Ecology and Evolution. 2019;00:1–12.  | 1www.ecolevol.org

Received:25April2019  |  Revised:3July2019  |  Accepted:9July2019DOI:10.1002/ece3.5506

O R I G I N A L R E S E A R C H

Future hydrological constraints of the Montseny brook newt (Calotriton arnoldi) under changing climate and vegetation cover

José L. J. Ledesma1,2  | Albert Montori3 | Vicent Altava‐Ortiz4 | Antonio Barrera‐Escoda5 | Jordi Cunillera5 | Anna Àvila6

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium,providedtheoriginalworkisproperlycited.©2019TheAuthors.Ecology and EvolutionpublishedbyJohnWiley&SonsLtd.

1CenterforAdvancedStudiesofBlanes,SpanishNationalResearchCouncil,Blanes,Spain2DepartmentofAquaticSciencesandAssessment,SwedishUniversityofAgriculturalSciences,Uppsala,Sweden3GRENP(GrupdeRecercadel'EscoladelaNaturadeParetsdelVallès),Life‐TritódelMontseny,DiputaciódeBarcelona,ParetsdelVallès,Spain4DepartmentofAppliedResearchandModelling,MeteorologicalServiceofCatalonia,Barcelona,Spain5DepartmentofClimatology,MeteorologicalServiceofCatalonia,Barcelona,Spain6CREAF,CampusdeBellaterra(UAB),CerdanyoladelVallès,Spain

CorrespondenceJoséL.J.Ledesma,CenterforAdvancedStudiesofBlanes,SpanishNationalResearchCouncil(CEAB‐CSIC),AccésalaCalaSantFrancesc14,17300Blanes,Spain.Email:jose.ledesma@ceab.csic.es

Funding informationSvenskaForskningsrådetFormas,Grant/AwardNumber:2015‐1518;SpanishGovernmentthroughaJuandelaCiervagrant,Grant/AwardNumber:FJCI‐2017‐32111;SpanishGovernmentthroughaMICINNproject,Grant/AwardNumber:CGL2017‐84687‐C2‐2‐R;LIFE‐TritóMontsenyprojectfromtheEuropeanCommission,Grant/AwardNumber:LIFE15NAT/ES/000757

AbstractTheMontsenybrooknewt (Calotriton arnoldi) is a criticallyendangeredamphibianspecieswhichinhabitsasmall20km2holmoakandbeechforestareainNESpain.Calotriton arnoldi strictly lives in runningwatersandmightbehighlyvulnerable tohydrological perturbations expected to occur under climate and vegetation coverchanges.Knowledgeaboutthepotentialresponseofthespecieshabitattoenviron‐mentalchangescanhelpassessingtheactionsneededforitsconservation.Basedonknowledgeofthespeciesandsupportedbyobservations,weproposeddailylowandhighstreamfloweventthresholdsfortheviabilityofC. arnoldi.Weusedtherainfall–runoffmodelPERSiSTtosimulatechanges inthefrequencyanddurationoftheseevents,whichwerepredictedundertwoclimateandfourvegetationcoverscenariosfornear‐future(2031–2050)andfar‐future(2081–2100)periodsinareferencecatch‐ment.Allfuturescenariosprojectedasignificantdecreaseinannualstreamflow(from21%toasmuchas67%)withrespecttothereferenceperiod.Thefrequencyandlengthof lowstreamfloweventswilldramatically increase. Incontrast, the riskofcatastrophicdriftlinkedtohighstreamfloweventswaspredictedtodecrease.Thepotentialchangeinvegetationtowardanexpansionofholmoakforestswillbemoreimportant thanclimatechanges indetermining threshold low flowconditions.Wethusdemonstratedthatconsiderationofpotentialchangesinvegetationandnotonlychangesinclimatevariablesisessentialinsimulatingfuturestreamflows.ThisstudyshowsthatfuturelowstreamflowconditionswillposeaseverethreatforthesurvivalofC. arnoldiandmayhelptakingmanagementactions,includinglimitingtheexpan‐sionofholmoakforest,foramelioratingthespecieshabitatandhelpitsconservation.

K E Y W O R D S

amphibians,Calotriton arnoldiconservation,catchmentmanagement,endangeredspecies,environmentalchange,Mediterraneanclimate,PERSiSTmodel,statisticaldownscaling

2  |     LEDESMA Et AL.

1  | INTRODUC TION

TheMontsenybrooknewt(Calotriton arnoldi;Figure1)wasfirstde‐scribedin2005asanewamphibiantaxonintheMontsenymoun‐tains, 40 km NE of Barcelona (Spain) (Carranza & Amat, 2005).Phylogeneticanalysisbasedon12Sand16SDNAsequencesindi‐catedthatthenewtpopulationsfromMontsenyhadevolvedsepa‐ratelyfromthePyreneanbrooknewt(Calotriton asper)populationsformorethanonemillionyearsandnowtheyconstitutetwoclearlydifferentiatedspecies(Valbuena‐Ureña,Amat,&Carranza,2013).Calotriton arnoldi is found inonlyseven locationsdistributed inasmall20‐km2 area, andcatchmentdivides appear to constitute anatural barrier that hampers interbreeding between populations(Carranza & Amat, 2005). Because of this lack of connectivityandthe lowpopulationdensities (anestimatedtotalofca.2,000adults), C. arnoldi is highly endangered and it is included in theIUCNRedListofThreatenedSpecies.TheDiputació de Barcelona wasawardedaLIFEproject(LIFE‐TritóMontseny)in2016devisedtoprotectC. arnoldi.Amongothers,projectobjectivesincludedin‐creasingthescientificknowledgeoftheecologicalrequirementsofthespeciesandassessingpossiblethreatsthatmayaffectit.Thereisstill littleknowledgeaboutthespeciesecologicalrequirementsanditshabitatpotentialresponsetoenvironmentalchanges.Thisinformation is of paramount importance to guide efforts for itsconservation.

Calotriton arnoldi is found in oligotrophic, cold, fast runningwaters draining typical holm oak (Quercus ilex) and beech (Fagus sylvatica)forestsinMontsenyanditappearstocompletelydependonaquaticresources,asneitherlarvaenoradultshavebeenfoundto inhabit theterrestrialenvironment. Its remarkablemorpholog‐ical traits, including reduced lung volume thatmay reducebuoy‐ancy,anditsthinskinprovidefurtherevidenceofitsadaptationtotheaquaticenvironment(Valbuena‐Ureñaetal.,2013).Therefore,drying out of headwater streamswill pose a serious risk for thespecies, either directly because of the unfavorable habitat con‐ditions, or indirectly due to the need of the individuals tomovetowetted refugeareas in streambanksor to thehyporheiczone.

