General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

Users may download and print one copy of any publication from the public portal for the purpose of private study or research.

You may not further distribute the material or use it for any profit-making activity or commercial gain

You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from orbit.dtu.dk on: Mar 20, 2020

Integrated ecological-economic fisheries models - evaluation, review and challengesfor implementation

Nielsen, J. Rasmus; Thunberg, Eric; Holland, Daniel S.; Schmidt, Jörn O.; Fulton, E. A.; Bastardie,Francois; Punt, A.E.; Allen, Icarus; Bartelings, Heleen; Bertignac, MichelPublished in:Fish and Fisheries

Link to article, DOI:10.1111/faf.12232

Publication date:2018

Document VersionPublisher's PDF, also known as Version of record

Link back to DTU Orbit

Citation (APA):Nielsen, J. R., Thunberg, E., Holland, D. S., Schmidt, J. O., Fulton, E. A., Bastardie, F., ... Waldo, S. (2018).Integrated ecological-economic fisheries models - evaluation, review and challenges for implementation. Fishand Fisheries, 19(1), 1-29. https://doi.org/10.1111/faf.12232

Fish and Fisheries. 2018;19:1–29.  | 1wileyonlinelibrary.com/journal/faf

Received:19November2016  |  Accepted:2May2017DOI: 10.1111/faf.12232

O R I G I N A L A R T I C L E

Integrated ecological–economic fisheries models—Evaluation, review and challenges for implementation

J Rasmus Nielsen1,¶  | Eric Thunberg2,¶ | Daniel S Holland3,¶ | Jorn O Schmidt4,¶ |  Elizabeth A Fulton5,# | Francois Bastardie1,# | Andre E Punt6,# | Icarus Allen7,* |  Heleen Bartelings8,* | Michel Bertignac9,* | Eckhard Bethke10,* | Sieme Bossier1,* |  Rik Buckworth11,* | Griffin Carpenter12,* | Asbjørn Christensen1,* |  Villy Christensen13,* | José M Da-Rocha14,* | Roy Deng11,* | Catherine Dichmont11,* |  Ralf Doering10,* | Aniol Esteban12,* | Jose A Fernandes7,* | Hans Frost15,* |  Dorleta Garcia16,* | Loic Gasche17,* | Didier Gascuel18,*  | Sophie Gourguet19,* |  Rolf A Groeneveld20,* | Jordi Guillén21,* | Olivier Guyader19,* | Katell G Hamon8,* |  Ayoe Hoff15,* | Jan Horbowy22,* | Trevor Hutton11,* | Sigrid Lehuta17,* |  L Richard Little5,* | Jordi Lleonart21,* | Claire Macher19,* | Steven Mackinson23,* |  Stephanie Mahevas17,* | Paul Marchal24,* | Rosa Mato-Amboage14,* | Bruce Mapstone5,* |  Francesc Maynou21,* | Mathieu Merzéréaud19,* | Artur Palacz1,* | Sean Pascoe11,* |  Anton Paulrud25,* | Eva Plaganyi11,* | Raul Prellezo16,* | Elizabeth I van Putten5,* |  Martin Quaas4,* | Lars Ravn-Jonsen26,*  | Sonia Sanchez27,* | Sarah Simons10,* |  Olivier Thébaud19,* | Maciej T Tomczak28,* | Clara Ulrich1,* | Diana van Dijk29,* |  Youen Vermard17,* | Rudi Voss4,* | Staffan Waldo30,*1TechnicalUniversityofDenmark,NationalInstituteofAquaticResources,Kgs.Lyngby,Denmark2SocialSciencesBranch,NortheastFisheriesScienceCenter,NOAAFisheriesOfficeofScienceandTechnology,WoodsHole,MA,USA3ConservationBiologyDivision,NorthwestFisheriesScienceCenter,NMFS,NOAAFisheriesOfficeofScienceandTechnology,Seattle,WA,USA4DepartmentofEconomics,Christian-Albrechts-UniversitätzuKiel,Kiel,Germany5CSIROOceansandAtmosphere,MarineLaboratories,Hobart,TAS,Australia6SchoolofAquaticandFisherySciences,UniversityofWashington,Seattle,WA,USA7PlymouthMarineLaboratory(SeaandSociety)ProspectPlace,Plymouth,UK8WageningenEconomicResearch,WageningenUniversity,WUR,TheHague,TheNetherlands9UnitéScienceetTechnologiesHalieutique,STHFrenchResearchInstituteforExploitationoftheSea,Ifremer,Plouzané,France10ThünenInstituteofSeaFisheries,Hamburg,Germany11CSIROOceansandAtmosphere,QueenslandBiosciencesPrecinct,StLucia,QLD,Australia12NewEconomicsFoundation,London,UK13TheUniversityofBritishColumbia,InstitutefortheOceansandFisheries,Vancouver,BC,Canada

¶Authorshipequalfortheseleadauthors.#Authorshipequalfortheseauthors.

*Authorshipequalfortheseauthors.

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttribution-NonCommercial-NoDerivsLicense,whichpermitsuseanddistributioninanymedium,providedtheoriginalworkisproperlycited,theuseisnon-commercialandnomodificationsoradaptationsaremade.©2017TheAuthors.Fish and FisheriesPublishedbyJohnWiley&SonsLtd.

2  |     NIELSEN Et aL.

14EscueladeComercio,UniversidadedeVigo,Vigo,Spain15DepartmentofFoodandResourceEconomics,UniversityofCopenhagen,Copenhagen,Denmark16AZTI,TxatxarramendiUgarteaz/g,Sukarrieta,Spain17UnitéEcologieetModèlespourl’Halieutiques,EMHFrenchResearchInstituteforExploitationoftheSea,Ifremer,Nantes,France18UMREcologyandEcosystemHealth(ESE),UniversitéBretagneLoire(UBL),Rennes,France19Unitéd’EconomieMaritime,AMUREFrenchResearchInstituteforExploitationoftheSea,Ifremer,Plouzané,France20EnvironmentalEconomicsandNaturalResourcesGroup,WageningenUniversityWUR,Wageningen,TheNetherlands21SpanishNationalResearchCouncil,InstitutdeCiènciesdelMar,CSIC,Barcelona,Spain22NationalMarineFisheriesResearchInstitute,Gdynia,Poland23ScottishPelagicFishermen’sAssociation,Fraserburgh,UK24UnitéHalieutiquedeMancheMerduNord,HMMM,IURHFFrenchResearchInstituteforExploitationoftheSea,Ifremer,Boulogne-sur-Mer,France25SwedishAgencyforMarineWaterManagement,Göteborg,Sweden26DepartmentofEnvironmentalandBusinessEconomics,UniversityofSouthernDenmark,Esbjerg,Denmark27AZTI,HerreraKaia–Portualdeaz/g,Pasaia,Spain28BalticSeaCentre,StockholmUniversity,Stockholm,Sweden29SwissFederalInstituteofAquaticScienceandTechnology,Dübendorf,Switzerland30DepartmentofEconomics,SwedishUniversityofAgriculturalSciences,Lund,Sweden

Correspondence

JRasmusNielsen,TechnicalUniversityofDenmark,NationalInstituteofAquaticResources,Kgs.Lyngby,Denmark.Email:rn@aqua.dtu.dk

Present addressesRikBuckworth,CharlesDarwinUniversity,POBox304,NT0815,Australia.

CathyMDichmont,CathyDichmontConsulting,47PioneerRoad,Sheldon,QLD,Australia.

ArturPalacz,InstituteofOceanologyofthePolishAcademyofSciences,PowstańcówWarszawy55,81-712Sopot,Poland.

AbstractMarineecosystemsevolveundermanyinterconnectedandarea-specificpressures.Tofulfilsociety’sintensifyinganddiversifyingneedswhileensuringecologicallysustainabledevelopment,moreeffectivemarinespatialplanningandbroader-scopemanagementofmarineresourcesisnecessary.Integratedecological–economicfisheriesmodels(IEEFMs)ofmarinesystemsareneededtoevaluateimpactsandsustainabilityofpotentialman-agementactionsandunderstand,andanticipateecological,economicandsocialdynam-icsatarangeofscalesfromlocaltonationalandregional.Tomakethesemodelsmosteffective,itisimportanttodeterminehowmodelcharacteristicsandmethodsofcom-municatingresultsinfluencethemodelimplementation,thenatureoftheadvicethatcanbeprovidedandtheimpactondecisionstakenbymanagers.Thisarticlepresentsaglobalreviewandcomparativeevaluationof35IEEFMsappliedtomarinefisheriesandmarineecosystemresourcestoidentifythecharacteristicsthatdeterminetheirusefulness,ef-fectivenessandimplementation.Thefocusisonfullyintegratedmodelsthatallowforfeedbacks between ecological and human processes although not all themodels re-viewedachievethat.Modellersmustinvestmoretimetomakemodelsuserfriendlyandtoparticipateinmanagementforawheremodelsandmodelresultscanbeexplainedanddiscussed.Suchinvolvementisbeneficialtoallparties,leadingtoimprovementofmo- delsandmoreeffective implementationofadvice,but demandssubstantial resourceswhichmustbebuiltintothegovernanceprocess.Ittakestimetodevelopeffectivepro-cessesforusingIEEFMsrequiringalong-termcommitmenttointegratingmultidiscipli-narymodellingadviceintomanagementdecision-making.

K E Y W O R D S

bio-economicmodels,comparativemodelevaluation,fisheriesmanagementadvice,integratedecological–economicfisheriesmodels,marinespatialplanningandcross-sectormanagement,performancecriteriaandscalesandrisks,useandacceptanceandimplementationandcommunicationandflexibilityandcomplexity

     |  3NIELSEN Et aL.

1  | INTRODUCTION

Thereisagrowingneedfortoolstoevaluatepoliciesandassesstrade-offsinmanagementofmarineresourcesandprovisionofecosystemservices such as fishing, aquaculture, renewable energy, shipping,conservationandrecreation (Cormier,Kannen,Elliott,&Hall,2015;Degnbol&Wilson,2008;EU2014;Langlois,Fréon,Steyer,Delgenés,&Hélias,2014;Whiteetal.,2012). It isnecessarytoelaborateandapplycommonprinciplesandbroader, interdisciplinarymanagementevaluationintheuseofmarinespaceinvolvingseveraltypesofactivi-tiesandsectors(Ramosetal.,2013;Somaetal.,2013;Stelzenmülleretal., 2013;Sundbladetal., 2014).Policymakersneed toknow thecosts and benefits of conserving ecosystem goods and services tomanage them sustainably. Moreover, according to an ecosystem-based approach to management, specific pressures, associated un-certaintiesandrisksneedtobetakenintoaccount(Douvere,2008;Ehler&Douvere,2009;Gilliland&Laffoley,2008;Hicksetal.,2016;Stelzenmülleretal.,2011).

Tomeettheseneeds, therehasbeen increasingdevelopmentofIntegratedEcological–EconomicFisheriesModels (IEEFMs)over thelasttwodecades(Bjørndal,Lane,&Weintraub,2004;Conrad,1995;Kaplan,Holland,&Fulton,2014;Kaplan,Horne,&Levin,2012;Kelletal., 2007; Knowler, 2002; Mullon etal., 2009; Österblom etal.,2013; Prellezo etal., 2012; Punt etal., 2011). Thesemodels incor-porate and integrate natural and human processes that have beenthe focusofvariousdisciplines such asoceanography, fish ecology,fisherieseconomics,anthropologyandsociology (Dichmont,Pascoe,Kompas,Punt,&Deng,2010;Heal&Schlenker,2008;Mullon,2013;Nielsen & Limborg, 2009; Ulrich etal., 2012). Fundamentally, anIEEFM isamathematical representationofecologicalandeconomicsystemswhichcanalsointegratesocialsystemsinsomecasesbasedonlinkingcomponents,parametersandprocessesofeachdimension(e.g.DeMarchi, Funtowicz, LoCascio, &Munda, 2000;Österblom,Crona,Folke,Nyström,Troell2016;Puntetal.,2010;Thébaudetal.,2013).

OneofthepotentialbenefitsofIEEFMsisthatonecandevelopabetterandmorecomprehensiveunderstandingofthefeedbackef-fectsbetweenhumanmulti-actoractivity,humaneconomicstructuresandecosystemdynamics.Thisunderstandingmayhelpmanagers toavoid the well-documented unintended consequences of manage-mentactionsthatmightnotbepredictedbysimplermodelsthatdonotaccountforinteractionsandfeedbackprocessesbetweensystemcomponents (Beddington,Agnew,&Clark,2007;Hicksetal.,2016;Hilborn, 2007; Hilborn, 2011; Hilborn etal., 2015; Holling, 2001;Marchaletal.,2016;Ostrom,2009;Walters1998;Wilenetal.,2002;Wormetal.,2009).Complexfeedbacksandimpactsbetweenecosy-stems,exploitedspeciesandfisheriessystemshavebeeninvestigatedand discussed extensively (Branch etal., 2010; Garcia & Cochrane,2005; Gascuel etal., 2016; Hill etal., 2007; Howarth, Roberts,Thurstan, & Stewart, 2013; Marasco etal., 2007; Murawski etal.,2010; Neubauer, Jensen, Hutchhings, & Baum, 2013; Österblom,Jouffray,Spijkers,2016;Paulyetal.,2013;PlagányiandButterworth2004;Rose etal., 2010). Comprehensive reviews of ecosystem and

biological models have been conducted addressing this complex-ity and feedback processes (e.g. Hyder etal., 2015; Piroddi etal.,2015;Plagányi etal., 2014;Roseetal., 2010;Tedescoetal., 2016).Holistic (“end-to-end”)models havebeendevelopedduring the lastdecade including management and socio-economic modules to si-mulateecosystemcomplexityfromdiverseperspectives(Christensen,Steenbeek, & Failler, 2011; Fulton, Smith, Smith, & Johnson, 2014;Fulton etal., 2011;Girardin etal., 2016;Kaplan etal., 2012, 2014)allowing both strategic (long term) and tactical (medium term)ma-nagementadviceonmarineresourcesanddecisionsaccordingtobestpractices(FAO2008;Plagányi2007).However,increasedcomplexitywithineachdimensionandgreaterintegrationofthedimensions,forexampleincludingeconomicdynamicsinecosystemmodels,mayalsoincreasethedifficultyofparameterizingthemodelsandunderstand-ingandcommunicatingtheresults(e.g.Stokesetal.,1999;McAllister,Starr, Restrepo, & Kirkwood, 1999; Rochet and Rice 2009, 2010;Butterworthetal.,2010;Kraak,Kelly,Codling,&Rogan,2010;Fultonetal.,2011,2014;Christensenetal.,2011).Therearealwaystrade-offsinvolvedwithmovingtothesemorecomplexintegratedmodelsinmanagementadvice.This isespeciallythecasewhenseveralsec-tors and their markets are considered which increases complexityandaccordingly limitsmodel implementation(e.g.Hicksetal.,2016;Österblometal.,2016).

While a variety of fisheries IEEFMs, often referred to as bio-economic models, have been developed in the past, only a smallnumber of reviews comparing their capabilities and implementationin practice have been published. For example, Conrad (1995) andKnowler(2002)reviewmodelsinwhichenvironmentalinfluencesareinterlinkedwitheconomicaspects.Ageneral introductionandover-view of bio-economicmodels can be found already in Seijo, Defeo,andSalas(1998),butapplicationstospecificempiricalcasesremainli- mited. Reviews of more restricted types and coverage of modelsinclude the following: Bjørndal etal., (2004), which also includesaquaculture; the review conducted by the Scientific, Technical andEconomic Committee for Fisheries (STECF) of the European Union(SEC,2006);andthereviewofregionaleconomicmodelsforfisheriesmanagement in theUSAby Seung (2006). Finally, the reviews pro-ducedinPrellezoetal.,(2012)andLehuta,Girardin,Mahevas,Travers-Trolet,andVermard(2016)focusedonEuropeanoperationalmodels.ThereviewbyLehutaetal.,(2016)concentratesonmethodologyandmodel development on a subset of complex models that focus onEuropeanfisheriesadvice.OthertypesofmodelsbasedonnetworktheorysuchasMullonetal., (2009)andMullon(2013)withaglobalfishmealmodelhaveemerged.Individual-basedandfleet-basedpre-dictionmodelsonfuelconsumptionandtripplanningevaluatingthecarbonfootprintandenergyconsumptioninfisherieshavealsopro-gressedrecently (e.g.Bastardie,Nielsen,Andersen,&Eigaard,2013;Bastardie,Nielsen,&Miethe, 2014;Bastardie,Nielsen, etal., 2015;Basurko, Gabina, & Uriondo, 2013; Grimm etal., 2010; Sala etal.,2011;Trenkeletal.,2013;WaldoandPaulrud2016).The latteren-ablesthedevelopmentofenergyefficientapproachesforfishingves-sels(e.g.Suuronenetal.,2012)andpredictionoffuelcosts(Daurès,Trenkel,&Guyader,2013).

4  |     NIELSEN Et aL.

We conduct a global comparative review and evaluation of 35IEEFMs to provide potential users an overview of when and howIEEFMs can be and have been usedworldwide and to identify thecharacteristicsthatdeterminetheirusefulness,effectivenessandim-plementation infisheriesadvice.Thereviewevaluatesmodeldesignchoices such as scope, spatial and temporal dimensions and scales,functionsandprocessesincluded,levelofcomplexityandrealism,theability tomodel uncertainty and stochastic process impact, and thetypeandrobustnessofadvicethatcanbeprovidedaswellasthedataand expertise needed to develop and parameterize IEEFMs.Modellinking, couplingand levelof integrationofbiological andeconomicand,tosomeextent,socialcomponentsinthemodelsareconsidered.Thisarticleisprimarilyfocusedonfullyintegratedmodelsthatallowforfeedbacksbetweenecologicalandhumanprocessesalthoughnotallthemodelsreviewedachievethat.

The review covers selected IEEFMs representing a rangeof ap-proaches and perspectives rather than providing a comprehensiveanalysisofallexistingmodelsworldwide.Thereviewservestoiden-tify some common features and failings of models and hence mayguideresearchersinselectingexistingmodelsandfurtherdevelopingthemratherthancreatingacompletelynewmodel.Italsohighlightsmodellingchallengesandfuturedirectionsofresearchespeciallywhenitcomestoimplementationofthemodels.Thereviewdemonstratesthat modellers face inevitable trade-offs between complexity andcomprehensiveness,flexibilityanduser-friendliness.Thosetrade-offsimpact model design, performance andmodel acceptance and alsomustbeconsideredindeterminingthebestapproachtocommunicatemodelresults.Nomodeldesignfitsallcasesanduses,butthereviewprovidesinsightsthatmayhelpbothdevelopersandusersofmodelstodetermine themodelcharacteristics thatbest suit their intendedimplementation, uses and how to more effectively communicatemodelresultstoensureuptakeinmanagementadviceanddecisions.

Thearticle isorganizedas follows: initially, theselected IEEFMsarelistedwithrelevantreferencesfortheirdevelopment.Second,theanalysismethodsandtoolsusedforevaluationofthemodelsarede-scribed.Thetoolsareusedtodescribe,categorizeandevaluatethedifferenttypeofmodelsaccordingtoasetofspecificcriteriacoveringtheaboveissues.Thiscategorizationandevaluationissummarizedinsemi-quantitativespiderwebplotstocomparethefocusandcapabi-lityofthedifferentmodelsandwhatmaindirectionsofdevelopmentthedifferentmodelsrepresent.Theresultsof thismeta-analysisarethendiscussedwithafocusonuseandcharacteristicsthatcontributetoeffectiveimplementation.Needsforfurtherresearchareidentifiedwith emphasis on specific needs for furthermodel implementation.Thespecificobjectivesofthestudyareto

-Provideasetoftoolsandcriteriatomakeacomparativeevalua-tionofIEEFMs;

-Evaluate use and implementation of different types of IEEFMsthroughselectedexamplesfromaroundtheworld;

-ElucidatelimitationsandprogressofIEEFMimplementationandthegovernanceprocessincludingnecessarystakeholderinvolvement;

-Providepotentialuserswithanoverviewandframeworkthatcanbeusedtoguideinselectionofthemostappropriatemodelsaccording

totheirspecificneeds,purposeandquestionstobeanswered,thatisprovidingguidelinesforgoodpracticeinselection,useandcommuni-cationofthemodelsaccordingtorequirementsandtrade-offs.

2  | MATERIALS AND METHODS

2.1 | Surveyed models

Asubsetofmodelshasbeenselected toprovideaglobalperspec-tiveforthereview.ThesemodelsrepresentawiderangeofdifferenttypesofcurrentandemergingIEEFMs.The35IEEFMsevaluatedarelistedinTable1withnameandabbreviationandthemodelcharacter-isticsdetailedintheannexes(SupplementaryMaterialTablesS1,S2andS3).AgeographicaloverviewofthemainimplementationofthedifferentmodelsisgiveninFigure1.Themodelsandtheirdevelop-mentarepublished inacomprehensivescientific literaturegiven inTable 2.

2.2 | Meta- analysis of bio- economic models

Weusethreemodelmeta-analysistoolstocomparetheIEEFMsonaglobalscaleaccordingtomodeltype,purpose,coverage,dimensions,scales, capacity, uses and level of implementation and to evaluatetrade-offsassociatedwithcomplexityandflexibility.Thosetoolscon-sistofadetailedModelCharacteristicsandPerformanceEvaluationMatrix(TableS1)completedbyadeveloperofeachmodel,aModelCategorizationandDescriptorsSummaryTable(TableS2)alsocom-pletedbyadeveloperofeachmodel, andaModelUseandTrade-OffSummaryTable(TableS3)thatcompilesinformationaboutallthemodels.Thetoolsandtheirstructureaswellasthedetailsoftheclas-sificationaregivenintheSupplementaryMaterialTablesS1,S2andS3,respectively.Furthermore,theresultsandthefourthtoolofthecomparativeevaluationandmeta-analysisaregiveninsummaryplotsofthetabulationsintheresultssection(Figures2–7).Thisfourthtoolisintheformofspiderwebplotswithfrequencyclassificationofthedifferenttypesofmodelswithrespecttotheirproperties,character-istics,usesandtrade-offs.

