke1984)Forexamplewhiletherehasbeenmuchworkidentifyingarea-restrictedsearch(KnellampCodling2012)thereislittleinforma-tionontherelationshipbetweensearchandpreycaptureValidationofsearchbehaviorisdifficultparticularlyinanimalswheredirectob-servationischallengingsuchasthoseinmanybiotelemetrystudies
Manymovementstudiesusepathsegmentationtechniquestodetectsearchbehaviorhowevermanyoftheseareunvalidatedestimatesofsearchduetothe lackofaseconddatastreamforground-truthingValidationofpreycaptureattemptshasbeenachievedusinganimal-bornecameras(BicknellGodleySheehanVotierampWitt2016MollMillspaugh Beringer Sartwell amp He 2007) timendashdepth recorders(Deanetal2012Shojietal2015TinkerCostaEstesampWieringa2007) stomach loggers (Weimerskirch Gault amp Cherel 2005) andaccelerometers(HansenLascellesKeeneAdamsampThomson2007Satoetal2007)amongothersHowevermanyofthesetechnologiesareeitherexpensive resulting in small sample sizesor are too largeto deploy on animals in combinationwith location loggerswithoutsignificant adverse impacts (Barron Brawn ampWeatherhead 2010HammerschlagGallagheramp Lazarre 2011Vandenabeele ShepardGroganampWilson 2012)As a resultmany studies still rely on thesoleuseoflocationdataandpathsegmentationapproachestoiden-tify behavior The determination of behavior from movement dataisanactiveareaofresearchandthesubjectofmanyreviews(AllenMetaxasampSnelgrove2017Edelhoffetal2016Haysetal2016JacobyBrooksCroftampSims2012)Thereareseveraldifferentmeth-ods for undertaking behavioral annotation or detecting importantareasofhighusebyanimalsFrequentlyusedaremovementpatterndescriptionandprocessidentificationMethodsbasedaroundmove-mentpatterndescriptionareoftenaimedat trying tosplitbetweendifferentbehavioralperiodsortolocatechangesinbehavior(Edelhoffetal2016)Processidentificationaimstotakethingsastepfurtherandconcentratesonmethodsthatarefocussedtowardbeingabletodescribetheunderlyingprocesseswhetherextrinsicorintrinsicanddescribehowtheseinformbehavior
Northerngannets(Morus bassanus)hereaftergannetsareawell-studiedspecies thatoccurprincipally in thetemperateshelfseasofthe North Atlantic during the breeding season Gannets are visualpredators (Cronin 2012) and undertake plunge-diving from heightentering thewater at speeds of up to 24ms (Chang etal 2016)Prior todiving gannets typically slow their flight and increase theirpathsinuosity(Wakefieldetal2013Bodeyetal2014Patricketal2014Warwick-Evans etal 2015) The relationship between slowspeedduringsearchandpreycaptureattemptshasbeenestablishedboth theoretically (Bartoń amp Hovestadt 2013 Benhamou 2004)andempirically in avarietyofmobilemarine and terrestrial species(Anderson amp Lindzey 2003 Byrne amp Chamberlain 2012 EdwardsQuinn Wakefield Miller amp Thompson 2013 McCarthy HeppellRoyerFreitasampDellinger2010Towneretal2016Wakefieldetal2013Williamsetal2014)Suchchanges inmovementandclearlyidentifiablepreycaptureattemptsintheformofdives(Cleasbyetal2015GartheBenvenutiampMontevecchi2000)aswellastheirabilitytocarrymultipledevicesandeaseofrecapturemakegannetsasuit-ablemodelspeciestoexploretheabilityofmovement-basedanalysistoidentifysearchbehaviorandpreycaptureattempts
Inthisstudyweapplyandcomparesevenmethodologiescoveringmovementpatterndescriptionandprocess identification topredictsearchbehavioringannetsusingGPSlocationdataIfsearchbehaviorisaprecursortopreycaptureattemptsdiveswilloccurprimarilywithin
emspensp emsp | emsp15BENNISON Et al
areasidentifiedassearchWithconsiderationgiventoopportunisticforagingwehypothesizethatmoresuccessfulmethodsofsearchclas-sificationwillcontainmoretruepositives(diveeventsoccurringwithinidentifiedsearch) fewer falsepositives (searchcontainingnodives)and fewer false negatives (dives occurring outside identified searchbehavior)Using this frameworkwewill also provide recommenda-tionsontheappropriateuseofmethodologicalapproaches
2emsp |emspMATERIALS AND METHODS
21emsp|emspData collection
Breedingadults at two islandcoloniesGreatSalteeCoWexfordIreland(5211286minus662189)andBassRockScotlandUK(5607672 minus264139)weretrackedwhileattending2to7-week-oldchicksovera38-dayperiodfromlateJunetoearlyAugust2011NinebirdsatGreatSalteeandeightbirdsatBassRockwerecaughtusingametalcrookorwirenoosefittedtoa4to6-mpoleandfittedwithGPSlog-gerscoupledwithtimendashdepthrecorders(TDRs)GPSloggers(i-gotUGT-200MobileActionTechnologyIncTaipeiTaiwan37g)sealedinheatshrinkplasticrecordedlocationsevery2minCEFASG5TDRs(CEFASTechnologyLowestoftUK25g)weredeployedusingthefast-logdivesensorat4Hzandusedtoidentifydiveeventsbasedona1mdepththresholdbeingexceededhereafterTDRdivesThiswas to ensure dives reflected prey capture attempts (median divedepthof46minplunge-divinggannetsand8mwhenpursuitdiv-ing(Gartheetal2000)ratherthanothersurface-relatedactivitiessuchasrestingwashingorpreeningDeviceswereattachedfollow-ing (Greacutemillet etal 2004) and involved affixing loggers ventrallyto2ndash4central tail feathersusingstripsofwaterproofTesacopytapeTotalinstrumentmasswasle2ofbodymassbelowthemaximumrecommended for seabird biologging studies (Phillips Xavier ampCroxall2003)andtagpositionwasconsideredtominimallyimpedegannetsaerodynamicallyorhydrodynamically (Vandenabeeleetal2012)Deploymentandretrievalhandlingtimeswereapproximately 10min
22emsp|emspData processing
GPStrackswereprocessedusingtheAdehabitatLTpackage(Calenge2011)intheRstatisticalFrameworkLocationdataweretransformedinto Cartesian coordinates using a Universal Transverse Mercator(UTM)30Nprojectionbeforecalculatingsteplengthandturningan-gles AlthoughGPS tagswere programmed to take locations every2min if therewasnoavailableGPSsignal (becauseabirdwasdiv-ing forexample) locationsmaynothavebeenexactly twominutesapartandsotrackswerestandardizedthroughlinearinterpolationtoatwo-minuteintervalSpeedsteplengthturningangleanddistancefromcolonywerecalculatedforeverypointalongabirdrsquostrackPointswithin5kmofthecolonywereremovedtoavoidpotentiallocationsassociated with colony rafting and bathing (Carter etal 2016) aswerethoseoccurringatnight(betweencivilsunsetandsunrise)be-causegannetsarevisualdiurnal foragers (Nelson2002)TDRdivesweresplitintodiveeventsandproducedasingletimestamppointrep-resentingthestartofanydiveeventover1mforappendingtotracksfollowingbehavioralclassification
Weappliedasuiteofmethodscommonlyusedtoidentifysearch-ingor infer foragingbehaviors frommovementdata summarized inTable1Themethods are not considered exhaustive but representa range of approaches coveringmovement pattern description andprocess identification (Edelhoff etal 2016)Movementpatternde-scriptionapproachesincludekerneldensityfirstpassagetime(FPT)and speedtortuosity thresholds while process identification tech-niquesappliedcoveredk-meansclusteringandtwostate-spacemod-elshiddenMarkovmodels(HMM)andeffectivemaximizationbinaryclustering (EMbC)The two formsof state-spacemodelswereusedtorepresentdivergingclassesofstate-spacemodelmaximumlikeli-hoodmethods (EMbC) andBayesianMonteCarlomethods (HMM)(Patterson Thomas Wilcox Ovaskainen amp Matthiopoulos 2008)While not predictingidentifying search behavior directly we alsoappliedmachinelearning(generalizedboostedregressionmodels)topredictdivesfromtrackmetricsratherthansearchbehaviorWefol-lowedthestandardmethodologyforeachtechniqueoutlined inthe
TABLE 1emspSummaryofcommonmethodologicalapproachestoidentifyingsearchandforagingbehaviorinmovementdataWhileallmethodsrequirevalidationdatatoassesshowwellthemethodworksitisnotnecessarilyrequiredtoimplementthemethod
MethodAnalysis complexity
Requires validation data
Suitable for investigating relationships with environmental variables
Immediately applicable to other specieslocations
Machinelearning
Highamplargedatarequirement
Yes Yes No
k-Means Low No Yes Yes
Thresholds Medium Yes Yes No
FPT Medium No Yes Yes
HMM Medium Noa Yes Yes
Kerneldensity
Low No Dependentonscale Yes
EMbC Low No Yes Yes
aHMMdonotrequirevalidationdatainthiscontextbutcanemployifdesired
16emsp |emsp emspensp BENNISON Et al
publishedliteratureandprovidereferencesfordetailedguidanceonapplyingeachapproach
MethodsofpredictingsearchbehaviorroutinelyidentifychainsofsearchinsuccessivelocationsChainscanbeasinglepointinlengthormay includemultipleconsecutivepointsalongamovement track(seeFigure1)Giventhatingannetsindividualpreycaptureattempts(dives)occuratdiscretelocationstimesweextractedmetricsofdivesoccurringwithinsearchdivesoutsideofsearchandsearchcontainingnodiveDatafromthetwocolonieswereprocessedindependentlytoaccountforpotentialdifferencesinmovementmetricsassociatedwithdifferencesinlocalhabitatandpreyavailability
23emsp|emspKernel density
Time in space is considered to be a goodproxy for foraging effort(Warwick-Evans etal 2015) GPS locations (excluding locationswithin5kmofthecolonyandlocationsatnight)wereusedtoesti-matekerneldensitiesinArcMap102whichusesakernelsmoothingfunctionbasedonthequartickernelfunctionbySilverman(1986)andhadabandwidthsearchdistanceof10kmThiswasusedtocreateakerneldensitysquaregridwithsidesof10kmThemethodproducesa10km2gridwithrelativeintensityofbothTDRdivesandGPStracksDutilleulrsquos modified spatial t test (Dutilleul Clifford Richardson ampHemon1993)wasusedtodeterminethespatialcorrelationbetweenthe intensityofdives and intensityof tracks accounting for spatialautocorrelationinthedata
24emsp|emspFirst passage time
Firstpassagetime(FPT)analysiswasundertakenfollowingFauchaldand Tveraa (2003) Although tracks were rediscretized in time forallotheranalysisFPTrequirestrackstoberedistributedinspacetoaccountforchanges inbirdspeedandsotrackswereredistributedusing linear interpolation to 500-m distances Analysis was under-takenusingtheAdehabitatLTpackageinR(Calenge2011)Basedonthebehavioral response ranges reportedbyBodeyetal (2014) forgannetscirclesofradiirangingfrom50mto12000mwereusedtoconstructfirstpassagetimevaluesThemaximumlog-varianceoffirstpassagetimevalueswasthenusedtodetermineappropriatesearch
radiiforeachindividualbirdTheslowestsextileofpassagetimeswasconsideredtoberelativelyhigher intensitysearchbehaviorasusedbyNordstromBattaileCotte andTrites (2013) andalso indicatedinworkbyHameretal(2009)followingFauchaldandTveraa(2003)SearchradiiwereusedtocreateanamalgamatedareaofsearchalonganindividualbirdrsquostrackwithGPSpointsalongthistracktreatedasldquosearchrdquopointsAlthoughFPTcanbeusedtodeterminenestedlev-elsofarea-restrictedsearch(Hameretal2009)wehaveconsideredonlythehighestlevelsofsearchbehaviortomaximizethenumberofdivespotentiallyoccurringwithinsearch
25emsp|emspk- Means clustering
k-Means clustering is amethodof vectorquantization that aims topartitionn observations intok clusters andhasbeenused to clus-ter data points consistent with different behaviors (Jain 2010) k-Means clustering was undertaken using the MacQueen algorithm(MacQueen1967)onsteplengthandturninganglebetweensucces-siveGPSlocationsTheoptimumnumberofclusterswasdeterminedusingtheldquoelbowmethodrdquowherethepercentageofvarianceexplained(theratioofthebetween-groupvariancetothetotalvariance)isplot-tedasafunctionofthenumberofclustersandthepointwhereaddi-tionoffurtherclustersresultsinonlymarginalincreasesinexplainedvariance(KetchenampShook1996)Thisresultedinthreeclustersandthesewere then assignedbehavioral states basedon logical differ-encesbetweenthemeansofvariablesineachgroupTheclusterwithlargeststeplengthandsmallesttortuositywasdefinedastravelshortstep lengthand intermediate tortuositywere consideredconsistentwithrestandintermediatesteplengthandhightortuositywerecon-sidered consistent with search behavior following Zhang OrsquoReillyPerryTaylorandDennis(2015)
26emsp|emspSpeedndashtortuosity thresholds
Speedndashtortuosity thresholds fromWakefield etal (2013) were ap-plied to thedataTheseweredevelopedbasedonpriorknowledgeof gannet foragingbehavior and an iterative examinationof plausi-ble thresholds of movement indices from those initially suggestedbyGreacutemillet etal (2004) Thresholds suggestedbyWakefield etal(2013)wereappliedastheywerebasedondatafromtrackedgannetsfroma rangeof colonies including the data analyzed in this studySuccessiveGPSlocationswereconsideredtorepresentsearchiftheymetanyoneofthreeconditions
1 Tortuosity lt09 and speed gt1ms2 Speedgt15msandlt9ms3 Tortuosityge09andaccelerationltminus4ms2
SpeedandaccelerationwerecalculatedbetweenLminus1andL0 where L0 isthefocalpointwhiletortuosityistheratioofthestraightlinetoalongthetrackdistancebetweenLminus4andL4CriteriaweredefinedbasedonGPSandTDRdatafromBassRockdeploymentsusedinthisstudyandarethereforecreatedfromaprioriinformation
F IGURE 1emspConceptualdiagramoflocationsthroughtimeidentifyingpointsofsearchbehaviorwithintheseriesthatrevealsearchchainsofdifferinglengths
emspensp emsp | emsp17BENNISON Et al
27emsp|emspHidden Markov Models
HiddenMarkovModels(HMM)areanexampleofstate-spacemod-elingwheremodelsareformedoftwopartsanobservableseriesandanonobservablestatesequence(Langrocketal2012)Theobserv-ableseriesinthiscontexttaketheformofGPSrelocationswithcon-sequentialsteplengthandturninganglewhilethenonobservablearebehavioralstatesHMMuseatimeseriestodeterminewhatdenotesthe underlying states and the changes between them The applica-tionof state-switchingmodels tomovementdataallowsbehavioralmodestobeexaminedwhileconsideringthehighdegreeofautocor-relationpresentintelemetrydata(Pattersonetal2008)WhentheobservationalerrorislowhiddenMarkovmodelsofferamoretrac-table approach to discretize behavioralmodes from telemetry datathanBayesianapproaches(Langrocketal2012)UsingtheRpack-agemoveHMM (Michelot Langrockamp Patterson 2016) themove-mentofeachindividualalongaforagingtripwasclassifiedintooneofthreeunderlyingstatesbycharacterizationofthedistributionsofsteplengthsand turninganglesbetweenconsecutive locationsA three-statemodelwasabetterfittothedatathanatwo-statemodelandisconsistentwithpreviousworkdescribinggannetmovement(Bodeyetal2014)aswellastheidentificationofthreestatesinEMbCandk-means clustering approaches within this study Model iterationssuccessfully converged to three states suggesting a good fit to thedataAgammadistributionwasusedtodescribethesteplengthsavonMisesdistributiondescribedtheturninganglesandtheViterbialgorithmwasusedtoestimatethemostlikelysequenceofmovementstates tohavegenerated theobservations (ZucchiniMacDonaldampLangrock2016)
28emsp|emspExpectationndashmaximization binary clustering
Expectationndashmaximizationbinaryclustering(EMbC)protocolsareanunsupervisedmultivariateexampleofastate-spacemodelingframe-work that canbeused forbehavioral annotationofmovement tra-jectories including search behavior (seeGarriga PalmerOltra andBartumeus(2016))EMbChasbeendesignedtobeasimplemethodofanalyzingmovementdatabasedonthegeometryaloneandcanbehaviorally annotate movement data with minimal supervisionEMbCisarelativelymoderntechniquethatisgainingtractionwithinmovementecologyIthaspreviouslybeenusedinavarietyofmove-mentstudiesincludingexploringbehavioraldifferencesbetweendis-tinctpopulationsofthered-footedbooby(Mendezetal2017)andcouplingenergybudgetswithbehavioralpatternsunderanoptimalforagingframework (LouzaoWiegandBartumeusampWeimerskirch2014)AnalysiswasundertakenusingtheEMbCpackageinR(GarrigaampBartumeus2015)usingcalculatedvelocitiesandturninganglestoinferbehavioralclassifications
29emsp|emspMachine learning
While themethodsoutlinedaboveall identifysearchbehaviorma-chinelearningmodelsaretrainedtospecificallyidentifypreycapture
dive events based on trackmetrics Analysiswas undertaken usingtheCaretpackageinR(Kuhn2008)usinggeneralizedboostedregres-sionmodelstoaccountforzero-inflation(ElithLeathwickampHastie2008)ModelswerebuiltusingsteplengthspeedturninganglehourofdayandtortuosityModelsweretrainedusing75ofthelinkedGPSTDRdivedatawiththeremaining25ofdatakeptforvalida-tionofpredictionsandunderwentcross-validation500timesduringthetrainingprocedureBycombiningallindividualanimalrsquosdatainthismannerweensurethatanyintra-individualvariationisaccountedforinthemodelingprocessReceiveroperatorcurves (ROCs)werecal-culated(FieldingampBell1997)todeterminethemodelofbestfitateachcolony
210emsp|emspComparison of methods using TDR dives
