Establishing Functional Relationships between Abiotic Environment, Macrophyte Coverage

19 Pages·2015·2.1 MB·English

RESEARCHARTICLE
Establishing Functional Relationships
between Abiotic Environment, Macrophyte
Coverage, Resource Gradients and the
Mytilus trossulus
Distribution of in a Brackish
Non-Tidal Environment
JonneKotta1*,KatarinaOganjan1,VeldaLauringson1,MerliPärnoja1,AntsKaasik2,
LiisaRohtla1,3,IlmarKotta1,HelenOrav-Kotta1
1 UniversityofTartu,EstonianMarineInstitute,DepartmentofMarineBiology,Mäealuse14,12618Tallinn,
Estonia,2 UniversityofTartu,InstituteofEcologyandEarthSciences,ChairofZoology,Vanemuise46,
51014,Tartu,Estonia,3 NovaSoutheasternUniversity,HalmosCollegeofNaturalSciencesand
Oceanography,8000NorthOceanDrive,DaniaBeach,Florida,UnitedStatesofAmerica
* [email protected]
OPENACCESS
Citation:KottaJ,OganjanK,LauringsonV,Pärnoja
Abstract
M,KaasikA,RohtlaL,etal.(2015)Establishing
FunctionalRelationshipsbetweenAbiotic
Environment,MacrophyteCoverage,Resource Benthicsuspensionfeedingmusselsareanimportantfunctionalguildincoastalandestua-
GradientsandtheDistributionofMytilustrossulusin
rineecosystems.Todatewelackinformationonhowvariousenvironmentalgradientsand
aBrackishNon-TidalEnvironment.PLoSONE10(8):
bioticinteractionsseparatelyandinteractivelyshapethedistributionpatternsofmussels
e0136949.doi:10.1371/journal.pone.0136949
innon-tidalenvironments.Opposingtotidalenvironments,musselsinhabitsolelysubtidal
Editor:FrankMelzner,GEOMARHelmholtzCentre
zoneinnon-tidalwaterbodiesand,thereby,drivingfactorsformusselpopulationsare
forOceanResearchKiel,GERMANY
expectedtodifferfromthetidalareas.Inthepresentstudy,weusedtheboostedregression
Received:March26,2015
treemodelling(BRT),anensemblemethodforstatisticaltechniquesandmachinelearning,
Accepted:August10,2015
inordertoexplainthedistributionandbiomassofthesuspensionfeedingmusselMytilus
Published:August28,2015 trossulusinthenon-tidalBalticSea.BRTmodelssuggestedthat(1)distributionpatternsof
Copyright:©2015Kottaetal.Thisisanopen M.trossulusarelargelydrivenbyseparateeffectsofdirectenvironmentalgradientsand
accessarticledistributedunderthetermsofthe partlybyinteractiveeffectsofresourcegradientswithdirectenvironmentalgradients.(2)
CreativeCommonsAttributionLicense,whichpermits
Withinitssuitablehabitatrange,however,resourcegradientshadanimportantroleinshap-
unrestricteduse,distribution,andreproductioninany
ingthebiomassdistributionofM.trossulus.(3)Contrarytotidalareas,musselswerenot
medium,providedtheoriginalauthorandsourceare
credited. competitivelysuperiorovermacrophyteswithpatternsindicatingeitherfacilitativeinterac-
tionsbetweenmusselsandmacrophytesorco-varianceduetocommonstressor.Tocon-
DataAvailabilityStatement:Allrelevantdataare
withinthepaperanddownloadableathttp://loch.ness. clude,directenvironmentalgradientsseemtodefinethedistributionpatternofM.trossulus,
sea.ee/gisservices2/LiikideInfoportaal/index.html. andwithinthefavourabledistributionrange,resourcegradientsininteractionwithdirect
Funding:Fundingforthisresearchwasprovidedby environmentalgradientsareexpectedtosetthebiomasslevelofmussels.
InstitutionalresearchfundingIUT02-20ofthe
EstonianResearchCouncil.Thestudyhasbeenalso
supportedbytheEstonianScienceFoundationgrant
8807,theproject"Thestatusofmarinebiodiversity
anditspotentialfuturesintheEstoniancoastalsea"
No3.2.0801.11-0029ofEnvironmentalprotectionand
technologyprogramoftheEuropeanRegionalFund.