In drawing conclusions about the effectiveness of models andtrade-offsfacedbymodellers,wealsoreliedondiscussionsatwork-shops,working groups and special sessions organized at three sci-entific conferences over four years inwhich themeta-analysiswasevaluated,severalofthesemodelswerepresented,andwheregeneralmodelling issueswerediscussedbypanels.Since2011,yearlymee-tingswereconvened focusingonevaluatingandcomparing IEEFMsintheICESWGIMM(InternationalCouncilofExplorationoftheSeaWorkingGroupon IntegratedManagementModelling,www.ices.dk01Apr2017;e.g. ICES2015a).The first twoconference special ses-sionswerespecialsessionsoftheInternationalInstituteforFisheriesEconomics and Trade (IIFET) held in Dar es Salaam, Tanzania andBrisbane,Australia,in2012and2014,respectively(Nielsen,Schmidt,etal., 2014; Thébaud etal., 2013; Thunberg, Holland, Nielsen, &Schmidt,2013).ThelastwasathemesessionheldattheICESAnnualScienceConferenceinCopenhagen,Denmark,in2015(ICES2015b;

     |  5NIELSEN Et aL.

Nielsen, Thunberg, Schmidt, Holland etal., 2015; Nielsen, Schmidt,Thunberg,Holland 2015) inwhich themeta-analysis of themodelswaspresented,evaluatedanddiscussed.

Themodels evaluated cover a broad range of IEEFMs coveringaspectsofcommercialmarinefisheriesandassociatedfishstocksandecosystems.Averybroadgroupofmodeldevelopersofthedifferenttypes of integrated ecological–economic marine models were con-tactedthroughtheICESWGIMMWorkingGroupsandIIFETSpecialSessions to complete thiswork. All model developers filling in themeta-analysis tools were directly involved in the review. Many ofthemodellersalsoattendedoneormoreoftheworkshops,workinggroups or conference sessions inwhich the models and the meta-analysiswerediscussed. In addition to theactualmeta-analysis,weattempttoconveysomeofthe insightsgainedfromtheevaluationsanddiscussionsattheworkinggroupmeetingsandconferencethemesessionstohelpusdrawsomesyntheticconclusionsfromthemeta-analysis that are not readily apparent just from comparing modelcharacteristics.

2.3 | Model Characteristics and Performance Evaluation Matrices

The Model Characteristics and Performance Evaluation Matricesgiven in SM Table S1 compile collective experience with and col-lective consensuson themodels as givenby themodel developersincludingfeedbacktothedevelopersfromusersduringthemodelde-velopment andmodel implementationprocesses.A full compilationofModel EvaluationMatrices for allmodels evaluated are given in

TABLE  1 Listoftabulatedmodelsandmodelabbreviationsusedintheevaluationandforreportingresults

No. Model nameModel abbreviation

1 CrabAllowableBiologicalCatchModel(CRABABC)

CRABABCa

2 CrabOceanAcidificationModel(CRABACID)

3 MultispeciesStockProductionModel MSPM

4 EcologicalModelingofMultiannualQuota(MAQ)

MAQ-ADJb

5 EcologicalModelingofMultiannualQuotawithAdjustmentRestriction(MAD-ADJ)

6 EconomicInterpretationofICESAdvisoryCommitteeforFisheriesManagement

EIAA

7 Bio-EconomicModelofEuropeanFleets(extendedEIAA)

BEMEF

8 IntegratedmodelforAustralianTorresStraitTropicalRockLobster

IMATSTRL

9 Bio-EconomicModuleConnectingEcologyandEconomy

ECOb

10 StochasticAge-StructureOptimizationModel+ITQWealthModel

STOCHHCR

11 IndividualVessel-BasedSpatialPlanningandEffortDisplacement

DISPLACE

12 IntegrationofSpatialInformationforSimulationofFisheries

ISIS-FISH

13 BalticCoupledFisheriesLibraryinRandStochasticMulti-speciesModel

BALTICFLR-SMS

14 ImpactAssessmentModelforFisheriesManagement

IAM

15 SpatialIntegratedbio-economicModelforFisheries(WageningenUniversity,NL)

SIMFISH

16 FISHRENTIFROUniversityofCopenhagen(DK)

FISHRENTc

17 FISHRENTTIThunenInstitute(D)

18 SwedishResourceRentModelfortheCommercialFisheries

SRRMCF

19 NewEnglandCoupledLobsterModel NECLH

20 20BalticSeaEcological-EconomicOptimizationModel

BSEAECON-ECOL

21 EffectsofLineFishingSimulator ELFSIM

22 AustraliaNorthernPrawnFisheryTigerPrawnsBio-economicModel

NPFTPBEM

23 SimplifiedBio-EconomicModelfortheAustralianNorthernPrawnFishery

NPFBIOECON

24 MediterraneanFisheriesSimulationTool MEFISTO

25 Bio-economicImpactAssessmentusingFisheriesLibraryinR

FLBEIA

26 FleetsandFisheriesForecastModelFcube

FCUBE

(Continues)

No. Model nameModel abbreviation

27 CoupledGeorgesbankFoodWebandComputableGeneralEquilibriumModel

GBFWCGE

28 BalticSeaAtlantisModel BSEAATL

29 CaliforniaCurrentAtlantisModel CACURRENTATL

30 SoutheastAustraliaAtlantisModel SEAUSATL

31 Size-spectrumbio-climateenvelopemodel&input/outputtables

SS-DBEM-IOT

32 GenericEcosystemModel GEM

33 PeruvianEcopathwithEcosimFoodwebModel

PERUEwE

34 BalticSeaEcopathwithEcosimFoodwebModel

BSEAEwE

35 NorthSeaEcopathwithEcosimandEcospace

NSEAEwE

aCrabOceanAcidification(CRABACID)isbasedontheCrabABCmodelsoresultsarecombinedforreporting.bMAQ-ADJisbasedonMAQwithanaddedrestrictiononquotaadjust-mentssoresultsarereportedonlyforMAQ-ADJ.cFISHRENTTIandIFROhavenearlyidenticalmodelcharacteristicsandarecombinedforpurposesofreporting.

TABLE  1  (Continued)

6  |     NIELSEN Et aL.

the SupplementaryMaterial Table S1 including the explanations ofthecategoriesherein.TheModelEvaluationMatrixsummarizesthefollowingmodelcharacteristics:(i)managementquestionsthemodeladdressed or can address; (ii) corresponding advice (biological andeconomic)themodelprovides;(iii)institutionalset-upandplatformsfor the model including needed partners; (iv) type of model inclu-dingmodellinking,couplingandlevelofintegration(linkedtotypeofmodel);(v)modeldimensionsandmodelstructure;(vi)usefulnessofthemodel;(vii)focusandtrade-offs(linkedtousefulnessabove);(viii)datarequirements;(ix)statusofthedevelopment,application,imple-mentationanduseofthemodelincasestudies;(x)disseminationofthemodel includingmodel platform, programming language, acces-sibility;and(xi)formatofoutput.Foreachoftheabovebullets,theanswerscouldbegivenaccordingtoascalingofthedegreeorlevelofthemodels,thatislow,medium,high.

2.4 | Model Categorization and Descriptors Summary Table

Eachoftheabovebulletsisusedasanaxis(roworcolumn)inamul-tidimensional diagram—the Model Categorization and DescriptorsSummary Table shown in SM Table S2, which has been filled infor each model evaluated. Detailed descriptions of the ModelCategorizationandDescriptorsSummaryTableandanexamplefor

onemodelaregivenintheTableS2.Furthermore,thecompiledma-terialisshowninthespiderwebsummaryplotsintheresultssectioninFigures2–6.

Inthesummarytable,theprimary-leveldescriptorsintherowsarecategorizedinto(i)advisorymodelsintheshortterm(fisheriesadvicewith fish stock assessment), (ii) assessmentof outcomesof existingTACorTAE(shortterm),(iii)managementstrategyevaluation(mediumterm, long term), (iv) strategic long-termadviceand (v)broaderbio-economicadvice(medium-longterm).Thesecondary-leveldescriptorsinthecolumnsofthetableiscategorizedintothreemajormodelde-scriptors covering (i)model dimensions and structure/resolution, (ii)modelcomplexityandflexibilityand(iii)modeltype(seefurtherde-scriptionsanddetailingofthisintheTableS2).

2.5 | The Model Use and Trade-Off Summary Table

TheModelUseandTrade-OffSummaryTablegiveninSMTableS3compilestheinformationthatmodeldevelopersprovidedintheModelCharacteristicsandPerformanceEvaluationMatricesandtheModelCategorizationandDescriptorsSummaryTable.Thistablenotesthepresenceorabsenceofparticularmodelcharacteristicsandqualitiesinanoverviewformthatfacilitatescomparisonacrossmodels.Thereisa rowforeachmodelandwith thecolumns indicating themodelcharacteristicsaccordingtotheprimaryuseandtypesofuse,aswell

F IGURE  1 Overviewofmainmodelapplicationsandimplementation

     |  7NIELSEN Et aL.

TABLE  2 Disseminationandpublicationofevaluatedmodels

No Model abbreviation Model publication

1 CRABABC Puntetal.,2012.

2 CRABACID Puntetal.,2014;Seungetal.,2015;Puntetal.,2016.

3 MSPM Horbowy,1996,2005.

4 EIAA Frost,Levring,Hoff,&Thøgersen,2009;.

5 BEMEF Frostetal.,2009;Carpenter&Esteban,2015;NewEconomicsFoundation2016.

6 MAQ VanDijketal.,2013.

7 MAQ-ADJ VanDijk,Hendrix,Haijema,Groeneveld,&vanIerland,2016.

8 IMATSTRL vanPuttenetal.,2012;vanPutten,Deng,etal.,2013;vanPutten,Gorton,Fulton,Thebaud2013;Plagányietal.,2012,2013;Pascoe,Hutton,vanPutten,Dennis,Plagányi,etal.,2013;Pascoe,Hutton,vanPutten,Dennis,Skewes,2013;Huttonetal.,(2016).

9 ECO² Bethke,2013a,b,2015,2016;Bethke,Bernreuther,&Tallman,2013;.

10 STOCHHCR(ITQWEALTH)

DaRocha&Gutiérrez,2011;Da-Rocha&Pujolas,2011;DaRocha&Mato-Amboage,2016;DaRocha&Sempere,2016;DaRocha,Cerviño,&Gutiérrez,2010;DaRocha,Gutiérrez,&Antelo,2012;DaRocha,Gutiérrez,&Cerviño,2012;DaRocha,Gutiérrez,Cerviño,&Antelo,2012;DaRocha,Gutiérrez,&Antelo,2013;DaRocha,Gutiérrez,Garcia-Cutrin,&Jardim,2015;DaRocha,Gutiérrez,Garcia-Cutrin,&Touza,2016;DaRocha,Gutiérrez,&Garcia-Cutrin,2016;DaRocha,Gutiérrez,Garcia-Cutrin,&Jardim,2017;Arnason,2002;WeningerandJust,2002;Heaps,2003;WeningerandWaters,2003;Weninger,2008;Kittsetal.,2011.

11 DISPLACE Bastardie,Nielsen,Andersen,&Eigaard,2010;Bastardieetal.,2013,2014;Bastardie,Nielsen,Eigaard,etal.,2015;Bastardie,Nielsen,Eero,Fuga,&Rindorf,2017;Nielsen,Kristensen,Lewy,&Bastardie,2014;www.displace-project.org(01Apr2017).

12 ISIS-FISH Mahevas&Pelletier,2004;Pelletieretal.,2009;Drouineau,Mahévas,Pelletier,&Beliaeff,2006;Drouineau,Mahévas,Bertignac,&Duplisea,2010;Duplisea,2010;Lehuta,Mahévas,Petitgas,&Pelletier,2010;Rocklin,Pelletier,Mouillot,Tomasini,&Culioli,2010;Lehuta,Mahévas,&LeFloc’h,2013;Lehuta,Petitgas,etal.,2013;Lehuta,Holland,&Pershing,2014;Lehuta,Vermard,&Marchal,2015;Rochet&Rice,2010;Marchal,Little,&Thebaud,2011;Marchal,DeOliveira,Lorance,Baulier,&Pawlowski,2013;Husseinetal.,2011a,b;Vermardetal.,2012;Gasche,Mahevas,&Marchal,2013;Reechtetal.,2015.

13 BALTICFLR-SMS Bastardieetal.,2009;Bastardie,Nielsen,&Kraus,2010;Bastardie,Vinther,Nielsen,Ulrich,&Storr-Paulsen,2010;Bastardie,Vinther,&Nielsen,2012;Bastardie,Nielsen,&Vinther,2015;Bastardie&Nielsen,2011;Nielsenetal.,2011;Feekingsetal.,(submitted).

14 IAM Macher,Guyader,Talidec,&Bertignac,2008;Macheretal.,2013;Merzéréaud,Biais,Lissardy,Bertignac,&Biseau,2013;Merzéréaudetal.,2011;Simmondsetal.,2011;Raveauetal.,2012;Guillénetal.,2013;Guillén,Macher,Merzéréaud,Fifas,&Guyader,2014;Guillén,Macher,Merzéréaud,Boncoeur,&Guyader,2015;EUSTECF,2015a,b,c.

15 SIMFISH Bartelings,Hamon,Berkenhagen,&Buisman,2015;Kempfetal.,2016.

16 FISHRENTIFRO Frost,Andersen,&Hoff,2011,2013;Lassen,AnkerPedersen,Frost,&Hoff,2013;Thøgersenetal.,2012;Salzetal.,2010.

17 FISHRENTTI Salzetal.,2011;Simons,Bartelings,etal.,2014;Simons,Döring,Temming2014;Simons,Döring,&Temming,2015a;Simons,Döring,&Temming,2015b.

18 SRRMCF WaldoandPaulrud2013a,b;2016;Paulrud&Waldo,2011.

19 NECLH Holland,2011a,b;Lehutaetal.,2014;.

20 BAL.ECON-ECOL Tahvonen,2009;Voss,etal.,2011;Voss,Quaas,Schmidt,Hoffmann2014;Voss,Quaas,Schmidt,Tahvonenetal.,2014;Skonhoftetal.,2012;Tahvonenetal.,2013.

21 ELFSIM Littleetal.,2007;Little,Punt,Mapstone,Begg,Goldman,Ellis2009;Little,Punt,Mapstone,Begg,Goldman,Williams,2009;.

22 NPFTPBEM Dichmont,Punt,Deng,Dell,&Venables,2003;Dichmontetal.,2010;Dichmont,Deng,Punt,Venables,&Hutton,2012;Puntetal.,2010;2011;Deng,Punt,Dichmont,Buckworth,&Burridge,2015;.

23 NPFBIOECON Gourguetetal.,2014,2016;Dichmontetal.,2003,2008;Puntetal.,2010,2011.

24 MEFISTO Lleonartetal.,1999,2003;Maynou,Sardà,Tudela,&Demestre,2006;Maynou,Martínez-Baños,Demestre,&Franquesa,2014;Mattos,Maynou,&Franquesa,2006;Merino,Karlou-Riga,Anastopoulou,Maynou,&Lleonart,2007;Tratniketal.,2007;SilvestriandMaynou2009;Guillénetal.,2012;Maynou,2014;Maouel,Maynou,&Bedrani,2014;Maravelias,Pantazi,&Maynou,2014;.

(Continues)

8  |     NIELSEN Et aL.

asmajortrade-offs.ThecompiledtableanddescriptionstoitaregivenintheSMTableS3.Alsoforthistable,thecompiledmaterialisshowninthespiderwebsummaryplotsintheresultssectioninFigures2–6.

Thecolumnsofthetablecategorizeeachmodel intermsofsixmajorfactors.Themainusesandfocusofthemodelare identifiedincluding: whether it is used to evaluate data needs (e.g. specifictypesofdataorspecificdatacollectionprograms);whetherithasasingle-stock,multispecies,mixed fishery or ecosystem orientation;andwhetheritprovideseconomicandsocialadvice(maincoverageofuse).Severalmodelsdo includesomesocialparameterssuchasemployment and distribution of impacts across fishing fleets andamongvesselownersandcrew.Mostmodelsincludeonlyeconomicparameters but may be used to evaluate the implications of ma-nagementchangesonbroadersocialconcernssuchassecurityofre-sourcesupplytoregionalorlocalcommunityindustry.Bio-economicmodelsmayalsoproxyforfamilystatusortraditionbymodificationsto parameters affecting fishing trip duration or fishing effort allo-cation.Thematrix table specifieswhat governancebody and leveleachmodelaremeanttoprovideadviceto (e.g.aspecificcountry,ICES, EU,Australian or NorthAmerican regional management bo-dies)andthedegreetowhichadvicefromthemodelhasbeenim-plemented(managementadvice).Thematrixtableindicateswhetherapaperbasedonthemodelhasbeenpublishedinapeer-reviewedjournaloronlya reportor internalagency/departmentdocuments,andwhether it hasbeen frequently cited.Theageof themodel isshownalongwiththelevelofmodeldevelopment(e.g.isitonlyfor

advanceusers,isthereabigmultiuserdevelopmentgroup,isthereawebsiteforthemodel?).Thiscoversthelevelofmodeldevelopment,applicationandimplementation.Finally,trade-offsinmodelusearenotedaccordingtowhetherthemodelissimpleorcomplex,whetheritisspecializedorflexible,andwhetherthemodelisusableonlybymodeldevelopersor isopenaccessanduser friendly. In theTableS3,furtherdetailsanddescriptionsofthedifferentcategoriesinthematrixtableareprovided.

2.6 | Spider web charts with frequency classification of the models

Asetofsemi-quantitativespiderwebplots(Figures2–7)isproducedbasedonthecompiledmodelsummaryanddescriptortables.Here,eachof the rowsor columns in the summary tables isdepicted inspiderwebplots inwhich the frequencyofmodelsbelonging toacertain category with respect to model properties, characteristicsortypeofmodelcanbesummarizedaccordingtocriteriausedforevaluatingthemodels.Thefrequencyplotsareusedtocomparethefocus and capabilityof thedifferentmodels andwhatmaindirec-tionsofdevelopmentthedifferentmodelsrepresent.Forexample,the figures summarize the findings in terms of the level of imple-mentationofthemodelsaccordingtothepurposeofthemodels,forexamplewhether it is foracademicpurposes,application inadviceandmanagement,andwhetherthemodelisfullydevelopedandin-tegratedornot.

No Model abbreviation Model publication

25 FLBEIA Garcia,Santurtun,Prellezo,Sanchez,&Andres,2012;Garcia,Urtizberra,Diez,Gil,&Marchal,2013;García,Prellezo,etal.,2016;García,Sanchez,Prellezo,Urtizberea,Andres2016;Jardimetal.,2013;Prellezoetal.,2016.

26 FCUBE ICES2006,2014a,b;Hoffetal.,2010;Ulrichetal.,2011,2017;Iriondoetal.,2012;Maravelias,Damalas,Ulrich,Katsanevakis,&Hoff,2012;Jardimetal.,2013;EUSTECF,2015b;ICES2015c,d.

27 GBFWCGE Seung2006;Steeleetal.,2007;Pan,Failler,&Floros,2007;.

28 BALTICATL Fultonetal.,2011;Palaczetal.,2014,2015,InRevision;Nielsen,Thunberg,etal.,2015;Nielsenetal.,2015b;Nielsen,Palacz,etal.,2015.

29 CACURRENTATL Kaplanetal.,2009,2012,2014;Fultonetal.,2011;Kaplan&Leonard,2012;.

30 SEAUSATL Fultonetal.,2011;vanPutten,Gorton,Fulton,Thebaud2013;vanPutten,Deng,etal.,2013;Fultonetal.,2014;.

31 SS-DBEM-IOT Fernandesetal.,2013;Fernandes,Kay,etal.,2016;Fernandes,Papathanasopoulou,etal.,2016;Queirósetal.,2015.

32 GEM Ravn-Jonsen2011;Andersen,Brander,Ravn-Jonsen2014;Andersen,Andersen,Mardle2014;Ravn-Jonsenetal.,2016.

33 PERUEwE Polovina1984;Christensen&Pauly,1992;Christensen&Walters,2004;WaltersandMartell2004;WaltersandChristensen2007;Waltersetal.,1997,1999,2000;Christensenetal.,2011,2014;Bevilacqua,Carvalho,Angelini,Steenbeek,&Christensen,Inprep.

34 BSEAEwE Polovina1984;Christensen&Pauly,1992;Christensen&Walters,2004;WaltersandMartell2004;WaltersandChristensen2007;Waltersetal.,1997,1999,2000;Tomczaketal.,2012,2013.

35 NSEAEwE Polovina1984;Christensen&Pauly,1992;Christensen&Walters,2004;WaltersandMartell2004;WaltersandChristensen2007;Waltersetal.,1997,1999,2000;PlagányiandButterworth2004;Mackinson,2014;Mackinson&Daskalov,2007;Mackinson,Deas,Beveridge,&Casey,2009;Heymans,Mackinson,Sumaila,Dyck,&Little,2011;ICES2011;Romagnonietal.,2015;Colléteretal.,2015.

TABLE  2  (Continued)

     |  9NIELSEN Et aL.

3  | RESULTS

Theresultsof theglobal reviewcoveracomparativeevaluationof35 IEEFMs (Tables1–2,Figure1).TheselectedmodelsrepresentabroadrangeofIEEFMs,butalladdresscommercialfisheriesandas-sociatedfishstocks.Themetadatacollectedforeachmodelprovidedinformation on capabilities, model structure, trade-offs andmodeluses.Throughoutitisimportanttokeepinmindthattheevaluationsof model characteristics are primarily based on self-assessmentsprovided by themodellers themselves. In this section,we presentsummaryinformationfortheseself-assessmentsacrossallmodelsoneachof theseaforementioneddimensions.Throughoutweuse themodelabbreviationsnotedinTables1-2.Thegeographicaldistribu-tionofmodel implementation isshown inFigure1.Several modelshave been widely implemented, for example Atlantis and EwE,andonlya fewexamplesofspecific implementationsareshown inFigure1.Someofthe35modelsanalysedareincludedwithseveral

implementationsandsimilarmodelshavebeenclustered(Tables1–2) resulting in 32 categories in the model meta-analysis plots inFigures2-8. The order and sequence of themodels in Tables1–2andaccordingly in theFigures2–8wasdeterminedby typeof ad-vice addressed and units included in the models (data collection,single-stock,multispecies,mixedfishery,bio-economics,ecosystem;Figure2Panel3)aswellasaccordingtocompletenessandintegra-tionofmodules(biologicalsuchassingle-/multispeciesonly,single-stock economic, multispecies economic, multispecies ecosystem/economic;Figure2Panel4).