Inordertocomparethepredictivepowerofthesevenmethodsout-lined above in predicting areas in which dives occurred TDR diveeventswere linked toGPS coordinates bymatching the timedatestampsofbothdatasetsforeachindividuallytrackedbirdTocomparehowwellthemethodscapturediveeventswithinareasofsearchtheproportionofdiveswithinidentifiedareasofsearch(truepositive)aswellasthenumberofsearchchainscontainingnodives(falseposi-tive)wascalculatedforFPTk-meansthresholdsHMMandEmbCThecorrelationbetweenkerneldensitiesofGPStracksandTDRdiveswasassessedusingaDutilleulrsquosmodifiedspatialttest(Dutilleuletal1993)Thisanalysisprovidesacorrelationcoefficientacrossthespa-tialextentofthetrackeddatatodeterminehowwellthetwodatasetscorrelatewhileaccountingforspatialautocorrelationModelperfor-manceformachinelearningwasassessedusingkappavaluesameas-ureofvariabilityexplainedbythemodelakintoR2valueswhere0isequaltonorelationshipand1isequaltoaperfectrelationshipasperLandisandKoch(1977)Furthertothisaconfusionmatrixwascalcu-latedbyrunningmodelsontheremaining25testdatatoassessthenumberofcorrectlyandincorrectlyidentifieddives
3emsp |emspRESULTS
NineGPSampTDRcombinationsweredeployedatGreatSalteeresult-ingin31716locationsafterstandardizationtoatwo-minuteintervalEightGPSampTDRcombinationsweredeployedatBassRockresultingin21208relocationswhenstandardizedTherewereatotalof2830TDRdivesamongthetrackedbirdsatGreatSalteeand2172atBassRockExamplesofmapsproducedbymethodsandshowingtheloca-tionofTDRdivescanbeseen in theSupplementaryMaterials (seeFigsS1ndash10)
FPT k-means EMbC thresholds and HMM all predict searchratherthanpreycaptureattemptsperseAllmethodspredictedcon-siderablesearcheffortacrossthetrackingperiod(Table2)FPTiden-tifiedthe longestcontiguouschainsofsearchbehavior (mean2474locationschain) followed by HMM (mean 858 locationschain)speed and tortuosity thresholds (mean 457 locationschain) andEMbC (mean 238 locationschain) k-Means method identified the
18emsp |emsp emspensp BENNISON Et al
mostdiscretesearchareaswiththeshortestchains(mean308loca-tionschain)UsingKendallrsquostaucorrelationtherewasaweakpositivecorrelationbetween the lengthof searchchainsand thenumberofdivesoccurringwithinthem(Table3)
The performance of behavioral classification methods was as-sessedbycomparingtheoccurrenceofTDRdivesinsideandoutsideof predicted search behavior (Table2) HMM captured the highestproportionofTDRdives(Figure2a)withinsearchareasandhadthesecond lowest false-positive rate (Figure2b) FPT had the longestidentifiedsearchchainsbuttheseactuallycapturedthelowestnum-berofdivesacrossallmethods (Table2Figure2a)Despitethe lowtrue-positiveratesFPThadthelowestfalse-positiverate(Figure2b)ThresholdsandEMbCwerecomparativelysimilarinboththeratesoftrueandfalsepositiveswhilek-meansclusteringhadthelowesttrue-positiveandhighestfalse-positiveratesofallmethodstested
KerneldensityofGPS locationsdidnotexplicitly identifysearchbehaviorbutidentifiedldquohotspotsrdquoofforagingcorrespondingtotimespentineach10times10kmgridcellwithahighproportionoftimespentintheareasurroundingcolonies(Figure3)Dutilleulrsquosmodifiedspatialttestdemonstratedagoodcorrelationbetweenthespatialdistribu-tionofTDRdivesandtimeinspace(Table4)withthebettercorrela-tion(086)atBassRockMachinelearningmodelsdirectlypredictedthe locationof prey capture eventsThemodels trained and testedontheirowncolony indicatedonlya fairorslightagreementwithinthedata(followingLandisandKoch1977)(Table5)Furthermoretheconfusionmatrix (Table6) showed that the predictive power of themodelsatbothcolonieswaspooronlysuccessfullypredicting22ofdivesinthetestdatasetWhenmodelsbuilt inonecolonywereap-pliedtootherstherewasafurtherlossofpredictivepowerindicatingthatmodelstructuresandmovementpatternsbetweencoloniesaredifferent(Table4)
4emsp |emspDISCUSSION
Sevenmethods of classifying search behaviorwere compared to avalidationdatasetofTDRdiveevents innortherngannetstodeter-minetheirabilitytoaccuratelycapturethetwocomponentsofforag-ingactivitymdashsearchingandpreyencountercaptureAcrossmethodsthenumberofpreycaptureattempts(TDRdives)withinsearchvariedconsiderablywiththehighestbeingcapturedbyhiddenMarkovmod-els(81)andthelowestcapturedbyfirstpassagetimeandk-meansclustering(30and31respectively)WhileHMMhadthehighestrateofcaptureofdiveeventsitalsohadoneofthelowestratesoffalsepositivesidentifyingfewersearchchainswherenodivewasre-cordedWhilethiswasstillrelativelyhigh(60)allmethodsproducedhighnumbersof search chains that containednoTDRdives (range53ndash76)TherewasaweakcorrelationbetweenchainlengthandnumberdiveswithinachainWhilepreycaptureattemptswillincreasewith trip and search duration (Sommerfeld Kato Ropert-CoudertGartheampHindell2013)theweakcorrelationrepresentssomelongersearchchainscontainingrelativelyfewpreycaptureattemptsduetoindividuals searching over poor-quality areas or simply that searchT
ABLE 2emspComparisonofsearchidentificationacrossmethodsatGreatSalteeandBassRockwithassociatedTDRdivesateachcolonyTruepositivesarewhenadiveoccurswithinachainof
locationsidentifiedassearchfalsepositivesarewhenachainoflocationsidentifiedassearchdoesnotcontainaTDRdiveandfalsenegativesarewhenadiveoccursoutsideofareasidentified
assearchandwillincludeopportunisticforagingevents
Met
hod
Gre
at S
alte
eBa
ss R
ock
No
of re
loca
tions
31
716
No
of d
ives
28
30N
o of
relo
catio
ns 2
120
8 N
o of
div
es 2
172
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
FPT
3059
5730
6941
1668
2968
4642
703
21663
k-Meansclustering
3752
740
06248
272
72191
7992
7809
1917
Thresholds
7681
6798
2319
3715
5750
6395
4250
2869
HMM
808
16305
1919
4153
813
05676
187
03667
EMbC
5091
7390
4909
207
14604
6861
5396
2726
Kerneldensity
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
Machinelearning
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
a MachinelearningandkerneldensityassessedwithothermetricsduetonatureofanalysisseeTables3ndash5
b NighttimeandlocationsclosetothecolonyhavebeenomittedTheremainingproportionofrelocationsisconsideredtobeacombinationofrestandtravel
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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AllenAMampSinghNJ (2016)Linkingmovementecologywithwild-lifemanagementandconservationFrontiers in Ecology and Evolution3155httpsdoiorg103389fevo201500155
Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
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CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
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novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
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ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
14emsp |emsp emspensp BENNISON Et al
1emsp |emspINTRODUCTION
Movement ismajor part of a speciesrsquo ecologyThe underlying pro-cessesdrivingthemovementofindividualsandpopulationsarestud-iedwidelyhoweveritisoftenunfeasibletodirectlyobserveanimalsthroughconstanteffortAsaresultmovementstudieshavefocussedonremotedetectionofanimalsthroughtechnologiessuchasGPSandsatellite tracking The development miniaturization and reductionofcostinremotetrackingtechnologieshaveenableditswidespreaduse inecological studies (CagnacciBoitaniPowellampBoyce2010)Remote tracking enables behaviors to be inferred from an animalsrsquotrajectory (BuchinDriemelKreveldampSacristaacuten2010)andhas ledto rapid advances in theunderstandingof speciesrsquo ecology (Nathanetal2008)
While movement patterns are often used to distinguish activephasesfromrestorsearchbehaviorfromtraveling(vanBeestampMilner2013 Dzialak OlsonWebb Harju ampWinstead 2015) identifyingthesebehavioralstatestypicallyreliesonmorecomplicatedmodelingprocedurestodetectpotentialunderlyingmechanismswithinbehav-ior identification (JonsenMyersampJames 2006Kerk etal 2015)Considerableprogresshasbeenmadeindevelopingmethodsthatcancategorize behaviors based on simple movement metrics (EdelhoffSignerampBalkenhol 2016)Thesemethods commonly identifymul-tiplestatesandascribethesetopredefinedbehaviorssuchassearchrest or travel (Evans Dall Bolton Owen ampVotier 2015 Guilfordetal 2008 Hamer PhillipsWanless Harris ampWood 2000 KingGlahnampAndrews1995PalmerampWoinarski1999ShepardRossampPortugal2016Weimerskirchetal2006)HoweverGurarieetal(2016) argued for closer and more detailed exploratory analysis ofmovementdata topreventmis-specificationofbehavior suggestingthat the strengths of particularmethods need to bemore carefullyconsideredsotheyaresuitablyattunedtothespecificquestionsbeingaskedbyresearchers
Withinconservationmanagementthereisanincreasingrelianceon identifying space use by species of conservation concern (AllenampSingh2016)Forexamplewithin themarineenvironment forag-ingareascouldbeconsideredfortheprotectionandmanagementofseabirdspecies (Lascellesetal2016)Theuseof theseapproachesmaycontributetotheestablishmentofconservationmeasuresinclud-ingdesignationofmarineprotectedareas (GruumlssKaplanGueacutenetteRobertsampBotsford2011)Foragingactivity is akeycomponent inananimalrsquostimeandenergybudgetanditiswellestablishedthatan-imals inenvironmentswithpatchy resourcesmustengage in searchbehaviortooptimizetheirforagingeffortintermsofmaximizingpreyencounters (MacArthurampPianka1966)Therefore foraging canbeconsideredatwo-partsystemcontainingbothsearchandpreycap-tureattempts(Charnov1976)Understandingtheinteractionbetweensearchandpreycaptureisakeycomponentinoptimalforagingtheory(Pyke1984)Forexamplewhiletherehasbeenmuchworkidentifyingarea-restrictedsearch(KnellampCodling2012)thereislittleinforma-tionontherelationshipbetweensearchandpreycaptureValidationofsearchbehaviorisdifficultparticularlyinanimalswheredirectob-servationischallengingsuchasthoseinmanybiotelemetrystudies
Manymovementstudiesusepathsegmentationtechniquestodetectsearchbehaviorhowevermanyoftheseareunvalidatedestimatesofsearchduetothe lackofaseconddatastreamforground-truthingValidationofpreycaptureattemptshasbeenachievedusinganimal-bornecameras(BicknellGodleySheehanVotierampWitt2016MollMillspaugh Beringer Sartwell amp He 2007) timendashdepth recorders(Deanetal2012Shojietal2015TinkerCostaEstesampWieringa2007) stomach loggers (Weimerskirch Gault amp Cherel 2005) andaccelerometers(HansenLascellesKeeneAdamsampThomson2007Satoetal2007)amongothersHowevermanyofthesetechnologiesareeitherexpensive resulting in small sample sizesor are too largeto deploy on animals in combinationwith location loggerswithoutsignificant adverse impacts (Barron Brawn ampWeatherhead 2010HammerschlagGallagheramp Lazarre 2011Vandenabeele ShepardGroganampWilson 2012)As a resultmany studies still rely on thesoleuseoflocationdataandpathsegmentationapproachestoiden-tify behavior The determination of behavior from movement dataisanactiveareaofresearchandthesubjectofmanyreviews(AllenMetaxasampSnelgrove2017Edelhoffetal2016Haysetal2016JacobyBrooksCroftampSims2012)Thereareseveraldifferentmeth-ods for undertaking behavioral annotation or detecting importantareasofhighusebyanimalsFrequentlyusedaremovementpatterndescriptionandprocessidentificationMethodsbasedaroundmove-mentpatterndescriptionareoftenaimedat trying tosplitbetweendifferentbehavioralperiodsortolocatechangesinbehavior(Edelhoffetal2016)Processidentificationaimstotakethingsastepfurtherandconcentratesonmethodsthatarefocussedtowardbeingabletodescribetheunderlyingprocesseswhetherextrinsicorintrinsicanddescribehowtheseinformbehavior
Northerngannets(Morus bassanus)hereaftergannetsareawell-studiedspecies thatoccurprincipally in thetemperateshelfseasofthe North Atlantic during the breeding season Gannets are visualpredators (Cronin 2012) and undertake plunge-diving from heightentering thewater at speeds of up to 24ms (Chang etal 2016)Prior todiving gannets typically slow their flight and increase theirpathsinuosity(Wakefieldetal2013Bodeyetal2014Patricketal2014Warwick-Evans etal 2015) The relationship between slowspeedduringsearchandpreycaptureattemptshasbeenestablishedboth theoretically (Bartoń amp Hovestadt 2013 Benhamou 2004)andempirically in avarietyofmobilemarine and terrestrial species(Anderson amp Lindzey 2003 Byrne amp Chamberlain 2012 EdwardsQuinn Wakefield Miller amp Thompson 2013 McCarthy HeppellRoyerFreitasampDellinger2010Towneretal2016Wakefieldetal2013Williamsetal2014)Suchchanges inmovementandclearlyidentifiablepreycaptureattemptsintheformofdives(Cleasbyetal2015GartheBenvenutiampMontevecchi2000)aswellastheirabilitytocarrymultipledevicesandeaseofrecapturemakegannetsasuit-ablemodelspeciestoexploretheabilityofmovement-basedanalysistoidentifysearchbehaviorandpreycaptureattempts
Inthisstudyweapplyandcomparesevenmethodologiescoveringmovementpatterndescriptionandprocess identification topredictsearchbehavioringannetsusingGPSlocationdataIfsearchbehaviorisaprecursortopreycaptureattemptsdiveswilloccurprimarilywithin
emspensp emsp | emsp15BENNISON Et al
areasidentifiedassearchWithconsiderationgiventoopportunisticforagingwehypothesizethatmoresuccessfulmethodsofsearchclas-sificationwillcontainmoretruepositives(diveeventsoccurringwithinidentifiedsearch) fewer falsepositives (searchcontainingnodives)and fewer false negatives (dives occurring outside identified searchbehavior)Using this frameworkwewill also provide recommenda-tionsontheappropriateuseofmethodologicalapproaches
2emsp |emspMATERIALS AND METHODS
21emsp|emspData collection
Breedingadults at two islandcoloniesGreatSalteeCoWexfordIreland(5211286minus662189)andBassRockScotlandUK(5607672 minus264139)weretrackedwhileattending2to7-week-oldchicksovera38-dayperiodfromlateJunetoearlyAugust2011NinebirdsatGreatSalteeandeightbirdsatBassRockwerecaughtusingametalcrookorwirenoosefittedtoa4to6-mpoleandfittedwithGPSlog-gerscoupledwithtimendashdepthrecorders(TDRs)GPSloggers(i-gotUGT-200MobileActionTechnologyIncTaipeiTaiwan37g)sealedinheatshrinkplasticrecordedlocationsevery2minCEFASG5TDRs(CEFASTechnologyLowestoftUK25g)weredeployedusingthefast-logdivesensorat4Hzandusedtoidentifydiveeventsbasedona1mdepththresholdbeingexceededhereafterTDRdivesThiswas to ensure dives reflected prey capture attempts (median divedepthof46minplunge-divinggannetsand8mwhenpursuitdiv-ing(Gartheetal2000)ratherthanothersurface-relatedactivitiessuchasrestingwashingorpreeningDeviceswereattachedfollow-ing (Greacutemillet etal 2004) and involved affixing loggers ventrallyto2ndash4central tail feathersusingstripsofwaterproofTesacopytapeTotalinstrumentmasswasle2ofbodymassbelowthemaximumrecommended for seabird biologging studies (Phillips Xavier ampCroxall2003)andtagpositionwasconsideredtominimallyimpedegannetsaerodynamicallyorhydrodynamically (Vandenabeeleetal2012)Deploymentandretrievalhandlingtimeswereapproximately 10min
22emsp|emspData processing
GPStrackswereprocessedusingtheAdehabitatLTpackage(Calenge2011)intheRstatisticalFrameworkLocationdataweretransformedinto Cartesian coordinates using a Universal Transverse Mercator(UTM)30Nprojectionbeforecalculatingsteplengthandturningan-gles AlthoughGPS tagswere programmed to take locations every2min if therewasnoavailableGPSsignal (becauseabirdwasdiv-ing forexample) locationsmaynothavebeenexactly twominutesapartandsotrackswerestandardizedthroughlinearinterpolationtoatwo-minuteintervalSpeedsteplengthturningangleanddistancefromcolonywerecalculatedforeverypointalongabirdrsquostrackPointswithin5kmofthecolonywereremovedtoavoidpotentiallocationsassociated with colony rafting and bathing (Carter etal 2016) aswerethoseoccurringatnight(betweencivilsunsetandsunrise)be-causegannetsarevisualdiurnal foragers (Nelson2002)TDRdivesweresplitintodiveeventsandproducedasingletimestamppointrep-resentingthestartofanydiveeventover1mforappendingtotracksfollowingbehavioralclassification
Weappliedasuiteofmethodscommonlyusedtoidentifysearch-ingor infer foragingbehaviors frommovementdata summarized inTable1Themethods are not considered exhaustive but representa range of approaches coveringmovement pattern description andprocess identification (Edelhoff etal 2016)Movementpatternde-scriptionapproachesincludekerneldensityfirstpassagetime(FPT)and speedtortuosity thresholds while process identification tech-niquesappliedcoveredk-meansclusteringandtwostate-spacemod-elshiddenMarkovmodels(HMM)andeffectivemaximizationbinaryclustering (EMbC)The two formsof state-spacemodelswereusedtorepresentdivergingclassesofstate-spacemodelmaximumlikeli-hoodmethods (EMbC) andBayesianMonteCarlomethods (HMM)(Patterson Thomas Wilcox Ovaskainen amp Matthiopoulos 2008)While not predictingidentifying search behavior directly we alsoappliedmachinelearning(generalizedboostedregressionmodels)topredictdivesfromtrackmetricsratherthansearchbehaviorWefol-lowedthestandardmethodologyforeachtechniqueoutlined inthe
