gigapixel big data movies provide cost-effective seascape
TRANSCRIPT
Ecology and Evolution 20181ndash12 emsp|emsp1wwwecolevolorg
Received16November2017emsp |emsp Revised14May2018emsp |emsp Accepted17May2018DOI101002ece34301
O R I G I N A L R E S E A R C H
Gigapixel big data movies provide cost- effective seascape scale direct measurements of open- access coastal human use such as recreational fisheries
David J H Flynn12emsp|emspTim P Lynch1 emsp|emspNeville S Barrett2emsp|emspLincoln S C Wong12emsp|emsp Carlie Devine1emsp|emspDavid Hughes1
ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicensewhichpermitsusedistributionandreproductioninanymediumprovidedtheoriginalworkisproperlycitedcopy2018TheAuthorsEcology and EvolutionpublishedbyJohnWileyampSonsLtd
TweetBigdataseascapescalemoviesallowforcost-effectivehigh-frequencymonitoringofcoastalrecreationaluseCSIROCRAGSTuna
1TheCommonwealthScientificandIndustrialResearchOrganisation(CSIRO)OceansampAtmosphereHobartTASAustralia2TheInstituteforMarineandAntarcticStudies(IMAS)UniversityofTasmaniaHobartTASAustralia
CorrespondenceTimPLynchTheCommonwealthScientificandIndustrialResearchOrganisation(CSIRO)OceansampAtmosphereCastrayEsplanadeHobartTAS7000AustraliaEmailtimlynchcsiroau
Funding informationUniversityofTasmaniaPartoftheIMASHonoursProjectTheCoastsProgramCSIROCAPEXGigapanruggerdizationMkII201415
AbstractCollectingdataonunlicensedopen-accesscoastalactivitiessuchassometypesofrecreationalfishinghasoftenreliedontelephoneinterviewsselectedfromlandlinedirectoriesHoweverthisapproachisbecomingobsoleteduetochangesincommuni-cationtechnologysuchasaswitchtounlistedmobilephonesOthermethodssuchasboatrampinterviewsareoftenimpracticalduetohighlaborcostWetrialedanau-tonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirect measurements of a recreational fishery Our sequential photosampling wasbatchedprocessedusinganovelsoftwareapplicationtoproduceldquobigdatardquotimese-riesmoviesfromaspatialsubsetofthefisheryandwevalidatedthiswitharegionalbus-routesurveyandinterviewswithparticipantsataccesspointsWealsocomparedlaborcostsbetweenthesetwomethodsMost trailerboatuserswererecreationalfishers targeting tuna sppOur camera systemcloselymatched trends in temporalvariationfromthelargerscaleregionalsurveybutasthecameradatawereatmuchhigher frequency we could additionally describe strong daily variability in effortPeakswerenormallyassociatedwithweekendsbutconsecutiveweekendtunafish-ingcompetitionsledtoananomalyofhigheffortacrossthenormalweekdaylullsByreducingfieldtimeandbatchprocessingimagerymonthlylaborcostsforthecamerasamplingwereaquarterofthebus-routesurveyandindividualcamerasamplescost25of bus route samples toobtainGigapixel panoramic cameraobservationsoffishingwererepresentativeofthetemporalvariabilityofregionalfishingeffortandcould be used to develop a cost-efficient indexHigh-frequency sampling had theaddedbenefitofbeingmorelikelytodetectabnormalpatternsofuseCombinationsofremotesensingandon-siteinterviewsmayprovideasolutiontodescribinghighlyvariableeffortinrecreationalfisherieswhilealsovalidatingactivityandcatch
K E Y W O R D S
bigdatacoastalCSIRORuggerdisedAutonomousGigapixelSystemGigaPanopen-accessfisheriesphotomosaicremotesensingsouthernbluefintunatimeintervalcounttime-lapse
2emsp |emsp emspensp FLYNN et aL
1emsp |emspINTRODUC TION
Worldwidecoastalzonesaresomeofthemostheavilyimpactedpartsoftheoceanbypeoplebothintermsofactivitytypesandparticipationrates(Halpernetal2008)Aparticularfocusofre-search on coastal use has been recreational fishing as this pop-ular activity can significantly affect fish and invertebrate stocks(Arlinghaus 2006 Cooke amp Cowx 2004 Lewin Arlinghaus ampMehner2006McPheeLeadbitterampSkilleter2002)asformanyspecies harvest can exceed the take of the commercial fishery(GiriampHall2015LyleStarkampTracey2014ZischkeGriffithsampTibbetts2012)
Unlikecommercialfisherieswhicharecommonlydocumentedbyoperatorsloggingtheircatchandeffortassessmentsofopen-accessnonreporting activities suchas recreational fisheries require sam-plingThiscanbebothdifficultandexpensiveduetotheiroftenlargespatialextenthightemporalvariationandforthefisheriesagenciesundertaking the work which often requires weekend and holidaysurveyinghighlaborcosts(Maetal2018PollockJonesampBrown1994 Rocklin Levrel Drogou Herfaut amp Veron 2014 VenturelliHyderampSkov2017)Due to these issues assessmentshave typi-callyrelieduponindirectdatacollectionfromoff-sitetelephonesur-veysbasedondataframesdevelopedaroundregionallsquowhitepagesrsquoor landline telephonedirectorieswhichtraditionallyhavehadhighresponseratesandeasilyscalablesamplesizes(Mooreetal2015Pollocketal1994)Suchapproachesmaynowriskundersamplingactivefishersbecauseofdemographicallybiasandoveralldecreasedlandlinephoneuseunlistedandnonregionalcodedmobilephonesandlowresponsesuccessfromvoicecallstomobilephones(Badcocketal 2016 Blumberg amp Luke 2009 Teixeira Zischke ampWebley2016)
Other formsof recreational fisheryassessmentsemploydirecton-sitesurveymethodssuchasbus-routesurveyswhichuseclerkstraveling around a fisherymdashfollowing randomly selected predeter-minedschedulesmdashinterviewingandobservingfisherstotrackfish-ingeffortcatchandrelease(McGlennonampKinloch1997Pollocketal 1994) On-sitemethods that cover broad geographic areashowever are seldom performed due to high labor costs (JonesampPollock2012)
Given these limitations remote sensing tools and technol-ogies such as autonomous photography are an emerging fieldofferingdirectmonitoringasanalternativeorsupplementtoon-site interviews (HartillPayneRushampBian2016KellerSteffeLowryMurphyampSuthers2016ParnellDaytonFisherLoarieampDarrow2010PowersampAnson2016vanPoortenCarruthersWardampVarkey 2015Wood LynchDevineKelleramp Figueira2016) One such system is the CSIRO Ruggedised AutonomousGigapixel System (CRAGS) which is a programmable weather-proofcameratrapthatprovidesultra-highresolutiontime-lapseandhigh-frequencypanoramic imagesTheCRAGSutilizesmod-ified commercially available hardware and software known as aGigaPanreg (httpgigapancom)tocapturephotographicsampleswith such high pixel density (gigapans) that wide fields of view
andassociatedobjectsofinterestscanbeexaminedcloselywith-out the loss of broader environmental context at landscape orseascape scales (LynchAldermanampHobday 2015) (SupportingInformationAppendixS1)
Thisroboticcamerasystemallowsformuchbroaderscaledphoto-graphicassessmentsthansimplecameratrapsbyautonomouslytak-ingandthenstitchingtogethermultipletelephotomegapixelimagesinto high-resolution gigapixel panorama The resulting tiled imageallowsfullyzoomableviewingforeithermultiplesmalltargetssuchasAlbatrossnestsacrossacolony(Lynchetal2015)ormorewidelyspacedlargertargetssuchasidentifyingcommercialvsrecreationalvessels around an offshore artificial reef (Wood etal 2016) Thebenefitofthesystemcomparedtosimplercameratrapsisbeingabletoobserve inhighdetail fromtheone imagestreammanyobjectssimultaneously across landscapeor seascape scalesWith somuchinformationdatahandlingcanbecomeoverwhelmingsoabatchpro-cessingmethod calledGigapanTimemachinehasbeendevelopedOriginallyusedforviewingcosmologicalsimulations(Yuetal2011)TimeMachineproducesavideostreamthatallowsviewerstofluidlyexploregigatotetrapixel-scaledvideosacrossbothspaceandtime
InAustraliaannualrecreationalfishingparticipationhasbeenes-timatedat195(HenryampLyle2003)whichiswellabovetheglobalaverageofaround10(ArlinghausTillnerampBork2015)AroundtheAustralian island state of Tasmania the annual participation rate is293whichwellexceedsthenationalaverageTheTasmanPeninsulainTasmaniarsquossoutheast (Figure1) isapopularfishingregionneartothelargestcityandstatecapitalofHobartwhereasteepbathymetricprofileandmigratorygame-fishpathwayoverlapprovidingcoastalac-cesstonormallyoffshorefishingopportunitiestorecreationalfisherintrailerboatsPelagicgamefishsuchassouthernbluefintuna(Thunnus maccoyii)aretargetedinlateAustralSummerandAutumn(MortonampLyle2004)butasthefisheryisunlicensedandepisodictrendsinef-fortandcatcharepoorlyunderstood(LowryampMurphy2003Mooreetal2015)Thisparticularrecreationalfisheryisalsoofmoregeneralinterestasthetargetsarecommerciallyimportantspecieswhichin-cludethosewithinternationallynegotiatedquotas(Ponsetal2017Zischke etal 2012) Recreational fishers are also often associatedwithotherusersofthemarineenvironment(FarrStoecklampSutton2014Kearney2002)orinthiscasetouristsenjoyingthelocalnationalparksHencehighusesitesmaybeimportanttomonitornotonlyforfisheriesandecologicalreasonsbutformanagingsocialvaluescon-flict resolutionandplanning (AlessaKliskeyampBrown2008Lynchetal2004)
WetrailedourCRAGSmethodtoseewhetherwecoulddevelopa high-frequency time series of temporal trends of observed trailerboat fishingeffortatanoffshoreconcentrationpoint for the recre-ationalgamefisheryWealsotestedboththesuitabilityandlaborcost-effectivenessofthehardwareandanovelapplicationofTimeMachinebatchprocessingsoftwaretoautomaticallyproducealdquobigdatardquointer-activemoviesmadefromourgigapixelpanoramasAsthepeninsulais isolatedandservicedbyonlya limitednumberofboatrampsweaimed tovalidate the representativenessof the temporal trendsweobservedwithCRAGSbycorrelatingtomatchsamplesobtainedfrom
emspensp emsp | emsp3FLYNN et aL
asimultaneousregionallyscaledbus-routetrailerboatsurveyatthethreerampsonthepeninsularsuitableforlaunchinglargetrailerboatsWe also undertook on-ground interviewswith trailer boat users togroundtruthactivitytype(fishingornonfishing)andtargetspeciesByundertakingbothcameraandboatrampsurveysourover-overarchingaimwastoinvestigatehowremoteimagerymayreplacecomplimentoroptimizeconventionalon-siteapproachesforrecreationalfishing
2emsp |emspMATERIAL S AND METHODS
WedeployedtheCRAGSbetween1May2015and30June2015toassesstrailerboatsoffshorefromwateradjacenttotheTasman
Peninsula (Figure1) This period was chosen as it correspondswith the main tuna fishing season For large trailer boats (gt5mlength) localaccess to the fishery is limitedto threeboat ramps(Figure1)althoughtheareamayalsobevisitedbylargervesselssteaming frommarinas and anchorages further north (MortonampLyle2004)butforthepurposesofourstudyweexcludedtheserelativelyrare largervesselsWefocusedourCRAGSsurveyataknown game fishing concentration the Hippolyte Rock MarineConservationArea (HRMCA)which are a group of small islandsabout6-kmoffshore (Figures1and2)aroundwhichrecreationalfishingispermittedWhiletheCRAGSdeploymentwasmarginallycloser to the FortescueBayRampwe commonly observe boatsapproachingtheHRMCAfromdirectionsthatcorrespondedtothe
F IGURE 1emspTheTasmanPeninsulainSETasmaniaBus-routesurveysamplingoccurredatramps locatedatPiratesFortescueandStuartsBayTheCRAGSlocationisshownasacameraiconandtheareaobservedbythecamerawhichincludestheHippolyteRocksMarineConservationArea(HRMCA)isdescribedasatriangle
Text
Pirates Bay
Fortesque Bay
StewartsBay
0 3 6 9 1215Kilometers
0 30 60 90 12015Kilometers
Tasman Is
Australia
Tasmania
Hobart
TasmanPeninsular
HippolyteRocksMCA
43deg 01 30
147deg 52 30
TAS
4emsp |emsp emspensp FLYNN et aL
locationof all three rampsWe fixed theCRAGS to a clifftop atWaterfall Baywhich overlooks the site fromvery high sea cliffs(~250m)withthecamerafocusingonthewesternsideofHRMCA(Figure1)ForeachsampleCRAGScapturedaseriesof68pho-tographsthroughatelephotolenswhichwhenstitchedtogetherprovide a high-resolution panoramic image with a wide field ofview (~140deg) (Supporting Information Appendix S1) The size ofareasurveyedbyCRAGScomparedtotheregionalareaaccessibleby trailer boatswas estimated using google earth polygons andtheUniversityofNewHampshireKMLextensiontoolmdashhttpsex-tensionunhedukmlToolsindexcfm
Thetimeof the initialCRAGSpanoramasamplewasrandomlyselected Samples were then collected sequentially through-out daylight hours (0600ndash1800) As less recreational fishing fortuna occurred at night no samples were taken with the CRAGSprogrammed to ldquoblankrdquo these hours from the sampling sched-ule (Supporting InformationAppendix S1) Photographic sessionswereprogrammedtobeseparatedbyarecurring93-mingapthus
producing a forward shift in sample capture time andminimizingpotentialautocorrelationsbetweenboatingactivitiesandsamplingtimesPhotographswerebatchprocessedtobothformpanoramicimagesforeachsampleandforcompilingintoaninteractivetime-lapsemovieusingCREATELabrsquosTimeMachinesoftware (CREATELabregPittsburgPAUSA)