Figure2reportstherangeofcapabilitiesintermsoftypeofman-agementadvice fromshort to long term (Panel1)and input/outputtypeofadvice(Panel2),structuralcomponentsintermsofadvicelevel(Panel3)andstructuralmodulesandlinkagesincludedinthemodels(Panel4).

Panel1showsthemanagementadvicecapabilitiesasconcentricringswheretheinnermostringrepresentsmodelsthatmaybeused

F IGURE  2 ModelcapabilitiesPanel1—modeldesigncapabilitiestoprovideshort-termtactical,medium-termMSEorlong-termstrategicadvice;Panel2—modelcapabilitytoprovidemanagementadviceonsettingTACs,effortlimits,ITEandITQ;Panel3—modelstructuralcharacteristicsintermsofadviceondatacollection,stocks,fleets,economicandecosystemcomponents;Panel4—modeluseindexintermsofincludedmodulesandtheirlinkagesforbiology(stocks),economicandecosystems

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENT

SRRMCFNECLH

ELFSIMMEFISTO

NPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwE

B SEA EwEN SEA EwE

1 = Short-term advice2 = Medium- term MSE3 = Long-term strategic

Panel 1

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENT

SRRMCFNECLH

ELFSIMMEFISTO

NPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwE

B SEA EwEN SEA EwE

Panel 2

1 = TAC or Quota2 = Effort3 = ITE4 = ITQ

0

1

2

3

4

5

6CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENT

SRRMCFNECLH

ELFSIMMEFISTO

NPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwE

B SEA EwEN SEA EwE

1 = Data collec on2 = Single stock3 = Multispecies4 = Mixed fiskery5 = Bio-economic6 = Ecosystem

Panel 3

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

Model Use Index1 = Single/multispecies only2 = Single stock/economic3 = Multispecies/economic4 = Multispecies/ecoystem/economic

Panel 4

10  |     NIELSEN Et aL.

toprovideshort-termadviceonTACsorimpacts,andtheouterringrepresentsmodels that aredesigned toprovide long-termstrategicadvice.Foranygivenmodel,therangeofcapabilitiescanbetraced

along the ray emanating from theorigin to themodel abbreviationwhereamarkeroneachringdenotes thepresenceofeachcapability(short-term tactical advice (1), medium-term MSE advice (2) and

F IGURE  3 ModelcharacteristicsPanel1—modelfishingfleetcharacteristics(entirefishery,métiersoragent-based);Panel2—modelspatialresolutioncharacteristics(VMStrack,subarea,stockarea,regions,orecosystem);Panel3—modelbiologicalcharacteristics(age-structured,size-structured,orbiomass);Panel4—modeltimestep(season,year,multiyear);Panel5—modelcharacteristicsintermsofstatic,dynamicorequilibriumwithrespecttocoupling;Panel6—modelcharacteristicsintermsofsimulationand/oroptimizationalgorithms

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1= Full Fishery2 = Single Métier3 = Multiple Métiers4 = Agent-based

Panel 1

0

1

2

3

4

5CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-…

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Ecoystem2 = Region3 = Stock area4 = Stock subareas5 = VMS track

Panel 2

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE1 = Biomass2 = Size3 = Age

Panel 3

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Year2 = Muliple years3 = Season

Panel 4

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Static2 = Equilibrium3 = Dynamic

Panel 5

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Simulation2 = Optimization3 = Both

Panel 6

     |  11NIELSEN Et aL.

long-term strategic advice (3). For example, 15models include thecapabilitytoprovideshort-term,medium-termMSEandlonger-termstrategicadvice.Bycontrast,MAQ-ADJandGEMaredesignedonly

for long-term strategic advice.However, these twomodels are theexceptionasallothermodelsareconstructedtoprovidemultiplead-visorycapabilities.

F IGURE  4 ModelcharacteristicsPanel1—fishingsectorcomponents(catchsector,fisherysystemincludingprocessinganddistribution,communities,andmultiplesectorsofalocalorregionaleconomy);Panel2—estimationofmodelparameters(qualitativeindicators,deterministicorstochasticparameters),Panel3—modelcharacteristicsintermsofuseofmarketprices,considerationofthevaluechainandinclusionofnon-marketvalues;Panel4—typeofembeddedinteractions(linear,nonlinearorboth);Panel5—natureofembeddedeconomicbehaviouralmodel(tactical,strategicornobehaviouralmodule);Panel6—includedfunctions(recruitment,catchability,fishpricesandharvestcosts)

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

Panel 1

1 = Catch sector2 = Fishery system3 = Communities4 = Multi-sectors

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

Panel 2

1 = Qualitative indicator2 = Deterministic3 = Stochastic

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

Panel 3

1 = Market values2 = Value chain3 = Non-market values

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Linear2 = Nonlinear3 = Both

Panel 4

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Tactical2 = Strategic3 = None

Panel 5

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Recruitment2 = Catchability3 = Fish prices4 = Harvest costs

Panel 6

12  |     NIELSEN Et aL.

Panel 2 of Figure2 shows structural components of themodelcapabilitywith respect to typeofadviceprovidedby themodel.AswasthecaseforPanel1,markersoneachconcentricringdenotethepresenceofeachoffourcomponentssuchasoutputadviceonTACandquota(1),inputeffortadvice(2),individualtradeableeffortquotaadvice(3)orindividualtransferablequotaadvice(4).Mostmodelspro-videTAC-Quotaadvice(22models).Threemodelsprovideadviceonall four levels.Another threemodelsprovideadviceonthree levels,whileeightprovidebothTAC-quotaandeffort-basedadvice,butnoadvice relevant to individual effort or catch quotas. In total, threemodels provide only individual-based advice covering both output(ITQ)andinput(ITE)advice,twomodelsprovideonlyITQadvice,andonemodelprovidesonlyITEadvice.Finally,onemodelprovidesonlyeffort-basedadvice.

Panel3ofFigure2showsthestructuralcomponentsincludedineachmodelintermsofadvicelevel.Markersoneachconcentricringdenotethepresenceofeachofsixcomponentswithadviceondatacollectionlevel(1)single-stocklevel(2),multispecieslevel(3),mixed

fisherylevel(4),bio-economiclevel(5)andecosystemlevel(6).Withafewexceptions,single-speciesmodelscanbescaleduptomultispe-cies,althoughthisdoesnotnecessarilymeanthattheoppositeisalsotrue. In total, 28models includemultispecies,25 includemixed fish- eries, 34 include bio-economic functions or parameters, and nineincludeecosystemconsiderations.Allninemodels that includeeco-systemsalso includemixedfisheries,bio-economicandmultispeciesstructural componentsexcept foronenot includingmixed fisheries.TheAtlantismodeldoesincludethecapacitytocoverindividualspe-cies and to have that in a foodwebwith functional groups (eitherage or size resolved or biomass pools).TheECO2 has the potentialtoformulatesimplebiologicalmodelsatpresentuptofullecosystemmodels in future.The termmultispecies here should inmost cases(exceptforthebelowmentioned)beinterpretedasmultistockwhereseveral species single-stock assessments have been included. Onlyvery few models include dynamic full feedback biological/trophicinteractions and/or estimate fish naturalmortality (mortality due tonaturalcauses)asfunctionof,forexample,predationpressure.Such

F IGURE  5 Modeltrade-offsPanel1—expertiserequiredtoconductmodelruns(developer,specializedexpertiseortraining,orgeneralexpertise);Panel2—modelapplications(specialized,simpleorflexible);Panel3—modelaccessibilitytoendusers(softwarerequired,openaccessanduser-friendliness);Panel4—relationshipbetweenmodelcomplexityanddataneeds(simplewithlowdataneeds,simplewithhighdataneedsandcomplexwithhighdataneeds)

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Developer2 = Specialized expertise3 = General expertise

Panel 1

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Specialized2 = Simple3 = Flexible

Panel 2

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE1 = Software requirement2 = Open Access3 = Manual/Website4 = User-Friendly

Panel 3

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

1 = Simple/Low data2 = Simple/High data3 = Complex/High data

Panel 4

     |  13NIELSEN Et aL.

F IGURE  6  : Summary of model use Panel1—modelimplementation(none,low,medium,high);Panel2—academicuse(modelsthatonlyhavetechnicalreports,modelsthathavebeenpublishedinthepeer-reviewedliteratureandmodelsthathavebeenwidelycited),Panel3—levelofadviceformodels(National,EU,NationalandEU,EUandICES,National,EUandICES)

0

1

2

3

4CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

DISPLACE

STOCH HCR

ECO²

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEN

PERU EwEB SEA EwE

N SEA EwE

Model Implmentation1 = None2 = Low3 = Medium4 = High

Panel 1

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

STOCH HCR

ECO²

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCF

NECLHELFSIM

MEFISTONPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwEB SEA EwE

N SEA EwE

Academic Use1 = Report only2 = Peer-reviewed3 = Freq citations

Panel 2

0

1

2

3

4

5MSPM

MAQ-ADJEIAA

BEMEF

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENTSRRMCFNECLH

NPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL.

MEFISTO

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

SS-DBEM-IOT

B SEA EwEN SEA EwE

Advice Level1 = Nation2= EU3 = Nation/EU4 = EU/ICES5 = Nation/EU/ICES

Panel 3

14  |     NIELSEN Et aL.

explicit modelling of biological interactions is only performed by afewecosystemandmultispecies-interactionmodelssuchasAtlantis,EwE,SS-DBEM-IOT,GBFWCGE,Baltic-FLR-SMSandtheBaltic-Econ-Ecolmodels.TheGEMexplicitmodelsabio-energeticbudgetoftheindividualpredatorandtherebylinkssomaticgrowthwiththepreda-tionmortalityinflictedonitsprey.

Panel4ofFigure2providesascoreforstructurallinkagesintermsofsingleormultispecies,bio-economicsandecosystem.Inthiscase,the position of eachmodel on the concentric circles is interpretedas the levelofstructural linkagewhereascoreof1meansthat themodelonlyincludessingleormultiplespecies;themodelhasneitherbio-economicnorecosystemlinkages.Ascoreof2denotesasingle-species model that is linked with bio-economics. The majority ofmodels(17)hadascoreof3,whichdenotesmodelsthatincludemulti-speciesandbio-economicslinkages.Modelsthatincludemultispecies,bio-economicandecosystemlinkages(9)werescoreda4.

ModelcharacteristicsarereportedinFigure3intermsoffishingfleet(Panel1),spatialresolutionand/orcoverageofadvice(Panel2),biologicalcharacteristics(Panel3),timestepand/orcoverageinadvice(Panel 4), dynamics (Panel 5) and algorithmused toproducemodeloutputs(Panel6).Ineachpanel,concentricringswithmarkersindicatethepresenceofaspecificmodelcharacteristic.

Panel1ofFigure3showsthedifferentwaysmodelsincorporatefishingfleetswherethetreatmentoffleetsineachmodelcanbeas-certained along the ray from the origin to themodel abbreviation.Nearlyallmodels incorporatefull fishingfleets,while24models in-corporatemultiplemétiersandonlytwoincorporateexclusivelysinglemétier.Onlythreemodels(DISPLACE,IAMandELFSIM)capturefish-ingfleetsasindividualvessels.

Panel2ofFigure3reportsthespatialresolutionandcoverageinadvicesupportedbyeachmodelwheretheresolutionforeachmodel(ecosystem(1),region(2),stockareas(3),stocksubareas(4)andVMStrack(5))isdenotedbyamarkerineachconcentriccircle.EcosystemisamorecomplexbutspatialcoarseresolutionthanVMStrack.OnlyDISPLACEincludesaspatialresolutionatthelevelofVMStrack.Note

thatDISPLACEmayalsobeappliedatastockareaorregionalspatialresolution.Twelvemodelshaveaspatialresolutionneededtoevaluatestocksubareas,ofwhich11canbescaleduptoastockarea.Ninemod-els(allAtlantisandEwEapplications,GBFWCGE,SS-DBEM-IOT,GEM)supportanecosystemspatialresolution,althoughallbutGEMandtheEwEapplicationsarescalabletoaregion,stockorstockarea.ISIS-FISHisscalabletoaregion,stockorstocksubarea.Thisclassificationofthemodelsenables theuser todistinguishwhether themodels are spa-tiallyexplicitornot,thatisdotheyonlycoveronearea(region,orstockdistributionarea,orfisheryarea,orecosystem)ordotheycontainandcoverseveralareasandspatialunits (stocksubareas,ecosystemsub-areas,otherspatialdistinctionsuchas ICESsubareas,statisticalrect-angles)ordotheyfollowveryhighspatialresolutiononahaultohaulbasis (or similar)oronanagent-based levelwhen forexampleusingVMSdata.

Panel3ofFigure3showsthebiologicalcharacteristics(biomass,e.g.,productionmodels(1),size-based(2),andage-based,forexamplevirtualpopulationanalysis(VPA)(3))embeddedineachmodel.Inthemajorityofmodels (21),stockdynamicswereoftheage-basedVPAtype.Oftheseage-basedmodels,10models(CRABABC,DISPLACE,ISIS-FISH,NECLH,ELFSIM,NECLH,MEFISTO;NPFTBEM,SS-DBEM-IOT and GEM) also include size-based biological considerations. ItshouldbenotedthatcertainecosystemmodelssuchastheAtlantismodelhasemergentsize-at-age,thatisnotafixedgrowthcurve,soit also takes size-based interactions into account (e.g. throughgapelimitationandsizeconstrainedreproduction).Whetherage-basedorsize-based,most of thesemodels are scalable up to an estimateofbiomass. Age-based models like DISPLACE, Baltic-FLR-SMS, IAM,NECLH,MEFISTO,Baltic-Econ-EcolandGEMcertainlydoincludebio-massestimation.Sevenofthemodelsincludedinthisstudy(MSPM,MAQ-ADJ,EIAA,BEMEF,ECO2,FISHRENTIFROandSRRMCF)areproductionmodels, for example of the Schaeffer or Cobb–Douglastype,basedsolelyonbiomass.

Panel 4 of Figure3 reports the time steps and time resolutionand/ortemporalcoverageinadviceforeachmodelasseasonal(e.g.

F IGURE  7 Effectofmodelaccessibilityandrequiredexpertiseonmodelimplementation

0

1

2

3CRAB ABC

MSPMMAQ-ADJ

EIAA

BEMEF

IMATSTRL

ECO²

STOCH HCR

DISPLACE

ISIS-FISH

BALTIC FLR-SMS

IAM

SIMFISH

FISHRENT

SRRMCFNECLH

ELFSIMMEFISTO

NPFTPBEM

NPF BIOECON

BALTIC ECON-ECOL

FLBEIA

FCUBE

GBFWCGE

BALTIC ATL

CA CURRENT ATL

SE AUS ATL

SS-DBEM-IOT

GEM

PERU EwE

B SEA EwEN SEA EwE

ImplementationExpertiseAccessibility

     |  15NIELSEN Et aL.

less than an annual time step) (1), a year (2) or multiyear (3) timeperiod, where the time step capability is indicated by a mark oneachconcentriccircle.AllbutfiveoftheIEEFMsareannuallybased,thatiswithyearlytimestepsandcoverage,andmany(20)ofthoseoperatewithmultiannualaspects.Morethanhalf(18)ofthemodelsareseasonallyexplicitaswellindicatinggeneralhightimeresolution.Severalof themodels thatcanberunandprovideadvisoryoutputforayearormultipleyearshavefinerscaletimesteps/resolutionintheirmodellingprocess,forexampleAtlantiscanberunwith12-to24-hourtimestepsthatisthenrunouttoyearormultipleyears.Intotal,20modelscanberunformultipleyears,27modelscanberunonanannualbasisand18onaseasonalbasis.MSPMisanannualmodelbut canbe runovermultipleyearswhileFLBEIA, aswell asseveralothers(eightmodels),includesseason,annualandmultiyearmodellingcapabilities.

Panel5ofFigure3identifiesmodelperformanceintermsofwhethertheprocessesconsideredarestatic (1),equilibrium(2)ordynamic (3).The majority of models (26) incorporate dynamic processes while 3(MSPM,STOCHHCRandFISHRENTIFRO)alsoincorporateprocessesbased on equilibrium conditions. Only two models (BALTIC ECON-ECOLandGBFWCGE)haveprocessesexclusivelybasedonequilibriumconditions.ISIS-FISHhaselementsofbothstaticanddynamicprocesseswhileEIAA,BEMEF,SS-DBEM-IOTandSRRMCFarestaticmodels.

Panel6ofFigure3indicatesthetypesofalgorithmsusedtopro-ducemodeloutputs.Amarkeron the inner ring (1)means that themodelusesa simulationalgorithm.Amarkeron the second ring (2)denotesmodels that arebasedonanoptimization algorithm, andamakeron theouter ring (3) indicatesmodels that incorporateboth,simulationandoptimizationalgorithms.Lessthanhalfofthemodels(14)aresimulationmodels,2arestrictoptimizationmodels,whilethelasthalf(16)incorporatebothtypesofalgorithms.

Figure4 reports additional model characteristics of the IEEFMswithfocusoneconomiccharacteristicsandsectorcoverage.

Panel 1 of Figure4 explores fishing sector components in themodelcoveragecategorized intocatchsector (1), fisherysystem in-cludingprocessinganddistribution (2), societalcommunities (3)andmultiplesectorsofalocalorregionaleconomy(4).Allmodelsaddressthecatchsectorandofthose21alsoaddressthewiderfisherysystemand8 also address communities.Only twomodels (GBFWCGEandSS-DBEM-IOT)covermultiplesectors.

Panel 2 of Figure4 evaluates the estimation of model parame-ters covering qualitative indicators (1), deterministic parameters (2)orstochastic(3).Mostmodels(25)includedeterministicparameters,while12ofthe25alsoincludestochasticparameterestimation.Afewmodels includebothqualitative indicatorsand stochasticparameterestimation (3) or deterministic parameters (1) while only five mo-dels includeexclusively stochastic parameter estimation (MAC-ADJ,STOCHHCR,DISPLACE,BALTICFLR-SMSandELFSIM).

Panel3ofFigure4showsmodelcharacteristicsintermsofuseofmarketprices/values(1),considerationofthevaluechain(2)andinclu-sionofnon-marketvalues(3).Allmodels,excepttheMSPM,includemarketvalues,whilesixalsoconsiderthevaluechainandtwoincludebothmarketandnon-marketvalues.

Panel4ofFigure4explores the typeof embedded interactionscoveringlinear(1),nonlinear(2)orboth(3).Mostmodels(23)includenonlinear interactions,whileeight includeboth.Onlyonemodel in-cludedonlylinearinteractions.

Panel5ofFigure4investigatesthenatureoftheembeddedeco-nomicbehaviouralmodelcoveringnobehaviouralmodule(1),astra-tegicmodule(2)oratacticalmodule(3)included.Mostmodelsincludetacticalmodules(21)andofthosenineincludealsostrategicmodules.Only fourmodels include only strategic behaviour, and fivemodelshave no behaviouralmodule included (CrabABC,MSPM, SRRMCF,NPFBIOECONandFCUBE).

Panel 6 of Figure4 explores some basic functions included inthemodelsinrelationtorecruitment(1),catchability(2),fishprices(3) and the harvest costs (4).Mostmodels include indicators andparameters for recruitment, catchability, costs and prices. Somemodelshavethoseindicatorsincludedasendogenousrelationships,other models use exogenous relationships for those indicators,whileothermodelsincludelinearornonlinearinteractionsforthoseparameters.

Modelstypicallyrequiretrade-offsthatneedtobemadethatcanaffecthowthemodelmaybeappliedtoaddressamanagementques-tion.Someofthekeytrade-offsamongmodelsthatwereevaluatedfor this studyare reported inFigure5.Someof these trade-offs in-cludetheexpertiserequiredtoconductanalyses (Panel1), rangeofapplicationsanddegreeofspecialization(Panel2),accessibilitytoendusers (Panel3) and the relationshipbetweenmodel complexity anddataneeds(Panel4).

Amarkerintheinnerring(1)ofFigure5,Panel1denotesmodelswhere analyses ormodel runs need to be conducted by themodeldeveloper.Thereare15modelsthatfallintothiscategory.Amarkerinthesecondring(2)ofPanel1meansthatanalysesdonotnecessarilyneedtobeconductedbythemodeldeveloperbutrequirespecializedexpertiseorsignificanttrainingbeforeobtainingproficiencyinusingthemodel.Fourteenmodelsrequirespecializedexpertise.Theouterring(3)denotesmodelsthatcanbeusedwithsometrainingbutcanbeusedby individualswithgeneralexpertise.Thesemodels includeFLBEIAandMEFISTO.

Panel 2 of Figure5 reports trade-offs along a continuum fromspecialized to flexible in terms of possible uses and managementapplications the model can address.With very few exceptions, allmodelswereself-assessedasbeingcomplex.For this reason,com-plexitywasnotincludedinPanel2sincedoingsowouldnotprovideanymeaningful information for thepurposeofmodelcomparisons.Amarkerintheinnerring(1)indicatesmodelsthathavebeendevel-opedtoaddressaspecializedfisheryorspecificapplicationforspecialmanagementissues.Thesemodels(7)includeCRABABC,IMATSTRL,BALTIC ECON-ECOL, NPFTPBEM, SS-DBEM-IOT, MEFISTO andPERU EwE. Twomodels (NECLH and STOCHHCR) are placed onthesecondring,whichdenotessimplemodels, that is lesscomplexmodelswithanintermediatelevelofapplicationwithrespecttoap-plicationandmanagement issuesthatcanbeaddressed,that isbe-tweenthespecialized/specificapplicationandthecapabilityofgeneralapplicationaddressingseveralmanagementissues.Allothermodels

16  |     NIELSEN Et aL.

lieontheouterring(3),whichdenotesmodelsthatmaybeappliedinawide rangeof fisheriesand/or toaddressmanydifferentman-agementissues.

