Journal of Ecology. 2019;107:1633–1644. wileyonlinelibrary.com/journal/jec  | 1633© 2019 The Authors. Journal of Ecology © 2019 British Ecological Society

Received:7November2018  |  Accepted:24April2019DOI: 10.1111/1365-2745.13204

M A C R O E V O L U T I O N A R Y P E R S P E C T I V E S O N B I O T I C I N T E R A C T I O N S

Integrated metabolic strategy: A framework for predicting the evolution of carbon‐water tradeoffs within plant clades

Ellie M. Goud1  | Jed P. Sparks1 | Mark Fishbein2 | Anurag A. Agrawal1

1DepartmentofEcologyandEvolutionaryBiology,CornellUniversity,Ithaca,NewYork2DepartmentofPlantBiology,Ecology,andEvolution,OklahomaStateUniversity,Stillwater,Oklahoma

CorrespondenceEllieM.GoudEmail:emg244@cornell.edu

Funding informationDivisionofEnvironmentalBiology,Grant/AwardNumber:1457510/1457473;DivisionofIntegrativeOrganismalSystems,Grant/AwardNumber:1645256

HandlingEditor:RichardShefferson

Abstract1. The fundamental tradeoff between carbon gain andwater loss has long beenpredicted as an evolutionary driver of plant strategies across environments.Nonetheless,challengesinmeasuringcarbongainandwaterlossinwaysthatin-tegrateoverleaf lifetimehavelimitedourunderstandingofthevariationinandmechanisticbasesofthistradeoff.Furthermore,themicroevolutionofplanttraitswithinspeciesversusthemacroevolutionofstrategiesamongcloselyrelatedspe-ciesmaynotbe the same,andaccordingly, the lattermustbeaddressedusingcomparativephylogeneticanalyses.

2. Hereweintroducetheconceptof‘integratedmetabolicstrategy’(IMS)todescribetheratiobetweencarbonisotopecomposition(δ13C)andoxygenisotopecompo-sitionabovesourcewater (Δ18O)of leafcellulose. IMS isameasureof leaf‐levelconditionsthatintegrateseveralmechanismscontributingtocarbongain(δ13C)andwaterloss(Δ18O)overleaflifespan,withlargervaluesreflectinghighermetabolicefficiencyandhencelessofatradeoff.WetestedhowIMSevolvesamongcloselyrelatedyetecologicallydiversemilkweedspecies,andsubsequentlyaddressedphe-notypicplasticityinresponsetowateravailabilityinspecieswithdivergentIMS.

3. Integratedmetabolicstrategyvariedstronglyamong20Asclepiasspecieswhengrownundercontrolledconditions,andphylogeneticanalysesdemonstratespe-cies‐specific tradeoffs between carbon gain andwater loss. Larger IMS valueswereassociatedwithspeciesfromdryhabitats,withlargercarboxylationcapac-ity,smallerstomatalconductanceandsmallerleaves;smallerIMSwasassociatedwithwet habitats, smaller carboxylation capacity, larger stomatal conductanceand larger leaves. The evolution of IMSwas dominated by changes in species’demandforcarbon(δ13C)moresothanwaterconservation(Δ18O).Althoughsomeindividualphysiologicaltraitsshowedphylogeneticsignal,IMSdidnot.

4. Inresponsetoexperimentaldecreasesinsoilmoisture,threespeciesmaintainedsimilarIMSacrosslevelsofwateravailabilitybecauseofproportionalincreasesinδ13CandΔ18O(orlittlechangeineither),whileonespeciesincreasedIMSduetodisproportionalchangesinδ13CrelativetoΔ18O.

5. Synthesis.IMSisabroadlyapplicablemechanistictool;IMSvariationamongandwithinspeciesmayshedlightonunresolvedquestionsrelatingtotheevolutionandecologyofplantecophysiologicalstrategies.

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1  | INTRODUC TION

Theneedtomaximizecarbongainwhileminimizingwaterlosshaslongbeenpresumedtodrivetheevolutionofdiversestrategiesbywhichplantsadapttovariableenvironments(Monson&Ehleringer,1993; Sage, 2004). Such strategies include succulence and alter-nativephotosyntheticpathways (e.g.,CAM)thatareevolutionarilyconvergent,coarse‐grainedmetabolicsolutionstothecarbon‐waterdilemma. While closely related species typically share a definedcoarse‐grainedstrategy,morefine‐grainedsolutionstobalancecar-bongainandwaterlossmaybeexpressedviadifferencesintraits,suchasstomata,thataffecttheexchangeratesofbothcarbondi-oxide(CO2)andwatervapour(Farquhar&Sharkey,1982;O'Leary,1988).Additionally,theleaf'sboundarylayer,internalresistancetogaseousdiffusion,andenzymaticactivitydrivingCO2consumptioncontribute substantially to carbon andwater flux, and are likely aconcertedpartofanoverallplantstrategytomanagethetradeoffbetweenthesetwofluxes.

Fordecades,thetradeoffbetweencarbonacquisitionandwaterlosshasbeenmeasuredastheratioofphotosyntheticcarbongaintotranspirationalwater loss, typicallymeasured instantaneously anddescribed as ‘water‐use efficiency’ (Keenan et al., 2013;Osmond,Bjorkman, & Anderson, 1980). Indeed, how this tradeoff variesamongspeciesandalongenvironmentalgradientsiscentraltoourunderstanding of plant ecology (Lambers, Chapin, & Pons, 2008;Pugnaire & Valladares, 1999). Though empirical measures of thisratioaremuchmoreinformativewhenintegratedoverlongerperi-odsoftime,themeasurementsoflong‐termcarbongainandwaterloss are challenging. At the leaf level, photosynthetic carbon gainovertimeisdrivenbytheaveragedifferenceinleafinternalandairexternalCO2concentrations(ci/ca),whichisinfluencedbythemet-abolicdemandforCO2andthesupplyofCO2viadiffusionthroughthestomataandleafboundarylayer.Similarly,waterlossovertimeisdefinedbytheaveragedifferenceinleafinternalandairexternalwatervapourconcentrations(ea/ei)whereeivarieswithtemperatureand eavarieswithrelativehumidity (Anyia,2004;vonCaemmerer&Farquhar, 1981; Farquhar&Sharkey, 1982).As such, traditionalmeasuresofinstantaneousleafgasexchangeofCO2andwaterva-pourfail toaccountforcontinuousvariation in light,humidityandairtemperature,andthusmayinaccuratelyreflectlong‐termcarbongainandwaterlossattheleaflevel(Seibt,Rajabi,Griffiths,&Berry,2008).

