Selecting the best forest management alternative by aggregating ecosystem services indicators over time: A case study in central Spain

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Ecological Indicators

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Original Articles

Selecting the best forest management alternative by aggregating ecosystem services indicators over time: A case study in central Spain

Luis Diaz-Balteiro

, Rafael Alonso

, María Martínez-Jaúregui

, Marta Pardos

aTechnicalUniversityofMadrid,ETSIngenierosdeMontes,CiudadUniversitariaS/N,28040Madrid,Spain

bINIA-CIFOR,CrtraCoru˜naKm7.5,28040Madrid,Spain

cSustainableForestManagementResearchInstitute,UniversityofValladolid&INIA,Avda.deMadrid57,34004Palencia,Spain

a r t i c l e i n f o

Articlehistory:

Received4April2016

Receivedinrevisedform30May2016 Accepted15June2016

Keywords:

Multi-criteriadecisionmakingtechniques Sustainableforestmanagement PICUS

Stakeholders Climatescenarios

a b s t r a c t

Sustainableforestmanagementhasbeenapproachedonmanyoccasionsbydefiningandsubsequently measuringasetofinitiallyacceptedindicators.Thismethodologypermitstheaggregationofmultiple goodsandserviceswithheterogeneouscharacteristicsintoforestmanagement.However,thecalculation oftheseindicatorshasusuallybeenstatic.Whenwefindourselvesinsituationsinwhichthereisaneedto makelong-termevaluationsoftheeffectsofpossiblescenariosaffectingforestmanagement,aprocedure hastobesetuptodefineandaggregatethedifferentindicatorsovertime,aswellastointegratethe preferencesofthestakeholdersinvolvedinmanagement.

Thisstudyshowsagoalprogramming-basedmethodology,whichpermitstoselectthebestmanage- mentalternativein6climatechangescenarioswhendifferentindicatorsareaggregatedover100years inamountainforestinCentralSpain.Theresultsrevealedthepredominanceofonemanagementalter- native(nomanagement)whenthepreferencesofthestakeholderswereaggregated.However,when thepreferentialweightscorrespondingtosomestakeholderswereincludedseparately,thesolutionmay notablyvary,especiallyinthecaseofforestowners.Itwasconcludedthatthemethodologyproposed allowsadynamicaggregationofdiversesustainableforestmanagementinadditiontopresentingagreat flexibilityatthemomentofselectingvarioussolutionsproposedbythegoalprogrammingmodel,and thepreferencesofthedifferentstakeholders.

©2016ElsevierLtd.Allrightsreserved.

1. Introduction

Nowadays,therearedifferentdefinitionsofsustainableforest management.Thereisaconsensusthatsustainableforestmanage- mentinvolvesaprocessofmanagingforeststhatareeconomically feasible,environmentallyconcernedandsociallyvaluable,balanc- ingpresentandfutureneeds(Higman etal.,2005).Thecurrent ideaofsustainableforestmanagementisnotonlyassociatedwith productionobjectives(Bettingeretal.,2009,chap.9),anditveers awayfromthemereachievementofclassicconditionsensuring sustainedyield(RecknagelandBentley,1919).

Ontheotherhand,thereiscurrentlyacertainunanimityinthe premisethatdefininganinitialsetofmultidisciplinarycriteriaand indicatorsmaywellcomenearertotheideaofsustainableforest management(Raisonetal.,2001).However,manyofthesestudies havepaidmoreattentiontothedefinitionandmeasurementofthe

∗ Correspondingauthor.

E-mailaddress:[email protected](L.Diaz-Balteiro).

1 Presentaddress:SorbusServiciosMedioambientalesSLL,C/ElMolino6,Cuzcur- ritadeJuarros,09198Burgos,Spain

indicatorsthantotheiraggregation,whichhasfailedtoanswerthe immediatequestionofwhetherforestmanagementissustainable ornot(Diaz-Balteiroetal.,2016a).

Giventhatmultidimensionalityisintrinsictothesustainable forest management idea, some studies have attempted to use diverse MCDM (Multiple Criteria Decision Making) techniques.

Thus,starting fromseveralyearsago, someforestmanagement casestudieshaveincorporatedsustainabilitybyapplyingMCDM techniques(Diaz-Balteiro andRomero,2008).Under theMCDM umbrella,goalprogramminghasbeenoneofthosemostwidely usedmethods tobuildsyntheticindexes inforest management applications, integrating several indicators and criteria (Diaz- BalteiroandRomero,2004;Giménezetal.,2013;Aldeaetal.,2014;

Diaz-Balteiroetal.,2016b).OtherstudiesemployingotherMCDM techniquesin similarsustainableforestmanagement issues are WolfslehnerandVacik(2008),Balanaetal.(2010),Jalilovaetal.

(2012)or Hernándezet al. (2014).Finally, it hasrecentlybeen shownhowthecombinationofMCDMtoolswithothertechniques maybevalidforintegratingtheecosystemservices(ES)associated withaforestsystem(Uhdeetal.,2015).

Theabovemethodologiesallowtheaggregationofabatteryof differentindicatorsintosyntheticindexesthatmeasuretheoverall http://dx.doi.org/10.1016/j.ecolind.2016.06.025

1470-160X/©2016ElsevierLtd.Allrightsreserved.

sustainabilityofaforestmanagement alternative.However,this methodologycanbeappliedtotheaggregationofindicatorsfor sustainableforestmanagementwithoutreallytakingintoaccount theaboveideaofsustainability.Thatis,youcanaggregateaset ofindicatorswithouttryingtodefineasustainabilityindex.This multi-indicatorapproachcanhelptoestablisharankingofdifferent forestmanagementalternatives,evenintegratingthepreferences ofdiversedecision-makers(Nordströmetal.,2009;Diaz-Balteiro etal.,2013).