Panel 3 of Figure5 reports accessibility trade-offs.Amarker ontheinnerring(1)ofPanel3denotesmodelsthatwouldrequireusertoobtainorpurchasespecializedorproprietarysoftwarepriortousingthemodel.Manymodelsbelongtothiscategory(10).Amarkeronthesecondringmeans that themodelhas,on thecontrary,beenmadeavailableasopenaccesswhichisthecasefor22ofthemodels.Inafew(5)ofthesecases,accesshasbeenprovidedasafreedownloadfromawebsite,andsometimesthereisalsoanelaboratedusermanualavailableatthepublicwebsite. Inthiscase,themodelhasamarkerin the third ring (3).Amarkeron theouter ring (4)denotesmodelsthatalsoarebothopenaccessanduserfriendly.ThesemodelsincludeBEMEF,ISIS-FISH,MEFISTOandtheEwEapplications.

Panel4ofFigure5showstherelationshipbetweenmodelcom-plexityanddataneedswheresimplewithlowdataneedsareplacedontheinnerring(1),simplewithhighdataneedsonthecentrering(2)andcomplexwithhighdataneedssituatedontheouterring(3).By far, the majority of models are highly complex with high dataneeds (23),while twoare in the secondcategoryand seven in thefirstcategory.

An important consideration in the present model evaluation iswhether and howmodels are used.Model usemay be conditionalonthestageofmodel implementation. Insomecases,theyareonlyusedinanacademicsettingtofurtherdeveloporimprovemodellingcapabilities.Inothercases,theyareused(orintendedtobeused)toprovideadvicetodifferentlevelsofmanagementorganizations.IntheSMTableS3,aModelUseandTrade-OffSummaryTableisgivenwithanoverviewofallIEEFMsevaluatedaccordingtomainuseandtypesofuse,aswellasmajortrade-offsinrelationtotheuse.Basedamongotheronthistable,theFigure6givesanoverviewandreportsmodelcomparisonsoneachofthedimensionsofmodeluse:model imple-mentation(Panel1),academicuse(Panel2)andmanagementadvicelevelandorganizations(Panel3).

Panel 1 of Figure6 provides an ordinal rating of eachmodel interms of level of implementation from models that have been de-velopedbuthavenotbeenappliedtoanyspecificissue(1)tolevelsoflow(2),medium(3)andhigh(4)implementation.ModelsthathaveahighlevelofimplementationincludeEIAA,IMATSTRL,STOCHHCR,ISIS-FISH, ELFSIM, NPFTPBEM, FLBEIA, FCUBE, SEAUS ATL, SS-DBEM-IOTandtheEwEapplications(intotal13).Bycontrast,mod-elsthathavenotyetbeenimplementedincludeCRABACID,BALTICECON-ECOL, NPF BIOECON, GBFWCGE and BALTIC ATL (5). Allothermodelswereratedaseitheralowormediumlevelofimplemen-tationwithsevenmodelsineachofthosemainratings.

Panel2ofFigure6isanordinalratingofeachmodelintermsofacademicdisseminationanduse.Modelswhereatechnicalreporthasbeenpreparedbutnot throughthepeer-reviewed literaturearede-notedas1,modelsthathavebeenpublishedinpeer-reviewedjournalsaredenotedas2,andpeer-reviewedmodelsthathavebeenfrequentlycitedaredenotedas3.BothBALTATLandBEMEFprovide techni-cal reports but have not appeared in the peer-reviewed literature;

however,apaperhasbeensubmittedonthefirst.Eightmodelshavebeenfrequentlycitedinpeer-reviewedacademicjournals.Thesefre-quentlycitedmodelsincludeIMATSTRL,ISIS-FISH,ELFSIM,FCUBE,SS-DBEM-IOT, GEM and EwE (8).All othermodels (22) have beendocumentedinpeer-reviewedliterature.

Panel3ofFigure6reportstheadvicelevelandtypesofmanage-mentorganizationsforwhicheachmodel isdesignedtoprovidead-vice.Here,we limit our focus tomodels that havebeendevelopedtoprovideadvicetoEuropeanmanagement institutions.For report-ingpurposes,weassigna1tomodelsthatseektoprovideadvicetomanagementorganizationsinasinglenation.Weassigna2tomodelsthatmayprovide advice toEUnationsormanagement institutions.A3isassignedtomodelsthataddressbothsinglenationandEUad-vice;a4isassignedtomodelsthatmayprovideadvicetoboththeEUandtoICES;a5isassignedtomodelsthatprovideadvicetoNationalmanagementbodies,theEUandICES.Sevenmodels(MSPM,ECO2,SRRMCF,NECLH,NPFTPBEM,NPFBIOECONandGBFWCGE)havebeendesignedtoonlyprovideadvicetoNationalmanagementbodieswhichcovertohighextentnon-EUmodels.Threemodels(MAQ-ADJ,STOCHHCRandBALTICECON-ECOL)addressEUmanagementcon-cernsalone,whileEIAAandMEFISTOaddressbothEUandNationalmanagementinstitutions.TheBALTICATLandSS-DBEM-IOTaddressbothEUand ICESmanagementconcerns,andallothermodels (11)aredesignedtoprovideadvicetomanagementbodiesattheNational,EUandICESlevels.

Theuseofamodelisdependentonthecombinationsandtrade-offsinrelationtomodelimplementation(experiencewiththemodel),modelexpertiseneededtousethemodelandtheaccessibilityofthemodeltousers.Figure7illustratestheintegratedcategorizationofthemodelsaccordingtothosethreecriteriaandevaluatestheeffectofmodelaccessibilityandrequiredexpertiseonmodelimplementation.Thelevelsofcategorizationoftheringsinthespiderwebchartinclude0:none,1:low,2:mediumand3:high.Therearenostrongorgeneraltrendsobserved;however,thereisatendencytowardshigherimple-mentationwhenaccessibility ishigherandwhencomplexityandex-pertiserequirementsaremoderate.Also,thereisatrade-offinmodeluseandlevelofimplementationwiththeageofthemodelswhichisanalysedinFigure8.Itappearsthatallmodelswithnoimplementa-tionhaveanageof5yearsorless,andmostofthemodelswithlowormedium implementation are also “young”modelswith an age of5yearsorless.However,arelativelyhighproportionofmodelswithhighimplementationalsohavealowageof5yearsorless,butinthiscategory,thesumofmodelswithhigherageof6-10and11-15yearsishigherthanyoungmodels.

4  | DISCUSSION AND CONCLUSIONS

Thisstudycomparesandcontrasts35IEEFMswithawidediversityofcharacteristicsanduses.Thisdiversityreflectsrecognitionbymo-dellersthatnosinglemodelapproach,structureororientationisap-propriateforallneeds.Thisrequiresmodellerstomaketrade-offstobestmeettheneedsoftheintendedusesandusersforeachmodel.

     |  17NIELSEN Et aL.

Our aim is to helpmanagers and scientists better understand howandwhythecharacteristicsofIEEFMsvarysomuch,whattrade-offsmodellers face, and what they have learned from developing andcommunicating thesemodels.Thedocumentationof thecharacter-istics of the specificmodels, the development of themethods andspecific tools toevaluate andcategorizemodel characteristics, andwhat themodeldevelopers seeas themodel strengths capabilitiesandlimitationsalsoprovidepotentialusersandothermodellerswithinformationabouthowmodels(andmodellers)maybeusefultothemeithertoprovidemanagementadviceorindevelopingnewmodels.Accordingly,wecanhelpmanagersandscientistschoosingthemostappropriatemodelsfortheirspecificsystems,advisoryandmanage-mentneeds,andquestionstobeaddressed.Givenpreviousexperi-encesandexpertknowledge,wecanprovidemethodsand insightsonwhataspectsofmodelstobeawareofandimplementationissuesofthemodels.

Thismeta-analysis,basedonself-assessmentsbymodeldevelop-ers,compilestheexperienceofmanydifferentmodellers.Wefoundthat it was important to collect metadata from model developersrather than just use a “standard literature review”becausemanyoftheabovequestionscanonlybeansweredwiththeinsightthemodeldevelopershaveontheirownmodels.However,responsescompiledinthedevelopedmeta-analysistoolsdependonmodellers’perceivedideasandinsight,forexamplecomplexityofamodeldependspartiallyon theeyeof thebeholder. For this reason, it is important tohavethesametypeofpeople(inthiscasemodeldevelopers)fillinginthematricesandsummarytables.Atthesametime, ithasbeenimport-anttohaveabalancedgroupevaluatingthemodelswithparticipationof economists, biologists, ecologists, theoretical people and peopleworkingwithappliedadviceandmodelimplementation.Thepresentgroupofmodeldevelopersrepresentssuchabalancedgroup,andithasbeenveryusefultohavegroupdiscussionsduringworkinggroupand conferencemeetings among scientists of different fields in thepresentevaluation.

4.1  | GENERAL CHARACTERISTICS OF THE EVALUATED IEEFMS OBTAINED FROM THE META- ANALYSIS

Mostofthemodelsreviewedarecase-specific—designedoratleastparameterizedforspecificfisheriesandareasandsometimestoad-dressspecificmanagementquestions.However,anumberofmodelsare basedonmore genericmodelling platforms but are parameter-izedforparticularareasandfisheriesandmayalsofocusondifferentoperatingmodelswithin themore generalmodels (e.g. various ap-plicationsoftheAtlantisandEwEecosystemmodels).Mostmodelsreviewedprovideshort-term(tactical)adviceandmedium-termman-agementstrategyevaluation(MSE),whileonlyabouthalfprovidebothshort-termandmedium-termadvice,aswellasmedium-termMSE.Inmanysituations,adequatedetailedecosystemdataand/orlong-termtime-seriesdataarenotavailabletoobtainadequateprecisiontopro-viderobustparametersforshort-termadvicewiththesemodels.Thisisparticularlytrueforthemorecomplexmodelswithmultiplespeciesorfine-scalespatialdynamics.However,nearlyallmodelscanprovidelong-termstrategicadvice.

Most models were classified as multistock (multispecies) andmixedfisheriesmodelshavingmodulesthatalsoconsideredeconomicsin relationtofisheries(métiers).Mostofthesemodelsareactuallymul-tistock models, that is considering several stocks in a mixed fish-eries context with technical interactions between fleets, but notmultispecies in the sense that they integratebiological interactions,for example predation, between the different fish stocks, or eco-system interactions. Only a few IEEFMs include biological interac-tions,forexampleactualfishmultispeciesprey–predatorinteractions,and/or trophic dynamics and interactions (the Atlantis and EwE applications, SS-DBEM-IOT, GBFWCGE, Baltic-FLR-SMS, Baltic-Econ-Ecol and the GEM models). All models contain biological– economicinteractionswithrespecttostocksandfisheries,exceptthe

F IGURE  8 Relationshipbetweenmodelageandmodeluse

High

Medium

Low

None

0

1

2

3

4

5

6

7

8

5 years or less 6–10 years 11–15 years > 15 years

Num

ber o

f Mod

els

Model Age

High Medium Low None

18  |     NIELSEN Et aL.

MSPMwhich isanexampleofastockproductionmodelwheretheeconomicmoduleisnotyetimplemented,whileonlyveryfewmodels(2)arealsomultisector,thatisincludenon-fisherysectorstoallowformarinespatialplanning(MSP).Thefocusonmultistockmodelsandbi-ological–economicinteractionreflectsbroadinterestinunderstandingthetechnical interactionsthatconnectfisheries.This is in largepartdrivenbyconcernsaboutby-catchanddiscardingthathavebeenanimportantpolicyfocusinrecentyears,particularlyinEurope.Althoughtheimportanceofunderstandingecologicalinteractionsisclearlyrec-ognized,parameterizingthesemodelsaccuratelyinawaythatenablesprovisionoftacticaladviceisoftenstillnotpossible,andtheend-to-endecosystemmodelsthathavebeendevelopedtendtobefocusedonlonger-termstrategicadvice.

In relation tomodel dimensions and scales, themajorityofmo-delsonlyoperatewithonegeographicalareaandunit,thatistheyarenotspatiallyexplicit.Somemodelsoperatewithseveralareassuchasstockorecosystemsubareasormanagementandadvisorysubregions,whileonlyafewmodelsareagent-basedoperatingatveryhighspa-tial (and time) resolution.Modelling spatial dynamics at a fine scalenotonlygreatlyincreasesmodelcomplexity,butitalsorequiresdataon ecological and human processes that is often lacking or patchy.Managementadvicealsostilltendstofocusonremovalsatthestocklevel.However,theincreasingamountofuseconflictsinmarineareas,notjustbetweenfisheries,butbetweenotherusessuchaselectricityproduction,aquacultureandmarinetransportwillcontinuetocreateinterestindevelopingmorespatiallyexplicitmodels.

Mostmodels are age-based or both age- and size-based,whileonly a very few are exclusively size-based. The broader ecosystemmodels usually operatewith age disaggregation for the vertebrates(fish, seamammals andbirds; higher trophic levels), but not for theinvertebratesandlowertrophiclevels.Age-andsize-structuremodelsare the standard for full analytical stock assessments, the data andinformationtoparameterizeageorsize-structuredmodelsareoftenavailable,andageorsize-structuredbio-economicmodelsareneces-sarytoprovideadvicecomparabletothatofthefullanalyticalstockassessments.Also,asmanagementisoftenfocusedonissuesofby-catchanddiscardingof juveniles, ageandsize-structuremodelsareoftennecessarytoaddresskeymanagementquestions.

Withrespecttothetypesofprocesses(andfunctions)consideredintheIEEFMsmostmodelsincorporatedynamicprocesses,whileonlya fewwere static models.Most models operatewith costs, prices,catchabilityandrecruitmentasexogenousvariablesorfunctions.Onlyafewmodelsincludeequilibriumprocesses.Abouthalfofthemodelsinclude both simulation and optimization routines with respect toestimationofoutputparameters,whileonlyveryfewareexclusivelyoptimizationmodels.Therestarepuresimulationmodels.Amongthemodelsthatincludesimulationandoptimizationroutines,mostopti-mizeoverfishingeffort (tomaximizeprofitorminimizecosts),whileecosystemandmultispeciesbiologicalinteractionsaresimulated.Thisisdue to the fact that the complexityofbiological interactions andecosystemdynamicsdoesnot lend itself tooptimization.Mosteco-systemandmultispeciesmodelsareeitherequilibriumorsimulationmodelswheredifferentscenariosofdifferentfactors(climatechange,

eutrophicationpressure levelsand/or fishingpressure levelsonvar-ious fish species, etc.) can be evaluated through “what if” scenarioevaluation.

Most models provide only deterministic quantitative estimates;however,afewprovideoutputparameterswithconfidencelimitsanduncertaintyindicated.Giventheirroleindecisionsupportformanage-ment,itisessentialtoknowhowthemodelsincorporateuncertainty,for example uncertainty from a distribution range of output frommultiplesimulations,stochasticvariables,deterministicprocessesorvariablesmodelledasrandomprocesses.Communicatinguncertaintyis clearly important,butalsoamajorchallenge. Itmay increase thecomplexity and computational needs ofmodels (e.g. requiring hun-dredsofstochasticruns).Modellersalsomaylackinformationonthecorrelation of stochastic processes in different model componentsevenwhentheyhavegoodinformationonvariationofindividualpro-cesses. Evenwhenmodellers can provide estimates of uncertainty,usersoftenfocusonthemeanormedianresults. Itcanbedifficultto conveywhether or how decisions should be adjusted to reflectuncertaintyanddoingsoisoftentheplaceofthemanagersnotthemodeller.

Withrespecttomodeldevelopment,complexity,user-friendlinessandflexibility,forexampletowhatextentthemodelsareeasilyusedand informative for policymakers and stakeholders (i.e. industry,NGOs, other interest groups, science,managers)—nearly half of themodelsrequireanalysestobeperformedbythedeveloper(duetodif-ficultyofmodeluse).Theremainderofthemodels(withtheexceptionoftwomodelswhichmaybeoperatedwithgeneralexpertise)couldbeanalysedbysomeoneotherthanthedeveloper,butthatpersonwouldrequire specialized training or expertise. Only four IEEFM modelsare characterized asuser friendly.Themajorityofmodelswerede-velopedusingopenaccesssoftwarebutafewhavespecificsoftwarerequirements. Most IEEFMs are characterized as flexible, and onlyfewofthemodelsarespecialized,andveryfewareconsideredtobesimple.Mostmodelshavehighdataneeds,whichaddstocomplexityof implementation and the need for a higher level expertise to usethem.This complexity and lackof user-friendliness almost certainlylimits theuseofmanymodelsunlessmodellers areable toactivelyengage with users of the model information. However, developinguser-friendlyinterfacesformodelscanbecostlyandmanymodellersdonothavethoseskills.

Somewhat fewer thanhalfof the IEEFMshaveachievedahighlevel of implementation, that is several cases of implementationanddirectuse in fisheriesmanagementadvice.Asimilarproportionhasamediumorlowlevelofimplementationinadvice,whileonlyafewmodelshavenoimplementationatall,thatisonlyscientificde-velopment.Formanyoftheimplementedmodels,thetargetedadvicehasbeenbroaderregional,ICESorEU,whileonlyafewmodelshavetargetedonlynationaladvice.The lattermodelshavetypicallybeenimplemented in single jurisdiction systems, such as United States,CanadaorAustralia.Mostof the IEEFMsarepublished in scientificpeer-reviewed journals; however, only about a fourth of the mo-delshavefrequentcitations.Afewmodelshavetheirownwebsitesthat are frequently used and sometimes involve model download.

     |  19NIELSEN Et aL.

Accordingtotheaboveresultsofthemeta-analysis,thereareseveralexamplesofIEEFMsthathavebeensuccessfulaccordingtopurpose,becausethemodelshavebeenusedinrealadviceandmanagementdecision, and they have been picked up by people other than theoriginaldeveloper.

4.2  | MAIN CONSIDERATIONS, TRADE- OFFS AND INSIGHTS GAINED FROM DISCUSSIONS OF THE META- ANALYSES AT CONFERENCE THEME SESSIONS AND WORKING GROUP MEETINGS CONCERNING MODEL IMPLEMENTATION AND USE

Theabovemeta-analysissuggestsanumberoffactorsthatdeterminetheusefulnessofmodels inprovidingmanagementadviceandcon-sequentlythedegreetowhichmodeladvice informsandinfluencesmanagementdecisions.Someofthesesuggesttrade-offsformodel-lerstoconsider.

Ingeneral,itisimportanttodetermineandassessthecontextoftheuseofthemodelinordertohaveawell-definedproblembeforedesigning and/or implementing a model, that is what managementobjectives,purposesanddecisionsaretobeaddressedandinformedintheapplicationofthemodel,orwhetherthemodelonly intendedfortheoretical(academic)use.Here,thereisatrade-offbetweensuc-cessfulimplementationofamodelandthepreviouseffortputintoan-alysisofthecontextthemodelshouldbeusedin.Theeffortsneededforapplicationofthemodelandtheexpectedoutcomesneedtobeconsideredandbalancedwiththepoliticalandmanagementadvisoryneedsandeconomicimportanceoftheadviceinordertobecosteffi-cientbecauseimplementationofmodelsisveryresourcedemanding.Similarly,itisnecessarytodefineandformulatequantifiableobjectivesandmaketheseaprioritywhichtheIEEFMsdirectlycanaddress.Thekeytodisseminationandtransmissionhasoftenbeenadvisorywork-inggroupsandbodies,largerresearchprojectsanddedicatedtrainingcourseswhereabroaderrangeofmodelexpertshaveparticipated.Inmostcases,thedevelopersareinvolvedinprovidingtechnicalsupportandintheformaluseofthemodel.Expandeduseofmodelwebsitesand platforms show that model developers can more efficientlycommunicatetheirworkandmodelsthroughcooperationwithvisualcommunicationsexpertsandgraphicdesignersandbyparticipatingincommunicationstrainings.

Morecomplexmodelsmaybeabletoaccountforinterconnectedecologicalandeconomicprocessesandprovidemorenuancedadvice,butunless themodeller is involved in themanagementprocessandcantailortheoutputsandmodelscenariostomeetmanagers’needs,themodelmayonlybeusedtoprovidegeneralstrategicadviceratherthaninformingspecificdecisions.Asimpler,user-friendlymodelmayprovidelessnuancedadvice,butifmanagersandstakeholderscanuseitthemselves,itmayhavemoreinfluenceondecisions.Consequently,thereisatrade-offbetweentheuseandextentofinclusionofecosystemor economic or social complexity in the IEEFMswhich givesmorenuancesbutalsohastheriskofreducinglikelihoodofuse.

Thereisatrade-offbetweenthemodelprojectionperiod,thatisthetimescale,intheadviceormanagementevaluationitinformsandtheprecisionof themodeloutputandadvice.Thedataneeded, theprecisionofthedata,thetoolsused,aswellastheoutputproducedvary depending onwhether themodel dealswith a strategic (whatshouldbedoneinthelong-run),versusatacticalapproach(whatcanbedoneintheshort-run).Modelsthatprovideusefultacticaladvicemayneedtoincorporatesingle-speciesbiologicalmodelscomparableto stock assessment models and may need to incorporate techni-cal interactions in fisheries.Models useful for strategic advice needto consider how ecological and economic and social processesmaychange and interact over time, but these processesmaybe hard toparameterize inways that provide both accurate short-termpredic-tionsandlonger-terminsights.Forexample,astatisticallyfittedstockassessmentmodelmayprovideaccurateshort-termpredictions,whileanecosystemmodelmaybemoreusefulforconsideringhowthefish-ery systemwill react to changes in theenvironmentover time.Thisorientationtowardstacticalvs.strategicadviceisparticularlyrelevantwith respect to human behavioural and social processes.Modellersfaceimportantchoicesaboutwhethertotrytosimulateobservedbe-haviourwithstatisticallyfittedmodels,usetheoreticallybasedmodelsorspecifybehaviourinthemodeltoachievesomeobjective(e.g.seteffortorcatchtomaximizeprofitsortofollowhistoricalpatternsofeffortallocation).Generally, the former ismostuseful formodels tobeusedfortacticaladvice,whilemodelsaimedatprovidingstrategicadviceandlong-terminsightsmayalsotakethelatterapproaches.Thechoice is alsodependenton themanagement context.Forexample,doesthemodelassumeanopenpoolresource,effortlimitations,indi-vidualtransferablequotas,orcommunalmanagement,orsomeotherrepresentation.ModellingbehaviourinITQorcommunalmanagementregimesmay requiremodellingstrategicbehaviourof fishermenandgroup dynamics,whilemodelling behaviour in a common pool, par-ticularlyoneobservedforsometime,maybesimpler.Ifthemodelisexpectedtomakepredictionswhenthemanagementregimeisfunda-mentallychanged,statisticallyfittedbehaviouralmodelsbasedonpriorobservedbehaviourarelikelytodoapoorjobofpredictingbehaviourinanewmanagementregime,anditmaybenecessarytoeitherspecifybehaviourorincorporateatheoreticallybasedbehaviouralmodel.

