Assessment of trawlable and untrawlable seafloor using multibeam-derived metrics

18 Pages·2015·1.34 MB·English

by  Jodi L. Pirtle

MethodsinOceanography12(2015)18–35
ContentslistsavailableatScienceDirect
Methods in Oceanography
journalhomepage:www.elsevier.com/locate/mio
Fulllengtharticle
Assessment of trawlable and untrawlable
seafloor using multibeam-derived metrics
JodiL.Pirtlea,∗,1,ThomasC.Webera,ChristopherD.Wilsonb,
ChristopherN.Rooperb
aUniversityofNewHampshire,CenterforCoastalandOceanMapping,24ColovosRoad,Durham,NH,
03824,USA
bNationalOceanicandAtmosphericAdministration,NationalMarineFisheriesService,AlaskaFisheries
ScienceCenter,7600SandPointWayNE,Seattle,WA,98115,USA
a r t i c l e i n f o a b s t r a c t
Articlehistory: Groundfishthatassociatewithruggedseafloortypesaredifficult
Received28January2015 toassesswithbottom-trawlsamplinggear.SimradME70multi-
Receivedinrevisedform beamechosounder(MBES)dataandvideoimagerywerecollected
22May2015
tocharacterizetrawlableanduntrawlableareas,andtoultimately
Accepted5June2015
improveeffortstodeterminehabitat-specificgroundfishbiomass.
Availableonline1July2015
Thedatawerecollectedduringtwoacoustic-trawlsurveysofthe
GulfofAlaska(GOA)during2011and2012byNOAAAlaskaFish-
Keywords:
eriesScienceCenter(AFSC)researchers.MBESdatawerecollected
Acousticbackscatter
Multibeamechosounder continuouslyalongthetrackline,whichincludedparalleltransects
Terrainanalysis (1–20nmispacing)andfine-scalesurveylocationsin2011.Video
Seafloorcharacterization datawerecollectedatcamerastationsusingadeployedcamera
Bottom-trawlsurvey system. Multibeam-derived seafloor metrics were overlaid with
Groundfishhabitat thelocationsofpreviouslyconductedAFSCbottom-trawl(BT)sur-
Trawlable vey hauls and 2011 camera stations. Generalized linear models
Untrawlable
wereusedtoidentifythebestcombinationofmultibeammetrics
GulfofAlaska
todiscriminatebetweentrawlableanduntrawlableseafloorforthe
regionofoverlapbetweenthecamerastationsorhaulpathsand
theMBESdata.Thetwobestmodelsweredevelopedusingdatacol-
lectedatcamerastationswitheitherobliqueincidencebackscatter
strength(S )ormosaicS incombinationwithbathymetricposi-
b b
∗
Correspondingauthor.Tel.:[phone];fax:[phone].
E-mailaddress:[email protected](J.L.Pirtle).
1 Presentaddress:NationalAcademyofSciences,NationalResearchCouncil,VisitingScientistatNationalOceanicand
AtmosphericAdministration,NationalMarineFisheriesService,AlaskaFisheriesScienceCenter,17109PointLenaLoopRoad,
Juneau,AK,99801,USA.
http://dx.doi.org/10.1016/j.mio.2015.06.001
2211-1220/©2015ElsevierB.V.Allrightsreserved.
J.L.Pirtleetal./MethodsinOceanography12(2015)18–35 19
tionindexandseafloorruggedness;thesedescribedover54%of
thevariationbetweentrawlableanduntrawlableseafloortypes.A
mapofpredictedseafloortrawlabilityproducedfromthemodelus-
ingmosaicS andbenthic-terrainmetricsdemonstratedthat58%
b
of the area mapped (5987 km2) had ≥50% probability of being
trawlableand42%ofbeinguntrawlable.Themodelcorrectlypre-
dicted69%oftrawlableanduntrawlablehaullocations.Success-
fulhaulsoccurredinareaswith62%probabilityofbeingtrawlable
andgeardamageoccurredinareaswitha38%probabilityofbeing
trawlable.Thismodelandmapproducedfrommultibeam-derived
seafloormetricsmaybeusedtorefineseafloorinterpretationfor
the AFSC BT surveys and to advance efforts to develop habitat-
specificbiomassestimatesforGOAgroundfishpopulations.
©2015ElsevierB.V.Allrightsreserved.