TABLE 1emspSummaryofcommonmethodologicalapproachestoidentifyingsearchandforagingbehaviorinmovementdataWhileallmethodsrequirevalidationdatatoassesshowwellthemethodworksitisnotnecessarilyrequiredtoimplementthemethod
MethodAnalysis complexity
Requires validation data
Suitable for investigating relationships with environmental variables
Immediately applicable to other specieslocations
Machinelearning
Highamplargedatarequirement
Yes Yes No
k-Means Low No Yes Yes
Thresholds Medium Yes Yes No
FPT Medium No Yes Yes
HMM Medium Noa Yes Yes
Kerneldensity
Low No Dependentonscale Yes
EMbC Low No Yes Yes
aHMMdonotrequirevalidationdatainthiscontextbutcanemployifdesired
16emsp |emsp emspensp BENNISON Et al
publishedliteratureandprovidereferencesfordetailedguidanceonapplyingeachapproach
MethodsofpredictingsearchbehaviorroutinelyidentifychainsofsearchinsuccessivelocationsChainscanbeasinglepointinlengthormay includemultipleconsecutivepointsalongamovement track(seeFigure1)Giventhatingannetsindividualpreycaptureattempts(dives)occuratdiscretelocationstimesweextractedmetricsofdivesoccurringwithinsearchdivesoutsideofsearchandsearchcontainingnodiveDatafromthetwocolonieswereprocessedindependentlytoaccountforpotentialdifferencesinmovementmetricsassociatedwithdifferencesinlocalhabitatandpreyavailability
23emsp|emspKernel density
Time in space is considered to be a goodproxy for foraging effort(Warwick-Evans etal 2015) GPS locations (excluding locationswithin5kmofthecolonyandlocationsatnight)wereusedtoesti-matekerneldensitiesinArcMap102whichusesakernelsmoothingfunctionbasedonthequartickernelfunctionbySilverman(1986)andhadabandwidthsearchdistanceof10kmThiswasusedtocreateakerneldensitysquaregridwithsidesof10kmThemethodproducesa10km2gridwithrelativeintensityofbothTDRdivesandGPStracksDutilleulrsquos modified spatial t test (Dutilleul Clifford Richardson ampHemon1993)wasusedtodeterminethespatialcorrelationbetweenthe intensityofdives and intensityof tracks accounting for spatialautocorrelationinthedata
24emsp|emspFirst passage time
Firstpassagetime(FPT)analysiswasundertakenfollowingFauchaldand Tveraa (2003) Although tracks were rediscretized in time forallotheranalysisFPTrequirestrackstoberedistributedinspacetoaccountforchanges inbirdspeedandsotrackswereredistributedusing linear interpolation to 500-m distances Analysis was under-takenusingtheAdehabitatLTpackageinR(Calenge2011)Basedonthebehavioral response ranges reportedbyBodeyetal (2014) forgannetscirclesofradiirangingfrom50mto12000mwereusedtoconstructfirstpassagetimevaluesThemaximumlog-varianceoffirstpassagetimevalueswasthenusedtodetermineappropriatesearch
radiiforeachindividualbirdTheslowestsextileofpassagetimeswasconsideredtoberelativelyhigher intensitysearchbehaviorasusedbyNordstromBattaileCotte andTrites (2013) andalso indicatedinworkbyHameretal(2009)followingFauchaldandTveraa(2003)SearchradiiwereusedtocreateanamalgamatedareaofsearchalonganindividualbirdrsquostrackwithGPSpointsalongthistracktreatedasldquosearchrdquopointsAlthoughFPTcanbeusedtodeterminenestedlev-elsofarea-restrictedsearch(Hameretal2009)wehaveconsideredonlythehighestlevelsofsearchbehaviortomaximizethenumberofdivespotentiallyoccurringwithinsearch
25emsp|emspk- Means clustering
k-Means clustering is amethodof vectorquantization that aims topartitionn observations intok clusters andhasbeenused to clus-ter data points consistent with different behaviors (Jain 2010) k-Means clustering was undertaken using the MacQueen algorithm(MacQueen1967)onsteplengthandturninganglebetweensucces-siveGPSlocationsTheoptimumnumberofclusterswasdeterminedusingtheldquoelbowmethodrdquowherethepercentageofvarianceexplained(theratioofthebetween-groupvariancetothetotalvariance)isplot-tedasafunctionofthenumberofclustersandthepointwhereaddi-tionoffurtherclustersresultsinonlymarginalincreasesinexplainedvariance(KetchenampShook1996)Thisresultedinthreeclustersandthesewere then assignedbehavioral states basedon logical differ-encesbetweenthemeansofvariablesineachgroupTheclusterwithlargeststeplengthandsmallesttortuositywasdefinedastravelshortstep lengthand intermediate tortuositywere consideredconsistentwithrestandintermediatesteplengthandhightortuositywerecon-sidered consistent with search behavior following Zhang OrsquoReillyPerryTaylorandDennis(2015)
26emsp|emspSpeedndashtortuosity thresholds
Speedndashtortuosity thresholds fromWakefield etal (2013) were ap-plied to thedataTheseweredevelopedbasedonpriorknowledgeof gannet foragingbehavior and an iterative examinationof plausi-ble thresholds of movement indices from those initially suggestedbyGreacutemillet etal (2004) Thresholds suggestedbyWakefield etal(2013)wereappliedastheywerebasedondatafromtrackedgannetsfroma rangeof colonies including the data analyzed in this studySuccessiveGPSlocationswereconsideredtorepresentsearchiftheymetanyoneofthreeconditions
1 Tortuosity lt09 and speed gt1ms2 Speedgt15msandlt9ms3 Tortuosityge09andaccelerationltminus4ms2
SpeedandaccelerationwerecalculatedbetweenLminus1andL0 where L0 isthefocalpointwhiletortuosityistheratioofthestraightlinetoalongthetrackdistancebetweenLminus4andL4CriteriaweredefinedbasedonGPSandTDRdatafromBassRockdeploymentsusedinthisstudyandarethereforecreatedfromaprioriinformation
F IGURE 1emspConceptualdiagramoflocationsthroughtimeidentifyingpointsofsearchbehaviorwithintheseriesthatrevealsearchchainsofdifferinglengths
emspensp emsp | emsp17BENNISON Et al
27emsp|emspHidden Markov Models
HiddenMarkovModels(HMM)areanexampleofstate-spacemod-elingwheremodelsareformedoftwopartsanobservableseriesandanonobservablestatesequence(Langrocketal2012)Theobserv-ableseriesinthiscontexttaketheformofGPSrelocationswithcon-sequentialsteplengthandturninganglewhilethenonobservablearebehavioralstatesHMMuseatimeseriestodeterminewhatdenotesthe underlying states and the changes between them The applica-tionof state-switchingmodels tomovementdataallowsbehavioralmodestobeexaminedwhileconsideringthehighdegreeofautocor-relationpresentintelemetrydata(Pattersonetal2008)WhentheobservationalerrorislowhiddenMarkovmodelsofferamoretrac-table approach to discretize behavioralmodes from telemetry datathanBayesianapproaches(Langrocketal2012)UsingtheRpack-agemoveHMM (Michelot Langrockamp Patterson 2016) themove-mentofeachindividualalongaforagingtripwasclassifiedintooneofthreeunderlyingstatesbycharacterizationofthedistributionsofsteplengthsand turninganglesbetweenconsecutive locationsA three-statemodelwasabetterfittothedatathanatwo-statemodelandisconsistentwithpreviousworkdescribinggannetmovement(Bodeyetal2014)aswellastheidentificationofthreestatesinEMbCandk-means clustering approaches within this study Model iterationssuccessfully converged to three states suggesting a good fit to thedataAgammadistributionwasusedtodescribethesteplengthsavonMisesdistributiondescribedtheturninganglesandtheViterbialgorithmwasusedtoestimatethemostlikelysequenceofmovementstates tohavegenerated theobservations (ZucchiniMacDonaldampLangrock2016)
28emsp|emspExpectationndashmaximization binary clustering
Expectationndashmaximizationbinaryclustering(EMbC)protocolsareanunsupervisedmultivariateexampleofastate-spacemodelingframe-work that canbeused forbehavioral annotationofmovement tra-jectories including search behavior (seeGarriga PalmerOltra andBartumeus(2016))EMbChasbeendesignedtobeasimplemethodofanalyzingmovementdatabasedonthegeometryaloneandcanbehaviorally annotate movement data with minimal supervisionEMbCisarelativelymoderntechniquethatisgainingtractionwithinmovementecologyIthaspreviouslybeenusedinavarietyofmove-mentstudiesincludingexploringbehavioraldifferencesbetweendis-tinctpopulationsofthered-footedbooby(Mendezetal2017)andcouplingenergybudgetswithbehavioralpatternsunderanoptimalforagingframework (LouzaoWiegandBartumeusampWeimerskirch2014)AnalysiswasundertakenusingtheEMbCpackageinR(GarrigaampBartumeus2015)usingcalculatedvelocitiesandturninganglestoinferbehavioralclassifications
29emsp|emspMachine learning
While themethodsoutlinedaboveall identifysearchbehaviorma-chinelearningmodelsaretrainedtospecificallyidentifypreycapture
dive events based on trackmetrics Analysiswas undertaken usingtheCaretpackageinR(Kuhn2008)usinggeneralizedboostedregres-sionmodelstoaccountforzero-inflation(ElithLeathwickampHastie2008)ModelswerebuiltusingsteplengthspeedturninganglehourofdayandtortuosityModelsweretrainedusing75ofthelinkedGPSTDRdivedatawiththeremaining25ofdatakeptforvalida-tionofpredictionsandunderwentcross-validation500timesduringthetrainingprocedureBycombiningallindividualanimalrsquosdatainthismannerweensurethatanyintra-individualvariationisaccountedforinthemodelingprocessReceiveroperatorcurves (ROCs)werecal-culated(FieldingampBell1997)todeterminethemodelofbestfitateachcolony
210emsp|emspComparison of methods using TDR dives
Inordertocomparethepredictivepowerofthesevenmethodsout-lined above in predicting areas in which dives occurred TDR diveeventswere linked toGPS coordinates bymatching the timedatestampsofbothdatasetsforeachindividuallytrackedbirdTocomparehowwellthemethodscapturediveeventswithinareasofsearchtheproportionofdiveswithinidentifiedareasofsearch(truepositive)aswellasthenumberofsearchchainscontainingnodives(falseposi-tive)wascalculatedforFPTk-meansthresholdsHMMandEmbCThecorrelationbetweenkerneldensitiesofGPStracksandTDRdiveswasassessedusingaDutilleulrsquosmodifiedspatialttest(Dutilleuletal1993)Thisanalysisprovidesacorrelationcoefficientacrossthespa-tialextentofthetrackeddatatodeterminehowwellthetwodatasetscorrelatewhileaccountingforspatialautocorrelationModelperfor-manceformachinelearningwasassessedusingkappavaluesameas-ureofvariabilityexplainedbythemodelakintoR2valueswhere0isequaltonorelationshipand1isequaltoaperfectrelationshipasperLandisandKoch(1977)Furthertothisaconfusionmatrixwascalcu-latedbyrunningmodelsontheremaining25testdatatoassessthenumberofcorrectlyandincorrectlyidentifieddives
3emsp |emspRESULTS
NineGPSampTDRcombinationsweredeployedatGreatSalteeresult-ingin31716locationsafterstandardizationtoatwo-minuteintervalEightGPSampTDRcombinationsweredeployedatBassRockresultingin21208relocationswhenstandardizedTherewereatotalof2830TDRdivesamongthetrackedbirdsatGreatSalteeand2172atBassRockExamplesofmapsproducedbymethodsandshowingtheloca-tionofTDRdivescanbeseen in theSupplementaryMaterials (seeFigsS1ndash10)
FPT k-means EMbC thresholds and HMM all predict searchratherthanpreycaptureattemptsperseAllmethodspredictedcon-siderablesearcheffortacrossthetrackingperiod(Table2)FPTiden-tifiedthe longestcontiguouschainsofsearchbehavior (mean2474locationschain) followed by HMM (mean 858 locationschain)speed and tortuosity thresholds (mean 457 locationschain) andEMbC (mean 238 locationschain) k-Means method identified the
18emsp |emsp emspensp BENNISON Et al
mostdiscretesearchareaswiththeshortestchains(mean308loca-tionschain)UsingKendallrsquostaucorrelationtherewasaweakpositivecorrelationbetween the lengthof searchchainsand thenumberofdivesoccurringwithinthem(Table3)
The performance of behavioral classification methods was as-sessedbycomparingtheoccurrenceofTDRdivesinsideandoutsideof predicted search behavior (Table2) HMM captured the highestproportionofTDRdives(Figure2a)withinsearchareasandhadthesecond lowest false-positive rate (Figure2b) FPT had the longestidentifiedsearchchainsbuttheseactuallycapturedthelowestnum-berofdivesacrossallmethods (Table2Figure2a)Despitethe lowtrue-positiveratesFPThadthelowestfalse-positiverate(Figure2b)ThresholdsandEMbCwerecomparativelysimilarinboththeratesoftrueandfalsepositiveswhilek-meansclusteringhadthelowesttrue-positiveandhighestfalse-positiveratesofallmethodstested
KerneldensityofGPS locationsdidnotexplicitly identifysearchbehaviorbutidentifiedldquohotspotsrdquoofforagingcorrespondingtotimespentineach10times10kmgridcellwithahighproportionoftimespentintheareasurroundingcolonies(Figure3)Dutilleulrsquosmodifiedspatialttestdemonstratedagoodcorrelationbetweenthespatialdistribu-tionofTDRdivesandtimeinspace(Table4)withthebettercorrela-tion(086)atBassRockMachinelearningmodelsdirectlypredictedthe locationof prey capture eventsThemodels trained and testedontheirowncolony indicatedonlya fairorslightagreementwithinthedata(followingLandisandKoch1977)(Table5)Furthermoretheconfusionmatrix (Table6) showed that the predictive power of themodelsatbothcolonieswaspooronlysuccessfullypredicting22ofdivesinthetestdatasetWhenmodelsbuilt inonecolonywereap-pliedtootherstherewasafurtherlossofpredictivepowerindicatingthatmodelstructuresandmovementpatternsbetweencoloniesaredifferent(Table4)
4emsp |emspDISCUSSION
Sevenmethods of classifying search behaviorwere compared to avalidationdatasetofTDRdiveevents innortherngannetstodeter-minetheirabilitytoaccuratelycapturethetwocomponentsofforag-ingactivitymdashsearchingandpreyencountercaptureAcrossmethodsthenumberofpreycaptureattempts(TDRdives)withinsearchvariedconsiderablywiththehighestbeingcapturedbyhiddenMarkovmod-els(81)andthelowestcapturedbyfirstpassagetimeandk-meansclustering(30and31respectively)WhileHMMhadthehighestrateofcaptureofdiveeventsitalsohadoneofthelowestratesoffalsepositivesidentifyingfewersearchchainswherenodivewasre-cordedWhilethiswasstillrelativelyhigh(60)allmethodsproducedhighnumbersof search chains that containednoTDRdives (range53ndash76)TherewasaweakcorrelationbetweenchainlengthandnumberdiveswithinachainWhilepreycaptureattemptswillincreasewith trip and search duration (Sommerfeld Kato Ropert-CoudertGartheampHindell2013)theweakcorrelationrepresentssomelongersearchchainscontainingrelativelyfewpreycaptureattemptsduetoindividuals searching over poor-quality areas or simply that searchT
ABLE 2emspComparisonofsearchidentificationacrossmethodsatGreatSalteeandBassRockwithassociatedTDRdivesateachcolonyTruepositivesarewhenadiveoccurswithinachainof
locationsidentifiedassearchfalsepositivesarewhenachainoflocationsidentifiedassearchdoesnotcontainaTDRdiveandfalsenegativesarewhenadiveoccursoutsideofareasidentified
assearchandwillincludeopportunisticforagingevents
Met
hod
Gre
at S
alte
eBa
ss R
ock
No
of re
loca
tions
31
716
No
of d
ives
28
30N
o of
relo
catio
ns 2
120
8 N
o of
div
es 2
172
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
FPT
3059
5730
6941
1668
2968
4642
703
21663
k-Meansclustering
3752
740
06248
272
72191
7992
7809
1917
Thresholds
7681
6798
2319
3715
5750
6395
4250
2869
HMM
808
16305
1919
4153
813
05676
187
03667
EMbC
5091
7390
4909
207
14604
6861
5396
2726
Kerneldensity
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
Machinelearning
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
a MachinelearningandkerneldensityassessedwithothermetricsduetonatureofanalysisseeTables3ndash5
b NighttimeandlocationsclosetothecolonyhavebeenomittedTheremainingproportionofrelocationsisconsideredtobeacombinationofrestandtravel
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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AllenAMampSinghNJ (2016)Linkingmovementecologywithwild-lifemanagementandconservationFrontiers in Ecology and Evolution3155httpsdoiorg103389fevo201500155
Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
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ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
emspensp emsp | emsp15BENNISON Et al
areasidentifiedassearchWithconsiderationgiventoopportunisticforagingwehypothesizethatmoresuccessfulmethodsofsearchclas-sificationwillcontainmoretruepositives(diveeventsoccurringwithinidentifiedsearch) fewer falsepositives (searchcontainingnodives)and fewer false negatives (dives occurring outside identified searchbehavior)Using this frameworkwewill also provide recommenda-tionsontheappropriateuseofmethodologicalapproaches
2emsp |emspMATERIALS AND METHODS