It is important to use an appropriate spatial scale tomatch thebiologicalscaleandthescaleofkeyhumanprocesses.Forexample,themanagementareasandunitsaddressedinamodelideallyshouldmatchtheresourcedistributionareas,that isdistributionofthefishstockstobemanaged.Ifthemanagementareaandthemodeldomainonlycoverpartsofthestocksdistributionareas,importantecologicalparametersandpopulationdynamicsmaynotbecapturedandtakenfullyintoaccountinthemodels(e.g.migrations,growthandrecruit-ment in relation to spawningor feeding areas)whichwill bias theiroutput.Ontheotherhand,boundariesmustbedrawnatsomepointandenlarging themwill necessarily add complexity.Modellersmustultimatelydecidewhetherprocessesexternaltothemodeldomainareconsequentialenoughtorequiremodellingorcanbespecifiedratherthanmodelleddirectly(e.g.acertaincatchornaturalmortalityappliedoutsidethemodeldomain).

20  |     NIELSEN Et aL.

Theuptakeanduseofmodelsmaydependonhowflexibletheyare.Whilemodelsbuiltfromscratchtailoredtospecificpurposesmayprovidemore accurate answers to the specific questions theyweredesigned for, models that enable users tomodify assumptions andprocessesmayultimatelybemoreusefulandcanprovideusers theabilitytodeterminethesensitivityofresultstoassumptionsorexplorequestionsnotoriginallyenvisionedby themodeldeveloper.Modelsthathavebeenaroundlongerandaremorefamiliartomanagersareprobablymorelikelytobeusedbecausetheyaremorelikelytohavebeenreviewedandpeoplehavesomebasisfordecidingwhethertheyprovideusefulandaccurateadvice.Thenumberoftimesthemodelhaspreviouslybeenimplementedorbroughttoapolicyinstitutionasadecisionsupporttool,themorelikelytheadvicewillbeusedbecausepolicymakersarecomfortablewithitandperhapshavehadachancetoseewhetherprioradvicewasuseful.Thus,theremaybeatrade-offbetweenintroducinganewmodel,evenifitisanimprovement,andstickingwithoradaptinganexistingmodel.

4.3  | GLOBAL EXPERIENCES IN IMPLEMENTATION AND USE OF IEEFMS—ADDITIONAL INSIGHTS FROM THE CONFERENCE THEME SESSION AND WORKING GROUP DISCUSSIONS

TheeffectiveintegrationofIEEFMsintotheprovisionofmanagementadvicecanbedrivenbyanddependonhavingadvisoryand/orma-nagementbodiesandfora(institutionalset-up)wherethemodelscanbeusedincooperationwithstakeholders.Itcantaketimeforbuildingtrust inthesefora,forthebodiestodevelopandforparticipantstolearntousemodelseffectively.Forexample,intheAustralianfisher-iesmanagementandadvisorysystem,theparticipatorymanagementandadvicebetweenmanystakeholdershasbeenthemaindriverofthe implementation of themodels (Smith etal., 1999, 2001, 2014;Sainsburyetal.,2000;Rayns2007).Suchasystemrequires thees-tablishmentofappropriatefacilitatinglegislationandcomanagementbodies which can be a long process (5–10 years). Importantly, thecomanagementstructureoradaptivemanagementprocessneedstobecross-sectorinvolvinganumberofparties,including,conservationand recreational fishery sectors alongwith the commercial. Such along-term,cross-sectoralviewhasbeentakeninthecontestedenvi-ronmentontheGreatBarrierReef(Mapstoneetal.,2008).

Effectively using IEEFMs to provide management advice canbe enhanced by simulation tests of management plans to evaluatetrade-offs and robustness to uncertainty, and it is important to in-volvestakeholdersinthisprocess.InAustralia,formalmethodsofthemanagement strategy evaluation have been used to assess impactsofalternativesetsofmeasuresaimed tomeetavarietyofmanage-ment goals (Fulton etal., 2014). Involving stakeholders directly inmanagementand/oradviceisimportantbecauseitcreatesincentivesfor involvement in advance anddrives theneed for adequateman-agement strategyevaluation tools toaddresscomplexquestions in-volvingmanystakeholdersandbothecologicalandeconomicaspects

of management and advice. Thus, it is important that governancestructures are inplace forestablishingprocesses thatenable stake-holders toparticipate inmanagement strategyevaluations (see, e.g.Fultonetal.,2011,2014).

Thepreeminentmanagementobjectivesmandatedby legislationcanbeimportant indeterminingwhetherandhowIEEFMsareusedtoprovidemanagementadvice,particularlyfortactical managementdecisions such as setTACs eachyear? For example,whilemanage-ment of fisheries in Australia is supported through the applicationof bio-economic models, these play virtually no role in fisheriesmanagement inNewZealand (Pascoeetal.,2016).Thisdiscrepancyis adirect resultof thedifferingemphasisonhoweconomicobjec-tivesareachieved,withAustraliatargetingmaximumeconomicyield(MEY),whileNewZealandtargetsmaximumsustainableyield (MSY)(Pascoeetal.,2016).SimilartoNewZealand,fisheriesmanagementinEuropeandUSAtendstobedrivenprimarilybybiologicaltargetsandreference points related toMSY. Economic and social factors entermostlyinallocationdecisionsanddesigningmanagementapproachestoachievedesiredcatchlevels.Incontrast,whenMEYistheobjective,itbecomesnecessaryto integratehumanbehaviour,economicsandperhapssocial factors into integratedmodels thatcan identifywhatMEYisandhowitcanbeachieved.

When integrating models into comanagement structures andprocesses, model flexibility, transparency, portability, build-up time,expert knowledge of the system tomodel and themodel interfaceavailablecanbecriticaldeterminantsofsuccess. Itseemsnecessarytoconcentratemoreonmakingmodelsflexible,moreunderstandabletostakeholders,portableandmoreuserfriendlytoincreasethelevelofimplementationandusebystakeholdersingeneral.Here,itshouldbenotedthatflexibilitytobeimplementedindifferentcasesdoesnotnecessarilycomewithgreatercomplexity.

InvolvementofstakeholdersandestablishingsuitableadvisoryandmanagementstructurestoenhanceimplementationofIEEFMsmaybeparticularlychallengingintheEUwhichconsistsofavarietyofmem-bercountriesboundtogetherwithseveralsupra-nationalinstitutions(Marchal etal., 2016). The scientific management advice in the EUandIcelandforconservationandutilizationoftheresourcesismainlyconductedbyscientistsusing IEEFMs forprovidingadvicealthoughthereareinformalconsultationsindecision-making.Incontrast,therearemandatory and formalized consultationswith stakeholders bothinscientificadviceandindecision-makinginAustralia,USAandNewZealandallowingIEEFMstobeusedinaninteractiveandintegrativewayforprovidingcommonlyagreedadviceformanagement.(Marchaletal.,2016).InUSA,therehavebeensomeproblemswithinsufficienttrust in themanagement institutionsorprocessesor a lackof trustbetween different stakeholders; in this case, integratedmodelswillnotevolveandnotbeused. Ittakesa longtimetobuilduptrust inthemanagementstructuresandbetweentheusergroupsinordertocooperateon IEEFMapproaches. In a reviewon implementationofecosystemmodels,Hyderetal., (2015)concludethat it isnecessarytoestablishastrongerlinktosocialandeconomicsystemstoincreasethe range of policy-related questions that themodels can address,and it is also important to improve communication between policy

     |  21NIELSEN Et aL.

andmodellingcommunitiessothereisasharedunderstandingofthestrengthsandlimitationsintheuseofecosystemmodels.

TheEUandmemberstateshaveinvestedconsiderableresourcestodevelopvariousmultispeciesandecosystemmodelsfordifferentma-rineecosystemsandregionalseasand,inparallel,toconductfieldpro-gramsadvancingprocessknowledgeonbiologicalandtrophodynamicinteractionsandtheresponseoffoodwebstoanthropogenicchangesinenvironmentalconditions.Strongevidencehasaccumulatedacrossall EUwaters for the importanceof accounting for thedynamicsofspecies interactionswhenattempting tounderstandandpredict theresponseoffisheriesresourcestoecosystemchange.Asaresult,mul-tispeciesandecosystemmodelsexistforallregions.ForeveryproposalofanewEUfisheriesregulation,theEuropeanCommissionisrequiredtoprovideanassessmentofecological,economicandsocialimpactsoftheregulation.Overthelastdecade,severalimpactassessmentshavebeenundertakenapplyingtheavailablebio-economicmodels.Inpar-ticular,inEUresearchprojects,themodelsforthishavebeenfurtherdevelopedandimplementedtobeabletoprovidethenecessarytoolsfortheassessments(seeSupplementaryMaterialS4fordetailsofim-plementationofvariousIEEFMsthrougharowofextensiveprojects).

ICEShasinitslatestStrategicPlan(www.ices.dk;05Apr2017)ex-plicitlyrequestedintegratedfisheriesmanagementadviceanddefinedadvisoryneedsforIEEFMs.Itseemsthatadequatemethodsandre-levantadvancedIEEFMsarealreadydevelopedandinplacetomeetthese advisory demands according to the management types usedin ICES context.Also, relevantmodel developer expertise exists onnationalbasiswithin the ICESmember countriesbesides theglobalexperiencesandmethodsformodelevaluationoutlinedinthispaperwhichcanbedirectlyusedinICEScontext.Giventhemodelevalua-tionmethodsdevelopedand theexperiencesoutlinedabove itwill,however,benecessarytoformallyestablishintegratedICESworkinggroupswhereeconomists,biologistsandsociologistscan interact. Itwillalsobeimportanttoallowforandpromoteinvolvementofstake-holdersinusingIEEFMsformanagementadvice.

4.4  | CONCLUSIONS

Managersofmarineresourcesmustbalancediverseandoftencom-peting interestsandmustmakedecisionsabouthighlycomplexsys-temswith limited and imprecise knowledge. IEEFMs are playing anincreasingly important role in supporting this challenging task.Theycanprovidemanagerswithabetterandmoreexplicitunderstandingofhownaturalandhumanprocessesinteracttoinfluenceoutcomes.IEEFMscanprovideameanstoquantifythetrade-offsbetweendif-ferentmanagementobjectivesandhowbenefitsandcostsfordiffer-entgroupsofstakeholdersareaffectedbymanagementdecisions.Ifmodelresultscanbeeffectivelyconveyedtostakeholders,orprefer-ablyifstakeholdercanbeinvolvedindevelopmentanduseofIEEFMs,thiscangenerategreateracceptanceofmanagementactionsandfa-cilitatemoreeffectiveimplementation.

IEEFMsrepresentcomplexsystems,andmodellersfacetrade-offswhen attempting to limit complexity tomakemodelsmore tractable

andeasierformanagersandstakeholderstouse.Ourreviewsuggeststhatmodellersaresometimesreticenttomakethesetrade-offs.Manyof themodels reviewed are extremely complex and are designed toprovideboth short-term tactical and long-term strategic adviceon arangeofmanagementdecisions.Manyattempttomodelmultiplespe-cies, sometimeswith both technical and ecological interactions.Thiscomplexitymayoftenbejustified,butitplacesmuchgreaterdemandson the modellers and the managers to use the models effectively.Modellersneed tobewilling to invest time intomakingmodelsuserfriendlyorbepreparedtoparticipatedirectly,andprobablyrepeatedly,inmanagementforawheremodelsandmodelresultscanbeexplainedanddiscussed.Thisinvolvementcanbebeneficialtoallparties,leadingbothtoimprovementofmodelsandmoreeffectiveimplementationofadvice,butcandemandsubstantialtimeandresourceswhichmustbebuilt into the governance process. Itmay also take time to developeffective processes for using IEEFMs requiring a long-term commit-menttointegratingmultidisciplinarymodellingadviceintomanagementdecision-making.Giventhemismatchbetweenthetimerequiredforamodel tobecomemature (6ormoreyears)andthefundingdurationtypicallyavailable(3–4years),thereisaneedfornewfundingschemesthat support development of modelswith good documentation anduser-friendly, open-source platforms that enable replicability andcontinuingdevelopmentandadaptationofthemodels.

This article is a step towards developing methods and specifictoolstoevaluatemodelcharacteristicsandapplyingacategorizationsystemforthesecomplexmodels.Futurestudiesshouldstandardizeanddetail those toolsmore, for example by quantifying anddetail-ingfurthertherangesofthedifferentcategorizationsintheclasses,forexample levelof implementationandthe timeranges forshort-,medium-orlong-termmanagementadvice.Theevaluation,discussionandfeedbackonthemeta-analysisconductedintheworkinggroup,workshopandconferencemeetings in ICESand IIFETcontexthaveledtoamorestandardizedwayformodeldeveloperstoconductself-assessmentsoftheirmodels.

ACKNOWLEDGEMENTS

ThisworkhasbeenconductedpartlyundertheICESWorkingGroupforIntegratedManagementModelling(ICESWGIMM).

REFERENCES

Andersen,P.,Andersen,J.L.,&Mardle,S. (2014).What’s going to happen with the EU’s discard policy? IIFET 2014 Australia Conference Proceedings. Availableat:http://ir.library.oregonstate.edu/xmlui/bitstream/handle/ 1957/54625/Mardle%20333.pdf?sequence=4

Andersen,K.H.,Brander,K.,&Ravn-Jonsen,L.J. (2014).Trade-offsbe-tween objectives for ecosystemmanagement of fisheries. Ecological Applications,25,390–1396.

Arnason,R.(2002).A review of international experiences with ITQs: An annex to Future options for UK fish quota management.CEMARERep.58,64p.

Bartelings,H.,Hamon,K.G.,Berkenhagen,J.,&Buisman,F.C.(2015).Bio-economicmodelling formarine spatial planning application inNorthSea shrimpand flatfish fisheries.Environmental Modelling & Software,74,156–172.

22  |     NIELSEN Et aL.

Bastardie,F.,Baudron,A.,Bilocca,R.,Boje,J.,Bult,T.P.,Garcia,D.,…Ulrich,C. (2009). Evaluating biological robustness of innovative manage-mentalternatives.InK.H.Hauge,&D.C.Wilson(Eds.),Comparative evaluations of innovative fisheries management: Global experiences and European prospects.Dordrecht,TheNetherlands:SpringerPublishing,Dordrecht.

Bastardie,F.,Nielsen,J.R.,Andersen,B.S.,&Eigaard,O. (2010).Effectsof fishingeffortallocationscenariosonenergyefficiencyandprofit-ability:Anindividual-basedmodelappliedtoDanishfisheries.Fisheries Research,106,501–516.

Bastardie,F.,Nielsen,J.R.,Andersen,B.S.,&Eigaard,O.(2013).Integratingindividual trip planning in energy efficiency – Building decision treemodels for Danish fisheries. Fisheries Research,143,119–130.

Bastardie,F.,Nielsen,J.R.,Eero,M.,Fuga,F.,&Rindorf,A.(2017).Impactsof changes in stock productivity and mixing on stock sustainabilityand fishery economic viability. ICES Journal of Marine Science, 74(2),535–551.

Bastardie,F.,Nielsen,J.R.,Eigaard,O.,Fock,O.H.,Jonsson,P.,&Bartolino,V.(2015).Competitionformarinespace:ModellingtheBalticSeafish-eriesandeffortdisplacementunderspatialrestrictions.ICES Journal of Marine of Science,72(3),824–840.

Bastardie,F.,Nielsen,J.R.,&Kraus,G.(2010).Managementstrategyeval-uationframeworkfortheEasternBalticcodfisherytotestrobustnessofmanagementagainstenvironmentalconditionsand fleet responsescenarios. ICES Journal of Marine Science,67,71–86.

Bastardie, F., Nielsen, J. R., & Miethe, T. (2014). Displace: A dynamic,individual-basedmodelforspatialfishingplanningandeffortdisplace-ment–Integratingunderlyingfishpopulationmodels.Canadian Journal of Fisheries and Aquatic Science,71,1–21.

Bastardie, F., Nielsen, J. R., & Vinther, M. (2015). Causes and conse-quences of technical, biological and spatial interactions in fisheries man-agement modelled from the individual distribution of fishing effort.CM2015/M:03.

Bastardie,F.,&Nielsen,J.R.(2011).Stock-based and fleet-based evaluation of the multi-annual management plan for the cod stocks in the Baltic Sea with respect to cod stock mixing and TAC vs. effort regulation under differ-ent constraints and stock conditions.Work.Doc.,40pp. In:Simmondsetal. (eds).2011.ReportoftheSubGrouponImpactAssessmentofBalticcodmulti-annualplans.EUSTECFEWG-11-07a.Hamburg(D),20-24June2011. Luxembourg: PublicationsOffice of theEuropeanUnion,2011.ISSN1018-5593(print).

Bastardie,F.,Vinther,M.,&Nielsen,J.R.(2012).Impact assessment (IA) of al-ternative HCRs to the current multiannual Baltic Sea plan on the bio-econ-omy of fleets – Coupling the SMS model to the FLR Baltic model.WorkingDocument,36pp.Appendix3.In:.Multi-SpeciesManagementPlansfortheBaltic(SimmondsandJardimeds).EU-EWG-12-02RostockMarch2012 STECF 12-06 Report, March 2012. Luxembourg: PublicationsOfficeoftheEuropeanUnion.ISSN1831-9424(print).

Bastardie, F., Vinther, M., Nielsen, J. R., Ulrich, C., & Storr-Paulsen, M.(2010). Stock-based vs. fleet-based evaluation of the multi-annualmanagement plan for the cod stocks in the Baltic Sea. Fisheries Research,101,188–202.

Basurko,O.C.,Gabina,G.,&Uriondo,Z. (2013).Energyperformanceoffishingvesselsandpotentialsavings.Journal of Cleaner Production,54,30–40.

Beddington,J.R.,Agnew,D.J.,&Clark,C.W.(2007).Currentproblemsinthemanagementofmarinefisheries.Science,316(5832),1713–1716.

Bethke,E.(2013a).The Eco² model – A basic bio-economic module for the de-scription of the dynamics of cohort biomass on exploitation (July 12, 2013). Available at: SSRN: http://ssrn.com/abstract=2309280 or https://doi.org/10.2139/ssrn.2309280(01Nov2016).

Bethke, E. (2013b). Beverton and Holt reloaded – Incorporating variable growth into a yield per recruit model. Available at: SSRN: http://ssrn.com/abstract=2310545 (01 Nov 2016) or https://doi.org/10.2139/ssrn.2310545(01Nov2016).

Bethke,E. (2015).Maximum feeding rations and maximum growth rates as a function of temperature – Derived by using a traditional feeding chart for rainbow trout (Oncorhynchus mykiss).Availableat:SSRN:http://ssrn.com/abstract=2634000 (01 Nov 2016) or https://doi.org/10.2139/ssrn.2634000(01Nov2016).

Bethke,E.(2016).A simple age- and growth-dependent sequential selectivity function for trawls – An additional module for the Eco²-model.Availableat:SSRN,http://ssrn.com/abstract=2737823(01Nov2016)orhttps://doi.org/10.2139/ssrn.2737823(01Nov2016).

Bethke,E.,Bernreuther,M.,&Tallman,R.F.(2013).Feed efficiency versus feed conversion ratio – Demonstrated on feeding experiments with Juvenile Cod (Gadus Morhua).Availableat:SSRN,http://ssrn.com/abstract=2313137orhttps://doi.org/10.2139/ssrn.2313137(01Nov2016).

Bevilacqua,A.H.,Carvalho,A.R.,Angelini,R.,Steenbeek,J.,&Christensen,V.(InPrep).Valuechainanalysisofincomedistributioninasmall-scalefishery. Ecological Economics(MSinPrep).

Bjørndal, T., Lane, D. E., &Weintraub, A. (2004). Operational researchmodels and themanagementof fisheriesandaquaculture:A review.European Journal of Operational Research,156,533–540.

Branch, T. A., Watson, R., Fulton, E. A., Jennings, S., McGilliard, C. R.,Pablico,G.T.,…Tracey,S.R.(2010).Thetrophicfingerprintofmarinefisheries. Nature,468,431–435.

Butterworth,D.S.,Bentley,N.,DeOliveira,J.A.A.,Donovan,G.P.,Kell,L.T.,Parma,A.M.,…Stokes,T.K.(2010).Purportedflawsinmanagementstrategyevaluation:Basicproblemsormisinterpretations?ICES Journal of Marine Science,67,567–574.

Carpenter, G., & Esteban, A. (2015).Managing EU fisheries in the public interest. New Economics Foundation Available at: http://b.3cdn.net/ nefoundation/e2a0356a6c69ec0cc6_ygm6bznj3.pdf

Christensen,V.,De laPuente,S.,Sueiro,J.C.,Steenbeek,J.,&Majluf,P.(2014).Valuing seafood:ThePeruvian fisheries sector.Marine Policy,44,302–311.

Christensen,V.,&Pauly,D.(1992).ECOPATHII—Asoftwareforbalancingsteady-state ecosystemmodels and calculating network characteris-tics.Ecological Modelling,61,169–185.