Prey abundance in these refugemicrositesmaybe very low, po‐tentially impairing an appropriatedevelopmentof the individuals(Clergue‐Gazeau,1969,1971).Otherhydrologicalthreatsconcernjusttheopposite,therisksassociatedwithveryhighflows.Newtsandotherorganismsinhabitingrunningwatersarehighlyvulnera‐bletohighstreamwatervelocityandflooding (Walls,Barichivich,&Brown,2013).Theseeventshavebeenreportedtocausecata‐strophicdrift inC. asper (Colomer,Montori,García,&Fondevilla,2014;Montorietal.,2012).

Consequently,C. arnoldimight be highly vulnerable to hydro‐logical perturbations, either linked to low flows during dry peri‐odsor linkedtohighhydraulicdischargeassociatedwithextremeprecipitation events. Both situations may be more frequent in aworld undergoing climate change. Precipitation has not shown aclear trend in recent decades inNE Iberian Peninsula (González‐Hidalgo,López‐Bustins,Štepánek,Martín‐Vide,&Luisa,2009;delRío,Herrero, Fraile, & Penas, 2011), but air temperature is rising(Vicente‐Serranoetal.,2014)and,particularly,theMontsenymas‐sifhasexperiencedanincreaseofaround0.3°Cperdecadeinthesecondhalfofthe20thcentury(Peñuelas&Boada,2003).Atem‐peratureincreasecanleadtoenhancedevapotranspiration(Wang,Dickinson,&Liang,2012),whicheventuallywill result in reducedstreamflow.Besidesclimate, landscapecover typehasalsoan in‐fluenceonwaterresources.Severalcatchmentstudieshaveshownthatanexpansionofforestcoverleadstoadecreaseinwateryield(Bosch&Hewlett,1982;Gallart&Llorens,2004).InMontseny,theholmoak forest appears tobeexpandingwithaltitudeat theex‐penseofbeechforestsandheathlands(Peñuelas&Boada,2003),which may further contribute to reduce streamflow by increas‐ing evapotranspiration. Thus, both climate and vegetation coverchangesmay threatenC. arnoldi populations.However, so far nostudieshavequantifiedthemagnitude,duration,andfrequencyoffuturehydrologicalextremesintheregionthatcanaffectthespe‐ciessurvival.Hydrologicalconsequencesofclimateandvegetationchangesarebetterexploredatthecatchmentscaleusingrainfall–runoffmodels,which canbe calibratedagainstobserved stream‐flow(Lupon,Ledesma,&Bernal,2018;Onietal.,2016;Seibert&McDonnell,2010).

Here,weusedtherainfall–runoffmodelPERSiST(Precipitation,Evapotranspiration and Runoff Simulator for Solute Transport;Futteretal.,2014)toinvestigatehowchangesinhereinproposeddaily low and high hydrological thresholds might threat popula‐tions ofC. arnoldi. Streamflowwas simulated at two future inter‐vals(near‐future2031–2050andfar‐future2081–2100)undertwoclimate scenarios (RCP4.5 and RCP8.5) in a reference catchmentinMontseny.Thehydrologicaleffectsoffourplausiblevegetationcoverscenarioswerealsoconsidered:(i)vegetationaspresent, (ii)displacementofholmoakandbeechintoheathland,(iii)expansionof holm oak over the beech but similar heathland cover, and (iv)holmoakcoveringthewholecatchment.Therelativeroleofclimateversusvegetationcover in futurehydrologicalextremes threaten‐ingC. arnoldiwasalsoexamined.Finally,managementimplicationswerediscussed.

F I G U R E 1  AdultindividualofMontsenybrooknewt(Calotriton arnoldi)initsnaturalhabitat

     |  3LEDESMA Et AL.

2  | MATERIAL S AND METHODS

2.1 | Catchment selection and characterization

BecauseC. arnoldiiscataloguedascriticallyendangeredintheIUCNRedListofThreatenedSpecies,providingdetailsonspecific loca‐tionswherepopulationscanbefoundisnotadvisedinthecontextof the Life‐Tritó Montseny project. TheDiputació de Barcelona isdevelopingstrictplanstoensurethesurvivalofC. arnoldi,andthisinformation is considered sensitive.For this reason,hereweusedrealdata fromanactual catchmentatMontsenybut refer to it as“referencecatchment”(RC0hereafter),andavoidedprovidinggeo‐graphicaldetailsofitslocationandonwhetheritharborsC. arnoldi ornot.Becauseofthehighsimilarityinclimate,lithology,vegetation,andtopographyofRC0withotherneighboringcatchments (whichmightormightnotharborpopulationsofC. arnoldi),weconsiderthatanyresultsandconclusionsobtainedbystudyingthiscatchmentcanbeextrapolatedtoothersitesatMontsenywhereC. arnoldiinhabits.

RC0 isa2km2Mediterraneanforestheadwatercatchment lo‐catedintheMontsenyNaturalPark,NESpain(Figure2).Theclimatehere is subhumid Mediterranean, with mild winters, wet springsand autumns, and dry summers. Mean annual air temperature is11.5±0.5°C(average±SD)andtotalannualprecipitationaverages983±247mm, less of 3% falling as snow (griddeddata from theMeteorological Service of Catalonia for the period 1971–2005).Gentleslopesarepresentintheupperpartsofthecatchment,andsteeperslopesarefoundinmid‐anddownstreamvalleys(Figure2).

Higher altitudesaredominatedbyheathlandsprimarily composedofheather (Calluna vulgarisandgramineae)andcomprise32.7%ofthecatchment.Holmoak(Quercus ilex)forestdominatesthelowerhalf (52.2%)ofthecatchment,whereasbeech(Fagus sylvatica)for‐estoccupiesa200mstripeonthenorth‐facingslopebetweentheheathlandsandtheholmoakforestandcovers15.1%ofthecatch‐ment.Apartfromlow‐intensitygrazingintheheathland‐dominatedareas, there is currently no relevant human‐related activity in thecatchment.