TimeMachinemovies are fully interactive and are able to bezoomedintopausedandplayedatvariousspeedsandarealsotimeanddate-stamped(Figure3)UsingthisinteractivemoviesoftwareeachGigaPanmoviewassystematicallypausedateachsamplefullyzoomedandscannedlefttorighttoptobottomforobjectsintheseascapebytheleadauthor(Figure3)Vesselcountswererecordedforeachsamplewithobjectslargerthan~10minlength(nottrailerboats)or requiring longer than10seconds todistinguish (eg lowresolutionandimageartifact)beingdiscountedSampleswerecat-egorized into two strataweekdays andweekendspublic holidaysusing the time and date stamp provided on each GigaPan scene(Figure3)
F IGURE 2emspAnunmodifiedGigaPanEpicPROunit(left)andaCRAGSdeployedatWaterfallBaySETasmania(right)
F IGURE 3emspScreencaptureoutputsfromtheTimeMachineinteractivedataviewershowinganozoomandpanoramicextentoftheHippolyteRockMarineConservationArea(HRMCA)b=13zoommainHippolyterock~6kmfromcamerac=12zoomofalargetrailerboatd=fullzoom
emspensp emsp | emsp5FLYNN et aL
Thecountofboatsforeachsamplewasthenextrapolatedintoanestimateofdailyaverageboatingeffort(hoursplusmnSE)usingthein-stantaneouscountmethod(Equation1)
where ei is theextrapolatedboatinghours for the ithday Ii is theaveragenumberoftrailerboatsobservedacrosstheinstantaneoussamplesfordayiandTisthedailysamplingperiod(daylighthours)Asbadweathercanbeamajorfactoraffectingthedecisiontoboatorfish(ForbesTraceyampLyle2009)boatingeffortwasassumedtobe0whenadverseweathermadeobservationsofboatsimpossible
Next total effort for each day-type strata (Ejk) within sam-pling months (k) was calculated to estimate the monthly effort(Equation2)AsCRAGSallowedforcontinuoussamplingthroughoutthesurveyperiodthesamplingprobability(πk)wassetto1
Standarderrorandeffortvarianceformonthlyestimateswerecalculated(Equation3)accordingtoPollocketal(1994)forstrata
where nj is thenumberofsampleddaysofstrata j Njk is thetotalnumberofdaysofstratajinmonthk
Asvariationforeachstratawascalculatedseparatelyweusedasumofsquaredstandarderrorsapproach(Equation4)toestimatethetotalmonthlyvariation
Assampleswereobtainedinaserialandcontinuousfashionatimeseriesmodelusingrollingaverageswasalsoappliedtothedata(Diggle1995)(Equation5)
where smt is the smoothed average of extrapolated effort e persampletwhichisweightedacrossthreesampleseachseparatedby93minThisprovidedasmoothedaveragewithindays
Wealsoconductedabus-routestylesurveytoestimatefishingeffortfromtrailerboatsfortheregion(Pollocketal1994Robsonamp Jones1989)WhileCRAGSsampleswerecollected throughoutMayandJunethecomparablebus-routesurveywasonlyconductedinMaydue to logistical constraintsWesurveyed the threemajorboatrampsintheregionatPiratesBayFortescueBayandStewartsBaybetween3May2015and30May2015(Figure1)
Atotalof20bus-routesampleswerescheduledWeundertookrandomstratifiedsamplingwithdaytypedividedintoweekdaysorweekendpublic holidays To reflect expected higher recreationaleffortduringweekendsandpublicholidays(McCluskeyampLewison2008)thesamplingprobability(πk)wasweightedtowardthisstrata
over weekdays at 12 to eight samples (Supporting InformationAppendix S2) We stratified the sample time into morning (am0600ndash1159)andafternoon(pm1200ndash1800)withequalproba-bilitiesofsamplingTominimizetemporalautocorrelationthestart-ing location and travel direction of the routewere also randomlyselectedwithoutreplacementEqualsamplingweightwasprovidedtoeachrampduetolackofpriorknowledgeofuserates
At each boat ramp surveys were conducted by first countingparkedboattrailersthenbymonitoringvesselentryandexitsduringa1-hrsamplingperiod(KinlochMcGlennonNicollampPike1997Pollocketal1994)The1-hrperiodwaschosendueto travel timeanddis-tancebetweensitesallowingforabus-routesampletobecompletedforallrampswithinthetemporalstrata(AMorPM)ofthedailysam-plingframeInterviewswereconductedwithallpartieslaunchingandretrievingvesselsduringtheclerkrsquoswaittimedeterminingpartysizetargetspeciesgroupandactivity(iefishingornot)forestimatesoffishing effort This well-established procedure produced a monthlyextrapolatedestimationofboateffort instandardizedunitsofefforthoursplusmnSEwhichcouldthenbeadjustedtofishingeffortviathepro-portionofintervieweesactivities(Pollocketal1994RobsonampJones1989)(SupportingInformationAppendixS3)PartysizebetweenrampswastestedforanydifferenceusingaonefactorANOVA
Thebus-route surveyhenceprovidesanestimationofall fish-ing effort from trailer boats around this isolated peninsular TheCRAGSdataarethusahigh-frequencyspatialsubsetoftheentiretrailer boat fishing effort for the period thatwas extrapolated bytheregionalsurveyThisallowedustobothtesttherelativeuseoftheHRMCAcomparedtowiderpeninsularuseandtheabilityofourremoteobservationswithourCRAGsunitofanactualsubsetofthefisherytotracklargertemporalpatternsofpeninsularwideeffort
TovalidatetherepresentativenessoftheCRAGShigh-frequencytrackingofeffortmetricsover timeweundertookaSpearmanrankcorrelationbetweentime-matchedeffortestimationsbyCRAGSandthe regional bus-route survey For graphing bus-routeextrapolationestimates were overlaid onto extrapolations from daily averages ofboatfishingeffortfromCRAGSWealsocarefullyloggedalltimespentontheprojecttocomparetotallaborcostsbetweenCRAGSandthebusrouteforbothdatacollectionandprocessingtodetermineeffortacrossthecomparativemonthaswellasforcostpersample
3emsp |emspRESULTS
The CRAGS operated successfully and continuously throughoutthestudycapturing545plusmn198SE(May)and483plusmn088SE(June)panoramasperdaymakinga totalof314 samplesWemade895observationsofboatsbut51samplesacross21daysreturnedzerocountsofboats(17oftotalsamples)Thesewereconcentratedonweekdaysmdashwith 44 zero-count samples across 18daysmdashwhile onweekendsandpublicholidaytherewereonlysevensamplesacross3dayswithnoactivityInclementweatherrendered27samplesun-usableTheimagesandbatch-processedTimeMachinemoviescon-sumed4274GBofmemory
(1)ei=IitimesT
(2)Ejk=
nsum
i=1
(
ei
120587k
)
(3)SEjk=
1
njminus1
sumn
i=1(eiminus ei)
2
njtimes (Njk)
2
(4)SEk=
radic
SE21k+SE2
2khellip+SE2
jk
(5)smt=etminus1+ et+ et+1
3
6emsp |emsp emspensp FLYNN et aL
Based on boat ramp interviews the average trailer boat partysize (P) was 287 people per vessel throughout May (plusmn0133 SEn=84)andpartysizebetweensitesshowednosignificantdiffer-ence (df=2F = 302p = 068) The averagemonthly boat fishingeffort(hours)extrapolatedforourCRAGSobservationsofHRMCAwas5629hr(plusmn711SE)with4096hr(plusmn607SE)inMayand1534hr(plusmn370 SE) in June (Figure4)mdashthis could be extrapolated to fisherhoursbymultiplyingbytheaveragepartysizebutasnointerviewswereconductedinJuneweleftboththeCRAGSandbusrouteex-trapolationsasboatfishingeffortonly
The total subset area observed by CRAGSwas 96km2 which comparedto318km2thatwaseasilyavailabletotrailerboatsaroundtheTasmanPeninsularwhichcorrespondstoaround3ofthetotalareaComparedtothetotalfishingeffortforMayextrapolatedfromtheregionalbus-routesurvey therelativelysmallareaaroundtheHRM(~3)receivedalargeamountofthefishingeffort(~23)
ThehighfrequencyofCRAGSsamplingpermittedtheexamina-tionoffine-scaleinter-dailytemporalvariationwhichdisplayedarag-gedsinusoidoscillation(Figure5)Effortwasgenerallylowwithinthe
weekdaystrataandthen increasedrapidly to largepeaksonweek-ends (Figure5) One exception to this was between the 18th and22ndMaywhichhadmoreeffortonweekdays thanotherperiods(Figure5)Bracketingthisperiodweretwoweekendsoffishingcom-petitions theldquoTunaClubofTasmaniarsquosrsquoFarSouthClassicrdquo (16ndash17thMay)andRally4Northerninvitationalweekendrally(23rdMay)
A total of 16 bus-route samples were conducted logistic fail-uresledtotheabandonmentoffour(20)oftheplannedsamplesRegionalTrailerboateffortfromaroundtheTasmanPeninsularwasestimatedtobe19650hr (plusmn2858SE) inMay (Figure4)Notrailersat individual rampswere recorded four timeswith all of theseoc-curringonweekdaysThetotalnumberoftrailerssightedperramprangedfrom0to16duringweekdaysand1ndash54forweekendswiththehighestnumberrecordedon16May2015atPiratesBayDuringhigh-intensitydays(particularlyweekendsholidaysmornings)therewereoftenlargeoverflowsfromrampcarparkswithparkedcarsandboattrailersextendingasmuchasonekilometerfromtheboatramp
During thebus-route survey 84 interviewswere conductedThemajorityofmariners(905n=96)indicatedfishingwastheir
F IGURE 4emspExtrapolatedmonthlytrailerboateffort(hoursplusmnSE)fortheentireTasmanPeninsulaassessedbyabus-routesurvey(May)andforthesubsetoftheareaaroundtheHRMCAsampledwithCRAGS(MayandJune)
F IGURE 5emspHigh-frequencyinterdailyextrapolatedCRAGSestimationsofboatefforthoursaroundthe(HRMCA)
0
100
200
300
400
500
600
Rolli
ng m
ean
daily
effo
rt in
hou
rs
DayWeekdays Weekends + Public Holidays
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
2emsp |emsp emspensp FLYNN et aL
1emsp |emspINTRODUC TION
Worldwidecoastalzonesaresomeofthemostheavilyimpactedpartsoftheoceanbypeoplebothintermsofactivitytypesandparticipationrates(Halpernetal2008)Aparticularfocusofre-search on coastal use has been recreational fishing as this pop-ular activity can significantly affect fish and invertebrate stocks(Arlinghaus 2006 Cooke amp Cowx 2004 Lewin Arlinghaus ampMehner2006McPheeLeadbitterampSkilleter2002)asformanyspecies harvest can exceed the take of the commercial fishery(GiriampHall2015LyleStarkampTracey2014ZischkeGriffithsampTibbetts2012)
Unlikecommercialfisherieswhicharecommonlydocumentedbyoperatorsloggingtheircatchandeffortassessmentsofopen-accessnonreporting activities suchas recreational fisheries require sam-plingThiscanbebothdifficultandexpensiveduetotheiroftenlargespatialextenthightemporalvariationandforthefisheriesagenciesundertaking the work which often requires weekend and holidaysurveyinghighlaborcosts(Maetal2018PollockJonesampBrown1994 Rocklin Levrel Drogou Herfaut amp Veron 2014 VenturelliHyderampSkov2017)Due to these issues assessmentshave typi-callyrelieduponindirectdatacollectionfromoff-sitetelephonesur-veysbasedondataframesdevelopedaroundregionallsquowhitepagesrsquoor landline telephonedirectorieswhichtraditionallyhavehadhighresponseratesandeasilyscalablesamplesizes(Mooreetal2015Pollocketal1994)Suchapproachesmaynowriskundersamplingactivefishersbecauseofdemographicallybiasandoveralldecreasedlandlinephoneuseunlistedandnonregionalcodedmobilephonesandlowresponsesuccessfromvoicecallstomobilephones(Badcocketal 2016 Blumberg amp Luke 2009 Teixeira Zischke ampWebley2016)
Other formsof recreational fisheryassessmentsemploydirecton-sitesurveymethodssuchasbus-routesurveyswhichuseclerkstraveling around a fisherymdashfollowing randomly selected predeter-minedschedulesmdashinterviewingandobservingfisherstotrackfish-ingeffortcatchandrelease(McGlennonampKinloch1997Pollocketal 1994) On-sitemethods that cover broad geographic areashowever are seldom performed due to high labor costs (JonesampPollock2012)
Given these limitations remote sensing tools and technol-ogies such as autonomous photography are an emerging fieldofferingdirectmonitoringasanalternativeorsupplementtoon-site interviews (HartillPayneRushampBian2016KellerSteffeLowryMurphyampSuthers2016ParnellDaytonFisherLoarieampDarrow2010PowersampAnson2016vanPoortenCarruthersWardampVarkey 2015Wood LynchDevineKelleramp Figueira2016) One such system is the CSIRO Ruggedised AutonomousGigapixel System (CRAGS) which is a programmable weather-proofcameratrapthatprovidesultra-highresolutiontime-lapseandhigh-frequencypanoramic imagesTheCRAGSutilizesmod-ified commercially available hardware and software known as aGigaPanreg (httpgigapancom)tocapturephotographicsampleswith such high pixel density (gigapans) that wide fields of view
andassociatedobjectsofinterestscanbeexaminedcloselywith-out the loss of broader environmental context at landscape orseascape scales (LynchAldermanampHobday 2015) (SupportingInformationAppendixS1)
Thisroboticcamerasystemallowsformuchbroaderscaledphoto-graphicassessmentsthansimplecameratrapsbyautonomouslytak-ingandthenstitchingtogethermultipletelephotomegapixelimagesinto high-resolution gigapixel panorama The resulting tiled imageallowsfullyzoomableviewingforeithermultiplesmalltargetssuchasAlbatrossnestsacrossacolony(Lynchetal2015)ormorewidelyspacedlargertargetssuchasidentifyingcommercialvsrecreationalvessels around an offshore artificial reef (Wood etal 2016) Thebenefitofthesystemcomparedtosimplercameratrapsisbeingabletoobserve inhighdetail fromtheone imagestreammanyobjectssimultaneously across landscapeor seascape scalesWith somuchinformationdatahandlingcanbecomeoverwhelmingsoabatchpro-cessingmethod calledGigapanTimemachinehasbeendevelopedOriginallyusedforviewingcosmologicalsimulations(Yuetal2011)TimeMachineproducesavideostreamthatallowsviewerstofluidlyexploregigatotetrapixel-scaledvideosacrossbothspaceandtime