Christensen,V.,Steenbeek,J.,&Failler,P.(2011).Acombinedecosystemand value chainmodeling approach for evaluating societal cost andbenefitoffishing.Ecological Modelling,222,857–864.

Christensen,V.,&Walters, C. J. (2004). Ecopathwith Ecosim:Methods,capabilitiesandlimitations.Ecological Modelling,172,109–139.

Colléter,M.,Valls,A.,Guitton,J.,Gascuel,D.,Pauly,P.,&Christensen,V.(2015).GlobaloverviewoftheapplicationsoftheEcopathwithEcosimmodelling approach using the EcoBasemodels repository. Ecological Modelling,302,42–53.

Conrad,J.(1995).Bioeconomicmodelsofthefishery.In:D.Bromley(Ed.),Handbook of environmental Economics (pp. 405–432). Oxford, UK:Blackwell.

Cormier,R.,Kannen,A.,Elliott,M.,&Hall,P. (2015).Marine spatial plan-ning quality management system.ICESCooperativeResearchReportNo.327,106pp.

Da-Rocha, J.M., Cerviño, S., & Gutiérrez,M. J. (2010).An endogenousbio-economic optimization algorithm to evaluate recovery plans:Anapplication to Southern Hake. ICES Journal of Marine Science, 67(9),1957–1962.

Da-Rocha,J.M.,&Gutiérrez,M.J.(2011).Lessonsfromthenorthernhakelong-termmanagementplan:Couldtheeconomicassessmenthaveac-ceptedit?ICES Journal of Marine Science,68(9),1937–1941.

Da-Rocha,J.M.,Gutiérrez,M.J.,&Antelo,L.T.(2012).Pulsevs.optimalstationaryfishing:TheNorthernstockofhake.Fisheries Research,121–122,51–62.

Da-Rocha, J. M., Gutiérrez, M. J., & Antelo, L. T. (2013). Selectivity,pulse fishing and endogenous lifespan in Beverton-Holt models.Environmental Resource Economics,54(1),139–154.

Da-Rocha,J.M.,Gutiérrez,M.J.,&Cerviño,S. (2012).Referencepointsbasedondynamicoptimisation:Aversatilealgorithmformixedfishery

     |  23NIELSEN Et aL.

managementwithbio-economicage-structuredmodels. ICES Journal of Marine Science,69(4),660–669.

Da-Rocha, J. M., Gutiérrez, M. J., Cerviño, S., & Antelo, L. T. (2012).“logMSY”andoptimalharvestingcontrolrules:Newtoolsfortheim-plementationoftheEuropeanCommonFisheriesPolicy.Ocean Coastal Management,70,48–53.

Da-Rocha, J. M., Gutiérrez, M. J., & Garcia-Cutrin, J. (2016).Harvesting Control Rules that deals with Scientific Uncertainty WP. Available at:https://www.researchgate.net/publication/301887234_Harvesting_Control_Rules_that_deal_with_Scientific_Uncertainty(01Mar2017).

Da-Rocha,J.M.,Gutiérrez,M.J.,Garcia-Cutrin,J.,&Jardim,E.(2015).Fleetbehaviour,managementplansandendogenousfishingmortalitiesfluc-tuations. In:E.Jardim& I.Mosqueira (Eds.),Multiannual management plans SWW and NWW, STECF-15-08 EUR– Scientific andTechnicalResearchseries.ISSN1831-9424(online);ISSN1018-5593(print).

Da-Rocha, J. M., Gutiérrez, M. J., Garcia-Cutrin, J., & Jardim, E. (2017).Endogenous fishing mortalities: A state-space bioeconomic model.ICES Journal of Marine Science.https://doi.org/10.1093/icesjms/fsx067

Da-Rocha, J. M., Gutiérrez, M. J., Garcia-Cutrin, J., & Touza, J. (2016).Reconcilingyieldstabilitywithinternationalfisheriesagenciesprecau-tionarypreferences:Theroleofnonconstantdiscountfactorsinagestructuredmodels.Fisheries Research,173,282–293.

Da-Rocha,J.M.,&Mato-Amboage,R. (2016).Onthebenefitsof includ-ingage-structure inHarvestcontrol rule.Environmental and Resource Economics,64(4),619–641.

Da-Rocha, J.M., & Sempere, J. (2016). ITQs, firm dynamics andwealthdistribution: Does full tradability increase inequality? Environmental and Resource Economics.Availableat:https://doi.org/10.1007/s10640-016-0017-3(01Mar2017).

Da-Rocha,J.M.,&Pujolas,P.(2011).Vesseldynamics,itq’sandendogenousvesselsdistributions.In:J.Simmonsetal.(Eds.),Impact Assessment of multi-annual plans for Southern hake, angler fish and Nephrops.STECF-11-06EUR–ScientificandTechnicalResearchseriesISSN1831-9424(online),ISSN1018-5593(print).

Daurès,F.,Trenkel,V.M.,&Guyader,O.(2013).ModellingthefishingcostsofFrenchcommercialvessels intheBayofBiscay.Fisheries Research,146,74–85.

De Marchi, B., Funtowicz, S. O., Lo Cascio, S., & Munda, G. (2000).Combining participative and institutional approaches with multicri-teria evaluation.An empirical study forwater issues inTroina.Sicily. Ecological Economics,34,267–282.

Degnbol,D.,&Wilson,D.C.(2008).SpatialplanningontheNorthSea:Acaseofcross-scalelinkages.Marine Policy,32,189–200.

Deng,R.A.,Punt,A.E.,Dichmont,C.M.,Buckworth,R.C.,&Burridge,C.Y.(2015).ImprovingcatchpredictionfortigerprawnsintheAustraliannorthernprawnfishery.ICES Journal of Marine Science,72(1),117–129.

Dichmont,C.,Deng,A.,Punt,A.E.,Ellis,N.,Venables,W.,Kompas,T.,…Bishop, J. (2008). Beyond biological performancemeasures in man-agementstrategyevaluation:Bringingineconomicsandtheeffectsoftrawlingonthebenthos.Fisheries Research,94,238–250.

Dichmont,C.M.,Deng,R.A.,Punt,A.E.,Venables,W.N.,&Hutton,T.(2012).Frominputtooutputcontrolsinashort-livedspecies:ThecaseofAustralia’sNorthernPrawnFishery.Marine and Freshwater Research,63(8),727–739.

Dichmont,C.M., Pascoe, S.,Kompas,T., Punt,A. E.,&Deng,R. (2010).On implementing maximum economic yield in commercial fisheries.Proceedings of the National Academy of Sciences,107(1),16–21.

Dichmont, C.M., Punt,A. E.,Deng,A.,Dell,Q., &Venables,W. (2003).Applicationof aweeklydelay-differencemodel to commercial catchandeffortdatafortigerprawnsinAustralia’sNorthernPrawnFishery.Fisheries Research,65,335–350.

DISPLACE Model Website. Available at: www.displace-project.org (01Apr 2017).

Douvere,F.(2008).Theimportanceofmarinespatialplanninginadvancingecosystem-basedseausemanagement.Marine Policy,32,762–771.

Drouineau,H.,Mahévas,S.,Bertignac,M.,&Duplisea,D.(2010).Alength-structuredspatiallyexplicitmodelforestimatinghakegrowthandmi-grationrates.ICES Journal of Marine Science,67(8),1697–1709.

Drouineau,H.,Mahévas,S.,Pelletier,D.,&Beliaeff,B.(2006).Assessingtheimpactofdifferentmanagementoptionsusing ISIS-Fish:TheFrenchMerlucciusmerluccius–NephropsnorvegicusmixedfisheryoftheBayof Biscay. Aquatic Living Resources,19(1),15–29.

Duplisea,D. (2010).A length-structured spatially explicitmodel for esti-matinghakegrowthandmigrationrates.ICES Journal of Marine Science,67(8),1697–1709.

Ehler, C., & Douvere, F. (2009). Marine spatial planning: A step-by-step approach toward ecosystem-based management. IntergovernmentalOceanographicCommissionandManandtheBiosphereProgramme.IOCManuals andGuidelinesNo 53. ICAMDossierNo 6,UNESCO,Paris,98pp.

EU. (2014).Directive 2014/89/EU OF THE European Parliament and of the Council of 23 July 2014 establishing a framework for maritime spatial planning.PublicationsOfficeoftheEuropeanUnion,LuxembourgOJL157.28.82014,p.135.

EUSTECF.(2015a).Scientific, Technical and Economic Committee for Fisheries (STECF) Multiannual management plans SWW and NWW. PublicationsOfficeoftheEuropeanUnion,LuxembourgSTECF-15-08EUR27406EN,JRC96964,82pp.

EUSTECF.(2015b).Scientific, Technical and Economic Committee for Fisheries (STECF) – Evaluation of management plans: Evaluation of the multi-an-nual plan for the North Sea demersal stock. PublicationsOfficeof theEuropeanUnion,LuxembourgSTECF-15-02,152pp.

EU STECF. (2015c). Scientific, Technical and Economic Committee for Fisheries. Evaluation of management plans.Evaluationof themulti-an-nualplanfortheNorthSeademersalstocks.PublicationsOfficeoftheEuropeanUnion,LuxembourgSTECF-15-04.

FAO. (2008). Best practices in ecosystem modelling for informing an eco-system approach to fisheries. FAO Fisheries Technical Guidelines forResponsibleFisheriesNo.4,Suppl.2,Add.1,Rome,FAO,1–78.

Feekings,J.P.,Bastardie,F.,Nielsen,J.R., Lund,H.,&Frandsen,R..Can changing the MLS for Norway Lobster (Nephrops norvegicus) improve the biological and economic performance of the Skagerrak and Kattegat demersal trawl fisheries? A bio-economic analysis of consequences under both landings and catch quota management systems.(Submitted).

Fernandes,J.A.,Cheung,W.W.,Jennings,S.,Butenschön,M.,deMora,L.Frölicher,T.L.,…Grant,A.,(2013).Modellingtheeffectsofclimatechangeonthedistributionandproductionofmarinefishes:Accountingfortrophicinteractionsinadynamicbioclimateenvelopemodel.Global Change Biology,19,2596–2607.

Fernandes,J.A.,Kay,S.,Hossain,M.A.R.,Ahmed,M.,Cheung,W.W.L.,Lazar,A.N.,&Barange,M.(2016).ProjectingmarinefishproductionandcatchpotentialinBangladeshinthe21stcenturyunderlong-termenvironmental change and management scenarios. ICES Journal of Marine Science,https://doi.org/10.1093/icesjms/fsv217.

Fernandes,J.A.,Papathanasopoulou,E.,Queirós,A.M.,Cheung,W.W.W.L.,Yool,A.,Artioli,Y.,…Barange,M.(2016).Estimatingtheecological,economicandsocialimpactsofoceanacidificationandwarmingonUKfisheries. Fish and Fisheries,https://doi.org/10.1111/faf.12183.

Frost, H. S.,Andersen, P., & Hoff,A. (2011).An application of fisherieseconomicstheory:100yearsafterWarming’spaper: “Rentoffishinggrounds.Nationaloekonomisk Tidsskrift,149(1–3),55–84.

Frost,H.,Andersen,P.,&Hoff,A. (2013).Managementof complex fish-eries: Lessons learned from a simulationmodel.Canadian Journal of Agricultural Economics,61(2),283–307.

Frost, H.Levring, J. A., Hoff, A., & Thøgersen, T. (2009). The EIAA model: Methodology, definitions and model outline. Institute of Food andResourceEconomics,UniversityofCopenhagenFOIReportNo.200.Availableat:https://curis.ku.dk/portal/files/44693774/Report_200.pdf

Fulton, E. A., Link, J. S., Kaplan, I. C., Savina-Rolland, M., Johnson, P.,Ainsworth, C., … Smith, D. C. (2011). Lessons in modelling and

24  |     NIELSEN Et aL.

managementofmarineecosystems:TheAtlantisexperience.Fish and Fisheries,12(2),171–188.

Fulton,E.A.,Smith,A.D.M.,Smith,D.C.,&Johnson,P.(2014).Aninte-gratedapproachisneededforecosystembasedfisheriesmanagement:Insights fromecosystem-levelmanagementstrategyevaluation.PLoS ONE,9,e84242.

Garcia,S.M.,&Cochrane,K.L. (2005).Ecosystemapproachtofisheries:Areviewofimplementationguidelines.ICES Journal of Marine Science,62,311–318.

García,D.,Prellezo,R.,Sampedro,P.,Da-Rocha,J.M.,Castro,J.,Cerviño,S.,…Gutiérrez,M.-J.(2016).Bioeconomicmultistockreferencepointsasatoolforovercomingthedrawbacksofthelandingobligation.ICES Journal of Marine Science,https://doi.org/10.1093/icesjms/fsw030.

Garcia,D.,Urtizberea,A.,Diez,G.,Gil,J.,&Marchal,P.(2013).Bio-economicmanagement strategyevaluationofdeepwater stocksusingFLBEIAmodel. Aquatic Living Resources,26,365–379.

Garcia,D., Sanchez, S., Prellezo, R.,Urtizberea,A.,&Andres,M. (2016).FLBEIA: A toolbox to conduct Bio-Economic Impact Assessment of fisheries management strategies.SoftwareX(Underreview).

Garcia,D., Santurtun,M., Prellezo,R., Sanchez, S.,&Andres,M. (2012).FLBEIA: A toolbox for bioeconomic impact assessment of fisheries man-agement strategies.ICESAnnualScienceConferenceBergenICESCM2012/K:01.

Gasche, L., Mahevas, S., & Marchal, P. (2013). Supporting FisheriesManagementbyMeansofComplexModels:CanWePointoutIslesofRobustnessinaSeaofUncertainty?PLoS ONE,8(10).

Gascuel,D.,Coll,M.,Fox,C.,Guénette,S.,Guitton,J.,Kenny,A.,…Shephard,S.(2016).FishingimpactandenvironmentalstatusinEuropeanseas:Adiagnosisfromstockassessmentsandecosystemindicators.Fish and Fisheries,17,31–55.

Gilliland,P.M.,&Laffoley,D.(2008).Keyelementsandstepsintheprocessofdevelopingecosystem-basedmarinespatialplanning.Marine Policy,32,787–796.

Girardin, R., Fulton, E. A., Lehuta, S., Savina-Rolland, M., Thébaud, O.,Travers-Trolet, M., … Marchal, P. (2016). Identification of the mainprocesses underlying ecosystem functioning in the Eastern EnglishChannel,withafocusonflatfishspecies,asrevealedthroughtheap-plicationoftheAtlantisend-to-endmodel.Estuarine and Coastal Shelf Science,https://doi.org/10.1016/j.ecss.2016.10.016.

Gourguet,S.,Thébaud,O.,Dichmont,C.,Jennings,S.,Little,L.R.,Pascoe,S.,…Doyen,L.(2014).Riskversuseconomicperformanceinamixedfishery. Ecological Economics,99,110–120.

Gourguet,S.,Thébaud,O.,Jennings,S.,Little,L.R.,Dichmont,C.,Pascoe,S.,…Doyen,L.(2016).Thecostofco-viabilityintheAustralianNorthernPrawnFishery.Environmental Modeling & Assessment,21(3),371–389.

Grimm,V.,Berger,U.,DeAngelis,D.L.,Polhill,J.G.,Giske,J.,&Railsback,S.F. (2010).TheODDprotocol:Areviewandfirstupdate.Ecological Modelling,221,2760–2768.

Guillén,J.,Macher,C.,Merzéréaud,M.,Bertignac,M.,Fifas,S.,&Guyader,O. (2013).EstimatingMSYandMEY inmulti-species andmulti-fleetfisheries,consequencesandlimits:AnapplicationtotheBayofBiscaymixedfishery.Marine Policy,40,64–74.

Guillén,J.,Macher,C.,Merzéréaud,M.,Boncoeur,J.,&Guyader,O.(2015).EffectsoftheShareRemunerationSystemonFisheriesManagementTargets and Rent Distribution. Marine Resource Economics, 30(2),123–138.

Guillén,J.,Macher,C.,Merzéréaud,M.,Fifas,S.,&Guyader,O.(2014).TheeffectofdiscardsandsurvivalrateontheMaximumSustainableYield estimation based on landings or catches maximisation:Application to the nephrops fishery in the Bay of Biscay.Marine Policy,50,207–214.

Guillén,J.,Maynou,F.,Floros,C.,Sampson,D.,Conides,A.,&Kostas,K.(2012).Abio-economicevaluationof thepotential forestablishingacommercial fishery on two newly developed stocks: The Ionian redshrimpsfishery.Scientia Marina,76(3),597–605.

Heal,G.,&Schlenker,W.(2008).Economics:Sustainablefisheries.Nature,455,1044–1045.

Heaps,T. (2003).The effects onwelfare of the imposition of individualtransferable quotas on a heterogeneous fishing industry. Journal of Environmental Economics and Management,46,557–576.

Heymans,J.J.,Mackinson,S.,Sumaila,U.R.,Dyck,A.,&Little,A.(2011).Theimpactofsubsidiesontheecologicalsustainabilityandfutureprof-itsfromNorthSeafisheries.PLoS ONE,6(5),e20239.

Hicks,C.C.,Levine,A.,Agrawal,A.,Basurto,X.,Breslow,S.J.,Carothers,C.,…Levin,P.S.(2016).Engagekeysocialconceptsforsustainability.Social indicators,bothmatureandemerging,areunderused.Science,352,38–40.

Hilborn,R.(2007).Managingfisheriesismanagingpeople:Whathasbeenlearned?Fish and Fisheries,8,285–296.

Hilborn,R.(2011).Futuredirectionsinecosystembasedfisheriesmanage-ment:Apersonalperspective.Fisheries Research,108,235–239.

Hilborn,R.,Fulton,E.A.,Green,B.S.,Hartmann,K.,Tracey,S.R.,&Watson,R.A.(2015).Whenisafisherysustainable?Canadian Journal of Fisheries and Aquatic Science,72,1433–1441.

Hill, S. L.,Watters,G.M., Punt,A. E.,McAllister,M.K., LeQuére,C.,&Turner,J.(2007).Modeluncertaintyintheecosystemapproachtofish-eries. Fish and Fisheries,8,315–336.

Hoff,A., Frost, H., Ulrich, C., Damalas, D.,Maravelias, C. D., Goti, L., &Santurtún, M. (2010). Economic effort management in multispeciesfisheries: The FcubEcon model. ICES Journal of Marine Science, 67,1802–1810.

Holland, D. S. (2011a). Optimal intra-annual exploitation of the MaineLobsterFishery.Land Economics,87(4),699–711.

Holland,D.S.(2011b).PlanningforChangingProductivityandCatchabilityintheMaineLobsterFishery.Fisheries Research,110(1),47–58.

Holling,C.S.(2001).Understandingthecomplexityofeconomic,ecologicalandsocialsystems.Ecosystems,4,390–405.

Horbowy,J. (1996).ThedynamicsofBaltic fishstockson thebasisofamultispecies stock-production model. Canadian Journal Fisheries Aquatic Sciences,53,2115–2125.

Horbowy,J. (2005).ThedynamicsofBaltic fishstocksbasedofamulti-species stock production model. Journal of Applied Ichthyology, 21,198–204.

Howarth,L.M.,Roberts,C.M.,Thurstan,R.H.,&Stewart,B.D.(2013).Theunintendedconsequencesofsimplifyingthesea:Makingthecaseforcomplexity.Fish and Fisheries,15(4),690–711.

Hussein, C., Verdoit-Jarraya, M., Pastor, J., Ibrahim, A., Saragoni, G.,Pelletier,D.,…Lenfant, P. (2011a).Assessing the impactof artisanalandrecreationalfishingandprotectiononawhiteseabream(Diplodussargus sargus) population in the north-western Mediterranean Seausingasimulationmodel.Part2:Sensitivityanalysisandmanagementmeasures. Fisheries Research,108(1),174–183.

Hussein, C., Verdoit-Jarraya, M., Pastor, J., Ibrahim, A., Saragoni, G.,Pelletier,D.,…Lenfant,P.(2011).Assessingtheimpactofartisanalandrecreationalfishingandprotectiononawhiteseabream(Diplodussar-gussargus)populationinthenorth-westernMediterraneanSeausingasimulationmodel.Part1:Parameterizationandsimulations.Fisheries Research,108(1),163–173.

Hutton,T., Putten, I.V., Pascoe, S., Deng, R., Plagányi, E., &Dennis, D.(2016).Trade-offs intransitionsbetween indigenousandcommercialfishingsectors:TheTorresStraitTropicalRockLobsterFishery.Fisheries Management and Ecology,23,463–477.

Hyder,K.,Rossberg,A.G.,Allen,I.,Austen,M.C.,Barciela,R.M.,Bannister,H.J.,…Paterson,D.M.(2015).Makingmodellingcountincreasingthecontributionofshelf-seascommunityandecosystemmodelstopolicydevelopmentandManagement.Marine Policy,61,291–302.

ICES. (2006). Report of the Working Group on Workshop on Simple Mixed Fisheries Management Models.ICESCM2006/ACFM:14

ICES. (2011).Annex 5 Report on Key Run for the North Sea Ecopath with Ecosim Ecosystem Model, 1991–2007. in ICES. 2012. Report of the

     |  25NIELSEN Et aL.

Working Group on Multispecies Assessment Methods (WGSAM),10–14October2011,WoodsHole,USA.ICESCM2011/SSGSUE:10,229pp.

ICES.(2014a).Report of the Working Group on Mixed Fisheries Advice for the North Sea (WGMIXFISH-NS),26–30May2014,ICESHQ,Copenhagen,Denmark.ICESCM2014/ACOM:22,95pp.

ICES. (2014b). Report of the Working Group on Mixed Fisheries Methods (WGMIXFISH-METH), 20–24 October 2014, Nobel House, London,UnitedKingdom.ICESCM2014/ACOM:23,79pp.

ICES. (2015a). First Interim Report of the Working Group on Integrating Ecological and Economic Models (ICES WGIMM). ICES CM 2015/SSGIEA:05,15pp.