1. Introduction
Multi-species bottom-trawl surveys are a common fishery-independent assessment method to
obtainbiomassestimatesfordemersalfishpopulations.Inherentintrawlsurveybiomassestimatesis
theissueofcatchability,whichisinfluencedbytherelativeproportionoftrawlableanduntrawlable
ground in a survey area, or the area accessible by the survey (Cordue, 2007). Management areas
like the Gulf of Alaska and US West Coast have a mix of trawlable and untrawlable seafloor
types(Zimmermann,2003;VonSzalayetal.,2010).Certaingroundfishspecies,suchasrockfishes
(Sebastesspp.)havemixeddistributionintrawlableanduntrawlablehabitatorpreferruggedhabitat
inaccessible to bottom-trawl gear (Stein et al., 1992; Clausen and Heifetz, 2002; Jagielo et al.,
2003;Rooperetal.,2007).Consequently,theproportionofthepopulationinuntrawlablehabitatis
undersampledornotsampledatall,andthesampledpopulationisassumedtoberepresentativeofthe
entirepopulationforthepurposeofthestockassessment.Disproportionatesurveyaccesstoallareas
occupiedbytheharvestedpopulationcanintroducenon-randomerrortobiomassestimatesfrom
trawlsurveytime-series(Cordue,2007).Thus,moreaccurateaccountingoftheextentoftrawlableand
untrawlablesurveyareaisneededasafirststeptowardassessingandcorrectingthispotentialbias.
NationalOceanicandAtmosphericAdministration(NOAA)AlaskaFisheriesScienceCenter(AFSC)
Resource Assessment and Conservation Engineering (RACE) Division researchers conduct a bien-
nial area-wide bottom-trawl survey (BT survey) for groundfish in the Gulf of Alaska (GOA), from
theIslandsofFourMountains(169°59′0′′W52°43′11′′N)intheAleutianIslandstoDixonEntrance
(133°13′53′′W54°30′38′′N)(VonSzalayetal.,2010).TheRACEBTsurveyisconductedaboardchar-
teredcommercialfishingvessels.Stationsin59surveystrataareallocatedfromasamplinggridina
stratified-randomdesign.Asurveyvesselskippersearchestolocatetrawlablegroundwithinasta-
tiongridcellforaminimumoftwohours,orabandonsthatcellandsearcheswithinanotheruntil
trawlablegroundislocated.Thesamplinggridispopulatedwiththelocationsofknowntrawlable
anduntrawlablefeatures.However,knowledgeofseafloortrawlabilityisnotcomprehensiveacrossa
surveygridcellandisqualitativeatbest.Untrawlableseafloorisoftenencounteredinareasthought
tobetrawlable,whichcanresultinconsiderablegeardamageandlossofthesampleandsurveytime.
Giventhedifficultiesinselectingtrawlableseafloor,weknowthattherearesimilardifficultiesas-
sociatedwithidentifyinguntrawlableseafloor.Abetterestimateofthearealextentoftrawlableand
untrawlablegroundintheGOAwillimprovebiomassestimatesforgroundfish,increasesurveyeffi-
ciency,andreducedamagetogearandbenthichabitat.Inthisstudy,wetestmetricsderivedfrom
multibeam echo sounder (MBES) data for their ability to discriminate between trawlable and un-
trawlableseafloortypesintheRACEBTsurveyarea.
20 J.L.Pirtleetal./MethodsinOceanography12(2015)18–35
MBESsurveyscanacquirehigh-resolutionbathymetryandbackscatterdataconcurrentlytopro-
ducedetailedimagesoftheseafloor.SeveralquantitativemetricscanbederivedfromMBESdatato
distinguishseafloorfeaturesofvariedmorphology(e.g., Wilsonetal.,2007).Whencombinedwith
groundtruthdataofsimilarspatialscale,itispossibletomodelandmapseafloortypesandlandscape
featuresthatinfluencemarinespeciesdistribution(Dolanetal.,2008;Buhl-Mortensenetal.,2009;
Reynoldsetal.,2012),includinghabitatforharvestedspecies(Shotwelletal.,2008;ToddandKostylev,
2011).MetricsextractedfromMBESdatathatmaybeusefulpredictorstodiscriminatebetweenar-
easoftrawlableanduntrawlableseafloorincludethosederivedfromangle-dependentbackscatter
strength,backscattermosaics,andbathymetry.
Seafloorbackscatterstrength(S )isdependentontheincidentangleofsoundwiththeseafloor.For
b
example,thepredictedS (decibels;dB)ofacobbleseafloorisquitesimilartofinesandandsiltwhen
b
examinedatanglesnearnormalincidence(0°).Incontrast,theS ofcobbleispredictedtobewellsep-
b
aratedfromsedimentsoffinergrainsizesatobliqueincidenceanglesbetween30°and50°(APL,1994;
Weberetal.,2013).Studieshaveusedmultibeam-derivedangle-dependentS metricstodescribethe
b
natureofseafloorsediments(FonsecaandMayer,2007;Fonsecaetal.,2009),terrainfeatures(Weber
etal.,2013),andbenthichabitat(Hasanetal.,2012).Theabilityofangle-dependentS metricstodis-
b
criminatebetweentrawlableanduntrawlableseafloorhasbeentestedforasmallregioncontaining
asubsetoftheseafloortypespresentintheGOA(Weberetal.,2013).
