Fishery Simulator for Conger Eel Fishery in Ise Bay

ContentslistsavailableatScienceDirect

Ecological Modelling

jo u r n al ho m e p ag e :w w w . e l s e v i e r . c o m / l o c a t e / e c o l m o d e l

Combined modeling of fish behavior and fishing operations for conger eel fishery in Ise Bay

Shigeru Tabeta

^∗

, Shota Suzuki, Yoshiharu Nakamura

DepartmentofEnvironmentSystems,GraduateSchoolofFrontierSciences,TheUniversityofTokyo,5-1-5Kashiwanoha,Kashiwa,Chiba277-8563,Japan

a r t i c l e i n f o

Articlehistory:

Received2March2015

Receivedinrevisedform15June2015 Accepted30June2015

Keywords:

Fishmigration Fishingoperation Congermyriaster IseBay

Bottomottertrawling

a b s t r a c t

Inordertosimulatethecongereel(Congermyriaster)fisheryinIseBay,atypicalsemi-enclosedbayin Japan,wedevelopedafisherysimulatorthatconsistsofafishbehavioralmodelandafishingoperations model.Thefishbehavioralmodelconsidersthemigrationeffectaswellasthegrowthrateofindividuals andchangesinpopulationsize.Inthebehavioralmodelwithmigration,thedirectionoffishmovement isdeterminedbythepreferenceintensitiesfortheenvironmentalfactorsofwatertemperatureand dissolvedoxygen.Thechangeinpopulationsizeiscalculatedbyusingageneralpopulationdynamics modelconsideringnaturalandfishingmortalities.Inthefirststep,thefishingmortalitywasestimatedby theactualfishcatchdata.Studieshaveclarifiedthatenvironmentalconditionssuchasoxygen-deficient waterinsummerhavealargeinfluenceonthemigrationanddistributionofC.myriaster.Althoughthe simulatedcatchperuniteffort(CPUE)basedonactualfishingoperationsdatawasabletodescribegeneral featuresoftheannualvariability,someimprovementofthemodelwillbenecessaryformoreaccurate prediction.Inthenextstep,thefishbehavioralmodelwascombinedwithafishingoperationsmodel, whichpredictsthebehavioroftrawlingboatsbasedoneconomicconditionsandresourcedistribution.

Thecombinedmodelreproducedtheboats’fishinglocationsandtheirfishcatchamountswell.Themodel wasappliedtoevaluatetheeffectsoffisherymanagementandclarifiedthatthecontroloffine-netuse isaneffectivemeanstoincreasethefisheryprofit.

©2015ElsevierB.V.Allrightsreserved.

1. Introduction

Whenconsideringtheenvironmentalmanagementandsustain- ableuseofacoastalzone,revitalizationofcoastalfisheriesisan importantissuethat isdeeply connected toecosystemservices andthelocaleconomy.Withregardtofisheryproductioninthe Japanesecoastalzone,itsrelationshipwithwaterqualityand/or terrestrialloadhasoftenbeendiscussed,aswellastheeffectsof environmentalregenerationprojectssuchasthedevelopmentof seagrassbedsandtidalflats(e.g.,KodamaandTabeta,2012).How- ever,coastalfisheriesinJapanarefacingserioussituationsduenot onlytothedeclineof fisheryresourcescaused byenvironmen- taldegradationandotherreasons,butalsotoeconomicproblems includingpricesettinganddistributioninfrastructure,decreased consumption,andunstableincomeoffishermen.Inordertorevital- izecoastalfisheriesanddistressedlocalcommunities,appropriate fisheriesmanagementconsideringbothenvironmentalandeco- nomicfactorsisnecessary,aswellasasystemforassessingthese

∗Correspondingauthor.Tel.:+81471364718.

E-mailaddress:tabeta@k.u-tokyo.ac.jp(S.Tabeta).

factors.Forexample,moreefficientandsustainablefishingopera- tionsshouldbeinvestigatedbasedontheresourceconditionsand businessprofitability.

Recently,anumberofmodelsconsideringbothecologicaland economicconditionshavebeendevelopedandappliedtoassessing fisherymanagement(e.g.,Bastardieetal.,2014;Blenckneretal., 2011;Fultonetal.,2011;KaplanandLeonard,2012;Romagnoni etal.,2015;Russoet al.,2015).However,therehavebeenvery fewapplicationsofbio-economicmodelstoJapanesecoastalfish- eries.Additionally,modelingandimplementingoffishmigration incoastalareawherecomplexenvironmentalfactorsaffectthefish behaviorisstillachallengingissue.