Anotherapproachistouseproxies(e.g.,specificleafarea)tode-scribecarbonandwaterexchangeoveraleaf'slifetime.Suchmea-sures are based on assumed relationships between traits and gasexchange(Wrightetal.,2004,2005)thatmaybeinconsistentamongclosely related species (Edwards, Chatelet, Sack, & Donoghue,

2014;Mason&Donovan,2015),because leaf traitshavemultiplefunctions in addition to carbongain andwater regulation.A thirdapproach has been to use the carbon stable isotope composition(δ13C)of leafmaterialasaproxy forwater‐useefficiency (Seibtetal.,2008).Duringcarbonfixation,theRubiscoenzymediscriminatesagainst the heavier 13C‐CO2; when CO2 is abundant (larger ci/ca),morediscriminationresultsintissuesthatarerelativelydepletedin13C.Thus,δ13CreflectsameasureofCO2supplyanddemandthatintegratesover the lifespanof the leafand isproportional toci/ca (Farquhar, Ehleringer,&Hubick, 1989).However, δ13C cannot dis-tinguishtheindividualinfluencesofcarboxylationrateandstomatalconductance(Ehleringer,1993).

Analternatemethodology thathasbeenapplied recently is tointegrate leaf carbon‐water relations over time through the sepa-rate, concerteduseof carbonandoxygenstable isotopes (Grams,Kozovits,Haberle,Matyssek,&Dawson,2007;Roden&Farquhar,2012; Scheidegger, Saurer, Bahn, & Siegwolf, 2000). The oxygenincellulosecomesfromwater,andthestable isotopecomposition(δ18O)of leafcellulose is influencedbybothsourceand leafwaterδ18O.Sourcewaterδ18Ovarieswithtemperatureandevaporation,whileleafwaterδ18Oprimarilyvarieswithevaporativeenrichmentduring transpirationalwater loss, aswell as leaf temperature, thedegreeofmixingbetweensourceandleafwaterswithintheplant,and the degree of isotopic exchange between organic moleculesandwater(Barbour,Roden,Farquhar,&Ehleringer,2004;Roden&Ehleringer,2000).Whencalculatedasanenrichmentabovesourcewater (Δ18O = δ18Ocellulose−δ

18Osource),Δ18Oreflectstheevapora-

tiveenvironmentatthetimeofcelluloseproductionandisinverselyproportionaltoea/ei(Farquhar,Cernusak,&Barnes,2007;Roden&Farquhar,2012).Althoughnotadirectmeasureoffluxes,therela-tionshipbetweenδ13CandΔ18Oofcelluloserepresentsanendpointintegrationofthetradeoffbetweencarbongainandwaterlossoverthelifetimeofaleaf.

1.1 | Integrated metabolic strategy

Here, we introduce the concept of integrated metabolic strategy (IMS)todescribetherelationshipbetweencarbonmetabolismandleafevaporativeconditionsusingδ13CandΔ18Oof leaf cellulose.There have beenmany attempts to describe the relationship be-tweencarbongainandwaterloss,themostprominentbeing‘water‐use efficiency’. Additionally, ‘metabolic set point’ has been usedtodescribe long termci/ca, similar tobasalmetabolism inanimals(Ehleringer,1993).Wepurposefullydonotredefinethesetermsinordertoavoidconfusion.Rather,weusetheterm‘IMS’becauseitisareadilyaccessibleandgeneraltermtodescribeastrategytoman-agethetradeoffbetweencarbonandwaterfluxesattheleaf‐level.

K E Y W O R D S

carbonstableisotope,ecophysiologicaltraits,leafeconomicspectrum,oxygenstableisotope,phenotypicplasticity,plantstrategies,tradeoffs,water‐useefficiency

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The relationship between δ13C andΔ18O can be visualized byplottingthesetwovariables indual‐isotopespace(Figure1a),pro-vidingarepresentationofintegratedcarbonmetabolicsetpointandtheevaporativestateofthewaterusedtoformcellulose.Assuch,itisagrossrepresentationofthecarbonforwatertradeoff.Becausetherelationshipbetweenδ13Candci/caispositiveandtherelation-shipbetweenΔ18O and ea/ei isnegative(atleastovertherangeofvalues of interest), it is convenient to rescale the denominator inorderto1makeallvaluespositive,and2distortthevaluesaslittleaspossiblewithoutgeneratingratioslargerthanone.Forourdata-set,Δ18Owassubtractedfromaconstantof100,andwedefineIMSasthefollowingratio:

IncreasingIMSvaluescorrespondtoincreasesinleaf‐levelmet-abolicefficiency (i.e., ahighermetaboliccarbonsetpointperunitof increasing evaporative condition in leaves; Figure1b).We taketheabsolutevalueofδ13Casthenumeratortorepresentthegen-eral, positive relationship between δ13C and ci/ca. In this dataset,IMSvaluesrangebetween0and1,with1representingplantswiththelargestcarbonsetpointataminimumofevaporativewaterloss.Importantly,similarIMSvaluescanbeachievedbyplantsthatdifferinmagnitudesofcarbonmetabolicsetpointandevaporativeenvi-ronment,iftheyhaveasimilarratiobetweenthetwo.Forexample,speciesBandC(Figure1a)differinδ13CandΔ18Ovalues,yetreachsimilar metabolic strategies because they exchange carbon andwaterinapproximatelythesame2/5ratio.Incontrast,plantsdifferin IMSwhen the ratiobetween these fluxesdiffer (speciesA andD,Figure1b).Wepredict,then,thatwhenrespondingtoadrivingforce (e.g.,water availability), plantswith fixed IMSmay differen-tiallyadjustanatomyand/orphysiology,butδ13CandΔ18O will vary proportionally(e.g.,movementbetweenpointsCandB,Figure1a).Alternatively,ifIMSisphenotypicallyplastic,weexpectchangesinδ13CandΔ18OtoresultinachangeinIMS(e.g.,movementbetweenpointsAandB,AandC,orAandD,Figure1a).