Mosaics are a commonly used form of backscatter data. Mosaics are generated by normalizing
S valuesacrossthemultibeamswathtotherangeofobservedvaluesatobliqueincidenceangles
b
(e.g., Rzhanovetal.,2012).Onecaveattoworkingwithmosaicsistheassumptionthatnormalinci-
dencebackscatterstrengthhasthesamediscriminatorypowerasbackscatterstrengthfromoblique
incidence angles. Some angular resolution of the S data is also lost when mosaics are produced.
b
However,backscattermosaicshavethepotentialtosimplifyinterpretationofseafloorproperties,as
opposedtoretainingandanalyzingthefullangle-dependentS .Greaterspatialresolutionisalsoavail-
b
ablefrombackscattermosaics,asopposedtoangle-dependentS metricsthatapplytodiscretesec-
b
tionsofthemultibeamswath.
Severalbenthicterrainmetricscanbederivedfromhigh-resolutionmultibeambathymetrydata
thatdescribeattributesofseafloormorphology.Seafloorslope,curvature,andbathymetricposition
indexaremeasuresofterrainvariability.Seafloorslopeistherateofchangeinbathymetryovera
definedarea(Horn,1981).Seafloorcurvaturehighlightsconcaveandconvexslopesanddefinesslop-
ingterrainalongfeatures(Evans,1980;Schmidtetal.,2003).Bathymetricpositionindex(BPI)isthe
equivalentoftopographicpositionindex,whichdescribestheelevationofonelocationrelativetothe
meanofneighboringlocations(Guisanetal.,1999;Weiss,2001).BPIwillemphasizefeaturesthatare
shallowerordeeperthanthesurroundingarea,suchasridgesandvalleys.Seafloorruggednessisa
measureofterraincomplexitythathighlightsthepresenceofroughandbathymetricallyuneventer-
rain(Sappingtonetal.,2007).Examiningtheseterrainmetricsatfineandbroadspatialscalesmaybe
meaningfultodeterminetherelativeinfluenceoffeaturesofdifferentscaletodistinguishbetween
trawlableanduntrawlableseafloortypes.
WederivedseveralmetricsthatdescribetheseafloorintheGOAfromdatacollectedopportunis-
ticallywithaSimradME70MBES,duringongoingfisheryresourcesurveys.TheME70isacalibrated
MBESdesignedtocollectquantitativeacousticdataontargetsthroughoutthewatercolumn(Trenkel
etal.,2008;Cutteretal.,2010).Bathymetryandbackscatterdataareacquiredconcurrentlywhenthe
ME70isoperatedinthestandardfisherymode.Userconfigurationispossibleandbottomdetections
canbeextractedfromthedatawithcustomizedsoftwaredesignedtocharacterizetheseafloorinthe
areaofstudy(Weberetal.,2013).
Inthiswork,wedemonstratethevalueofmultibeammetricsaspredictorvariablestodiscrimi-
natebetweentrawlableanduntrawlableseafloortypesintheGOA,extendingthepreliminarystudy
byWeberetal.(2013).Ourobjectiveswereto(1)determinewhetherornottrawlableanduntrawlable
groundcanbedistinguishedusingavarietyofmultibeam-derivedbackscatterandbathymetrymet-
rics;(2)identifywhichmultibeammetrics,singularlyorincombination,aremostusefultodiscrim-
inatebetweentrawlableanduntrawlablelocations;and(3)generateprobabilitymapsofpredicted
seafloortrawlabilitywithintheGOA.
J.L.Pirtleetal./MethodsinOceanography12(2015)18–35 21
2. Methods
2.1. Multibeamsurvey
NOAAAFSCRACEresearchersconductacoustic-trawlsurveys(ATsurveys)withtheNOAAship
OscarDysontoassesssemi-demersalwalleyepollock(Gaduschalcogrammus)biomassanddistribution
during summer and winter in the GOA (Jones and Guttormsen, 2012; Jones et al., 2015). ME70
data were collected during the summer 2011 GOA-wide biennial AT survey from 14 June to 12
AugustfromtheIslandsofFourMountainsintheAleutianIslands(169°59′0′′W52°43′11′′N)tothe
easternsideofKodiakIsland(151°5′25′′W57°20′46′′N).AdditionalME70datawerecollectedduring
thesmaller-scalewinter2012ATsurveyduring13–22Februaryand17–27MarchbetweenSanak
Island (163°14′42′′W 54°48′37′′N) and the eastern side of Kodiak Island. Survey activities for the
pollockassessmentweregenerallyconductedduringdaylighthoursinthesummerandbothdayand
nightduringthewinterpre-spawningpollockATsurveys.Nominalunderwayfree-running(versus
trawling)vesselspeedwas5.5–6ms−1(10.7–11.7kt),butvarieddependingonweatherandseastate.
TheME70wasoperatedinfisherymode,usingacustomconfigurationof31-beamsatfrequencies
from73–117kHz,withbeamopeninganglesfrom2.8°–11.0°,steeredto0°inthealongshipdirection
and−66°to+66°intheathwartshipdirection,withthelowestfrequenciessteeredtothehighest
beamangles,andapulsedurationof1.5ms−1(sensuWeberetal.,2013).TheME70wascalibrated
inThreeSaintsBay,KodiakIsland(153°30′54′′W57°10′30′′N),priortothestartofthesummer2011
survey,usingthestandardspherecalibrationmethod(Footeetal.,1987).