In this study, we have focused onPinus sylvestris mountain forestsintheSierradeGuadarramainSpain.Weusedthehybrid patch modelPICUS v1.6 (e.g.,Seidl et al.,2005)to analysethe provisionofmultipleESovera100-yearsimulationscenario,pro- vidingrelatedESindicators(Seidletal.,2011;Pardosetal.,2015).

Planningandimplementingmultifunctionalforestmanagementis challengingbecauseEScouldbeaffecteddifferentlybychangesin climateandmanagement.Inthissense,themodelPICUSassesses theimpactofclimatechangeandallowstodesignmanagement alternatives.

Sinceforestmanagementis,ingeneral,ofaninherentlydynamic nature,theprimaryobjectiveofthisstudywastoshowamethod- ologywhich aggregatesasetofindicatorswithdifferentvalues overthetime,toobtainthemostpreferredmanagementalterna- tivethroughouttheplanninghorizon.Thisideahasbeenapplied toamountainforestcasestudywheretheobjectivewastoselect theoptimalforestmanagementalternativeunderseveralclimate scenarios,introducingthepreferentialweightsofdifferentstake- holders. A secondary objective of this study was to examine whethertheresultsobtainedovertimewereaffectedbychanges inthestakeholders’preferentialweightsattachedtothedifferent indicatorsanalysed.

Althoughthereisanextensiveliteratureontheaggregationof indicators(PolleschandDale,2015), innotmanystudiesisthis aggregationhasbeencarriedoutovertime.Thus,manyofthem usuallyoffermethodsforbuildingcompositeindexes,butfroma staticpointofview.However,whenassessingthesustainability ofdifferentsystems,itisessentialtointegrateadynamiccompo- nent(SchlaepferandElliot,2000,pp.14).Someexamplesofthe calculationofasetofindicatorsatdifferenttimesovertimeare foundinLeetal.(2010),andButleretal.(2012).Inthelatterstudy, theauthorshavedefinedasyntheticbiodiversityindex(farmland birdindex)withvaluestakenbetween1970and2006.Finally,in Brice ˜no-Elizondoetal.(2008),amulti-attributeutilitymodelin astochasticcontexthasbeenemployedtoselectthebeststand treatmentprogrammeunderdifferentclimatescenariosandusing aphysiologicalgrowthmodeltosimulatethevalueofdifferentcri- teriathroughout100years.However,nopreferentialstakeholders‘

preferenceshavebeenintegratedintothemodel.

2. Materialandmethods

2.1. Casestudy:PinardeValsaínforest

PinardeValsaínisa7622hapublicforestlocatedontheNorth facingslopesoftheSierradeGuadarrama(CentralMountainRange ofSpain,40^◦49^N,4^◦1^W).Atelevationsbetween1400and1900m a.s.l.theforestedareaisclearlydominatedbypureeven-agedPinus sylvestris, while mixed Pinus sylvestris-Quercuspyrenaica stands (10%oftheforestedarea)arefoundatbelow1400ma.s.l.Above 1900ma.s.l.,alpineshrubsistheprevailingvegetationtype.The climateissub-Mediterranean,withameanannualtemperatureof 8.5^◦Cat1500m,averageannualrainfallof1275mm,andprecip- itationbetweenMayandSeptemberof651mm.Moderatelydeep dystriccambisolsandferricluvisolshavedevelopedover acidic bedrockasmajorsoiltypes.

Table1

Baselineclimateandclimatechangescenariosat1500ma.s.l.

Baselineclimate Climatescenarios

C0 C1 C2 C3 C4 C5

T(^◦C) 7.2 T +3.7 +3.8 +3.9 +4.6 +5.9

P(mm) 1366.3 P −17% −10% +4% −11% −17%

Psummer(mm) 337 Psummer −25% −30% −24% −38% −58%

Thelackof anyformalplanningforcenturies ledtooverex- ploitation,regenerationfailureandseveredegradationoftheforest.

Thesituationchangedwiththeimplementationofthefirstman- agementplansin1889.Sincethen,even-agedforestmanagement basedonnaturalregenerationhasbeenthecommonpractice.For decades,theuseofauniformshelterwoodsystemwitharotation of120yearsanda20-yearregenerationperiodfavouredtimber productionasthemainES.Startingfromthe1980’s,multifunction- alitygainedimportanceandthesilviculturalsystemwaschanged toa shelterwood groupsystemthat extended theregeneration periodto40years,toensuresufficientnaturalregeneration.Cur- rently,thisisthe“businessasusual”managementapproachfor Pinussylvestrisstands.ThemainESdemandedcurrentlyatPinarde Valsaínaretimber,carbonstorage,biodiversityandhabitatcon- servation,recreationandgame.Since2013,3326haoftheValsaín forest(above1875ma.s.l.)areincludedinthe“SierradeGuadar- rama”NationalPark,wheremanagementishighlyregulatedand evenrestrictedinsomeareas.