OurintroductionofIMSbuildsonpreviousworkthathasconsid-ered δ13CandΔ18Oseparately ingreenhouse (Barbouretal.,2004)and field settings (Cernusak, Farquhar, & Pate, 2005; Ehleringer,

Phillips,&Comstock,1992;Sparks&Ehleringer,1997).Examiningtherelationshipbetweenδ13CandΔ18Oindual isotopespacehasbeenusedtodifferentiatephotosyntheticandstomatalresponsestochang-ingenvironmentalconditions,suchasprecipitationandtemperature,primarilyinthecontextofimprovingpaleoclimatemodels(Offermannetal.,2011;Roden&Farquhar,2012).Fieldmeasurementshavebeenusedtorelatevariationinδ13CandΔ18Otovariationinenvironmentalwateravailabilitydueto, forexample,gradients inprecipitationandtopography(Flanagan&Farquhar,2014;MorenoGutiérrez,Dawson,Nicolás,&Querejeta, 2012; Prieto,Querejeta, Segrestin,Volaire,&Roumet, 2017).However, unlike previouswork,we convert the re-lationshipbetweenδ13CandΔ18Otoanindex(IMS)thatprovidesasinglemeasureoftherelationshipbetweencarbonsetpointandwateravailability.

1.2 | Evolution of metabolic strategies

To evaluate the evolution of IMS, we combine common gar-den and experimentalwatermanipulations across species span-ningtherangeofecologicaldiversitywithinthemilkweedgenusAsclepias (Apocynaceae) (Figure2).Asclepias includes about140NewWorld,mostlyherbaceous,perennialplantsthatdisplayre-markable variation inmorphology and habitat affiliations.ManyspeciesliveindesertandaridenvironmentsofthesouthwesternUnitedStates.Othersoccupymoremesicenvironments,suchasgrasslands and forests, while still others are restricted to wet-lands,suchasmarshesandswamps(Woodson,1954).Totestforintrinsic,species‐specificvariationinmetabolicstrategies,wese-lected20taxa(19speciesplusonesubspecies),representingeachmajor clade in Asclepias(Fishbein,Chuba,Ellison,&Mason‐Gamer,2011;Fishbeinetal.,2018)thatvaryinleafmorphology(e.g.,leafsizeandthickness),andthatnaturallyoccurinhabitatsthatdiffersubstantially inwater availability. Specifically,we asked the fol-lowingquestions: (a)Does IMSdiffer amongclosely related, yetecologicallydiversespeciesundercommongardenconditions?(b)Can leaf traits, physiologicalmeasures, andevolutionaryhistorybeusedtopredictIMS?

Toevaluatewhethervariationinsoilwateravailabilitywouldim-pactIMSofindividualplants(i.e.,phenotypicplasticity),weselected

IMS=

|||�13C

|||

100−Δ18O

F I G U R E 1  Conceptualmodelforvariationin(a)leaf‐levelδ13CandΔ18O and(b)associatedintegratedmetabolicstrategies(IMS)valuesfordifferenthypotheticalplantspeciesorindividuals

1636  |    Journal of Ecology GOUD et al.

fourofthe20taxatogrowunderexperimentalwatertreatments.ThesefourspeciesrepresentedoppositeendsofIMSandmorpho-logicalvariationwithinthegenus;twospecieswiththelargestIMSvalues and small, thick leaves, and two species with the smallestIMS values and large, thin leaves. Additionally, these four speciesincluded a dryland and a wetland‐specialist, and two ecologicallybroadspecies.Thus,ourfinalquestionwas,(c)HowdoesagradientofsoilmoistureimpactIMS?Wehypothesizedthatdryland‐adaptedspecieswouldhavelargerIMSvalues,indicatingamorewatercon-servativestrategy(Figure1b),relativetowetland‐adaptedspecies.WefurtherhypothesizedanassociationofhigherIMSwithsmallerleaves, lower rates of stomatal conductance and larger rates ofcarboxylation.

2  | MATERIAL S AND METHODS

2.1 | Plant growth

InMay2016,seedsfrom20Asclepiasspecies(Figure2,wildcollectedorpurchasedfromnativeplantsuppliers),weregerminatedbymois-teningandstratifyingat4°Cforat least10daysandthenat28°Cfor3days.SeedlingswereplantedinMetroMixsoil(Scotts‐Sierra,Marysville,OH,USA)in500mlplasticpots.Plantsweregrownfor6weeksinawalk‐ingrowthchamber(ConvironCMP6050)thatwasmaintainedat26°C(14hrday)and24°C(10hrnight)withanaveragerelativehumidityof50%.Plantsweremonitoreddailyandsoilwatercontentsweremaintainedat fieldcapacity.Volumetricwatercon-tent(VWC)atfieldcapacitywasdeterminedbysaturatinga500mlpotofsoilwithwater,sealingthetopwithparafilmandallowingallexcesswatertodrainviagravityfor48hr.Atthispoint,thesoilwas

atfieldcapacity(Colman,1947)andsoilVWCwasmeasuredusingaHydroSenseIIsoil‐watersensor(CampbellScientific,Logan,UT).Thewater content of the soil at field capacitywas approximately30%.