ME70datawerecollectedduringthesurveysalongparalleltransectsspaced2–20nmiapartover
20–500mbottomdepths.Fine-scalemultibeamsurveyswith100%bottomcoveragewereconducted
duringeveninghoursofthesummer2011surveytotargetbothtrawlableanduntrawlableseafloor
types.AnApplanixPOS/MVV4systemoutputdynamicmotionandpositiondatadirectlytotheME70
tocompensatethebeamdirectionsforpitchandrolloftheshipandtogeoreferencethemultibeam
data.AC-NavMBX-4systemapplieddifferentialcorrectiondatatothePOS/MVtoimproveposition
accuracy.Expendablebathythermograph(XBT)profileswereconductedapproximatelyevery3–6h
during the survey and conductivity temperature and depth (CTD) sensor profiles were conducted
nightlyatfine-scalesurveylocationstomeasuresoundspeedthroughthewatercolumnandcorrect
fortheeffectofsoundrefraction.Conductivityandtemperaturewerealsocontinuouslymeasured
nearthetransducerbytheME70system.
2.2. Camerastations
Videoimagerywascollectedatcamerastationstocharacterizetheseafloorastrawlableorun-
trawlableforcomparisonwiththeME70data.Camerastationsweresampledduringeveninghoursof
thesummer2011surveywithinthefine-scalemultibeamsurveyareas.Camerastationswerelocated
inapatternthatsampledacentrallocationandthentargetedtheremainingfine-scalesurveyarea
equally.Onecameradeploymentwasconductedateachcamerastation.Atleastoneandasmanyas
fivecamerastationsweresampledduringeachfine-scalesurvey.
Videoimagerywascollectedatcamerastationsusingoneoftwocamerasystems,includingasingle
camera(DC)andstereocamera(SDC)system.TheDCwasfittedwithonedigitalvideorecorderand
twolightsplacedabovethecamerahousingforillumination.TheSDChadtwo-SonyTRD-900progres-
sivescancamcorderswith1280×720pixelresolution(SonyCorp.,Tokyo,Japan)andtwolightsplaced
abovethecamerahousings(Williamsetal.,2010).Bothsystemsweremountedinaluminumcages.
EachDCdeploymentviastandardshipboardwinchincluded5minoftotalbottomtimeprovidinga
visualassessmentoftheseafloor.EachSDCdeploymentincluded30mintotalbottomtime.Unlikethe
DC,theSDCusedareal-timevideofeedandadedicatedwinchwhichallowedverticalmaneuverability
inthewatercolumnandoverthebenthicterrain.Thissystemwasoptimalforgreaterspatialcoverage
andwasusedwhenmoreshiptimewasavailable.
Seafloor substrate was classified from video, using a two-code system with the following
> .
categories: rock with vertical relief (R), flat bedrock (F), boulder (B; 255 cm), cobble (C;
6.5–25.5cm),pebble(P;2–6.5cm),gravel(G;2–4mm),sand(S;grainsdistinguishable),andmud
22 J.L.Pirtleetal./MethodsinOceanography12(2015)18–35
(M)(Wentworth,1922;Steinetal.,1992).Grainsizewasestimatedfromatetheredweight(28cm
diameter)inthefieldofview(DC),ordirectlyfromthehorizontalfieldofview(2.4m;SDC).The
firstcodewiththissystemrepresented50%–80%ofthesubstratecomposition,andthesecondcode
represented20%–50%(e.g.,MBisatleast50%mudandatleast20%boulder,andBBis≥80%boulder).
Anewcodewasassignedwhenachangeinsubstratewasencounteredforanarealastinggreaterthan
10sofvideoduration.
The seafloor substrate and overall benthic terrain at camera stations was further classified as
trawlableoruntrawlable.AstandardPoly-Nor’eastern4-seambottom-trawlisusedintheGOABT
survey(Stauffer,2004).Untrawlableareasforthisstudyweredefinedasanysubstratecontaining
> .
boulders( 255cmatthegreatestdimension)reachinghigherthan20cmoffbottom,orbedrock
withverticalreliefand–orruggednessthatwouldlikelypreventthebottom-trawlfrompassingover
itwithoutdamagefromseafloorcontact.
2.3. Haullocations
HistoricBTsurveyhaullocationsweresampledwiththeME70wheneverpossibleduringtheAT
surveys.Becausethebottom-trawlhaullocationsoccurredinareasthatwereassumedtobetrawlable
by the BT survey, the haul locations represent only a subset of the seafloor types in the GOA. In
contrast,thecamerastationssampledduringthemultibeamsurveycapturedavarietyoftrawlable
anduntrawlableseafloortypesthatwerecharacterizedfromvideo.Duetothisdifference,thehaul
locationsandcamerastationswerecomparedtotheME70dataasseparatedatasets.