2.2. Climatescenarios

We used a baseline climate (C0) and five transient climate changescenarios(C1toC5).Eachclimateconsistedofa100-year timeseriesthatincludeddailydatafortemperature,precipitation, radiationandvapourpressuredeficit.Dataavailablebetween1961 and1990(PuertodeNavacerradaweatherstation,40^◦47^N,4^◦01^W) wereusedtogeneratethebaselineclimateat1500ma.s.l.Thefive climatechangescenarioswerebasedonregionalclimatemodel simulationsfromtheENSEMBLESproject(HewittandGriggs,2004;

www.ensembleseu.org).Climatescenariosincreasemeantemper- ature between3.7^◦C and 5.9^◦C, decreasesummerprecipitation (May–September)between25%and58%,whilechangesinannual precipitationarenotsomarked(Table1).

2.3. Forestmanagementalternatives

Forest management alternatives have focusedonly on Pinus sylvestris,bothinthepureandmixedstands.Thebusiness-as-usual managementregime(BAU),analternativemanagement(AM1)and a“nomanagement”alternative(NM)havebeendefined.Thealter- nativemanagementAM1focusesonchangesinthethinningregime tofavourmorediversestandstructuresandtotriggertreevigour whilepromotingqualitytimber,atthesametimemaintainingthe multifunctionalityofthestands.

IntheBAUalternative,thefocusisontheproductionofvaluable timberwhilekeepingupasatisfactorylevelofotherES.Threelight thinningsfrombelowareappliedatages40,60,and80years.Four regenerationfellingsareappliedduringa20-yearperiod.Inthe finalregenerationcutafter20years,someresidualtreesperhectare arekeptstandingtoprovideanestinghabitatforbirds.Rotation lengthis120years.IntheAM1alternative,themanagementobjec- tiveistopromotequality timbersimilarly toBAU.However,in contrasttoBAU,selectivecrownthinnings(35–40%of standing volumeisremoved)areappliedtopromotegrowthandvigourof goodqualitytrees.Therotationconsistsof120yearsemploying theirregularshelterwoodasinBAUfornaturalregeneration.The mainobjectiveoftheNMalternativeistoallownaturalprocesses,

includingmortalityandnaturalsuccession,todevelopwithoutany managementinterventiontocreatenatural,ecologicallyvaluable habitats.

2.4. SimulationmodelPICUSv1.6

ThehybridforestpatchmodelPICUSvl.6wasusedtosimulate the100-year(2010–2110)forestdynamicsunderthedifferentcli- matescenarios.Thismodelcombinesa3Dgapmodel approach (LexerandHönninger,2001)withthephysiologically-basedpro- ductionapproachofthe3PGmodel(LandsbergandWaring,1997).

Itsimulates individualtreedynamicson10×10mpatches and structurestreecrownswithin5mcells.Forestdynamicsisassessed throughkeyprocessessuchasgrowth,mortalityandregeneration (seeLexerandHönninger,2001andSeidletal.,2005fordetails).

The model incorporates a flexible management module allow- ingforallsortsofthinningandharvestingoperations.ThePICUS v1.6runsondailyclimatedataofminimum,maximumandmean temperature,precipitation,radiationandvapourpressuredeficit.

EarlierversionsofthePICUSmodelhavebeenevaluatedsuccess- fullyforPinussylvestris,andPICUSv1.6hasrecentlybeencalibrated andvalidatedspecificallytoPinussylvestrisstandsatValsaínforest (Pardosetal.,2016).

Forthesimulations,currentforestconditionsinPinardeVal- saínwereobtainedfromforestinventorydata.Basedontheforest conditions,wedefined18RepresentativeStandTypes(RST)that differedinspeciesmixture(purePinussylvestrisstandsvsmixed P.sylvestris-Quercuspyrenaicastands),standdevelopmentstage (thicket,pole,matureandovermature)andsitetype(plantavail- able nitrogen and water holding capacity, WHC). Initial stand structuresforthemodelsimulationsweregeneratedfromtheDBH (diameteratbreastheight)distributionandheight-diameterequa- tion,thelatterbeingextractedfromtheValsaínmanagementplan.

OutputsprovidedbyPICUSonayearlybasiswereaggregatedin 20-yearperiodsforanalysispurposes.

2.5. Indicatorsconsidered

WeassessedfivemainES(timberproduction,carbonsequestra- tion,habitatandbiodiversityconservation,recreationandgame habitatquality)throughdifferentindicators.Timberproduction, carbon sequestrationand habitatand biodiversity conservation hadbeenaddressedamongthemainEStostudythesustainable provision of ES in seven case study regions in major moun- tainrangesthroughoutEurope(ARANGEEUproject,http://www.

arange-project.eu). Recreation and game habitat quality were addedtothelistofESastheyareespeciallyrelevantinValsaín forest.Timberproductionwasrepresentedbythetotalannualvol- umeoftimberharvested fromastand(TVH,m³ha⁻¹yr⁻¹),thus givinganideaofthemanagement intensityin it.Sincenodata forcarbonstorageinsoilanddeadwoodwereavailable,carbon sequestration(CS,tha⁻¹yr⁻¹)wascalculatedastotalcarbonstored inaboveground(C_above)andbelowground(C_below)treebiomass.We chosethewoodvolumemethod(IPCCC,2006)forcalculations:

C_above=(V×D×BEF)×CF (1)

(2)C_below = C_above.R

whereVistimbervolume(m³ha⁻¹),Disthewooddensity(tdry matterm⁻³),BEFisthebiomassexpansionfactorforconversion ofvolumetoabovegroundtreebiomass,CFisthecarbonfraction ofdrymatter(tCtdrymatter⁻¹),andRistheroot-to-shootratio (Bugmannetal.,2016).

