英文【世界銀行】全球海洋保護(hù)區(qū)的擴(kuò)大和捕撈努力的重新分配（英）2025

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PolicyResearchWorkingPaper11030

GlobalExpansionofMarineProtectedAreasandtheRedistributionofFishingEffort

GavinMcDonald

JenniferBone

ChristopherCostello

GabrielEnglander

JenniferRaynor

WORLDBANKGROUP

DevelopmentEconomics

DevelopmentResearchGroupJanuary2025

PolicyResearchWorkingPaper11030

Abstract

Theexpansionofmarineprotectedareas(MPAs)isacorefocusofglobalconservationefforts,withthe“30x30”ini-tiativetoprotect30%oftheoceanby2030servingasaprominentexampleofthistrend.ThispaperexaminesaseriesofproposedMPAnetworkexpansionsofvarioussizesandforecaststheimpactthatincreasedprotectioncouldhaveonglobalpatternsoffishingeffort.Thisisaccom-plishedusingapredictivemachinelearningmodeltrainedonaglobaldatasetofsatellite-basedfishingvesselmoni-toringdata,currentMPAlocations,andspatiotemporalenvironmental,geographic,political,andeconomicfea-tures.ThemodelpredictsfuturefishingeffortundervariousMPAexpansionscenarios,comparedtoabusiness-as-usualcounterfactualscenariothatincludesnonewMPAs.The

differencebetweenthesescenariosrepresentsthepredictedchangeinfishingeffortresultingfromMPAexpansion.Theresultsshowthat,regardlessoftheMPAnetwork’sobjec-tiveorsize,fishingeffortwoulddecreaseinsidetheMPAs,thoughbymuchlessthan100%.Moreover,thisreduc-tioninfishingeffortwithinMPAsdoesnotsimplyshiftoutside—fishingeffortoutsideMPAsalsodeclines.Theoverallmagnitudeofthepredicteddecreaseinglobalfishingeffortprincipallydependsonwherenetworksareplacedinrelationtoexistingfishingeffort.MPAexpansionwillleadtoaglobalredistributionoffishingeffort,whichshouldbeconsideredinnetworkdesign,implementation,andimpactevaluation.

ThispaperisaproductoftheDevelopmentResearchGroup,DevelopmentEconomics.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebat

/prwp

.Theauthorsmaybecontactedataenglander@.

ThePolicyResearchWorkingPaperSeriesdisseminatesthefindingsofworkinprogresstoencouragetheexchangeofideasaboutdevelopmentissues.Anobjectiveoftheseriesistogetthefindingsoutquickly,evenifthepresentationsarelessthanfullypolished.Thepaperscarrythenamesoftheauthorsandshouldbecitedaccordingly.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheInternationalBankforReconstructionandDevelopment/WorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.

ProducedbytheResearchSupportTeam

Globalexpansionofmarineprotectedareasandtheredistributionoffishingeffort

GavinMcDonald1,2,3*,JenniferBone1,2,3,ChristopherCostello1,2,3,GabrielEnglander4,JenniferRaynor5

1MarineScienceInstitute,UniversityofCalifornia,SantaBarbara,USA.

2BrenSchoolofEnvironmentalScienceandManagement,UniversityofCalifornia,SantaBarbara,USA.

3EnvironmentalMarketsLab,UniversityofCalifornia,SantaBarbara,USA.

4DevelopmentResearchGroup,TheWorldBank,USA.

5DepartmentofForestandWildlifeEcology,UniversityofWisconsin-Madison,USA.

*Correspondingauthor(s).E-mail(s):gmcdonald@;

Contributingauthors:jbone@;ccostello@;

aenglander@;jraynor@;

JELclassiication:Q22,Q28

Keywords:marineprotectedareas|isherieseconomics|isherbehavior|predictivemachinelearning

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1Introduction

Theexpansionofmarineprotectedareas(MPAs)isacrucialpartofglobalconservationefforts[1].The“30x30”initiative,forexample,aimstopro-tectatleast30%oftheworld’soceansby2030throughacombinationoffullyprotectedareas(noextractiveactivitiesallowed)andpartiallyprotectedareas(someactivitiesremainpermit-ted)[2,3].Currently,fullyprotectedMPAscoverlessthan3%oftheworld’soceans,butthisisexpectedtoincrease[4,5].AsfullyprotectedMPAsexpand,itiscrucialtounderstandtheirimpactonglobalfishingeffort.ThecreationoffullyprotectedMPAswillshiftthelocationandintensityofindustrialfishingeffort—asfishersmoveoutofnewly-createdMPAs,increasedcon-gestionandalteredeconomicopportunitiesfromfishingcouldleadtoacascadeofredistributionthatripplesfromproximatetofarflungareas[6].Butwhereandhowmuchfishingeffortwillmoveremainsanunansweredquestion.