21emsp|emspData collection
Breedingadults at two islandcoloniesGreatSalteeCoWexfordIreland(5211286minus662189)andBassRockScotlandUK(5607672 minus264139)weretrackedwhileattending2to7-week-oldchicksovera38-dayperiodfromlateJunetoearlyAugust2011NinebirdsatGreatSalteeandeightbirdsatBassRockwerecaughtusingametalcrookorwirenoosefittedtoa4to6-mpoleandfittedwithGPSlog-gerscoupledwithtimendashdepthrecorders(TDRs)GPSloggers(i-gotUGT-200MobileActionTechnologyIncTaipeiTaiwan37g)sealedinheatshrinkplasticrecordedlocationsevery2minCEFASG5TDRs(CEFASTechnologyLowestoftUK25g)weredeployedusingthefast-logdivesensorat4Hzandusedtoidentifydiveeventsbasedona1mdepththresholdbeingexceededhereafterTDRdivesThiswas to ensure dives reflected prey capture attempts (median divedepthof46minplunge-divinggannetsand8mwhenpursuitdiv-ing(Gartheetal2000)ratherthanothersurface-relatedactivitiessuchasrestingwashingorpreeningDeviceswereattachedfollow-ing (Greacutemillet etal 2004) and involved affixing loggers ventrallyto2ndash4central tail feathersusingstripsofwaterproofTesacopytapeTotalinstrumentmasswasle2ofbodymassbelowthemaximumrecommended for seabird biologging studies (Phillips Xavier ampCroxall2003)andtagpositionwasconsideredtominimallyimpedegannetsaerodynamicallyorhydrodynamically (Vandenabeeleetal2012)Deploymentandretrievalhandlingtimeswereapproximately 10min
22emsp|emspData processing
GPStrackswereprocessedusingtheAdehabitatLTpackage(Calenge2011)intheRstatisticalFrameworkLocationdataweretransformedinto Cartesian coordinates using a Universal Transverse Mercator(UTM)30Nprojectionbeforecalculatingsteplengthandturningan-gles AlthoughGPS tagswere programmed to take locations every2min if therewasnoavailableGPSsignal (becauseabirdwasdiv-ing forexample) locationsmaynothavebeenexactly twominutesapartandsotrackswerestandardizedthroughlinearinterpolationtoatwo-minuteintervalSpeedsteplengthturningangleanddistancefromcolonywerecalculatedforeverypointalongabirdrsquostrackPointswithin5kmofthecolonywereremovedtoavoidpotentiallocationsassociated with colony rafting and bathing (Carter etal 2016) aswerethoseoccurringatnight(betweencivilsunsetandsunrise)be-causegannetsarevisualdiurnal foragers (Nelson2002)TDRdivesweresplitintodiveeventsandproducedasingletimestamppointrep-resentingthestartofanydiveeventover1mforappendingtotracksfollowingbehavioralclassification
Weappliedasuiteofmethodscommonlyusedtoidentifysearch-ingor infer foragingbehaviors frommovementdata summarized inTable1Themethods are not considered exhaustive but representa range of approaches coveringmovement pattern description andprocess identification (Edelhoff etal 2016)Movementpatternde-scriptionapproachesincludekerneldensityfirstpassagetime(FPT)and speedtortuosity thresholds while process identification tech-niquesappliedcoveredk-meansclusteringandtwostate-spacemod-elshiddenMarkovmodels(HMM)andeffectivemaximizationbinaryclustering (EMbC)The two formsof state-spacemodelswereusedtorepresentdivergingclassesofstate-spacemodelmaximumlikeli-hoodmethods (EMbC) andBayesianMonteCarlomethods (HMM)(Patterson Thomas Wilcox Ovaskainen amp Matthiopoulos 2008)While not predictingidentifying search behavior directly we alsoappliedmachinelearning(generalizedboostedregressionmodels)topredictdivesfromtrackmetricsratherthansearchbehaviorWefol-lowedthestandardmethodologyforeachtechniqueoutlined inthe
TABLE 1emspSummaryofcommonmethodologicalapproachestoidentifyingsearchandforagingbehaviorinmovementdataWhileallmethodsrequirevalidationdatatoassesshowwellthemethodworksitisnotnecessarilyrequiredtoimplementthemethod
MethodAnalysis complexity
Requires validation data
Suitable for investigating relationships with environmental variables
Immediately applicable to other specieslocations
Machinelearning
Highamplargedatarequirement
Yes Yes No
k-Means Low No Yes Yes
Thresholds Medium Yes Yes No
FPT Medium No Yes Yes
HMM Medium Noa Yes Yes
Kerneldensity
Low No Dependentonscale Yes
EMbC Low No Yes Yes
aHMMdonotrequirevalidationdatainthiscontextbutcanemployifdesired
16emsp |emsp emspensp BENNISON Et al
publishedliteratureandprovidereferencesfordetailedguidanceonapplyingeachapproach
MethodsofpredictingsearchbehaviorroutinelyidentifychainsofsearchinsuccessivelocationsChainscanbeasinglepointinlengthormay includemultipleconsecutivepointsalongamovement track(seeFigure1)Giventhatingannetsindividualpreycaptureattempts(dives)occuratdiscretelocationstimesweextractedmetricsofdivesoccurringwithinsearchdivesoutsideofsearchandsearchcontainingnodiveDatafromthetwocolonieswereprocessedindependentlytoaccountforpotentialdifferencesinmovementmetricsassociatedwithdifferencesinlocalhabitatandpreyavailability
23emsp|emspKernel density
Time in space is considered to be a goodproxy for foraging effort(Warwick-Evans etal 2015) GPS locations (excluding locationswithin5kmofthecolonyandlocationsatnight)wereusedtoesti-matekerneldensitiesinArcMap102whichusesakernelsmoothingfunctionbasedonthequartickernelfunctionbySilverman(1986)andhadabandwidthsearchdistanceof10kmThiswasusedtocreateakerneldensitysquaregridwithsidesof10kmThemethodproducesa10km2gridwithrelativeintensityofbothTDRdivesandGPStracksDutilleulrsquos modified spatial t test (Dutilleul Clifford Richardson ampHemon1993)wasusedtodeterminethespatialcorrelationbetweenthe intensityofdives and intensityof tracks accounting for spatialautocorrelationinthedata
24emsp|emspFirst passage time
Firstpassagetime(FPT)analysiswasundertakenfollowingFauchaldand Tveraa (2003) Although tracks were rediscretized in time forallotheranalysisFPTrequirestrackstoberedistributedinspacetoaccountforchanges inbirdspeedandsotrackswereredistributedusing linear interpolation to 500-m distances Analysis was under-takenusingtheAdehabitatLTpackageinR(Calenge2011)Basedonthebehavioral response ranges reportedbyBodeyetal (2014) forgannetscirclesofradiirangingfrom50mto12000mwereusedtoconstructfirstpassagetimevaluesThemaximumlog-varianceoffirstpassagetimevalueswasthenusedtodetermineappropriatesearch
radiiforeachindividualbirdTheslowestsextileofpassagetimeswasconsideredtoberelativelyhigher intensitysearchbehaviorasusedbyNordstromBattaileCotte andTrites (2013) andalso indicatedinworkbyHameretal(2009)followingFauchaldandTveraa(2003)SearchradiiwereusedtocreateanamalgamatedareaofsearchalonganindividualbirdrsquostrackwithGPSpointsalongthistracktreatedasldquosearchrdquopointsAlthoughFPTcanbeusedtodeterminenestedlev-elsofarea-restrictedsearch(Hameretal2009)wehaveconsideredonlythehighestlevelsofsearchbehaviortomaximizethenumberofdivespotentiallyoccurringwithinsearch
25emsp|emspk- Means clustering
k-Means clustering is amethodof vectorquantization that aims topartitionn observations intok clusters andhasbeenused to clus-ter data points consistent with different behaviors (Jain 2010) k-Means clustering was undertaken using the MacQueen algorithm(MacQueen1967)onsteplengthandturninganglebetweensucces-siveGPSlocationsTheoptimumnumberofclusterswasdeterminedusingtheldquoelbowmethodrdquowherethepercentageofvarianceexplained(theratioofthebetween-groupvariancetothetotalvariance)isplot-tedasafunctionofthenumberofclustersandthepointwhereaddi-tionoffurtherclustersresultsinonlymarginalincreasesinexplainedvariance(KetchenampShook1996)Thisresultedinthreeclustersandthesewere then assignedbehavioral states basedon logical differ-encesbetweenthemeansofvariablesineachgroupTheclusterwithlargeststeplengthandsmallesttortuositywasdefinedastravelshortstep lengthand intermediate tortuositywere consideredconsistentwithrestandintermediatesteplengthandhightortuositywerecon-sidered consistent with search behavior following Zhang OrsquoReillyPerryTaylorandDennis(2015)
26emsp|emspSpeedndashtortuosity thresholds
Speedndashtortuosity thresholds fromWakefield etal (2013) were ap-plied to thedataTheseweredevelopedbasedonpriorknowledgeof gannet foragingbehavior and an iterative examinationof plausi-ble thresholds of movement indices from those initially suggestedbyGreacutemillet etal (2004) Thresholds suggestedbyWakefield etal(2013)wereappliedastheywerebasedondatafromtrackedgannetsfroma rangeof colonies including the data analyzed in this studySuccessiveGPSlocationswereconsideredtorepresentsearchiftheymetanyoneofthreeconditions
1 Tortuosity lt09 and speed gt1ms2 Speedgt15msandlt9ms3 Tortuosityge09andaccelerationltminus4ms2
SpeedandaccelerationwerecalculatedbetweenLminus1andL0 where L0 isthefocalpointwhiletortuosityistheratioofthestraightlinetoalongthetrackdistancebetweenLminus4andL4CriteriaweredefinedbasedonGPSandTDRdatafromBassRockdeploymentsusedinthisstudyandarethereforecreatedfromaprioriinformation
F IGURE 1emspConceptualdiagramoflocationsthroughtimeidentifyingpointsofsearchbehaviorwithintheseriesthatrevealsearchchainsofdifferinglengths
emspensp emsp | emsp17BENNISON Et al
27emsp|emspHidden Markov Models
HiddenMarkovModels(HMM)areanexampleofstate-spacemod-elingwheremodelsareformedoftwopartsanobservableseriesandanonobservablestatesequence(Langrocketal2012)Theobserv-ableseriesinthiscontexttaketheformofGPSrelocationswithcon-sequentialsteplengthandturninganglewhilethenonobservablearebehavioralstatesHMMuseatimeseriestodeterminewhatdenotesthe underlying states and the changes between them The applica-tionof state-switchingmodels tomovementdataallowsbehavioralmodestobeexaminedwhileconsideringthehighdegreeofautocor-relationpresentintelemetrydata(Pattersonetal2008)WhentheobservationalerrorislowhiddenMarkovmodelsofferamoretrac-table approach to discretize behavioralmodes from telemetry datathanBayesianapproaches(Langrocketal2012)UsingtheRpack-agemoveHMM (Michelot Langrockamp Patterson 2016) themove-mentofeachindividualalongaforagingtripwasclassifiedintooneofthreeunderlyingstatesbycharacterizationofthedistributionsofsteplengthsand turninganglesbetweenconsecutive locationsA three-statemodelwasabetterfittothedatathanatwo-statemodelandisconsistentwithpreviousworkdescribinggannetmovement(Bodeyetal2014)aswellastheidentificationofthreestatesinEMbCandk-means clustering approaches within this study Model iterationssuccessfully converged to three states suggesting a good fit to thedataAgammadistributionwasusedtodescribethesteplengthsavonMisesdistributiondescribedtheturninganglesandtheViterbialgorithmwasusedtoestimatethemostlikelysequenceofmovementstates tohavegenerated theobservations (ZucchiniMacDonaldampLangrock2016)
28emsp|emspExpectationndashmaximization binary clustering
Expectationndashmaximizationbinaryclustering(EMbC)protocolsareanunsupervisedmultivariateexampleofastate-spacemodelingframe-work that canbeused forbehavioral annotationofmovement tra-jectories including search behavior (seeGarriga PalmerOltra andBartumeus(2016))EMbChasbeendesignedtobeasimplemethodofanalyzingmovementdatabasedonthegeometryaloneandcanbehaviorally annotate movement data with minimal supervisionEMbCisarelativelymoderntechniquethatisgainingtractionwithinmovementecologyIthaspreviouslybeenusedinavarietyofmove-mentstudiesincludingexploringbehavioraldifferencesbetweendis-tinctpopulationsofthered-footedbooby(Mendezetal2017)andcouplingenergybudgetswithbehavioralpatternsunderanoptimalforagingframework (LouzaoWiegandBartumeusampWeimerskirch2014)AnalysiswasundertakenusingtheEMbCpackageinR(GarrigaampBartumeus2015)usingcalculatedvelocitiesandturninganglestoinferbehavioralclassifications
29emsp|emspMachine learning
While themethodsoutlinedaboveall identifysearchbehaviorma-chinelearningmodelsaretrainedtospecificallyidentifypreycapture
dive events based on trackmetrics Analysiswas undertaken usingtheCaretpackageinR(Kuhn2008)usinggeneralizedboostedregres-sionmodelstoaccountforzero-inflation(ElithLeathwickampHastie2008)ModelswerebuiltusingsteplengthspeedturninganglehourofdayandtortuosityModelsweretrainedusing75ofthelinkedGPSTDRdivedatawiththeremaining25ofdatakeptforvalida-tionofpredictionsandunderwentcross-validation500timesduringthetrainingprocedureBycombiningallindividualanimalrsquosdatainthismannerweensurethatanyintra-individualvariationisaccountedforinthemodelingprocessReceiveroperatorcurves (ROCs)werecal-culated(FieldingampBell1997)todeterminethemodelofbestfitateachcolony
210emsp|emspComparison of methods using TDR dives
Inordertocomparethepredictivepowerofthesevenmethodsout-lined above in predicting areas in which dives occurred TDR diveeventswere linked toGPS coordinates bymatching the timedatestampsofbothdatasetsforeachindividuallytrackedbirdTocomparehowwellthemethodscapturediveeventswithinareasofsearchtheproportionofdiveswithinidentifiedareasofsearch(truepositive)aswellasthenumberofsearchchainscontainingnodives(falseposi-tive)wascalculatedforFPTk-meansthresholdsHMMandEmbCThecorrelationbetweenkerneldensitiesofGPStracksandTDRdiveswasassessedusingaDutilleulrsquosmodifiedspatialttest(Dutilleuletal1993)Thisanalysisprovidesacorrelationcoefficientacrossthespa-tialextentofthetrackeddatatodeterminehowwellthetwodatasetscorrelatewhileaccountingforspatialautocorrelationModelperfor-manceformachinelearningwasassessedusingkappavaluesameas-ureofvariabilityexplainedbythemodelakintoR2valueswhere0isequaltonorelationshipand1isequaltoaperfectrelationshipasperLandisandKoch(1977)Furthertothisaconfusionmatrixwascalcu-latedbyrunningmodelsontheremaining25testdatatoassessthenumberofcorrectlyandincorrectlyidentifieddives
3emsp |emspRESULTS
NineGPSampTDRcombinationsweredeployedatGreatSalteeresult-ingin31716locationsafterstandardizationtoatwo-minuteintervalEightGPSampTDRcombinationsweredeployedatBassRockresultingin21208relocationswhenstandardizedTherewereatotalof2830TDRdivesamongthetrackedbirdsatGreatSalteeand2172atBassRockExamplesofmapsproducedbymethodsandshowingtheloca-tionofTDRdivescanbeseen in theSupplementaryMaterials (seeFigsS1ndash10)
FPT k-means EMbC thresholds and HMM all predict searchratherthanpreycaptureattemptsperseAllmethodspredictedcon-siderablesearcheffortacrossthetrackingperiod(Table2)FPTiden-tifiedthe longestcontiguouschainsofsearchbehavior (mean2474locationschain) followed by HMM (mean 858 locationschain)speed and tortuosity thresholds (mean 457 locationschain) andEMbC (mean 238 locationschain) k-Means method identified the
18emsp |emsp emspensp BENNISON Et al
mostdiscretesearchareaswiththeshortestchains(mean308loca-tionschain)UsingKendallrsquostaucorrelationtherewasaweakpositivecorrelationbetween the lengthof searchchainsand thenumberofdivesoccurringwithinthem(Table3)
The performance of behavioral classification methods was as-sessedbycomparingtheoccurrenceofTDRdivesinsideandoutsideof predicted search behavior (Table2) HMM captured the highestproportionofTDRdives(Figure2a)withinsearchareasandhadthesecond lowest false-positive rate (Figure2b) FPT had the longestidentifiedsearchchainsbuttheseactuallycapturedthelowestnum-berofdivesacrossallmethods (Table2Figure2a)Despitethe lowtrue-positiveratesFPThadthelowestfalse-positiverate(Figure2b)ThresholdsandEMbCwerecomparativelysimilarinboththeratesoftrueandfalsepositiveswhilek-meansclusteringhadthelowesttrue-positiveandhighestfalse-positiveratesofallmethodstested
KerneldensityofGPS locationsdidnotexplicitly identifysearchbehaviorbutidentifiedldquohotspotsrdquoofforagingcorrespondingtotimespentineach10times10kmgridcellwithahighproportionoftimespentintheareasurroundingcolonies(Figure3)Dutilleulrsquosmodifiedspatialttestdemonstratedagoodcorrelationbetweenthespatialdistribu-tionofTDRdivesandtimeinspace(Table4)withthebettercorrela-tion(086)atBassRockMachinelearningmodelsdirectlypredictedthe locationof prey capture eventsThemodels trained and testedontheirowncolony indicatedonlya fairorslightagreementwithinthedata(followingLandisandKoch1977)(Table5)Furthermoretheconfusionmatrix (Table6) showed that the predictive power of themodelsatbothcolonieswaspooronlysuccessfullypredicting22ofdivesinthetestdatasetWhenmodelsbuilt inonecolonywereap-pliedtootherstherewasafurtherlossofpredictivepowerindicatingthatmodelstructuresandmovementpatternsbetweencoloniesaredifferent(Table4)
4emsp |emspDISCUSSION