Ise Bay (Fig. 1) is a typical semi-enclosed bay located in south-central Honshu, Japan, witha large urban areaalong its shore.Coastaldevelopmentandterrestrialloadshavecausedenvi- ronmental degradationand affected themarine ecosystem and fisheriesinthebay. Inparticular,oxygen-deficientwaterinthe bottomlayerduringsummerhascausedseriousimpactsonthe bay’sbenthicecosystem(Fujiwaraetal.,2002;Suzuki,2001).Itis reportedthatoxygen-deficientwaterappearsfromJunetoOctober almosteveryyearinIseBay,whoseareahasbeenincreasingand thedurationhasbecomelonger(KurodaandFujita,2006).

http://dx.doi.org/10.1016/j.ecolmodel.2015.06.043 0304-3800/©2015ElsevierB.V.Allrightsreserved.

Fig.1.IseBayandthethreemajorfishingportsforbottomottertrawling.

However,variousfisheriesactivitiesarestillcontinuingunder theseharshsocialconditions(Funakoshi,2008;General,2008).Bot- tomottertrawlingisoneofthemajorfisheriesactivitiesinIseBay, andthesuccessofsuchtrawlingissensitivetomarineenviron- mentalconditionssuchasthedissolvedoxygenlevel.Thefishcatch ofbottomottertrawlinginIseBaywasabout7200tin2000,but itdeclinedtoabout4600tin2009.Thenumberoffisheryman- agemententities,whichwasabout3000in1998,alsodeclinedto about1900in2013.Themaintargetspeciesarecongereel(Conger

myriaster),mantisshrimp(Oratosquillaoratoria),Japanesepuffer- fish(Takifugurubripes),prawn(Trachysalambriacurvirostris),and Japaneseseabass(Lateolabraxjaponicus)(JapanFisheriesAgency, 2007,2012).

Inthepresentstudy,wedevelopedafisherysimulatorforC.myr- iaster,whichisoneoftherepresentativetargetspeciesfortrawl fishingin Ise Bay.Thefish catchofC. myriasterin thebay has beendeclininginrecentyears,withmorethan1000tcaughtannu- allyinthe1990sbutlessthan500tcaughtin2012(Kurogietal., 2013).Thesimulatorconsistsofafishbehavioralmodelthatpre- dictsspatiotemporalvariabilityoffishbiomassandpopulationsize andafishingoperationsmodelthatpredictsthefishingactivities oftrawlingboats.

Inthefishbehavioralmodel,themigration,growthrate,and changesinpopulationsizeofthefisharecalculated.Wepreviously developedasimilarmodelforPagrusmajorintheSetoInlandSea (HakutaandTabeta,2013),butthemodelvalidationwasdifficult becausetherearelimitedquantitativedataaboutthebehaviorof thetargetfish.Inthepresentstudy,wewereabletoobtainbetter quantitativedatabyusingfisheryinformationsuchasfishcatch andcatchperuniteffort(CPUE).

Inthefirststep,thefishingcoefficientwasestimatedbyusing actualfishcatchdatatovalidatethefishbehavioralmodel.Inthe nextstep,thefishbehavioral modelwascombinedwithafish- ing operations model, which predicts theactivities of trawling boatsbasedoneconomicconditionsandresourcedistribution.The combinedmodelwasthenappliedtoevaluatetheeffectsoftrawl fishingmanagementonthecongereelfisheryinIseBay.

Fig.2.Thelower-trophicecosystemmodelusedforcalculatingenvironmentalconditions(fromHakutaandTabeta,2013).

Fig.3. Theselectivityoffishinggear(trawlingnet)usedatthemajorfishingports onIseBay.

2. Models

2.1. Fishbehavior

2.1.1. Environmentalconditions

Alower-trophicecosystemmodel(Fig.2)wasemployedtocal- culatethe environmentalconditions of water temperature and dissolved oxygen. The details of the model were described by HakutaandTabeta(2013)andMizumukaietal.(2008).Inorderto reproducetherealisticspatioemporalvariabilityofenvironmental conditions,theobservedvaluesofwatertemperatureanddissolved oxygenwereassimilatedwithanudgingscheme.