Haullocationsampleswereconstrainedtosurveyyears1996–2011,duetopositionalprecision
andreliabletrawlgeardamagereporting.HaulperformanceischaracterizedbyRACEassuccessful,
marginally-successful(hereaftermarginal),orfailed,basedonthelevelofgeardamagesustainedfrom
seafloorcontact.Successfulhaulsdonotsustaingeardamageandprovideanacceptablecatchsample.
Marginalhaulsarethosewithsomegeardamage,butwherethedamagewasjudgedtonotaffectthe
catch.Failedhaulshaveextensivegeardamage,orthedamagewaslocatedinanareaofthenetwhere
thecatchwasjudgedtobeaffected.Bottom-trawlhaulsusedinouranalysisincurredseveraltypes
offailureresultingingear-damage,includinggearhang-ups(i.e.,gearwassnaggedorentangledon
theseafloor),havingtohaul-backearlyduetohangs,majorhangsthatstoppedtheforwardprogress
ofthevessel,andontwooccasionsthenetwasdestroyed.Successfulhaulswereassumedtooccur
inareasoftrawlableseafloorandfailedhaulsinareasofuntrawlableseafloor.Marginalhaulswere
pooledwithfailedhaulsfortheanalysisbecauseallhaulswithgeardamagewereassumedtooccur
inlocationswithuntrawlablefeatures.
2.4. Multibeamdata
2.4.1. Multibeamdataprocessing
TheSimradME70datacollectionandprocessingworkflowforthisstudyisshowninFig.S1(see
AppendixA).RawacousticdatafilesweregeneratedbytheME70workstationduringdatacollection.
BathymetryandS datawereextractedfromtherawME70filesusingalgorithmsthatperformbottom
b
detectionsandapplyuncertaintycorrections(sensuWeberetal.,2013).Thebottomdetectionswere
then used to calculate angle-dependent S metrics for data analysis, or written to Generic Sensor
b
Format(GSF)filesforfurtherprocessing.
2.4.2. Angle-dependentS
b
Threedifferentangle-dependentS metricswerederivedfromtheseafloorbackscatterstrength.
b
These were based on the predicted S at incidence angles (Jackson and Richardson, 2007). These
b
metricswerenormal-incidenceS (0°–1°),obliqueincidenceS (30°–50°),andtheslopeoftheangle-
b b
dependentbackscatterwithin10°ofnormalincidence(S -slope).
b
2.4.3. Backscattermosaicsandbathymetrygrids
BottomdetectionsfromtheME70datawereinputasGSFfilesintothehydrographicdatapro-
cessingsoftwareCARISHIPS(version7.2,CARIS).Soundingswerecorrectedforvesseloffsets,sound
J.L.Pirtleetal./MethodsinOceanography12(2015)18–35 23
refraction,andtides,andmergedtogeneratea6m2resolutionsurface,usingtheCombinedUncer-
taintyandBathymetricEstimatoralgorithms(CUBE).Cursorycleaningwasconductedtoremoveobvi-
oussoundingerrorsaffectingthesurface.ProcessedGSFfileswereexportedtoFledermausGeocoder
Tools(FMGT)andDMagictogeneratebackscattermosaics(Sb,dB)andbathymetrygrids(m-depth),
eachat6m2resolution(version4.0,IVS).
2.4.4. Benthicterrainanalysis
Benthicterrainmetricswerederivedfrombathymetrygrids(6m2)inArcGIS(version10.1,ESRI).
CalculationsforseafloorslopeandcurvaturewereperformedusingDEMSurfaceTools(Jenness,2013).
SeafloorruggednessandBPIwerecalculatedusingfeaturesoftheBenthicTerrainModeler(Wright
etal.,2012).Seafloorslope,curvature,andruggednesswerecalculatedfromamovinganalysiswin-
dowofa3×3arrayofgridcells,whichusesthebathymetryofeachcelland8surroundingneighbors.
Ruggednesswasalsocalculatedusing11and21cellarrays.Thesespatialscaleswereselectedbased
onthescaleofterrainfeaturesderivedbythesemetricsthatarelikelyencounteredbyabottom-trawl
haulpath(16mwidth).Slopewascalculatedalongeast–westandnorth–southgradients,usingHorn’s
directionalmethod(Horn,1981).Twomeasuresofcurvaturewerecalculated.Profilecurvatureisthe
curvatureofthesurfaceinthemaximumdirectionofslope,andisusefultohighlightconcaveandcon-
vexslopes.Plancurvatureisthecurvatureperpendiculartothemaximumdirectionofslope,andis
usefultodefineslopingterrainalongfeatures(Evans,1980;Schmidtetal.,2003).Seafloorruggedness
wascalculatedusingavectorruggednessmeasure(VRM)thatquantifiesthe3-dimensionaldisper-
sionofvectorsorthogonaltotheplanarterrainsurfaceandaccountsforvariabilityinbothslopeand
aspect(Sappingtonetal.,2007).BPIcomparesthebathymetricpositionofagridcelltothemean
ofneighboringcellsinradialdirections(Guisanetal.,1999;Weiss,2001).BPIwascalculatedfrom
neighborhoodswitharadiusof20,50,100,and200cells.