Sevenmethods of classifying search behaviorwere compared to avalidationdatasetofTDRdiveevents innortherngannetstodeter-minetheirabilitytoaccuratelycapturethetwocomponentsofforag-ingactivitymdashsearchingandpreyencountercaptureAcrossmethodsthenumberofpreycaptureattempts(TDRdives)withinsearchvariedconsiderablywiththehighestbeingcapturedbyhiddenMarkovmod-els(81)andthelowestcapturedbyfirstpassagetimeandk-meansclustering(30and31respectively)WhileHMMhadthehighestrateofcaptureofdiveeventsitalsohadoneofthelowestratesoffalsepositivesidentifyingfewersearchchainswherenodivewasre-cordedWhilethiswasstillrelativelyhigh(60)allmethodsproducedhighnumbersof search chains that containednoTDRdives (range53ndash76)TherewasaweakcorrelationbetweenchainlengthandnumberdiveswithinachainWhilepreycaptureattemptswillincreasewith trip and search duration (Sommerfeld Kato Ropert-CoudertGartheampHindell2013)theweakcorrelationrepresentssomelongersearchchainscontainingrelativelyfewpreycaptureattemptsduetoindividuals searching over poor-quality areas or simply that searchT
ABLE 2emspComparisonofsearchidentificationacrossmethodsatGreatSalteeandBassRockwithassociatedTDRdivesateachcolonyTruepositivesarewhenadiveoccurswithinachainof
locationsidentifiedassearchfalsepositivesarewhenachainoflocationsidentifiedassearchdoesnotcontainaTDRdiveandfalsenegativesarewhenadiveoccursoutsideofareasidentified
assearchandwillincludeopportunisticforagingevents
Met
hod
Gre
at S
alte
eBa
ss R
ock
No
of re
loca
tions
31
716
No
of d
ives
28
30N
o of
relo
catio
ns 2
120
8 N
o of
div
es 2
172
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
FPT
3059
5730
6941
1668
2968
4642
703
21663
k-Meansclustering
3752
740
06248
272
72191
7992
7809
1917
Thresholds
7681
6798
2319
3715
5750
6395
4250
2869
HMM
808
16305
1919
4153
813
05676
187
03667
EMbC
5091
7390
4909
207
14604
6861
5396
2726
Kerneldensity
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
Machinelearning
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
a MachinelearningandkerneldensityassessedwithothermetricsduetonatureofanalysisseeTables3ndash5
b NighttimeandlocationsclosetothecolonyhavebeenomittedTheremainingproportionofrelocationsisconsideredtobeacombinationofrestandtravel
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
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CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
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EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
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Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
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LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
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MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
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PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
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SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
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24emsp |emsp emspensp BENNISON Et al
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TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
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vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
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Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
16emsp |emsp emspensp BENNISON Et al
publishedliteratureandprovidereferencesfordetailedguidanceonapplyingeachapproach
MethodsofpredictingsearchbehaviorroutinelyidentifychainsofsearchinsuccessivelocationsChainscanbeasinglepointinlengthormay includemultipleconsecutivepointsalongamovement track(seeFigure1)Giventhatingannetsindividualpreycaptureattempts(dives)occuratdiscretelocationstimesweextractedmetricsofdivesoccurringwithinsearchdivesoutsideofsearchandsearchcontainingnodiveDatafromthetwocolonieswereprocessedindependentlytoaccountforpotentialdifferencesinmovementmetricsassociatedwithdifferencesinlocalhabitatandpreyavailability
23emsp|emspKernel density
Time in space is considered to be a goodproxy for foraging effort(Warwick-Evans etal 2015) GPS locations (excluding locationswithin5kmofthecolonyandlocationsatnight)wereusedtoesti-matekerneldensitiesinArcMap102whichusesakernelsmoothingfunctionbasedonthequartickernelfunctionbySilverman(1986)andhadabandwidthsearchdistanceof10kmThiswasusedtocreateakerneldensitysquaregridwithsidesof10kmThemethodproducesa10km2gridwithrelativeintensityofbothTDRdivesandGPStracksDutilleulrsquos modified spatial t test (Dutilleul Clifford Richardson ampHemon1993)wasusedtodeterminethespatialcorrelationbetweenthe intensityofdives and intensityof tracks accounting for spatialautocorrelationinthedata
24emsp|emspFirst passage time
Firstpassagetime(FPT)analysiswasundertakenfollowingFauchaldand Tveraa (2003) Although tracks were rediscretized in time forallotheranalysisFPTrequirestrackstoberedistributedinspacetoaccountforchanges inbirdspeedandsotrackswereredistributedusing linear interpolation to 500-m distances Analysis was under-takenusingtheAdehabitatLTpackageinR(Calenge2011)Basedonthebehavioral response ranges reportedbyBodeyetal (2014) forgannetscirclesofradiirangingfrom50mto12000mwereusedtoconstructfirstpassagetimevaluesThemaximumlog-varianceoffirstpassagetimevalueswasthenusedtodetermineappropriatesearch
radiiforeachindividualbirdTheslowestsextileofpassagetimeswasconsideredtoberelativelyhigher intensitysearchbehaviorasusedbyNordstromBattaileCotte andTrites (2013) andalso indicatedinworkbyHameretal(2009)followingFauchaldandTveraa(2003)SearchradiiwereusedtocreateanamalgamatedareaofsearchalonganindividualbirdrsquostrackwithGPSpointsalongthistracktreatedasldquosearchrdquopointsAlthoughFPTcanbeusedtodeterminenestedlev-elsofarea-restrictedsearch(Hameretal2009)wehaveconsideredonlythehighestlevelsofsearchbehaviortomaximizethenumberofdivespotentiallyoccurringwithinsearch
25emsp|emspk- Means clustering
k-Means clustering is amethodof vectorquantization that aims topartitionn observations intok clusters andhasbeenused to clus-ter data points consistent with different behaviors (Jain 2010) k-Means clustering was undertaken using the MacQueen algorithm(MacQueen1967)onsteplengthandturninganglebetweensucces-siveGPSlocationsTheoptimumnumberofclusterswasdeterminedusingtheldquoelbowmethodrdquowherethepercentageofvarianceexplained(theratioofthebetween-groupvariancetothetotalvariance)isplot-tedasafunctionofthenumberofclustersandthepointwhereaddi-tionoffurtherclustersresultsinonlymarginalincreasesinexplainedvariance(KetchenampShook1996)Thisresultedinthreeclustersandthesewere then assignedbehavioral states basedon logical differ-encesbetweenthemeansofvariablesineachgroupTheclusterwithlargeststeplengthandsmallesttortuositywasdefinedastravelshortstep lengthand intermediate tortuositywere consideredconsistentwithrestandintermediatesteplengthandhightortuositywerecon-sidered consistent with search behavior following Zhang OrsquoReillyPerryTaylorandDennis(2015)
26emsp|emspSpeedndashtortuosity thresholds
Speedndashtortuosity thresholds fromWakefield etal (2013) were ap-plied to thedataTheseweredevelopedbasedonpriorknowledgeof gannet foragingbehavior and an iterative examinationof plausi-ble thresholds of movement indices from those initially suggestedbyGreacutemillet etal (2004) Thresholds suggestedbyWakefield etal(2013)wereappliedastheywerebasedondatafromtrackedgannetsfroma rangeof colonies including the data analyzed in this studySuccessiveGPSlocationswereconsideredtorepresentsearchiftheymetanyoneofthreeconditions
1 Tortuosity lt09 and speed gt1ms2 Speedgt15msandlt9ms3 Tortuosityge09andaccelerationltminus4ms2
SpeedandaccelerationwerecalculatedbetweenLminus1andL0 where L0 isthefocalpointwhiletortuosityistheratioofthestraightlinetoalongthetrackdistancebetweenLminus4andL4CriteriaweredefinedbasedonGPSandTDRdatafromBassRockdeploymentsusedinthisstudyandarethereforecreatedfromaprioriinformation
F IGURE 1emspConceptualdiagramoflocationsthroughtimeidentifyingpointsofsearchbehaviorwithintheseriesthatrevealsearchchainsofdifferinglengths
emspensp emsp | emsp17BENNISON Et al
27emsp|emspHidden Markov Models
HiddenMarkovModels(HMM)areanexampleofstate-spacemod-elingwheremodelsareformedoftwopartsanobservableseriesandanonobservablestatesequence(Langrocketal2012)Theobserv-ableseriesinthiscontexttaketheformofGPSrelocationswithcon-sequentialsteplengthandturninganglewhilethenonobservablearebehavioralstatesHMMuseatimeseriestodeterminewhatdenotesthe underlying states and the changes between them The applica-tionof state-switchingmodels tomovementdataallowsbehavioralmodestobeexaminedwhileconsideringthehighdegreeofautocor-relationpresentintelemetrydata(Pattersonetal2008)WhentheobservationalerrorislowhiddenMarkovmodelsofferamoretrac-table approach to discretize behavioralmodes from telemetry datathanBayesianapproaches(Langrocketal2012)UsingtheRpack-agemoveHMM (Michelot Langrockamp Patterson 2016) themove-mentofeachindividualalongaforagingtripwasclassifiedintooneofthreeunderlyingstatesbycharacterizationofthedistributionsofsteplengthsand turninganglesbetweenconsecutive locationsA three-statemodelwasabetterfittothedatathanatwo-statemodelandisconsistentwithpreviousworkdescribinggannetmovement(Bodeyetal2014)aswellastheidentificationofthreestatesinEMbCandk-means clustering approaches within this study Model iterationssuccessfully converged to three states suggesting a good fit to thedataAgammadistributionwasusedtodescribethesteplengthsavonMisesdistributiondescribedtheturninganglesandtheViterbialgorithmwasusedtoestimatethemostlikelysequenceofmovementstates tohavegenerated theobservations (ZucchiniMacDonaldampLangrock2016)
28emsp|emspExpectationndashmaximization binary clustering
Expectationndashmaximizationbinaryclustering(EMbC)protocolsareanunsupervisedmultivariateexampleofastate-spacemodelingframe-work that canbeused forbehavioral annotationofmovement tra-jectories including search behavior (seeGarriga PalmerOltra andBartumeus(2016))EMbChasbeendesignedtobeasimplemethodofanalyzingmovementdatabasedonthegeometryaloneandcanbehaviorally annotate movement data with minimal supervisionEMbCisarelativelymoderntechniquethatisgainingtractionwithinmovementecologyIthaspreviouslybeenusedinavarietyofmove-mentstudiesincludingexploringbehavioraldifferencesbetweendis-tinctpopulationsofthered-footedbooby(Mendezetal2017)andcouplingenergybudgetswithbehavioralpatternsunderanoptimalforagingframework (LouzaoWiegandBartumeusampWeimerskirch2014)AnalysiswasundertakenusingtheEMbCpackageinR(GarrigaampBartumeus2015)usingcalculatedvelocitiesandturninganglestoinferbehavioralclassifications
29emsp|emspMachine learning
While themethodsoutlinedaboveall identifysearchbehaviorma-chinelearningmodelsaretrainedtospecificallyidentifypreycapture
dive events based on trackmetrics Analysiswas undertaken usingtheCaretpackageinR(Kuhn2008)usinggeneralizedboostedregres-sionmodelstoaccountforzero-inflation(ElithLeathwickampHastie2008)ModelswerebuiltusingsteplengthspeedturninganglehourofdayandtortuosityModelsweretrainedusing75ofthelinkedGPSTDRdivedatawiththeremaining25ofdatakeptforvalida-tionofpredictionsandunderwentcross-validation500timesduringthetrainingprocedureBycombiningallindividualanimalrsquosdatainthismannerweensurethatanyintra-individualvariationisaccountedforinthemodelingprocessReceiveroperatorcurves (ROCs)werecal-culated(FieldingampBell1997)todeterminethemodelofbestfitateachcolony
210emsp|emspComparison of methods using TDR dives
Inordertocomparethepredictivepowerofthesevenmethodsout-lined above in predicting areas in which dives occurred TDR diveeventswere linked toGPS coordinates bymatching the timedatestampsofbothdatasetsforeachindividuallytrackedbirdTocomparehowwellthemethodscapturediveeventswithinareasofsearchtheproportionofdiveswithinidentifiedareasofsearch(truepositive)aswellasthenumberofsearchchainscontainingnodives(falseposi-tive)wascalculatedforFPTk-meansthresholdsHMMandEmbCThecorrelationbetweenkerneldensitiesofGPStracksandTDRdiveswasassessedusingaDutilleulrsquosmodifiedspatialttest(Dutilleuletal1993)Thisanalysisprovidesacorrelationcoefficientacrossthespa-tialextentofthetrackeddatatodeterminehowwellthetwodatasetscorrelatewhileaccountingforspatialautocorrelationModelperfor-manceformachinelearningwasassessedusingkappavaluesameas-ureofvariabilityexplainedbythemodelakintoR2valueswhere0isequaltonorelationshipand1isequaltoaperfectrelationshipasperLandisandKoch(1977)Furthertothisaconfusionmatrixwascalcu-latedbyrunningmodelsontheremaining25testdatatoassessthenumberofcorrectlyandincorrectlyidentifieddives
3emsp |emspRESULTS
NineGPSampTDRcombinationsweredeployedatGreatSalteeresult-ingin31716locationsafterstandardizationtoatwo-minuteintervalEightGPSampTDRcombinationsweredeployedatBassRockresultingin21208relocationswhenstandardizedTherewereatotalof2830TDRdivesamongthetrackedbirdsatGreatSalteeand2172atBassRockExamplesofmapsproducedbymethodsandshowingtheloca-tionofTDRdivescanbeseen in theSupplementaryMaterials (seeFigsS1ndash10)
FPT k-means EMbC thresholds and HMM all predict searchratherthanpreycaptureattemptsperseAllmethodspredictedcon-siderablesearcheffortacrossthetrackingperiod(Table2)FPTiden-tifiedthe longestcontiguouschainsofsearchbehavior (mean2474locationschain) followed by HMM (mean 858 locationschain)speed and tortuosity thresholds (mean 457 locationschain) andEMbC (mean 238 locationschain) k-Means method identified the
18emsp |emsp emspensp BENNISON Et al
mostdiscretesearchareaswiththeshortestchains(mean308loca-tionschain)UsingKendallrsquostaucorrelationtherewasaweakpositivecorrelationbetween the lengthof searchchainsand thenumberofdivesoccurringwithinthem(Table3)
The performance of behavioral classification methods was as-sessedbycomparingtheoccurrenceofTDRdivesinsideandoutsideof predicted search behavior (Table2) HMM captured the highestproportionofTDRdives(Figure2a)withinsearchareasandhadthesecond lowest false-positive rate (Figure2b) FPT had the longestidentifiedsearchchainsbuttheseactuallycapturedthelowestnum-berofdivesacrossallmethods (Table2Figure2a)Despitethe lowtrue-positiveratesFPThadthelowestfalse-positiverate(Figure2b)ThresholdsandEMbCwerecomparativelysimilarinboththeratesoftrueandfalsepositiveswhilek-meansclusteringhadthelowesttrue-positiveandhighestfalse-positiveratesofallmethodstested
KerneldensityofGPS locationsdidnotexplicitly identifysearchbehaviorbutidentifiedldquohotspotsrdquoofforagingcorrespondingtotimespentineach10times10kmgridcellwithahighproportionoftimespentintheareasurroundingcolonies(Figure3)Dutilleulrsquosmodifiedspatialttestdemonstratedagoodcorrelationbetweenthespatialdistribu-tionofTDRdivesandtimeinspace(Table4)withthebettercorrela-tion(086)atBassRockMachinelearningmodelsdirectlypredictedthe locationof prey capture eventsThemodels trained and testedontheirowncolony indicatedonlya fairorslightagreementwithinthedata(followingLandisandKoch1977)(Table5)Furthermoretheconfusionmatrix (Table6) showed that the predictive power of themodelsatbothcolonieswaspooronlysuccessfullypredicting22ofdivesinthetestdatasetWhenmodelsbuilt inonecolonywereap-pliedtootherstherewasafurtherlossofpredictivepowerindicatingthatmodelstructuresandmovementpatternsbetweencoloniesaredifferent(Table4)
4emsp |emspDISCUSSION
Sevenmethods of classifying search behaviorwere compared to avalidationdatasetofTDRdiveevents innortherngannetstodeter-minetheirabilitytoaccuratelycapturethetwocomponentsofforag-ingactivitymdashsearchingandpreyencountercaptureAcrossmethodsthenumberofpreycaptureattempts(TDRdives)withinsearchvariedconsiderablywiththehighestbeingcapturedbyhiddenMarkovmod-els(81)andthelowestcapturedbyfirstpassagetimeandk-meansclustering(30and31respectively)WhileHMMhadthehighestrateofcaptureofdiveeventsitalsohadoneofthelowestratesoffalsepositivesidentifyingfewersearchchainswherenodivewasre-cordedWhilethiswasstillrelativelyhigh(60)allmethodsproducedhighnumbersof search chains that containednoTDRdives (range53ndash76)TherewasaweakcorrelationbetweenchainlengthandnumberdiveswithinachainWhilepreycaptureattemptswillincreasewith trip and search duration (Sommerfeld Kato Ropert-CoudertGartheampHindell2013)theweakcorrelationrepresentssomelongersearchchainscontainingrelativelyfewpreycaptureattemptsduetoindividuals searching over poor-quality areas or simply that searchT