Theeffectivenessandlongevityoffullypro-tectedMPAexpansiondependsonhowfishingeffortresponds.Iffishingeffortsimplymoveselsewhere,itcouldincreasefishingintensityandthreatenbiodiversityoutsideofMPAs,possiblyevenreversingthepresumedbiodiversitybene-fitsofprotection[7].However,iffishingeffortdecreases(e.g.,duetoincreasedcompetitionandreducedprofitability),itcouldprotectbiodiversity[8]butpotentiallyharmtheeconomiesoffishery-dependentnationsandthefeasibilityoflong-termprotectioncommitments.

Forexample,twoofthelargestfullyprotectedMPAsevercreated,PhoenixIslandsProtectedAreaandPalauNationalMarineSanctuary,wererecentlyre-openedtofishingbecauseoftheirper-ceivednegativeeconomiceffects.Infact,protectedareadowngrading,downsizing,anddegazettementofMPAshasbeenobservedindozensofMPAsaroundtheworld,withcommercialfishinginter-estsbeingoneofthedrivingfactors[9].Thus,understandinghowfishingeffortwillredistributemustbeacentralcomponentofmarinespatialplanning.

PreviousresearchhasmeasuredtheeffectofindividualMPAsonfishingresponses,buttyp-icallyforasinglefleetinalimitedarea;noneoftheexistingmethodscancapturetheeffectsofglobalinterventionsaffectingallfishingfleets.

Simulationmethodsdevelopedinthefisherieslit-erature[10]andlocationchoicemodelsdevelopedintheeconomicsliterature[6,11–14]arehelp-fulforunderstandingthestructureofindividualbehavior,butareunlikelytoapplywhencon-sideringcomplexinteractionsbetweenmultiplefleetsattheglobalscale.Theyalsooftenrequiredetailedvessel-leveldata,whicharerarelyavail-ablegloballyevenwithmodernsatellitetracking.Causalinferencemethodshavebeeneffectiveinexaminingregionaleffectsofindividualmarineprotectionpolicies[15–18],butthesemethodsrequireanunaffectedcontrolgroup,whichbydef-initiondoesnotexistforapolicythatinducesglobaleffects.Asaresult,simulationsoflarge-scaleMPAexpansionshavereliedonheuristicassumptionsoffisherresponses,suchasassumingnochangeoutsideMPAsorauniformrealloca-tionofeffortfromwithinMPAstoareasoutside[19–23].Thisapproachprovidesan“allelseequal”referencepoint,butitisanassumedscenario,notanempirically-drivenresponse,sodoesnotcapturethetrueneteffectofthepolicy.

Wedevelopthefirstdata-driven,predictivebehavioralmodelofglobalfishingeffortresponsefollowinglarge-scalespatialclosures.WebeginbycompilingaglobaldatasetoffishingeffortforallindustrialfishingvesselsthatusedAutomaticIdentificationSystem(AIS)transpondersbetween2016and2021[24],whichisouroutcomevari-able(Fig.1).Wethengenerate42modelfeaturesthatincludespatialandtemporalinformationonthegeographicdistributionoffullyprotectedMPAs,environmentalandeconomicconditions,andgeographicandgovernancecharacteristics;wealsoassessAISreceptionquality,whichcanaffectperceivedfishingeffortfromAIStranspon-ders(seeMaterialsandMethodsforacompletelistofmodelfeatures).Inordertopromotecom-putationaltractability,weaggregatealldatatoa1x1degreepixellevelannually;however,themodelcanbeimplemented,inprinciple,atanygeographicresolution.Nextwetrainaseriesoftwo-stagehurdlerandomforestmodelstopre-dictfishingeffortone,two,andthreeyearsinthefuture:thefirststagepredictswhetheranyfishingoccursinapixel,andthesecondstagepredictstheintensityoffishingifitoccurred.Wetunethemodelhyperparametersusingcross-validation(CV)withtime-basedfolds,andwequantifyout-of-sampleperformanceoverbothtimeandspace.