2.1.2. Fishmigration

Basedonthelife-historytraitsofC.myriaster,inthemodelit isassumedthatthefishstayinthebottomlayer.Thedirectionof thefishmovementisdeterminedbythepreferenceintensityofthe environmentalfactors.Foreachtimestep,thepreferenceintensity ofeachgridiscalculatedbyusingthefollowingequation:

P^∗=P1×P2 (1)

Fig.4. Thecombinedmodeloffishbehaviorandfishingoperations.

whereP^*istheinclusivepreferenceintensity,whichtakesavalue between0and1,andP₁andP₂arethepreferenceintensitiesfor watertemperatureanddissolvedoxygen,respectively.Thelikeli- hoodthatthefishwillstayormoveisproportionaltothepreference intensitiesinthetargetgridandtheadjacentones.Thetimeinter- valforthecalculationoffishmovementcorrespondstothetime requiredforthefishtomovethehorizontaldistanceofonegridat thespecies’typicalmovementspeed.

The preference intensity for each environmental factor also takesthevaluebetween0and1.ForthewatertemperatureT,the preferenceintensityP1isdeterminedbythefollowingformula:

P1=

⎧ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎨

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎪

⎪ ⎩

0 if T≤Tcl

_T−T

cl

Tol−Tcl

if T_cl<T≤T_ol 1 if Tol<T≤Toh

Tch−T Tch−Toh

if Toh<T<Tch

0 if T≥Tch

(2)

whereT_cl(^◦C)isthelowerlimittemperature,T_ol(^◦C)theloweropti- mumtemperature,T_oh(^◦C)theupperoptimumtemperature,and

Fig.5.SimulatedcongereelannualdistributionpatternsinIseBay.

Fig.6. Theannualvariabilitiesofwatertemperatureanddissolvedoxygen.

T_ch(^◦C)upperlimittemperature.Forthedissolvedoxygenconcen- tration,DO,thepreferenceintensityP₂iscalculatedasfollows:

P2=

⎧ ⎪

⎪ ⎪

⎪ ⎪

⎨

⎪ ⎪

⎪ ⎪

⎪ ⎩

0 if DO≤DOc

DO−DOc

DOo−DOc

if DOc<DO≤DOo

1 if DO>DOo

(3)

whereDOc (mg/L)isthecriticaldissolvedoxygenconcentration andDOo(mg/L)isthelowerlimitoftheoptimumdissolvedoxygen concentration.Theparametervaluesweredeterminedbasedonthe studieslistedinTable1.

2.1.3. Fishgrowth

The relationship between body weight and body length is obtainedbytheanalysisofC.myriastersamplescaughtinIseBay, asfollows:

W=0.0007×L^3.24 (4)

Table1

Thepreferenceintensityparametersforthefishbehaviormodel.

Symbol Value Reference Tcl 7.0^◦C Shimoetal.(2000) Tol 13.0^◦C Shimoetal.(2000) Toh 23.0^◦C Shimoetal.(2000) Tch 29^◦C Shimoetal.(2000)

DOc 2.0mg/L OsakaPrefecturalFisheryExperimentalStation(1963) DOo 3.0mg/L Kobayashi(1993),Nakamuraetal.(2003)

whereW(g)istheaverageweightofanindividualfish,andL(cm) istheaveragebodylength.

Tocalculatethebody lengthweemployedVonBertalanffy’s growthfunction,whoseparametervalueswerealsodetermined bysampleanalysis:

L=45.8{1−e−0.51(t+0.97)} (5)

wheret(year)isthetimefromApril1.

2.1.4. Fishpopulation

Ageneralpopulationdynamicsmodelisemployedtopredict thevariabilityofthefishpopulationsize:

dNi

dt =−(M+Fi)·Ni (6)

whereN_iisthepopulationsizeingridi,Mthecoefficientofnat- uralmortality,andF_ithecoefficientoffishingmortalityingridi.

ThenaturalmortalityrateofM=0.25year⁻¹wasestimatedbythe methodofTauchandTanaka(Tanaka,1960).

Finally,thetotalfishbiomassingridi,B_iiscalculatedfromthe fishpopulationsize,N_i,andtheweightofanindividualfish,W:

Bi=Ni·W (7)

2.2. Fishingoperation

Thefishingoperationsmodel wasdeveloped basedoninfor- mationobtainedthroughafield survey(Tabetaet al.,2012).In thesimulator,theactivitiesoffishingboats,suchastheselection offishingareas,arepredictedbasedontheresourcedistribution andeconomicconditions.Thecalculationforeachdayinvolvesa three-stepprocedure.

First,itisassumedthateachfishingboatchoosesthegridin whichthecostfunctionVtakesmaximumvalue:

V=Pj·Ui(1−ıDi,j) (8)

whereP_jisthepriceoffishatthelocalmarketofportj,U_iisthe expectedCPUEofgridi,D_i,jisthedistancebetweenportjandgridi, andıistheweightcoefficientforthedistancewhichisdetermined bythesensitivityanalysis.

