Table Of ContentFisheries Research xxx (xxxx) xxx–xxx
ContentslistsavailableatScienceDirect
Fisheries Research
journal homepage: www.elsevier.com/locate/fishres
Unraveling the recruitment problem: A review of environmentally-informed
forecasting and management strategy evaluation
M.A. Haltucha,⁎, E.N Brooksb, J. Brodziakc, J.A. Devined, K.F. Johnsona,e, N. Klibanskyf,
R.D.M. Nashd, M.R. Payneg, K.W. Shertzerf, S. Subbeyd, B.K. Wellsh
aNorthwestFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,Seattle,WA,UnitedStates
bNortheastFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,WoodsHole,MA,UnitedStates
cPacificIslandsFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,Honolulu,HI,UnitedStates
dInstituteforMarineResearch,Bergen,Norway
eSchoolofAquaticandFisheryScience,UniversityofWashington,Seattle,WA,UnitedStates
fSoutheastFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,Beaufort,NC,UnitedStates
gNationalInstituteofAquaticResources,Kgs.Lyngby,Denmark
hSouthwestFisheriesScienceCenter,NationalMarineFisheriesService,NationalOceanicandAtmosphericAdministration,SantaCruz,CA,UnitedStates
ARTICLE INFO ABSTRACT
HandledbyA.E.Punt Studiesdescribingandhypothesizingtheimpactofclimatechangeandenvironmentalprocessesonvitalratesof
Keywords: fishstocksareincreasinginfrequency,andconcomitantwiththatisinterestinincorporatingtheseprocessesin
Fisheries fishstockassessmentsandforecastingmodels.Previousresearchsuggeststhatincludingenvironmentaldrivers
Recruitment offishrecruitmentinforecastingisoflimitedvalue,concludingthatforecastingimprovementsareminimal
Forecasting whilepotentialspuriousrelationshipsweresufficienttoadviseagainstinclusion.Thisreviewevaluatesprogress
Environment inimplementingenvironmentalfactorsinstock-recruitmentprojectionsandManagementStrategyEvaluations
Managementstrategyevaluation (MSEs),fromtheyear2000through2017,byreviewingstudiesthatincorporateenvironmentalprocessesinto
recruitmentforecasting,full-cycleMSEs,orsimulationsinvestigatingharvestcontrolrules.Theonlysuccesses
identifiedwereforspecieswithashortpre-recruitsurvivalwindow(e.g.,opportunisticlife-historystrategy),
wheretheabbreviatedlife-spanmadeiteasiertoidentifyoneoralimitedsetofkeydriversthatdirectlyimpact
dynamics.Autoregressivemethodsappearedtoperformaswell,ifnotbetter,forspecieswithalongerpre-recruit
survivalwindow(e.g.,seasonal,inter-annual)duringwhichtheenvironmentcouldpotentiallyexertinfluence.
Thisreviewsuggeststhattheinclusionofenvironmentaldriversintoassessmentsandforecastingismostlikely
tobesuccessfulforspecieswithshortpre-recruitsurvivalwindows(e.g.,squid,sardine)andforthosethathave
bottlenecksintheirlifehistoryduringwhichtheenvironmentcanexertawell-definedpressure(e.g.,anadro-
mousfishes,thosereliantonnurseryareas).Theeffectsofenvironmentmaybemorecomplicatedandvariable
forspecieswithalongerpre-recruitsurvivalwindow,reducingtheabilitytoquantifyenvironment-recruitment
relationships. Species with more complexearly life historiesand longer pre-recruit survival windows would
benefit from future research that focuses on relevant species-specific spatio-temporal scales to improve me-
chanisticunderstandingofabiotic-bioticinteractions.
1. Introduction recruitmentforecastingmodelsresultsinlittleornoimprovementwith
respecttofisherymanagementperformance.Thislackofimprovement
Interest in incorporating environmental processes in fish stock as- is largelybasedon researchrelatedtogadoid-like life histories.More
sessments and forecasting models is increasing in step with studies specifically,since1999,thirtyeightstudieshavecitedBasson(1999),
describing and hypothesizing impacts of climate change and environ- which we consider to be a benchmark analysis on the topic of using
mentaldriversonvitalratesoffishes.However,muchoftheresearch environmentalfactorsforshort-termrecruitmentpredictionsforaga-
on the importance of environmental factors in the design of manage- doid-likelifehistory.Thisincongruitymakesitdifficulttojustifycalls
ment procedures found that including environmental indices in for increased complexity that potentially increase risk without
⁎Correspondingauthor.
E-mailaddress:[email protected](M.A.Haltuch).
https://doi.org/10.1016/j.fishres.2018.12.016
Received31May2018;Receivedinrevisedform13December2018;Accepted17December2018
0165-7836/ Published by Elsevier B.V.
Please cite this article as: Haltuch, M.A., Fisheries Research, https://doi.org/10.1016/j.fishres.2018.12.016
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
increasingbenefits. andtheresultantrecruits,ratherthanfocusingonolder,fullyvulner-
InthisstudywehighlightthesimulationstudyofBasson(1999)and able ages where survival is less likely to be influenced by the en-
then trace developments in the two decades since its publication. vironmentandmorelikelytovarywithfishingpressure.
Basson(1999)foundnobenefitstoeitherstockconservationoraverage Since Basson (1999), the inclusion of environmental variables in
yield from including an environmental factor in a stock assessment fisheries assessments remains rare. A recent examination of 1250 as-
model. Furthermore, reductions in fishery management uncertainty sessments worldwide identified only 24 that included ecosystem in-
took place only given a strong environment-recruitment relationship. formation regarding stock productivity, of which 14 were environ-
Basson(1999)recommendedthatfutureworkexplicitlyincorporatethe mentalfactors(Skern-Mauritzenetal.,2016).However,areviewwitha
assessment procedure within the simulation framework and that the wider scope, which summarized the use of all ecosystem information
stock-recruitrelationshipberefittedduringeveryassessment,bothwith usedinstockassessmentsintheU.S.ratherthanjustrecruitment,found
and without environmental factors. A more general recommendation that24%ofallU.S.stockassessmentsincludedecosysteminformation
wastoexploreadditionallifehistoriestodeterminewhichmaybenefit (Marshalletal.,2018).SinceBasson(1999),severalstudieshavemade
frominclusionofenvironmentalcovariatesinstockassessmentmodels. recruitmentforecasts(e.g.,Wardetal.,2014),someofwhichinclude
The implicit nature of the relationship assumed between the en- environmentaldrivers.Mostlikely,thecombinationoflife-historytraits
vironmental driver and recruitment, and the linkage with the harvest andmanagementtacticswillaffecttheutilityofenvironmentallybased
strategy,mayhavecontributedtothenegativeresultinBasson’sstudy. recruitmentforecasting.Weundertookareviewofliteraturefromthe
Forexample,Basson(1999)modeledagadoid-likeperiodiclifehistory, year 2000 through 2017, to evaluate progress in implementing en-
basedlargelyonthestrongnegativerelationshipbetweenIrishSeacod, vironmental factors in Management Strategy Evaluations (MSEs) and
Gadus morhua (ICES sub-area 7a; ICES, 1999), age-0 recruitment and stock-recruitment forecasts. We identified over sixty peer-reviewed
sea-surface temperature (SST; Planque and Fox, 1998). Drinkwater studies that address questions of environmentally-driven recruitment
(2005) also predicted a reduction in Irish Sea cod recruitment with forecastingandrelatedMSEs.Justoverone-thirdofthesestudieswere
increasedSSTwithclimatechange.PlanqueandFox(1998)hypothe- foundtoberelevantduringinitialscreening,whererelevancewasde-
sized several mechanisms to explain how warmer water might lower termined as a study going beyond mere correlation to model an en-
recruitment, such as decreased fecundity, negative effects on egg or vironmental driver explicitly or implicitly and the stock assessment
larvalphysiology,orfoodavailability.Themostimportanteffectofthe and/ormanagementimplicationswereconsidered.Therelevantstudies
gadoid life history was likely on the simulated management strategy, weconsiderbelowarebroadlycategorizedaseitherprimarilyfocused
where higher predicted temperatures, implying lower recruitment, onrecruitmentforecastingorelseonsimulationsoffull-cycleMSEsor
dictated a lower fishing mortality (F) reference point and subsequent harvestcontrolrules(HCRs;Table1).