ABLE 2emspComparisonofsearchidentificationacrossmethodsatGreatSalteeandBassRockwithassociatedTDRdivesateachcolonyTruepositivesarewhenadiveoccurswithinachainof
locationsidentifiedassearchfalsepositivesarewhenachainoflocationsidentifiedassearchdoesnotcontainaTDRdiveandfalsenegativesarewhenadiveoccursoutsideofareasidentified
assearchandwillincludeopportunisticforagingevents
Met
hod
Gre
at S
alte
eBa
ss R
ock
No
of re
loca
tions
31
716
No
of d
ives
28
30N
o of
relo
catio
ns 2
120
8 N
o of
div
es 2
172
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
FPT
3059
5730
6941
1668
2968
4642
703
21663
k-Meansclustering
3752
740
06248
272
72191
7992
7809
1917
Thresholds
7681
6798
2319
3715
5750
6395
4250
2869
HMM
808
16305
1919
4153
813
05676
187
03667
EMbC
5091
7390
4909
207
14604
6861
5396
2726
Kerneldensity
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
Machinelearning
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
a MachinelearningandkerneldensityassessedwithothermetricsduetonatureofanalysisseeTables3ndash5
b NighttimeandlocationsclosetothecolonyhavebeenomittedTheremainingproportionofrelocationsisconsideredtobeacombinationofrestandtravel
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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AllenAMampSinghNJ (2016)Linkingmovementecologywithwild-lifemanagementandconservationFrontiers in Ecology and Evolution3155httpsdoiorg103389fevo201500155
Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
emspensp emsp | emsp17BENNISON Et al
27emsp|emspHidden Markov Models
HiddenMarkovModels(HMM)areanexampleofstate-spacemod-elingwheremodelsareformedoftwopartsanobservableseriesandanonobservablestatesequence(Langrocketal2012)Theobserv-ableseriesinthiscontexttaketheformofGPSrelocationswithcon-sequentialsteplengthandturninganglewhilethenonobservablearebehavioralstatesHMMuseatimeseriestodeterminewhatdenotesthe underlying states and the changes between them The applica-tionof state-switchingmodels tomovementdataallowsbehavioralmodestobeexaminedwhileconsideringthehighdegreeofautocor-relationpresentintelemetrydata(Pattersonetal2008)WhentheobservationalerrorislowhiddenMarkovmodelsofferamoretrac-table approach to discretize behavioralmodes from telemetry datathanBayesianapproaches(Langrocketal2012)UsingtheRpack-agemoveHMM (Michelot Langrockamp Patterson 2016) themove-mentofeachindividualalongaforagingtripwasclassifiedintooneofthreeunderlyingstatesbycharacterizationofthedistributionsofsteplengthsand turninganglesbetweenconsecutive locationsA three-statemodelwasabetterfittothedatathanatwo-statemodelandisconsistentwithpreviousworkdescribinggannetmovement(Bodeyetal2014)aswellastheidentificationofthreestatesinEMbCandk-means clustering approaches within this study Model iterationssuccessfully converged to three states suggesting a good fit to thedataAgammadistributionwasusedtodescribethesteplengthsavonMisesdistributiondescribedtheturninganglesandtheViterbialgorithmwasusedtoestimatethemostlikelysequenceofmovementstates tohavegenerated theobservations (ZucchiniMacDonaldampLangrock2016)
28emsp|emspExpectationndashmaximization binary clustering
Expectationndashmaximizationbinaryclustering(EMbC)protocolsareanunsupervisedmultivariateexampleofastate-spacemodelingframe-work that canbeused forbehavioral annotationofmovement tra-jectories including search behavior (seeGarriga PalmerOltra andBartumeus(2016))EMbChasbeendesignedtobeasimplemethodofanalyzingmovementdatabasedonthegeometryaloneandcanbehaviorally annotate movement data with minimal supervisionEMbCisarelativelymoderntechniquethatisgainingtractionwithinmovementecologyIthaspreviouslybeenusedinavarietyofmove-mentstudiesincludingexploringbehavioraldifferencesbetweendis-tinctpopulationsofthered-footedbooby(Mendezetal2017)andcouplingenergybudgetswithbehavioralpatternsunderanoptimalforagingframework (LouzaoWiegandBartumeusampWeimerskirch2014)AnalysiswasundertakenusingtheEMbCpackageinR(GarrigaampBartumeus2015)usingcalculatedvelocitiesandturninganglestoinferbehavioralclassifications
29emsp|emspMachine learning
While themethodsoutlinedaboveall identifysearchbehaviorma-chinelearningmodelsaretrainedtospecificallyidentifypreycapture
dive events based on trackmetrics Analysiswas undertaken usingtheCaretpackageinR(Kuhn2008)usinggeneralizedboostedregres-sionmodelstoaccountforzero-inflation(ElithLeathwickampHastie2008)ModelswerebuiltusingsteplengthspeedturninganglehourofdayandtortuosityModelsweretrainedusing75ofthelinkedGPSTDRdivedatawiththeremaining25ofdatakeptforvalida-tionofpredictionsandunderwentcross-validation500timesduringthetrainingprocedureBycombiningallindividualanimalrsquosdatainthismannerweensurethatanyintra-individualvariationisaccountedforinthemodelingprocessReceiveroperatorcurves (ROCs)werecal-culated(FieldingampBell1997)todeterminethemodelofbestfitateachcolony
210emsp|emspComparison of methods using TDR dives
Inordertocomparethepredictivepowerofthesevenmethodsout-lined above in predicting areas in which dives occurred TDR diveeventswere linked toGPS coordinates bymatching the timedatestampsofbothdatasetsforeachindividuallytrackedbirdTocomparehowwellthemethodscapturediveeventswithinareasofsearchtheproportionofdiveswithinidentifiedareasofsearch(truepositive)aswellasthenumberofsearchchainscontainingnodives(falseposi-tive)wascalculatedforFPTk-meansthresholdsHMMandEmbCThecorrelationbetweenkerneldensitiesofGPStracksandTDRdiveswasassessedusingaDutilleulrsquosmodifiedspatialttest(Dutilleuletal1993)Thisanalysisprovidesacorrelationcoefficientacrossthespa-tialextentofthetrackeddatatodeterminehowwellthetwodatasetscorrelatewhileaccountingforspatialautocorrelationModelperfor-manceformachinelearningwasassessedusingkappavaluesameas-ureofvariabilityexplainedbythemodelakintoR2valueswhere0isequaltonorelationshipand1isequaltoaperfectrelationshipasperLandisandKoch(1977)Furthertothisaconfusionmatrixwascalcu-latedbyrunningmodelsontheremaining25testdatatoassessthenumberofcorrectlyandincorrectlyidentifieddives
3emsp |emspRESULTS
NineGPSampTDRcombinationsweredeployedatGreatSalteeresult-ingin31716locationsafterstandardizationtoatwo-minuteintervalEightGPSampTDRcombinationsweredeployedatBassRockresultingin21208relocationswhenstandardizedTherewereatotalof2830TDRdivesamongthetrackedbirdsatGreatSalteeand2172atBassRockExamplesofmapsproducedbymethodsandshowingtheloca-tionofTDRdivescanbeseen in theSupplementaryMaterials (seeFigsS1ndash10)
FPT k-means EMbC thresholds and HMM all predict searchratherthanpreycaptureattemptsperseAllmethodspredictedcon-siderablesearcheffortacrossthetrackingperiod(Table2)FPTiden-tifiedthe longestcontiguouschainsofsearchbehavior (mean2474locationschain) followed by HMM (mean 858 locationschain)speed and tortuosity thresholds (mean 457 locationschain) andEMbC (mean 238 locationschain) k-Means method identified the
18emsp |emsp emspensp BENNISON Et al
mostdiscretesearchareaswiththeshortestchains(mean308loca-tionschain)UsingKendallrsquostaucorrelationtherewasaweakpositivecorrelationbetween the lengthof searchchainsand thenumberofdivesoccurringwithinthem(Table3)
The performance of behavioral classification methods was as-sessedbycomparingtheoccurrenceofTDRdivesinsideandoutsideof predicted search behavior (Table2) HMM captured the highestproportionofTDRdives(Figure2a)withinsearchareasandhadthesecond lowest false-positive rate (Figure2b) FPT had the longestidentifiedsearchchainsbuttheseactuallycapturedthelowestnum-berofdivesacrossallmethods (Table2Figure2a)Despitethe lowtrue-positiveratesFPThadthelowestfalse-positiverate(Figure2b)ThresholdsandEMbCwerecomparativelysimilarinboththeratesoftrueandfalsepositiveswhilek-meansclusteringhadthelowesttrue-positiveandhighestfalse-positiveratesofallmethodstested
KerneldensityofGPS locationsdidnotexplicitly identifysearchbehaviorbutidentifiedldquohotspotsrdquoofforagingcorrespondingtotimespentineach10times10kmgridcellwithahighproportionoftimespentintheareasurroundingcolonies(Figure3)Dutilleulrsquosmodifiedspatialttestdemonstratedagoodcorrelationbetweenthespatialdistribu-tionofTDRdivesandtimeinspace(Table4)withthebettercorrela-tion(086)atBassRockMachinelearningmodelsdirectlypredictedthe locationof prey capture eventsThemodels trained and testedontheirowncolony indicatedonlya fairorslightagreementwithinthedata(followingLandisandKoch1977)(Table5)Furthermoretheconfusionmatrix (Table6) showed that the predictive power of themodelsatbothcolonieswaspooronlysuccessfullypredicting22ofdivesinthetestdatasetWhenmodelsbuilt inonecolonywereap-pliedtootherstherewasafurtherlossofpredictivepowerindicatingthatmodelstructuresandmovementpatternsbetweencoloniesaredifferent(Table4)
4emsp |emspDISCUSSION
Sevenmethods of classifying search behaviorwere compared to avalidationdatasetofTDRdiveevents innortherngannetstodeter-minetheirabilitytoaccuratelycapturethetwocomponentsofforag-ingactivitymdashsearchingandpreyencountercaptureAcrossmethodsthenumberofpreycaptureattempts(TDRdives)withinsearchvariedconsiderablywiththehighestbeingcapturedbyhiddenMarkovmod-els(81)andthelowestcapturedbyfirstpassagetimeandk-meansclustering(30and31respectively)WhileHMMhadthehighestrateofcaptureofdiveeventsitalsohadoneofthelowestratesoffalsepositivesidentifyingfewersearchchainswherenodivewasre-cordedWhilethiswasstillrelativelyhigh(60)allmethodsproducedhighnumbersof search chains that containednoTDRdives (range53ndash76)TherewasaweakcorrelationbetweenchainlengthandnumberdiveswithinachainWhilepreycaptureattemptswillincreasewith trip and search duration (Sommerfeld Kato Ropert-CoudertGartheampHindell2013)theweakcorrelationrepresentssomelongersearchchainscontainingrelativelyfewpreycaptureattemptsduetoindividuals searching over poor-quality areas or simply that searchT
ABLE 2emspComparisonofsearchidentificationacrossmethodsatGreatSalteeandBassRockwithassociatedTDRdivesateachcolonyTruepositivesarewhenadiveoccurswithinachainof
locationsidentifiedassearchfalsepositivesarewhenachainoflocationsidentifiedassearchdoesnotcontainaTDRdiveandfalsenegativesarewhenadiveoccursoutsideofareasidentified
assearchandwillincludeopportunisticforagingevents
Met
hod
Gre
at S
alte
eBa
ss R
ock
No
of re
loca
tions
31
716
No
of d
ives
28
30N
o of
relo
catio
ns 2
120
8 N
o of
div
es 2
172
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
FPT
3059
5730
6941
1668
2968
4642
703
21663
k-Meansclustering
3752
740
06248
272
72191
7992
7809
1917
Thresholds
7681
6798
2319
3715
5750
6395
4250
2869
HMM
808
16305
1919
4153
813
05676
187
03667
EMbC
5091
7390
4909
207
14604
6861
5396
2726
Kerneldensity
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
Machinelearning
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
a MachinelearningandkerneldensityassessedwithothermetricsduetonatureofanalysisseeTables3ndash5
b NighttimeandlocationsclosetothecolonyhavebeenomittedTheremainingproportionofrelocationsisconsideredtobeacombinationofrestandtravel
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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AllenAMampSinghNJ (2016)Linkingmovementecologywithwild-lifemanagementandconservationFrontiers in Ecology and Evolution3155httpsdoiorg103389fevo201500155
Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
18emsp |emsp emspensp BENNISON Et al
mostdiscretesearchareaswiththeshortestchains(mean308loca-tionschain)UsingKendallrsquostaucorrelationtherewasaweakpositivecorrelationbetween the lengthof searchchainsand thenumberofdivesoccurringwithinthem(Table3)
The performance of behavioral classification methods was as-sessedbycomparingtheoccurrenceofTDRdivesinsideandoutsideof predicted search behavior (Table2) HMM captured the highestproportionofTDRdives(Figure2a)withinsearchareasandhadthesecond lowest false-positive rate (Figure2b) FPT had the longestidentifiedsearchchainsbuttheseactuallycapturedthelowestnum-berofdivesacrossallmethods (Table2Figure2a)Despitethe lowtrue-positiveratesFPThadthelowestfalse-positiverate(Figure2b)ThresholdsandEMbCwerecomparativelysimilarinboththeratesoftrueandfalsepositiveswhilek-meansclusteringhadthelowesttrue-positiveandhighestfalse-positiveratesofallmethodstested
KerneldensityofGPS locationsdidnotexplicitly identifysearchbehaviorbutidentifiedldquohotspotsrdquoofforagingcorrespondingtotimespentineach10times10kmgridcellwithahighproportionoftimespentintheareasurroundingcolonies(Figure3)Dutilleulrsquosmodifiedspatialttestdemonstratedagoodcorrelationbetweenthespatialdistribu-tionofTDRdivesandtimeinspace(Table4)withthebettercorrela-tion(086)atBassRockMachinelearningmodelsdirectlypredictedthe locationof prey capture eventsThemodels trained and testedontheirowncolony indicatedonlya fairorslightagreementwithinthedata(followingLandisandKoch1977)(Table5)Furthermoretheconfusionmatrix (Table6) showed that the predictive power of themodelsatbothcolonieswaspooronlysuccessfullypredicting22ofdivesinthetestdatasetWhenmodelsbuilt inonecolonywereap-pliedtootherstherewasafurtherlossofpredictivepowerindicatingthatmodelstructuresandmovementpatternsbetweencoloniesaredifferent(Table4)
4emsp |emspDISCUSSION
Sevenmethods of classifying search behaviorwere compared to avalidationdatasetofTDRdiveevents innortherngannetstodeter-minetheirabilitytoaccuratelycapturethetwocomponentsofforag-ingactivitymdashsearchingandpreyencountercaptureAcrossmethodsthenumberofpreycaptureattempts(TDRdives)withinsearchvariedconsiderablywiththehighestbeingcapturedbyhiddenMarkovmod-els(81)andthelowestcapturedbyfirstpassagetimeandk-meansclustering(30and31respectively)WhileHMMhadthehighestrateofcaptureofdiveeventsitalsohadoneofthelowestratesoffalsepositivesidentifyingfewersearchchainswherenodivewasre-cordedWhilethiswasstillrelativelyhigh(60)allmethodsproducedhighnumbersof search chains that containednoTDRdives (range53ndash76)TherewasaweakcorrelationbetweenchainlengthandnumberdiveswithinachainWhilepreycaptureattemptswillincreasewith trip and search duration (Sommerfeld Kato Ropert-CoudertGartheampHindell2013)theweakcorrelationrepresentssomelongersearchchainscontainingrelativelyfewpreycaptureattemptsduetoindividuals searching over poor-quality areas or simply that searchT
ABLE 2emspComparisonofsearchidentificationacrossmethodsatGreatSalteeandBassRockwithassociatedTDRdivesateachcolonyTruepositivesarewhenadiveoccurswithinachainof
locationsidentifiedassearchfalsepositivesarewhenachainoflocationsidentifiedassearchdoesnotcontainaTDRdiveandfalsenegativesarewhenadiveoccursoutsideofareasidentified
assearchandwillincludeopportunisticforagingevents
Met
hod
Gre
at S
alte
eBa
ss R
ock
No
of re
loca
tions
31
716
No
of d
ives
28
30N
o of
relo
catio
ns 2
120
8 N
o of
div
es 2
172
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
Rate
of t
rue
posi
tives
( d
ives
in
sear
ch)
Rate
of f
alse
po
sitiv
es (
sear
ch
chai
ns w
ith n
o di
ve)
Rate
of f
alse
ne
gativ
es (
div
es
outs
ide
of se
arch
)
Tim
e sp
ent i
n se
arch
ingb (
of
relo
catio
ns se
arch
ing)
FPT
3059
5730
6941
1668
2968
4642
703
21663
k-Meansclustering
3752
740
06248
272
72191
7992
7809
1917
Thresholds
7681
6798
2319
3715
5750
6395
4250
2869
HMM
808
16305
1919
4153
813
05676
187
03667
EMbC
5091
7390
4909
207
14604
6861
5396
2726
Kerneldensity
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
Machinelearning
NA
aNA
aNA
aNA
aNA
aNA
aNA
aNA
a
a MachinelearningandkerneldensityassessedwithothermetricsduetonatureofanalysisseeTables3ndash5
b NighttimeandlocationsclosetothecolonyhavebeenomittedTheremainingproportionofrelocationsisconsideredtobeacombinationofrestandtravel
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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AllenAMampSinghNJ (2016)Linkingmovementecologywithwild-lifemanagementandconservationFrontiers in Ecology and Evolution3155httpsdoiorg103389fevo201500155
Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
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ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
emspensp emsp | emsp19BENNISON Et al