PLOSONE|DOI:10.1371/journal.pone.0136949 August28,2015 1/19
FactorsContributingtotheSpatialPatternsofMytilustrossulus
TheprojecthasreceivedfundingfromtheBONUS Introduction
projectBIO-C3,fundedjointlyfromtheEuropean
Union’sSeventhProgrammeforresearch, Akeymissioninecologyistounderstandbioticpatternsandtheirchangesinnature.Inorder
technologicaldevelopmentanddemonstrationand toachievesuchanunderstandinginthemarinerealm,ecologistshaveinitiatedamultitudeof
fromtheEstonianResearchCouncil.Thefunders projectsaimingtomapmarinebiotaorperformedexperimentstodemonstrateinteractions
hadnoroleinstudydesign,datacollectionand betweenphysicalenvironmentandorganisms.However,asdirectmappingofbiotais
analysis,decisiontopublish,orpreparationofthe
extremelycostlyinmarineenvironment,modellinghasbecomeanunavoidabletool,andsev-
manuscript.
eralrefinedstatisticalapproacheshavebeenalreadyappliedinthefield[1]
CompetingInterests:Theauthorshavedeclared Distributionpatternsofspeciesdependontheirecologicalniche,whichconsistsofamulti-
thatnocompetinginterestsexist.
dimensionalenvironmentalspace.Ingeneral,non-independenteffectsarecommoninnature
[2,3]and,therefore,neitherthespeciesnichenortheresultingdistributionrangecanbepre-
dictedfromseparateeffectsofanindividualenvironmentalvariable.Asuitablehabitatisoften
definedbycomplexinterrelationshipsamongamultitudeofenvironmentalvariablesthatcan
belargelydividedintothreebroadcategories[4].Theseincludeindirectenvironmentalgradi-
ents,resourcegradientsanddirectenvironmentalgradients.Indirectenvironmentalgradients
canoftenbeeasilymeasured,butrepresentonlyproxiesforasetofunderlyinggradients,
whichaffectorganismsdirectlywhileitmaybedifficulttomeasureordisentangletheeffectsof
theseunderlyinggradients[5,6].Waterdepthcanbeviewedasatypicalindirectenvironmental
gradientinthemarinerealm.Resourcegradientsaresubstancesbeingconsumedanddirect
environmentalgradientsrepresentfeaturesthathavedirectphysiologicalimpactongrowth
butarenotconsumed.Thepicturegetsmorecomplicatedasthesamefactormayhavean
impactsimultaneouslyviadifferentpathways.Forexample,watermovementcanindirectly
affectthehabitatofsuspensionfeedingbivalvesbymodifyingsedimentationratesoraffectses-
silemusselsdirectlybyphysicallydisturbingordetachinganimals[1,7].Furthermore,theben-
thicsuspensionfeedingmodeandsedentarylifestyleofmusselsprescribeanintrinsicneedfor
avectoroffooddelivery.Thereby,watermovementcanimpactbenthicsuspensionfeedersalso
throughathirdpathway,bymodifyingtheresourcesupplywhilelimitingtheamountoffood
reachedbymussels[8,9].
Althoughthenicheconceptintroducedresourceaxesandtheimportanceofcompetition
[10],mostoftheecologicalnichemodelinghasbeendealingwithabioticfactorsonly,without
consideringinterspecificinteractionsandresources[11–13].Therefore,itislargelyunknown
howbioticenvironmentinteractswithHutchinsonianfundamentalnichespaceinstructuring
realcommunities[14].Therealizednicheofaspecies,however,dependslargelyonbioticinter-
actionswithotherspecies[15–17].Thereby,herewedistinguishbesidesdirect/indirectenvi-
ronmentalandresourcegradientsalsothefourthtypeofgradients,namelytheabundancesof
ambientspeciesororganismgroupsotherthandirectresources,buteithercompetitors,preda-
torsorfacilitators.Werefertothisgradienttypeasbioticinteractiongradients.
Benthicsuspensionfeedingmusselsareanimportantfunctionalguildincoastalandestua-
rineecosystems.Thisfunctionalguildfeedsonsuspendedfood,usuallymicroalgae,frombot-
tom-reachingwatermasses[18].Despitealargebodyoffieldandexperimentalworks[19,20]
westilllackknowledgeonhowvariousenvironmentalgradientsseparatelyandinteractively
shapethedistributionpatternsofsuspensionfeedingmusselsindifferentecosystems.Therea-
sonforthisis,firstly,becausethedistributionofsuspensionfeedersiscontrolledbyalarge
numberofprocessesinvolvingbothbenthicandpelagicenvironments(e.g.substratecoloniza-
tion,watermovement,phytoplanktonproduction,physicaldisturbances)aswellasmany
interactionsbetweentheseprocesses[1,21].Secondly,duetothiscomplexityindrivingforces,
thedirectionandmagnitudeofenvironmentalimpactonmusselsisexpectedtovaryhighly
amongdifferentecosystems[22,23].Todate,thedistributionpatternsofmusselshavebeen
extensivelystudiedintidalhabitats[19,24–26]whereasstudiesonnontidalareasarestill
PLOSONE|DOI:10.1371/journal.pone.0136949 August28,2015 2/19
FactorsContributingtotheSpatialPatternsofMytilustrossulus
scarce.Contrastingtotidalareas,musselsinhabitsolelysubtidalzoneinnontidalwaterbodies
[27]anddrivingfactorsformusselpopulationsareexpectedtodifferfromthetidalareas[23].