total allowable catch (TAC) for that year. Recruits (age-0) were not Thegoalsofthisrevieware1)toexaminesuccessesandchallenges
availabletothefisheryuntilageone,sosettingalowerTACduringa across studies that include environmental considerations in MSEs or
yearofpoorerenvironmentalconditionswouldnothaveanimmediate stockrecruitmentforecastsand2)tohighlightstudycharacteristicsthat
effect on fishing mortality on recruits from that year. However, tem- resultindifferentoutcomes.Thisreviewislooselystructuredbytopics
perature was similar in consecutive years because of modelled auto- relatedtohowtherelevantstudiesaddressorfulfillissuesraisedin,or
correlationinthetimeseries,sotheTACsetduringthenextyearwould recommendedby, Basson(1999).Basson’smainrecommendationsin-
also be relatively low, relieving some pressure on the previous year’s cluded: 1) the consideration of a wide range of life-history types, ex-
recruits. The overall effectof the management tacticon Basson’sper- ploitation patterns, and stock-recruitment relationships, 2) identifica-
formancemeasures, yield andspawningstock biomass(SSB),was de- tion of strong interactions between environmental drivers and
layedandindirectbecauseage-1fishmadeupasmallproportionofthe recruitment,3)increasedpredictionskillforenvironmentalfactors,4)
catchandwerenotfullymatureuntilage3.Thesemanagementtactics increased realism with respect to management actions and im-
might be more effective for stocks that matured earlier, had higher plementation, and 5) the consideration of long-term trends in en-
natural mortality, or grew more quickly, with more of the spawning vironmentaltimeseries.Section2reviewsstudiesacrossawiderange
biomassatyoungerages.Analternativemanagementapproachwould of different stock-specific life histories orgeneral scenarios. Section3
betodelaytheimplementationofstricterTACstomorecloselymatch reviews the need for simulation and forecasting studies with more
thetimingoftheentryintothefisheryforcohortsspawnedduringyears realistic assessment, harvest, and management implementation mod-
ofpoorenvironment.Thisapproachmightbemoreeffectiveatlower ules. Section 4 summarizes the use of a) either univariate or multi-
stocksizesandatasteeperpartofthestock-recruitrelationshipbecause variate mechanistic drivers,b) historical data, c) spatialscale, andd)
reducedmortalityonspawningageclassesshouldhaveastrongerpo- temporalforecastscale.Finally,sections5,6,and7provideasynthesis
sitive effect on recruitment. Basson (1999) does not report the para- of successes, challenges, and future research recommendations to im-
metervaluesforthestock-recruitcurveused,butwhenanalyzingthe proverelevancetofisheriesmanagement.
samedata,PlanqueandFox(1998)foundrecruitmenttobeessentially
independentofSSB. 2. Life-historystrategiesandenvironmentally-informed
InreviewingtheworkthathasbuiltuponBasson(1999),weusethe recruitmentforecasts
term ‘recruitment’ to designate the single age class at the end of the
interval where population dynamics are governed by a stock-recruit 2.1. Characterizinglifehistories
function. In this case, the stock-recruit function generally models the
early-stagemortalitypriortorecruitmentasbeingdensity-dependent, Thevariabilityinlife-historystrategiesacrossfishtaxaandamong
whereas the mortality rates of individuals older than the recruitment ecoregionsresultsindifferencesinthedurationandabundanceofin-
agearetypicallymodeledasbeingdensity-independent.Wedistinguish dividualsindefinedstages(Houde,2002)(Fig.1).Severalauthorshave
thisuseof‘recruitment’fromearlierdefinitionswhereitwasdefinedas triedtoclassifyandquantifydifferencesinlifehistorywithrespectto
theagewhenfishbecomefullyvulnerabletothefishery(Ricker,1954, implicationsforfisherymanagement(e.g.,WinemillerandRose,1992;
1975). In contemporary stock assessment models, vulnerability to King and McFarlane, 2003; Winemiller, 2005). However, the classifi-
fishing gear at all ages can be directly modeled as part of the assess- cations of periodic, opportunistic, (Winemiller and Rose, 1992;
ment,aseitherafunctionofsizeorage,andpotentialtemporalchanges Winemiller, 2005) (Fig. 2) and salmonid (McCann and Shuter, 1997)
in vulnerability can be accommodated as well (Quinn and Deriso, lifehistoriesareusedtostructureourreviewofthevariedresponsesto
1999). Thus, the recruitment definition that we adopt places the em- environmentally-drivenrecruitmentprocesses.