ICES.(2015b).ICES ASC Theme Session M 2015.In:Nielsen,J.R.,Schmidt,J.,Thunberg,E.,andHollandD.2015.Social,economic,andecologi-cal impactassessmentacrossmarinesectors. ICESCM2015/ThemeSessionMSummary,3pp.Availableat:www.ices.dk(05Apr2017).

ICES. (2015c). Report of the Working Group on Mixed Fisheries Advice Methodology (WGMIXFISH-METH), 5–9 October 2015, DTU-Aqua,Charlottenlund,Denmark.ICESCM2015/ACOM:22,51pp.

ICES. (2015d). Report of the Working Group on Mixed Fisheries Advice (WGMIXFISH-ADVICE), 25–29 May 2015, ICES HQ, Copenhagen,Denmark.ICESCM2015/ACOM:21,171pp.

Iriondo,A.,García,D.,Santurtún,M.,Castro,J.,Quincoces,I.,Lehuta,S.,…Ulrich,C. (2012).Managingmixed fisheries in theEuropeanwesternwaters: Application of Fcube methodology. Fisheries Research, 134–136,6–16.

Jardim,E.,Urtizberea,A.,Motova,A.,Osio,C.,Ulrich,C.,Millar,C.,…Holmes,S..(2013).Bioeconomic modelling applied to fisheries with R/FLR/FLBEIA. JRCScientificandPolicyReportEUR25823ENJRC79217.

Kaplan,I.C.,Holland,D.S.,&Fulton,E.A.(2014).Findingtheacceleratorandbrake inan individualquotafishery:Linkingecology,economics,and fleetdynamicsofUSWestCoast trawl fisheries. ICES Journal of Marine Science,71,308–319.

Kaplan,I.C.,Horne,P.,&Levin,P.S.(2009).Ecosystem-based management of what? An emerging approach for balancing conflicting objectives in ma-rine resource management. The Future of Fisheries Science in NorthAmerica,Springer,pp.77–95.

Kaplan, I. C., Horne, P. J., & Levin, P. S. (2012). Screening CaliforniaCurrentFisheryManagementScenariosusingtheAtlantisEnd-to-EndEcosystemModel.Progress In Oceanography,102,5–18.

Kaplan, I. C., & Leonard, J. (2012). From krill to convenience stores:Forecastingtheeconomicandecologicaleffectsoffisheriesmanage-mentontheUSWestCoast.Marine Policy,36,947–954.

Kell,L.T.,Mosqueira,I.,Grosjean,P.,Fromentin,J.-M.,Garcia,D.,Hillary,R.,…Scott,R.D.(2007).FLR:Anopen-sourceframeworkfortheevalua-tionanddevelopmentofmanagementstrategies.ICES Journal of Marine Science,64,640–646.

Kempf,A.,Mumford,J.,Levontin,P.,Leach,A.,Hoff,A.,Hamon,K.G.,…Rindorf,A.(2016).TheMSYconceptinamulti-objectivefisheriesen-vironment–LessonsfromtheNorthSea.Marine Policy,69,146–158.

Kitts, A., Bing-Sawyer, E., Walden, J., Demarest, C., McPherson, M.,Christman,P.,…Clay,P.(2011).Final Report on the Performance of the Northeast Multispecies (Groundfish) Fishery (May 2010; April 2011). US Department of Commerce, Northeast Fisheries Science Centre, RefDoc.11-19,97pp.

Knowler, D. (2002). A Review of Selected Bioeconomic Models withEnvironmental Influences in Fisheries. Journal of Bioeconomics, 4,163–181.

Kraak,S.B.M.,Kelly,C.J.,Codling,E.A.,&Rogan,E.(2010).Onscientists’discomfort in fisheries advisory science:The example of simulation-based fisheries management-strategy evaluations. Fish and Fisheries,11,119–132.

Langlois,J.,Fréon,P.,Steyer,J.-P.,Delgenés,J.-P.,&Hélias,A.(2014).Sea-useimpactcategoryinlifecycleassessment:Stateoftheartandper-spectives.International Journal Life Cycle Assessment,19,994–1006.

Lassen,H.,AnkerPedersen,S.,Frost,H.,&Hoff,A. (2013).Fisheryman-agementadvicewithecosystemconsiderations.ICES Journal of Marine Science,70(2),471–479.

Lehuta,S.,Girardin,R.,Mahevas,S.,Travers-Trolet,M.,&Vermard,Y.(2016).Reconcilingcomplexsystemmodelsandfisheriesadvice:Practicalex-amplesandleads.Aquatic Living Resources,29(208),1–20.

Lehuta,S.,Holland,D.S.,&Pershing,A.J. (2014).Investigatingintercon-nectedfisheries:AcoupledmodelofthelobsterandherringfisheriesintheNortheastUS.Canadian Journal of Fisheries and Aquatic Sciences,71(2),272–289.

Lehuta, S., Mahévas, S., & Le Floc’h, P. (2013). Simulation-based bio-economicindicatorsofmanagementimpact:AssessingrelevanceandrobustnessforthepelagicfisheryoftheBayofBiscay.Canadian Journal of Fisheries and Aquatic Sciences,70(12),1741–1756.

Lehuta,S.,Mahévas,S.,Petitgas,P.,&Pelletier,D.(2010).Combiningsen-sitivityanduncertaintyanalysistoevaluatetheimpactofmanagementmeasures with ISIS-Fish: Marine Protected Areas for the Anchovy(Engraulis encrasicolus L.) fishery in theBayofBiscay. ICES Journal of Marine Science,67,1063–1075.

Lehuta,S.,Petitgas,P.,Mahévas,S.,Vermard,Y.,Huret,M.,Uriarte,A.,&Record, N. R. (2013). Selection and validation of a complex fisherymodelusinganuncertaintyhierarchy.Fisheries Research,143,57–66.

Lehuta, S.,Vermard,Y.,&Marchal, P.(2015).A spatial model of the mixed Demersal fisheries in the Eastern Channel. In Marine Productivity:Perturbations andResilienceof Socio-Ecosystems.Proceedings15thFrench-JapanOceanographicSymposium187–95.

Little,L.R.,Punt,A.E.,Mapstone,B.D.,Begg,G.A.,Goldman,B.,&Ellis,N.(2009).Differentresponsestoareaclosuresandeffortcontrolsforsedentaryandmigratoryharvestedspeciesinamultispeciescoralreefline fishery. ICES Journal of Marine Science,66,1931–1941.

Little, L. R., Punt, A. E., Mapstone, B. D., Begg, G. A., Goldman, B., &Williams,A.J.(2009).Anagent-basedmodelforsimulatingtradingofmulti-speciesfisheriesquota.Ecological Modelling,220,3404–3412.

Little,L.R.,Punt,A.E.,Mapstone,B.D.,Pantus,F.,Smith,A.D.M.,Davies,C. R., &McDonald, A. D. (2007). ELFSim –AModel for EvaluatingManagementOptions for Spatially-StructuredReefFishPopulations:AnIllustrationofthe“LarvalSubsidy”Effect.Ecological Modelling,205,381–396.

Lleonart,J.,Maynou,F.,&Franquesa,R.(1999).AbioeconomicmodelforMediterraneanfisheries.Fisheries Economy Newsletter,48,1–16.

Lleonart,J.,Maynou,F.,Recasens,L.,&Franquesa,R.(2003).Abioeconomicmodel forMediterranean Fisheries, the hake off Catalonia (WesternMediterranean)asacasestudy.Scientia Marina,67(suppl.1),337–351.

Macher,C.,Guyader,O.,Talidec,C.,&Bertignac,M.(2008).Acost-benefitanalysis of improving trawl selectivity in the case of discards: TheNephropsnorvegicus fishery in theBayofBiscay.Fisheries Research,92(1),76–89.

Macher,C.,Merzéréaud,M.,Guyader,O.,Lagiere,R.,Larabi,Z.,LeGrand,C.,&Morin,L.(2013).Governancesystemoffisheriesquota:Abio-eco-nomicapproachinthecaseofthebayofBiscaysolefishery.ESEE2013Conference:EcologicalEconomicsandInstitutionalDynamics.10thbi-ennialconferenceof theEuropeanSociety forEcologicalEconomics,18-21Jun2013,Lille(France).

Mackinson, S. (2014). Combined analyses reveal environmentally drivenchanges in the North Sea ecosystem and raise questions regardingwhat makes an ecosystem model’s performance credible? Canadian Journal of Fisheries and Aquatic Science,71,31–46.

Mackinson,S.,&Daskalov,G.(2007).An ecosystem model of the North Sea to support an ecosystem approach to fisheries management: Description and parameterisation. Scientific Series Technical Report, Cefas Lowestoft142,196pp.

Mackinson,S.,Deas,B.,Beveridge,D.,&Casey,J. (2009).Mixed-fisheryorEcosystemconundrum?Multi-speciesconsiderationsinformthink-ingonlong-termmanagementofNorthSeademersalstocks.Canadian Journal of Fisheries and Aquatic Science,66,1107–1129.

26  |     NIELSEN Et aL.

Mahevas,S.,&Pelletier,D.(2004).ISIS-Fish,agenericandspatiallyexplicitsimulationtoolforevaluatingtheimpactofmanagementmeasuresonfisheries dynamics. Ecological Modelling,171(1–2),65–84.

Maouel,D.,Maynou,F.,&Bedrani,S.(2014).BioeconomicanalysisofsmallpelagicfisheryinCentralAlgeria.Turkish Journal of Fisheries and Aquatic Sciences,14,1–8.

Mapstone, B.D., Little, L. R., Punt,A. E.,Davies,C. R., Smith,A.D.M.,Pantus, F.,…Jones,A. (2008).ManagementStrategyEvaluations forlinefishingintheGreatBarrierReef:Balancingconservationandmulti-sectorfisheryobjectives.Fisheries Research,94,315–329.

Marasco, R. J., Goodman, D., Grimes, C. B., Lawson, P.W., Punt, A. E.,Quinn, T. J. II(2007). Ecosystem-based fisheries management: Somepracticalsuggestions.Canadian Journal of Fisheries and Aquatic Sciences,64,928–939.

Maravelias,C.,Damalas,D.,Ulrich,C.,Katsanevakis,S.,&Hoff,A.(2012).Multispecies fisheries management in the Mediterranean Sea:Application of the Fcube methodology. Fisheries Management and Ecology,19(3),189–199.

Maravelias, C., Pantazi,M., &Maynou, F. (2014). Fisheriesmanagementscenarios: Trade-offs between economic and biological objectives.Fisheries Management and Ecology,21(3),186–195.

Marchal,P.,Andersen,J.L.,Aranda,M.,Fitzpatrick,M.J.L.,Goti,L.,Guyader,O.,…Ulrich,C.(2016).Acomparativereviewoffisheriesmanagementexperiences in the European Union and other countriesworldwide:Iceland,Australia,NewZealand.Fish and Fisheries,17,803–824.

Marchal,P.,DeOliveira,J.,Lorance,P.,Baulier,L.,&Pawlowski,L.(2013).Whatistheaddedvalueofincludingfleetdynamicsprocessesinfish-eries models?Canadian Journal of Fisheries and Aquatic Sciences, 70,992–1010.

Marchal,P.,Little,L.R.,&Thebaud,O.(2011).Quotaallocationinmixedfisheries:Abioeconomicmodellingapproachapplied to theChannelflatfishfisheries.ICES Journal of Marine Science,68(7),1580–1591.

Mattos,S.M.G.,Maynou,F.,&Franquesa,R.(2006).Abio-economicanal-ysisofthehand-lineandgillnetcoastalfisheriesofPernambucoState,north-easternBrazil.Scientia Marina,70(2),335–346.

Maynou,F.(2014).Co-viabilityanalysisofWesternMediterraneanfisher-iesunderMSYscenariosfor2020.ICES Journal of Marine Science,71(7),1563–1571.

Maynou,F.,Martínez-Baños,P.,Demestre,M.,&Franquesa,R.(2014).Bio-economicanalysisoftheMarMenor(Murcia,SESpain)small-scalela-goonfishery.Journal of Applied Ichthyology,30(5),978–985.

Maynou, F., Sardà, F., Tudela, S., & Demestre, M. (2006). Managementstrategiesforredshrimp(Aristeusantennatus)fisheriesintheCatalansea (NWMediterranean) based on bioeconomic simulation analysis.Aquatic Living Resources,19(2),161–171.

McAllister,M. K., Starr, P. J., Restrepo,V. R., & Kirkwood, G. P. (1999).Formulating quantitative methods to evaluate fishery-managementsystems:Whatfisheryprocessesshouldbemodelledandwhattrade-offsshouldbemade?ICES Journal of Marine Science,56,900–916.

Merino, G., Karlou-Riga, C., Anastopoulou, I., Maynou, F., & Lleonart,J. (2007). Bioeconomic simulation analysis of hake and red mulletfisheries in the Gulf of Saronikos (Greece). Scientia Marina, 71(3),525–535.

Merzéréaud,M.,Biais,G.,Lissardy,M.,Bertignac,M.,&Biseau,A.(2013).Evaluation of proposed harvest control rules for Bay of Biscay sole. ICESCM2013/ACOM:75,18p.

Merzéréaud, M., Macher, C., Bertignac, M., Frésard, M., Le Grand, C.,Guyader,O., … Fifas, S. (2011).Description of the impact assessment bio-economic model for fisheries management (IAM).Amure ElectronicPublications,WorkingPapersSeriesD-29-2011,19p.

Mullon, C. (2013).Network economics of marine ecosystems and their ex-ploitation.CRCPress.

Mullon,C.,Mittaine,J.-F.,Thébaud,O.,Péeron,G.,Merino,G.,&Barange,M.(2009).Modelingtheglobalfishmealandfishoilmarkets.Natural Resource Modeling,22(4),564–609.

Murawski,S.A.,Steele,J.H.,Taylor,P.,Fogarty,M.J.,Sissenwine,M.P.,Ford,M., & Suchman, C. (2010).Why comparemarine ecosystems?ICES Journal of Marine Science,67,1–9.

Neubauer, P., Jensen, O. P., Hutchhings, J. A., & Baum, J. K. (2013).Resilienceandrecoveryofoverexploitedmarinepopulations.Science,340,347–349.

NewEconomicsFoundation(2016).Bio-economic model of European fleet, documentation.Available at: http://fisheriesmodel.org/documentation(01Nov2016)

Nielsen, J. R., Bastardie, F., Egekvist, J., Jantzen, K., Raid, T., Goldmanis,E.,…Eero,M.(2011).EvaluationofEffortandTACQuotaUptakeandCapacityUsebyCountryaswellasefficiencyofeffortmeasuresaccord-ingtofishingmortalityandfishingpowerintheWesternandEasternBalticCodFishery during 2005–2010 in relation to themulti-annualcodmanagementplan.WorkingDocument,70pp.In:Simmondsetal.(eds).2011.ReportoftheSubGrouponImpactAssessmentofBalticcodmulti-annualplans.EUSTECFEWG-11-07a.Hamburg(D),20–24June 2011. Luxembourg: PublicationsOffice of the EuropeanUnion,2011.ISSN1018-5593(print).ICESWKROUNDACOM2011/ACOM:55,67pp.

Nielsen,J.R.,Kristensen,K.,Lewy,P.,&Bastardie,F. (2014).Astatisticalmodelforestimationoffishdensityincludingcorrelationinsize,space,timeandbetweenspeciesfromresearchsurveydata.PLoS ONE,9(6),e99151.p.1–15.

Nielsen,J.R.,&Limborg,M.(2009).Managingfleetsandfisheriesratherthansinglestocks–conceptualchangeinEuropeanfisheriesmanage-mentadvice.World Fishing,58(1),8–9.

Nielsen, J. R., Palacz, A., Christensen, A., Bastardie, F., Maar, M., Hoff,A., … Fulton, B. (2015). A Baltic Management Evaluation Framework: Ecosystem End2End ATLANTIS & Bio-Economic FISHRENT & DISPLACE Models – Models, Linkages, Scenarios, Initial Results.W.&A.deNottbeckFoundation and KONEEN SÄÄTIÖ International Symposium on“BalticSeaEcosystemModelsandtheirApplicationforManagement”Tvärminne,SF,13-16Sept.2015.Availableat:http://www.helsinki.fi/science/fem/workshop/(01Nov2016).

Nielsen,J.R., Schmidt,J.O.,Holland,D.,Thunberg,E.,Amboage,R.M.,Bastardie,F.,…Waldo,S.(2014).Evaluation of integrated ecological-eco-nomic models – What are they used for? IIFETConference,July7–11,BrisbaneAustraliaExtendedAbstractandOralPresentationID494

Nielsen,J.R.,Schmidt,J.,Thunberg,E.,&Holland,D. (2015).Social, eco-nomic, and ecological impact assessment across marine sectors.ICESCM2015/ThemeSessionMReport,3pp.

Nielsen, J. R., Thunberg, E., Schmidt, J. O., Holland, D., Bastardie, F.,Andersen,J.L.,…Buckworth,R.(2015).Evaluation of integrated ecolog-ical-economic models – Review and challenges for implementation. ICESASCCM2015/M:10.

Österblom, H., Crona, B. I., Folke, C., Nyström,M., &Troell,M. (2016).Marine ecosystem science on an intertwined planet. Ecosystems,https://doi.org/10.1007/s10021-016-9998-6(01Nov2016).

Österblom,H.,Jouffray,J.-B.,&Spijkers,J.(2016).Whereandhowtopri-oritizefisheryreform?Proceedings of the National Academy of Sciences,https://doi.org/10.1073/pnas.1605723113(01Nov2016).

Österblom, H., Merrie, A., Metian, M., Boonstra, W. J., Blenckner, T.,Watson,J.R.,…Folke,C.(2013).ModelingSocial-EcologicalScenariosinMarineSystems.BioScience,63(9),735–744.

Ostrom, E. (2009). A general framework for analyzing sustainability ofsocial-ecologicalsystems.Science,325,419–422.

Palacz,A.P.,Nielsen,J.R.,Christensen,A.,Hoff,A.,Frost,H.,Gislason,H.,…Fulton,E.A.(2015).An integrated end-to-end modelling framework for testing ecosystem-wide effects of human-induced pressures in the Baltic Sea.ICESASCCM2015/M:12

Palacz,A.P.,Nielsen,J.R.,Christensen,A.,Hoff,A.,Maar,M.,Frost,H.,…Fulton,E.A.(InRevision).Anintegratedend-to-endmodellingframe-workfortestingecosystem-wideeffectsofhuman-inducedpressuresintheKattegatandWesternBalticSearegion.PLoS ONE(inrevision).

     |  27NIELSEN Et aL.

Palcz,A.,Nielsen,J.R.,Christensen,A.,Gislason,H.,Bastardie,F.,Geitner,K.,…Fulton,E.(2014).The Baltic Atlantis model: Implementing a holis-tic framework to evaluate ecosystem wide responses to changes in climate and anthropogenic forcing. EU-FP7-VECTORS Symposium, LaGrandeMotte,France,18–20Nov.2014

Pan,H.,Failler,P.,&Floros,C.(2007).A regional computable general equilib-rium model for fisheries.CEMAREResearchPaperNo.163.Portsmouth,UK.

Pascoe,S.,Hutton,T.,vanPutten,E.I.,Dennis,D.,Plagányi,É.,&Deng,R.(2013). Implicationsofquotareallocation intheTorresStraitTropicalRockLobsterFishery.Canadian Journal of Agricultural Economics 61(2),335–352.

Pascoe, S.,Hutton,T.,vanPutten,E. I.,Dennis,D., Skewes,T., Plagányi,É.,&Deng,R. (2013).DEA-basedpredictorsforestimatingfleetsizechangeswhenmodelingtheintroductionofrights-basedmanagement.European Journal of Operational Research,230(3),681–687.

Pascoe,S.,Kahui,V.,Hutton,T.,&Dichmont,C.(2016).ExperienceswiththeuseofbioeconomicmodelsinthemanagementofAustralianandNewZealandfisheries.Fisheries Research,183,539–548.

Paulrud,A.,&Waldo,S.(2011).Mot nya vatten – vart leder individuella över-förbara fiskekvoter?RapporttillExpertgruppenförmiljöstudier,SwedishGovernment2011:4.ISBN978-91-38-23597-3.

Pauly,D.,Hilborn,R.,&Branch,T.A.(2013).Doescatchreflectabundance?Nature,494,303–306.

Pelletier,D.,Mahevas,S.,Drouineau,H.,Vermard,Y.,Thebaud,O.,Guyader,O.,&Poussind,B.(2009).Evaluationofthebioeconomicsustainabilityofmulti-speciesmulti-fleetfisheriesunderawiderangeofpolicyop-tionsusingISIS-Fish.Ecological Modelling,220(7),1013–1033.

Piroddi,C.,Teixeira,H.,Lynam,C.P.,Smith,C.,Alvarez,M.C.,Mazik,K.,…Uyarra,M.C.(2015).Usingecologicalmodelstoassessecosystemsta-tusinsupportoftheEuropeanMarineStrategyFrameworkDirective.Ecological Indicators,58,175–191.

Plagányi,É.E. (2007).Models for an ecosystem approach to fisheries. FAOFisheriesTechnicalPaperNo.477.Rome,FAO,108p.

Plagányi,É.E.,&Butterworth,D.S.(2004).Acriticallookatthepotentialof Ecopathwith Ecosim to assist in practical fisheriesmanagement.African Journal of Marine Science,26,261–288.

Plagányi,É.E.,Deng,R.,Dennis,D.,Hutton,T.,Pascoe,S.,vanPutten,E.I.,&Skewes,T. (2012).An integrated Management Strategy Evaluation (MSE) for the Torres Strait tropical rock lobster Panulirus ornatus fishery. CSIRO/AFMAFinalProjectReportAFMAProjectnumber2009/839.ISBN9780643108875.

Plagányi,É.E.,Punt,A.E.,Hillary,R.,Morello,E.B.,Thébaud,O.,Hutton,T.…Rothlisberg,P.C.(2014).Multispeciesfisheriesmanagementandconservation:Tacticalapplicationsusingmodelsofintermediatecom-plexity.Fish and Fisheries,15(1),1–22.