Inthetidalzonespeciesareconstantlychallengedbyfluctuatingenvironmentalconditionsand
thebioticpatternsoftenreflectthestresstoleranceofspecies[28–30].Ontheotherhand,subti-
dalareasofferspeciessomestability;thus,thedistributionpatternsofmusselsareexpectedto
beshapedprimarilybyhabitatandfoodavailabilityaswellaspredationpressure[31].
Therisinginterestinmarinehabitatmappinghasresultedinnumerousmodellingstudies
focussedonthedistributionofspecies[32–35].However,traditionalstatisticalmodellingmay
notbethemostrewardingwaytounderstandenvironmental-speciesrelationships,asitstarts
byassuminganappropriatedatamodel,andmodelparametersarethenestimatedfromthe
data[36].Duetothelackofasolidknowledgeonhowtheexternalenvironmentimpactsthe
speciesthatwearetryingtomodel,thepredictiveperformanceofthesemodelsisexpectedto
bemoderate.Ontheotherhand,duetotimeconstraintsandlimitedmanpower,experimental
studiescannotresolvecausalconnectionsbeyondoneortwoenvironmentalvariables.More-
over,experimentsareonlyseldomreplicatedinspaceandtime.Asaconsequence,theexperi-
mentalapproachcanprovideusaverylocalizedsnapshot,butnotagenericunderstandingon
environment-biotarelationships.
Machinelearningprovidesatheoreticalframeworkthatmovesbeyondtraditionalparadigm
boundaries.Considering„complexrealism”andourweaktheoreticalfoundations,modellingis
seenhereasasophisticatedtooltoimproveourunderstandingontherelationshipbetween
environmentandbiota.Bycontrasttotraditionalmethods,machinelearningavoidsstarting
withadatamodelandratherusesanalgorithmtolearntherelationshipbetweentheresponse
anditspredictors[37].Butevenheresomeecologicalunderstandingisaprerequisitewhenit
comestoselectingenvironmentalvariablesforthemodel.Specifically,inordertosucceedin
identifyingandquantifyingrelationshipsbetweentheenvironmentandbiota,themodel
shouldincorporateatleastthemostimportantdirectandresourcegradientsaswellasrecap-
turemultitudeofinteractionsbetweentheseenvironmentalgradientsandbiota.Thenovelpre-
dictivemodellingtechniquecalledBoostedRegressionTrees(BRT)combinesthestrengthsof
machinelearningandstatisticalmodelling.BRThasnoneedforpriordatatransformationor
eliminationofoutliersandcanfitcomplexnonlinearrelationships.TheBRTalsoavoidsover-
fittingthedata,therebyprovidingrobustestimates.Whatisthemostimportantintheecologi-
calperspective:itautomaticallydetectsandmodelsinteractiveeffectsbetweenpredictors.The
methodcopeswithdifferentnon-linearrelationshipsincludingthresholdsandunimodal
responseswhicharecommoninecologicaldatabutdifficulttoanalyseusingmoretraditional
methods.Duetoitsstrongpredictiveperformance,BRTisincreasinglyusedinecology
[38,39].Although,weadmitthattheresultsofdistributionmodellingarepurelycorrelative
andcausalinterpretationsneedtobevalidatedbyfutureexperimentalmanipulations,machine
learningalgorithmsenableapowerfulinitialinsighttothekeydriversaswellastotheinterac-
tionsbetweentheenvironmentandthebiota.
Bluemusselsconsistofagroupofthreecloselyrelatedtaxa,knownastheMytilusedulis
complex.ThecommonmusselMytilusedulisinsensustrictoisnativetotheNorthAtlantic,
theMediterraneanmusselMytilusgalloprovincialisisnativeintheMediterranean,theBlack
SeaandWesternEuropeandthebaymusselMytilustrossulusisnativetoNorthPacific,north-
ernpartsoftheNorthAtlanticandBalticSea.Thetaxacanhybridisewitheachother,ifpresent
atthesamelocality.M.trossulusinhabitsbothsubtidalaswellasintertidalareas,toleratesa
widerangeofenvironmentalconditionsandthereforegainshighbiomassesatdifferenthabitat
types[40].Thismakesthespeciesagoodmodelorganismtoimproveourunderstandingon
therolesofmultipleenvironmentalgradientsonthedistributionofbenthicsuspensionfeeders.