phasisonprocessesthatexertinfluenceonsurvivalbetweenspawning Periodic strategists, such as many groundfish species, are
2
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
e)
g
a
p
xt
Capturesenvironmentaluncertainty? No No No No No No No Yes Yes No No No NANo No No No Yes Yes NAYes Yes Yes Yes ntinuedonne
o
c
e (
Includeslargspatialscaledrivers? Yes Yes No No No Yes Yes No No Yes Yes No NAYes Yes No No NA No NAYes NA No No
e
ur
Includestemperatmetric? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No NAYes Yes Yes Yes NA Yes NAYes NA Yes Yes
1
ForecastLength Annual Near-term Annual Long-term,2008-2100 Annual Near-term,to5yearsLong-term,2006-2100Long-term,2009-2050Annual Annual Annual NAAnnual Long-term,2001-2050 Annual InSeason Long-term,50yearsLong-term,2006-2050NALong-term,50years Long-term,25and50yearsLong-term
elationships. >25YearsData No,2000-2010 Yes Yes,1950-1996 Yes,1900-2007 Yes Yes,1969-2000 Yes,1955-1999 Yes,1974-2005and1960-2009Yes,1979-2008 Yes,1970-2009 Yes,1952-2017 Yes,41(recruit),46(environment)YesNo,1997-2015 Yes Yes,1983-2012 No,1987-2000 Yes,1961-2004 Yes,1962-2005 NAYes,1963-2006(Cod) Yes,25and50years Yes,1976-2006 Yes,1963-2008
r
nt
e
nt-environm Mechanisticrelationship? Yes No Yes Yes Yes Yes Yes Yes Yes Yes No Yes NANo Yes Yes No Theoretical Yes NANo Theoretical Theoretical Yes
e
m
theoreticalrecruit Species Chinooksalmon Pacificsardine Pinksalmon Atlanticcroaker SpringSpawningHerring(NSSH)Cohosalmon Sprat Sprat WalleyePollock CohoSalmon CohoSalmon GulfMenhaden GeneralPinksalmon FlatheadSole,NorthernRocksole,ArrowtoothFlounderChinookSalmon Squid(Loligogahi) WalleyePollock WalleyePollock GeneralHerring,Cod,Plaice GeneralizedFlatfish,Hake,Rockfish Sardine WalleyePollock
informedMSEstudieswith LifehistoryType,Genus(Classification) Salmon,Oncorhynchus(Salmonid)CoastalPelagic,Clupea(Opportunistic)Salmon,Oncorhynchus(Salmonid)Groundfish,Micropogonias(Opportunistic)CoastalPelagic,Clupea(Opportunistic)Salmon,Oncorhynchus(salmonid)CoastalPelagic,Sprattus(Opportunistic)CoastalPelagic,Sprattus(Opportunistic)Groundfish,Gadus(Periodic) Salmon,Oncorhynchus(Salmonid)Salmon,Oncorhynchus(Salmonid) CoastalPelagic,Brevoortia(Opportunistic)GeneralSalmon,Oncorhynchus(Salmonid)Flatfish,Hippoglossoides,Lepidopsetta,Atheresthes(Periodic) Salmon,Oncorhynchus(Salmonid)Invertebrate,Teuthida(Opportunistic)Groundfish,Gadus(Periodic) Groundfish,Gadus(Periodic) FlatfishCoastalPelagic,Groundfish,Flatfish,Clupea,Gadus,Pleuronectes(OpportinisticandPeriodic)Groundfish,Eopsetta,Merluccius,Sebastes(Periodic) CoastalPelagic,Sardinops(Opportunistic)Groundfish,Gadus(Periodic)
y
analysestypicall Spatialscale NortheastPacific CaliforniaCurrent NortheastPacific U.S.mid-AtlanticregionNorwegianandBarentsSeaOregonandWashington,USABalticSea BalticSea EasternBeringSea Oregon,US Oregon,US NorthernU.S.GulfofMexicoGeneralSoutheastAlaska,USEasternBeringSea CentralCalifornia FalklandIslands GulfOfAlaska GulfOfAlaska GeneralNorthSea General CaliforniaCurrent EasternBeringSea
a
studies.Previousdat Publication Burkeetal.,2013 Deyleetal.,2013 Haesekeretal.,2005 Hareetal.,2010 ICES,2013 Logerwelletal.,2003Mackenzieetal.,2008Mackenzieetal.,2012Mueteretal.,2011 Ruppetal.,2012 PacificFisheryManagementCouncil(PFMC,2018Vaughanetal.,2011 Wardetal.,2014Wertheimeretal.,2017Wilderbueretal.,2013 Winshipetal.,2015 Agnewetal.,2002 A’maretal.,2009a A’maretal.,2009b Brooks,2013Bruneletal.,2010 HaltuchandPunt,2011 Hurtadoetal.,2010 Ianellietal.,2011
d /
able1ummaryofreviewe Forecasting MSE/Simulation/ReferencePointsHCRs
TS
3
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
characterized as slow growing, long-lived, and highly fecund species
thathaveevolvedtopersistinhighlyvariableenvironmentswithper-
Capturesenvironmentaluncertainty? Yes Yes NA NAYes isftoaetrgsdatiitscgetsgrr,oeiswcstrusiu,cnihgstum,acseshhnoAtartsstl-uamlcnivcateiencdsys,ac(wnWodiatihsPnteaaimcnlitpfiieleclrelmarsgeaaidlcnmiadsotpenRe,ocfeaisecersue,,n1cad9hri9etay3rc.a)h.cSatOaerlrpamicpztooeendrrtiiudzbneysditsritanaics--
termediatebodysize,ageatmaturity,andeggsize,aswellashaving
e
Includeslargspatialscaledrivers? No NA NA NANA s2shi0toi0orE3ntae)s.crhcloiflreirfeess-phpaionsntso,drfiynasgstttgroarotdewigstythin,ccaatnnsdtealxoghweisb,fietacnaudnndguiemtnybe(reKarilnloygf,bathnioedlodMguicrcFaaatliroltanrannoef-,
eachsuccessivelifestageincreasesasafishdevelops.Theinfluenceof
e environmental factors such as temperature, salinity, prey levels, and
Includestemperaturmetric? No NA NA YesNA pdlirenevkdeealdotipotmoneaenrntev.ieFrxoiprnsemtc,tetenhdteatoltimpcoliannydgiimtoifopnsopsratoawnrntcirnuogeleso,srdweuigtrhigngpdridffoideffrueicrnteginotnrestsiaspgooenfstseeonsf
between spawning strategy. For example, the total annual eggs to be
m,50m, m,50 m, spawned are fixedprior tothe spawning event fordeterminate spaw-
ForecastLength Long-ter2010-20Long-ter80yearsLong-ter2011-20NA NALong-ter50years nfoerrsi,nwdehteerremasineagtgesscpaanwdneevresl.oTphaetseantywtoimsteradtuerginiegstchoensfpearwdniffinegresnetasaodn-
vantagesandvulnerabilitiesdependingonthedistributionoffavorable
5YearsData 1956-2006 1951-2010 ˜1968-1978 1978-2010 eLsinpnikflavewuiwrenoniinsncemeg,ettnbyhtepeanerleth(Bciicosanloadsnpit,gairo1wan9nds7ei5ern)se,,txepoo.egfcc.e,teeasndnuvobigrsdroturanarpmithnueigmcntattcyhlopeniendflsaitpnuiaodewnncscnoeinnwdgreiiltlliasoteneaadffswoeitnclotl.