InAustraliaannualrecreationalfishingparticipationhasbeenes-timatedat195(HenryampLyle2003)whichiswellabovetheglobalaverageofaround10(ArlinghausTillnerampBork2015)AroundtheAustralian island state of Tasmania the annual participation rate is293whichwellexceedsthenationalaverageTheTasmanPeninsulainTasmaniarsquossoutheast (Figure1) isapopularfishingregionneartothelargestcityandstatecapitalofHobartwhereasteepbathymetricprofileandmigratorygame-fishpathwayoverlapprovidingcoastalac-cesstonormallyoffshorefishingopportunitiestorecreationalfisherintrailerboatsPelagicgamefishsuchassouthernbluefintuna(Thunnus maccoyii)aretargetedinlateAustralSummerandAutumn(MortonampLyle2004)butasthefisheryisunlicensedandepisodictrendsinef-fortandcatcharepoorlyunderstood(LowryampMurphy2003Mooreetal2015)Thisparticularrecreationalfisheryisalsoofmoregeneralinterestasthetargetsarecommerciallyimportantspecieswhichin-cludethosewithinternationallynegotiatedquotas(Ponsetal2017Zischke etal 2012) Recreational fishers are also often associatedwithotherusersofthemarineenvironment(FarrStoecklampSutton2014Kearney2002)orinthiscasetouristsenjoyingthelocalnationalparksHencehighusesitesmaybeimportanttomonitornotonlyforfisheriesandecologicalreasonsbutformanagingsocialvaluescon-flict resolutionandplanning (AlessaKliskeyampBrown2008Lynchetal2004)
WetrailedourCRAGSmethodtoseewhetherwecoulddevelopa high-frequency time series of temporal trends of observed trailerboat fishingeffortatanoffshoreconcentrationpoint for the recre-ationalgamefisheryWealsotestedboththesuitabilityandlaborcost-effectivenessofthehardwareandanovelapplicationofTimeMachinebatchprocessingsoftwaretoautomaticallyproducealdquobigdatardquointer-activemoviesmadefromourgigapixelpanoramasAsthepeninsulais isolatedandservicedbyonlya limitednumberofboatrampsweaimed tovalidate the representativenessof the temporal trendsweobservedwithCRAGSbycorrelatingtomatchsamplesobtainedfrom
emspensp emsp | emsp3FLYNN et aL
asimultaneousregionallyscaledbus-routetrailerboatsurveyatthethreerampsonthepeninsularsuitableforlaunchinglargetrailerboatsWe also undertook on-ground interviewswith trailer boat users togroundtruthactivitytype(fishingornonfishing)andtargetspeciesByundertakingbothcameraandboatrampsurveysourover-overarchingaimwastoinvestigatehowremoteimagerymayreplacecomplimentoroptimizeconventionalon-siteapproachesforrecreationalfishing
2emsp |emspMATERIAL S AND METHODS
WedeployedtheCRAGSbetween1May2015and30June2015toassesstrailerboatsoffshorefromwateradjacenttotheTasman
Peninsula (Figure1) This period was chosen as it correspondswith the main tuna fishing season For large trailer boats (gt5mlength) localaccess to the fishery is limitedto threeboat ramps(Figure1)althoughtheareamayalsobevisitedbylargervesselssteaming frommarinas and anchorages further north (MortonampLyle2004)butforthepurposesofourstudyweexcludedtheserelativelyrare largervesselsWefocusedourCRAGSsurveyataknown game fishing concentration the Hippolyte Rock MarineConservationArea (HRMCA)which are a group of small islandsabout6-kmoffshore (Figures1and2)aroundwhichrecreationalfishingispermittedWhiletheCRAGSdeploymentwasmarginallycloser to the FortescueBayRampwe commonly observe boatsapproachingtheHRMCAfromdirectionsthatcorrespondedtothe
F IGURE 1emspTheTasmanPeninsulainSETasmaniaBus-routesurveysamplingoccurredatramps locatedatPiratesFortescueandStuartsBayTheCRAGSlocationisshownasacameraiconandtheareaobservedbythecamerawhichincludestheHippolyteRocksMarineConservationArea(HRMCA)isdescribedasatriangle
Text
Pirates Bay
Fortesque Bay
StewartsBay
0 3 6 9 1215Kilometers
0 30 60 90 12015Kilometers
Tasman Is
Australia
Tasmania
Hobart
TasmanPeninsular
HippolyteRocksMCA
43deg 01 30
147deg 52 30
TAS
4emsp |emsp emspensp FLYNN et aL
locationof all three rampsWe fixed theCRAGS to a clifftop atWaterfall Baywhich overlooks the site fromvery high sea cliffs(~250m)withthecamerafocusingonthewesternsideofHRMCA(Figure1)ForeachsampleCRAGScapturedaseriesof68pho-tographsthroughatelephotolenswhichwhenstitchedtogetherprovide a high-resolution panoramic image with a wide field ofview (~140deg) (Supporting Information Appendix S1) The size ofareasurveyedbyCRAGScomparedtotheregionalareaaccessibleby trailer boatswas estimated using google earth polygons andtheUniversityofNewHampshireKMLextensiontoolmdashhttpsex-tensionunhedukmlToolsindexcfm
Thetimeof the initialCRAGSpanoramasamplewasrandomlyselected Samples were then collected sequentially through-out daylight hours (0600ndash1800) As less recreational fishing fortuna occurred at night no samples were taken with the CRAGSprogrammed to ldquoblankrdquo these hours from the sampling sched-ule (Supporting InformationAppendix S1) Photographic sessionswereprogrammedtobeseparatedbyarecurring93-mingapthus
producing a forward shift in sample capture time andminimizingpotentialautocorrelationsbetweenboatingactivitiesandsamplingtimesPhotographswerebatchprocessedtobothformpanoramicimagesforeachsampleandforcompilingintoaninteractivetime-lapsemovieusingCREATELabrsquosTimeMachinesoftware (CREATELabregPittsburgPAUSA)
TimeMachinemovies are fully interactive and are able to bezoomedintopausedandplayedatvariousspeedsandarealsotimeanddate-stamped(Figure3)UsingthisinteractivemoviesoftwareeachGigaPanmoviewassystematicallypausedateachsamplefullyzoomedandscannedlefttorighttoptobottomforobjectsintheseascapebytheleadauthor(Figure3)Vesselcountswererecordedforeachsamplewithobjectslargerthan~10minlength(nottrailerboats)or requiring longer than10seconds todistinguish (eg lowresolutionandimageartifact)beingdiscountedSampleswerecat-egorized into two strataweekdays andweekendspublic holidaysusing the time and date stamp provided on each GigaPan scene(Figure3)
F IGURE 2emspAnunmodifiedGigaPanEpicPROunit(left)andaCRAGSdeployedatWaterfallBaySETasmania(right)
F IGURE 3emspScreencaptureoutputsfromtheTimeMachineinteractivedataviewershowinganozoomandpanoramicextentoftheHippolyteRockMarineConservationArea(HRMCA)b=13zoommainHippolyterock~6kmfromcamerac=12zoomofalargetrailerboatd=fullzoom
emspensp emsp | emsp5FLYNN et aL
Thecountofboatsforeachsamplewasthenextrapolatedintoanestimateofdailyaverageboatingeffort(hoursplusmnSE)usingthein-stantaneouscountmethod(Equation1)
where ei is theextrapolatedboatinghours for the ithday Ii is theaveragenumberoftrailerboatsobservedacrosstheinstantaneoussamplesfordayiandTisthedailysamplingperiod(daylighthours)Asbadweathercanbeamajorfactoraffectingthedecisiontoboatorfish(ForbesTraceyampLyle2009)boatingeffortwasassumedtobe0whenadverseweathermadeobservationsofboatsimpossible
Next total effort for each day-type strata (Ejk) within sam-pling months (k) was calculated to estimate the monthly effort(Equation2)AsCRAGSallowedforcontinuoussamplingthroughoutthesurveyperiodthesamplingprobability(πk)wassetto1
Standarderrorandeffortvarianceformonthlyestimateswerecalculated(Equation3)accordingtoPollocketal(1994)forstrata
where nj is thenumberofsampleddaysofstrata j Njk is thetotalnumberofdaysofstratajinmonthk
Asvariationforeachstratawascalculatedseparatelyweusedasumofsquaredstandarderrorsapproach(Equation4)toestimatethetotalmonthlyvariation
Assampleswereobtainedinaserialandcontinuousfashionatimeseriesmodelusingrollingaverageswasalsoappliedtothedata(Diggle1995)(Equation5)
where smt is the smoothed average of extrapolated effort e persampletwhichisweightedacrossthreesampleseachseparatedby93minThisprovidedasmoothedaveragewithindays
Wealsoconductedabus-routestylesurveytoestimatefishingeffortfromtrailerboatsfortheregion(Pollocketal1994Robsonamp Jones1989)WhileCRAGSsampleswerecollected throughoutMayandJunethecomparablebus-routesurveywasonlyconductedinMaydue to logistical constraintsWesurveyed the threemajorboatrampsintheregionatPiratesBayFortescueBayandStewartsBaybetween3May2015and30May2015(Figure1)
Atotalof20bus-routesampleswerescheduledWeundertookrandomstratifiedsamplingwithdaytypedividedintoweekdaysorweekendpublic holidays To reflect expected higher recreationaleffortduringweekendsandpublicholidays(McCluskeyampLewison2008)thesamplingprobability(πk)wasweightedtowardthisstrata
over weekdays at 12 to eight samples (Supporting InformationAppendix S2) We stratified the sample time into morning (am0600ndash1159)andafternoon(pm1200ndash1800)withequalproba-bilitiesofsamplingTominimizetemporalautocorrelationthestart-ing location and travel direction of the routewere also randomlyselectedwithoutreplacementEqualsamplingweightwasprovidedtoeachrampduetolackofpriorknowledgeofuserates
At each boat ramp surveys were conducted by first countingparkedboattrailersthenbymonitoringvesselentryandexitsduringa1-hrsamplingperiod(KinlochMcGlennonNicollampPike1997Pollocketal1994)The1-hrperiodwaschosendueto travel timeanddis-tancebetweensitesallowingforabus-routesampletobecompletedforallrampswithinthetemporalstrata(AMorPM)ofthedailysam-plingframeInterviewswereconductedwithallpartieslaunchingandretrievingvesselsduringtheclerkrsquoswaittimedeterminingpartysizetargetspeciesgroupandactivity(iefishingornot)forestimatesoffishing effort This well-established procedure produced a monthlyextrapolatedestimationofboateffort instandardizedunitsofefforthoursplusmnSEwhichcouldthenbeadjustedtofishingeffortviathepro-portionofintervieweesactivities(Pollocketal1994RobsonampJones1989)(SupportingInformationAppendixS3)PartysizebetweenrampswastestedforanydifferenceusingaonefactorANOVA
Thebus-route surveyhenceprovidesanestimationofall fish-ing effort from trailer boats around this isolated peninsular TheCRAGSdataarethusahigh-frequencyspatialsubsetoftheentiretrailer boat fishing effort for the period thatwas extrapolated bytheregionalsurveyThisallowedustobothtesttherelativeuseoftheHRMCAcomparedtowiderpeninsularuseandtheabilityofourremoteobservationswithourCRAGsunitofanactualsubsetofthefisherytotracklargertemporalpatternsofpeninsularwideeffort
TovalidatetherepresentativenessoftheCRAGShigh-frequencytrackingofeffortmetricsover timeweundertookaSpearmanrankcorrelationbetweentime-matchedeffortestimationsbyCRAGSandthe regional bus-route survey For graphing bus-routeextrapolationestimates were overlaid onto extrapolations from daily averages ofboatfishingeffortfromCRAGSWealsocarefullyloggedalltimespentontheprojecttocomparetotallaborcostsbetweenCRAGSandthebusrouteforbothdatacollectionandprocessingtodetermineeffortacrossthecomparativemonthaswellasforcostpersample
3emsp |emspRESULTS
The CRAGS operated successfully and continuously throughoutthestudycapturing545plusmn198SE(May)and483plusmn088SE(June)panoramasperdaymakinga totalof314 samplesWemade895observationsofboatsbut51samplesacross21daysreturnedzerocountsofboats(17oftotalsamples)Thesewereconcentratedonweekdaysmdashwith 44 zero-count samples across 18daysmdashwhile onweekendsandpublicholidaytherewereonlysevensamplesacross3dayswithnoactivityInclementweatherrendered27samplesun-usableTheimagesandbatch-processedTimeMachinemoviescon-sumed4274GBofmemory
(1)ei=IitimesT
(2)Ejk=
nsum
i=1
(
ei
120587k
)
(3)SEjk=
1
njminus1
sumn
i=1(eiminus ei)
2
njtimes (Njk)
2
(4)SEk=
radic
SE21k+SE2
2khellip+SE2
jk
(5)smt=etminus1+ et+ et+1
3
6emsp |emsp emspensp FLYNN et aL
Based on boat ramp interviews the average trailer boat partysize (P) was 287 people per vessel throughout May (plusmn0133 SEn=84)andpartysizebetweensitesshowednosignificantdiffer-ence (df=2F = 302p = 068) The averagemonthly boat fishingeffort(hours)extrapolatedforourCRAGSobservationsofHRMCAwas5629hr(plusmn711SE)with4096hr(plusmn607SE)inMayand1534hr(plusmn370 SE) in June (Figure4)mdashthis could be extrapolated to fisherhoursbymultiplyingbytheaveragepartysizebutasnointerviewswereconductedinJuneweleftboththeCRAGSandbusrouteex-trapolationsasboatfishingeffortonly
The total subset area observed by CRAGSwas 96km2 which comparedto318km2thatwaseasilyavailabletotrailerboatsaroundtheTasmanPeninsularwhichcorrespondstoaround3ofthetotalareaComparedtothetotalfishingeffortforMayextrapolatedfromtheregionalbus-routesurvey therelativelysmallareaaroundtheHRM(~3)receivedalargeamountofthefishingeffort(~23)
ThehighfrequencyofCRAGSsamplingpermittedtheexamina-tionoffine-scaleinter-dailytemporalvariationwhichdisplayedarag-gedsinusoidoscillation(Figure5)Effortwasgenerallylowwithinthe
weekdaystrataandthen increasedrapidly to largepeaksonweek-ends (Figure5) One exception to this was between the 18th and22ndMaywhichhadmoreeffortonweekdays thanotherperiods(Figure5)Bracketingthisperiodweretwoweekendsoffishingcom-petitions theldquoTunaClubofTasmaniarsquosrsquoFarSouthClassicrdquo (16ndash17thMay)andRally4Northerninvitationalweekendrally(23rdMay)