InthebrackishnontidalBalticSea,M.trossulusisanimportantorganisminvarioushardand
PLOSONE|DOI:10.1371/journal.pone.0136949 August28,2015 3/19
FactorsContributingtotheSpatialPatternsofMytilustrossulus
mixedbottomsubtidalhabitats.HereM.trossuluscoexistswithM.edulis,butasakeyecologi-
caldifferentiationM.trossulustolerateslowersalinitycomparedtoM.edulisandtherebydis-
tributesalmostthewholerangeoftheBalticSea[41,42].However,therearenopureM.
trossulusintheBalticSeawithallmytilidsbeinghybrids,withvaryingfractionsofM.edulis
allelesintheirgenomes[43].
Inthepresentstudy,weaimedtodescribetherealizednicheofthemusselM.trossulusin
thenortheasternBalticSea,bothintermsofdistributionandthesizeofpopulations.Weused
theBRTmodelling(1)toquantifytherelativecontributionofresource,abioticenvironmental
andbioticinteractiongradientsonthedistributionofM.trossulusintheBalticSea(2)Wealso
soughthowtheavailabilityofresourcesaffectsthestandingstockofspeciesand(3)howbiotic
interactionsanddifferentdirectenvironmentalgradientsincludingkeydisturbanceseither
separatelyorinteractivelymodulatetheresource-biomassrelationship.
Weexpectedthatattheregionalscale,salinityisconsideredasthemainfactordrivingthe
distributionofMytilustrossulus[42].Locally,however,alargearrayofenvironmentalvariables
suchassubstratetype,watertemperature,flowvelocity,winter-timeicescour,areexpectedto
eitherseparatelyorinteractivelyshapethedistributionpatternofmussels[7,44–47].Wealso
expectedthatwithinafavourablehabitat,theavailabilityoffoodresourcesdefinesthebiomass
ofspecies[47].Nevertheless,resourcegradientsinthisspacemayinteractwithdirectenviron-
mentalgradients,whichactasvalvesregulatingtheavailabilityofresources.Asbenthicsuspen-
sionfeederslinktwospatiallydistinctsystems,specificabioticenvironmentalconditionsmay
beofutmostimportanceforthemindeterminingtheamountofresourcetobereceived[47–
49].Inaddition,disturbancemayreduceorultimatelyevendisruptthelinkbetweenresource
parametersandthedistributionofspecies[50].Thismayexplainwhysomehighlytrophic
areaswithe.g.sufficientamountofhardbottomandsuitablesalinitylackdensemusselpopula-
tions[51].Finally,weexpectthattheinterspecificcompetitionbetweenmusselsandother
biotaismoderate,rarelyoutperformingtheeffectsofabioticenvironmentaldisturbances[44].
Itisexpectedthatmacroalgaecompetewithmusselsforsubstrate,although,thishasnotbeen
experimentallydemonstratedintheBalticSea.Instead,musselsareknowntofacilitatethe
growthofmacroalgae[52]and,thus,mutualisticinteractionsbetweenmusselsandmacroalgae
(e.g.dampeningdifferenttypesofdisturbances,intensifyingturbulentflowsatthebottom-
waterinterface)mayactuallyoutweighapotentialreductioninadvectionbycanopymacroal-
gae[53].Ascomparedtotheoceanicwaters,theBalticSealacksthemajorepibenthicpredators
andthereforethepredationpressureonmusselsisalsolow[27,54].Predationbyvertebratesin
thestudyareaisrare,decliningandhardlydetectable,therefore,wedecidednottoincludepre-
dationtothedistributionmodel[55].
Methods
1.Studyarea
ThestudyarealiesinthenortheasternBalticSea,intheEstoniancoastalsea(Fig1).Itischar-
acterizedbyfullysubmergedhabitatduetotheabsenceoftides,althoughveryshallowwaters
maybeirregularlyexposedbytheactionofwind.Salinityisconstantlylowandclosetothe
physiologicaltoleranceofmussels.Opposingtomoresalinerangeofthespecies,invertebrate
predationisabsentinthestudyarea[27,54].Thestudyareaencompassesmajorgeomorpho-
logicalstructuresincludingdifferenttypesofsoft,limestoneandgranitebedrock,allowingthus
togeneralizetheobtainedresultsoverlargepartsoftheBalticSea[56,57].Largepartsofthe
studyareaarerelativelyflatandshallow,lackingsteepslopes.Shallowareasmayalsobesub-
jectedtointensewinter-timeicescour.Waveenergyislowerthanonthecoastsoflargeoceans,
butmaystillberemarkableforthebottomfaunaatshallowexposedsites,especiallyduring
PLOSONE|DOI:10.1371/journal.pone.0136949 August28,2015 4/19
FactorsContributingtotheSpatialPatternsofMytilustrossulus
Fig1.Mapofthesamplingstationsinthestudyarea.FilledcirclesindicatethelocationsofM.trossulus.
doi:10.1371/journal.pone.0136949.g001
autumnandwinterstorms.Someareasaresubjectedtolocalupwellingeventsinducedbywind
conditions.Often,angiospermormacroalgalcommunitiesinhabitthesebottomsatdepths
downto20m.ThemusselM.trossulusexhibitsgenerallylowbiomassandsparsedistribution.