>2 Yes, Yes, Yes, NAYes, broadcastpelagicspawners,andenvironmentaleffectsmaybelessin-
fluentialforeggsofspecieswithparentalcareorlivebirth.Variability
inenvironmentalconditionscaninfluenceeggandlarvaldevelopment
Mechanisticrelationship? Theoretical Theoretical Theoretical TheoreticalYes rLmaeetgengsteat(lte.cagon.n,ddPDietiepoBinnlso,ic1sa,9n1991a9)l,s4o)w,ihnleiflcahudesinntcoetvujarunrvi,eanbthillereomuggroohrwtastlthiatgyreartadetusersaa.ntEidonnvthir(eosrenee--
fore mortality rates (Rice et al., 1993; Houde, 1997). At the juvenile
Species PacificOceanPerch Rocklobster MultipleAlaskaCrabStocks GeneralizedGadidSnowcrab sjl22uet.0aa2v1gde.6enPt),io.eliermdsioepfdnroioscrmittasytnraatddtefueapglctiesstntoasdrnsednintifcmltuhodorestaewlaithryeeat(hsBeearcrekthleeimtnaiutle.r,dse2ir0ny0e1gxr;toeBunontg,dswstahsdeicpehatrcaaaltn.e,
LifehistoryType,Genus(Classification) Rockfish,Sebastes(Periodic) Invertebrate,Panulirus(Equilibruim)Invertebrate,Paralithodes,Chionoecetes,Lithodes(Equilibruim/Periodic)Groundfish,Gadus(Periodic)Invertebrate,Chionoecetes(Equilibruim/Periodic) stpvusreettiesromvceracraadWtpuklte,e-uitgartharrmielaffeeslcettseeru,yeanucrentthiemitdanfpemoasgocprrohlreerlledoeacocdneantcrhlasgkiustrotstioeutbi(srcm(Tgei2dhherae0eunetbn1lrautaat0lanto(gs–iCrdoa2ariainnea0nnssdn5flchcenh0uedaiaep)erellsacnvclbihrocetaeeogelsotrfeteaopaldhxamcelop.rudo,muvlsn2aaag.ai0lh)nmIS0,ntsp4sotuature)dori.rbnveggMsligmoatvtauvdaoenweeildd,mttriiiennaaaarmdtalnkeenvdeetdaanaalnrgotfliadap.enalle-(tgl02irwP-oit0toanaese1ndurtm1eimeirncr)--l
ent Seads Sea on ClimateChange (IPCC) climate model output. Results suggest that
Spatialscale CaliforniaCurr Australia EasternBering/AluteanIslan GeneralEasternBering rcmeoecmnrWuitn.ihtgmiledenetcht,aedsrpeeasi,wscnaoemgrobpoiadormemdaeswcsh,iaathnnidsfotcircaetcucahnsdtwseorasutslasdnudmliiknienglgyordfaeencndlvionimreoonrvmeecerrnutthiate-l
factors that drive walleye pollock recruitment, MSEs suggested that
Publication Punt,2011 Puntetal.,2013 Puntetal.,2014 Skagenetal.,2013SzuwalskiandPunt,2012 mppwcorriaeortnphcdiaoousgruicaoetttimnvi,nieathgnnyitgr(ehsAegte’nrirmamvbtaieeriragossineeh,tmsiafanetilsnnd.,tciamo2nlr0ipsp0molr9ironaaadkt)tc.uianhcgTgetehis,verbietcegyoutiuwmrbtrpeeeeeecnnarstfuhotmsihrfemteasntelheardveegseeuolmsslvtteroeeanrfdtatecligaslnttirecraishlsotkewaginneoydrf-,
overfishing during periods of stock decline was lower. In later work,
A’mar et al. (2009b) found that management strategies incorporating
ed) environment often exceeded historical catch levels because manage-
u
ntin mentdidnotrespondquicklytodecliningproductivity,thusincreasing
co the risk of overfishing. The authors postulated that the management
(
1 strategy might be improved when fluctuations or trends in stock pro-
e
bl ductivityoccurbyusingafisherycontrolrulewithafixedlowerlimit
a
T for spawning biomass, at which point fishing would be restricted or
4
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
Fig.1.Intrinsicversusextrinsicfactorsthatdrivedifferencesinthedurationandabundanceofindividualsatdefinedstages.
eliminated if the model estimated spawning biomass declines below usedaconstantrecruitmentrelationship.Iffuturerecruitmentremains
thislimit. similar to past recruitments, then both status quo management, with
Ianellietal.(2011)usedIPCCclimateprojectionsacrossarangeof static reference points and current ecosystem considerations, and the
scenariosthatincorporatewarmingSSTstoevaluatealternativeman- climate-based alternatives, perform similarly. There is no gain in
agement strategies for the Eastern Bering Sea walleye pollock stock. management performance using climate-basedreference points. How-
Outcomesfromclimate-basedstockprojectionsresultedinlowercatch ever, if warming conditions result in reduced recruitment, then the
and declining biomass due to reduced recruitment compared to pro- status quo management results in lower average catches and an in-
jectionsbasedonthecurrentmanagementscenarioandstrategiesthat creased likelihood of fishery closures. The climate-based reference
Fig.2.Fourendpointlifehistorystra-
tegies,withafifthintermediatestrategy
inthecenter.Adaptedfromaprincipal
components plot from King and
McFarlane (2003, Fig. 1). The five
points corresponding to each strategy
wereplacedattheapproximatecenter
ofeachclusterofspeciesfromKingand
McFarlane’s figure. Note that in King
andMcFarlane’s(2003)originalfigure,
thelabelsforopportunisticandperiodic
strategistswereinadvertentlyswitched,
but have been placed correctly in the
currentfigure.
5
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
pointsandcontrolrulestrackthedeclineinproductivity,allowingthe sole,Eopsettajordani)andopportunisticNorthPacifichake(Merluccius
targetstocksizetoshiftlowerwithdecliningrecruitmentresultingin productus) examples. This work suggests that applying the wrong en-
higherfisherycatchesandfewerfisheryclosures. vironmental relationship may actually make recruitment forecasts
ResearchonNorthSeacod,(Gadusmorhua)aperiodicgadid,byKell worse. Haltuch and Punt (2011) suggested that criteria for including
etal.(2005)evaluatedtheeffectofincludingclimateimpactsonshort- environment-recruitment relationships within a management frame-
andlong-termmanagementstrategies.Resultssuggestasmalleffectof work could include 1) the use of an a priori conceptual mechanistic
climate on short-term stock recovery, which was more dependent on modelbasedonenvironmentaldataatspatialandtemporalscalesthat
fishing effort reductions and that incorporating climate into stock as- makebiologicalsense,2)clearlyexplainingtheextentofdatadredging
sessment projections did not lead to an improvement. However, over (Burnham and Anderson, 2002), 3) using data-reduction techniques
the long-term, climate had a greater effect,although reducing fishing (e.g., principal component analysis) to produce a smaller number of
mortalitystillresultedinhighercatchesandstocksize. potentially more informative indices, and 4) using the most parsimo-
Investigations of the inclusion of several environmental variables nious model that explains a high proportion of the variability in the
intostock-recruitmodelsforthreeEasternBeringSeaperiodicflatfish data(Guisanetal.,2002).
species (northern rock sole, Lepidopsetta polyxystra; flathead sole, Evaluatingharveststrategiesforaslow-growing,low-productivity,
Hippoglossoideselassodon;andarrowtoothflounder,Atheresthesstomias) periodicspecies(Pacificoceanperch,Sebastesalutus),Punt(2011)in-
found that springtime wind pattern was important for predicting re- corporateddifferentdelaysinthespringtransitionperiodaspredicted
cruitment for all three species (Wilderbuer et al., 2013). The authors under global warming scenarios. Stock rebuilding was affected by
hypothesizedthatwindsactingtotransportpelagiclarvaetofavorable forecasts of spring transition, but the impact could be reduced if the
nursery grounds were responsible for above-average recruitment. target F was allowed to adapt during the rebuilding. However, re-
However, the Arctic oscillation was a more important recruitment buildingtimedidnotimprovewhenincludingthecorrectformofthe
predictor for arrowtooth flounder, which the authors hypothesized environment-stock-recruit relationship. Instead, the current manage-
mightbeduetoeffectsonlarvalsurvivalthroughsettlementpatterns. ment system was responsive to climate-induced changes in biological
Recruitment forecasts using environmentally modified stock-recruit parametersdespitenotbeingincludedexplicitlyintheoperatingmodel
relationshipscoupledwithIPCCclimatemodelpredictionswereusedto orprojections.