doesnotalwaysresultinpreycaptureattemptsThesefindingssug-geststhatsignificanteffortisspentinunsuccessfulsearchbehaviorconsistentwithlowpreyencounterratesassociatedwithforagingonspatiallyandtemporallypatchypreyresources(Weimerskirch2007)
Whilethespatialdistributionoftrackedgannetswillencompassavarietyofbehaviors includingforagingtravelandrestperiodssim-plermethodologiessuchaskerneldensityestimationoftrackdatacor-relatedwellwithkerneldensitiesofTDRdiveeventsThissupportstheassertionthattimeinareaisagoodproxyforforagingeffort(Greacutemilletetal2004Warwick-Evansetal2015)Howeverthisapproachuti-lizes largerareasofspacebeyondmovementpathsandso it isnotcapableofidentifyingforaginginassociationwithtemporallyephem-eraleventsorfeaturesthatmaydirectlychangeananimalrsquosmovementtrajectoryWithinmore process-driven approaches FPT is arguablyoneofthemostubiquitousmethodsusedtoidentifyforagingareasinbothterrestrialandmarinesystems (BattaileNordstromLiebschampTrites2015ByrneampChamberlain2012Evansetal2015Hameretal2009LeCorreDussaultampCocircteacute2014)FPTcapturessearchbehavioracrossmultiplespatialscalesandisparticularlynotedforitsabilitytodetectnestedscalesofarea-restrictedsearch(Hameretal2009)WhilewedidnotinvestigatenestedscalesofsearchFPTalongwithk-meansclusteringhadthelowestrateofdivesoccurringwithinbroad areas of identified search However in contrast to k-meansFPThadthelowestrateoffalsepositives(searchcontainingnodives)likelyasaresultofidentifyingverylargecontiguousareasofsearchk-MeansclusteringandFPThadhighratesoffalsenegativeswithap-proximately70ofalldivesoccurringoutside identifiedsearchbe-haviorAcertainamountofopportunisticforagingisanticipatedinanywide-rangingpredator (MontevecchiBenvenutiGartheDavorenampFifield2009)resultingindiveeventsoccurringoutsideclassicalpat-ternsofsearchmovementHoweverthehighrateofdivesoccurringoutsidesearchasdefinedbyFPTandk-meanssuggeststhateitherthemajorityofpreycaptureattemptsoccuropportunisticallyorthatthescaleofARSchangesspatiallyresultinginsearchbehaviorassociatedwithdivesbeingmissed
Speedndashtortuositythresholdsldquocapturedrdquo68ofTDRdiveswithinareasidentifiedassearchThereisevidencetosuggestthathumansaremorecapable thanmachinesatpattern recognitionwhenpre-sentedwith limited data (Samal amp Lyengar 1992) It is thereforeunsurprising that thresholdsperformedwell considering that theywere constructed based on prior knowledge of foraging behavioranditerativeexaminationofthresholdsagainstavalidationdataset
ingannets(Wakefieldetal2013)Therelativelyhighratesoffalsepositives(66ofsearchchainscontainingnoTDRdive)werewithinthespreadofvaluesforothermethodshighlightingsignificantef-fortspentsearching forprey interspersedwith relatively fewpreyencounters
Thestate-spacemodelingframeworkhasbeenacknowledgedasparticularlyuseful inmovementecology(Pattersonetal2008)andis rapidlyexpandingwithinpath segmentation techniques (Michelotetal2016RobertsampRosenthal2004)BoththeEMbCandHMMapproaches model the changes in step length and turning anglethroughtimeandspacetoannotatethetrajectoryofananimalwithbehavioral states (Garrigaetal 2016Michelot etal 2016) EMbCprotocolsresultedinshortersearchchainsthatencapsulated49ofalldiveeventswhileHMMidentifiedlongerchainsofsearchthatcap-turedthehighestnumberofdives(81)ofanymethodWhileHMMdefined thehighest numberof points as search across allmethodsitalsohadoneofthe lowestratesoffalsepositivesLessthan20ofdivesoccurredoutsideof searchThiswouldbemore consistentwithopportunistic foraging andprovides further empirical evidenceofsearchbehaviorleadingtopreycaptureattempts(DiasGranadeiroampPalmeirim2009WeimerskirchPinaudPawlowskiampBost2007)ThehighnumberofshortersearchchainsidentifiedbyEMbCcoupledwiththefactthatitispossibletolinkstatetransitionstoenvironmen-talcovariatesinaHMMframeworksuggeststhatboththesemeth-odsmayalsobesuitable foror investigatingbehavioral response toephemeralenvironmentalcues
Regional differences in habitat and prey aswell as inter- andintraspecificcompetitionare likely to influence thewayananimalforages(HuigBuijsampKleyheeg2016Schultz1983ZachampFalls1979)Toaccountforthisthecoloniesweretreatedindependentlyduring analysis Machine learning did highlight slight differencesbetween colonies in the movement metrics considered to be ofmost predictive power suggesting local differences in movementassociatedwithforagingandsearchMachinelearningwastheonlymethod that directly predicted prey capture events rather thansearch behaviorWhile the explanatory power of themodelswasdeemedtobesatisfactorythepredictiveabilityofmodelswaspooronlycorrectlyidentifying22ofdivesinthetestdatasetThesuc-cessofthismethodmayhavebeenlimitedbytheavailablesamplesizeAs a powerful tool machine learning approaches do requirelargeamountsofdataarecomputationallycomplexandrequireaprioriknowledgeofdiveevents to train themodelHoweverma-chinelearningprotocolsarestillbeingdevelopedwithinecologicalresearch and such data mining remains a challenge for accurateclassification(Hochachkaetal2007)
An interestingconsiderationthroughoutthemethodspresentedhere is the ability to identify multiple behavioral states HMM k-meansandEMbCarecapableofidentifyingbehaviorconsistentwithrestwithinthetrackingperiod(typicallyverylowspeedandamedium-to-hightortuosityvalues)InthiscontextkerneldensityFPTspeedndashtortuosity thresholds andmachine learningdidnot identifyperiodsofrestThemajorityofbehavioralannotationreliesontheprincipleof animals slowing down and paths becomingmore tortuouswhen
TABLE 3emspKendallrsquostaucorrelationbetweensearchchainlengthandnumberofdivescontainedwithineachchain
MethodCorrelation (tau) p Value Z statistic
FPT 043 lt01 1267
k-Meansclustering 030 lt01 2176
Thresholds 045 lt01 3372
HMM 047 lt01 2379
EMbC 039 lt01 3129
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
REFERENCES
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Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
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ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
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ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
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FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
20emsp |emsp emspensp BENNISON Et al
searching (Bartoń ampHovestadt 2013 Benhamou 2004) Howeverslowing down and turningmore could also be an indication of restbehavior especially when considering potential error from closelypositionedGPSrelocations (Hurford2009JerdeampVisscher2005)Theabilitytoexcludeaperiodthatcloselyresemblessearchpatternscould have the potential to reduce false-positive periods of searchandweaccountedforthisasmuchaspossiblebyremovinglocationsinproximitytothecolonyaswellaslocationsoccurringatnightbeforecomparingmethodsWhile not directly assigning a rest period it is
F IGURE 2emspProportionof(a)TDRdivesoccurringwithinlsquosearchrdquobehavior(truepositives)and(b)searchchainscontainingnoTDRdives(falsepositives)usingEMbCFPTHMMk-meansandspeedndashtortuositythresholds
F IGURE 3emspKerneldensitiesofgannettracksatbothGreatSalteeandBassRockfor(a)divelocationsand(b)individualbirdtracksScaleisofrelativetimeinspaceacrossthespatialboundaryof10kmthroughoutthetrackingarea
(a) (b)
TABLE 4emspDutilleulrsquoscorrelationbetweenkerneldensitiesofallGPSlocationsandconfirmeddivelocations
Colony Correlation p Value F statisticDegrees of freedom
GreatSaltee 079 lt01 12337 6957
BassRock 087 lt01 99188 32990
TABLE 5emspKappavaluesformachinelearningmodelswheremodelsdevelopedusingcolony-specificdataareappliedatthecolonyfromwhichtrainingdataweretakenandatadifferentcolonyLowvaluesformodelstrainedatonecolonyappliedtotheothercolonysuggestverypoormodelfit
Model trained
Model applied
Great Saltee Bass Rock
GreatSaltee 02456 minus00006757
BassRock 002792 01885
TABLE 6emspConfusionmatrixtabletotalsofpredictionsmadeacrossmachinelearningmodelsatbothGreatSalteeandBassRock
Predicted result
Reference (true value) in test data set
Dive No dive
Dive 222 258
No dive 779 5332
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
REFERENCES
AllenRMMetaxasAampSnelgrovePV (2017)ApplyingmovementecologytomarineanimalswithcomplexlifecyclesAnnual Review of Marine Science10httpsdoiorg101146annurev-marine-121916- 063134
AllenAMampSinghNJ (2016)Linkingmovementecologywithwild-lifemanagementandconservationFrontiers in Ecology and Evolution3155httpsdoiorg103389fevo201500155
Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
BartońKAampHovestadtT(2013)Preydensityvalueandspatialdistribu-tionaffecttheefficiencyofarea-concentratedsearchJournal of Theoretical Biology31661ndash69httpsdoiorg101016jjtbi201209002
BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
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Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
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ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
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Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
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Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
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Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
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LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
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MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
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Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
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PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
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SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
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24emsp |emsp emspensp BENNISON Et al
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TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
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vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
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Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
emspensp emsp | emsp21BENNISON Et al
importanttonotethatspeedndashtortuositythresholdscouldbeadaptedtoincludetheannotationofrestandtravelaswellasspecificsearchbehaviorInasimilarfashionmachinelearningprotocolscouldalsobeappliedtopredictbehaviorsotherthandiving
Carefulchoicesmustbemade in theselectionandapplicationofbehavioralclassificationmethodswheninferringforagingWhileallmethodstestedgenerallysupportedthehypothesisthatsearchbehavior leadstopreyencounterandsubsequentpreycaptureat-tempts inawide-rangingpelagicpredator therewasconsiderablevariationinthedegreetowhichthiswasnotedTheHMMmethodproducedestimatesof foragingbehavior thatmosteffectivelyen-capsulatedboth searchandpreycapturecomponentsof foragingAs such itwould seema sensible recommendation thatHMMbeused when identifying foraging (including both search and preycapture)areasisapriorityAcrossmethodsratesoffalsenegatives(dives occurring outside of search behavior) ranged from 19 to70Whilesomeofthismaybeattributedtoopportunisticfeedingoutsideofsearchbehaviormethodswithhighratesoffalsenega-tivessuggestthatcareshouldbetakenwhenusingbehavioralclas-sificationmethodsThat animals spend considerable time activelysearchingforpreywhilepreycaptureoccurslargelyoutsideofthisactivityseemsimprobableandpoorclassificationofbehaviorscanhaveimplicationswhenconsideringtimendashenergybudgetsandsub-sequent reproductivesuccessorsurvivalMethodssuchasHMMEMbCandthresholdshadthelowestratesofdivesoccurringout-sideof searchThesemethodsmaybemoreattuned to capturingdiveeventsandtherefore representamore inclusivedefinitionofforagingwhileFPTandk-meansclusteringmaybemoregeneralintheiridentificationofsearchInvestigatingthedifferencesbetweenmethodsmayleadtoincreasedunderstandingoftheenvironmentalcuesusedbypredatorstoinitiatesearchandpreycaptureaswellasthescalesatwhichthesecuesoccurNeverthelesswereiteratetheneedfordetailedexploratoryanalysisofmovementdatatopreventmis-specification of behavior (Gurarie etal (2016)) and argue formethods tobeusedbasedon suitability and thequestionsbeingaskedbyresearchers
ACKNOWLEDGMENTS
Wewouldliketothankallthoseinvolvedinfieldworkaswellasland-ownersofbothcoloniesforallowingaccessforresearchinparticularSirHewHamilton-Dalrymple forpermitting fieldworkatBassRocktheNealefamilyforpermittingworkonGreatSalteeandtheScottishSeabird Centre for logistical support and advice This study wasfundedbyNERCStandardGrantNEH0074661ABisfundedbytheIrishResearchCouncil (ProjectIDGOIPG2016503)MJisfundedunderMarineRenewableEnergyIreland(MaREI)TheSFICentreforMarineRenewableEnergyResearch(12RC2302)andEWisfundedbytheNERC(IRFNEM0179901)
CONFLICT OF INTEREST
Nonedeclared
AUTHOR CONTRIBUTIONS
ABMJandSBconceivedtheinitialideasanddesignedmethodologyWJGundertook analysis forHMMABundertook remaining analy-sisMJWJGEWTWBSCVandKHprovidedadviceandguidanceonanalyticalframeworksandmanuscriptpreparationABandMJledthewritingofthemanuscriptAllauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication
DATA ACCESSIBILITY
Data reported in this article are archived by Birdlife International(wwwseabirdtrackingorg)
ORCID
Ashley Bennison httporcidorg0000-0001-9713-8310
Thomas W Bodey httporcidorg0000-0002-5334-9615 W James Grecian httporcidorg0000-0002-6428-719X
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Anderson C R Jr amp Lindzey FG (2003) Estimating cougar predationrates fromGPS locationclustersThe Journal of Wildlife Management67307ndash316httpsdoiorg1023073802772
Barron D G Brawn J D ampWeatherhead P J (2010)Meta-analysisof transmitter effects on avian behaviour and ecology Methods in Ecology and Evolution 1 180ndash187 httpsdoiorg101111mee320101issue-2
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BattaileBCNordstromCALiebschNampTritesAW(2015)Foraginganewtrailwithnorthernfurseals(Callorhinusursinus)Lactatingsealsfrom islands with contrasting population dynamics have differentforaging strategies and forage at scales previously unrecognized byGPSinterpolateddivedataMarine Mammal Science311494ndash1520httpsdoiorg101111mms201531issue-4
BenhamouS(2004)HowtoreliablyestimatethetortuosityofananimalrsquospathStraightnesssinuosityorfractaldimensionJournal of Theoretical Biology229209ndash220httpsdoiorg101016jjtbi200403016
BicknellAW Godley B J Sheehan EVVotier S C ampWittM J(2016) Camera technology for monitoring marine biodiversity andhumanimpactFrontiers in Ecology and the Environment14424ndash432httpsdoiorg101002fee1322
BodeyTWJessoppMJVotierSCGerritsenHDCleasby IRHamer K C hellip Bearhop S (2014) Seabird movement reveals theecological footprint of fishingvesselsCurrent Biology24 514ndash515httpsdoiorg101016jcub201404041
BuchinMDriemelAvanKreveldMampSacristaacutenV(2010)Analgorith-micframeworkforsegmentingtrajectoriesbasedonspatio-temporalcriteriaProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp202ndash211)NewYorkNYACM
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
22emsp |emsp emspensp BENNISON Et al
ByrneMEampChamberlainMJ(2012)Usingfirst-passagetimetolinkbehaviourandhabitat in foragingpathsofa terrestrialpredator theracoon Animal Behaviour 84 593ndash601 httpsdoiorg101016janbehav201206012
CagnacciFBoitaniLPowellRAampBoyceMS(2010)AnimalecologymeetsGPS-basedradiotelemetryAperfectstormofopportunitiesandchallengesPhilosophical Transactions of the Royal Society B Biological Sciences3652157ndash2162httpsdoiorg101098rstb20100107
Calenge C (2011) Analysis of animal movements in R The adehabitatLT packageViennaAustriaRFoundationforStatisticalComputing
CarterMICoxSLScalesKLBicknellAWNicholsonMDAtkinsKMhellipPatrickSC(2016)GPStrackingrevealsraftingbehaviourofNorthernGannets(Morus bassanus)ImplicationsforforagingecologyandconservationBird Study6383ndash95httpsdoiorg1010800006365720151134441
ChangBCrosonM Straker LGart SDoveCGerwin JampJungS (2016) How seabirds plunge-divewithout injuries Proceedings of the National Academy of Sciences of the United States of America11312006ndash12011httpsdoiorg101073pnas1608628113