2.2 | Streamflow and climate data

StreamflowwasmeasuredattheoutletofRC0betweenJanuary2011andDecember2015inastillingpondusinga120°V‐notchweirequippedwithalimnimetricscale.Apressuresensorsystem,consistingofasubmergedSchlumbergerMini‐DiversensorandaSchlumberger Baro‐Diver above thewater surface,was used torecordwater levels every 15min. Sensorwater‐level dataweretested againstmanualwater‐level readings from the limnimetricscale,showingastrongcorrelation (R2= .98;p < .0001; N=96).Subsequently, high‐frequencymeasurementswere converted tostreamflow through the specific stage‐flow rating curve for thelocation, and aggregated into daily data. A total of 1,618 dailystreamflowobservationswereavailable,that is,89%ofthedayswithin the 5‐year observed period. Streamflow will be denotedherebyareallynormalizedunitsofmm/day(ormm/yearwhereap‐propriate)foreasycomparisontoothersites.Theproposeddaily

F I G U R E 2  Referencestudycatchmentsiteincludingvegetationcovers,griddedclimatedatalocations(blacksquares),andstreamflowmeasurementlocation(invertedblacktriangle).ThelocationofthecatchmentwithinSpainisshownintheinset

4  |     LEDESMA Et AL.

lowandhighstreamfloweventthresholdswillbedenotedbyunitsofL/s,whicharemoreintuitiveforspecificeventsinanecologicalcontext.

Gridded climate data were computed at the MeteorologicalService of Catalonia (SMC). Instrumental measurements of dailytemperature(maximumandminimum)andprecipitationwereinter‐polatedinspaceusingalinearmultiregressionmethod(Altava‐Ortiz,Bladé,&LlasatBotija,2010).Theprocessconsideredaltitude, lati‐tude, longitude,andseadistancewithindifferentclimaticclustersfrommore than 1,250 rain gauges and 660 temperature stationsdistributedoverCataloniaforthe1970–2015period.Climaticclus‐tersweredefinedasa resultof themaximizationofmonthlydataspatialcorrelationintheentireregion.Residualvalues(i.e.,interpo‐latedminusobserved values)were geographically correctedusingakriginginterpolationmethod,whichtookintoaccountthespatialautocorrelationoftheinvolveddata.Theinterpolationwasdonefor1‐km2gridcells.TwoofthedatapointsfromthegridlaidwithintheRC0catchmentarea,oneintheupperpartofthecatchmentandthesecondoneinthevalleyneartheoutlet(Figure2).Dailyaveragesofthesetwopoints(forbothmeantemperatureandtotalprecipitation)werecalculated for the5‐yearperiod2011–2015andassumed toberepresentativeofthewholecatchment.Inanycase,therewereonlyminordifferencesbetweenthedataseriesfromthesetwogridpoints(datanotexplicitlyshown).

2.3 | Rainfall–runoff modeling: characterization and present‐day calibration

Therainfall–runoffmodelPERSiST(Futteretal.,2014)wasusedtosimulate streamflowat theoutletof theRC0catchment (invertedblacktriangleinFigure2).PERSiSTisaconceptual,semi‐distributedtoolforsimulatingwaterfluxes,includingstreamflow,throughphys‐icallybasedroutingschemesatdailytimesteps.Itinvolvesaflexibleframeworkbasedonanumberof interconnectedbucketswithinamosaicoflandscapeunits,whicharespecifiedbythemodelerbasedontheconceptualperceptionoftherunoffgenerationprocess.Themodelrequiresdailytimeseriesofairtemperatureandprecipitationasinputdata.Parameterdescriptionsandothermodeldetailscanbefoundelsewhere(Futteretal.,2014;Ledesma&Futter,2017;Luponetal.,2018).

TheapplicationofPERSiSTtoRC0consistedofthreelandscapeunits,whichcorrespondedtothecatchmentvegetationcoversde‐scribedabove(holmoakforest,beechforest,andheathland)inap‐propriateproportions,andthreesoilbucketsincludinga“quick”box

thatroutedthewaterinputsfromprecipitationtothesoil,ashallow“soil” layer for fast runoff responses, and adeeper “groundwater”layerforbaseflowregulation.Theaverageofthegriddedmeandailytemperatureandtotaldailyprecipitation fromthe twogridpointslocatedwithin the catchment generated at the SMCwas used asinputdata.Griddedclimatedataproductsareareliablealternativeto instrumentalmeasurements as inputs to rainfall–runoffmodels(Ledesma&Futter,2017).

Themodelwascalibratedforthe5‐yearperiodwithstreamflowobservations (2011–2015) using a combination ofmanual and au‐tomaticcalibrationtechniques.ManualcalibrationhasbeenprovenasarobustmethodforobtainingacceptablesimulationswithintheIntegratedCatchment(INCA)familyofmodels(Cremona,Vilbaste,Couture,Nõges,&Nõges,2017;Futteretal.,2014;Ledesma,Köhler,& Futter, 2012), of which PERSiST is the common hydrologicalmodel.An initialmanualcalibrationemployingsensiblevaluesandthresholdsbasedonexpertjudgementwasusedtoapproximatethedynamicsandmagnitudeofthesimulatedstreamflowtothoseoftheobservedstreamflow.Thisstepalsohelpedidentifyingcrediblepa‐rameterrangesforasubsequentautomatedcalibrationprocedure,whichconsistedonaMonteCarloschemethatoptimizedthevaluesoffourcalibrationcriteria (i.e.,performancemetrics): (i)theNash–Sutcliffe (NS)efficiency index (importanttofithighflows;Nash&Sutcliffe,1970), (ii) the log(NS) (importanttofit lowflows), (iii) therelative volume differences (RVD) of simulated versus observedstreamflow(importanttoensurethatcumulativemodeledandob‐servedstreamflowsareassimilaraspossible),and(iv)theratioofob‐servedtosimulatedvariance(VAR)instreamflowvalues(importantbecauseautomatedcalibrationstrategiestendtoproducemodeledseries thatare lessvariable thanobserveddata) (Onietal.,2016).TheMonteCarloprocesswascarriedoutin100iterationsof1,000runseach,andasinglebest‐performingparametersetwasselectedforfinalmanualtuning.

Modelparameter sensitivitywas assessedusing the100best‐performingparametersetsobtainedintheautomatedMonteCarlocalibration.Foreachparameter,theensembleofvaluesfromthe100parametersetswascomparedtoarectangulardistributionusingaBonferroni‐correctedKolmogorov–Smirnov (KS) test. A significantKSstatistic(adjustedp<.01)impliedthattheposteriordistributionwasnotrectangularandthusthatstreamflowsimulationsweresen‐sitivetothespecificparameter(Futteretal.,2014).