produce40-yearforecasts,thoughcomparableforecastsexcludingen- AslightlydifferentapproachwasusedbySkagenetal.(2013)ona
vironmentaleffectswerenotcompleted. hypothetical periodic species, where trends in environmental factors
Winter mortality of estuarine-dependent juveniles has a strong ef- forcedchangesinallbiologicalparametersthatmightbeaffected(e.g.,
fect on Atlantic croaker (Micropogonias undulatus) recruitment (Hare growth, condition, weight, maturity, and recruitment). Incentives to
etal.,2010).Coldairtemperaturescanresultincoolingestuarinewater increase or decrease the total allowable catch were implemented, in
temperatures to critically low levels in nursery areas, leading to high addition to the current management tactics. However, the authors
mortality of overwintering age-0 croaker and reduced recruitment. found that using only environmentally-driven processes to inform
Hare et al. (2010) found that inclusion of minimum winter air tem- management decisions (indicator based management) was not suffi-
perature in the stock-recruitment model resulted in a significantly cient.Stockassessmentestimatesofstockstatuswerealsoneeded,and
better fit tothe stock-recruit data. This temperature-dependent stock- without that information the environmental indicator was an in-
recruit model produced recruitment predictions that were generally sufficientbasisforsustainablemanagementdecisions.Theauthorsex-
consistent with the stock assessment. Long-term recruitment forecasts presscautionatgeneralizingthisresulttoobroadlyhowever,astheir
incorporatingIPCCclimatemodeltemperaturepredictionsshowedin- primaryintentwastoillustrateasimulationframework.
creases in recruitment concurrent with warmer, milder winters (Hare
etal.,2010). 2.3. Opportunisticstrategists
Workontwoperiodicstrategists(Atlanticcod,Gadusmorhua,and
European plaice, Pleuronectes platessa) and one opportunist (Atlantic Researchontwosmallpelagicopportunisticspecies,sprat(Sprattus
herring,Clupeaharengus)byBruneletal.(2010)echoedthefindingsof sprattus)andsardine(Sardinopssagax),foundwatertemperaturetobe
Basson(1999);thegainfromincorporatingenvironmentalfactorsinto an important predictor of recruitment. Studies of Baltic Sea sprat
the management strategy was dependent on the strength of the en- (MacKenzie et al., 2008; MacKenzie et al., 2012) cite several earlier
vironment-recruitment relationship. Marginal improvements in man- papersdemonstratingeffectsofwatertemperatureonsurvivalofeggs
agementperformancewerefoundgiventheinclusionofenvironmental andlarvae(seecitationsinMacKenzieetal.,2008).Earlierworkshows
factors,withthemostimprovementunderunfavorableenvironmental that recruitment of sprat can be predicted by incorporating SST into
conditions.Theauthorssuggestthatmovingbeyonduseofrecenthis- models(MacKenzieandKöster,2004).One-yearrecruitmentforecasts
torical data to include forecasted environmental data might improve including low predicted SSTare substantially lowerthan forecasts ig-
outcomes, but this is hindered by difficulty of making reliable ocea- noringtheenvironment(MacKenzieetal.,2008).
nographic forecasts. Historical observations may not fully capture fu- SST was found to influence Pacific sardine recruitment via spatial
ture conditions adequately under climate change and it is unknown shifts in the distribution of age-1 recruits during warmer years that
whether current environment-recruitment relationships will remain results in increased availability to fisheries (Jacobson and MacCall,
stationaryintothefuture. 1995).Later,SSTwasincorporatedintoatemperature-basedHCRfor
Using a simulation model parameterized for three different life- Pacificsardine(Hilletal.,2011).Re-evaluationoftheSSTrelationship
historytypes(i.e.,long-livedandunproductive,moderatelylong-lived indicatedalackofimprovedrecruitmentpredictions(McClatchieetal.,
andproductive,andshort-livedandvariableproduction),Haltuchand 2010),butrecentworksuggestsSSTremainsimportant(Jacobsonand
Punt (2011) provide further reason for caution when incorporating McClatchie, 2013) and that SST impacts on recruitment vary, de-
apparent environment-recruitment relationships into forecasts. They pendingonpopulationsize(Deyleetal.,2013).Infact,includingSST,
oftenfoundfalsedetections(i.e.,Type-Ierror)givenacorrelationbe- or a proxy, improved short-term sardine forecast skill by about 30%
tween a periodic environmental index and recruitment, for all three (Deyle et al., 2013). Given the persistent influence of SST for Pacific
life-historytypes.Failuretodetecttruecorrelations(i.e.,Type-IIerror) sardinerecruitment,Tommasietal.(2017a)usedaMSEtocomparethe
wasalsocommon;however,resultsvarieddependingonthelengthof performance of harvest policies that either included or excluded sea-
thesimulatedtimeseriesandtheassessmentmethodapplied.Type-II sonal SST forecasts. The harvest policy that incorporated both stock
errors were more problematic for the long-lived periodic rockfish biomass andskillful SSTpredictions led toincreases instockbiomass
(canaryrockfish,Sebastespinniger)thanfortheperiodicflatfish(petrale andcatchandreductionsintheprobabilityofcatchandbiomassfalling
6
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
belowsocioeconomicorecologicallyacceptablelevels(Tommasietal., et al.,2014). Ifsalmon prey items arenot available,salmon survival,
2017a). However, they recommended combining the SST informed growthandconditionofsalmonarereducedandsalmonpredatorsmay
harvest policy with additional catch restrictions at low stock sizes to switchtopreyingonsmaller,poorconditionsalmonjuveniles(Tucker
buffer against SST forecast errors. While work on Pacific sardine has et al., 2016). Such spatially restricted dynamics and life-history bot-
focused on temperature-dependent harvest policies, SST-recruitment tlenecks, e.g., ocean entry point, suggest that a well-defined mechan-
relationshipshavebeenusedtoproducelong-term(90years)recruit- istic conceptual model could lead to improved forecasts for Pacific
ment forecasts for another sardine stock that estimated increased salmonpopulations.
spawningbiomassduetoincreasingSST(MacKenzieetal.,2012). A large body of research has been dedicated to improving Pacific
Vaughan et al. (2011) found that recruitment forecasts of age-0.5 salmon forecast models in the largely bottom-up driven California
Gulfmenhaden(Brevoortiapatronus),a smallpelagicestuarine-depen- Current(CheckleyandBarth,2009).Burkeetal.(2013)examinedthe
dent clupeiform, are improved using discharge from the Mississippi value of incorporating environmental variables identified in previous
River. Early observations (Govoni, 1997) led to the hypothesis that processstudies(rangingfromlocalconditionsandecosystemprocesses
greaterriverdischargehindersthemovementofGulfmenhadenlarvae to large-scale oceanographic and atmospheric variables) into the Co-
fromthecontinentalshelfspawningareasintoestuarinenurseryareas. lumbiaRiver Chinooksalmon adultforecast ofreturns,findingthata
One-year recruitment forecasts dependent on river discharge always forecast model fit retrospective abundance data well. Using a similar
contained the observed recruitment within the 95% confidence inter- rangeoflocalandlarge-scaleenvironmentaldrivers,Ruppetal.(2012)
valsineachyearofaneight-yearretrospectiveanalysis(Vaughanetal., andLogerwelletal.(2003)successfullyfitenvironmentaldatatotime-
2011). series of Oregon coho salmon (Oncorhyncus kisutch) recruitment. Pro-
Incorporating environment into MSEs for opportunistic species is cess studies for Atlantic salmon (Salmo salar) are also demonstrating
likelytobeusefulbecausetherecruitingyearclassisalargeproportion clear connections between environmental conditions and recruitment
of the total stock size. Agnew et al. (2002) investigated whether in- (e.g.,Millsetal.,2013).Theseexamplesexplicitlyconsiderthebottom-
corporating SST six months prior to Falkland squid (Loligo gahi) re- updeterminantsoftheproductivityoftheshelfecosystemandsuggest
cruitment could improve single-cohort fishery management. The me- successfulforecastingofsalmonrecruitmentispossible.However,the
chanisticlinkbetweenSSTandFalklandsquidrecruitmentisunknown effectofbottom-updeterminantsonsurvivalisneitherdirectnorlinear
butlikelyrelatedtogrowthrates.UsingSSTpredictionstosetfishing (Wells et al., 2017). Rather, when production on the shelf is reduced
mortality resulted in increased catch and lower risk of early fishery andtheforagetaxaarelessavailable,predatorscanswitchfrompre-
closure, but fishing mortality varied widely unless the amount of ferred prey to salmon near shore, imparting significant variability in
changeincatchwaslimited. recruitment(Emmettetal.,2006;Wellsetal.,2017).Suchsignificant
Hurtado-Ferroetal.(2010)conductedanMSEforJapanesesardine cascadingeffectsrelatedtobottom-updriverscanreducethevalueof
(Sardinops melanosticutus), where reference points changed at an en- usingenvironmentalcovariatesinforecastmodels.