ThreeindicatorsofHabitatandBiodiversityConservation(dead woodvolume (SDWV),abundance(LSDTN)and volume oflarge

standing dead trees (LSDTV)) and two indicators of Recreation (treesizediversity(H)andabundanceoflargelivingtrees(LLTN)) werepreliminarilychosenforthecurrentstudy.Largetreesbear moremicrohabitatsforforestdwellingspecies(Vuidotetal.,2011;

Larrieuand Cabanettes,2012; Nilsson et al., 2002; Michel and Winter,2009;WinterandMöller,2008;Gil-Tenaetal.,2007)and arevisuallymoreattractiveforrecreationpurposes.Largeliving treeswerecomputedastreeswithadbhover50cm,whilelarge standingdeadtreesincludedtreeswithadbhover30cm.TheH indicator(NeumannandStarlinger,2001)correspondstothemean oftheShannon entropyindices appliedtodiameterandheight classesinsteadofspecies,using5-cmclassesfordiametersand2-m classesforheights.CalculationsoftheabovementionedESindi- catorsaredetailedinBugmannetal.(2016).Ananalysisofthese indicatorsshowedthattheyhadahighcorrelationbetweeneach other;thus,wefinallychosethevolumeoflargestandingdeadtrees (LSDTV,m³ha⁻¹)toassesshabitatandbiodiversityconservation, andtreesizediversity(H)asarecreationindicator.

Toassessthegamehabitatquality,roedeer(Capreoluscapreo- lusL.)waschosenasabiggameindicator.Thisspeciesishighly demanded in the territorial context (3120 roe deer harvest in Segoviaand 244roe deerharvestin Madridin the2013–2014 huntingseason,MAGRAMA,2013)and itshabitatrequirements compriseotherfaunahabitatrequirementslinkedtoforests(Tixier and Duncan, 1996;San José etal., 1997; Acevedo et al., 2005;

CamprodonandBrotons,2006;Gil-Tenaetal.,2007;Mangasetal., 2008).Twoindicatorswereinitiallydefined:FoodandShelter.Itis worthnotingthatPinardeValsaínisexcellentatsupplyingroedeer withfoodandshelterinallRSTs.Foodisacategoricalvariablewith twoclassescalculatedthroughoutthecanopycover(COV,in%).We analysedtheCOVdistributioninPinardeValsaínandthenobtained aCOVvalueforeachRST.ThoseRSTswithaCOVvaluebelowthe medianwereassignedavalueof1,explainingtheavailabilityof openareasandthereforetheaccessibilitytoherbaceousandshrub resources(i.e.brambles),whileCOVvaluesabovethemedianwere assignedavalueof0.Shelterisalsoacategoricalvariablerelated tothebrushand regenerationcover(BRREG)obtainedfromthe 2009LIDARsurveyoftheforestarea.Thethresholdwasobtained similarlytoFood,i.e.,bycalculatingthemedian.ThoseRSTswitha BRREGvaluebelowthemedianwereassignedavalueof0,indicat- ingscarcityofrefugezones,whileBRREGvaluesabovethemedian wereassignedavalueof1.Whilealltheabove-mentionedindica- torswereobtainedfromPICUSsimulations,BRREGisnotanoutput fromthemodel.Thus,BRREGwascalculatedthroughapredictive modelusingananalysisofcovariancewithCOV,BA(basalarea, m²ha⁻¹),Dg(meansquareddiameter,cm),VI(Currentannualvol- umeincrement,m³ha⁻¹yr⁻¹)andSV(standvolume,m³ha⁻¹)as continuousvariablesandRSTasacategoricalvariable.OnlyCOV andRSTweresignificant,andwereusedasinputvariables.The applicationofthegamehabitatqualityESattheforestRSTlevel includedtheaggregationofFoodandShelterbysummingupboth indicators,thusresultingina soleordinalindicator(FOSH)with threeclasses:0–2,headed“poor”(bothFoodandShelterarepro- videdbelow themedian),“medium”(either Foodor Shelterare providedabovethemedian)and “good”(bothFoodand Shelter arepresentedabovethemedian)forroedeerhabitatquality.

Alltheindicatorswereavailableonanannualbasisoverthe entiresimulationperiod(2010–2110).Foraggregationpurposes, theindicatorswerecalculatedat20-yearintervals.Table2sum- marizestheindicatorsusedandthemanagementalternatives.

2.6. Methods

Therewere l forest management alternatives,each of them evaluatedaccordingtomsustainabilityindicatorsalongthekmile- stones(at20,40,60,80and100years)toobtainarankingofthe

Table2

Indicatorsandforestmanagementalternativesofthemodel.

Indicators Acronym Meaning

Timberproduction TVH totalvolumeharvested

Carbonsequestration CS carbonsequestered

Biodiversityconservation LSDTV volumeoflargestandingdeadtrees

Recreation H treesizediversity

Game FOSH ordinalindicatorwhichincludesfoodandshelteravailability

ForestManagementalternatives Acronym

Businessasusualmanagementregime BAU

AlternativeManagement AM1

NoManagement NM

differentforestmanagementalternativesbentonsustainability.It isimportanttonotethatourhypothesiswastochoosethemost sustainableforestmanagementalternativeforthenext100years.