Second,thefishingcoefficientineachgrid,F_i,iscalculatedas:

F_i=

NS_i

k

ATk

AGi·NT_k·GE_k (9)

whereNS_iisthenumberofboatsoperatingingridi,AT_kisthe trawlingareaperhaulbyfishingboatk,AG_iistheareaofgridi,NT_k isthenumberofhaulsperdaybyboatk,andGE_kistheefficiency offishinggear(i.e.,trawlingnet)onboatk.

Finally,thechangeintheresourceamountineachgriddueto fishcatch,mortality,andgrowthofthefishiscalculatedbythefish behavioralmodel.

Thetrawlingareaperhaul,AT_k,iscalculatedbymultiplyingthe widthofthetrawl(18m)and thedistanceof onetrawlingrun (3500m).Thenumberofhaulsperdaybyaboat,NT_k,wassetto10 basedoninterviewswiththefishermenofIseBay.

Fig.7.ComparisonofannualvariabilityofactualandsimulatedCPUEforthemajor fishingportsinIseBay.

Forthegearefficiency,GE_k,theselectivitybasedonthemesh sizeofthetrawlingnetisused,asmodeledbyGorieandNagasawa (2010):

GEk=˛·r(L,p) (10)

r(L,p)= 1

1+e−0.99(L/p−12.1) (11)

Fig.8. ThecongereelpriceatthemajorfishingportsinIseBay.

Table2

Theparametersusedinthefishingcoefficientcalculation.

Port Fishingefforts Netmeshsize

Wakamatsu 409boats/year 20.2mm

Aritaki 1596boats/year 21.6mm

Toyohama 8024boats/year 23.3mm

where˛istheparameterdenotingtheprobabilityoffishentering thenet,andpisthemeshsizeofthetrawlingnet(Table2).The selectivitycurvesforthethreeportsareshowninFig.3.

The fishing efficiency could also be affected by ecological characteristics of the fish. Conger eels tend tohide in benthic

mudwhenthewatertemperatureislow,whichmakesthegear efficiencylower.Thiseffectisincorporatedintotheparameter˛, asfollows:

˛=˛0·P1 (12)

whereP1 isthepreferenceintensityofwatertemperatureinthe grid.˛0wasempiricallydeterminedtobe0.2,becauseitisneces- sarytoseineseveraltimestocatchallthefishinacertainarea.

Inthepresentstudy,threemajorfishingportswerechosenfor thetargetsoftheanalysis:Wakamatsu,Aritaki,andToyohama,the locationsofwhichareshowninFig.1.Thefishingboatsarepro- hibitedfromenteringthegridsadjacenttothecoastline,because theseareasareprotectedfromtrawlfishing.

2.3. Couplingofmodels

Afisherysimulatorwasdevelopedbylinkingthefishbehavioral modelandthefishingoperationsmodel,asshowninFig.4.

Thecomputationalprocedureofthecombinedmodelinvolves sevensteps:(1)calculatethelengthandweightofthefish,usingthe growthmodel;(2)determinethefishingground(grid)foreachboat usingcostfunctionVforeachgrid;(3)calculatethefishingcoef- ficientF_iforeachgridbyusingEq.(9);(4)calculatethefishcatch C_iingridi(C_i=F_i·W·N_i);(5)calculatethechangeintheresource amountineachgridduetofishcatch,mortality,and growthof thefishbyusingthefishpopulationandgrowthmodel;(6)calcu- latethepreferenceintensitybasedonenvironmentalconditionsto determinethemigrationofthefish;and(7)gotothenexttime step.

Thetimeintervalofthefishbehavioralmodelwassetto30min, andthatofthefishingoperationsmodelwas60minforonetrawl- ingperiod.Sincetheboatstrawls10timesinaday,theyoperate for10hinaday.

Fig.9.ThefishinggroundsusedbytheboatsfromeachportinAugustandDecember.

3. Simulationunderactualfishingoperation

3.1. Computationalconditions

Asimulation of fishbehavior was firstcarried out by using actualfishingoperationsdata.Inthesimulation,thefishingcoeffi- cientsF_iarecalculatedbasedoncollectedfishingrecordsforIseBay (KurogiandKatayama,2011;Tabetaetal.,2012).Thegridsizefor thesimulationswas900m×900mfortheentirecomputational domain,andtherewere26verticallayers.Thesimulationswere carriedoutforApril2007throughMarch2008.Theforcingfac- torsforthehydrodynamicmodelaretide,riverdischarge,wind, andsurfaceheatandsalinityfluxes,whichwerebasedonobserva- tions.