vironmental threshold. The authors found marginal improvements in Forexample,althoughPacificsalmonhaveaclearlife-historybot-
outcomesforlong-termmanagementstrategies,alongwithnobenefit tleneck,analysesbyWinshipetal.(2015)wereunabletoidentifyre-
forshort-termmanagementstrategies.Hurtado-Ferroetal.(2010)ar- liable environmental covariates for improving the prediction of adult
guedthatgoodenvironmentalpredictionsarenotneededformanage- abundance from sibling models (i.e., estimating remaining cohort
ment of this fishery; simply knowing the environmental threshold at abundanceatseafromtheabundanceofyounger,jack,salmonreturns).
whichanewmanagementstrategyshouldbeimplementedwasenough. ForecastsofSacramentoRiver,Californiafall-runChinooksalmonadult
abundancehavebeeneffectivelyestimatedfromtheabundanceofre-
2.4. Salmonidstrategists(PacificSalmonids) turningjacks(PacificFisheryManagementCouncil(PFMC,2018),but
this relationship has weakened (Winship et al., 2015). Regression
ThefirstmonthsatseaforPacificsalmonareconsideredacritical models incorporating different temporal combinations of eight en-
period during which recruitment may be determined (Beamish and vironmentalvariables,20intotalconsideringseasonalinvestigations,
Mahnken, 2001; Wells et al., 2012; Kilduff et al., 2014a, b; Woodson relatedtomaturationorsurvivalwereevaluatedforpredictionofadult
and Litvin, 2015). Following Pearcy (1992), researchers focusing on ocean abundance (Winship et al., 2015). While the models demon-
Pacificsalmonhaveattemptedtolinkoceanographicconditionsduring strated improved accuracy over models without environmental vari-
or near the time of out-migration (juveniles migrating from fresh to ables, cross-validation methods (mimicking the assessment timing re-
saltwater)toearlysurvivalandlaterrecruitment.Forsalmonpopula- quirements)didnotconsistentlyidentifyaone-yearforecastmodelthat
tions from Alaska through California, Hare et al. (1999) and Mantua includedenvironmentalvariablesasthebestperformer.Simplerauto-
etal.(1997)demonstratedtheimpactbasin-scalefactorscanhaveon regressive models based on jack returns had similar or better perfor-
influencing salmon recruitment. Roughly, Northeast Pacific Ocean mance(Winshipetal.,2015).Theseresultshighlighttheimportanceof
temperatures areinverselyrelatedtoproductionofsalmonintheCa- modelvalidationindevelopingrecruitment-predictionmodels.
lifornia Current but positively associated with production of salmon Siblingmodelsmaynotbeavalidmeanstoestimatetheabundance
fromAlaska.Mechanically,theserelationshipsaretheresultofagen- ofsalmonatseaforspecieswithashortermarineresidenceperiod,such
erallyupwelling-dominatedsysteminCaliforniaCurrentandadown- aspinksalmon(Oncorhynchus.gorbuscha)andcohosalmon,asthereare
wellingsysteminAlaska(Hareetal.,1999;Mantuaetal.,1997).Un- few fish that return early to serve as the basis for estimating the re-
fortunately,whileinformative,therelationshipsbetweenproductionof mainingcohortstrength.Spawner-recruitrelationshipshavebeenused
salmonandsolelybasin-scaledynamicshaveproventoocoarsetore- toestimateabundanceatseaforanumberofpinksalmonpopulations.
liably forecast population-specific salmon recruitment. Wells et al. However, escapement (i.e., spawner abundance) surveys can be im-
(2016) provides a mechanistic understanding of the relationships be- precise. Marine environmental indicators have been investigated as a
tweenthebasin-,regional-,andmeso-scaleenvironmentandsurvivalof mechanismforimprovingtherecruitmentestimates.However,forecast
a single central California Chinook salmon (Oncorhyncus tsawytscha) modelsacross43stocksofpinksalmoninthestatesofWashingtonand
population.Namely,thestrengthandlocationoftheNorthPacificHigh AlaskasuggestthatrecruitmentestimatesincludingSSTbenefitedonly
pressurecellinwinterrelatestolate-winterupwellingalongtheCali- afewstocks(21%)(Haesekeretal.,2005).Inrecentyears,pinksalmon
forniacoastwhichprovidesnutrientstothesystemandsetspotential abundanceinSoutheastAlaskahasbeenestimatedusingoceansurvey
productivityinspring assalmonout-migrate(Logerwelletal., 2003). estimates ofjuvenile abundanceandbiophysical indicators(e.g., zoo-
As spring approaches, regional upwelling and transport dynamics de- plankton abundance, local physical conditions, and basin-scale in-
terminetheproductivityandretentionofsalmonpreyitems(Schroeder dicators) (Wertheimer et al., 2017). While model validation suggests
7
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
thatmodelswithbiophysicalindicatorsareanimprovementoverthose Introducing environment-recruitment uncertainty into the forecast
without them, the best performing biophysical model has varied be- whenitwasnotincludedinthestockassessmentisalsosuspect.
tweencohorts,potentiallyindicatingvaryingprocessesaffectingearly Severalrecentstudiesfeaturegreaterbiologicalcomplexity,testing
marinesurvival.EstimatesofoceanabundanceofOregoncoastnatural a range of potential environmental linkages and evaluating whether
cohosalmonattempttoaccountforcohort-specificprocessesaffecting forecasts improve when accounting for future environmental state
survivalandrecruitmentbyusinganensemblemodelingapproach.The (Tables2and3).Mostforecastvalidationsfitthemodeltoasubsetof
ensemble model includes six models with nine marine environmental available data and predict the remaining years so that forecasts are
variables indicating basin conditions, low frequency variability, and comparedwithobservations.Forexample,Winshipetal.(2015)eval-
regional environmental characteristics (Pacific Fishery Management uates the predictability of the Sacramento Index (a proxy for adult
Council(PFMC,2018).Cross-validationhasdemonstratedthattheen- Chinooksalmonabundance)bytestingvariousfunctionalrelationships
sembleapproachissuperiortoanyindividualmodel. with explicit termsfor environmental variables (SST, wind speed and
The application of MSE for salmon has been uncommon. Winship stress,andotheroceanographicmeasures)orwithflexiblemodelsthat
etal.(2013)usedanMSEapproachconsideringstrategiesforreducing allowedforsmoothchanges,reflectinglow-frequencyenvironmentalor
fishing mortality on a protected salmon population from Sacramento ecological variation. A greater emphasis on this type of validation is
River, California but did not include environmental covariates. This warrantedinfutureworkandcouldhelpreducethefrequencyoffalse
lack of examples is, in part, due to the complex biological and en- positives that lead to acceptance of environmental drivers, which are
vironmentalinteractionsaffectingsalmonthroughtheirlifecycle.The thendroppedafteroneormoreyearsofadditionaldataareconsidered.