2.5. Dataanalysis
2.5.1. Extractmultibeammetrics
Twosetsofsamplelocationswereusedforthisanalysis.Theseincludedthecamerastationswithin
theME70fine-scalesurveyareasandBTsurveytrawlhaullocations.Multibeam-derivedseafloormet-
rics were overlaid with the location of camera stations and haul path locations to extract metrics
thatcouldpotentiallydiscriminatebetweentrawlableanduntrawlableseafloortypesandtogener-
atemodelsandprobabilitymapsofpredictedseafloortrawlability.Multibeammetricswereextracted
fromanareawithin20mofthecamerastations.Multibeammetricswereextractedathaullocations
fortheareaofoverlapbetweentheME70swathandbottom-trawlpathwherethenetwasincontact
withtheseafloor,takingintoaccountthedistanceofwireout,trawlwarplength,trawlwidth(16m),
andthepositionaluncertaintyofthesurveyvessel.A28mbufferaroundthehaulpathswasappliedto
additionallyincludeonehalfofthetrawlnetwidth(8m).Haulpathstargetedduringthemultibeam
surveyweresampledwith100%coverage.Occasionally,onlytheedgeorasectionofahaulpathwas
sampledbecauseoftheopportunisticnatureofME70operationsduringtheATsurveys.Multibeam
surveycoverageofindividualhaulpathlocationswasdeterminedbasedontheproportionofthehaul
pathareasampledbythemultibeamswath.Analysiswaslimitedtohaullocationswheretheregion
>
ofoverlapbetweenthemultibeamdataandbottom-trawlhaulpathlocationwas 10%ofthehaul
patharea.
The multibeam data extracted for analysis with the camera and haul data sets included angle-
dependentS metrics,backscatterandbathymetrymetricsaswellastheirassociatedmeanandstan-
b
darddeviation(SD).ThesecalculationswereperformedforS metrics(dB)byfirstconvertingthose
b
values to intensities. Metrics used in data analysis are listed in Table 1. To determine whether a
multibeam-derivedmetriccoulddiscriminatebetweentrawlableanduntrawlableareas,themetric
valuesofcamerastationsandhaullocationsthatwerecharacterizedastrawlableoruntrawlablewere
firstcomparedusinga2-samplet-test(α =0.05).
24 J.L.Pirtleetal./MethodsinOceanography12(2015)18–35
Table1
SeafloormetricsderivedfromdatacollectedwiththeME70MBESduringcruisesin2011
and2012aboardtheNOAAshipOscarDysonintheGulfofAlaska.Metricsderivedfrom
theangle-dependentseafloorbackscatterstrength(Sb)werenormal-incidenceseafloorSb,
obliqueincidenceSb,andtheslopeoftheangle-dependentbackscatterwithin10°ofnormal
incidence (Sb-slope). The mean mosaic value (mosaic Sb) was derived from backscatter
mosaics.Metricsderivedfromgriddedbathymetrydatawereseafloorslope,plancurvature,
profilecurvature,ruggedness(VRM),andbathymetricpositionindex(BPI).
Multibeammetric Angleofincidenceorscale
Angle-dependentSb Rawdataresolution
NormalincidenceSb 0°–1°
ObliqueincidenceSb 30°–50°
Sb-slope 0°–10°
Backscattermosaic 6m2
Mosaicvalue Swathnormalizedtovaluesfrom30°–50°
Bathymetrygrid 6m2
Slope 3×3cells
Profilecurvature 3×3cells
Plancurvature 3×3cells
VRM 3×3,11×11,21×21cells
BPI 20,50,100,200cellradius
2.5.2. Modelseafloortrawlability
Theabilityofeachmultibeam-derivedmetrictodiscriminatebetweentrawlableanduntrawlable
areaswastestedwithpredictivemodels.Seafloortrawlabilitywasmodeledseparatelyforcamera
stationsandhaullocationswithlogisticregressionusingageneralizedlinearmodel(GLM)Y = ∝
+β X + βX + E and the logit link function p(Y) = exp(Y)/(1 + exp(Y)) for binary response
1 1 i i
data(McCullaghandNelder,1983).GLMcanaccommodatedatathatarenotnormallydistributed
andthemodelresultscanbeimplementedasprobabilitymapsforprediction(Guisanetal.,1999;
Duff,2008).Theresponseforthesemodelswasthetrawlableoruntrawlablecharacterizationforeach
samplelocation.Predictorvariableswerethemultibeam-derivedseafloormetricsatthesampleloca-
tions(Table1).Multibeammetricswerestandardizedpriortoanalysisbysubtractingthemeanand
dividingbytheSDofthemetric.Best-fittingmodelsweredeterminedinaforwardstep-wisefashion
beginningwiththecamerastations.