2.4 | Selection of critical low and high streamflow thresholds for Calotriton arnoldi

Calotriton arnoldihasbeenveryrecentlydiscovered,andatpresent,nodirectobservationsexistthatcanprovidecriticalthresholdvaluesfordeleteriousloworhighstreamflowsforthespecies.Furthermore,anexhaustivebibliographicexplorationdidnot return relevant re‐sultsconcerningthedefinitionofthresholdvaluesforanyamphibianspecies,whichmayhavehelpedtoconstrainandcompareourpro‐posedlowandhighflowthresholds.Thus,hereweproposethresh‐oldvaluesbasedonknowledgeofC. arnoldibiologicalcycle (larval

TA B L E 1  Summaryofvegetationcoverproportionscenarios

ScenarioHolm oak forest (%)

Beech forest (%)

Heathland (%)

(i)Vegetationaspresent 52.2 15.1 32.7

(ii)Beechincreased 59.8 23.9 16.4

(iii)Heathlandprotected 59.8 7.6 32.7

(iv)Holmoakwhole 100 0 0

     |  5LEDESMA Et AL.

andadultphases),feedinghabitsandsurvivalstrategy,andobserva‐tionoftheRC0hydrologicalbehavior.AdailylowflowthresholdofQlow=0.33L/swasselected.Atthisstreamflowvalue,downstreamreachesnear theRC0 catchmentoutlet showno superficialwaterflowbecausetheyhavelowslopesandarecoveredbysand,gravels,andpebbles(personalobservation).Therefore,Qlowhasbeenconsid‐eredasanadequateworkingthresholdfordryconditions.

NoobservationormodelresultsexistforC. arnolditodeterminetheriskofcatastrophicdriftforhighflowthresholds.Studiesinves‐tigatingtherelatedC. asperspecieshaveshownthatintenseprecip‐itationevents (>50mm/day)causecatastrophicdriftof larvaeandadults, leading to reducedannualpopulationnumbers (Montorietal.,2012).IntheRC0andothercatchmentsatMontseny,highflowpeaksstronglydependontheantecedentconditionsofhumidityinthecatchmentsoils(Àvila,Piñol,Rodà,&Neal,1992).Precipitationevents of 50 mm/day will result in very different streamflow re‐sponses depending on whether they occur in the dry (April toSeptember)orwet season (October toMarch).Flow increase rateis thus the relevant variable to assess risk in thepresent case.Todepict this,we generated the variable “flowdifference in consec‐utivedays”(Qdiff),whichwasdefinedasthedifferenceinobservedstreamflowbetweentwoconsecutivedays.Thresholdvaluesweredefinedasthe99thpercentileoftheQdiffvaluesforthewholeyear(Qdiff‐all=97L/s;N=1,612)andforthedryseason(Qdiff‐dry=42L/s;N=776).AlldaysaboveQdiff‐all(orQdiff‐dry)wereconsideredassin‐gle‐dayeventsindependentlyonwhethertheywerefollowedbyan‐otherdayaboveQdiff‐all(orQdiff‐dry)ornot.

Besides optimizing the performance metrics described above(“hard calibration” criteria), the PERSiST calibration also aimed atsimulating a number of days belowQlow as similar as possible totheobservednumberofdaysbelowQlow(“softcalibration”criteria)(Luponetal.,2018).Analogously,thecalibrationaimedatsimulatinganumberofdaysaboveQdiff‐allandQdiff‐dryassimilaraspossibletotheobservednumberofdaysaboveQdiff‐allandQdiff‐dry.

2.5 | Future climate and hydrological projections

Two emission scenarios from the Representative ConcentrationPathways(RCP)wereconsidered:theRCP4.5(moderatecarbondi‐oxide [CO2]emissionscenario)andtheRCP8.5 (extreme‐highCO2 emission scenario), to obtain near‐future (2031–2050) and far‐fu‐ture(2081–2100)dailytimeseriesoftemperatureandprecipitationat thesamegridpointsused forgenerating inputs for streamflowcalibration.HistoricalCO2concentrationvalueswereusedtoobtainthe reference period 1981–2000. Reference and future projecteddatawerebasedonastatisticaldownscalingtechniqueperformedattheSMC(Altava‐Ortizetal.,2010)anddrivenbyaglobalsetofdailysimulationsfromtheMax‐PlanckInstituteEarthSystemModel(MPI‐ESM; Giorgetta et al., 2013), whichwere available from theWorld Data Centre for Climate Portal CERA (https://cera‐www.dkrz.de).DailyprecipitationandtemperaturedatafromthetwogridpointsusedforcalibrationwereagainaveragedpriorusingthemasinputsinPERSiSTtosimulatefuturestreamflows.

Fourlandscapescenarioswerealsoconsideredtobecombinedwiththeclimatescenariosinordertoaccountforpotentialchangesin vegetation cover proportions linked to the changes in climate.BasedontheresultspresentedbyPeñuelasandBoada(2003),thefollowingscenarioswereconsidered:(i)Vegetationproportionsaremaintained as present; (ii) holmoak forest takes over 50%of thebeech forestareaand, in its turn,beech forest takesover50%oftheheathlandarea;(iii)holmoakforesttakesover50%ofthebeechforestareaandtheheathlandareadoesnotchange;and (iv)holmoakforesttakesoverthewholecatchmentarea(Table1).Thesinglebest‐performing parameter set obtained during model calibrationwasusedtosimulatefuturestreamflowsforthe16futurescenarios(2periods×2climates×4vegetationcovers)andforthereferencescenario.

2.6 | Statistical analyses

Analysisofvariance (ANOVA)wasappliedtoestimatetherelativecontributionofperiod(nearfutureandfarfuture),climatescenario(RCP4.5andRCP8.5),andvegetationcoverscenario(Table1)tothetotalvariationinthenumberofdayswithdailysimulatedstreamflownotreaching/exceedingthespecifiedthresholds:Qlow=0.33L/sforlowflows,andQdiff‐all=97L/s(wholeyear)andQdiff‐dry=42L/s(dryseason)forflowincreaseeventsinconsecutivedays.Thiswasdonetodisentangletherelativeimportanceofclimateversusvegetationcover in future hydrological extremes thatmight affectC. arnoldi. ThefractionoftotalvariationascribedtoeachANOVAcomponent(i.e.,period,climatescenario,orvegetationcoverscenario)wasequaltothesumofsquaresforthatcomponentdividedbythetotalsumofsquaresfromtheANOVA,followingFutteretal.(2011)andLedesmaetal.(2013).