ThelarvaeofC.myriastercometothemouthofIseBayfromthe PacificOceanfromFebruarythroughMay,andthefrywithbody lengthsof10–20cmarecaughtbybottomtrawlingwithincoastal areasthat areshallower than10minJune and July(Nakajima, 1997).Therefore,inthesimulationitwasassumedthatinAprilthe fishconveneacrossthewholeareainthebaythatisshallowerthan 10m,withauniformpopulationdensity.Theinitialbodylengthand populationsizeweresetto17.9cmand32.6million,respectively.

Thesimulation wasconductedfor 3years consideringmultiple agegroups,in whichenvironmentalconditionsfromApril2007 toMarch2008wererepeatedlyused.

3.2. Fishbehavior

Fig.5showstheannualvariabilityoffishbiomassdistribution basedonthesimulation.Thevariabilitiesofwatertemperatureand dissolvedoxygenarealsoshowninFig.6.InAprilandMay,the fishdensitywasnearlyhomogeneousacrossthewholebay.The influenceofoxygen-deficientwater,alongthemarginalzone of whichthefishdensitybecomeshigher,begantoappearinJune.

FromJulytoSeptember,veryfewfishstayedinthelargecentral areaofthebaybecauseofthespreadofoxygen-deficientwater, whichresultedinareasofhighfishdensitynearthecoast.Because ofthebottomtopographicfeatures andseasonalwindpatterns, theoxygen-deficientwaterinIseBaytendstospreadacrossthe westernsideofthebay.Theinfluenceofoxygen-deficientwater begantodecline inOctober,andthefishdistributionwasagain nearlyhomogeneousinDecemberandJanuary.InFebruary,when thewatertemperaturewaslowest,thefishtendedtogatheratthe centerorthemouthofthebay,wherethewatertemperaturewas higher.

The simulation also shows that less than 1% of the initial populationdiedduetothelowoxygen,whichmeansthattheenvi- ronmentalconditionshavelargeinfluencesonthedistributionof congereel,butaffectlittleonthestockofcongereel.

3.3. CPUE

Inordertovalidatethefishbehavioralmodel,simulatedCPUE wascomparedtotheactualCPUEfortheboatsbelongingtoeach fishingport(Fig.7).TheCPUEincreasedfromspringtosummer anddeclinedinautumn,becauseareasofhighresourcedensity appearedduringsummerduetotheinfluenceofoxygen-deficient water.ThepeakCPUEvalueswerelargerforWakamatsuandAri- takithanforToyohama,andthesefeatureswerereproducedwell bythesimulation.However,thesimulationunderestimatedthe CPUEfor Aritakiin springandsummer.Accordingtothesimu- lation,theresourcedensityinthesouthwesternpartofthebay, which is themain fishing ground for theboats of Aritaki,was notveryhighinJune andJuly(Fig.5).Inreality,however,it is possiblethatthehigh-resource-densityareacorrespondingtothe marginalzoneofoxygen-deficientwateralsoformedintheAritaki

Fig.10.ComparisonofannualvariabilityofsimulatedCPUEforthemajorfishing portswith(Cal.B)orwithout(Cal.A)thefishingoperationsmodel.

fishingarea.Thesimulationalsofailedtoreproducetheveryhigh CPUEinspringatAritaki,whichwasanunusualphenomenonthat occurredin2007.Althoughthereweresomedifferencesbetween thesimulationandactualdata,themodelwasabletoreproduce thegeneralfeaturesofannualCPUEvariabilityforC.myriasterin IseBay.

Fig.11.Theinfluencesofmanagingfishinggearbychangingtheperiodoffine-netuseon(a)thepopulationcaughtbyfishingand(b)thepopulationsurvivingtoescapethe bay.Thebaselineofincreaserateisthepopulationbythesimulationinwhichthefine-netisusedallyearround.

4. Simulationbycombinedmodel

4.1. Computationalconditions

Thefisherysimulatorcombiningthefishbehavioralmodeland fishingoperationsmodelwasappliedtothesimulationofthecon- gereelfisheryinIseBay.Theexpectedfishpriceisnecessaryto calculatethecostfunctionVintheoperationsmodel.Thefishprice variabilityatthemajorlocalmarketswasobtainedbyanalyzing thevouchers.Therelationshipbetweenthepriceandbodylength offishwasdifferentateachmarketasshowninFig.8.Thefish priceatthe Wakamatsulocal marketwashigher thanthose at othermarketsbecauseithasbrandvalue.ThecoefficientıforV wasdeterminedtobe0.02bysensitivityanalysis.