A total of 16 bus-route samples were conducted logistic fail-uresledtotheabandonmentoffour(20)oftheplannedsamplesRegionalTrailerboateffortfromaroundtheTasmanPeninsularwasestimatedtobe19650hr (plusmn2858SE) inMay (Figure4)Notrailersat individual rampswere recorded four timeswith all of theseoc-curringonweekdaysThetotalnumberoftrailerssightedperramprangedfrom0to16duringweekdaysand1ndash54forweekendswiththehighestnumberrecordedon16May2015atPiratesBayDuringhigh-intensitydays(particularlyweekendsholidaysmornings)therewereoftenlargeoverflowsfromrampcarparkswithparkedcarsandboattrailersextendingasmuchasonekilometerfromtheboatramp
During thebus-route survey 84 interviewswere conductedThemajorityofmariners(905n=96)indicatedfishingwastheir
F IGURE 4emspExtrapolatedmonthlytrailerboateffort(hoursplusmnSE)fortheentireTasmanPeninsulaassessedbyabus-routesurvey(May)andforthesubsetoftheareaaroundtheHRMCAsampledwithCRAGS(MayandJune)
F IGURE 5emspHigh-frequencyinterdailyextrapolatedCRAGSestimationsofboatefforthoursaroundthe(HRMCA)
0
100
200
300
400
500
600
Rolli
ng m
ean
daily
effo
rt in
hou
rs
DayWeekdays Weekends + Public Holidays
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
emspensp emsp | emsp3FLYNN et aL
asimultaneousregionallyscaledbus-routetrailerboatsurveyatthethreerampsonthepeninsularsuitableforlaunchinglargetrailerboatsWe also undertook on-ground interviewswith trailer boat users togroundtruthactivitytype(fishingornonfishing)andtargetspeciesByundertakingbothcameraandboatrampsurveysourover-overarchingaimwastoinvestigatehowremoteimagerymayreplacecomplimentoroptimizeconventionalon-siteapproachesforrecreationalfishing
2emsp |emspMATERIAL S AND METHODS
WedeployedtheCRAGSbetween1May2015and30June2015toassesstrailerboatsoffshorefromwateradjacenttotheTasman
Peninsula (Figure1) This period was chosen as it correspondswith the main tuna fishing season For large trailer boats (gt5mlength) localaccess to the fishery is limitedto threeboat ramps(Figure1)althoughtheareamayalsobevisitedbylargervesselssteaming frommarinas and anchorages further north (MortonampLyle2004)butforthepurposesofourstudyweexcludedtheserelativelyrare largervesselsWefocusedourCRAGSsurveyataknown game fishing concentration the Hippolyte Rock MarineConservationArea (HRMCA)which are a group of small islandsabout6-kmoffshore (Figures1and2)aroundwhichrecreationalfishingispermittedWhiletheCRAGSdeploymentwasmarginallycloser to the FortescueBayRampwe commonly observe boatsapproachingtheHRMCAfromdirectionsthatcorrespondedtothe
F IGURE 1emspTheTasmanPeninsulainSETasmaniaBus-routesurveysamplingoccurredatramps locatedatPiratesFortescueandStuartsBayTheCRAGSlocationisshownasacameraiconandtheareaobservedbythecamerawhichincludestheHippolyteRocksMarineConservationArea(HRMCA)isdescribedasatriangle
Text
Pirates Bay
Fortesque Bay
StewartsBay
0 3 6 9 1215Kilometers
0 30 60 90 12015Kilometers
Tasman Is
Australia
Tasmania
Hobart
TasmanPeninsular
HippolyteRocksMCA
43deg 01 30
147deg 52 30
TAS
4emsp |emsp emspensp FLYNN et aL
locationof all three rampsWe fixed theCRAGS to a clifftop atWaterfall Baywhich overlooks the site fromvery high sea cliffs(~250m)withthecamerafocusingonthewesternsideofHRMCA(Figure1)ForeachsampleCRAGScapturedaseriesof68pho-tographsthroughatelephotolenswhichwhenstitchedtogetherprovide a high-resolution panoramic image with a wide field ofview (~140deg) (Supporting Information Appendix S1) The size ofareasurveyedbyCRAGScomparedtotheregionalareaaccessibleby trailer boatswas estimated using google earth polygons andtheUniversityofNewHampshireKMLextensiontoolmdashhttpsex-tensionunhedukmlToolsindexcfm
Thetimeof the initialCRAGSpanoramasamplewasrandomlyselected Samples were then collected sequentially through-out daylight hours (0600ndash1800) As less recreational fishing fortuna occurred at night no samples were taken with the CRAGSprogrammed to ldquoblankrdquo these hours from the sampling sched-ule (Supporting InformationAppendix S1) Photographic sessionswereprogrammedtobeseparatedbyarecurring93-mingapthus
producing a forward shift in sample capture time andminimizingpotentialautocorrelationsbetweenboatingactivitiesandsamplingtimesPhotographswerebatchprocessedtobothformpanoramicimagesforeachsampleandforcompilingintoaninteractivetime-lapsemovieusingCREATELabrsquosTimeMachinesoftware (CREATELabregPittsburgPAUSA)
TimeMachinemovies are fully interactive and are able to bezoomedintopausedandplayedatvariousspeedsandarealsotimeanddate-stamped(Figure3)UsingthisinteractivemoviesoftwareeachGigaPanmoviewassystematicallypausedateachsamplefullyzoomedandscannedlefttorighttoptobottomforobjectsintheseascapebytheleadauthor(Figure3)Vesselcountswererecordedforeachsamplewithobjectslargerthan~10minlength(nottrailerboats)or requiring longer than10seconds todistinguish (eg lowresolutionandimageartifact)beingdiscountedSampleswerecat-egorized into two strataweekdays andweekendspublic holidaysusing the time and date stamp provided on each GigaPan scene(Figure3)
F IGURE 2emspAnunmodifiedGigaPanEpicPROunit(left)andaCRAGSdeployedatWaterfallBaySETasmania(right)
F IGURE 3emspScreencaptureoutputsfromtheTimeMachineinteractivedataviewershowinganozoomandpanoramicextentoftheHippolyteRockMarineConservationArea(HRMCA)b=13zoommainHippolyterock~6kmfromcamerac=12zoomofalargetrailerboatd=fullzoom
emspensp emsp | emsp5FLYNN et aL
Thecountofboatsforeachsamplewasthenextrapolatedintoanestimateofdailyaverageboatingeffort(hoursplusmnSE)usingthein-stantaneouscountmethod(Equation1)
where ei is theextrapolatedboatinghours for the ithday Ii is theaveragenumberoftrailerboatsobservedacrosstheinstantaneoussamplesfordayiandTisthedailysamplingperiod(daylighthours)Asbadweathercanbeamajorfactoraffectingthedecisiontoboatorfish(ForbesTraceyampLyle2009)boatingeffortwasassumedtobe0whenadverseweathermadeobservationsofboatsimpossible
Next total effort for each day-type strata (Ejk) within sam-pling months (k) was calculated to estimate the monthly effort(Equation2)AsCRAGSallowedforcontinuoussamplingthroughoutthesurveyperiodthesamplingprobability(πk)wassetto1
Standarderrorandeffortvarianceformonthlyestimateswerecalculated(Equation3)accordingtoPollocketal(1994)forstrata
where nj is thenumberofsampleddaysofstrata j Njk is thetotalnumberofdaysofstratajinmonthk
Asvariationforeachstratawascalculatedseparatelyweusedasumofsquaredstandarderrorsapproach(Equation4)toestimatethetotalmonthlyvariation
Assampleswereobtainedinaserialandcontinuousfashionatimeseriesmodelusingrollingaverageswasalsoappliedtothedata(Diggle1995)(Equation5)
where smt is the smoothed average of extrapolated effort e persampletwhichisweightedacrossthreesampleseachseparatedby93minThisprovidedasmoothedaveragewithindays
Wealsoconductedabus-routestylesurveytoestimatefishingeffortfromtrailerboatsfortheregion(Pollocketal1994Robsonamp Jones1989)WhileCRAGSsampleswerecollected throughoutMayandJunethecomparablebus-routesurveywasonlyconductedinMaydue to logistical constraintsWesurveyed the threemajorboatrampsintheregionatPiratesBayFortescueBayandStewartsBaybetween3May2015and30May2015(Figure1)
Atotalof20bus-routesampleswerescheduledWeundertookrandomstratifiedsamplingwithdaytypedividedintoweekdaysorweekendpublic holidays To reflect expected higher recreationaleffortduringweekendsandpublicholidays(McCluskeyampLewison2008)thesamplingprobability(πk)wasweightedtowardthisstrata
over weekdays at 12 to eight samples (Supporting InformationAppendix S2) We stratified the sample time into morning (am0600ndash1159)andafternoon(pm1200ndash1800)withequalproba-bilitiesofsamplingTominimizetemporalautocorrelationthestart-ing location and travel direction of the routewere also randomlyselectedwithoutreplacementEqualsamplingweightwasprovidedtoeachrampduetolackofpriorknowledgeofuserates
At each boat ramp surveys were conducted by first countingparkedboattrailersthenbymonitoringvesselentryandexitsduringa1-hrsamplingperiod(KinlochMcGlennonNicollampPike1997Pollocketal1994)The1-hrperiodwaschosendueto travel timeanddis-tancebetweensitesallowingforabus-routesampletobecompletedforallrampswithinthetemporalstrata(AMorPM)ofthedailysam-plingframeInterviewswereconductedwithallpartieslaunchingandretrievingvesselsduringtheclerkrsquoswaittimedeterminingpartysizetargetspeciesgroupandactivity(iefishingornot)forestimatesoffishing effort This well-established procedure produced a monthlyextrapolatedestimationofboateffort instandardizedunitsofefforthoursplusmnSEwhichcouldthenbeadjustedtofishingeffortviathepro-portionofintervieweesactivities(Pollocketal1994RobsonampJones1989)(SupportingInformationAppendixS3)PartysizebetweenrampswastestedforanydifferenceusingaonefactorANOVA
Thebus-route surveyhenceprovidesanestimationofall fish-ing effort from trailer boats around this isolated peninsular TheCRAGSdataarethusahigh-frequencyspatialsubsetoftheentiretrailer boat fishing effort for the period thatwas extrapolated bytheregionalsurveyThisallowedustobothtesttherelativeuseoftheHRMCAcomparedtowiderpeninsularuseandtheabilityofourremoteobservationswithourCRAGsunitofanactualsubsetofthefisherytotracklargertemporalpatternsofpeninsularwideeffort
TovalidatetherepresentativenessoftheCRAGShigh-frequencytrackingofeffortmetricsover timeweundertookaSpearmanrankcorrelationbetweentime-matchedeffortestimationsbyCRAGSandthe regional bus-route survey For graphing bus-routeextrapolationestimates were overlaid onto extrapolations from daily averages ofboatfishingeffortfromCRAGSWealsocarefullyloggedalltimespentontheprojecttocomparetotallaborcostsbetweenCRAGSandthebusrouteforbothdatacollectionandprocessingtodetermineeffortacrossthecomparativemonthaswellasforcostpersample
3emsp |emspRESULTS
The CRAGS operated successfully and continuously throughoutthestudycapturing545plusmn198SE(May)and483plusmn088SE(June)panoramasperdaymakinga totalof314 samplesWemade895observationsofboatsbut51samplesacross21daysreturnedzerocountsofboats(17oftotalsamples)Thesewereconcentratedonweekdaysmdashwith 44 zero-count samples across 18daysmdashwhile onweekendsandpublicholidaytherewereonlysevensamplesacross3dayswithnoactivityInclementweatherrendered27samplesun-usableTheimagesandbatch-processedTimeMachinemoviescon-sumed4274GBofmemory
(1)ei=IitimesT
(2)Ejk=
nsum
i=1
(
ei
120587k
)
(3)SEjk=
1
njminus1
sumn
i=1(eiminus ei)
2
njtimes (Njk)
2
(4)SEk=
radic
SE21k+SE2
2khellip+SE2
jk
(5)smt=etminus1+ et+ et+1
3
6emsp |emsp emspensp FLYNN et aL
Based on boat ramp interviews the average trailer boat partysize (P) was 287 people per vessel throughout May (plusmn0133 SEn=84)andpartysizebetweensitesshowednosignificantdiffer-ence (df=2F = 302p = 068) The averagemonthly boat fishingeffort(hours)extrapolatedforourCRAGSobservationsofHRMCAwas5629hr(plusmn711SE)with4096hr(plusmn607SE)inMayand1534hr(plusmn370 SE) in June (Figure4)mdashthis could be extrapolated to fisherhoursbymultiplyingbytheaveragepartysizebutasnointerviewswereconductedinJuneweleftboththeCRAGSandbusrouteex-trapolationsasboatfishingeffortonly
The total subset area observed by CRAGSwas 96km2 which comparedto318km2thatwaseasilyavailabletotrailerboatsaroundtheTasmanPeninsularwhichcorrespondstoaround3ofthetotalareaComparedtothetotalfishingeffortforMayextrapolatedfromtheregionalbus-routesurvey therelativelysmallareaaroundtheHRM(~3)receivedalargeamountofthefishingeffort(~23)
ThehighfrequencyofCRAGSsamplingpermittedtheexamina-tionoffine-scaleinter-dailytemporalvariationwhichdisplayedarag-gedsinusoidoscillation(Figure5)Effortwasgenerallylowwithinthe
weekdaystrataandthen increasedrapidly to largepeaksonweek-ends (Figure5) One exception to this was between the 18th and22ndMaywhichhadmoreeffortonweekdays thanotherperiods(Figure5)Bracketingthisperiodweretwoweekendsoffishingcom-petitions theldquoTunaClubofTasmaniarsquosrsquoFarSouthClassicrdquo (16ndash17thMay)andRally4Northerninvitationalweekendrally(23rdMay)
A total of 16 bus-route samples were conducted logistic fail-uresledtotheabandonmentoffour(20)oftheplannedsamplesRegionalTrailerboateffortfromaroundtheTasmanPeninsularwasestimatedtobe19650hr (plusmn2858SE) inMay (Figure4)Notrailersat individual rampswere recorded four timeswith all of theseoc-curringonweekdaysThetotalnumberoftrailerssightedperramprangedfrom0to16duringweekdaysand1ndash54forweekendswiththehighestnumberrecordedon16May2015atPiratesBayDuringhigh-intensitydays(particularlyweekendsholidaysmornings)therewereoftenlargeoverflowsfromrampcarparkswithparkedcarsandboattrailersextendingasmuchasonekilometerfromtheboatramp