Onlyatveryexposedopen-seaareas,thebiomassmayexceed1kgdwm-2[58].
2.Biologicaldata
Altogether3585stationsweresampledwithintheEstonianterritorialwatersduringtheice-
freeseasonsbetween2005and2009.Themajorityofstationsweresampledonlyonce.Within
eachwaterbodyapproximately15stationsweresampledannually.Inordertoestablishthe
samplingstations,agridofrectangularcellswasgeneratedwithcellsizesof300musingthe
SpatialAnalysttoolofArcInfo10[59].Thenwecalculatedthevaluesofwaveexposureand
inclinationofcoastalslopesforeachgridcell(seebelow).Theexposureandslopeclasseswere
combinedtotheavailableinformationondepthandbottomsediments(dividedintoclay,silt,
sand,gravel,boulderandrockbottoms)availableinthedatabasesoftheEstonianMarineInsti-
tute.Samplingsiteswerelocatedrandomlyinawaythateachcombinationofexposure,slope,
depthandsedimentclasshadacomparablenumberofsamplingsites(Table1).
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FactorsContributingtotheSpatialPatternsofMytilustrossulus
Table1. MeasuredenvironmentalvariablesintheoverallsamplingareaandintheareawhereM.trossuluswasfound.
Variable Unit Samplingarea Distributionarea
Mean Min Max Mean Min Max
Depth m 11.77 0.10 75 8.87 0.2 47
Exposure m2s-1 229020 5672 968957 277950 5672 968957
Slope ° 0.66 0 13.47 0.79 0 10.56
Icethickness m 0.28 0 0.50 0.26 0 0.48
Temperature °C 12.95 0.03 22.23 12.88 0.03 22.23
Salinity psu 6.26 3.70 8.05 6.66 4.42 7.93
Oxygen mmolm-3 319 0 376 325 0 375
Velocity cms-1 3.75 0 15.26 3.58 0 13.34
Siltclaycover % 13.34 0 100 6.22 0 100
Sandcover % 38.12 0 100 21.96 0 100
Bouldercover % 37.87 0 100 58.15 0 100
Chlorophylla mgm-3 19.54 0.66 45 19.00 0.66 45
Plantcover % 31.65 0 100 44.43 0 100
doi:10.1371/journal.pone.0136949.t001
Ateachsamplingsitethecoverageofdifferentsedimenttypes(rock,boulders,pebbles,
gravel,sand,silt)andmacrophytes(bothmacroalgaeandhigherordervegetation)wasesti-
matedeitherdirectlybydiverorremoteunderwatervideodevice.Theunderwatercamerawas
setatanangleof35°belowhorizontomaximisethefieldofviewandtherangeoftheforward
viewwasabout2minclearwaters.
InadditionateachsamplingsitequantitativesamplesofM.trossuluswerecollectedinthree
replicateseitherbyadiverusingastandardbottomframe(0.04m2)onhardbottoms,orbya
quantitativeEkman-Lenzgrabsampler(0.02m2)onsoftbottoms.Althoughthesampleswere
collectedusingtwodifferentmethodswithdifferentaccuracy,thesetwomethodsarecompara-
bleinthecaseofM.trossuluswithlimitedescapeabilitiesandrelativelyhomogenousseafloor
area.Samplesweresievedatthefieldon0.25mmmeshscreens.Theresidueswerestoredat
−20°Candsubsequentsortingandcountingofspecieswasperformedinthelaboratoryusinga
stereomicroscope.Thedryweightofmusselswasobtainedafterdryingtheindividualswith
shellsat60°Cfor2weeks.
BiomasssamplingandanalysisfollowedtheguidelinesdevelopedfortheHELCOMCOM-
BINEprogramme[60].AccordingtotheProtectionRulesoftheEstoniancoastalwaters,bio-
logicalsamplingdoesnotrequirespecificpermitsorapprovals.Thestudyareaisnotprivately-
ownedandthestudydidnotinvolveendangeredorprotectedspecies.
3.Environmentaldata
Asetofenvironmentalvariableswerechosenfortheanalysesbasedonthetheoreticalassump-
tionsoftheroleofenvironmentonthemusseldistribution(Table1).Thevaluesofwatertem-
perature,salinityandwatervelocitywereobtainedfromtheresultsofhydrodynamicalmodel
calculationsfrom2005−2009.Asannualaverages,minimaandmaximaofthestudiedhydro-
logicalvariableswerehighlyintercorrelated,weusedannualaveragesinthefinalmodels.The
calculationswerebasedontheCOHERENSmodelwhichisaprimitiveequationoceancircula-
0 0
tionmodel.Itwasformulatedwithsphericalcoordinatesona1 ×1 minutehorizontalgrid
and30verticalsigmalayers.Themodelwasforcedwithhourlymeteorologicalfieldsof2mair
temperature,windspeed,windstressvector,cloudcoverandrelativehumidity.Themeteoro-
logicalfieldswereobtainedfromanoperationalatmosphericmodel.Themodelwasvalidated
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FactorsContributingtotheSpatialPatternsofMytilustrossulus
againstwaterlevel,temperature,salinityandwatervelocitymeasurementsfromthestudyarea
[61].