Thus,wedidnotcontemplatethepossibilityofchangingaforest managementalternativeevery20years.Afterobtainingthevalues ofeachindicatorunderallalternativespreviouslydefined,andfor everymilestone,theindicatorswereaggregatedintoasynthetic index using a binary goal programming model (Romero, 2001, 2004).However,first,asthefiveindicatorshadbeenmeasuredin differentunits(cubicmeters,tofcarbon,etc.),itwasnecessaryto normalizethemfollowingtheprocedureproposedbyDiaz-Balteiro andRomero(2004).Oncetheindicatorsandotherelementsofthe model(targets)hadbeennormalized,thenextstepwastosetup thegoalprogrammingmodel.Wemustrememberthatatargetina goalprogrammingmodelisthedecisionmakersdesiretoachievea criterion(JonesandTamiz,2010).Inourcase,eachdecisionmaker setanumerictarget(inpercentageoftheidealvalue)foreachgoal.

Thefollowingequationsshowthemaincomponentsofthismodel:

Achievementfunction:

Min(1−)·D+



j=1



k=1

(˛_ijn_jk+ˇ_jkp_jk) (3)

Goalsandconstraints subjectto:

(a_jkn_jk+ˇ_jkp_jk)−D≤0 (4)

l



i=1

¯R_ijkX_ik+n_jk−P_jk= ¯tj j∈



j,...,m



k ∈



1,...,n



(5)



i−1

X_ik=1␬ ∈



1,...,n



(6)

X_ijk ∈{0,1} (7)



=1

X_ik=q_ii∈



1,...,l



(8)

q_i ∈



0,1



i ∈



1,...,l



(9)



i=1

q_i=1 (10)

n≥0 p≥0 (11)

wheren_jkandp_jkarethedeviationvariables(negativeandpositive, respectively)thatquantifytheunderorover-achievementofthejth indicatorwithrespecttoitstargetvalue,˛_jkandˇ_jkaretheprefer- entialweightsassociatedwithbothdeviationvariables.VariableD measuresthemaximumdeviationbetweenthevalueachievedby anindicatoranditsrespectivetargetvalue.R_ijkisthenormalised valuereachedbytheithforestalternativemanagementwhenit

isevaluatedaccordingtothejthindicatorand alongthek tem- poralmilestones.Inordertocompletethemeaningofthegeneric goalshowninEq.(3),itwasnecessarytodefinethe“satisficing”

andnormalizedtargetst_jtobeattachedtoeachindicator.These figuresrepresent“goodenough”achievementsforeachindicator consideredandhavetobeobtainedbyaskingforthesefiguresfrom thedecisionmakers.

TheaboveEqs.(3)–(4)showedanextendedgoalprogramming model(Romero,2001,2004)whichallowsthebuildingupofdif- ferentsolutions.Thus,representsacontrolparameter,andfor

=1,thesolutionobtainedisthemostefficientone,optimizing the“average”achievement.Ontheotherhand,for=0themost

“balanced” solutionwasdetermined. Otherintermediate val- uesbetweentheopeninterval(0,1)allowedtheachievementofa compromisebetweenthetwosolutionsabove(Diaz-Balteiroetal., 2011).

Inthisbinarygoalprogrammingmodel,thedecisionvariables X_ikarebinary.So,theyareequalto1iftheithforestmanagement alternativeischosen,and,intheothercase,theyareequalto0.The Eq.(8)–(9)ensuredthattheprevioushypothesisrelatedtochoos- ingoneforestmanagementalternativethroughoutthe100years wouldbefulfilled.Bysolvingthismodel,themostsustainableforest managementalternativewasdeterminedforeachclimatescenario.

Thelaststepwastoapplythisproceduretotherestoftheclimate scenarios.

Allthecomputations wereimplemented byresorting tothe softwareLINGO15(LindoSystems,2015).

Inordertoobtainsomeinputsfortheabovebinarygoalpro- grammingmodel,asurveyamongseveralstakeholdersinvolvedin thiscasestudywasundertaken.Wehavesent43questionnaires todifferentstakeholders(publicauthorities,forestmanagers,pri- vateforestowners,researchersanduniversitylecturers,ecologists, membersofdifferentforestryassociations,hunters,mycologists and professionals fromdiversewood-basedindustries),and we havereceived34valid questionnaires.Oneofthefirstgroupof questionsaskedaboutthepreferentialweightstobeattachedto thefiveindicators consideredin thisstudy. Theprocedurewas evolvedthrougha“pairwise”comparisonformatbyusingSaaty’s verbalscale(Saaty,1977).Thatis,toeachofthestakeholderswe posedthefollowingtypeofquestion:“betweentheithindicator and thejthindicator”which oneis themostimportant andby whichratio?Thisprocedurehasbeenwidelyusedinpractice(Diaz- Balteiroetal.,2009,2016c).Anotherquestionfocusedonthetarget valuesattachedbythedifferentstakeholderstoeachofthegoals regardingthepercentageofachievementontheidealvalueofeach goal,usingascalefrom10%to100%.Inshort,startingfromthebasis thatnotalltheindicatorscanreachanoptimalvalueforallforest managementalternativesandclimatechangescenarios,weasked thestakeholderswhichvalueswouldbeacceptabletothem.Both elements(preferentialweightsandtargetvalues)wereaggregated byusingthearithmeticalmeanofalltheresponsesgivenbythe stakeholders.Besides,incontrasttootherstudies,noothergroup

Table3

Preferentialweightsandtargetsforeachindicator.