During thebus-route survey 84 interviewswere conductedThemajorityofmariners(905n=96)indicatedfishingwastheir
F IGURE 4emspExtrapolatedmonthlytrailerboateffort(hoursplusmnSE)fortheentireTasmanPeninsulaassessedbyabus-routesurvey(May)andforthesubsetoftheareaaroundtheHRMCAsampledwithCRAGS(MayandJune)
F IGURE 5emspHigh-frequencyinterdailyextrapolatedCRAGSestimationsofboatefforthoursaroundthe(HRMCA)
0
100
200
300
400
500
600
Rolli
ng m
ean
daily
effo
rt in
hou
rs
DayWeekdays Weekends + Public Holidays
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
4emsp |emsp emspensp FLYNN et aL
locationof all three rampsWe fixed theCRAGS to a clifftop atWaterfall Baywhich overlooks the site fromvery high sea cliffs(~250m)withthecamerafocusingonthewesternsideofHRMCA(Figure1)ForeachsampleCRAGScapturedaseriesof68pho-tographsthroughatelephotolenswhichwhenstitchedtogetherprovide a high-resolution panoramic image with a wide field ofview (~140deg) (Supporting Information Appendix S1) The size ofareasurveyedbyCRAGScomparedtotheregionalareaaccessibleby trailer boatswas estimated using google earth polygons andtheUniversityofNewHampshireKMLextensiontoolmdashhttpsex-tensionunhedukmlToolsindexcfm
Thetimeof the initialCRAGSpanoramasamplewasrandomlyselected Samples were then collected sequentially through-out daylight hours (0600ndash1800) As less recreational fishing fortuna occurred at night no samples were taken with the CRAGSprogrammed to ldquoblankrdquo these hours from the sampling sched-ule (Supporting InformationAppendix S1) Photographic sessionswereprogrammedtobeseparatedbyarecurring93-mingapthus
producing a forward shift in sample capture time andminimizingpotentialautocorrelationsbetweenboatingactivitiesandsamplingtimesPhotographswerebatchprocessedtobothformpanoramicimagesforeachsampleandforcompilingintoaninteractivetime-lapsemovieusingCREATELabrsquosTimeMachinesoftware (CREATELabregPittsburgPAUSA)
TimeMachinemovies are fully interactive and are able to bezoomedintopausedandplayedatvariousspeedsandarealsotimeanddate-stamped(Figure3)UsingthisinteractivemoviesoftwareeachGigaPanmoviewassystematicallypausedateachsamplefullyzoomedandscannedlefttorighttoptobottomforobjectsintheseascapebytheleadauthor(Figure3)Vesselcountswererecordedforeachsamplewithobjectslargerthan~10minlength(nottrailerboats)or requiring longer than10seconds todistinguish (eg lowresolutionandimageartifact)beingdiscountedSampleswerecat-egorized into two strataweekdays andweekendspublic holidaysusing the time and date stamp provided on each GigaPan scene(Figure3)
F IGURE 2emspAnunmodifiedGigaPanEpicPROunit(left)andaCRAGSdeployedatWaterfallBaySETasmania(right)
F IGURE 3emspScreencaptureoutputsfromtheTimeMachineinteractivedataviewershowinganozoomandpanoramicextentoftheHippolyteRockMarineConservationArea(HRMCA)b=13zoommainHippolyterock~6kmfromcamerac=12zoomofalargetrailerboatd=fullzoom
emspensp emsp | emsp5FLYNN et aL
Thecountofboatsforeachsamplewasthenextrapolatedintoanestimateofdailyaverageboatingeffort(hoursplusmnSE)usingthein-stantaneouscountmethod(Equation1)
where ei is theextrapolatedboatinghours for the ithday Ii is theaveragenumberoftrailerboatsobservedacrosstheinstantaneoussamplesfordayiandTisthedailysamplingperiod(daylighthours)Asbadweathercanbeamajorfactoraffectingthedecisiontoboatorfish(ForbesTraceyampLyle2009)boatingeffortwasassumedtobe0whenadverseweathermadeobservationsofboatsimpossible
Next total effort for each day-type strata (Ejk) within sam-pling months (k) was calculated to estimate the monthly effort(Equation2)AsCRAGSallowedforcontinuoussamplingthroughoutthesurveyperiodthesamplingprobability(πk)wassetto1
Standarderrorandeffortvarianceformonthlyestimateswerecalculated(Equation3)accordingtoPollocketal(1994)forstrata
where nj is thenumberofsampleddaysofstrata j Njk is thetotalnumberofdaysofstratajinmonthk
Asvariationforeachstratawascalculatedseparatelyweusedasumofsquaredstandarderrorsapproach(Equation4)toestimatethetotalmonthlyvariation
Assampleswereobtainedinaserialandcontinuousfashionatimeseriesmodelusingrollingaverageswasalsoappliedtothedata(Diggle1995)(Equation5)
where smt is the smoothed average of extrapolated effort e persampletwhichisweightedacrossthreesampleseachseparatedby93minThisprovidedasmoothedaveragewithindays
Wealsoconductedabus-routestylesurveytoestimatefishingeffortfromtrailerboatsfortheregion(Pollocketal1994Robsonamp Jones1989)WhileCRAGSsampleswerecollected throughoutMayandJunethecomparablebus-routesurveywasonlyconductedinMaydue to logistical constraintsWesurveyed the threemajorboatrampsintheregionatPiratesBayFortescueBayandStewartsBaybetween3May2015and30May2015(Figure1)
Atotalof20bus-routesampleswerescheduledWeundertookrandomstratifiedsamplingwithdaytypedividedintoweekdaysorweekendpublic holidays To reflect expected higher recreationaleffortduringweekendsandpublicholidays(McCluskeyampLewison2008)thesamplingprobability(πk)wasweightedtowardthisstrata
over weekdays at 12 to eight samples (Supporting InformationAppendix S2) We stratified the sample time into morning (am0600ndash1159)andafternoon(pm1200ndash1800)withequalproba-bilitiesofsamplingTominimizetemporalautocorrelationthestart-ing location and travel direction of the routewere also randomlyselectedwithoutreplacementEqualsamplingweightwasprovidedtoeachrampduetolackofpriorknowledgeofuserates
At each boat ramp surveys were conducted by first countingparkedboattrailersthenbymonitoringvesselentryandexitsduringa1-hrsamplingperiod(KinlochMcGlennonNicollampPike1997Pollocketal1994)The1-hrperiodwaschosendueto travel timeanddis-tancebetweensitesallowingforabus-routesampletobecompletedforallrampswithinthetemporalstrata(AMorPM)ofthedailysam-plingframeInterviewswereconductedwithallpartieslaunchingandretrievingvesselsduringtheclerkrsquoswaittimedeterminingpartysizetargetspeciesgroupandactivity(iefishingornot)forestimatesoffishing effort This well-established procedure produced a monthlyextrapolatedestimationofboateffort instandardizedunitsofefforthoursplusmnSEwhichcouldthenbeadjustedtofishingeffortviathepro-portionofintervieweesactivities(Pollocketal1994RobsonampJones1989)(SupportingInformationAppendixS3)PartysizebetweenrampswastestedforanydifferenceusingaonefactorANOVA
Thebus-route surveyhenceprovidesanestimationofall fish-ing effort from trailer boats around this isolated peninsular TheCRAGSdataarethusahigh-frequencyspatialsubsetoftheentiretrailer boat fishing effort for the period thatwas extrapolated bytheregionalsurveyThisallowedustobothtesttherelativeuseoftheHRMCAcomparedtowiderpeninsularuseandtheabilityofourremoteobservationswithourCRAGsunitofanactualsubsetofthefisherytotracklargertemporalpatternsofpeninsularwideeffort
TovalidatetherepresentativenessoftheCRAGShigh-frequencytrackingofeffortmetricsover timeweundertookaSpearmanrankcorrelationbetweentime-matchedeffortestimationsbyCRAGSandthe regional bus-route survey For graphing bus-routeextrapolationestimates were overlaid onto extrapolations from daily averages ofboatfishingeffortfromCRAGSWealsocarefullyloggedalltimespentontheprojecttocomparetotallaborcostsbetweenCRAGSandthebusrouteforbothdatacollectionandprocessingtodetermineeffortacrossthecomparativemonthaswellasforcostpersample
3emsp |emspRESULTS
The CRAGS operated successfully and continuously throughoutthestudycapturing545plusmn198SE(May)and483plusmn088SE(June)panoramasperdaymakinga totalof314 samplesWemade895observationsofboatsbut51samplesacross21daysreturnedzerocountsofboats(17oftotalsamples)Thesewereconcentratedonweekdaysmdashwith 44 zero-count samples across 18daysmdashwhile onweekendsandpublicholidaytherewereonlysevensamplesacross3dayswithnoactivityInclementweatherrendered27samplesun-usableTheimagesandbatch-processedTimeMachinemoviescon-sumed4274GBofmemory
(1)ei=IitimesT
(2)Ejk=
nsum
i=1
(
ei
120587k
)
(3)SEjk=
1
njminus1
sumn
i=1(eiminus ei)
2
njtimes (Njk)
2
(4)SEk=
radic
SE21k+SE2
2khellip+SE2
jk
(5)smt=etminus1+ et+ et+1
3
6emsp |emsp emspensp FLYNN et aL
Based on boat ramp interviews the average trailer boat partysize (P) was 287 people per vessel throughout May (plusmn0133 SEn=84)andpartysizebetweensitesshowednosignificantdiffer-ence (df=2F = 302p = 068) The averagemonthly boat fishingeffort(hours)extrapolatedforourCRAGSobservationsofHRMCAwas5629hr(plusmn711SE)with4096hr(plusmn607SE)inMayand1534hr(plusmn370 SE) in June (Figure4)mdashthis could be extrapolated to fisherhoursbymultiplyingbytheaveragepartysizebutasnointerviewswereconductedinJuneweleftboththeCRAGSandbusrouteex-trapolationsasboatfishingeffortonly
The total subset area observed by CRAGSwas 96km2 which comparedto318km2thatwaseasilyavailabletotrailerboatsaroundtheTasmanPeninsularwhichcorrespondstoaround3ofthetotalareaComparedtothetotalfishingeffortforMayextrapolatedfromtheregionalbus-routesurvey therelativelysmallareaaroundtheHRM(~3)receivedalargeamountofthefishingeffort(~23)
ThehighfrequencyofCRAGSsamplingpermittedtheexamina-tionoffine-scaleinter-dailytemporalvariationwhichdisplayedarag-gedsinusoidoscillation(Figure5)Effortwasgenerallylowwithinthe
weekdaystrataandthen increasedrapidly to largepeaksonweek-ends (Figure5) One exception to this was between the 18th and22ndMaywhichhadmoreeffortonweekdays thanotherperiods(Figure5)Bracketingthisperiodweretwoweekendsoffishingcom-petitions theldquoTunaClubofTasmaniarsquosrsquoFarSouthClassicrdquo (16ndash17thMay)andRally4Northerninvitationalweekendrally(23rdMay)
A total of 16 bus-route samples were conducted logistic fail-uresledtotheabandonmentoffour(20)oftheplannedsamplesRegionalTrailerboateffortfromaroundtheTasmanPeninsularwasestimatedtobe19650hr (plusmn2858SE) inMay (Figure4)Notrailersat individual rampswere recorded four timeswith all of theseoc-curringonweekdaysThetotalnumberoftrailerssightedperramprangedfrom0to16duringweekdaysand1ndash54forweekendswiththehighestnumberrecordedon16May2015atPiratesBayDuringhigh-intensitydays(particularlyweekendsholidaysmornings)therewereoftenlargeoverflowsfromrampcarparkswithparkedcarsandboattrailersextendingasmuchasonekilometerfromtheboatramp
During thebus-route survey 84 interviewswere conductedThemajorityofmariners(905n=96)indicatedfishingwastheir
F IGURE 4emspExtrapolatedmonthlytrailerboateffort(hoursplusmnSE)fortheentireTasmanPeninsulaassessedbyabus-routesurvey(May)andforthesubsetoftheareaaroundtheHRMCAsampledwithCRAGS(MayandJune)
F IGURE 5emspHigh-frequencyinterdailyextrapolatedCRAGSestimationsofboatefforthoursaroundthe(HRMCA)
0
100
200
300
400
500
600
Rolli
ng m
ean
daily
effo
rt in
hou
rs
DayWeekdays Weekends + Public Holidays
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
emspensp emsp | emsp5FLYNN et aL
Thecountofboatsforeachsamplewasthenextrapolatedintoanestimateofdailyaverageboatingeffort(hoursplusmnSE)usingthein-stantaneouscountmethod(Equation1)
where ei is theextrapolatedboatinghours for the ithday Ii is theaveragenumberoftrailerboatsobservedacrosstheinstantaneoussamplesfordayiandTisthedailysamplingperiod(daylighthours)Asbadweathercanbeamajorfactoraffectingthedecisiontoboatorfish(ForbesTraceyampLyle2009)boatingeffortwasassumedtobe0whenadverseweathermadeobservationsofboatsimpossible
Next total effort for each day-type strata (Ejk) within sam-pling months (k) was calculated to estimate the monthly effort(Equation2)AsCRAGSallowedforcontinuoussamplingthroughoutthesurveyperiodthesamplingprobability(πk)wassetto1
Standarderrorandeffortvarianceformonthlyestimateswerecalculated(Equation3)accordingtoPollocketal(1994)forstrata
where nj is thenumberofsampleddaysofstrata j Njk is thetotalnumberofdaysofstratajinmonthk
Asvariationforeachstratawascalculatedseparatelyweusedasumofsquaredstandarderrorsapproach(Equation4)toestimatethetotalmonthlyvariation
Assampleswereobtainedinaserialandcontinuousfashionatimeseriesmodelusingrollingaverageswasalsoappliedtothedata(Diggle1995)(Equation5)
where smt is the smoothed average of extrapolated effort e persampletwhichisweightedacrossthreesampleseachseparatedby93minThisprovidedasmoothedaveragewithindays
Wealsoconductedabus-routestylesurveytoestimatefishingeffortfromtrailerboatsfortheregion(Pollocketal1994Robsonamp Jones1989)WhileCRAGSsampleswerecollected throughoutMayandJunethecomparablebus-routesurveywasonlyconductedinMaydue to logistical constraintsWesurveyed the threemajorboatrampsintheregionatPiratesBayFortescueBayandStewartsBaybetween3May2015and30May2015(Figure1)