Modelswithmetricsasindividualpredictorswereexaminedfirst.Commondiagnosticsforlogis-
ticregressionwereevaluated,includingtheproportionofdevianceexplained(D2)andananalysis
ofdeviancetest.AnanalysisofdeviancetestisalikelihoodratiotestforGLMsusedtoevaluatethe
performanceofeachmodelcomparedtoanullmodel(α = 0.05).Themostdiscriminatorymodels
werechosenbasedontheAkaikeinformationcriterion(AIC).ModelswithAICvalueswithin2digits
arenotconsidereddifferentwithregardtothepredictedresponse(BurnhamandAnderson,2002).
Modelswithmorethanonepredictorwereexaminednext,usingcombinationsofthebestindivid-
ualpredictormodels.Multi-collinearityamongmultibeammetricswasexaminedpriortochoosing
metricsformodelswithmorethanonepredictor,sothatcollinearmetrics(|r| > 0.7)werenotin-
cludedinthesamemodel(e.g., Dormannetal.,2013).Predictorswereaddedonebyoneaslongas
themodelfitimproved.Thebestmodelswithmorethanonepredictorwereevaluatedandcompared
totheresultsofthesinglepredictormodels.AllanalysiswasconductedinR(RDevelopmentCore
Team,2013).
2.5.3. Mapseafloortrawlability
PredictedprobabilityofseafloortrawlabilityfortheGOAmultibeamsurveyareawasmappedus-
ingthemodelcoefficientsandmultibeammetricsfromthebestmodel.Amodelprobabilitythreshold
of0.5wasusedtodiscriminatebetweentrawlableanduntrawlableareas.Theabilityofthemodelto
correctlypredictthepresenceoftrawlableanduntrawlableseafloorfeatureswasevaluatedforeither
camerastationsorhaullocations,dependingonwhichdatasetwasusedwiththeMBESsurveydata
todevelopthebestmodel.Thiscross-validationanalysiswasconductedinArcGISbyextractingthe
J.L.Pirtleetal./MethodsinOceanography12(2015)18–35 25
Fig. 1. Distribution of angle-dependent seafloor backscatter strength values (Sb; dB) for the range of incidence angles
(−55°–+55°)sampledbythemultibeamswathatcamerastationsclassifiedastrawlableoruntrawlable.Camerastations
(n=47)weresampledduringthe2011ME70MBESsurveyaboardtheNOAAshipOscarDysonintheGOA.
modelvaluesofpredictedseafloortrawlabilityfromtheregionofoverlapwithsamplelocationsthat
wereclassifiedastrawlableoruntrawlable.
3. Results
Atotalof47camerastationsweresampledatfine-scaleME70surveylocationsduringthe2011AT
survey.Trawlableseafloorwasidentifiedat27camerastationsanduntrawlableseafloorat20stations
(TableS1;seeAppendixA).
Seafloorsubstrateattrawlablecamerastationsincludedpoorlysorted,fine-grainsediments,which
weredifficulttodistinguishfromoneanotherinthevideodata(e.g.,mixedsandandmudwithsilt).
Othersubstratetypesatthetrawlablecamerastationsweremorereadilydiscernibleandincluded
areasoffine-grainsedimentswithscatteredgravel,pebbles,cobbles,andtheoccasionalembedded
smallboulder,orcontinuousareasofgravelandpebbleswithscatteredcobblesofvariedsize.Un-
trawlablestationsincludedareasoffine-grainsedimentwithbouldersandrockoutcrops,areasof
continuousgravel,pebbles,andcobblewithisolatedbouldersandrock,orareasofcontinuousboul-
derandrock.
ThetotalareamappedwiththeME70duringtheATsurveyswas5987km2,whichcorresponded
withthelocationof450historicbottom-trawlhaulpathsacrosstheBTsurveyarea.Theseincluded
373 successful hauls and 77 marginal and failed hauls. The range of spatial overlap between the
multibeamdata(swath)andindividualhaullocationswas10%–100%withmeanoverlapof55%(±26%
SD). Examining the distribution of seafloor scattering strength data at the range of angles across
the multibeam swath demonstrated that areas of trawlable seafloor generally exhibited lower S
b
valuesthanareaswithuntrawlableseafloor.Areasoftrawlableseaflooratcamerastationsgenerally
had lower S values at all incidence angles than untrawlable seafloor (Fig. 1). The BT survey haul
b
performancecategories,successful,marginal,andfailed,showseparationinS valuesacrossincidence
b
anglesathaullocations,withsuccessfulhaullocationshavinglowervaluesoverall(Fig.2).Marginal
andfailedhaullocationsgroupedtogetherintherangeofhigherS valuesacrossthemultibeamswath.
b
MeanS valuesatmarginalandfailedhaullocationswerenotsignificantlydifferent.
b
3.1. Multibeammetricsandcamerastations
Angle-dependentS metricvalues(Table1)forcamerastationsclassifiedastrawlableweresig-
b
nificantlylowerthanthoseclassifiedasuntrawlable(Fig.3).Similarresultswereobtainedforvalues
26 J.L.Pirtleetal./MethodsinOceanography12(2015)18–35
Fig. 2. Distribution of angle-dependent seafloor backscatter strength values (Sb; dB) for the range of incidence angles
(−55°–+55°)sampledbythemultibeamswathathaullocationswheretrawlperformancewasclassifiedassuccessful,
marginal,orfailed.Haullocationssampled(n=450)wereconductedduringthesummer1996–2011RACEBTsurveys.