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Finally,weusethetrainedmodelstopredict:(i)abusiness-as-usual(BAU)counterfactualsce-nario,whichrepresentsfuturefishingeffortifnonewfullyprotectedMPAsareimplementedand(ii)MPAexpansionscenarios,whichrepre-sentfuturefishingeffortasfullyprotectedMPAcoverageincrementallyincreasesfromcurrentlev-els.ThedifferencebetweeneachMPAexpansionscenarioandtheBAUcounterfactualscenariorep-resentsthepredictedchangeinfishingeffortasaresultoftheMPAexpansion.Wefocusouranal-ysisonthepotentialimpactsoffullyprotectedMPAs,althoughinpracticethelevelsofprotec-tionaffordedbyMPAexpansionwillvaryacrossspecificpoliciesandregions.Forexample,under30x30theEuropeanUnionhasproposedtoaffordsomelevelofprotectionto30%ofitswaters,whilefullyprotectingonly10%[25].

Inforecastingtheeffectsoffuturefullypro-tectedMPAs,wedonotfavororlimitouranalysistoanyparticularMPAnetworkproposal.Rather,weexplorehowasuiteofalternativeproposedMPAnetworkswouldeachaffectfishingeffort.Weincludeanumberofnetworksthataretheout-putsofglobalprioritizationanalyses.Theseareanetworkthatfocusesonareasbeyondnationaljurisdictioninthehighseas[26],andasuiteofnetworksthatprioritizeeitherbiodiversitypro-tection,carbonsequestration,foodprovision,ormultipleobjectives[20](Fig.2).Wealsoconsideranexpert-designedbottom-upnetworkofEco-logicallyorBiologicallySignificantMarineAreas

(EBSAs)proposedbytheConventiononBiolog-icalDiversity[27].Wefinallyevaluateasetofnetworksthatrandomlyprotectpixelstoachievecertainarea-basedtargets,aswellasnetworksthatprotecteitherthecurrentlymost-fishedareasortheareasthatarecurrentlynotfished.Impor-tantly,eachnetworkdiffersinitsoverlapwithcurrentfishingeffort(Fig.1);forthesameoceancoveragepercentage,somenetworkswouldplaceMPAsinregionswithsignificantlyhighercurrentfishingactivitythanothers(Fig.3).ComparingresultsacrossMPAnetworksthereforeallowsustoexplorenotonlythepotentialimpactoffullyprotecting,forexample,15%oftheocean,butalsowhetheritmatterswhich15%oftheoceanisprotected.

Fishing

hours0101,000100,000

Fig.1:Mapofobservedfishingeffort(hours)in2021,shownusingalog10scaleforvisualiza-tionpurposes.Pixelshavea1x1degreegeographiccoordinateresolution,thespatialunitofouranal-ysis.

Wemakethreedistinctscientificcontributions.First,ourresultscontributetoatimely,interna-tionalpolicydebateontheimpactsoflarge-scaleMPAexpansionandthebestimplementationstrategies.Whilepreviousworkhasmodeledthepotentialimpactsoflarge-scaleexpansionofter-restrialprotectedareasonland[28],nosuchsimilarworkhasmodeledthepotentialimpactsofmarineprotectedareasintheocean.Second,wedevelopanovelmachinelearningtechniqueforpredictingglobalchangesinfishingeffortasaresultofMPAimplementationthatistractable,flexible,anddata-driven.Machinelearningletsusmovebeyondrigidassumptionsaboutthestructureofcomplexeconomicandecologicalinteractionsbecausethealgorithmallowsfornon-linear,data-drivenrelationships[29].WealsousemachinelearningtobuildaplausibleBAUcoun-terfactual,whichallowsforinferencewhenthereisnounaffectedcontrolgroup[30].Finally,ourmodelcanprovideadecision-supporttoolforlocalmarinemanagerstopredicttheeffectsoffuturespatialclosures(fullyprotectedMPAsorotherspatially-explicitfishingprohibitions).Byprovidingmoreclarityonpotentialfishingeffortoutcomes,ourmodelcanhelpmangersreducetheprobabilityofdowngrading,downsizing,anddegazettementoffutureMPAs,thusdecreasingtheuncertaintyandregulatoryburdenassociatedwithMPAexpansion.Importantly,ourmodelisgeneralenoughthatitcouldalsopredictfishingredistributionfromotherspatiotemporalchangessuchasclimatechange.