4.2. SimulationoftrawlfisheryinIseBay

Thesimulatedfishinggroundusedbytheboatsfromeachport wascomparedtoactualdatafromthefishingrecords.Fig.9shows thefishinggroundsusedbytheboatsfromeachportinAugustand December.Theareasoffishinggroundsbasedonthefishingrecords appeartobelargerthanthesimulatedareas,becausethespatial

resolutionofthefishingrecords(4km×4km)iscoarserthanthat ofthecomputationalgrids(900m×900m).TheboatsfromWaka- matsumainlyusedtheeasternpartofthebayinsummer,whereas theyusedthecoastalareaneartheportinwinter.Thisactivitypat- ternisstronglyrelatedtotheenvironmentalconditionofdissolved oxygen.Ontheotherhand,theboatsfromAritakiandToyohama usedtheareasrelativelynearertotheirhomeportsthroughoutthe year.Thesefeatureswerereproducedwellbythesimulationusing thecombinedmodel.

TheCPUEatthethreemainportscalculatedbythecombined modelwerecomparedtothosebythesimulationbasedonactual fishingoperations.Fig.10showsannualvariabilityofsimulated CPUEforthemajorfishingportswithorwithoutthefishingoper- ations model. The combinedmodel overestimated the CPUE in summerforWakamatsu.Thereasonforthis discrepancyisthat congereelscongregateinaquitenarrowareaalongthemargins oftheoxygen-deficientwaterinsummer,andthefishermenwere assumedtoidentifytheareawithcompleteaccuracyintheopera- tionsmodel.Incontrast,forAritakiandToyohamaportstheresults ofboth modelsimulationswereconsistent,whichindicatesthe fishingoperationsmodelpredictedtheactivitiesofthefishingboats wellfortheseports.

Fig.12.Theeconomiceffectofmanagingfishinggearbychangingtheperiodoffine-netuse.

4.3. Assessmentoffisherymanagement

Controllingtheefficiency offishinggear isan effectivefish- eriesmanagementscheme.Forbottomottertrawling,meshsizeof thetrawlingnetisoftencontrolledtoavoidcatchingsmallyoung fish.Thesimulatorwasappliedtoevaluatetheeffectofcontrolling fishinggearefficiencyforbottomottertrawlinginIseBay.Inthe simulation,low-efficiencygear(coarsenet)isusuallyusedtoavoid catchingsmallfish,andhigh-efficiencygear(finenet)isusedonly duringaspecificperiodtoincreasethecatchofmaturehigh-value fish.

Fig.11(a) showsthe influencesof managingfishing gearby changingtheperiodoffine-netuseonthepopulationofcongereel caughtbythefishing.Theincreaserateofcongereelpopulation whichsurvivetoescapethebayisalsoshowninFig.11(b).Asthe periodoffine-netusebecomesshorter,thepopulationcaughtby fishingdeclinesandthesurvivingpopulationincreases.Theeco- nomiceffectofmanagingfishinggearbychangingtheperiodof fine-netuseis shownin Fig.12.The simulatedresultsshowed thatiffishermenstart tousethefinernettooearly(i.e.,before May)orcontinuetouseittoolong(i.e.,afterOctober),thecatchof smallyoungfishwillincrease,whichadverselyaffectstheresource andeconomicconditions.However,iftheystarttousethefiner netafterAugust,toomanylargefishwillescapethebay,which willalsoreducetheprofit.Thesalessignificantlydecreasedwhen thefinenetwasuseduntilOctober,becausesmallfishofthenext age-classbegantobecaught,whichwilldecreasethefishcatchin thefollowingyear.Oursimulationshowedthatthesalesvalueof caughtfishbecomeslargestwhenthefinernetisusedfromJuneto September.

5. Conclusion

Inthepresentstudy,afisherysimulatorforC.myriasterinIseBay wasdevelopedbycombiningafishbehavioralmodelandafishery operationsmodel.Thefishbehavioralmodelconsidersthemigra- tioneffectaswellasthegrowthrateofindividualsandchangesin populationsize.Inthebehavioralmodelwithmigration,thedirec- tionoffishmovementisdeterminedbythepreferenceintensities fortheenvironmentalfactorsofwatertemperatureanddissolved oxygen.Thechangeinpopulationsizeiscalculatedbyusingagen- eralpopulationdynamicsmodelconsideringnaturalandfishing mortalities.

Numericalsimulationswerecarriedouttoreproducethespa- tialdistributionandseasonalvariationoffishmovementinIseBay.