Atotalof20bus-routesampleswerescheduledWeundertookrandomstratifiedsamplingwithdaytypedividedintoweekdaysorweekendpublic holidays To reflect expected higher recreationaleffortduringweekendsandpublicholidays(McCluskeyampLewison2008)thesamplingprobability(πk)wasweightedtowardthisstrata
over weekdays at 12 to eight samples (Supporting InformationAppendix S2) We stratified the sample time into morning (am0600ndash1159)andafternoon(pm1200ndash1800)withequalproba-bilitiesofsamplingTominimizetemporalautocorrelationthestart-ing location and travel direction of the routewere also randomlyselectedwithoutreplacementEqualsamplingweightwasprovidedtoeachrampduetolackofpriorknowledgeofuserates
At each boat ramp surveys were conducted by first countingparkedboattrailersthenbymonitoringvesselentryandexitsduringa1-hrsamplingperiod(KinlochMcGlennonNicollampPike1997Pollocketal1994)The1-hrperiodwaschosendueto travel timeanddis-tancebetweensitesallowingforabus-routesampletobecompletedforallrampswithinthetemporalstrata(AMorPM)ofthedailysam-plingframeInterviewswereconductedwithallpartieslaunchingandretrievingvesselsduringtheclerkrsquoswaittimedeterminingpartysizetargetspeciesgroupandactivity(iefishingornot)forestimatesoffishing effort This well-established procedure produced a monthlyextrapolatedestimationofboateffort instandardizedunitsofefforthoursplusmnSEwhichcouldthenbeadjustedtofishingeffortviathepro-portionofintervieweesactivities(Pollocketal1994RobsonampJones1989)(SupportingInformationAppendixS3)PartysizebetweenrampswastestedforanydifferenceusingaonefactorANOVA
Thebus-route surveyhenceprovidesanestimationofall fish-ing effort from trailer boats around this isolated peninsular TheCRAGSdataarethusahigh-frequencyspatialsubsetoftheentiretrailer boat fishing effort for the period thatwas extrapolated bytheregionalsurveyThisallowedustobothtesttherelativeuseoftheHRMCAcomparedtowiderpeninsularuseandtheabilityofourremoteobservationswithourCRAGsunitofanactualsubsetofthefisherytotracklargertemporalpatternsofpeninsularwideeffort
TovalidatetherepresentativenessoftheCRAGShigh-frequencytrackingofeffortmetricsover timeweundertookaSpearmanrankcorrelationbetweentime-matchedeffortestimationsbyCRAGSandthe regional bus-route survey For graphing bus-routeextrapolationestimates were overlaid onto extrapolations from daily averages ofboatfishingeffortfromCRAGSWealsocarefullyloggedalltimespentontheprojecttocomparetotallaborcostsbetweenCRAGSandthebusrouteforbothdatacollectionandprocessingtodetermineeffortacrossthecomparativemonthaswellasforcostpersample
3emsp |emspRESULTS
The CRAGS operated successfully and continuously throughoutthestudycapturing545plusmn198SE(May)and483plusmn088SE(June)panoramasperdaymakinga totalof314 samplesWemade895observationsofboatsbut51samplesacross21daysreturnedzerocountsofboats(17oftotalsamples)Thesewereconcentratedonweekdaysmdashwith 44 zero-count samples across 18daysmdashwhile onweekendsandpublicholidaytherewereonlysevensamplesacross3dayswithnoactivityInclementweatherrendered27samplesun-usableTheimagesandbatch-processedTimeMachinemoviescon-sumed4274GBofmemory
(1)ei=IitimesT
(2)Ejk=
nsum
i=1
(
ei
120587k
)
(3)SEjk=
1
njminus1
sumn
i=1(eiminus ei)
2
njtimes (Njk)
2
(4)SEk=
radic
SE21k+SE2
2khellip+SE2
jk
(5)smt=etminus1+ et+ et+1
3
6emsp |emsp emspensp FLYNN et aL
Based on boat ramp interviews the average trailer boat partysize (P) was 287 people per vessel throughout May (plusmn0133 SEn=84)andpartysizebetweensitesshowednosignificantdiffer-ence (df=2F = 302p = 068) The averagemonthly boat fishingeffort(hours)extrapolatedforourCRAGSobservationsofHRMCAwas5629hr(plusmn711SE)with4096hr(plusmn607SE)inMayand1534hr(plusmn370 SE) in June (Figure4)mdashthis could be extrapolated to fisherhoursbymultiplyingbytheaveragepartysizebutasnointerviewswereconductedinJuneweleftboththeCRAGSandbusrouteex-trapolationsasboatfishingeffortonly
The total subset area observed by CRAGSwas 96km2 which comparedto318km2thatwaseasilyavailabletotrailerboatsaroundtheTasmanPeninsularwhichcorrespondstoaround3ofthetotalareaComparedtothetotalfishingeffortforMayextrapolatedfromtheregionalbus-routesurvey therelativelysmallareaaroundtheHRM(~3)receivedalargeamountofthefishingeffort(~23)
ThehighfrequencyofCRAGSsamplingpermittedtheexamina-tionoffine-scaleinter-dailytemporalvariationwhichdisplayedarag-gedsinusoidoscillation(Figure5)Effortwasgenerallylowwithinthe
weekdaystrataandthen increasedrapidly to largepeaksonweek-ends (Figure5) One exception to this was between the 18th and22ndMaywhichhadmoreeffortonweekdays thanotherperiods(Figure5)Bracketingthisperiodweretwoweekendsoffishingcom-petitions theldquoTunaClubofTasmaniarsquosrsquoFarSouthClassicrdquo (16ndash17thMay)andRally4Northerninvitationalweekendrally(23rdMay)
A total of 16 bus-route samples were conducted logistic fail-uresledtotheabandonmentoffour(20)oftheplannedsamplesRegionalTrailerboateffortfromaroundtheTasmanPeninsularwasestimatedtobe19650hr (plusmn2858SE) inMay (Figure4)Notrailersat individual rampswere recorded four timeswith all of theseoc-curringonweekdaysThetotalnumberoftrailerssightedperramprangedfrom0to16duringweekdaysand1ndash54forweekendswiththehighestnumberrecordedon16May2015atPiratesBayDuringhigh-intensitydays(particularlyweekendsholidaysmornings)therewereoftenlargeoverflowsfromrampcarparkswithparkedcarsandboattrailersextendingasmuchasonekilometerfromtheboatramp
During thebus-route survey 84 interviewswere conductedThemajorityofmariners(905n=96)indicatedfishingwastheir
F IGURE 4emspExtrapolatedmonthlytrailerboateffort(hoursplusmnSE)fortheentireTasmanPeninsulaassessedbyabus-routesurvey(May)andforthesubsetoftheareaaroundtheHRMCAsampledwithCRAGS(MayandJune)
F IGURE 5emspHigh-frequencyinterdailyextrapolatedCRAGSestimationsofboatefforthoursaroundthe(HRMCA)
0
100
200
300
400
500
600
Rolli
ng m
ean
daily
effo
rt in
hou
rs
DayWeekdays Weekends + Public Holidays
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
6emsp |emsp emspensp FLYNN et aL
Based on boat ramp interviews the average trailer boat partysize (P) was 287 people per vessel throughout May (plusmn0133 SEn=84)andpartysizebetweensitesshowednosignificantdiffer-ence (df=2F = 302p = 068) The averagemonthly boat fishingeffort(hours)extrapolatedforourCRAGSobservationsofHRMCAwas5629hr(plusmn711SE)with4096hr(plusmn607SE)inMayand1534hr(plusmn370 SE) in June (Figure4)mdashthis could be extrapolated to fisherhoursbymultiplyingbytheaveragepartysizebutasnointerviewswereconductedinJuneweleftboththeCRAGSandbusrouteex-trapolationsasboatfishingeffortonly
The total subset area observed by CRAGSwas 96km2 which comparedto318km2thatwaseasilyavailabletotrailerboatsaroundtheTasmanPeninsularwhichcorrespondstoaround3ofthetotalareaComparedtothetotalfishingeffortforMayextrapolatedfromtheregionalbus-routesurvey therelativelysmallareaaroundtheHRM(~3)receivedalargeamountofthefishingeffort(~23)
ThehighfrequencyofCRAGSsamplingpermittedtheexamina-tionoffine-scaleinter-dailytemporalvariationwhichdisplayedarag-gedsinusoidoscillation(Figure5)Effortwasgenerallylowwithinthe
weekdaystrataandthen increasedrapidly to largepeaksonweek-ends (Figure5) One exception to this was between the 18th and22ndMaywhichhadmoreeffortonweekdays thanotherperiods(Figure5)Bracketingthisperiodweretwoweekendsoffishingcom-petitions theldquoTunaClubofTasmaniarsquosrsquoFarSouthClassicrdquo (16ndash17thMay)andRally4Northerninvitationalweekendrally(23rdMay)
A total of 16 bus-route samples were conducted logistic fail-uresledtotheabandonmentoffour(20)oftheplannedsamplesRegionalTrailerboateffortfromaroundtheTasmanPeninsularwasestimatedtobe19650hr (plusmn2858SE) inMay (Figure4)Notrailersat individual rampswere recorded four timeswith all of theseoc-curringonweekdaysThetotalnumberoftrailerssightedperramprangedfrom0to16duringweekdaysand1ndash54forweekendswiththehighestnumberrecordedon16May2015atPiratesBayDuringhigh-intensitydays(particularlyweekendsholidaysmornings)therewereoftenlargeoverflowsfromrampcarparkswithparkedcarsandboattrailersextendingasmuchasonekilometerfromtheboatramp
During thebus-route survey 84 interviewswere conductedThemajorityofmariners(905n=96)indicatedfishingwastheir
F IGURE 4emspExtrapolatedmonthlytrailerboateffort(hoursplusmnSE)fortheentireTasmanPeninsulaassessedbyabus-routesurvey(May)andforthesubsetoftheareaaroundtheHRMCAsampledwithCRAGS(MayandJune)
F IGURE 5emspHigh-frequencyinterdailyextrapolatedCRAGSestimationsofboatefforthoursaroundthe(HRMCA)
0
100
200
300
400
500
600
Rolli
ng m
ean
daily
effo
rt in
hou
rs
DayWeekdays Weekends + Public Holidays
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
emspensp emsp | emsp7FLYNN et aL
primaryactivity (Table1)Overall643 (n=54) indicated theirprimary target were tuna with 179 (n=15) specifically tar-geting southern bluefin tuna (Thunnus maccoyii)Other commonrecreationaltargetsincludedflathead(Platycephalusspp595)Southernrocklobster(Jasus edwardsii107)andsquidcalamari(Sepioteuthis australisandNototodarus gouldi476)(Table1)
Basedon the responsesofactivity type from interviews totalboatfishingeffortinMaywasestimatedtobe17783hr(plusmn2586SE)Asmostnonfishingactivitieswerenear shoreweassumed trailerboatingactivityatHMRCAobservedbyCRAGSwasallfishing
Therewere16directcomparisonsamplesbetweenthebusrouteandCRAGSmethodsbetweenthe2ndandthe29thofMaySamplesconsisted of 7 weekdays and 9 weekendspublic holiday samples
(Figure6)Infourcaseswheretwobus-routesamplesweretakenonthe same day (Supporting Information Appendix S2) they are com-bined toadailyestimation for tabulation (Table2)Theproportionaldifferencebetweenestimatedeffortsusingthetwomethodsrangedfrom078-to418-folddifferencehoweverranksestimatesbetweenCRAGS and bus-route samples were strongly associated (n=16ρ(12)=087p=lt001)Interdailysamplingstrata(ampm)werealsoexaminedseparatelyforcorrelationwhichremainedstrongforbotham(n=9ρ(7)=080p=lt001)andpm(n=7ρ(5)=089p=lt001)
ForthecomparativemonthCRAGStook169samplescomparedtothebusroutesrsquo16(Table3)WehadtwofieldtripdaysforCRAGSwith four people on the setup and three on the breakdown daywhichresultedinsevenfull-timeequivalent(FTE)daysofworkThiscomparedto16fielddaysforthebus-routemethodwhichincludedtraveltothefieldsiteforbetweentwoandthreestaffwhichaccu-mulatedto40FTEdaysofworkSettingupourCRAGSautomatedstitching took one FTE day (though this is faster for experiencedofficers)whiledataextractionperimagewasestimatedtotakeon
TABLE 1emsp Intervieweesreportedprimarytargettaxaoractivity
Primary target taxaspeciesactivity Interview responses
Tuna(unspecified) 37 440
SouthernBluefinTuna 15 179
AlbacoreTuna 2 238
AllTuna 54 643
Flathead 5 595
Southernrocklobster 9 107
Squidcalamari 4 476
Abalone 1 119
SmallPelagics 1 119
SnottyTrevalla 2 238
Allnearshorespecies 22 262
Surfing 1 119
SCUBA 3 357
DemonCavevisitors 3 357
PleasureCruise 1 119
Allnonextractive 8 952
Note ldquoTunardquo responsesdenote anglers targeting all specieswithin thefamilyThunnini
F IGURE 6emspDailyboatingeffort(hours)extrapolatedusingbus-routeaccesspointsurvey(bar)andCRAGS(line)Effortestimatefrombus-routesurveywascalculatedfrommorning(amdarkgray)andafternoon(pmlightgray)surveyCRAGSsurveywasconductedbetweenMayandJunewhilethebus-routesurveywasonlyconductedinMay
TABLE 2emspRankingofeffortforpairedsamplesbetweenCRAGSandthebusroute
DateCRAGS hours (rank)
Bus- route hours (rank) Δ Rank
2052015 330(9) 1208(10) minus1
3052015 303(8) 1471(11) minus3
12052015 0(1) 48(2) minus1
14052015 0(1) 24(1) 0
16052015 387(11) 1794(12) minus1
19052015 93(4) 89(4) 0
20052015 57(3) 117(5) minus2
22052015 228(7) 283(6) 1
23052015 513(12) 414(8) 4
24052015 345(10) 1155(9) 1
25052015 122(6) 323(7) minus1
29052015 102(5) 79(3) 1
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
8emsp |emsp emspensp FLYNN et aL
average75mincomparedto1minforenteringthebus-routedatasheetWhiledataextraction forCRAGS took longer compared tothe bus routemdashat 381 FTE daysmdashtotal monthly labor for CRAGStookonlyaquarterofthetimeandonaper-samplecomparisonwasonly25ofthebus-routelaborcost
4emsp |emspDISCUSSION