Winter-timeicedisturbanceisthekeydisturbanceformacrophyteandbenthicinvertebrate
communitiesintheBalticSearange[62,63].FinnishMeteorologicalInstituteprovidedice
coveroverthestudyareafortheinvestigatedperiod.Icecoverandthicknesswereproducedon
dailybasisatanominalresolutionof500mandwerebasedonthemostrecentavailableice
chartandsyntheticapertureradar(SAR)image.Theiceregionsintheicechartswereupdated
accordingtoaSARsegmentationandnewiceparametervalueswereassignedtoeachSARseg-
mentbasedontheSARbackscatteringandtheicethicknessrangeatthatlocation.
Theamountofavailablefoodresourcesaffectsthedensitiesofspecies[64].Forsuspension
feeders,thiscouldbetranslatedfromtheamountoforganicsestoninthewater.Waterchloro-
phyllaisagoodproxyforthefoodsupplyofmussels[8].Inthisstudyweusedthesatellitesen-
sorMODISAquaderivedwaterchlorophyllavalues.Thismeasureislimitedtosurfacewaters
only;however,duetointensivemixinginourshallowwaterecosystem,thesatellitederivedval-
uesrepresentwellnear-bottomconditions.Satelliteobservationswererecordedonweekly
basisoverthewholeice-freeperiod.Cloud,landandotherprocessingflagswereidentifiedand
maskedbyNASALevel2OceanColorProcessing.Thespatialresolutionofsatellitedatawas1
km.Erroneouszerochlorophyllavaluesmayoccurduetodifferentproblemsinimagepro-
cessingchain.Theerroneousvalueshavetoberemovedpriortostatisticalanalysis.Asinall
yearroundchlorophyllavaluesonlyveryrarelydropbelow0.2inthestudyarea,weuseda
thresholdof0.1tofilteroutallthesefalsezeroconcentrations.
Anothervariableaffectingmusselsalongdifferentpathwaysisexposuretowaves[1,7].
Exposuredefinesthewaterexchangebothbetweencoastalandopenseaaswellasbetween
watersurfaceandbottomlayers[65].Thus,theinteractionbetweenchlorophyllaandexposure
isexpectedtoindicatethefluxoffoodintothesite[50].Besidesbeingimportantforresource
allocation,waveexposureisalsoadirectvariabletransportinglarvaeandaffectingadults
directly[45].TheSimplifiedWaveModelmethodwasusedtocalculatethewaveexposurefor
meanwindconditionsrepresentedbythetenyearperiodbetween1January1997and31
December2006[66].Anested-gridtechniquewasusedtotakeintoaccountlongdistance
effectsonthelocalwaveexposureregime.Theresultinggridshadaresolutionof25m.Inthe
modellingtheshorelinewasdividedintosuitablecalculationareas,fetchandwaveexposure
gridswerecalculatedandsubsequentlytheseparategridswereintegratedintoaseamless
descriptionofwaveexposurealongthestudyarea.Thismethodresultsinapatternwherethe
fetchvaluesaresmoothedouttothesides,andaroundislandandskerriesinasimilarwaythat
refractionanddiffractionmakewavesdeflectaroundislands.
Althoughdepthistraditionallyregardedamongstthemostimportantparametersdescrib-
ingspatialpatternofmussels[1,48],initiallywedidnotincludedepthinourmodel.Thisis
becausedepthisasurrogateofseveraldirectvariablessuchaslightavailability,temperature,
salinity,pressure,waveaction,icescouringortheircombinations[13].Thus,spatialmodels
thatincorporatedepthasaindependentvariablearedifficulttointerpretduetoamultitudeof
thecause-effectrelationshipsinvolved.Moreover,itislikelythatthedepth-biotarelationship
changeswhenmovingfromonegeographicregiontoanother,orwhenextendingthestudy
areatoincludealargerregion[1].However,forenvironmentalmanagementitmightbestill
appealingtofindagoodapproximationofspatialdistributionasafunctionofasingleandeasy
tomeasureparameteraswaterdepth.Therefore,werunadditionalmodelwhereonlydepth
wasusedasapredictorofthespatialpatternofmussels.
InordertomatchtemporalpatternsrelevanttothelifespanofM.trossulusandtogetridof
potentialnoiseduetotheshort-termvariabilityofenvironmentalvariables,annualaveragesof
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FactorsContributingtotheSpatialPatternsofMytilustrossulus
hydrophysicalvariables,waveexposureandwaterchlorophyllaandawintertimeaverageof
icedisturbancewereusedwhenmodellingthepatternsofmussels.