In order to investigate how the resultswould have changed ifa different value ofQlow would have been selected, we obtainedthenumberofsimulateddaysbelowQlow foreachscenario (refer‐ence+16futurescenarios)usingfiveothervaluesofQlow(namely2,1,0.5,0.25,and0.2L/s).WeconsideredthattheconclusionsofthestudywouldholdifthesimulateddaysbelowQlowestimatedforthereferenceandthefuturescenariosusingdifferentQlowthresh‐oldswere linearly correlatedwith the analogous values using theselectedQlowof0.33L/s.

3  | RESULTS

3.1 | Streamflow model calibration and sensitivity analysis

Fortheobservationperiod(2011–2015),streamflowwasdominatedby low base flow. RC0 showed fast responses to some (but notall)precipitationevents,which in the large stormcasesgeneratedasmuchas52mm/dayofstreamflow.Across‐correlationanalysisshowed that the time lag thatmaximized thecorrelationbetweendailyprecipitationanddailyobservedstreamflowwas1day(FigureS1).PERSiSTcaptured remarkablywell thispattern includingboth

6  |     LEDESMA Et AL.

the dynamics and themagnitude of streamflow and even the ex‐tremelyhigh flowevents, except for an intermediateevent at theendof2015(Figure3).Theratioofobservedtosimulatedvariancewas0.97, indicating justaslightlyhighervariance in thesimulatedflows.Cumulativeobservedstreamflowwasonlyslightlyunderes‐timated by the cumulative simulated streamflow (3%). TheNS ef‐ficiency index was 0.89, indicating a very good fit of high flows.Finally,thelog(NS)was0.89,indicatingaverygoodfitoflowflows.

PERSiST was also able to simulate a very similar amount ofdaysbelowandabovetheproposeddailylowandhighstreamflowthresholdvaluescomparedwiththeobservedamountofdays.Thenumberofdays inwhichstreamflowwasbelowQlow intheobser‐vationrecord(N=1,618)was44.Forthesamerecord,thenumberofdailysimulatedstreamflowsbelowthisthresholdwas42.Therewere16observedsingle‐dayeventsaboveQdiff‐all,whereasPERSiSTsimulated 14 single‐day events. For the Qdiff‐dry threshold, therewereeightobservedversussixsimulatedsingle‐dayevents.

Simulatedstreamflowwassensitivetofouroutofthe28modelparameterstestedusingtheBonferroni‐correctedKStest(TableS1).Three of the sensitive parameters related to the residence time of

water in the “soil” and “groundwater” layersof theholmoak forestandheathlandvegetationcovers,withlowervaluesproducingbettermodelperformances.Theothersensitiveparameterwastheso‐called“evapotranspirationadjustment”intheholmoakforest,whichisusedtolimitevapotranspirationunderdryconditions.Highervaluesoftheevapotranspirationadjustmentgeneratedbettermodelperformances.

3.2 | Projected future climate

AccordingtobothRCP4.5andRCP8.5scenarios,meanannualtem‐perature in the near future (2031–2050) is projected to increase1.3°Cwithrespecttothemeanannualtemperature(11.6°C)ofthereferenceperiod (1981–2000). Inthefarfuture (2081–2100), tem‐peraturewillincrease1.8°CaccordingtotheRCP4.5scenarioandasmuchas3.9°CaccordingtotheRCP8.5scenario.Inthenearfuture,the increasewill bemoremoderate inwinter and springmonths,whereasitwillbemorepronouncedinsummerand,especially,au‐tumn(Figure4a).Theincreaseintemperatureinthefarfuturewillbemore evenly distributed throughout the year, although slightlylesspronouncedinwinterandslightlymorepronouncedinsummer.

F I G U R E 3  Dailyobservedandsimulatedstreamflow(lowerpanel)andcorrespondingprecipitation(upperpanel)timeseriesinthereferencecatchment

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Averageannualprecipitationwill decreasewith respect to thereferenceperiodvalue(941mm/year)inallfuturescenarios.Inthenear future, RCP4.5 projected an 11% decrease,whereas RCP8.5projecteda16%decrease(downto805mm/year).Similarly,RCP4.5projected a 17%decrease in the far future,whereasRCP8.5 pro‐jecteda28%decreaseforthisperiod(i.e.,downto714mm/year).Inallcases,thedecreasewillberelativelymorepronouncedinthealready dry summermonths,whereaswinter precipitationwill berelativelysimilartoreferenceperiodprecipitation(Figure4b).

3.3 | Projected future streamflow

Dailyextremehighflowevents(Q>30mm/day)atRC0willbeasfrequent inthenearfutureas inthereferenceperiod (ca.0.5%ofthetime)accordingtoallscenarios(Figure5a).Onthecontrary,allfar‐futurescenariosprojectedlessfrequencyoftheseevents.Daily

streamfloweventsbetween10and30mm/daywillbelessfrequentin both the near‐ and, especially, the far future. Intermediate andbase flowconditionswill yield lower streamwaterdischarge thanreferenceconditionsaccordingtoallscenarios,thatis,foranygivenexceedancetimeabove10%,streamflowmagnitudewillbelowerinallcases(Figure5b).

Consequently, all scenarios projected a significant overall de‐creaseinaverageannualstreamflowwithrespecttothereferenceperiod (346 mm/year), from a 21% decrease (near‐future RCP4.5withvegetationaspresent) toasmuchasa67%decrease (far‐fu‐tureRCP8.5withwholeholmoakforestcover;Table2).Inparallel,thenumberofdayswithsimulatedstreamflowlowerthanQlow will dramaticallyincreasewithrespecttothereferenceperiod(Table2;Figure5b).MostscenariossimulatedstreamflowbelowQlowduring10%–20%of the time,with amaximumof 30% for the far‐futureRCP8.5 scenario with whole holm oak forest cover, whereas forthe reference period the same estimatewas 2.6%. Far‐future andRCP8.5scenarioswilltendtobedrierthantheircorrespondingnear‐futureandRCP4.5scenarios, respectively.Wholeholmoak forestcoverscenariosprojectedremarkablydryconditions,whereassce‐narioswithanincreasedbeechforestcoveroraprotectedheathlandcoverprojectedsimilarconditions,whichwereslightlydrierthanthecorrespondingvegetationaspresentscenarios.