Indicator Weight Target

TVH 0.246 64.8

CS 0.178 56.7

LSDTV 0.311 69.4

H 0.158 50.3

FOSH 0.106 41.2

Table4

Optimalforestmanagementalternativeforeachclimatescenario.

Climatescenario Goalprogrammingsolution

␭=1 ␭=0

0 NM NM

1 NM NM

2 NM NM

3 NM NM

4 NM NM

5 AM1 NM

decision-makingwasimplementedtoaggregatetheopinionsofthe differentstakeholders(Diaz-Balteiroetal.,2009).

3. Results

First,wecompiledthevaluesforeachindicatorundereachfor- estmanagement alternative,milestone,andclimatescenario.In Annex1,thenormalizedresultsforallclimatescenariosareshown.

Ontheotherhand,Table3givestheresultsoftwoelementsofthe goalprogrammingmodel:thepreferentialweightsattachedbythe stakeholderstoeachindicatorandthetargetsforeachgoal.

Theresults for thetwo solutions of theextended goal pro- gramming model, taking into account the preferences of the stakeholders,areshowninTable4.Sincethistabledoesnotreveal howthesolutionsaremodified over100yearsofsimulation, it hasbeendeemedappropriatetoshowtheresultsonrelaxingEqs.

(8)–(9).Thus,theconstraintofchoosingonlyoneforestmanage- mentalternative over the planninghorizon hasbeenremoved.

TheseresultsareshowninTable5.

Finally,asensitivityanalysiswasperformedbyincludinginthe goalprogrammingmodelascenarioinwhichalltheindicatorshad thesameweight,andotherscenariosassociatedwiththepreferen- tialweightsoffoursubgroupsofstakeholders:owners,ecologists, forestcompaniesandhunters.Nosubgroupassociatedwithdiffer- entforestryassociationshasbeenintroducedbecausetheresult wasthesameaswhenallthestakeholders’weightswereconsid- ered.TheresultsareshowninTable6.

4. Discussion

TheresultsshowninTables4and5demonstratethat,regard- lessoftheclimatescenarioanalysed,thereisagreatpredominance oftheNMsolutioncomparedtotherest.Thissituationaroseeither

ifa singleforestmanagement alternativeis chosenfor the100 yearsplanninghorizonorifitischangedevery20years.Whenthe forestmanagementchangesevery20years,acertainvariability wasindeedproducedbytheselectionoftheoptimalmanagement accordingtoeachclimatescenario,butonlyduringthefirstperiod (0–20years).Langneretal.(2016)alsoobservedthispreponder- anceoftheNMsolutionforthesamecasestudy.

Whatmightattract one’sattentionis that thecurrent man- agement alternative was not considered on any scenario. This circumstance hasalso occurredin otherstudies combiningthe provision of different ES under different more reduced plan- ning horizons, and using multi-criteria tools for their analysis (Cordingleyetal.,2016).Theresultcouldbeexplainedbythefact thatsociety,bymeansofagreementsandnorms,isadaptingthe provisionofEStotherelationshipswhichareestablishedbetween humansandnature(Janssenetal.,2007).Thus,mostofthestake- holdersofsuchanemblematicterritorynearalargecitylikeMadrid shouldalreadybepreparedforthechangesinNMimposedbythe NationalParkdeclaration.However,itseemsthattheforestowners and,inpart,hunters(when␭=0)havebeenlesscapableofassum- ingthenewmanagementrestrictionsfromatopdownperspective andtendtoperpetuatetraditionalmanagement(similarresultsare showninGómez-BaggethunandKelemen,2008).

Inanotherdirection,theinsensitivityobtainedwithregardtoan optimalmanagementalternativeinthedifferentclimatescenarios shouldnotsuggestthatthosescenariosareneutralwithrespectto thedifferentESinthestudycase.Thus,inPardosetal.(2016)itis seenhowsomeclimatescenarios(4and5)haveanegativeimpact ontheprovisionoftheESinthiscasestudy.

Although,likeinBrice ˜no-Elizondoetal.(2008),theobjectiveof thisstudywasnottoevaluatethedifferentmanagementalterna- tivesundereconomiccriteria,wedidcalculatethevalueofthenet presentvalueintheplanninghorizonforeachmanagementalter- nativeandclimatescenario.However,astheonlyincomeexpected wasinthefinalcuts,thecashflowsforthealternativeNMsolution willneverbepositive,unliketheothertwo.Forthatreason,the resultsofthateconomicindicatorhavenotbeenincluded.

TheresultsinTable4showlittlevariabilitybetweentheoptimal managementalternatives.Itmightbewonderedifthatinsensitivity isduetocertainaspectsofthemodel,suchasthestakeholders’pref- erenceswhichhavebeenentered.Thesensitivityanalysisshowed inTable6confirmthatthesolutionpreviouslyshowninTable4is verysimilartothatwhichwouldbeobtainedif,insteadoftakingthe starting-outhypothesis(indicatorweightsobtainedastheaverage oftheopinionsofalltheindicators),thesameweightforalltheindi- catorsweretobetaken.However,therewerenotabledifferences betweenthestarting-outhypothesisandthatwhichwouldhave beenobtainediftheopinionsofdifferentgroupsofstakeholders hadbeenconsideredseparately,especiallyforthecaseofthefor- estownersorwhen␭=0.Inaddition,andsimilartootherstudies, thesolutionsobtainedwiththeweightsassignedbyhunterswere differenttothoseofowners(Nordströmetal.,2009).Inshort,and likeinotherstudies,thevariationinthesolutionswhenthepref-

Table5

Optimalforestmanagementalternativeforeach20-yearmilestone.