Charnov E L (1976) Optimal foraging the marginal value the-orem Theoretical Population Biology 9 129ndash136 httpsdoiorg1010160040-5809(76)90040-X
Cleasby IRWakefieldEDBodeyTWDaviesRDPatrickSCNewtonJhellipHamerKC(2015)Sexualsegregationinawide-rangingmarinepredatorisaconsequenceofhabitatselectionMarine Ecology Progress Series5181ndash12httpsdoiorg103354meps11112
CroninTW (2012)Visual opticsAccommodation in a splashCurrent Biology22R871ndashR873httpsdoiorg101016jcub201208053
Dean B Freeman R Kirk H Leonard K Phillips R A Perrins CM ampGuilfordT (2012) Behaviouralmapping of a pelagic seabirdCombiningmultiple sensors andahiddenMarkovmodel reveals thedistributionofat-seabehaviourJournal of the Royal Society Interface1020120570httpsdoiorg101098rsif20120570
Dias M P Granadeiro J P amp Palmeirim J M (2009) Searching be-haviour of foraging waders Does feeding success influence theirwalkingAnimal Behaviour771203ndash1209httpsdoiorg101016janbehav200902002
Dutilleul P Clifford P Richardson S ampHemon D (1993)ModifyingthettestforassessingthecorrelationbetweentwospatialprocessesBiometrics49305ndash314httpsdoiorg1023072532625
DzialakMROlsonCVWebb S LHarju SMampWinstead J B(2015)Incorporatingwithin-andbetween-patchresourceselectioninidentificationofcriticalhabitatforbrood-rearinggreatersage-grouseEcological Processes45httpsdoiorg101186s13717-015-0032-2
EdelhoffHSignerJampBalkenholN(2016)Pathsegmentationforbegin-nersAnoverviewofcurrentmethodsfordetectingchangesinanimalmovementpatternsMovement Ecology421httpsdoiorg101186s40462-016-0086-5
EdwardsEWJQuinnLRWakefieldEDMillerPIampThompsonPM(2013)TrackinganorthernfulmarfromaScottishnestingsitetotheCharlie-GibbsFractureZoneEvidenceoflinkagebetweencoastalbreeding seabirds and Mid-Atlantic Ridge feeding sites Deep Sea Research Part II Topical Studies in Oceanography98(PartB)438ndash444httpsdoiorg101016jdsr2201304011
ElithJLeathwickJRampHastieT(2008)Aworkingguidetoboostedregression trees Journal of Animal Ecology77 802ndash813 httpsdoiorg101111j1365-2656200801390x
EvansJCDallSRXBoltonMOwenEampVotierSC(2015)SocialforagingEuropeanshagsGPStrackingrevealsbirdsfromneighbour-ingcolonieshavesharedforaginggroundsJournal of Ornithology15723ndash32httpsdoiorg101007s10336-015-1241-2
FauchaldPampTveraaT (2003)Using first-passage time in the analysisofarea-restrictedsearchandhabitatselectionEcology84282ndash288httpsdoiorg1018900012-9658(2003)084[0282UFPTIT]20CO2
FieldingAHampBell J F (1997)A reviewofmethods for the assess-mentof prediction errors in conservationpresenceabsencemodelsEnvironmental Conservation 24 38ndash49 httpsdoiorg101017S0376892997000088
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-MaximizationBinaryClusteringforBehaviouralAnnotationPLoS ONE11e0151984
Garriga J Palmer J ROltraA amp Bartumeus F (2016) Expectation-maximizationbinaryclusteringforbehaviouralannotationPLoS ONE11e0151984httpsdoiorg101371journalpone0151984
GartheSBenvenutiSampMontevecchiWA(2000)Pursuitplungingbynortherngannets(Sula bassana)rdquofeedingoncapelin(Mallotus villosus)rdquoProceedings of the Royal Society of London B Biological Sciences2671717ndash1722httpsdoiorg101098rspb20001200
GreacutemilletDDellrsquoOmoGRyanPGPetersGRopert-CoudertYampWeeksSJ(2004)Offshorediplomacyorhowseabirdsmitigateintra-specificcompetitionAcasestudybasedonGPStrackingofCapegan-nets fromneighbouringcoloniesMarine Ecology- Progress Series268265ndash279httpsdoiorg103354meps268265
GruumlssAKaplanDMGueacutenette SRobertsCMampBotsford LW(2011) Consequences of adult and juvenile movement for marineprotected areas Biological Conservation 144 692ndash702 httpsdoiorg101016jbiocon201012015
GuilfordTMeadeJFreemanRBiroDEvansTBonadonnaFhellipPerrinsC(2008)GPStrackingoftheforagingmovementsofManxShearwatersPuffinus puffinus breedingonSkomer IslandWalesIbis 150 462ndash473 httpsdoiorg101111j1474-919X2008 00805x
GurarieEBracisCDelgadoMMeckleyTDKojolaIampWagnerCM (2016)What istheanimaldoingToolsforexploringbehaviouralstructureinanimalmovementsJournal of Animal Ecology8569ndash84httpsdoiorg1011111365-265612379
HamerKHumphreysEMagalhaesMGartheSHennickeJPetersGhellipWanlessS(2009)Fine-scaleforagingbehaviourofamedium-ranging marine predator Journal of Animal Ecology 78 880ndash889httpsdoiorg101111jae200978issue-4
HamerKPhillipsRWanlessSHarrisMampWoodA(2000)Foragingrangesdietsandfeeding locationsofgannetsMorus bassanus in theNorthSeaEvidencefromsatellitetelemetryMarine Ecology Progress Series200257ndash264httpsdoiorg103354meps200257
HammerschlagNGallagherAampLazarreD (2011)Areviewofsharksatellite tagging studies Journal of Experimental Marine Biology and Ecology3981ndash8httpsdoiorg101016jjembe201012012
HansenBDLascellesBDXKeeneBWAdamsAKampThomsonAE(2007)Evaluationofanaccelerometerforat-homemonitoringofspontaneousactivity indogsAmerican Journal of Veterinary Research68468ndash475httpsdoiorg102460ajvr685468
HaysGCFerreiraLCSequeiraAMMeekanMGDuarteCMBaileyHhellipCostaDP (2016)Keyquestions inmarinemegafaunamovementecologyTrends in Ecology amp Evolution31463ndash475httpsdoiorg101016jtree201602015
Hochachka W M Caruana R Fink D Munson A Riedewald MSorokinaDampKellingS(2007)Data-miningdiscoveryofpatternandprocessinecologicalsystemsThe Journal of Wildlife Management712427ndash2437httpsdoiorg1021932006-503
HuigNBuijsR-JampKleyheegE(2016)SummerinthecityBehaviouroflargegullsvisitinganurbanareaduringthebreedingseasonBird Study63214ndash222httpsdoiorg1010800006365720161159179
HurfordA(2009)GPSmeasurementerrorgivesrisetospurious180degturning angles and strong directional biases in animal movementdataPLoS ONE4e5632httpsdoiorg101371journalpone000 5632
JacobyDMPBrooksEJCroftDPampSimsDW(2012)Developingadeeperunderstandingofanimalmovementsandspatialdynamicsthrough
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
emspensp emsp | emsp23BENNISON Et al
novelapplicationofnetworkanalysesMethods in Ecology and Evolution3574ndash583httpsdoiorg101111j2041-210X201200187x
Jain A K (2010) Data clustering 50 Years beyond K-means Pattern Recognition Letters 31 651ndash666 httpsdoiorg101016jpatrec200909011
JerdeCLampVisscherDR(2005)GPSmeasurementerrorinfluencesonmovementmodel parameterization Ecological Applications15 806ndash810httpsdoiorg10189004-0895
JonsenIDMyersRAampJamesMC(2006)Robusthierarchicalstatendashspacemodels reveal diel variation in travel rates ofmigrating leath-erback turtles Journal of Animal Ecology751046ndash1057httpsdoiorg101111j1365-2656200601129x
Kerk M Onorato D P Criffield M A Bolker B M AugustineB C McKinley S A amp Oli M K (2015) Hidden semi-Markovmodels reveal multiphasic movement of the endangered Floridapanther Journal of Animal Ecology 84 576ndash585 httpsdoiorg1011111365-265612290
KetchenD J Jramp ShookC L (1996)The application of cluster anal-ysis in strategic management research An analysis and critiqueStrategic Management Journal17441ndash458httpsdoiorg101002(SICI)1097-0266(199606)176lt441AID-SMJ819gt30CO2-G
KingDTGlahnJFampAndrewsKJ(1995)DailyactivitybudgetsandmovementsofwinterroostingDouble-crestedCormorantsdeterminedbybiotelemetryinthedeltaregionofMississippiColonial Waterbirds18152ndash157httpsdoiorg1023071521535
KnellASampCodlingEA(2012)Classifyingarea-restrictedsearch(ARS)usingapartialsumapproachTheoretical Ecology5325ndash339httpsdoiorg101007s12080-011-0130-4
KuhnM(2008)CaretpackageJournal of Statistical Software281ndash26LandisJRampKochGG (1977)Themeasurementofobserveragree-
ment for categorical data Biometrics 33 159ndash174 httpsdoiorg1023072529310
LangrockRKingRMatthiopoulosJThomasLFortinDampMoralesJM(2012)FlexibleandpracticalmodelingofanimaltelemetrydataHidden Markov models and extensions Ecology 93 2336ndash2342httpsdoiorg10189011-22411
LascellesBGTaylorPMillerMDiasMOppelSTorresLhellipShafferSA(2016)Applyingglobalcriteriatotrackingdatatodefineimport-antareasformarineconservationDiversity and Distributions22422ndash431httpsdoiorg101111ddi201622issue-4
LeCorreMDussaultCampCocircteacuteSD(2014)Detectingchangesintheannual movements of terrestrial migratory species Using the first-passagetimetodocumentthespringmigrationofcaribouMovement Ecology21ndash11httpsdoiorg101186s40462-014-0019-0
Louzao M Wiegand T Bartumeus F amp Weimerskirch H (2014)Coupling instantaneous energy-budget models and behaviouralmode analysis to estimate optimal foraging strategy An examplewith wandering albatrosses Movement Ecology 2 (1)8 httpsdoiorg1011862051-3933-2-8
MacArthur R H amp Pianka E R (1966) On optimal use of a patchyenvironment The American Naturalist 100 603ndash609 httpsdoiorg101086282454
MacQueenJ(1967)Somemethodsforclassificationandanalysisofmul-tivariateobservationsInLMLeCamampJNeyman(Eds)Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (pp281ndash297)OaklandCAUniversityofCaliforniaPress
McCarthyA LHeppell S Royer F FreitasCampDellingerT (2010)Identificationof likelyforaginghabitatofpelagic loggerheadseatur-tles(Carettacaretta)intheNorthAtlanticthroughanalysisofteleme-trytracksinuosityProgress in Oceanography86224ndash231httpsdoiorg101016jpocean201004009
MendezLBorsaPCruzSDeGrissacSHennickeJLallemandJhellipWeimerskirchH(2017)Geographicalvariationintheforagingbe-haviourof thepantropical red-footedboobyMarine Ecology Progress Series568217ndash230httpsdoiorg103354meps12052
MichelotTLangrockRampPattersonTA(2016)moveHMMAnRpack-age for thestatisticalmodellingofanimalmovementdatausinghid-denMarkovmodelsMethods in Ecology and Evolution71308ndash1315httpsdoiorg1011112041-210X12578
MollRJMillspaughJJBeringerJSartwellJampHeZ(2007)Anewlsquoviewrsquoofecologyandconservationthroughanimal-bornevideosystemsTrends in Ecology amp Evolution22660ndash668httpsdoiorg101016jtree200709007
Montevecchi W Benvenuti S Garthe S Davoren G amp Fifield D(2009)Flexibleforagingtacticsbyalargeopportunisticseabirdpreyingonforage-andlargepelagicfishesMarine Ecology Progress Series385295ndash306httpsdoiorg103354meps08006
NathanRGetzWMRevillaEHolyoakMKadmonRSaltzDampSmousePE (2008)Amovementecologyparadigmforunifyingor-ganismalmovement researchProceedings of the National Academy of Sciences of the United States of America10519052ndash19059httpsdoiorg101073pnas0800375105
NelsonB(2002)The Atlantic gannetAmsterdamFenixBooksNordstromCABattaileBCCotteCampTritesAW(2013)Foraging
habitatsof lactatingnorthernfursealsarestructuredbythermoclinedepthsandsubmesoscalefronts intheeasternBeringSeaDeep Sea Research Part II Topical Studies in Oceanography8878ndash96httpsdoiorg101016jdsr2201207010
PalmerCampWoinarskiJ(1999)Seasonalroostsandforagingmovementsof the black flying fox (Pteropus alecto) in the Northern TerritoryResource tracking in a landscapemosaicWildlife Research26 823ndash838httpsdoiorg101071WR97106
PatrickSCBearhopSGreacutemilletDLescroeumllAGrecianWJBodeyTWhellipVotierSC(2014)Individualdifferencesinsearchingbehaviourand spatial foraging consistency in a central place marine predatorOikos12333ndash40httpsdoiorg101111more2014123issue-1
PattersonTAThomasLWilcoxCOvaskainenOampMatthiopoulosJ (2008) Statendashspace models of individual animal movementTrends in Ecology amp Evolution 23 87ndash94 httpsdoiorg101016jtree200710009
PhillipsRAXavierJCampCroxallJP(2003)Effectsofsatellitetrans-mittersonalbatrossesandpetrelsAuk1201082ndash1090httpsdoiorg1016420004-8038(2003)120[1082EOSTOA]20CO2
PykeGH(1984)OptimalforagingtheoryAcriticalreviewAnnual Review of Ecology and Systematics15523ndash575httpsdoiorg101146an-nureves15110184002515
RobertsGOampRosenthalJS(2004)GeneralstatespaceMarkovchainsand MCMC algorithms Probability Surveys 1 20ndash71 httpsdoiorg101214154957804100000024
SamalAampLyengarPA (1992)Automatic recognitionandanalysisofhumanfacesandfacialexpressionsAsurveyPattern Recognition2565ndash77httpsdoiorg1010160031-3203(92)90007-6
SatoKWatanukiYTakahashiAMillerPJTanakaHKawabeRhellipWatanabeY(2007)Strokefrequencybutnotswimmingspeedisrelatedtobodysizeinfree-rangingseabirdspinnipedsandcetaceansProceedings of the Royal Society of London B Biological Sciences274471ndash477httpsdoiorg101098rspb20060005
Schultz J C (1983) Habitat selection and foraging tactics of caterpil-lars in heterogeneous trees In R FDennoampM SMcClure (Eds)Variable plants and herbivores in natural and managed systems (pp61ndash90) Cambridge MA Academic Press httpsdoiorg101016B978-0-12-209160-550009-X
ShepardELRossANampPortugalSJ(2016)Movinginamovingme-diumNewperspectivesonflightPhilosophical Transactions of the Royal Society B Biological Sciences37120150382httpsdoiorg101098rstb20150382
ShojiAElliottKHGreenwoodJGMcCleanLLeonardKPerrinsCMhellipGuilfordT(2015)DivingbehaviourofbenthicfeedingBlackGuillemotsBird Study62217ndash222httpsdoiorg1010800006365720151017800
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593
24emsp |emsp emspensp BENNISON Et al
SilvermanBW(1986)Density estimation for statistics and data analysisBocaRatonFLCRCPresshttpsdoiorg101007978-1-4899-3324-9
SommerfeldJKatoARopert-CoudertYGartheSampHindellMA(2013) Foraging parameters influencing the detection and inter-pretation of area-restricted search behaviour inmarine predatorsAcasestudywiththemaskedboobyPLoS ONE8e63742httpsdoiorg101371journalpone0063742
TinkerMCostaDEstesJampWieringaN(2007)Individualdietaryspe-cializationanddivebehaviourintheCaliforniaseaotterUsingarchivaltimendashdepth data to detect alternative foraging strategiesDeep Sea Research Part II Topical Studies in Oceanography54330ndash342httpsdoiorg101016jdsr2200611012
TownerAV Leos-BarajasV Langrock R Schick R S SmaleM JKaschkeThellipPapastamatiouYP(2016)Sex-specificandindividualpreferencesforhuntingstrategiesinwhitesharksFunctional Ecology301397ndash1407httpsdoiorg1011111365-243512613
vanBeest FMampMilner JM (2013)Behavioural responses to ther-malconditionsaffectseasonalmasschangeinaheat-sensitivenorth-ern ungulatePLoS ONE8 e65972 httpsdoiorg101371journalpone0065972
VandenabeeleSPShepardELGroganAampWilsonRP(2012)Whenthreepercentmaynotbethreepercentdevice-equippedseabirdsex-periencevariableflightconstraintsMarine Biology1591ndash14httpsdoiorg101007s00227-011-1784-6
Wakefield E D Bodey TW Bearhop S Blackburn J Colhoun KDavies R hellip Hamer K C (2013) Space partitioningwithout terri-toriality in gannets Science 341 68ndash70 httpsdoiorg101126science1236077
Warwick-EvansVAtkinsonPGauvainR Robinson LArnouldJampGreenJ (2015)Time-in-area represents foragingactivity inawide-rangingpelagicforagerMarine Ecology Progress Series527233ndash246httpsdoiorg103354meps11262
Weimerskirch H (2007) Are seabirds foraging for unpredictable re-sourcesDeep Sea Research Part II Topical Studies in Oceanography54211ndash223httpsdoiorg101016jdsr2200611013
Weimerskirch H Gault A amp Cherel Y (2005) Prey distribution andpatchinessFactorsinforagingsuccessandefficiencyofwanderingal-batrossesEcology862611ndash2622httpsdoiorg10189004-1866
Weimerskirch H Le Corre M Marsac F Barbraud C Tostain O ampChastel O (2006) Postbreeding movements of frigatebirds trackedwith satellite telemetry The Condor 108 220ndash225 httpsdoiorg1016500010-5422(2006)108[0220PMOFTW]20CO2
WeimerskirchHPinaudDPawlowskiFampBostCA(2007)Doespreycaptureinducearea-restrictedsearchAfine-scalestudyusingGPSinamarine predator thewandering albatrossThe American Naturalist170734ndash743httpsdoiorg101086522059
Williams TMWolfe L Davis T Kendall T Richter BWangY hellipWilmers CC (2014) Instantaneous energetics of puma kills revealadvantage of felid sneak attacks Science 346 81ndash85 httpsdoiorg101126science1254885
ZachRampFallsJB(1979)Foragingandterritorialityofmaleovenbirds(Aves Parulidae) in a heterogeneous habitat The Journal of Animal Ecology4833ndash52httpsdoiorg1023074098
ZhangJOrsquoReillyKMPerryGLTaylorGAampDennisTE(2015)Extendingthefunctionalityofbehaviouralchange-pointanalysiswithk-means clustering A case study with the little Penguin (Eudyptula minor) PLoS ONE 10 e0122811 httpsdoiorg101371journalpone0122811
ZucchiniWMacDonaldILampLangrockR(2016)Hidden Markov models for time series An introduction using RBocaRatonFLCRCPress
SUPPORTING INFORMATION
Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle
How to cite this articleBennisonABearhopSBodeyTWetalSearchandforagingbehaviorsfrommovementdataAcomparisonofmethodsEcol Evol 2018813ndash24 httpsdoiorg101002ece33593