2.2 | Experimental water treatments

InMay2018,weselectedfourspeciesbasedontheir2016IMSvalues: Asclepias curassavica (Figure S1a), Asclepias incarnata (Figure S1b), Asclepias pumila (Figure S1d) and Asclepias verti‐cillata (Figure S1e). These species were selected because theyrepresentoppositeendsofIMSvariationthatalsocoincidewithdifferencesinleafmorphology,andbecausethesespeciesareinthesameclade(Figure2)(Fishbeinetal.,2011,2018).Asclepias curassavica and A. incarnata represent the smallest IMS valuesandhavelarge,thinleaves(FigureS1a,b).Asclepias curassavicaisanecologicallywidespreadtropicalandsubtropicalspecieswhileA. incarnataisrestrictedtotemperatewetlands.Asclepias pumila and A. verticillata represent the largest IMS values and havesmall,thickleaves(FigureS1d,e).Asclepias pumilaoccursintem-perateshort‐grassprairieintheGreatPlains,whileA. verticillata occurs in temperate grasslands and forest openings across theeasternandmidwesternUnitedStatesandsouthernmostCanada(Woodson1954).

WegrewindividualsofA. curassavica,A. incarnata,A. pumila and A. verticillata inconditions identical to2016 (pot size,growthme-dium, chamber conditions), but under threedifferentwatering re-gimesrelativetofieldcapacity(measuredas30%VWC,asdescribedabove): dry (one‐third field capacity, approximately 10% VWC),mesic(fieldcapacity,approximately30%VWC),andwet(saturated,

F I G U R E 2  Maximumlikelihoodchronogrambasedonplastomesequencesfrom19Asclepiastaxaandtherangeofassociatedintegratedmetabolicstrategies(IMS).Bootstrapvalueslessthan100%areindicatedatnodes.IMScoloursamongtaxacorrespondtotheaverageIMSvalueofeachTukeypost‐hocgroup,rangingfromthelowestIMSvalues(0.39,blackcircles)tothelargestIMSvalues(0.51,whitecircles).ThisphylogenyismissingA. fascicularis(seeMethods),whichwouldbeplacedinthecladecontainingA. perennis and A. mexicana(Fishbeinetal.,2011)

     |  1637Journal of EcologyGOUD et al.

approximately 60%VWC).WemeasuredVWCdaily andwateredthepotsasnecessarytomaintaintreatmentVWCs.

2.3 | Gas exchange and leaf traits

Wemeasuredleafgasexchangein2016andin2018usingaLI‐CORLI‐6400CO2 gas exchange analyzer (LI‐COR, Lincoln,NE) on fivetosixreplicateplantsperspeciesat32and39daysold.Wemeas-uredlight‐saturatedmaximumratesofphotosynthesis(Amax)bygen-erating light responsecurvesonthreereplicateplantsperspeciestoobtainthe light intensity (photosyntheticallyactiveradiation)atwhich photosynthesis saturated.We estimatedmaximum rates ofcarboxylation(Vcmax)fromA/cicurvesonthesamethreereplicateplants per species used to generate light response curves.Whenplantswere 45 days old,we recorded the total number of leavesandplantheightofeachindividual,removedleaves,separatedandwashedrootstoremovesoil.Wemeasuredtotalleafarea(LA)usingaLI‐CORLI‐3100 leaf‐areametre (LI‐COR)andweighedfresh leafmass.Wethenoven‐driedleaf,stemandrootmaterialsat60°Cfor48hr.Averageleafarea(leafsize,LS)wascalculatedbydividingLAbythetotalnumberofleaves.

2.4 | Sample processing for cellulose extraction

Approximately,300mgofgroundleafmaterialwasloadedintofibrefilterbags,heat‐sealedandplacedinaSoxhletapparatustorefluxa2:1solutionoftoluene:ethanolfora24‐hrperiodfollowedbyape-riodofdryingandanother24‐hrperiodofextraction(forlipidsandresins)with95%ethanol.Bagswereair‐driedandboiledinwaterfor1hrtoextractsolublesugarsandlowmolecularweightpolysaccha-rides.Toobtainholocellulose,thesamplesweresoakedina0.7%w/vsodiumchlorite/aceticacidsolutionthatwascontinuouslystirredonastirplateandperiodically replacedovera3‐dayextraction toex-tractligninandothernitrogen‐containingcompounds.Toobtainpureα‐cellulose, thesamplesweresoaked ina17%w/vsodiumhydrox-ide(NaOH)solutionfollowedbyan11%w/vaceticacidsolutiontoneutralizethepHwitheachstepfollowedbyextensiverinsingwithdistilledwater.Theα‐cellulosewasdriedat65°Cfor48hr(Leavitt&Danzer,2002).

2.5 | Isotope analyses

Isotoperatiosandpercentelementofallsamplesweremeasuredusinga continuous flow isotope ratiomass spectrometer (ThermoScientificDeltaVAdvantage).Forδ13C,themassspectrometerwascoupledtoanelementalanalyzer(CarloErbaNC2500)andforδ18O itwascoupledtoaThermoScientificTC/EApyrolysisanalyzerwithaCostechZeroBlankautosampler.Isotoperatiosareexpressedasδvalues(permil):

where Rsample and Rstandard are the ratios of heavy to light isotopeof the sample relative to the international standards for C and O,

Vienna‐Pee‐DeeBelemniteandViennaStandardMeanOceanWater,respectively.δ18Oofirrigation(source)waterwas−10.1‰in2016and−9.9‰in2018.Withinrunisotopicprecisionforqualitycontrolstan-dardswas0.2‰forcarbonand0.3‰foroxygen.MassspectrometrywasperformedattheCornellUniversityStableIsotopeLaboratory.