Environmentalconditionssuchasoxygen-deficientwaterinsum- merwereshowntohavealargeinfluenceonthemigrationand distributionofC.myriaster.ThesimulatedCPUEusingactualfishing operationsdatadescribedthegeneralfeaturesofannualvariabil- ityfairlywell,althoughsomeimprovementofthemodelwillbe necessaryformoreaccurateprediction.

Thecombinedmodelofthefishbehaviorandthefisheryoper- ationsalsoreproducedwellthefishinglocationsoftheboatsand theircatchamounts.Thesimulationrevealedthatthespatialpat- ternsoffishinggroundusebyboatsfromeachporthaddifferent featuresbecausetheyareaffectedbyboththeenvironmentaland theeconomicconditions.Thesimulatorwasappliedtoevaluatethe effectsoffisherymanagementandclarifiedthatthecontroloffish- inggearisaneffectivemeansforincreasingtheprofitoftheconger eelfishery.

Inthepresentstudy,wetriedtodeterminethemodelparame- tersbytheexternalinformationaspossible,althoughsomeofthem areestimatedsoastofitthesimulationtotheobservation.How- ever,weneedmoreobservationstoconfirmthatthemodelcould bemoregenerallyapplicable.Forourfutureresearch,wearetrying toimprovethefishingrecordsbygatheringdatamorefrequently andatafinerspatialscalebyusingdigitaldevicewithGPS.Toana- lyzevariousmanagementscenariosofthecongereelfishery,itwill alsobenecessarytoimplementadecision-makingmodeltopredict whetherthefishermengofishingornotconsideringtheexpected fishcatchandoperationscosts.

Acknowledgment

The authors thank Dr. Hiroaki Kurogi (Fisheries Research Agency),Mr.TakuyaMaruyama(MiePrefectureFisheriesResearch Institute), and Dr. Manabu Hibino (Aichi Prefecture Fisheries ResearchInstitute)forprovidingimportantdataandadvice.This researchwaspartiallysupportedbytheAgriculture,Forestry,and FisheriesResearchCouncilandJSPSKAKENHI(grant15H04210).

References

Bastardie,F.,Nielsen,J.R.,Miethe,T.,2014.DISPLACE:adynamic,individual-based modelforspatialfishingplanningandeffortdisplacement–integratingunder- lyingfishpopulationmodels.Can.J.Fish.Aquat.Sci.71,366–386.

Blenckner,T.,Dring,R.,Ebeling,M.,Hoff,A.,Tomczak,M.,Andersen,J.,Kuzebski,E., Kjellstrand,J.,Lees,J.,Motova,A.,Vetemaa,M.,Virtanen,J.,2011.FishSTERN:

afirstattemptatanecological-economicevaluationoffisherymanagement scenariosintheBalticSearegion.Swed.Environ.Prot.AgencyRep.Ser.6428, 89.

Fujiwara,T.,Takahashi,T.,Kasai,A.,Sugiyama,Y.,Kuno,M.,2002.Theroleofcircu- lationinthedevelopmentofhypoxiainIseBay,Japan.Estuar.Coast.ShelfSci.

54(1),19–31.

Fulton,E.A.,Link,J.S.,Kaplan,I.C.,Savina-Rolland,M.,Johnson,P.,Ainsworth,C., Horne,P.,Gorton,R.,Gamble,R.J.,Smith,A.D.M.,Smith,D.C.,2011.Lessonsin modellingandmanagementofmarineecosystems:theAtlantisexperience.Fish Fish.12,171–188.

Funakoshi,S.,2008.Changesinthecatchvolumeandspeciescaughtbysmall-scale trawlfisheriesinIseBay.Bull.AichiFish.Res.Inst.14,7–16.

GeneralCouncilforIseBay,2008.CurrentIndustrialSituationofIseBay.IseEnviron- mentalDatabase,Availableathttp://www.isewan-db.go.jp/sites/jkl/datafour.

pdf

Gorie,S.,Nagasawa,K.,2010.Habitatpreferenceandfeedinghabitsofjuvenile whitespottedcongerCongermyriasterintheeasternSetoInlandSea,Japan.

Aquacult.Sci.58(2),167–179.

Hakuta,K.,Tabeta,S.,2013.BehavioralmodellingofPagrusmajorintheeastSeto InlandSea.J.Mar.Sci.Technol.18(2),213–219.