In the reference period, most events with consecutive daysbelowQlow were shorter than a week and only two events werelongerthanamonth(Figure6).However,thenumberandlengthoftheseeventswilldramaticallyincreaseinthefuture,withallscenar‐iospredictingatleastoneeventwithconsecutivedaysbelowQlow longer than 3months (Table 2; Figure 6). The number and lengthof these events was especially magnified during the whole holmoakforestcoverscenarios.Inthenear‐futurescenarios,daysbelowQlowwillbeapproximatelyhomogeneouslydistributedoverwinter,summer,andautumnmonths,withlowerproportionsduringspring(TableS2).Inthefarfuture,asinthereferenceperiod,daysbelowQlowwillbemorepredominantduringsummer.

Duringthereferenceperiod,maximumQdiffwasca.35mmandthe single‐day events larger thanQdiff‐all occurredmainly throughNovember toMarch, typically thewettest and coldestmonths inMontseny. Inall futurescenarios,especiallytheRCP8.5scenarios,thenumberofsingle‐dayeventsaboveQdiff‐allwilldecrease(Table2)and concentrate inMarch andDecember.Anumberof single‐dayeventsaboveQdiff‐drywereslightlylessthanhalfofthoseaboveQdiff‐all inallcases.Furthermore,Qdiffhadnoclearrelationshipwiththeamountofprecipitationduringthesingle‐dayeventforanyof thescenariosortheobservations,thatis,thesameprecipitationamount(includinghighrainfallevents)generatedawiderangeofQdiffvalues,especiallyduringthedryseason(Figure7).

TheANOVA(TableS3)revealedthatvegetationcoverscenariosexplainedmoreofthevarianceinthenumberofdayswithsimulatedstreamflowbelowQlow (50%)thantheclimatescenarios (11%)andtheperiodconsidered(21%)(Table3).Incontrast,climatescenariosexplainedmostofthevarianceinthenumberofsimulatedsingle‐dayevents aboveQdiff‐all (47%) andQdiff‐dry (53%),whereas vegetation

F I G U R E 4  Monthlyaveragetemperature(a)andprecipitation(b)forthereferenceperiod(1981–2000)andforRepresentativeConcentrationPathway(RCP)scenarios4.5and8.5fornear‐(2031–2050)andfarfuture(2081–2100).Errorbarsin(a)arestandarddeviations.Dottedlightlinesin(b)are90th(above)and10th(below)percentiles

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8  |     LEDESMA Et AL.

coverscenariosexplainedamoderateamountofvariance.Thepe‐riod considered explained 17% of the variance in the number ofsimulatedsingle‐dayeventsaboveQdiff‐allandanegligiblevarianceof above simulatedQdiff‐dry single‐day events (Table 3). SimulateddaysbelowQlowusingarangeofQlowthresholdswerelinearlycor‐relatedwiththesimulateddaysbelowQlowfromtheselectedQlowof0.33L/s,asobtainedfromthereferenceandthe16futurescenarios(FigureS2).

4  | DISCUSSION

4.1 | Overall streamflow model performance and catchment hydrological behavior

PERSiST reproduced remarkably well the flow magnitude anddynamicsatRC0, aswell as thenumberofdaysbelowQlow andnumberofsingle‐dayeventsaboveQdiff‐allandQdiff‐dry.Combining

F I G U R E 5  Flowdurationcurvesfor0%–10%(a)and10%–100%(b)timeexceedanceforeachofthe16scenariosconsidered(2futureperiods×2climatescenarios×4vegetationcoverscenarios)andforthereferenceperiod.Notethelogarithmicscaleinthex‐axisin(a)andthey‐axisin(b).DottedlinecorrespondstothedailylowflowthresholdQlow=0.33L/s

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1981-20002031-2050 RCP4.5 i2031-2050 RCP8.5 i2031-2050 RCP4.5 ii2031-2050 RCP8.5 ii2031-2050 RCP4.5 iii2031-2050 RCP8.5 iii2031-2050 RCP4.5 iv2031-2050 RCP8.5 iv2081-2100 RCP4.5 i2081-2100 RCP8.5 i2081-2100 RCP4.5 ii2081-2100 RCP8.5 ii2081-2100 RCP4.5 iii2081-2100 RCP8.5 iii2081-2100 RCP4.5 iv2081-2100 RCP8.5 iv

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(a) (b)

TA B L E 2  Numberofdayswithdailysimulatedstreamflownotreaching/exceedingthespecifiedthresholds:Qlow=0.33L/sforlowflows,andQdiff‐all=97L/s(wholeyear)andQdiff‐dry=42L/s(dryseason)forflowincreaseeventsinconsecutivedays

Period Climate scenario Vegetation scenario Qlow (days) Qlow (events) Qdiff‐all (days) Qdiff‐dry (days) Q (mm/year)

1981–2000 Reference i 187 11 74 33 346

2031–2050 RCP4.5 i 758 31 63 25 272

2031–2050 RCP8.5 i 722 24 49 22 238

2031–2050 RCP4.5 ii 853 32 52 20 240

2031–2050 RCP8.5 ii 802 23 46 17 208

2031–2050 RCP4.5 iii 760 33 61 25 270

2031–2050 RCP8.5 iii 721 24 50 20 236

2031–2050 RCP4.5 iv 1,527 58 49 17 196

2031–2050 RCP8.5 iv 1,376 39 40 15 166

2081–2100 RCP4.5 i 854 31 54 26 238

2081–2100 RCP8.5 i 1,369 62 45 18 165

2081–2100 RCP4.5 ii 936 32 48 22 208

2081–2100 RCP8.5 ii 1,533 63 36 15 143

2081–2100 RCP4.5 iii 852 30 54 26 235

2081–2100 RCP8.5 iii 1,369 62 43 17 164

2081–2100 RCP4.5 iv 1,393 48 46 27 166

2081–2100 RCP8.5 iv 2,152 68 34 14 114

Note: Dataareforeachofthe16scenariosconsidered(2futureperiods×2climatescenarios×4vegetationcoverscenarios)andforthereferenceperiod.NumberofeventswithconsecutivedaysbelowQlowandannualaveragestreamflowarealsoshown.AllsimulateddaysaboveQdiff‐all or Qdiff‐drywereconsideredindependent(i.e.,single‐dayevents),andthus,thenumberofdaysreportedcorrespondstothenumberofevents.

     |  9LEDESMA Et AL.

specific streamflow characteristics (here the specific thresholdsQlow,Qdiff‐all,andQdiff‐dry)withcommonlyusedmodelperformancemetrics(hereNS,log(NS),RVD,andVAR)duringcalibrationproce‐duresisrecommendedtoachieveaccuratestreamflowsimulationsforpracticalapplications (Pool,Vis,Knight,&Seibert,2017;Vis,Knight,Pool,Wolfe,&Seibert,2015).Anaccuratemodelsimula‐tionwas indeed achieved in the present application, suggestingthatarobustparameterizationwasusedforfuturesimulationsofstreamflow.