2.6 | Phylogenetic relationships

Weestimatedthephylogenyofthe20sampledspeciesofAsclepias (Figure2)byaddingnewplastidgenome(plastome)sequencesforA. pumila and A. verticillatatoarecentlypublisheddatasetof108samples of Asclepias plus four outgroup sequences (Fishbein etal.,2018).TheA. pumilasamplewaspreparedusingtheNEBNextUltra IIDNALibraryPrepKit for Illumina (NewEnglandBioLabs,Ipswich, MA) with a NEXTflex‐HT™ Barcode (Bioo Scientific,Austin, TX) and sequenced on an Illumina NextSeq 500 at theOklahomaStateUniversityGenomicsandProteomicsCenter.TheA. verticillatasamplewaspreparedasin(Straubetal.,2012).BothplastomeswereassembledusingGeneious10(Kearseetal.,2012;BiomattersLtd.,Auckland,NewZealand)byobtainingdenovocon-tigsusingtheproprietaryGeneiousassembler,mappingcontigstoan A. niveareference(NCBINC_022431.1),andre‐mappingunas-sembledreadstothealignedcontigstocompletetheassemblies.The two copies of the inverted repeatwere not distinguished intheseassemblies.Thephylogenyofthe114sampleswasobtainedfollowingFishbeinetal. (2018).Briefly,plastomesequenceswerealignedusingMAFFTv.7(Katoh,Rozewicki,&Yamada,2017)andambiguously aligned regionsweremaskedwith the implementa-tionofGblocks (Castresana,2000) inMesquite3.5 (Maddison&Maddison,2018).Themaximumlikelihoodphylogenyofthesese-quenceswas estimatedwith IQ‐TREE v. 1.6.7 (Nguyen, Schmidt,vonHaeseler,&Minh,2015)withnodesupportestimatedbyul-trafastbootstrap(Minh,Nguyen,&vonHaeseler,2013).Themaxi-mumlikelihoodtreewasconvertedtoachronogramwithrelativenodedatesusingpenalizedlikelihoodoptimizationofratevariationamongbranches,implementedintreePL(Smith&O'Meara,2012).The resulting time tree was pruned to contain only the speciessampledhereusingthedrop.tipfunctionintheapev.4.1package(Paradis,Claude,&Strimmer,2004)forr(RCoreTeam,2016).Thisphylogenycontained19ofthe20sampledspeciesduetoalackofsequenceinformationforA. fascicularis.

2.7 | Statistical analyses

WeassessedrelationshipsbetweenIMSandleaftraitsusingsimplelinearregressionsandphylogeneticindependentcontrastsusingthepglsfunctionofthecaperpackageinr(Orme,Freckleton,Thomas,&Petzoldt,2018).WeassessedrelationshipsbetweenIMSandex-perimentalwatertreatmentsusingone‐wayANOVA.WeestimatedphylogeneticsignalbycalculatingPagel'sλandBlomberg'sKusingthe phylosig function in the picante package in r (Kembel et al.,2010).We included the standarderrorof themean foreachvari-able.Allanalyseswereperformedwiththefull20speciesexceptfor

�13C or �18O =

(Rsample∕Rstandard−1

)×1000 (‰)

1638  |    Journal of Ecology GOUD et al.

phylogeneticsignalandindependentcontrasts(19species)inr3.2.4 (RCoreTeam,2016).

3  | RESULTS

3.1 | Metabolic diversity and evolution among Asclepias species

Speciesleafcellulosevariedinδ13CandΔ18O(Figure3a),andassoci-atedIMS(Figure3b).DifferencesamongspeciesinIMSweredrivenmorebyvariation inδ13C,asevidencedbya stronger relationshipbetweenIMSandδ13C(FigureS2a;R2=0.91,p<0.0001)thanΔ18O (FigureS2b;R2=0.23,p=0.033).

Wepredicted that IMSwouldbe influencedby leaf traits, andfoundthatIMSnegativelycorrelatedwithleafsizeandstomatalcon-ductance (gs) andpositively correlatedwith leaf nitrogen and car-boxylationcapacity(Vcmax),aftercorrectingforbiasesduetosharedevolutionary history using phylogenetically independent contrasts(allp<0.05,Figure4).Together, these resultssuggest that IMS ismechanistically determined by anatomical and physiological traitsthataffectcarboxylationcapacity,leafboundarylayerandstomatalconductances.

Integratedmetabolicstrategy,δ13C,leafsizeandstomatalcon-ductance showed little evidence of phylogenetic signal (λ and K<0.50,p>0.05),whileΔ18O,leafnitrogenandcarboxylationcapac-ity showed phylogenetic signal consistent with Brownian motionevolution(λandK>0.50,p<0.05)(TableS1).

Species with the lowest IMS (shaded black, Figures 2 and 3)werefromwetlandsormesictropicalandsub‐tropicalregions,andhave large, thin leaves (e.g.,A. curassavica, both subspecies ofA. incarnata; Figure S1a,b). In our subsample of the genusAsclepias,thesethreetaxawerefoundintheIncarnataeclade.Nonetheless,among the two major clades best sampled here (Incarnatae andthe north temperate clade containingA. syriaca), therewas largediversityinIMS,witheachcladecontainingshiftsamongthethreehighest IMScategories (white,hatched,grey inFigure2).Species

withmid‐rangeIMS(grey,hatched,Figure3b)werefromdeserts,grasslandsandwoodlandsandhavevariablysizedleavesgenerallywithhairsorwaxes (e.g.,A. californica,A. eriocarpa;FigureS1c,d),while specieswith thehighest IMS (white, Figure3b) have small,thinleavesandarefromgrasslandsandmorearidhabitats(e.g.,A. pumila,A. subulata;FigureS1e). Inotherwords,milkweedspeciesfromdrierhabitatsshowedrelativelyhigher IMSthanthosefromwetlands, indicative of higher carbon gain for a givenwater lossunder common growth conditions. Moreover, each of the majorAsclepias clades were well represented in this study (Incarnataeand north temperate clades) and spanned the range of IMS val-uesandleafmorphologies,andhadatleasttwohabitataffiliations(Figure2),suggestingthatdifferentmetabolicstrategiesandtheirrespectivemechanisticunderpinningsmayhavehadmultipleinde-pendentorigins.