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Business?as?usualExpert?derivedEBSAVisallietal.2020

Salaetal.2021biodiversitySalaetal.2021carbonSalaetal.2021food

Salaetal.2021multi?objectiveRandomProtectingmost?fishedpixels

Protectingunfishedpixels

Areaprotected

2.5%3%5%10%

16%20%30%

Fig.2:Mapsofbusiness-as-usual(BAU)networkandhypotheticalglobalMPAnetworksusedinoursimulations.ThefillcoloroftheglobalMPAnetworkmapsisbytheglobalareacoveragesize,andonlypixelsthatarefullyenclosedinMPAsarecolored.TheBAUscenarioholdsfixedtheexistingfullyprotectedMPAcoverageasoftheendof2020(2.5%ofoceanarea).SincetheSalaetal.2021networkscenarios,protectingmost-fishedpixelscenario,andrandom,unfished,andmost-fishedscenarioseachprotectpixelsindescendingorderofpriority,thenetworkforeachareaprotectedsize(3%,5%,10%,16%,20%,and30%)isinclusiveofallpixelsinsmallercoveragesizes.TheVisallietal.2020andexpert-derivedEBSAscenariosareeachonlyavailableforasinglecoveragesize(16%and20%).Pixelshavea1x1degreegeographiccoordinateresolution,thespatialunitofouranalysis.

2Results

Howwellcanagloballytunedmachinelearningmodelactuallypredictfishing?Animportantfirststepinvalidationistotestthepredictionsofourtrainedmodelsagainstout-of-samplefishingeffortdata.Wefindthatthemodelperformswellin

theseout-of-sampletestsandissufficientforourpurposeofpredictingfuturefishingeffortundertheexpansionoffullyprotectedMPAs.Totestthemodel’sabilitytopredictfuturefishingeffort,weperformatemporalout-of-sampletestusingamodeltrainedonearlyyearsofthedatasetandtestedonheld-outlateryearsofthedataset.Using

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Current

fishinghours

overlapping

with

areaprotected

90%-

80%-

70%-

60%-

50%-

40%-

30%-

20%-

10%-

0%-

Business?as?usual

MPAnetworkscenario

Protectingmost?fishedpixelsSalaetal.2021biodiversitySalaetal.2021carbon

Salaetal.2021multi?objectiveRandom

Salaetal.2021foodExpert?derivedEBSAVisallietal.2020

Protectingunfishedpixels

0%10%20%30%

Areaprotected

Fig.3:Percentofglobalfishingeffort(hours)thatspatiallyoccurswithinpixelsthatwouldbeprotectedbyhypotheticalMPAnetworksversuspercentofglobaloceanareaprotected,coloredbyMPAscenario.ColorsdifferentiatethevarioushypotheticalMPAnetworks.Linetypesareusedtofurtherdifferentiatenetworksthatcanhavevariouslevelsofprotection,whileshapesareusedtodifferentiatenetworksthatonlyhaveasinglelevelofprotection.Thescenariosarelistedinthelegendinthesameorderastheyappearinthefigureattheleveloftheirlargestareaprotected.

thistest,theROCarea-under-the-curveinthefirststagepredictionisapproximately0.97andtheF1scoreisapproximately0.91(Fig.S4AandTableS1).Inthesecondstagepredictionoffishingintensity,theR2isapproximately0.8(Fig.S4BandTableS1).Additionally,acrossperformancemetrics,thereislittlereductioninperformanceaswepredictfishingeffortadditionalyearsintothefuture.Totestthemodel’sabilitytopredictbothfuturefishingeffortandinspatialareasthathaveneverbeforeseenfullyprotectedMPAs,weperformaspatiotemporalout-of-sampletestthatusesasuiteofmodelsforeachoceantrainedonearlyyearsofthedatasetandinotheroceans,andtestedonheld-outlateryearsofthedatasetandintheoceanofinterest.Wedothisleave-one-outtestforeachocean,allowingustoseehowwellthemodelcanpredictfishingeffortinspatialareaswherethemodelhasnotseenanytrainingdata.Again,wefindhighperformanceforthespatiotemporalout-of-sampletesting(Fig.S5),indicatingthatthemodelcanforecastacrossbothtimeandspace.Finally,wetestourmodel’sperformanceagainstaseriesofsimplermodels,againusingtemporalout-of-sampletesting.Wefindthatourmodeloutperformsthesesimpler

modelsacrossallperformancemetrics(Fig.S6).Theseout-of-sampleevaluationsgivecredencetooursubsequentMPAnetworkscenariopredictionsbecausetheMPAnetworkscenariopredictionsconsiderMPAsinfutureyearsandinlocationsthatmaynotyethaveMPAs.