4.BoostedRegressionTrees(BRT)modelling
PriortomodellingthePearsoncorrelationanalysisbetweenallenvironmentalvariableswas
runinordertoavoidsituationsofincludinghighlycorrelatedvariablesintothemodelling.The
correlationanalysisshowedthatmostofvariableswereonlyweaklyintercorrelatedatr<0.1.
However,moreexposedareaswerealsocharacterizedbyhighersalinity(r=0.60,p<0.001),
lowerchlorophylla(r=-0.59,p<0.001)andlowericecover(r=-0.44,p<0.001).Inaddi-
tion,thecoverageofstoneswasinverselyrelatedtosandcover(r=-0.59,p<0.001).Neverthe-
less,thesevaluesarefarbelowacriticalthresholdwhencollinearitybeginstoseverelydistort
modelestimationandsubsequentprediction[67].
ThecontributionofdifferentenvironmentalvariablesonthedistributionofM.trossulus
wasexploredusingtheBoostedRegressionTreetechnique(BRT).BRTmodelsarecapableof
handlingdifferenttypesofpredictorvariablesandtheirpredictiveperformanceissuperiorto
mosttraditionalmodellingmethods(seee.g.comparisonswithGLM,GAMandmultivariate
adaptiveregressionsplines,[68,69]).Whileoverfittingisoftenseenasaprobleminstatistical
modelling,thisproblemcanbeovercomebyusingindependentdatasets.TheBRTmodelling
iterativelydevelopsalargeensembleofsmallregressiontreesconstructedfromrandomsubsets
ofthedata.Eachsuccessivetreepredictstheresidualsfromtheprevioustreetograduallyboost
thepredictiveperformanceoftheoverallmodel[38].
TheBRTmodellingconsistedofatwo-stageprocess.InthefirstBRTmodelallstudiedenvi-
ronmentalvariables(coverageofdifferentsedimentfractions,icethickness,oxygen,salinity,
slope,watertemperature,waveexposure,velocity,chlorophylla,coverageofmacroalgae)were
regressedtopredictthepresenceofM.trossulus.InthesecondBRTmodelonlythesamples
containingM.trossuluswereusedtopredictthebiomassofM.trossulus.Inaddition,thepres-
enceandbiomassofM.trossuluswereregressedusingonlydepthasasingleindependent
predictor.
InfittingaBRTthelearningrateandthetreecomplexitymustbespecified.Thelearning
ratedeterminesthecontributionofeachsuccessivetreetothefinalmodel,asitproceeds
throughtheiterations.Thetreecomplexityfixeswhetheronlymaineffects(treecomplexity=1)
orinteractionsarealsoincluded(treecomplexity>1).Ultimately,thelearningrateandtree
complexitycombineddeterminethetotalnumberoftreesinthefinalmodel.Followingthe
suggestionsbyElithetal.[38]themodellearningratewaskeptat0.1andtreecomplexityat5
forbothmodels.Itwasalsocheckedthatthefinalmodelshadmorethan1000trees.Neverthe-
less,aselectionofmodelparametershadonlymarginalimpactonmodelperformancewith
optimalmodelsimprovingpredictionslessthan1%.Inordertoavoidpotentialproblemsof
overfitting,unimportantvariablesweredroppedusingasimplifytool.Thistoolisacross-vali-
dationbasedprogramdescribedbyElithandcolleagues[[38],detailsinAppendixS2].In
ordertoeliminatenon-informativevariables,thetoolprogressivelysimplifiesmodel,thenre-
fitsthemodelandsequentiallyrepeatstheprocessuntilsomestoppingcriterionisreached.
Suchsimplificationismostusefulforsmalldatasetswhereredundantpredictorsmaydegrade
performancebyincreasingvariance.Asaconsequence,ourfinalmodelsdidnotincludeany
autocorrelatingvariables.Modelperformancewasevaluatedusingthecrossvalidationstatistics
calculatedduringmodelfitting[37].Thus,whenrunningmodelsarandomselectionof80%of
thedatawasusedfortrainingthemodelandtherestofthedatai.e.20%wasassignedfortest-
ingmodelaccuracy.TheBRTmodellingwasdoneinthestatisticalsoftwareRusingthegbm
package[70].