3.2 | Metabolic diversity within Asclepias species in response to different water levels

Wenextconductedmanipulationsofsoilwateravailabilityusingfourmilkweed species, from opposite ends of IMS variation. Althoughδ13CandΔ18Ovariedwithinspeciesinresponsetowatertreatments(Figure 5c,d, Figure S4), IMS did not vary among the threewatertreatmentsforA. curassavica,A. incarnata and A. pumila.However,IMSwashigherunderdry conditions forA. verticillata (Figure5a).ForlowerIMSspeciesA. curassavica and A. incarnata,δ13CandΔ18O jointlychanged fromwet todry treatments inapositivedirection(Figure5c,d, FigureS4) that is consistentwith theprediction thatplantsmayalterthemagnitudeofcarbonandwaterfluxestogetherandmaintainasimilarIMSinresponsetowaterlimitation(Figure1).Incontrast,δ13CandΔ18Oofhigher IMSspeciesA. pumila and A. verticillatachangedfromwettodrytreatmentsinanegativedirec-tion (Figure5c,d,FigureS4).This isconsistentwiththepredictionthatplantsdifferentiallychangethemagnitudeofcarbonandwaterfluxes,withconsequentchangesinIMSvaluesinresponsetowaterlimitation(Figure1).

F I G U R E 3  Relationshipsbetween(a)δ13CandΔ18Oofleafcellulose(p=0.3);(b)integratedmetabolicstrategy(IMS)values,inorderfromlowesttohighestIMSvaluesfor20Asclepiastaxagrownundercommonconditions.LargerIMSvaluesindicatehighermetabolicefficiency.Dataarespeciesmeanswithstandarderror.ColourscorrespondtofourdistinctgroupsinmeanIMSvaluesbasedonTukeypost‐hoccomparisons.1=A. brachystephana,2=A. californica,3=A. curassavica,4=A. eriocarpa,5=A. fascicularis,6=A. incarnatasubsp.incarnata,7 = A. labriformis,8=A. latifolia,9=A. linaria,10=A. mexicana,11=A. perennis,12=A. incarnatasubsp.pulchra,13=A. pumila,14=A. speciosa,15=A. subulata,16=A. subverticillata,17=A. syriaca,18=A. tuberosa,19=A. verticillata,20=A. viridis

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On average, biomasswasmuch lower for plants grown underthedriestconditions(Figure5b).Similarly,Δ18Owasrelativelylargerunder thedriestconditions (Figure5d), indicating less foliarwaterloss. Interestingly, δ13C andwas relatively larger under the driestconditions forA. curassavica and A. incarnata, butwas unchangedin A. pumila and relatively lower in A. verticillata (Figure 5c). Inotherwords,A. curassavica,A. incarnata and A. pumilarespondedtowater limitationby reducingwater loss andcarbongain in a simi-larstoichiometrythatultimatelydidnotaltertheirIMS.Incontrast,

A. verticillatareducedwaterlossinresponsetowaterlimitation,butdidnotreducecarbongain,allowingforgreatermetabolicefficiencyunderdryconditions.

Larger IMS values were associated with smaller leaf size andlargerstomatalconductancesacrossbutnotwithinspecies(FigureS6a,b). Larger IMS valueswere associatedwith larger amounts ofleafnitrogenandratesofcarboxylationacrossspeciesbutsmalleramountsofleafnitrogenwithinspecies,smallercarboxylationrateswithin wetland‐adapted species and larger carboxylation rates

F I G U R E 4  Linearrelationshipsbasedonordinaryleastsquaresregressions(OLS,solidlines)andphylogeneticindependentcontrasts(PIC,dashedlines)betweenIMSand(a)leafsize(OLSR2=0.26*,PICR2=0.36*);(b)stomatalconductance,gs(OLSR

2=0.06,PICR2=0.13*);(c)nitrogencontent(OLSR2=0.23*,PICR2=0.45**)and(d)maximumrateofcarboxylation,Vcmax (OLSR2=0.13*,PICR2=0.13*)for20Asclepiastaxagrownundercommonconditions.Dataarerawspeciesmeans,*p<0.05,**p<0.01,***p < 0.0001

F I G U R E 5  Resultsofone‐wayANOVAsbetween(a)integratedmetabolicstrategies(IMS),(b)biomass,(c)δ13Cand(d)Δ18OforfourAsclepiasspeciesgrownunderthreedifferentsoilwatertreatments:dry(one‐thirdfieldcapacity),mesic(fieldcapacity)andwet(twicefieldcapacity).Dataaremeans±SE(n=6).Asterisksindicatesignificantdifferencesbetweenwaterlevels(p<0.05),basedonpost‐hocTukeytests[Colourfigurecanbeviewedatwileyonlinelibrary.com]

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withindryland‐adaptedspecies (FigureS6c,d).SeeFiguresS3andS5foradditionaltraitmeansacrossIMSgroupsandwithinspeciesinresponsetowatertreatments(maximumphotosyntheticrates,sto-matalconductance,plantheight,totalleafarea,leafsize,leafnitro-gencontent,carboxylationcapacity,root/shoot).