IntheabsenceofanynewMPAs,ourBAUcounterfactualscenariopredictsgreatertotalglobalfishingeffortinthefuturecomparedtocur-rentlyobservedlevels(Fig.4).UnderanyofthehypotheticalMPAexpansionscenarioswecon-sider,ourmodelpredictsthattotalfutureglobalfishingeffortwillinvariablybelowerthantheBAUcounterfactualscenarioofnonewMPAs.TheextentofthisdecreasedependsontheMPAnetworkandthepercentageofoceanareaitencompasses.Forsomescenariossuchasprotect-ingunfishedpixels,“Visallietal.2020”,“Expert-derivedEBSA”,“Random”,andforMPAcover-agelevelsof5%to20%,wefindthatpredictedtotalfishingeffortmayberoughlyequaltoorevenabovethecurrentlevelsweseetoday.However,forotherscenarios,andallscenarioswith30%protection(otherthanprotectingunfishedpix-els),wefindthattotalglobalfishingeffortwouldbebothlowerthanthecurrentlyobservedlevel

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andlowerthanthepredictedfuturelevelunderbusiness-as-usual.

Intuitively,thedecreaseinfuturetotalfishingfromMPAexpansionissmallestunderthesce-nariosthatextendprotectiontoareasthatarecurrentlyunfished(-0.4%to-6%,darkgraylineinFig.5A).Scenariosthatextendprotectiontoareasthatarecurrentlymost-fishedwouldleadtothelargestdecreaseintotalfishingeffort(-6%to-55%,lightgrayline).Thesetwoscenariosarenotmeanttorepresentplausiblereal-worldMPAnetworks;rather,theyareintendedtodis-playarangeofpossibleeffectsfromthelarge-scaleexpansionoffullyprotectedMPAs.Mostoftheactualproposednetworksresultinreductionsinfishingeffortofabout10%to20%.Twoexcep-tionsare“Salaetal.2021carbon”and“Salaetal.2021biodiversity”,whichhavepredicteddeclinesof37%and38%,respectively,afterthreeyearsandwithfull30%areaprotection.Forallscenarios,asthepercentageofoceanareaprotectedincreases,wepredictincrementallylargerdecreasesintotalfishingeffort.

Wehaveshownthatacrosstherangeofproposedprotectionscenarios(i.e.excludingthe“Most-fished”,“Unfished”,and“Random”sce-narios”),globalfishingeffortislikelytodecrease,andthemagnituderangesfromabout-3%to-38%.WhatdrivesthemagnitudeofpredicteddecreasesfordifferentMPAnetworks?Regardlessofthenetwork’sobjectives,howitwasdesigned,thepercentageoftheoceancovered,orthefore-casthorizon,thekeydriveristheoverlapoftheproposednetworkwithcurrentfishingeffort(Fig.5B).Asthispercentageincreases,weconsistentlypredictlargerdecreasesinglobalfishingeffort.Forinstance,theproposedscenariothatresultsinthesmallestpredicteddecreasecoversonly4%ofcur-rentglobalfishinghours.Similarly,theproposedscenariopromptingthelargestpredicteddecreasecoversthegreatestpercentageofcurrentglobalfishinghours(78%).ThispatternimpliesthattheoverlapbetweencurrentfishingeffortandnewMPAswillplayacrucialroleindeterminingtheimpactsoffutureMPAexpansion.

Crucially,thesereductionsinaggregatefish-ingeffortarisebothinsideandoutsidethenewMPAs.AggregatefishingeffortinsidetheMPAsdiminishesunderallnetworkscenarios;however,itneverdropstozero,eventhoughthesenewMPAsostensiblyprohibitallcommercialfishing

(Figs.S11-S12).AggregatefishingeffortoutsideMPAsalsodecreasesacrossallscenarios,whichdrivesthemajorityoftheglobaldecline(Figs.S11-S12).ThiseffectismostprominentinlocationsclosertoMPAboundaries.PixelslocatedfullyinsidenewMPAsseethelargestmediandecreaseinfishingeffort(-18%),andthismediandecreasebecomessmallerandapproacheszeroasthedis-tanceincreasesfromtheMPAboundary(Fig.6B).Thisspatialdissipationisconsistentwiththebefore-versus-afterchangesweobserveinthehis-toricalrawdatafollowingtheimplementationofrealMPAs(withanobservedmediandecreaseof-57%forpixelsfullyinsidenewMPAs,anddimin-ishingmagnitudechangesasthedistancetothenearestMPAincreases)(Fig.6A).