JapanFisheriesAgency,2007.RecoveryPlanofFisheryResourcesforBottomTrawl- inginIseBayandMikawaBay,Availableathttp://www.jfa.maff.go.jp/j/suisin/

skeikaku/pdf/isewanmikawawankosoko.pdf

JapanFisheriesAgency,2012.SummaryandActionsofRecoveryPlanofFishery ResourcesforBottomTrawlinginIseBayandMikawaBay,Availableathttp://

www.jfa.maff.go.jp/j/suisin/skouiki/taiheiyo/pdf/tm19-2.pdf

Kaplan,I.C.,Leonard,J.,2012.Fromkrilltoconveniencestores:forecastingtheeco- nomicandecologicaleffectsoffisheriesmanagementontheUSWestCoast.

Mar.Pol.36(5),947–954.

Kobayashi,Y.,1993.Relationshipbetweendistributionoftheanoxiawatermassin TokyoBayandcatchbysmallbeamtrawlers.Bull.KanagawaPrefect.Fish.Exp.

Stn.14,27–39.

Kodama,M.,Tabeta,S.,2012.Impactofredtideandhypoxiaoninnerbayfish- eriesanditscountermeasuresforsustainablefisheries.Res.J.FoodAgric.35(3), 11–15.

Kuroda,N.,Fujita,K.,2006.Comparisonoflongtermandshorttermfluctuationof hypoxiabetweenIseBayandMikawaBay.Bull.AichiFish.Res.Inst.12,5–12.

Kurogi,H.,Katayama,S.,2011.StockassessmentandevaluationforCongermyri- aster(fiscalyear2010).In:MarineFisheriesStockAssessmentandEvaluation forJapaneseWaters(FiscalYear2010/2011).FisheriesAgencyandFisheries ResearchAgencyofJapan,pp.667–684.

Kurogi,H.,etal.,2013.StockassessmentandevaluationforCongermyriaster(fiscal year2012).In:MarineFisheriesStockAssessmentandEvaluationforJapanese Waters(FiscalYear2012/2013).FisheriesAgencyandFisheriesResearchAgency ofJapan,pp.650–665.

Mizumukai,K.,Sato,T.,Tabeta,S.,Kitazawa,D.,2008.Numericalstudiesoneco- logicaleffectsofartificialmixingofsurfaceandbottomwatersindensity stratificationin semi-enclosed bay and opensea. Ecol. Model. 214,251–

270.

Nakajima,H.,1997.CongereelfisheryandecologyinMiePrefecture(inJapanese).

In:CurrentStatusofResourcesandFisheriesofCongermyriaster,1(1).Japan FisheriesResourceConservationAssociation,pp.95–97.

Nakamura,Y.,Yoshida,T.,Ariyama,H.,Yamochi,S.,Tamai,K.,Irie,T.,Idemi,N.,Saka- gami,Y.,Koyano,Y.,2003.Habitatsuitabilitymodelbasedonthecharacteristics ofdistributionandmigrationforCongermyriasterinOsakaBay.Annu.J.Coast.

Eng.50(2),1151–1155.

OsakaPrefecturalFisheryExperimentalStation,1963.Short-termcultureexperi- mentofCongermyriaster(inJapanese).Bull.OsakaPrefect.Fish.Exp.Stn.S37, 75–86.

Romagnoni,G.,Mackinson,S.,Hong,J.,Eikeset,A.M.,2015.TheEcospacemodel appliedtotheNorthSea:evaluatingspatialpredictionswithfishbiomassand fishingeffortdata.Ecol.Model.300,50–60.

Russo,T.,Pulcinella,J.,Parisi,A.,Martinelli,M.,Belardinelli,A.,Santojanni,A., Cataudella,S.,Colella,S.,Anderlini,L.,2015.Modellingthestrategyofmid- watertrawlerstargetingsmallpelagicfishintheAdriaticSeaanditsdrivers.

Ecol.Model.300,102–113.

Shimo,S.,Akimoto,Y.,Takahama,H.,2000.Bibliographicalstudyofthethermal effectsonmarineorganisms.Rep.Mar.Ecol.Res.Inst.2,1–351.

Suzuki,T.,2001.Oxygen-deficientwatersalongtheJapanesecoastandtheireffects upontheestuarineecosystem.J.Environ.Qual.30(2),291–302.

Tabeta,S.,Nakamura,Y.,Suto,T.,Maruyama,T.,Sekine,M.,Irie,M.,Seki,I.,Furukawa, K.,2012.AsurveyonactualconditionsofthetrawlfisheryforCongermyri- asterinIseBaytodevelopanoperationsimulator.J.Coast.ZoneStud.25(3), 41–52.

Tanaka,S.,1960.Studiesonthedynamicsandthemanagementoffishpopulations.

Bull.TokaiReg.Fish.Res.Labor28,1–200.