4  | DISCUSSION

Weaskedwhethercloselyrelatedyetecologicallydiversemilkweedspecieswouldvary inIMSwhengrownundercontrolled,commonconditions.Wefoundthat IMSvariedsubstantiallyamongspecies(Figure 3), suggesting differential tradeoffs between carbon gainand water loss. Variation along both δ13C andΔ18O axes reflectfundamentallydifferentratesof leaf‐levelcarbongainamongspe-ciesthatlikelyresultfromdifferencesinlongtermCO2supplyanddemand,ci/ca.ExamininginterspecificvariationinΔ

18Oallowedusto tease apart the separate effects of water loss frommetaboliccarbongainthatareindependentofcarboxylation.Moreover,rela-tively greater interspecific variation in δ13C thanΔ18O (Figure S2)indicatedastrongereffectofphotosynthesisonci/carelativetosto-matal conductance (Grams et al., 2007; Scheidegger et al., 2000);thus,metabolicdiversityamongthesespeciesmaybedrivenmorebyCO2 demand than factors affectingCO2 supply andwater lossviadiffusion.However,weacknowledgethattheobservedvariationincarboxylationisalsoco‐controlledbyresourcesupply,especiallynitrogen and phosphorus, which were not limiting in this growthchamberstudy.Species‐specificvariationincarboxylationmaynotbeexpressedasstronglyinnatureundernutrientlimitations.

Wealsofoundthatdifferenttraitcombinationsresultedinsim-ilarIMSvaluesaspredictedbyourconceptualmodel.Forexample,showymilkweed(A. speciosa) istheonlybroad‐leafrepresentativeinthehighIMSgroup(Figure2),andA. speciosahadlowerΔ18O and disproportionatelysmallerδ13Cthanotherhigh‐IMSspecies.Pine‐needlemilkweed(A. linaria)hadmid‐rangeδ13CandΔ18O,butbasedonitsstiff,needle‐likeleaveswewouldhaveexpectedlargerΔ18O (morewater conservative). It is unclear if the rockyhabitatsofA. linaria provide access towater pockets, or if our growth chamberconditions did not necessitate conserving water at the leaf‐level.Rushmilkweed(A. subulata),adesertspecies,hadbyfarthelargestΔ18O,indicatingthatitisthemostwaterconservativespeciesinthisstudy.Thenarrow leavesofA. subulataareephemeralandphoto-synthesiscontinuesprimarilythroughitsgreenstem,althoughitisunclearwhether this relates towater conservation.These speciesdemonstratehowIMSisnotnecessarilypredictedbymacroclimateandthatsimilarmetabolicefficienciescanbereachedviadifferentmorphologicalsolutions.

4.1 | Variation in IMS: Leaf traits, habitat affiliations and evolutionary history

Our second goal was to determine the drivers of interspecificvariation in IMS, including anatomy, physiology, environment, and

evolutionaryhistory.Weclearlyhavenotmeasuredall anatomicaland physiological characters affecting δ13C and Δ18O. However,themost influentialtraitsarelikelytobethosethatdirectlyrelatetoenzymaticcarbonfixationandgaseousdiffusionrates.Assuch,weused leafnitrogencontentandmaximumrateofcarboxylation(Vcmax)torepresentfixation,andleafsizeandstomatalconductance(gs) to represent gaseous diffusion resistances. Accordingly, IMSpositivelycorrelatedwithleafnitrogenandVcmaxandnegativelycor-relatedwithleafsizeandgswhenaccountingforsharedevolution-aryhistory,usingphylogeneticallyindependentcontrasts(Figure4).Previouswork has suggested positive relationships between δ13Cand leafnitrogenandnegative relationshipsbetweenΔ18Oandgs(Ellsworth, Ellsworth, & Cousins, 2017; Moreno Gutiérrez et al.,2012;Sparks&Ehleringer,1997).Here,weconsidertheseisotopestogetherinasingleindex,whichallowsustoidentifypotentialmech-anisms thatunderlie integrated leafmetabolismas awhole ratherthanthesumofitsparts.LeafnitrogenandVcmaxexplainedmoreofthetotalvariationinIMSthanleafsizeandgs,furthersupportingourinterpretationthatIMSamongthesespeciesisdefinedprimarilybydifferencesinleaf‐levelCO2demandratherthandifferencesinCO2 andwatervapourdiffusionresistances.

Previous work using gas exchange measurements have foundwater‐useefficiencytobeequallycontrolledbyleafnitrogenandsto-matalconductanceacrosstropicalwoodyandherbaceousangiospermspecies(Cernusak,Aranda,Marshall,&Winter,2007),whilestomatalconductancewastheprimarydriveracrossadiverserangeoftropi-calgymnospermandangiospermtreesandlianas(Cernusak,Winter,Aranda,&Turner,2008).Atleasttworeasonscouldaccountfordiffer-encesbetweenotherstudiesandours:first,weuseintegratedisotopicmeasuresratherthaninstantaneousgasexchange,thuscontrollingfortemporal variability in fluxes. Second, by comparing closely relatedspeciesratherthanacrossabroadtaxonomicscale(e.g.,gymnospermsand angiosperms), we are controlling for confounding effects thatcharacterizesuchdiverseplants(Edwardsetal.,2014).

Dryland‐adapted plants are generally more water‐use effi-cient relative to those frommesicandwater‐loggedenvironments(Dudley,1996;Field,Merino,&Mooney,1983).Consistentwiththisidea,manyofthespecieswiththelargestIMSvaluesinthisstudyarefromaridhabitats(e.g.,A. brachystephana,A. subulata),whilethosewiththelowestIMSvaluesarefromwetlands(e.g.,A. incarnata,A. perennis) (Figure 2). Although there are few studies that compareδ13CandΔ18Oacrossmultiplespecies,ifweconvertpreviouslypre-sented isotopicdata to IMSvalues,Mediterraneanshrublandspe-ciesoccupyingmorexericmicrohabitatsalsohadlargerIMSvaluesrelativetospeciesrestrictedtomoremesicmicrohabitats(seeTableS2forconverteddatafromMorenoGutiérrezetal.,2012).LargerIMSvaluesindrylandspeciessuggestnotonlylessfoliarwaterloss,butalsoagreatercarboxylationefficiencyachievedbyfixingsimi-laramountsofcarbonatalowerinternalCO2concentration(ci)andlowerstomatalconductancerelativetowetland‐adaptedspecies.ItiswelldocumentedthatC3plantsfromaridecosystems,especiallydeserts, areable tomaintainhigh ratesof carboxylationwhile re-strictingwaterlossbyoperatingatalowerci.Moreover,plantsfrom