PLOSONE|DOI:10.1371/journal.pone.0136949 August28,2015 8/19
FactorsContributingtotheSpatialPatternsofMytilustrossulus
Results
1.Presenceofmussels
M.trossuluswasfoundat1635stationsoutof3585.TheBRTmodellingwiththesimplifytool
optionondescribed85%ofvariabilityinthepresenceofM.trossulus.Altogethertenindepen-
dentvariableswereretainedinthemodel.Over75%ofmodelvariabilitywasduetodirectabi-
oticenvironmentalgradientswhereasresourcegradients(exposureandchlorophylla)
contributedlessthan25%tothemodel.Ingeneral,directenvironmentalgradientshadstrong
separateeffectswhileresourcegradientsimpactedthedistributionpatternofM.trossulus
eitherseparatelyorinteractivelywithdirectenvironmentalgradients.FunctionsfittedbyBRT
modelswerehighlyvariableinshape,andweremostlynon-linear(Fig2).
Thecoverageofboulders,exposuretowaves,watersalinityexplainedover50%ofthe
modelvariability.OthervariablescontributedmuchlesstothepresenceofM.trossulus.The
increasingcoverofboulders,elevatedexposure,salinityaswellasmoderateicedisturbance
separatelyincreasedtheprobabilityofoccurrenceofM.trossulusinthestudyarea.Theproba-
bilitytofindM.trossulusincreasedwithalgalcoveruptoathresholdof10%.Abovethislevel
furtherincreaseinalgalcoverhadnoeffectonmussels.TheprobabilitytofindM.trossulus
increasedbothatlowandhighendsofchlorophyllagradient(Fig2).
Exposureandsurfacewaterchlorophyllainteractivelycontributedtothepresenceofmus-
selswithchlorophyllabeingimportantatlowexposurevaluesbutnotathighexposurevalues.
Interestingly,atlowexposurechlorophyllavaluewasinverselyrelatedtotheprobabilityof
occurrenceofM.trossulus.Inaddition,exposurestronglyinteractedwithiceandsiltcover.At
lowicethickness,theeffectofexposureonM.trossuluswasonlymarginalwhereasathighice
thicknesselevatedexposureexponentiallyincreasedtheprobabilityofoccurrenceofM.trossu-
lus.Similarly,atlowexposuretheeffectofsiltonM.trossuluswasmoderatewhereasathigh
exposure,elevatedsiltcoverlinearlydecreasedtheprobabilityofoccurrenceofM.trossulus
(Fig3).
Fig2.Standardizedfunctional-formrelationshipsshowingtheeffectofenvironmentalvariablesonthepresenceofM.trossulusinthestudyarea,
whilstallothervariablesareheldattheirmeans.ThevariablesareorderedbytheirrelativecontributionintheBRTmodel(showninbrackets).Upward
tickmarksonx-axisshowthefrequencyofdistributionofdataalongthisaxis.Seethesectionofmethodsforfurtherinformationonenvironmentalvariables.
doi:10.1371/journal.pone.0136949.g002
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FactorsContributingtotheSpatialPatternsofMytilustrossulus
Fig3.Three-dimensionalpartialdependenceplotsintheBRTmodelforthepresenceofM.trossulusinthestudyarea.
doi:10.1371/journal.pone.0136949.g003
TheBRTmodelincludingonlydepthasasingleindependentpredictorexplainedonly37%
ofvariabilityinthepresenceofM.trossulusinthestudyarea.
2.Biomassofmussels
Inareaswheremusselswerepresent,thebiomassofM.trossuluswasafunctionofonly3pre-
dictors:exposure,coverofmacroalgaeandsalinity.Nevertheless,themodeldescribedonly
65%ofvariabilityinthebiomassofmussels.Atlowexposurevalues,thebiomassofmussel
increasedslightlywithincreasingexposure.Abovecertainthreshold,smallincreaseinexposure
resultedinadramaticincreaseinthebiomassofmussels.Increaseinbothplantcoverand
salinityonlymoderatelyincreasedthebiomassofmussels.Similartothepresencemodel,func-
tionsfittedbytheBRTmodelswerehighlyvariableinshape,andnon-linear(Fig4).
Importantly,exposureandsurfacewaterchlorophyllainteractivelycontributedtothebio-
massofM.trossulusdemonstratingasignificantroleofresourcegradientinthemodelofmus-
sels’biomass.Highbiomasseswerefoundeitherunderconditionsoflowchlorophyllaand
highexposureorhighchlorophyllaandmoderateexposure.Inadditiontherewerealsostrong
interactionsbetweenexposureandthecoverofmacroalgaeandsalinityandexposure.Atlow
exposure,relationshipbetweentheplantcoverandM.trossuluswasweak.Athighexposure,
Fig4.Standardizedfunctional-formrelationshipsshowingtheeffectofenvironmentalvariableson
thebiomassofM.trossuluswithinthedistributionrangeofmussels,whilstallothervariablesare
heldattheirmeans.ThevariablesareorderedbytheirrelativecontributionintheBRTmodel(shownin
brackets).Upwardtickmarksonx-axisshowthefrequencyofdistributionofdataalongthisaxis.Seethe
sectionofmethodsforfurtherinformationonenvironmentalvariables.
doi:10.1371/journal.pone.0136949.g004
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