ObservedandsimulatedpatternsofstreamflowatRC0indicatethat the catchment is very flashy, that is runoff generation is no‐tablyfast,andthesensitivityanalysissuggestedthatstreamflowismostly sensitive to residence timeof soilwater and groundwater.Thissuggeststhatshortwaterresidencetimesrelatedtotopogra‐phy and seasonality drive runoff generation. Seasonality refers tothedifferenthydrologicalbehaviorduringwetversusdryconditionsas,forexample,highflowpeaksstronglydependontheantecedentsoilwetness(Àvilaetal.,1992).Here,streamflowwasalsosensitivetotheevapotranspirationadjustmentparameterusedtolimitevapo‐transpirationunderdryconditions,whichindicatesthatduringtheseperiodsthereislowhydrologicalconnectivitybetweensoilsandthestream and precipitation inputs contribute to increase catchmentwaterstorageratherthantostreamflowgenerationorevapotrans‐piration.Consistently,precipitationwasonlyweaklyrelatedtoQdiff duringtheobservationperiodandthisbehaviorwaswellcapturedbythefuturesimulations,especiallyduringthedryseason(Figure7).

4.2 | Vulnerability of Calotriton arnoldi to future low streamflows

Establishingcritical streamflowthresholds foramphibianspecies isdifficult,especiallyforendangeredspeciesbecauseobservationsanddataarescare,butweconsiderourproposedQlowvalueanadequateworkingthreshold.Nevertheless,smallchangesintheQlowproposed

herewouldhaveyieldedthesameresultsinrelativeterms(FigureS2),andtherefore,theconclusionsdiscussedbelowapplytoarangeoflowstreamflowvaluesthatcouldhavebeenselectedasthresholds.

Oursimulations indicate thatprojectedclimateandvegetationcoverchangeswillseverelythreatenC. arnoldisurvivalbyincreasingthefrequencyanddurationofverylowstreamflows,whichmayleadtoperiodsofprolongedlackofsurficialflow.PreviousstudieswithC. asperreportedthatclimatechangeseverelyreducedthespeciesdistribution range because of the reduced possibility of dispersal,leading to a loss of genetic diversity (de Pous, Montori, Amat, &Sanuy,2016).OurresultssuggestthatC. arnoldiwillneedtomigrateinsearchofsuitablehabitatsduringperiodsofdroughtandthelackofsuitablesitesmayleadtoimportantlossesbothinpopulationindi‐vidualsandgeneticdiversity.Furthermore,incaseofdwindlingpop‐ulationsofC. arnoldi,lowgeneticdiversityexistingintheremainingpopulations constitutes an additional bottleneck thatmay lead toextinction(Valbuena‐Ureñaetal.,2013).

Criticallowstreamflowprojectionswerenotverydifferentcon‐sideringthemoderate(RCP4.5)ortheextreme(RCP8.5)scenarios,whereasvegetationcoverhadanimportantroleindeterminingthelowstreamflowhydrologyofthecatchment.Scenariosinwhichtheopenheathlandsaremaintainedirrespectiveofthetreespeciescov‐eringtherestofthecatchmentpresentconsiderablylowernumberofdaysbelowQlow thanscenarioswhereholmoakforestcoveredthewholecatchment.Holmoakforestshavewaterdemandsthataresignificantlylargerthanthoseofopenheathlandsandcantranspireevenduringdryperiods(Ladekarl,Rasmussen,Christensen,Jensen,&Hansen,2005;Paçoetal.,2009),andsoevapotranspirationwillmakeupasignificantlylargerproportionofthewaterbalanceunderwholeholmoakforestcoverscenarios.OursimulationsthussuggestthatthepotentialchangeinvegetationcovertowardanexpansionofholmoakforestsattheexpenseofbeechforestsandheathlandswillbemoreimportantthanclimatechangeitselfindeterminingcriticallowstreamflowconditionsintheMontsenymountains.

F I G U R E 6  DurationandnumberofeventswithconsecutivedaysbelowthedailylowflowthresholdQlow=0.33L/sforeachofthe16scenariosconsidered(2futureperiods×2climatescenarios×4vegetationcoverscenarios)andforthereferenceperiod

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10  |     LEDESMA Et AL.

4.3 | Vulnerability of Calotriton arnoldi to future high streamflows

Riskofcatastrophicdrift inC. asperwas related toprecipitationeventslargerthan50mm/day(Colomeretal.,2014;Montorietal.,2012). In these studies, no indication of associated streamflowswas provided as the investigated catchments were ungauged.AtRC0, the threshold basedon actual streamflow increase ratebetterdescribes thepotential impactofhigh flowsonC. arnoldi or other species, as the relationship between precipitation andstreamflowisquiteloose(Figure7).Duringthedryseason(Aprilto September),Qdiff valueswere noticeably lower than those inthewetseasonandsingle‐dayeventsaboveQdiff‐dryconcentratedinAprilandMay,bothinthereferenceandfuturescenarios(datanot explicitly shown). The occurrence of these flashy events

duringspringmayhavedeleteriousconsequencesbecausethisisasensitiveperiodintheC. arnoldilifecycle(reproductionoccursat this time), anddrift risk is larger for eggs and larvae than foradults. Immature individuals of the relatedC. asper species caninhabit the terrestrial environment, and this represents a refugethatmighthelprecoveringpopulationsizesafterextremestream‐flowincreaseevents(Colomeretal.,2014;Montorietal.,2012).Bycontrast, immatureindividualsofC. arnoldidonotinhabittheterrestrial surroundings and remain in the aquatic environmentuntil sexualmaturity,whichmakes this speciesmore vulnerabletofloodsandhighwatervelocities.Nevertheless,oursimulationsshowthatthenumberofsingle‐dayeventsaboveQdiff‐dry will de‐crease in thefutureandsothepotential riskofhighstreamflowevents forC. arnoldi during its sensitive life cycleperiodwill bereduced,aswellasthepotentialriskofcatastrophicdriftoverall.