Over our autumn samplingmost trailer boat owners accessing thewaters around theTasmanPeninsula via the regional boating infra-structurewererecreationallyfishingwithfisherspredominatelytar-getingtunaTrailerboatrecreationalfishersconstitutealargetrancheof coastal users in Australia (McPhee etal 2002) and our resultssuggestedthatthisregionremainsonewhererecreationalfishing isconcentrated(MortonampLyle2004)EffortobservedbyCRAGSwasa spatial subsetof thegeneral areaeasily accessedby trailer boatsaroundtheTasmanPeninsularwhichweregionallyassessedwithourbus-routerovingsurveyAsCRAGSonlyobserved~3of thetotalarea but corresponded to ~23 of themonthly bus-route boat ef-fort theHippolyteRocksMarineConservationArea (HRMCA) is afurtherconcentrationpointwithintheregionforrecreationalfishingAsthisoffshorefishingeffortaroundtheHRMCAwasmostprobablyfocusedontotunamdashwhichaccountedfor634ofallinterviewedfish-ers activitiesmdashthe actual proportionofpeninsularwide tuna fishingthatwasobservedbyCRAGSwaspotentiallymuchhigherthan23
For developing effort metrics finding a representative site toobservemaybe important forprecise trackingof trendsThetightrankcorrelationbetweenpairedregionalbus-routeandCRAGScam-era samples suggest that effort atHRMCAwas representative ofregionaltemporaltrendsOurresultsarealsosimilartootherstud-ieswheresamplingmethodsforrecreationalfisheriesoverbroaderscaleswhencomparedtoindextrendsofeffortcollectedfrompointsourcesshowstrongcorrelations(Hartilletal2016)
CRAGSwithitsseascapescaleofdatacaptureobservesactualeffortonfishinggroundsratherthan indirectmetricsfrommove-ment past access points (Hartill etal 2016 Smallwood PollockWise Hall amp Gaughan 2012 van Poorten amp Brydle 2018) This
makesthechoiceofaccesspointstomonitorimmaterialfortrendcol-lectionandmaybeofparticularinterestforpelagicfisheriesWhilenonintuitiverecreationalfishingforwide-rangingmigratorypelagicspeciesisoftenspatiallyconcentratedintosmallareas(Lynch2006)duetotightoverlapsbetweeneaseofaccessbyfishersandfishbe-haviorrelatedtobathymetrymigrationhabitatandpreyavailability(Lynchetal2004PattersonEvansCarterampGunn2008)
Unlikespatialpatternstheintensityofcoastalrecreationaltemporaleffortcanbehighlyvariableovertime(Lynch20082014WiseTelferLaiHallampJackson2012)thoughitisgenerallythoughttofollowpre-dictablepatternsrelativetoholidayandnonholidayperiods(JonesampPollock2012)Ourresultsdemonstratedtheselargefluctuationsrang-ingfromzerouptogt400hroftrailerboateffortforadjacentdaysIncombinationwithholidayperiodsothersourcesofvariabilitymayalsooccurduetoindividualorcombinationsoffactorssuchasfishingcluborcompetitionactivitiesandweatherconditionsHigh-frequencysam-plingcanovercomethesecommontemporaldisjunctionsinobservedfishingeffortwherebychanceduetolownumbersofsampleslogis-ticallypermissiblewithtraditionalrovingoraccesspointsurveyslargeerrorsintheestimationofeffortcanbeintroduced(Hartilletal2016vanPoortenampBrydle2018vanPoortenetal2015)
With multiple daily observations abnormal episodes are morelikelytobeobservedForinstanceaweek-longperiodofintenseef-fortwasrecordedbetween15May2015and27May2015whichwasbracketedbytwoconsecutiveweekendswherefishingcompetitionswereheldontheTasmanPeninsularThenormalsinusoidpatternoflowweekdayandhighweekendeffortwasmuchlesspronouncedastheweekdaylullsineffortwerepartiallybridgedbyanglerscontinuingtofishAsbus-routedesignsusedaytypeasstratawithlowerprob-abilitiesforsamplingweekdaysandmuchfewersamplesoverallthistypeofeffectcouldeasilybemissedWhileourbus-routesamplesdidoverlapwiththetunafishingcompetitionsthiswaspurelybychanceSucheventswhichresultinlargespikesofeffortcanhavemarkedim-pactsonlocalecosystemsandharvestestimates(McPheeetal2002MonzPickeringampHadwen2013)andwithoutpriorknowledgearemorelikelytobecapturedwithmonitoringathigherfrequencies
Methodsforgatheringdataoncoastalusesuchasrecreationalfishing moved away from direct approaches and toward off-sitemethodssuchastelephoneinterviews(McCluskeyampLewison2008Pollock etal 1994) due to unsustainable costs from staffing andoperationsparticularlyduringperiodsof intenseusesuchasearlymornings weekends and public holidays which require overtimepaymentsHoweversamplingissueswithoff-sitemethodsareofpar-ticularconcernforopen-accessactivitiessuchasunlicensedfisheriesasthereisnolicensedatabaseofcontactphonenumbersonwhichtobaseasampleframeThisisparticularlysofornicherecreationalfisheriessuchaspelagicgamefishingasthefishersbecomeincreas-inglydilutewithinthegeneralpopulationandhencearehardtoac-cessparticularlyviaoff-sitemethodssuchastelephone interviews(Griffithsetal2010)
Remoteandautonomousdeploymentofsensorsmaythereforeprovideanalternativeandcost-effectivemethodforlong-termmon-itoring particularly for effort across a range of applicationsOur
TABLE 3emspFulltimeequivalent(FTE)labordaysforCRAGSandbusroutesurveystoestimatepatternsofeffort
CRAGS Bus route
Numberofsamples 169 16
Fielddays 2 16
Averagepeopleperfieldday 35 25
Field FTE days subtotals 7 40
StitchingsetupFTE 1 0
DataextractionFTE(persample) 0017 0002
Subtotal data extraction FTE 381 004
FTEdayspersample 006 250
Total FTE days 108 400
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
emspensp emsp | emsp9FLYNN et aL
comparisonsofmatchedpairsofdatafromtwomethodssuggestedthat theCRAGSoutputswerewell correlatedwith regionalefforttrendsOuranalysisoflabordemonstratesthattryingtounderstandthistemporalvariabilitywithsamplingviaboatrampsurveyswouldbeprohibitivelyexpensiveOurCRAGSallowsforatemporaleffortmetric derived fromactual observationsof fishing to be cheaplydeveloped with sampling frequency at subdaily scales This high-frequencysamplingapproachwasvalidatedagainstour largerbutmuchmorecostlybus-routesurveyOuranalysisoflaborrequiredtobothcollectandprocessdatashowedthatCRAGSrequiredconsid-erablylessresourcestocollectapproximatelyninetimesmorefre-quenttemporaldatathanthebus-routemethodMajorlaborsavingswereduetotheruggedizedandautonomouscapabilityofCRAGSasonlytwofieldtripswererequired(deploymentandretrievaltook7FTEdays)comparedtothebus-routersquos16fieldtrips inamonthwhichconsumed40FTEdaysandincludedanadditional780kmofinter-ramproadtravel(SupportingInformationAppendixS3)AsitisweatherproofautonomousandabletobeprogrammedwithatimedelaysetupCRAGSldquofireandforgetrdquonaturebothremovestheneedforobservertobephysicallylocatedattheregiontocountactivity(EdwardsampSchindler2017)andworkduringovertimeperiodsTheldquoblankingperiodrdquowhichstopsandstartssamplingmdashinthiscaseover-nightmdashalso reduces battery and memory use In combined thesetechnological advances have allowed long-term CRAGS deploy-mentsacross4yearsof4ndash6monthrotationsontooffshoreislandsand for several seasons in Antarctica to monitor seabirds (Lynchetal2017)Thelaborestimateforthebusroutewasconservativeasitdidnotincludeovertimeloadingforthe6weekenddaysofsam-plingorearlymorningcommencementofamsamples
Theadditional381FTEprocessingtimerequiredbythecameraapproach did not impact greatly on the overall labor budgetwhenconsideredagainstfieldworkTheuseoftheTimeMachinebatchpro-cessingsoftwaretoautomaticallyproducetheldquobigdatardquointeractivemoviewasalsoatechnologicalinnovationthatsignificantlyreducedthelaborofdatahandlingcomparedtothemanualopeningofindivid-ualGigaPansusedinpreviousstudies(Lynchetal2015Woodetal2016)IncomparativetermsattheindividualsampleleveltotracktheeffortmetriceachCRAGSsamplerequired24ofthelaborofthebus-routesurvey
Thedevelopmentandapplicationofimagerecognitionsoftware(machine learning) to detect classify and count boats automati-cally (Bousetouane amp Morris 2015) would further reduce post-processingcostsTherequiredfrequencyofsamplingshouldalsobeconsideredonacase-by-casebasisdependentontheresearchquestion as image collection can be somewhat open-ended andresult inunwieldydata sets that require subsampling tobe cost-effective(Parnelletal2010)Inourcasethesettingof~5imagesperdayappearedtoadequatelydescribetheinterdailyvariation
Whileouroneautonomouscameraprovidedastrongcorrelationwithtemporaltrendsineffortcomparedtotheregionalsurveyitdidnotprovide relativespatialdistributionsofeffortacross the fisheryThesedistributionshowevercanbepredictableandhighlyconcen-trated(Lynch20062014Parnelletal2010)anobservationthatwas
alsoshownbyourresultsandmuchsmallerlevelsofsamplingeffortwouldprobablyberequiredtoresolvevariationinspatialdistributionscomparedtowhatisrequiredtoresolvethehightemporalvariationsineffortUnderstandingfine-scalespatialdistributionsofrecreationalfishingeffortiscommonlyachievedusingaerialcounton-watersur-veysorvantagepointsurveys(Lynch2006Smallwoodetal2012)thoughotherremotemethodssuchashigh-resolutionsatelliteimag-ery(FretwellScofieldampPhillips2017)mayprovideacheapersolu-tionThesetypesofwidersurveyshavealsobeensuggestedashighlycompatiblewithpointsourcemetricssuchasremotecamerasystemstoallowforcalibrationorldquoup-ratingrdquoforeffortexpansionstoestimateoveralleffortwithinthefishery(Hartilletal2016)
Thelongdistance(~6km)betweentheHMRCAandthedeploy-ment location stretched the resolution limit of the current CRAGSsetup aswewere unable to distinguish party size or activity typepurelyusing thecamerasystemThis isunlikepreviousCRAGSde-ploymentwhichhadahighsuccessrateofidentifyingpartysizeac-tivity typeata shorter rangeofapproximately19km (Woodetal2016) Thus ground-truthing information about the fisherywas re-quiredforthesystemtoworkeffectivelyAutonomouscamerasystemalsodonotcollectcreel(catch)databutbyallowingforcost-effectivecollectionofthehighlyvariabletemporaleffortdatatheymayallowforhumanresourcestobespentelsewhereduringsurveyperiods(egtoundertakeboatrampinterviews)(vanPoortenampBrydle2018)
Our studywas a small-scale trial of new technology andwhilepreliminary results are encouraging validated seasonal effort esti-mationsobservedviaCRAGSwereoutsidethescopeofthisprojectOur comparisons would hence have been improved by extendingCRAGSdeploymentsandwithmorecomparisonstoregionalsurveysSimilarly usingmultiple CRAGS simultaneously would have bettertested for differences in subregional patterns of temporal trendsorconcentrationof infishingeffortfor instanceboatsaroundthenearbyTasmanIsland
Insummarytheprogrammablesamplinganddataextractionof-feredbyhigh-resolutionautonomouscamerasystemscanallowforfine-scalelong-termmonitoringoffishingefforttrendsorpotentiallymanyothertypesofactivitiesatseascapescalesMorewidespreadadoptionofhigh-frequencysamplingapproachesforeffortestima-tionwouldalsohelpavoidcollectionofmisrepresentativedatabyidentifyingeffortanomalies(McCluskeyampLewison2008)suchastheweekdaycontinuationeffectofraisedeffortbetweenadjacentweekendfishingcompetitionByimprovingtheinformationdensityovertraditionalsurveymethodsautonomouscamerasystemsandotherremotesystemsmayprovideanewwaveofoptimizationfordirectmeasurementofcoastalhumanusesuchasrecreationalfish-eriesalthoughcomplimentarymethodsarestillrequiredtodeter-minespatialdistributionstargetspeciesandharvest
ACKNOWLEDG MENTS
We thankMsGeorginaWoodMs SaraAdkinsMsBelleMonkMrRSherringtonandMsPaigeKellyforconstructiveinsighten-thusiasmandassistanceduringfieldworkWealsothankMrPhillip
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
10emsp |emsp emspensp FLYNN et aL
CulversonMrStuartDudgeonandMrMattStapenellKarenEvensprovidedhelpfulcommentsonaninternaldraftGreatestthankstothemany recreational anglerswhowillingly shared information tosurveyclerkswithouttheirinvolvementthisprojectwouldnothavecometofruitionThreeanonymousreviewersprovidedcommentsthatimprovedthemanuscript
CONFLIC T OF INTERE S TS
Theauthorsdeclarenoconflictofinterests
AUTHOR CONTRIBUTIONS
MrFlynnhelpeddesignthestudyledthefieldworkundertookmostoftheanalysisandhelpwritethemanuscriptDrLynchhelpdesignthestudyassisted inthefieldworkprovidedadviceontheanaly-sisanddidsomeanalysisconstructedfiguresandafterMrFlynncontributedthemosttothedraftingofthemanuscriptDrBarretthelpeddesign the study assisted in some fieldwork provided ad-viceonanalysisandeditedthemanuscriptMrWonghelpedexten-sivelywithfieldworkassistedwithanalysisconstructedfiguresandhelpedwritethemanuscriptMsDevineassistedwithfieldworkandconfigurationoftheequipmentincludingtheapplicationofthebigdatamoviesoftwareandhandlingofphotographsandalsohelpedwritethemanuscriptMrHughesdevelopedtheelectronicsfortheequipmentoversaw thebuildofgearwrote the software topro-gramtherobotandreviewedandeditedthemanuscript
DATA ACCE SSIBILIT Y
Survey sampling schedules data collection sheets extrapolationproceduresandtechnicalCRAGSspecificationsarealluploadedasonlinesupportinginformationappendixRawdatafromsurveysandCRAGSDOIhttpdoiorg105061dryadv10d218
RE SE ARCH E THIC S COMPLIANCE AND FUNDERS
