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Ecology and Evolution. 2018;8:13–24. | 13 www.ecolevol.org Received: 12 April 2017 | Revised: 6 October 2017 | Accepted: 11 October 2017 DOI: 10.1002/ece3.3593 ORIGINAL RESEARCH Search and foraging behaviors from movement data: A comparison of methods Ashley Bennison 1,2 | Stuart Bearhop 3 * | Thomas W. Bodey 3 | Stephen C. Votier 4 | W. James Grecian 5 | Ewan D. Wakefield 5,6 | Keith C. Hamer 7 * | Mark Jessopp 1 * This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. *Co-senior authorship. 1 MaREI Centre for Marine and Renewable Energy, Environmental Research Institute, University College Cork, Cork, Ireland 2 School of Biological, Earth, and Environmental Sciences (BEES), University College Cork, Cork, Ireland 3 Centre for Ecology & Conservation, University of Exeter, Penryn, UK 4 Environment & Sustainability Institute, University of Exeter, Penryn, UK 5 Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, Fife, Scotland 6 Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary, and Life Sciences, University of Glasgow, Glasgow, Scotland 7 Faculty of Biological Sciences, School of Biology, University of Leeds, Leeds, UK Correspondence Ashley Bennison, Environmental Research Institute, MaREI Centre for Marine and Renewable Energy, University College Cork, Cork, Ireland. Email: [email protected] Funding information Natural Environment Research Council, Grant/Award Number: IRF NE/M017990/1 and NE/H007466/1; Irish Research Council, Grant/Award Number: GOIPG/2016/503; Marine Renewable Energy Ireland (MaREI); The SFI Centre for Marine Renewable Energy Research, Grant/Award Number: 12/RC/2302 Abstract Search behavior is often used as a proxy for foraging effort within studies of animal movement, despite it being only one part of the foraging process, which also includes prey capture. While methods for validating prey capture exist, many studies rely solely on behavioral annotation of animal movement data to identify search and infer prey capture attempts. However, the degree to which search correlates with prey capture is largely untested. This study applied seven behavioral annotation methods to identify search behavior from GPS tracks of northern gannets (Morus bassanus), and compared outputs to the occurrence of dives recorded by simultaneously deployed time–depth recorders. We tested how behavioral annotation methods vary in their ability to iden- tify search behavior leading to dive events. There was considerable variation in the number of dives occurring within search areas across methods. Hidden Markov mod- els proved to be the most successful, with 81% of all dives occurring within areas identified as search. k-Means clustering and first passage time had the highest rates of dives occurring outside identified search behavior. First passage time and hidden Markov models had the lowest rates of false positives, identifying fewer search areas with no dives. All behavioral annotation methods had advantages and drawbacks in terms of the complexity of analysis and ability to reflect prey capture events while minimizing the number of false positives and false negatives. We used these results, with consideration of analytical difficulty, to provide advice on the most appropriate methods for use where prey capture behavior is not available. This study highlights a need to critically assess and carefully choose a behavioral annotation method suitable for the research question being addressed, or resulting species management frame- works established. KEYWORDS behavior, first passage time, hidden Markov models, kernel density, k-means, machine learning, movement, state-space models, telemetry

Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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Page 1: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

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

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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

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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

Page 2: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

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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

Page 3: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

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Rate

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Tim

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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

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Machinelearning

NA

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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

Page 4: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

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

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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

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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

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

Page 5: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

REFERENCES

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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

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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

Page 6: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

REFERENCES

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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

Page 7: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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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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

Page 8: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

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

Page 9: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

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

Page 10: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

Page 11: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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

Page 12: Search and foraging behaviors from movement data: A ...Received: 12 April 2017 ... Research, Grant/Award Number: 12/RC/2302 ... (Buchin, Driemel, Kreveld, & Sacristán, 2010), and

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


GIS projects 2012 Marc van Kreveld. Two phases Problem analysis (phase 1 report 1) –Literature study, reverse engineering –Statement of criteria –Dependency

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