In contrast to the importance of vegetation cover scenarios toexplain thenumberofdaysbelowQlow, climate scenariosappear toexplainmostofthevarianceinthenumberofsingle‐dayeventsaboveQdiff‐alland,especially,aboveQdiff‐dry.RCP8.5scenariosareextreme‐highCO2emissionscenarios inwhichtemperaturesarepredictedtobehigherandlargerainfalleventsmoresporadicthanthecorrespond‐ingRCP4.5scenarios.Theseconditionswilllimittheopportunitiesforgenerationofhighstreamflowincreaseeventsduetothecombinationofincreasedevapotranspirationandlowerprobabilityoflargerainfalleventsoccurringunderpre‐existentwetconditions.

4.4 | Implications for conservation and management

AsdiscussedbyO'Driscolletal.(2018),climateimpactassessmentstudies,suchastheonepresentedhere,canbeinterpretedinquali‐tativetermsbasedonalternativescenariosof internallyconsistentnarratives,or“storylines,”whicharebasedonarestrictedsetofpos‐siblesourcesofuncertaintyandthusarerepresentationsofasub‐setofallpossiblefutures(Cremonaetal.,2017;vanVuuren,Vries,Beusen,&Heuberger, 2008). In our case, combining an educatedselection of critical streamflow thresholds based on hydrologicalobservationsandC. arnoldiecologicalrequirements,withmoderate

F I G U R E 7  Scatterplotsof“flowdifferenceinconsecutivedays”(Qdiff),versusdailyprecipitationfor(a)thewholeyearand(b)thedryseason(ApriltoSeptember)forfutureRepresentativeConcentrationPathway(RCP)scenarios4.5and8.5andforthereferenceperiod.Dashedlinescorrespondtothethresholdvaluesdefinedasthe99thpercentileoftheQdiffvaluesfor(a)thewholeyear(Qdiff‐all=97L/s),andfor(b)thedryseason(Qdiff‐dry=42L/s)usingtheobserveddata,whicharealsoshown

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TA B L E 3  Relativecontributionofperiod(near‐andfar‐future),climatescenario(RCP4.5andRCP8.5),andvegetationcoverscenario(Table1)tothetotalvariationinthenumberofdayswithdailysimulatedstreamflownotreaching/exceedingthespecifiedthresholds:Qlow=0.33L/sforlowflows,andQdiff‐all=97L/s(wholeyear)andQdiff‐dry=42L/s(dryseason)forflowincreaseeventsinconsecutivedays

Component Qlow (%) Qdiff‐all (%) Qdiff‐dry (%)

Period 21 17 0.3

Climatescenario 11 47 53

Vegetationcoverscenario

50 33 22

Model 82 97 76

Residual 18 3 24

     |  11LEDESMA Et AL.

(e.g., RCP4.5) and extreme (RCP8.5) climate scenarios, and fourplausiblevegetationcover scenariosensured that awide rangeofpossiblefutureswascovered.Theserelevantstorylinesonpossiblefuturesofferastartingpointfordecision‐makingregardingC. arnoldi managementinthecontextoftheLIFE‐TritóMontsenyprojectthatcanguidefutureactionsandresearch.

Our study shows that predicted future low streamflow condi‐tionswillposea severe threat for thesurvivalofexistingC. arnoldi populations,ortheviabilityofthoseplannedtobereintroduced.Thisthreadwillbemostlydrivenbythehydrologicalconsequencesoftheexpectedexpansionofholmoakforests.Incontrast,theriskofcat‐astrophicdriftduetohighstreamfloweventswillbereduced,espe‐ciallyunderhighRCPemissionscenarios.Therefore,werecommendconsiderationoffuturepotentialchangesinlandscape(e.g.,changesinvegetationcover)andnotonlypredictedchangesinclimatevariablesinsimulationsoffuturestreamflowsusingrainfall‐runoffmodels.

Thisworkmayhelptotakemanagementactions(e.g., limittheexpansion of the holm oak forest) for ameliorating the C. arnoldi newthabitat.Thisisrelevantbecauseveryfewcasestudieshaveuptonowexaminedtheeffectsofhydrologicalregimesonamphibianpopulations(Cayuela,Besnard,Béchet,Devictor,&Olivier,2012)ortheefficacyoflandscapemanagementpracticesdevisedtohelptheconservationofsuchpopulations(Hartel,Băncilă,&Cogălniceanu,2011; Shoo et al., 2011).We used a holistic approach that bringstogether different scientific disciplines (hydrologicalmodeling, cli‐matology,biology,andecology) forprovidingabasis forC. arnoldi managementandconservationstrategies.Thistypeofapproachisencouragedforformulatingscience‐based,sustainablemanagementofbothamphibianspeciesandecosystems.

ACKNOWLEDG MENTS

JLJLwas funded by the Swedish Research Council Formas (grant2015‐1518)andtheSpanishGovernmentthroughaJuandelaCiervagrant(FJCI‐2017‐32111).AAacknowledgesfundingfromtheSpanishGovernment through aMICINN project (CGL2017‐84687‐C2‐2‐R)andtheLIFE‐TritóMontsenyprojectfromtheEuropeanCommission(LIFE15NAT/ES/000757).WewouldliketothankMartynFutterandJanSeibertforvaluablediscussionsontherainfall‐runoffmodelingandIreneFraileforfieldworkatMontseny.

CONFLIC T OF INTERE S T

Nonedeclared.

AUTHORS' CONTRIBUTIONS

JLJL andAA conceived the idea, designed themethodology, pro‐posedthestructure,andwrotethepaper.JLJLperformedthehydro‐logicalmodelingandanalyzedthedata.AMprovidedsupportontheCalotriton arnoldiecologyandcontributedtothewriting.VA,AB,andJCcarriedouttheclimatemodeling.Allauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication.

DATA AVAIL ABILIT Y S TATEMENT

Alldataassociatedwiththepapercanbeaccessedthroughfigshareat https://figshare.com/articles/Ledesma_et_al_2019_ECE_Dataset_xlsx/8869598(https://doi.org/10.6084/m9.figshare.8869598).

ORCID

José L. J. Ledesma https://orcid.org/0000‐0002‐4181‐5498

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SUPPORTING INFORMATION

Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle.

How to cite this article:LedesmaJLJ,MontoriA,Altava‐OrtizV,Barrera‐EscodaA,CunilleraJ,ÀvilaA.FuturehydrologicalconstraintsoftheMontsenybrooknewt(Calotriton arnoldi)underchangingclimateandvegetationcover. Ecol Evol. 2019;00:1–12. https://doi.org/10.1002/ece3.5506