ResearchabidesbytheAustraliancodeofhumanresearchandex-perimentation(NationalStatementonEthicalConductinHumanResearch of 2007) Permission to conduct research on humanparticipants was granted by the University of Tasmania SocialScience under reference H0014772mdashldquoRemote investigation ofcoastal area user group quantification and variationrdquo aswell asfromtheCSIROSocialScienceHumanResearchEthicsCommitteeunder reference 08115mdashldquoHawkesbury Region Project HumanUsePatternsrdquoFundingwasbyUTASasPartoftheIMASHonoursProject funding and through internal support from the CoastsProgram
ORCID
Tim P Lynch httporcidorg0000-0002-5346-8454
R E FE R E N C E S
Alessa L Kliskey A amp Brown G (2008) Socialndashecological hotspotsmapping A spatial approach for identifying coupled socialndasheco-logicalspaceLandscape and Urban Planning8527ndash39httpsdoiorg101016jlandurbplan200709007
ArlinghausR(2006)Overcominghumanobstaclestoconservationofrecreational fishery resources with emphasis on central EuropeEnvironmental Conservation 33 46ndash59 httpsdoiorg101017S0376892906002700
Arlinghaus R Tillner R amp Bork M (2015) Explaining participationratesinrecreationalfishingacrossindustrialisedcountriesFisheries Management and Ecology 22 45ndash55 httpsdoiorg101111fme12075
BadcockPBPatrickKSmithAMASimpsonJMPennayDRisselCEhellipRichtersJ(2016)Differencesbetweenlandlineandmobilephoneusers in sexualbehavior researchArchives of Sexual Behavior46(6)1711ndash1721
Blumberg S J amp Luke J V (2009) Reevaluating the need for con-cern regarding noncoverage bias in landline surveys American Journal of Public Health 99 1806ndash1810 httpsdoiorg102105AJPH2008152835
Bousetouane F ampMorris B (2015) Off-the-shelf CNN features forfine-grainedclassificationofvesselsinamaritimeenvironmentInGBebisRBoyleBParvinDKoracinIPavlidisRFerisTMcGrawMElendtRKopperERaganZYeampGWeber(Eds)Advances in visual computing 11th International symposium ISVC 2015 Las Vegas NV USA December 14ndash16 2015 proceedings Part II (pp379ndash388)Cham Switzerland Springer International Publishing httpsdoiorg101007978-3-319-27863-6
Cooke S J amp Cowx I G (2004) The role of recreational fish-ing in global fish crises BioScience 54 857ndash859 httpsdoiorg1016410006-3568(2004)054[0857TRORFI]20CO2
DiggleP J (1995)Time-series a biostatistical introductionOxfordUKClarendonPress
EdwardsJampSchindlerE (2017)Avideomonitoringsystemtoeval-uate ocean recreational fishing effort in Astoria OregonMarine recreational information program (pp 31) Newport OR OregonDepartmentofFishandWildlife
FarrMStoecklNampSuttonS(2014)RecreationalfishingandboatingArethedeterminantsthesameMarine Policy47126ndash137httpsdoiorg101016jmarpol201402014
ForbesETraceySampLyle JM (2009)Assessment of the 2008 rec-reational gamefish fishery of Southeast Tasmania with particular refer-ence to Southern Bluefin TunaHobartTASTasmanianAquacultureandFisheriesInstituteUniversityofTasmania
FretwellPTScofieldPampPhillipsRA(2017)Usingsuper-highres-olutionsatelliteimagerytocensusthreatenedalbatrossesIbis159481ndash490httpsdoiorg101111ibi12482
GiriKampHallK (2015)SouthAustralian recreational fishingsurveyFisheries Victoria internal report series (pp 63) Queenscliff VicFisheriesVictoria
Griffiths S P Pollock K H Lyle J M Pepperell J G TonksM L amp Sawynok W (2010) Following the chain to elu-sive anglers Fish and Fisheries 11 220ndash228 httpsdoiorg101111j1467-2979201000354x
Halpern B SWalbridge S Selkoe K A Kappel C V Micheli FDrsquoAgrosaChellipWatsonR(2008)AglobalmapofhumanimpactonmarineecosystemsScience319948ndash952httpsdoiorg101126science1149345
Hartill BW Payne GW Rush N amp Bian R (2016) Bridging thetemporal gap Continuous and cost-effective monitoring of dy-namic recreational fisheries by web cameras and creel surveysFisheries Research 183 488ndash497 httpsdoiorg101016jfishres201606002
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
emspensp emsp | emsp11FLYNN et aL
HenryGWampLyleJM(2003)NationalrecreationalandindigenousfishingsurveyFinal report to the fisheries research amp development cor-poration and the fisheries action program (pp 188) Canberra ACTFRDC
JonesCMampPollockKH(2012)Recreationalanglersurveymeth-odsEstimationofeffortharvestandreleasedcatchInAZaleDLParrishampTMSutton(Eds)Fisheries techniquesBethesdaMDAmericanFisheriesSociety
Kearney R E (2002) Co-management The resolution of conflict be-tween commercial and recreational fishers in Victoria AustraliaOcean amp Coastal Management45201ndash214httpsdoiorg101016S0964-5691(02)00055-8
KellerKSteffeASLowryMMurphyJJampSuthersIM(2016)Monitoringboat-basedrecreationalfishingeffortatanearshorearti-ficialreefwithashore-basedcameraFisheries Research18184ndash92httpsdoiorg101016jfishres201603025
KinlochMAMcGlennonDNicollGampPikePG(1997)Evaluationof the bus-route creel surveymethod in a largeAustralianmarinerecreationalfisheryISurveydesignFisheries Research33101ndash121httpsdoiorg101016S0165-7836(97)00068-4
LewinWCArlinghausRampMehnerT(2006)Documentedandpo-tential biological impacts of recreational fishing Insights forman-agementandconservationReviews in Fisheries Science14305ndash367httpsdoiorg10108010641260600886455
LowryMampMurphy J (2003)Monitoring the recreational gamefishfisheryoffsouth-easternAustraliaMarine and Freshwater Research54425ndash434httpsdoiorg101071MF01269
LyleJMStarkKEampTraceySR(2014)2012ndash2013 Survey of rec-reational fishing in TasmaniaHobart TAS Institute forMarine andAntarcticStudies
Lynch T P (2006) Incorporation of recreational fishing ef-fort into design of marine protected areas Incorporacioacuten delEsfuerzo de Pesca Recreativa en el Disentildeo de Aacutereas MarinasProtegidas Conservation Biology 20 1466ndash1476 httpsdoiorg101111j1523-1739200600509x
LynchTP(2008)Thedifferencebetweenspatialandtemporalvaria-tioninrecreationalfisheriesforplanningofmarineprotectedareasResponse toSteffeConservation Biology22486ndash491httpsdoiorg101111j1523-1739200800888x
LynchTP (2014)Adecadal time-seriesof recreational fishingeffortcollectedduringandafterimplementationofamultipleusemarinepark shows high inter-annual but low spatial variability Fisheries Research15185ndash90httpsdoiorg101016jfishres201309014
LynchTPAldermanRampHobdayAJ(2015)Ahigh-resolutionpan-orama camera system formonitoring colony-wide seabird nestingbehaviourMethods in Ecology and Evolution6491ndash499httpsdoiorg1011112041-210X12339
LynchTFlynnDWoodGKimJ-HParkJampHobdayAJ(2017)Metrics parameters and results from fisheries and wildlife studies with the CSIRO Ruggedized Autonomous Gigapixel Systems (CRAGS)DarwinNTAustralianMarineScienceAssociation
LynchTPWilkinsonEMellingLHamiltonRMacreadyAampFearyS(2004)ConflictandimpactsofdiversandanglersinamarineparkEnvironmental Management 33 196ndash211 httpsdoiorg101007s00267-003-3014-6
MaHOgawaTKSminkeyTRBreidtFJLesserVMOpsomerJDhellipVanVoorheesDA(2018)Pilotsurveystoimprovemonitoringofmarine recreational fisheries inHawaiʻiFisheries Research204197ndash208httpsdoiorg101016jfishres201802010
McCluskey S M amp Lewison R L (2008) Quantifying fish-ing effort A synthesis of current methods and their ap-plications Fish and Fisheries 9 188ndash200 httpsdoiorg101111j1467-2979200800283x
McGlennonDampKinlochMA(1997)Evaluationofthebus-routecreelsurveymethod in a largeAustralianmarine recreational fishery II
PilotsurveysandoptimalsamplingallocationFisheries Research3389ndash99httpsdoiorg101016S0165-7836(97)00067-2
McPheeDLeadbitterDampSkilleterGA(2002)SwallowingthebaitIs recreational fishing in Australia ecologically sustainable Pacific Conservation Biology840httpsdoiorg101071PC020040
Monz C A Pickering C M amp HadwenW L (2013) Recent ad-vances in recreation ecology and the implications of differentrelationships between recreation use and ecological impactsFrontiers in Ecology and the Environment11441ndash446httpsdoiorg101890120358
MooreAHallKGiriKTraceySPenroseLHansenShellipBrownP(2015)Developingrobustandcost-effectivemethodsforestimatingthenationalrecreationalcatchofSouthernBluefinTunainAustraliaFRDC Projectpp123
Morton A J amp Lyle J M (2004) Preliminary assessment of the rec-reational gamefish fishery in Tasmania with particular reference to Southern Bluefin Tuna Taroona TAS Tasmanian Aquaculture andFisheriesInstitute
ParnellPEDaytonPKFisherRALoarieCCampDarrowRD(2010)SpatialpatternsoffishingeffortoffSanDiegoImplicationsfor zonal management and ecosystem function Ecological Applications202203ndash2222httpsdoiorg10189009-15431
PattersonTAEvansKCarterTLampGunnJS(2008)Movementand behaviour of large southern bluefin tuna (Thunnus maccoyii)in the Australian region determined using pop-up satellite ar-chival tags Fisheries Oceanography 17 352ndash367 httpsdoiorg101111j1365-2419200800483x
PollockKH JonesCMampBrownTL (1994)Angler survey meth-ods and their applications in fisheries management Bethesda MDAmericanFisheriesSociety
PonsM Branch T AMelnychukM C JensenO P Brodziak JFromentinJMhellipHilbornR(2017)EffectsofbiologicaleconomicandmanagementfactorsontunaandbillfishstockstatusFish and Fisheries181ndash21httpsdoiorg101111faf12163
PowersSPampAnsonK (2016)Estimating recreationaleffort in theGulf of Mexico Red Snapper Fishery using boat ramp camerasReduction in federalseason lengthdoesnotproportionally reducecatchNorth American Journal of Fisheries Management 36 1156ndash1166httpsdoiorg1010800275594720161198284
Robson D amp Jones C M (1989) The theoretical basis of an ac-cess site angler survey design Biometrics 45 83ndash98 httpsdoiorg1023072532036
Rocklin D Levrel H Drogou M Herfaut J amp Veron G (2014)Combining telephone surveys and fishing catches self-reportTheFrenchseabass recreational fisheryassessmentPLoS ONE9e87271httpsdoiorg101371journalpone0087271
Smallwood C B Pollock K HWise B S Hall N G amp GaughanD J (2012) Expanding aerial-roving surveys to include counts ofshore-based recreational fishers from remotely operated camerasBenefitslimitationsandcosteffectivenessNorth American Journal of Fisheries Management321265ndash1276httpsdoiorg101080027559472012728181
Teixeira D ZischkeM T ampWebley J A C (2016) Investigatingbias in recreational fishing surveys Fishers listed in public tele-phone directories fish similarly to their unlisted counterpartsFisheries Research 181 127ndash136 httpsdoiorg101016jfishres201604012
van Poorten B T amp Brydle S (2018) Estimating fishing effortfrom remote traffic counters Opportunities and challengesFisheries Research 204 231ndash238 httpsdoiorg101016jfishres201802024
van Poorten B T Carruthers T RWardHGM ampVarkeyD A(2015)Imputingrecreationalanglingeffortfromtime-lapsecamerasusinganhierarchicalBayesianmodelFisheries Research172265ndash273httpsdoiorg101016jfishres201507032
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
12emsp |emsp emspensp FLYNN et aL
VenturelliPAHyderKampSkovC(2017)AnglerappsasasourceofrecreationalfisheriesdataOpportunitieschallengesandproposedstandardsFish and Fisheries18578ndash595httpsdoiorg101111faf12189
WiseBSTelferCFLaiEKMHallNGampJacksonG(2012)Long-termmonitoringofboat-basedrecreational fishing inSharkBayWesternAustraliaProvidingscientificadviceforsustainablemanagement in a World Heritage Area Marine and Freshwater Research631129ndash1141httpsdoiorg101071MF12054
WoodGLynchTPDevineCKellerKampFigueiraW(2016)High-resolutionphoto-mosaictime-seriesimageryformonitoringhumanuseofanartificialreefEcology and Evolution66963ndash6968httpsdoiorg101002ece32342
YuFRupertACCDiTizianaMNishikantaKRandySIllahNhellipJeffreyG(2011)TerapixelimagingofcosmologicalsimulationsThe Astrophysical Journal Supplement Series19718
Zischke M T Griffiths S P amp Tibbetts I R (2012) Catch and ef-fort from a specialised recreational pelagic sport fishery off
eastern Australia Fisheries Research 127ndash128 61ndash72 httpsdoiorg101016jfishres201204011
SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle
How to cite this articleFlynnDJHLynchTPBarrettNSWongLSCDevineCHughesDGigapixelbigdatamoviesprovidecost-effectiveseascapescaledirectmeasurementsofopen-accesscoastalhumanusesuchasrecreationalfisheriesEcol Evol 2018001ndash12 httpsdoiorg101002ece34301
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes
Graphical AbstractThecontentsofthispagewillbeusedaspartofthegraphicalabstractofhtmlonly
Itwillnotbepublishedaspartofmainarticle
Wetrailedaremoteautonomousultra-high-resolutionphotosamplingmethodasacosteffectsolutionfordirectseascape-scaledmeasure-mentsOursequentialphotosamplingwasbatchedprocessesusingnovelTimeMachinedatahandlingsoftwaretoproduceldquobigdatardquotimeseriesmoviesfromaspatialsubsetofanoffshorefisherywhichwecomparedtoaregionalbus-routesurveyofboattrailersandinterviewswithparticipantsOurcamerasamplescloselytrackedlargerscaleregionaltrendsbutwithlaborcostsaquarterofthebus-routesurveycostsforanindividualsample25ofthebusroutes