Climatescenario Goalprogrammingsolution

␭=1 ␭=0

20 40 60 80 100 20 40 60 80 100

0 BAU NM NM NM NM AM1 NM NM NM NM

1 BAU NM NM NM NM BAU NM NM NM NM

2 BAU NM NM NM NM BAU NM NM NM NM

3 BAU NM NM NM NM NM BAU NM NM NM

4 BAU NM NM NM NM AM1 NM NM NM NM

5 AM1 NM NM NM NM AM1 NM NM NM NM

Table6

Sensitivityanalysiswithdifferentpreferentialweights.

Stakeholders^a ForestOwners Ecologists ForestCompanies Hunters

Climatescenario Goalprogrammingsolutions

␭=1 ␭=0 ␭=1 ␭=0 ␭=1 ␭=0 ␭=1 ␭=0 ␭=1 ␭=0

0 NM BAU BAU BAU NM NM NM BAU NM BAU

1 NM NM BAU BAU NM NM NM NM NM BAU

2 NM NM BAU BAU NM NM NM NM NM BAU

3 NM NM BAU BAU NM NM NM NM NM BAU

4 NM NM BAU BAU NM NM NM NM NM AM1

5 BAU NM BAU BAU AM1 NM BAU NM NM AM1

a(Equalweightsforeachindicator).

erentialweightsconferredtoeachESaremodified,changesthe optimalmanagementinitiallyestablished(Cordingleyetal.,2016).

Finally,itshouldberememberedthat,unlikeotherrelatedstudies (Langneretal.,2016),thepreferenceswereobtainedthroughreal surveysfromthestakeholders.

Thiscircumstance suggests,as a future lineof research,the possibility of including in the analysis group decision-making techniquesinordertoaggregatethestakeholders’preferencesin anotherway(González-PachónandRomero,2007;Diaz-Balteiro etal.,2009).Similarly,interactionwiththestakeholdersshouldnot bereducedtotheweightsandtargetsshowninTable3,butthey couldeasilybeaskedwhich␭valuestheypreferred(Diaz-Balteiro etal.,2016b).Indicatoraggregationcouldalsobeapproachedwith other goal programming models, such as lexicographical ones, (Aldeaetal.,2014),implyinganMCMDmodel,whichassumesthat therearenofinitetrade-offsamongthegoals(seeEq.(5))placed atdifferentprioritylevels(Diaz-Balteiroetal.,2013).Finally,the methodologyexpoundedinthisstudycouldbeincludedwithina decisionsupportsystem(Garcia-Gonzaloetal.,2013,2015).

Itmustbeemphasizedthatthemethodologyproposedpermits thecomputationofindicatorsassociatedwithanyES,sinceinsome worksithasbeenobservedthattherewasnocomplete,homoge- neouscoveroftheESwhentoolssupportingforestmanagement wereanalysed(Filyushkinaetal.,2016).Ontheotherhand,itis widelyrecognizedthatoneofthefirststepsinasustainabilityanal- ysisconsistsoftheselectionofsuitableindicators(OECD,2008).In ourcase study,theindicatorselectionseemstoshowamarked influenceontheresults,sincethreeofthefivechosenindicators revealedhighestvaluesfortheNMscenario.Nevertheless,inthis casestudytheselectionofindicatorshasbeenhighlyconditioned byitsdynamiccharacter,asitisnoteasytoselectindicatorswith consistentvaluesalong100years.

However,ifwe cangetthedata,anytypeofindicatorcould havebeenincorporatedintotheanalysis,suchas,forexample,pro- tectionagainstgravitationalhazards(Pardosetal.,2016)without havingtoalterthegoalprogrammingmodelproposed(Giménez etal.,2013).Likewise,ifreliablemodelsweretobeavailable,the numberofmanagementalternativescouldbeincreasedthrough theemploymentofothersilviculturalalternativesensuringasus- tainablemanagement(BravoandDiaz-Balteiro,2004).

Finally,itisimportanttonotethatnodatawereavailableabout thepreferencesofthestakeholdersoverthenext100years,sothat ahypothesisisimplicitlybeingputforwardthattheywillnotvary

throughoutthetime,whenithasbeendemonstratedthattheycan varyand,furthermore,inshorterlapsesoftime(Ambjörnssonetal., 2016).However,ifthisinformationweretobeavailable,itcould beeasilyintegratedintothemodeldescribedabove.

5. Conclusions

Thisstudyhaspresentedamulti-criteriamethodologywhich permitstheaggregationintotheanalysisofdifferentES,regardless oftheirnature,andaselectionofthebestmanagementalterna- tivesinaccordancewiththoseES,theirevolutioninthetime,the differentclimatescenariosandthepreferencesofthestakeholders involved.Also,themodelspossessedagreatflexibilitysinceitwas possibletochoosesolutionsaccordingtothedifferentvaluestaken bytheparameter.

Inrelationtoourcasestudyarea,theresultsshowapoorvari- abilityintheoptimalsolutionsobtainedintermsofchangesinthe climatescenario;thesolutionmostcommonlypreferredbeingthe onethatincorporatedNM.However,iftheweightsassignedtoeach indicatoraremodified,inaccordancewiththepreferencesofsome groupsofstakeholders,theoptimalmanagementalternativefor eachclimatescenariomaynotablyvary,especiallywhenconsider- ingthemostbalancedsolutionoftheextendedgoalprogramming model(␭=0).