practiceofhatcherysupplementationcanalsobeseenasahindranceto Selectionoffishestoafisheryoftenoccursatayoungage,typically
conductingMSEsforsalmonbecausehatcheryfishalterlinksbetween less than age two; therefore, when a study explicitly investigates re-
stock size and recruitment. Life-cycle modelling may offer a way to lationships between recruitment and the environment, the time scale
realisticallymodelprocessesaffectingsalmonsurvivalduringtheirlife waseithercontemporaneous(nolag)oratmostwithaone-yearlag.For
cycleandtheinteractionsandfeedbacksoftheprocesses(seeGosselin example,Mackenzieetal.(2008)evaluatedwhetherincorporatingthe
et al., 2018). Currently, along the U.S. west coast, life-cycle models North Atlantic Oscillation index value from the same year as the as-
inclusiveofenvironmentalfactorsarewellparameterizedfortheearly sessmentwouldimprovetheassessmentestimateofage-1fishthatyear.
lifestagesinfreshwater(e.g.,Hendrixetal.,2014).Thesepartiallife- AnexceptiontothetimescaleoflagsinvestigatedwouldbetheMSEof
cycle models have informed temperature-dependent incubation sur- Punt (2011), where an environmental driver was incorporated by si-
vival, flow release dynamics, and habitat restoration alternatives mulating recruitment data that were linked to the timing of spring
(Martinetal.,2017;Dudley,2018).Uponcompletionandintegrationof transitionthreeyearsprior,althoughtheassessmentcomponentofthe
marinecomponents(e.g.,Fiechteretal.,2015;Hendersonetal.,2018), MSEdidnotattempt toaccountfortheenvironment. Noneofthere-
thesemodelswillbeimportanttoolsforconductingMSEsacrossthefull viewedstudiesinvestigatedmultiplelagsthatcouldoccurwithskipped
life cycle including the evaluation of potential mitigation efforts for spawningorrecruitmentatolderages,thoughDeyleetal.(2013)noted
climatechangeeffects,improvedhabitats,andmanagedfisheries. that multivariate state-space models have the potential to include
multiplelagsifsupportedbythedata.
3. Theneedformorerealisticassessment,harvest,and The complexity of interactions that could occur in the early life-
implementationmodules history stages prior to recruitment make it difficult to derive explicit
mechanistic models of how the environment directly affects survival.
During the past two decades, many studies have attempted to ad- Furthermore,longtimeseriesofdataforhypothesizeddriversthatspan
dressquestionsregardingimprovedyieldorstockconservationviain- sufficientcontrastinspawnerabundancearerare.Perhapsthisiswhya
corporating environmental factors into forecasts. Several studies es- majorityofstudiesincorporatetheenvironmentimplicitlybyallowing
sentially repeat the approach of Basson (1999), simulating the true reference points in HCRs to reflect recent years of recruitment and
population dynamics data with observation error before estimating biologicalparameters(e.g.,the“dynamicB0”,MacCalletal.,1985)or
future catches (e.g., Brunel et al., 2010; Hurtado-Ferro et al., 2010). bybasingforecastsofrecruitmentonarecenttimeperiodratherthan
Basson(1999)acknowledgedthatthis“short-cut”simulationneedsto thefulltimeseries(A’maretal.,2009a,Ianellietal.,2011;Puntetal.,
be more realistic; ideally, one would use MSE to simulate the entire 2013; Skagen et al., 2013). Using recent recruitment and biological
procedure. parametersmakestheimplicitassumptionthatthenear-termenviron-
In a full MSE, data are simulated, an assessment is performed to mentalstate,anditseffectonstockdynamics,willbesimilartorecent
determine stock status, a HCR is applied to forecast catch, and the observations,withouttheburdenofpredictingtheenvironmentalstate
procedure is repeated through time to provide a feedback loop (e.g., andestimatingthefunctionalrelationshipwithrecruitment.
Aömaretal.,2009a,b;Hurtado-Ferroetal.,2010;Ianellietal.,2011; Another challenge to developing direct or lagged empirical en-
Punt,2011;Puntetal.,2013;SzuwalskiandPunt,2012).Ofthestudies vironment-recruitment relationships is the lack of recruitment ob-
reviewed,fullMSEswerelesscommonthanHCRevaluationswithout servations.Surveysofage-0andage-1fishareuncommon(forexcep-
the full feedback loop, largely due to computational demands (Punt tions see Vaughan et al., 2011; ICES, 2013), and thus, most studies
etal.,2016).Whilesimulationstudiesreflectacontinuumofsimpleto investigaterelationshipsusingstockassessmentoutputthatshouldbe
complex, studies fell into two main categories: 1) those that evaluate interpreted with extreme caution (Brooks and Deroba, 2015). When
existing and alternative HCRs for a particular or hypothetical stock explicit versus implicit incorporation of the environment has been
(e.g.,Agnewetal.,2002;Ianellietal.,2011;Punt,2011;Skagenetal., compared, implicitly including the environment through autocorrela-
2013) and 2) those making forecasts with and without hypothetical tioninrecruitmentormodellingsmoothtrendsovertimeappearsless
climatescenariostodemonstratedifferentstocktrajectories(e.g.,Hare prone to prediction error (Punt, 2011; Winship et al., 2015; Johnson
et al., 2010; MacKenzie et al., 2012; Mueter et al., 2011; Wilderbuer et al., 2016a). At least one study found an improvement in forecast
etal.,2013).Noneofthereviewedstudiesincorporatestheuncertainty performance by explicitly including the environment (Agnew et al.,
ofmanagementimplementation,i.e.,theinabilitytoachievemanage- 2002), but this was forasimplistic simulation of anannuallife cycle
ment targets precisely even though implementation uncertainty is (squid).Futureworkshouldbevettedusingsimulationpriortobeing
prevalent in many fisheries. Also, many of the forecast-only studies implementedwithstockassessmentoutput(e.g.,Deyleetal.,2013)or
simplyusedassessmentoutputwithoutpropagatingtheuncertaintyand ideally should be included in the stock assessment itself (e.g., A’mar
correlation of those model estimates into their forecasts, thereby un- etal.,2009b).
derestimating overall uncertainty and potentially introducing bias. No single study performed a full factorial design using a range of
8
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
e)
g
es pa
NForecast PrincipleComponents 1 1 1 NA 4 1 1atatime 1 8covariatesacross6modelswith3variableach.Thisstudyusesmodelaveragingtoaddressuncertainty (continuedonnext
UsedtoForecast Aprinciplecomponentsfromallvariables SIOSSTandPDO,oneatatime SST MinimumwinterairtemperatureNA SSTduringwinterpriortoemigration,Springtransitiondateduringemigration,Sealevelduringemigration,SSTduringwinterfollowingemigrationJan-FebNAO Foursetsofoceanmodeltemperatureprojections SST SPR,logspawners,FallMEI,SummerUWI,SpringSST,SpringSSH,FallUWI,JanuarySST
8
NSelected 23 2 1 1 NA 4 3,butoneatatime 4,butoneatatime 2 12variablesacross1selectedmodels
Selected All SIOSST,PDO SummerSST MinimumwinterairtemperatureNA SSTduringwinterpriortoemigration,Springtransitiondateduringemigration,Sealevelduringemigration,SSTduringwinterfollowingemigrationEachfitinturnasanexampleAll PC1(averagetemperature),andPC3(predationpressure) May-JulyPDO(y=4yearaverage),SummerUWI,FallNPGO,SPR,LogSpawners,FallMEI,WinterNPI,Spring
Es.