Plagányi,E.,vanPutten,E.I.,Hutton,T.,Deng,R.,Dennis,D.,Pascoe,S.,…Campbell,R. (2013). Integrating indigenous livelihoodand lifestyleobjectivesinmanaginganaturalresource.Proceedings of the National Academy of Sciences,110(9),3639–3644.

Polovina, J. J. (1984).Model of a coral reef ecosystem I.TheECOPATHmodelanditsapplicationstoFrenchFrigateShoals.Coral Reefs,3,1–11.

Prellezo, R., Accadia, P., Andersen, J. L., Andersen, B. S., Buisman, E.,Little,A.,…Röckmann,C.(2012).AreviewofEUbio-economicmod-elsforfisheries:Thevalueofadiversityofmodel.Marine Policy,36,423–431.

Prellezo,R.,Carmona,I.,&Garcia,D.(2016).Thebad,thegoodandtheverygoodofthelandingobligationimplementationintheBayofBiscay:AcasestudyofBasquetrawlers.Fisheries Research,181,172–185.

Punt,A.E.,Deng,R.A.,Dichmont,C.M.,Kompas,T.,Venables,W.N.,Zhou,S.,…VanderVelde,T. (2010). Integratingsize-structuredassessmentand bioeconomic management advice in Australia’s northern prawnfishery. ICES Journal of Marine Science,67(8),1785–1801.

Punt,A. E.,Deng,R., Pascoe, S.,Dichmont,C.M., Zhou, S., Plagányi, E.E., … van derVelde, T. (2011). Calculating optimal effort and catch

trajectoriesformultiplespeciesmodelledusingamixofsize-structured,delay-difference and biomass dynamics models. Fisheries Research,109(1),201–211.

Punt,A.E.,Foy,R.J.,Dalton,M.G.,Long,W.C.,&Swiney,K.M.(2016).Effectsoflong-termexposuretooceanacidificationconditionsonfu-turesouthernTannercrab(Chionoecetesbairdi)fisheriesmanagement.ICES Journal of Marine Science,73,849–864.

Punt,A.E.,Poljak,D.,Dalton,M.G.,&Foy,R.J.(2014).Evaluatingtheim-pactofoceanacidificationonfisheryyieldsandprofits:TheexampleofredkingcrabinBristolBay.Ecological Modelling,285,39–53.

Punt,A.E.,Siddeek,M.S.M.,Garber-Yonts,B.,Dalton,M.,Rugolo,L.,Stram,D.,…Zheng,J.(2012).EvaluatingtheimpactofbufferstoaccountforscientificuncertaintywhensettingTACs:ApplicationtoredkingcrabinBristolBay,Alaska.ICES Journal of Marine Science,69,624–634.

Queirós,A.M., Fernandes,J.A., Faulwetter, S.,Nunes,J., Rastrick, S. P.,Mieszkowska,N.,…Widdicombe,S. (2015). Scalingupexperimentaloceanacidificationandwarmingresearch:Fromindividualstotheeco-system.Global Change Biology,21,130–143.

Ramos, J., Soma,K., Bergh,Ø., Schulze,T.,Gimpel,A., Stelzenmüller,V.,…Gault,J. (2013).MultipleinterestsacrossEuropeancoastalwaters:Theimportanceofacommonlanguage.ICES Journal of Marine Science,https://doi.org/10.1093/icesjms/fsu095.

Raveau,A.,Macher,C.,Méhault,S.,Merzéréaud,M.,LeGrand,C.,Guyader,O.,…Guillén,J.(2012).Abio-economicanalysisofexperimentalselec-tivedevicesintheNorwaylobster(Nephropsnorvegicus)fisheryintheBay of Biscay. Aquatic Living Resources,25(3),215–229.

Ravn-Jonsen, L. J. (2011). Intertemporal Choice of Marine EcosystemExploitation.Ecological Economics,70,1726–1734.

Ravn-Jonsen,L.J.,Andersen,K.H.,&Vestergaard,N.(2016).AnIndicatorFor Ecosystem Externalities In Fishing. Natural Resource Modeling,https://doi.org/10.1111/nrm.12094.

Rayns,N.(2007).TheAustraliangovernment’sharveststrategypolicy.ICES Journal of Marine Science,64,596–598.

Reecht,Y.,Gasche,L.,Lehuta,S.,Vaz,S.,Smith,R.,Mahévas,S.,&Marchal,P.(2015). Toward a dynamical approach for systematic conservationplanning of Eastern English Channel fisheries. In: H. J. Ceccaldi, Y.Hénocque,Y.Koike,T.Komatsu,G.Stora,&M.-H.Tusseau-Vuillemin(Eds.), Marine productivity: Perturbations and resilience of socio-eco-systems. Proceedings of the 15th French-Japanese OceanographySymposium,175–85

Rochet,M.-J.,&Rice,J.C.(2009).Simulation-basedmanagementstrategyevaluation:Ignorancedisguisedasmathematics?ICES Journal of Marine Science,66,754–762.

Rochet,M.-J.,&Rice,J.C.(2010).Commenton“Purportedflawsinman-agementstrategyevaluation:Basicproblemsormisinterpretations?”byButterworthetal.ICES Journal of Marine Science 67,575–576.

Rocklin,D.,Pelletier,D.,Mouillot,D.,Tomasini,J.A.,&Culioli,J.M.(2010).Simulating management scenarios for the spiny lobster fishery in a Mediterranean MPA using ISIS-Fish.Oralcommunication–InternationalMarineConservationCongress,WashingtonDC,19-24May2010.

Romagnoni,G.,Mackinson,S.,Hong,J.,&Eikeset,A.M.(2015).TheecospacemodelappliedtotheNorthSea:Evaluatingspatialpredictionswithfishbiomassandfishingeffortdata.Ecological Modelling 300,50–60.

Rose,K.A.,Allen,J. I.,Artioli,Y.,Barange,M.,Blackford,J.,Carlotti,F.,…Flynn,K. (2010). End-to-endmodels for the analysis ofmarine eco-systems:challenges,issues,andnextsteps.Marine Coastal Fisheries,2,115–130.

Sainsbury,K.J.,Punt,A.E.,&Smith,A.D.M.(2000).Designofoperationalmanagement strategies for achieving fishery ecosystem objectives.ICES Journal of Marine Science,57,731–741.

Sala,A.,DeCarlo,F.,Buglioni,G.,&Lucchetti,A. (2011).Energyperfor-manceevaluationoffishingvesselsbyfuelmassflowmeasuringsys-tem.Ocean Engineering,38,804–809.

Salz,P.,Buisman,E.,Soma,K.,Frost,H.,Accadia,P.,&Prellezo,R.(2010).Study on the remuneration of spawning stock biomass.MARE/2008/11.

28  |     NIELSEN Et aL.

CallforTendersMARE/2008/11–Lot3.Availableat:http://ec.europa.eu/fisheries/documentation/studies/remuneration_of_the_spawning_stock_biomass_en.pdf(01Nov2016),298p.

Salz,P.,Buisman,E.,Soma,K.,Frost,H.,Accadia,P.,&Prellezo,R.(2011).FISHRENT. Bio-economic simulation and optimisation model for fisheries. LEI-Report2011-024.

Seijo, J.C.,Defeo,O.,& Salas, S. (1998).Fisheries Bioeconomics.Theory,ModellingandManagement.FAOFisheriesTechnicalPaperNo.368.

Seung,C.K.(2006).AReviewofRegionalEconomicModelsforFisheriesManagementintheU.S.Marine Resource Economics,21,101–124.

Seung,C.K.,Dalton,M.G.,Punt,A.E.,Poljak,D.,&Foy,R.(2015).EconomicimpactsofchangesinanAlaskacrabfisheryfromoceanacidification.Climate Change Economics, 6(4), 1550017. https://doi.org/10.1142/S2010007815500177

Silvestri,S.,&Maynou,F.(2009).Applicationofabioeconomicmodelforsupporting the management process of the small pelagic fishery intheVenetoRegion,northernAdriaticSea.Italy. Scientia Marina,73(3),563–572.

Simmonds,J.,Biais,G.,Bertignac,M.,Macher,C.,Merzéréaud,M.,Scott,R.,&Vanhee,W.(2011).Impact Assessment of Bay of Biscay sole (STECF-11-01).Prepared inCopenhagenFebruary2011,adoptedby theEUSTECFduringits36thplenarymeetingheldfrom11-15April,2011inBarza,Italy.41pp.

Simons,S.L.,Bartelings,H.,Hamon,K.G.,Kempf,A.,Döring,R.,&Temming,A. (2014). Integrating stochasticage-structuredpopulationdynamicsintocomplexfisherieseconomicmodelsformanagementevaluations:TheNorthSea saithe fishery as a case study. ICES Journal of Marine Science: Journal du Conseil,71(7),1638–1652.

Simons, S. L., Döring, R., & Temming, A. (2014). Modelling the spatio-temporal interplay betweenNorth Sea saithe (Pollachiusvirens) andmultiple fleet segments for management evaluation. Aquatic Living Resources,27(01),1–16.

Simons,S.L.,Döring,R.,&Temming,A.(2015a).Modellingfishers’responsetodiscardpreventionstrategies:ThecaseoftheNorthSeasaithefish-ery. ICES Journal of Marine Science,72(5),1530–1544.

Simons,S.L.,Döring,R.,&Temming,A.(2015b).Combiningareaclosureswithcatchregulationsinfisherieswithspatio-temporalvariation:Bio-economicimplicationsfortheNorthSeasaithefishery.Marine Policy,51(1),281–292.

Skonhoft,A.,Vestergaard,N.,&Quaas,M.F.(2012).Optimalharvestinanagestructuredmodelwithdifferent fishing selectivity.Environmental and Resource Economics,51(4),525–544.

Smith,A.D.M.,Sainsbury,K.J.,&Stevens,R.A.(1999).Implementingef-fectivefisheries-managementsystems–managementstrategyevalu-ationandtheAustralianpartnershipapproach. ICES Journal of Marine Science,56,967–979.

Smith,A.D.M.,Smith,D.C.,Haddon,M.,Knuckey, I.,Sainsbury,K.J.,&Sloan,S.(2014).ImplementingharveststrategiesinAustralia:5yearson. ICES Journal of Marine Science,71,195–203.

Smith,D.C.,Smith,A.D.M.,&Punt,A.E.(2001).ApproachandprocessforstockassessmentintheSouthEastFishery:Aperspective.Marine and Freshwater Research,52,671–681.

Soma,K.,Ramos,J.,Bergh,Ø.,Schulze,T.,vanOostenbrugge,H.,vanDuijn,A.P.,…Buisman,E.(2013).The“mappingout”approach:EffectivenessofmarinespatialmanagementoptionsinEuropeancoastalwaters.ICES Journal of Marine Science, 71, 2630–2642. https://doi.org/10.1093/icesjms/fst193

Steele,J.H.,Collie,J.S.,Bisagni,J.J.,Gifford,D.J.,Fogarty,M.J.,Link,J.S.,…Stockhausen,W.T.(2007).Balancingend-to-endbudgetsoftheGeorgesBankecosystem.Progress in Oceanography,74,423–448.

Stelzenmüller,V.,Breen,P.,Stamford,T.,Thomsen,F.,Badalamenti,F.,Borj,A.,Buhl-Mortensen,L.,etal.(2013).Monitoringandevaluationofspa-tiallymanagedareas:Agenericframeworkforimplementationofeco-systembasedmarinemanagement and its application.Marine Policy,37,149–164.

Stelzenmüller, V., Schulze, T., Fock, H. O., & Berkenhagen, J. (2011).Integrated modelling tools to support risk-based decision-makingin marine spatial management.Marine Ecology Progress Series, 441,197–212.

Stokes,T.K.,Butterworth,D.S.,Stephenson,R.L.,&Payne,A.I.L.(1999).Confronting uncertainty in the evaluation and implementation offisheries-management systems. ICES Journal of Marine Science, 56,795–796.

Sundblad, E.-L.,Grimvall,A.,Gipperth, L.,&Morf,A. (2014). StructuringsocialdatafortheMarineStrategyFrameworkDirective.Marine Policy,45,1–8.

Suuronen,P.,Chopin,F.,Glass,C.,Løkkeborg,S.,Matsushita,Y.,Queirolo,D.,&Rihan,D.(2012).Lowimpactandfuelefficientfishing—Lookingbeyondthehorizon.Fisheries Research,119–120,135–146.

Tahvonen, O. (2009). Economics of Harvesting Age-Structured FishPopulations.Journal of Environmental Economics and Management,58,281–299.

Tahvonen, O., Quaas, M. F., Schmidt, J. O., & Voss, R. (2013). Optimalharvesting of an age-structured schooling fishery.Environmental and Resource Economics,54(1),21–39.

Tedesco, L., Piroddi, C., Kämäri,M., & Lynam, C. (2016). Capabilities ofBalticSeamodelstoassessenvironmentalstatusformarinebiodiver-sity.Marine Policy,70,1–12.

Thébaud,O.,Doyen,L.,Lample,M.,Mahêvas,S.,Mullon,C.,Planque,B.,…Vermard,Y.(2013).Buildingecological-economicmodelsandscenariosofmarine resourcesystems:Workshop report.Marine Policy,https://doi.org/doi.org/10.1016/j.marpol.2013.05.010.

Thunberg, E., Holland, D., Nielsen, J. R., & Schmidt, J. (2013). Coupled Economic-Ecological Models for Ecosystem-Based Fishery Management: Exploration of Trade-offs Between Model Complexity and Management Needs. Proceedings of the Sixteenth Biennial Conference of theInternationalInstituteofFisheriesEconomics&Trade(IIFET),July2012,Dar-Es-Salaam, Tanzania: Economics of Fish Resources and AquaticEcosystems: Balancing Uses, Balancing Costs, 2013, InternationalInstituteofFisheriesEconomics&Trade,Corvallis,OR.Alsoorallypre-sentedatIIFET2012.

Thøgersen,T.T.,Hoff,A.,&Frost,H.(2012).Linkingeffortandfishingmor-tality in amixed fisheriesmodel:Comparing linearversusnon-linearassumptions.Fisheries Research,127–128,9–17.

Tomczak,M.T., Heymans, J. J.,Yletyinen, J., Niiranen, S., Otto, S.A., &Blenckner, T. (2013). Ecological Network Indicators of EcosystemStatusandChangeintheBalticSea.PLoS ONE,8(10),e75439.

Tomczak,M.T.,Niiranen,S.,Hjerne,O.,&Blenckner,T.(2012).EcosystemflowdynamicsintheBalticProper-Usingamulti-trophicdatasetasabasisforfood-webmodelling.Ecological Modelling,230,123–147.

Tratnik, M., Radinović, S., & Pedišić, P.(2007). Upravljanje fondom srdele u Hrvatskom dijelu jadranskog mora. Managing stocks of sardines in the Croatian part of the Adriatic Sea. Agronomski Glasnik 1/2007. ISSN0002-1954.

Trenkel,V.M.,Daurès,F.,Rochet,M.-J.,&Lorance,P.(2013).InterannualVariabilityofFisheriesEconomicReturnsandEnergyRatiosIsMostlyExplained byGearType.PLoS ONE, https://doi.org/doi.org/10.1371/journal.pone.0070165.

Ulrich, C., Reeves, S. A., Vermard, Y., Holmes, S., &Vanhee,W. (2011).Reconciling single-speciesTACs in theNorth Sea demersal fisheriesusing the Fcube mixed-fisheries advice framework. ICES Journal of Marine Science,68,1535–1547.

Ulrich, C.,Vermard,Y., Dolder, P. J., Brunel, T., Jardim, E., Holmes, S. J.,…Rindorf,A. (2017).Achievingmaximum sustainableyield inmixedfisheries:Amanagement approach for theNorth Sea demersal fish-eries. ICES Journal of Marine Science,https://doi.org/10.1093/icesjms/fsw126.

Ulrich,C.,Wilson,D.C.,Nielsen,J.R.,Bastardie,F.,Reeves,S.,Andersen,B.S.,&Eigaard,O.R. (2012).Challengesandopportunitiesforfleet-and métier-based approaches for fisheries management under the

     |  29NIELSEN Et aL.

EuropeanCommonFisheryPolicy.Ocean & Coastal Management,70,38–47.

VanDijk,D.,Haijema,R.,Hendrix,E.M.T.,Groeneveld,R.A.,&vanIerland,E.C.(2013).Fluctuatingquotaandmanagementcostsundermultian-nualadjustmentoffishquota.Ecological Modelling,265,230–238.

VanDijk,D.,Hendrix,E.M.T.,Haijema,R.,Groeneveld,R.A.,&vanIerland,E.C.(2016).Anadjustmentrestrictiononfishquota:Resourcerents,overcapacity and recovery of fish stock. Environmental & Resource Economics,https://doi.org/10.1007/s10640-015-9983-0.

vanPutten,E.I.,Deng,R.,Dennis,D.,Hutton,T.,Pascoe,S.,Plagányi,É.,&Skewes,T.(2013).ThequandaryofquotamanagementintheTorresStrait rock lobster fishery. Fisheries Management and Ecology, 20(4),326–337.

vanPutten,I.E.,Gorton,R.J.,Fulton,E.A.,&Thebaud,O.(2013).Theroleofbehaviouralflexibilityinawholeofecosystemmodel. ICES Journal Marine Science,70,150–163.

vanPutten,E.I.,Lalancette,A.,Bayliss,P.,Dennis,D.,Hutton,T.,Norman-López,A.,…Skewes,T.(2012).ABayesianmodeloffactorsinfluencingindigenousparticipationintheTorresStraittropicalrocklobsterfish-ery. Marine Policy Special Issue,37,96–105.

Vermard,Y.,Lehuta,S.,Mahevas,S.,Thebaud,O.,Marchal,P.,&Gascuel,D. (2012). Combining fleet dynamics and population dynamics for a volatile fishery: the example of the anchovy fishery of the Bay of Biscay. IFREMER Report, 25pp Available at: http://archimer.ifremer.fr/doc/00107/21858/(01Nov2016)

Voss,R.,Hinrichsen,H.-H.,Quaas,M.F.,Schmidt,J.O.,&Tahvonen,O.(2011).TemperaturechangeandBalticsprat:Fromobservationstoecological-economicmodelling.ICES Journal of Marine Science,68(6),1244–1256.

Voss,R.,Quaas,M.F.,Schmidt,J.O.,&Hoffmann,J.(2014).Regionaltrade-offs frommultispeciesmaximum sustainable yield (MMSY)manage-mentoptions.Marine Ecology Progress Series,498,1–12.

Voss, R., Quaas, M. F., Schmidt, J. O., Tahvonen, O., Lindegren, M., &Möllman,C.(2014).Assessingsocial-ecologicaltrade-offstoadvanceecosystem-basedfisheriesmanagement.PLoS ONE,9(9),e107811.

Waldo,S.,&Paulrud,A.(2013a).ITQsinSwedishdemersalfisheries.ICES Journal of Marine Science,70(1),68–77.

Waldo,S.,&Paulrud,A.(2013b).The Swedish Resource Rent Model for the Commercial Fishery, SRRMCF.AgriFoodWorkingPaper2013:1.

Waldo, S.,&Paulrud,A. (2016). ReducingGreenhouseGasEmissions inFisheries: The Case of Multiple Regulatory Instruments in Sweden.Environmental and Resource Economics, https://doi.org/10.1007/s10640-016-0018-2.

Walters, C. J. (1998). Designing fisheries management systems that donotdependuponaccuratestockassessment. In:T.J.Pitcher,P.J.B.Hart&D.Pauly(Eds.),Reinventing fisheries management(pp.279–288).London:Chapman&Hall.

Walters, C., & Christensen, V. (2007). Adding realism to foraging arenapredictionsoftrophicflowratesinEcosimecosystemmodels:Sharedforagingarenasandboutfeeding.Ecological Modelling,209,342–350.

Walters,C.,Christensen,V.,&Pauly,D.(1997).Structuringdynamicmod-elsofexploitedecosystems from trophicmass-balanceassessments.Reviews in Fish Biology and Fisheries,7,139–172.

Walters,C.,&Martell,S.J.D.(2004).Fisheries Ecology and Management.NJ,USA:PrincetonUniversityPress.448pp.

Walters, C., Pauly,D., & Christensen,V. (1999). Ecospace: Prediction ofmesoscale spatial patterns in trophic relationships of exploited eco-systems, with emphasis on the impacts of marine protected areas.Ecosystems,2,539–554.

Walters,C.,Pauly,D.,Christensen,V.,&Kitchell,J.F.(2000).Representingdensitydependent consequencesof lifehistory strategies in aquaticecosystems:EcoSimII.Ecosystems,3,70–83.

Weninger,Q.(2008).EconomicBenefitsofManagementReformintheGulfofMexicoGrouperFishery:ASemi-ParametricAnalysis.Environmental and Resource Economics,41,479–497.

Weninger,Q.,&Just,R.E. (2002).FirmDynamicswithTradableOutputPrices. American Journal of Agricultural Economics,84,562–584.

Weninger,Q.,&Waters,J.R. (2003).Economicbenefitsofmanagementreform in the northern Gulf of Mexico reef fish fishery. Journal of Environmental Economics and Management,46(2),207–230.

White,C.,Halpern,B.S.,&Kappel,C.V.(2012).Ecosystemservicetradeoffanalysis reveals thevalueofmarinespatialplanning formultipleoceanuses. Proceedings of the National Academy of Sciences,109(12),4696–4701.

Wilen,J.E.,Smith,M.D.,Lockwood,D.,&Botsford,L.W.(2002).Avoidingsurprises:Incorporatingfishermanbehaviorintomanagementmodels.Bulletin of Marine Science,70(2),553–575.

Worm,B.,Hilborn,R.,Baum,J.K.,Branch,T.A.,Collie,J.S.,Costello,C.,…Zeller,D.(2009).RebuildingGlobalFisheries.Science,325,578–585.

SUPPORTING INFORMATION

Additional Supporting Informationmay be found online in the sup-portinginformationtabforthisarticle.

How to cite this article:NielsenJR,ThunbergE,HollandDS,etal.Integratedecological–economicfisheriesmodels—Evaluation,reviewandchallengesforimplementation.Fish Fish. 2018;19:1–29. https://doi.org/10.1111/faf.12232