Howdoourresultscomparetotheconven-tionalwisdom?Thetwomostcommonassump-tionsemployedinthepreviousliteratureare“fulldisplacement”(whereeachunitoffishingeffortcoveredbyanMPAisdisplacedoutsidetheMPA)and“completeexit”(whereeachunitoffishingeffortcoveredbyanMPAdisappears)[19–23].Ourresultssuggestthatneitherofthesehypothesesiscorrect.Instead,wefindthatthelarge-scaleexpansionoffullyprotectedMPAsislikelytotriggeraredistributionoffishingeffort,mostprominentlynearnewMPAsbutwitheffectsthatalsoarisefartheraway,whichinaggregateimplyaglobalreductioninfishing.Furthermore,asfullyprotectedMPAscoverlargerfractionsofcurrentfishingactivity,thisreductionmagnifies,under-scoringtheimportanceofcarefullyconsideringfishers’adaptationtoMPAexpansion.

3Discussion

Theecologicalandeconomicconsequencesoflarge-scaleMPAexpansionwillhingetoalargeextentonhowglobalfishingeffortrespondstoincreasedprotection,yetlittleisknownabouthowthiswillunfold.Understandingthepotentialredistributionoffishingeffort,andthesubse-quentchangesintheaggregatequantityofglobalfishing,isanimportantfirststep.WedevelopedanempiricalglobalmodeltopredicthowfishingactivitywillrespondtochangesinfullyprotectedMPAcoverage.Importantly,ourmodelisgeneralenoughthatitcouldalsobeusedtopredictfish-ingchangesinresponsetootheroceanicchanges,suchasclimatechangeornewfishingregulations.

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MPAcoverage:3%

MPAcoverage:5%

MPAcoverage:10%

40

20

Fishing

hours

(millions)

0

MPAcoverage:20%

MPAcoverage:30%

40

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MPAcoverage:16%

Business?as?usual

MPAnetworkscenario

ProtectingunfishedpixelsVisallietal.2020

Expert?derivedEBSASalaetal.2021foodRandom

Salaetal.2021multi?objectiveSalaetal.2021carbon

Salaetal.2021biodiversityProtectingmost?fishedpixels

?5?4?3?2?10123?5?4?3?2?10123?5?4?3?2?10123

Forecasthorizon(years)

Fig.4:Totalobservedglobalfishingeffort(hours)(whereforecasthorizons-5to0correspondtoobserveddatafromyears2016to2021),andtotalpredictedglobalfishingeffortinthebusiness-as-usual(BAU)scenarioandallMPAscenariosforthethreepredictedforecasthorizons.Averticaldashedlineisshownat0years(suchthatthelinetotheleftrepresentsobserveddata,andthelinestotherightrepresentpredictions).Ahorizontaldashedlineisshownatthelevelofcurrentlyobservedfishingeffortinthelastyearofhistoricallyobserveddata.EachpanelrepresentsglobalMPAnetworksthataresizedforagivenpercentagecoverage.ColorsandlinetypesdifferentiatethevarioushypotheticalMPAnetworksandtheBAUscenario.TheMPAnetworkscenariosarelistedinthelegendinthesameorderastheyappearinthefigureataforecasthorizonof3yearsandtheirlargestMPAcoverage.

AcrossawiderangeofhypotheticalMPAnet-works,ourkeyfindingisclear:aggregateglobalfishingislikelytodecline,andthemagnitudeofthisdeclineislargelydrivenbytheamountofcurrentfishingactivityinthenewlyprotectedareas.ThisfactorismoreimportantthaneithertheconservationobjectivesoftheMPAnetworkoreventhetotalareaitcovers.Specifically,whennewfullyprotectedMPAsareplacedinregionscurrentlyexperiencingintensefishingactivity,wepredictthemostsubstantialdecreasesintotalfish-ingeffort.WhilepolicymakersmaychoosetoplaceMPAsinlocationswithlimitedfishingactivity—forstrategicreasonssuchasprotectingcriticalhabitats,orforpoliticalreasonssuchasprotect-ingareasthatarecurrentlyunfishedorminimallyfished—ourresultssuggestthatsuchplacementswouldexertminimalimpactonfishers’decisions.Inotherwords:whichpartsoftheoceanareprotectedismoreimportantindeterminingover-allfishingeffortthanhowmuchoftheoceanisprotected.

Ourresultsshowthatnewful