S
M
or ed
ns, der
mulatio NConsi 23 9 1 1 6 10 3 4 10 >100
si
esconsideredandusedforforecasting, CovariatesConsidered PacificDecadalOscillation(PDO),OceanicNinoIndex,SST,Springtransition,Upwellingstrength,Upwellinglength,Deeptemperature,Deepsalinity,Freshwaterflow,Freshwatertemperature,Copepodrichnessandnorthernanomaly,Copepodcommunity,Biologicalspringtransition,Ichthyoplanktonbiomass,Ichthyoplanktoncommunityindexscore,Chinookdiet(May,June),Chinookcondition(May),ChinookGrowth(June),Copepodcommunityindexscorefromverticalnets,Junebiomassofbongosamples,Densityofage1anchovy,Densityofadulthake,PrevalenceofRenibacteriumsalmoninarum,JuneChinooksurveyCPUE,JuneandJulySalmonCPUEinVancouverScriptsInstituteofOceanography(SIO)Temperature(surfaceandbottom),NorthPacificIndex(NPI),NewportPierSST,PDO,NorthPacificGyreOscillation(NPGO),SouthernOscillationIndex(SOI),MultivariateElNinoIndex(MEI),andSouthernCaliforniaBight(SCB)SSTSummerSSTNortheastPacificregional Minimumwinterairtemperature 2models,1.predatorabundance(cod),preyabundance(Caplin);2.hatchdate,SST,proportionofrepeatspawners,andpredatorabundance(youngsaithe)NPI,Upwellingintensity,SST,Springtransitiondate,sealevel(alltreatedseasonallyduringemigrationandsomeduringthefirstyearatsea). Maywatertemperature,NAO(Jan-Feb),andicecover(Balticsea)ModeledAugusttemperaturefromthreeBalticSeaocean-biogeochemicalhindcastmodels,andobservedMaytemperatureIceretreat,Springtransitiondate,SummerSST,Summerwinds,Stratification,Predation,andPrinciplecomponents(PCs)1through4fromtheabovecovariatesSeasonalrunningaveragesforMEI,NPGO,NPI,OceanicNinoIndex(ONI),PDO,SpringTransitionDate(SPR),SeaSurfaceHeight,SSTandUpwellingWindIndex(UWI).Calculated3month(2monthfor
at
onmentalcovari Publication Burkeetal.,2013 Deyleetal.,2013 Haesekeretal.,2005Hareetal.,2010 ICES,2013 Logerwelletal.,2003 Mackenzieetal.,2008Mackenzieetal.,2012 Mueteretal.,2011 Ruppetal.,2012
vir
n
e
of ng
ble2mmary orecasti
au F
TS
9
M.A.Haltuchetal. Fisheries Research xxx (xxxx) xxx–xxx
e)
NForecast Ensembleof6modelsinclusiveofthreevariableseach 1 NAVariesinter-annually 1 6total:4modelswithJacks,and2modelswithJacksand2environmentalcovariateseach1 1atatime 12eachfrom6downscaledIPCCmodels(72total) NA4,eachinturn (continuedonnextpag
UsedtoForecast MeanMay-JulPDOaveragedover4yearspriorreturnyear,MEIOct-Dectheyearpriorreturn,SpringTransitiontheyearpriorreturn,SeaSurfaceHeightApr-Juntheyearpriorreturn,UpwellingJul-SepandSep-Novtheyearpriorreturn,SSTMay-JultheyearpriorreturnandJantheyearofreturn,logSpawners3yearspriorreturnMississippiRiverdischarge NAJuvenileCPUEorEcosystemRanksIndex.Additionalbiophysicalcovariatesarechosenthroughmodelselection. Springwinddirection 22 SST TwotheoreticalregimechangesbasedonobservedfrequencySeasonalIPCCmodeoutputsfor:precipitation,WME,andadvectionofoceanwater NAScenariowheretherangeofenvironmentalvariationissimilartohistoricalobservations.4Scenarios:constanttrendwithno
e
h
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y n
nnuall dingo
NSelected 9 1 NAVariesinter-a 1to3,depenstock 22 1 1atatime 12 NA1,3,1
Selected SST,winterONI,springSSH,FallUWI,JanuarySST Ensemblemeanpredictor MississippiRiverdischarge NAJuvenileCPUEorEcosystemRanksIndex.Additionalbiophysicalcovariatesarechosenthroughmodelselection. Springwinddirectionandstocksize(northernrocksole);AO,Springwinddirection,andStocksize(arrowtoothflounder);Stocksize(flatheadsole)NA,Manymodelsevaluated. SST TwotheoreticalregimechangesbasedonobservedfrequencySeasonalIPCCmodeloutputsfor:precipitation,WME,andadvectionofoceanwater NAJan-Junetemperature(Cod);NOAlagged2years,feedinggroundtemperaturelagged1year,spawninggroundminimum
5al
dot
NConsidered 9 1 NA22 6maineffectsaninteractions,11t 22 1 1atatime 10 2,oneatatimeNotClear,<10
CovariatesConsidered MEI)runningmeanstoget12timeseriesforeachvariable.Additionally,Dec-JanmeansforallbutMEI,andJanuarymeans.LogSpawners.MeanMay-JulPDOaveragedover4yearspriorreturnyear,MEIOct-Dectheyearpriorreturn,SpringTransitiontheyearpriorreturn,SeaSurfaceHeightApr-Juntheyearpriorreturn,UpwellingJul-SepandSep-Novtheyearpriorreturn,SSTMay-JultheyearpriorreturnandJantheyearofreturn,LogSpawners3yearspriorreturn MississippiRiverdischarge NASalmonharvestandbiophysicalvariables;JuvenilesalmonsurveyCPUE;Juvenilecondition;Predatorsabundance;Zooplankton;Local-scalephysics(watertemperature,mixedlayerdepth);Basin-scalephysics(PDO,NPI,MEI,NPGO);EcosystemRanksIndexEndingdriftdepth,Endingdriftdistancefromland,AverageAnnualArcticOscillation(AO),MaySST,Springwinddirection,andSpawningstocksize 3seasonalvaluesfor:SST,Windspeed,Northerlywindstress,Easterlywindstress,Windcurl,UpwellingIndex,SealevelandSpringtransitionindex.Also,JackspawningescapementSST Twotheoreticalregimechangesbasedonobservedfrequency WinterPrecipitation,WinterWindMixingEnergy(WME),Springeddyformation,SpringWME,SpringUpwelling,SpringTemperature,SpringPrecipitation,SummerWME,SummerSST,FallSSTTheoreticalgoodorbadenvironmentSimulationassumesrelationshipknowwithouterror.ModelswerefitwithhistoricaldataandselectedusingAIC.Covariates:NOA,temperature,salinity(variousmeasures)
d) Publication PacificFisheryManagementCouncil(PFMC,2018 Vaughanetal.,2011Wardetal.,2014Wertheimeretal.,2017 Wilderbueretal.,2013 Winshipetal.,2015 Agnewetal.,2002A’maretal.,2009a A’maretal.,2009b Brooks,2013Bruneletal.,2010
e
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