Fig.3. Multibeam-derivedseafloormetrics(mean,+SD)comparedbetweentrawlableanduntrawlablecamerastations
(t-test,α = 0.05).Metricsderivedfromtheangle-dependentseafloorbackscatterstrength(Sb)arenormal-incidenceSb,
obliqueincidenceSb,andSb-slope.MosaicSb isderivedfrombackscattermosaics(6m2).Metricsderivedfromgridded
bathymetrydata(6m2)areseafloorslope,profilecurvature,andplancurvature,calculatedfrom3×3cellarrays.Seafloor
ruggedness(VRM)iscalculatedfrom3×3,11×11,and21×21cells,andbathymetricpositionindex(BPI)iscalculated
from20,50,100,and200cellradiusanalysiswindows.Significancelevelsareasfollows:*(p < 0.05),**(p < 0.001),***
(p<0.0001).
derivedfrombackscattermosaics.MosaicS valuesweresignificantlylowerattrawlablecamerasta-
b
tions.
Differencesweredetectablebetweentrawlableanduntrawlablecamerastationsforonlysomeof
thebenthicterrainmetricsderivedfromthebathymetrygrids(Fig.3).AllBPIvaluesweresignifi-
cantlylessattrawlablecamerastations.Measuresofseafloorslopeandcurvaturedidnotdistinguish
betweentrawlableanduntrawlablestations.Differenceswerealsonotdetectablebetweentrawlable
J.L.Pirtleetal./MethodsinOceanography12(2015)18–35 27
Fig.4. Multibeam-derivedseafloormetrics(mean,+SD)comparedbetweentrawlableanduntrawlablehistoricbottom-trawl
haullocations(t-test,α = 0.05).Metricsderivedfromtheangle-dependentseafloorbackscatterstrength(Sb)arenormal-
incidenceSb,obliqueincidenceSb,andSb-slope.MosaicSbisderivedfrombackscattermosaics(6m2).Metricsderivedfrom
griddedbathymetrydata(6m2)areseafloorruggedness(VRM)calculatedfroma21×21cellarray,andbathymetricposition
index(BPI)calculatedfromananalysiswindowof200cellradius.Significancelevelsareasfollows:*(p<0.05),**(p<0.001),
***(p<0.0001).
anduntrawlablestationsforseafloorruggednesscalculatedfrom3×3and11×11cellarrays.How-
ever,broad-scaleruggedness(VRM21×21cells)wassignificantlylowerattrawlablestations.
There was a significant difference between oblique incidence S and mosaic S normalized to
b b
< .
obliqueincidenceangles(p 00001),demonstratingthatthemeansofthesemetricsweredifferent.
Thesemetricswerecorrelatedforcamerastations(r =0.78).
3.2. Multibeammetricsandhaullocations
Allangle-dependentS metricvaluesforbottom-trawlhaullocationsclassifiedastrawlablewere
b
significantlylessthanvaluesatuntrawlablelocations(Fig.4).TheseincludednormalincidenceS ,
b
obliqueincidenceS ,andS slopenearnormalincidence.MosaicS valuesattrawlablehaullocations
b b b
werealsosignificantlylowerthanuntrawlablehaullocations.
Onlybenthicterrainmetricsthatcoulddiscriminatebetweentrawlableanduntrawlablecamera
stations were evaluated in the analysis for haul locations, including VRM and BPI at one derived
spatialscale.Althoughthesemetricswerediscriminatoryatcamerastations,whichsampledabroader
rangeofseafloortypesthantheBThaullocations,theydidnotdiscriminatebetweentrawlableand
untrawlablehaullocations(Fig.4).
There was a significant difference between oblique incidence S and mosaic S normalized to
b b
< .
obliqueincidenceangles(p 00001),demonstratingthatthemeansofthesemetricsweredifferent.
Thesemetricswerecorrelatedforhaullocations(r =0.95).
3.3. Trawlabilitymodels
ThemostdiscriminatorysinglemetricmodelsforcamerastationsbasedonAICvaluesweremosaic
S (AIC = 37.2,D2 = 0.38)andobliqueincidenceS (AIC = 42.5,D2 = 0.28).Thebestmodelsfor
b b
camerastationswithmorethanonepredictorweremodelsthatincludedbroad-scalebathymetric
positionindex(BPI200cells)andseafloorruggedness(VRM21cells)withobliqueincidenceS (AIC=
b