Acknowledgements

ThisstudywasfinancedbytheEUFP7projectARANGE-289437.

The work of Luis Diaz-Balteiro was funded by the Ministry of EconomyandCompetitivenessofSpainunderprojectAGL2015- 68657-R.Weare especiallygratefultoJavierDonés, Directorof Valsaínforestsand toMiguel Cabrerafor facilitatingtheaccess todata.Theauthorsaregratefulforthehelpandassistancepro- videdbyMarConde(INIA-CIFOR)andProfessorsCarlosRomero andJacintoGonzález-Pachón(UPM).Wearehighlyappreciative ofthepatienceandcooperationofthestakeholderswhoanswered thesurvey.Wearegratefultotheanonymousreviewerswhichpro- videdusefulcommentsandsuggestionswhichimprovedaprevious versionofthismanuscript.Also,thanksaregiventoDianaBadder foreditingtheEnglish.

Annex1.

Normalizedresultsforclimatescenarios

ClimateScenario1 ClimateScenario2 ClimateScenario3 ClimateScenario4 ClimateScenario5 AlternativeManagement AlternativeManagement AlternativeManagement AlternativeManagement AlternativeManagement

Indicator Year AM1 BAU NM AM1 BAU NM AM1 BAU NM AM1 BAU NM AM1 BAU NM

TVH 20 0.881 0.987 0.000 0.908 1.000 0.000 0.872 0.972 0.000 0.872 0.967 0.000 0.885 0.934 0.000

40 0.923 0.890 0.000 0.973 0.864 0.000 0.920 0.858 0.000 0.928 0.880 0.000 0.920 0.815 0.000

60 0.318 0.292 0.000 0.322 0.323 0.000 0.315 0.305 0.000 0.299 0.291 0.000 0.299 0.282 0.000

80 0.245 0.176 0.000 0.242 0.174 0.000 0.241 0.176 0.000 0.216 0.154 0.000 0.199 0.150 0.000

100 0.394 0.231 0.000 0.351 0.201 0.000 0.368 0.222 0.000 0.301 0.172 0.000 0.301 0.177 0.000

CS 20 0.430 0.395 0.884 0.449 0.409 0.920 0.432 0.392 0.879 0.433 0.400 0.901 0.430 0.386 0.901

40 0.082 0.073 0.964 0.078 0.083 0.960 0.080 0.075 0.955 0.078 0.072 0.953 0.075 0.069 0.951

60 0.014 0.030 0.988 0.013 0.030 0.994 0.022 0.042 1.000 0.009 0.021 0.995 0.000 0.020 0.945

80 0.154 0.223 0.971 0.154 0.220 0.958 0.150 0.221 0.933 0.138 0.195 0.922 0.124 0.190 0.904

100 0.264 0.426 0.857 0.234 0.381 0.810 0.246 0.407 0.815 0.202 0.330 0.768 0.202 0.338 0.770

LSDTV 20 0.241 0.164 0.372 0.237 0.131 0.289 0.267 0.188 0.333 0.225 0.201 0.285 0.209 0.251 0.255

40 0.103 0.113 0.452 0.157 0.126 0.542 0.197 0.116 0.471 0.136 0.131 0.399 0.143 0.168 0.390

60 0.058 0.086 0.635 0.135 0.140 0.628 0.133 0.073 0.641 0.102 0.080 0.379 0.131 0.099 0.723

80 0.010 0.027 0.710 0.035 0.072 0.591 0.031 0.032 0.793 0.026 0.025 0.653 0.046 0.039 0.724

100 0.024 0.022 0.902 0.006 0.035 0.899 0.014 0.027 0.788 0.000 0.017 1.000 0.011 0.012 0.935

H 20 0.539 0.538 0.770 0.580 0.532 0.785 0.511 0.514 0.742 0.551 0.507 0.763 0.549 0.497 0.736

40 0.196 0.188 0.814 0.031 0.129 0.823 0.198 0.109 0.831 0.143 0.108 0.829 0.176 0.111 0.826

60 0.141 0.190 0.822 0.059 0.000 0.838 0.273 0.186 0.844 0.128 0.077 0.814 0.093 0.029 0.807

80 0.334 0.361 0.834 0.275 0.161 0.886 0.328 0.353 0.878 0.310 0.223 0.897 0.280 0.248 0.878

100 0.489 0.501 0.982 0.376 0.373 1.000 0.442 0.434 0.968 0.315 0.302 0.952 0.310 0.322 0.961

FOSH 20 1.000 0.803 0.357 0.990 0.803 0.326 0.990 0.888 0.357 0.990 0.803 0.068 1.000 0.888 0.098

40 1.000 0.981 0.000 1.000 0.981 0.072 1.000 0.981 0.072 1.000 0.981 0.072 1.000 0.981 0.072

60 1.000 1.000 0.028 1.000 1.000 0.070 1.000 1.000 0.028 1.000 1.000 0.118 1.000 1.000 0.120

80 1.000 0.871 0.637 1.000 0.354 0.637 1.000 0.871 0.637 1.000 0.871 0.726 1.000 0.943 0.800

100 1.000 0.348 0.700 1.000 0.483 0.811 1.000 0.348 0.729 1.000 0.483 0.780 1.000 0.483 0.751

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