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arid environments often have larger leaf nitrogen content and in-vestproportionallymoreoftheir leafnitrogenintophotosynthesis(i.e.,Rubiscoenzyme).ThisallowsforagreaterdrawdownofinternalCO2 (lowerci), inordertoachieverelativelyhigherphotosyntheticratesatagivenstomatalconductancethannon‐aridplants(Prentice,Dong,Gleason,Maire,&Wright,2014;Wright,Reich,&Westoby,2003).Inagreementwiththis,milkweedsfromdrylandshadlargertotal leaf nitrogen contents and maximum rates of carboxylation(FigureS3f,g),whilephotosyntheticratesdidnotvary(FigureS3a).

Integratedmetabolicstrategydidnotshowphylogeneticsignal(TableS1),indicatingthatthebalancebetweenleaf‐levelcarbongainandwaterlossisnotprimarilydefinedbysharedancestry.AsIMSistheend‐point integrationofmanytraits,eachwithpotentiallydif-ferentratesofevolution(Ackerly,2009),itisperhapsnotsurprisingthatthereisnophylogeneticsignal.Interestingly,Δ18O,leafnitrogencontentandcarboxylationcapacity(Vcmax)showedphylogeneticsig-nal,whileδ13C,leafsizeandstomatalconductancedidnot.AstrongphylogeneticsignalinΔ18O,butalackofphylogeneticsignalinthewater‐userelatedtraitsthatwemeasured (leafsize,stomatalcon-ductance),couldariseifothertraitsimportanttowaterlossthatwedid not measure are phylogenetically conserved and contributedto phylogenetic signal inΔ18O. For example, stomatal pore indexandstemandleafhydraulicconductancesacrosstheMagnoliaceaeshowedphylogeneticsignal,butnotinstantaneousratesoftranspi-rationandstomatalconductance(Liuetal.,2015).Similarly,leafsizedidnotshowphylogeneticsignalacrossAsclepias(Agrawal,Fishbein,Halitschke,etal.,2009a)orEricaceae(Goud&Sparks,2018).

A lackofphylogeneticsignal inδ13Ccouldbe indicativeofdif-ferentialtraitcombinationsamongspeciestoachieveanoverallcar-bonmetabolismthatisnotnecessarilyphylogeneticallyconserved,despite conservatism in biochemical traits such as leaf nitrogenand Vcmax. Leaf nitrogen has been reported to have phylogeneticsignal for Magnoliaceae species (Liu et al., 2015) but not acrosstheEricaceae(Goud&Sparks,2018)orcloselyrelatedAsteraceae(Münzbergová&Šurinová,2015).Wepreviouslyuseddiscrete fo-liartraitstocharacterizeAsclepiashabitataffiliations,withhairy&waxyspeciesbeingfromdrierenvironmentsthanglabrousspecies(Agrawal,Fishbein,Jetter,etal.,2009b).Consistentwiththisstudy,wetlandspeciesalsohadlargerδ13Candlowernitrogencontentthandrylandspecies(Figure3a,FigureS3f).

AnadditionalconsiderationfortheinterpretationofourresultsisthelimitedsamplingofAsclepiasspeciesinthecurrentstudy.Mechanisticstudiescanbelimitedinthenumberoftaxatocompare,and20speciesisquitehighforthesetypesofdetailedphysiologicalmeasurements.However,itiswellestablishedthatphylogeneticallycontrolledanalysesaresuspecttosamplingbias,includingAsclepias(Fishbeinetal.,2018).IncreasedsamplingmayimproveourunderstandingabouttheshiftsinIMSamongmilkweedspecies.Forexample,weexpectthatafewotherAsclepiasspeciesoutsideoftheIncarnataeclade(e.g.,A. lanceolata and A. rubrainthenorthtemperateclade;Figure2)willfallintothelowestIMScategory(typicalofwetlandspecies),andsuchindependentoriginsmayhelptoclarifytherepeatedevolutionofphysiology‐environmentassociationsovermacroevolutionarytime.

4.2 | Within‐species IMS across an experimental soil moisture gradient

Giventhatinstantaneousgasfluxesandleaftraitscanvarybyordersofmagnitudewithenvironment, itwouldbe reasonable toexpectIMSvaluestobesimilarlyplasticandpotentially respond inmulti-pledirectionsbasedongrowthconditions.Ourresultssuggestthatsome speciesmay be constrained by an intrinsic strategy for bal-ancingcarbonandwaterlossattheleaf‐level.Thequestionis,howfixedarethesestrategies?OnemightexpectplantstoaltertheirIMSunderseverelydryandconstantlysaturatedconditions relative tomesic conditions, and that dryland‐ and wetland‐adapted speciesmay have differential responses. Surprisingly, IMS did not changewithin speciesacrossanexperimental soilmoisturegradient,withtheexceptionofA. verticillata (larger IMSvaluesunderdrycondi-tions)(Figure5a).SimilarIMSvaluesweremaintainedacrosswaterlevelsbecauseofproportionalchangesinδ13CandΔ18OforA. curas‐savica and A. incarnata,andbecauseofnochangeinδ13CandΔ18O valuesforA. pumila(Figure5c,d).

Consistent IMSacross levelsofwateravailability inA. curassa‐vica and A. incarnatademonstratea leaf‐levelstrategytominimizecarbon‐watertradeoffs.Thisisinagreementwithpreviousstudiesthat observed changes in δ13C andΔ18O between years that dif-feredinwateravailability,buttherelativerankingamongspeciesinacommunityremainedconsistent (Garten&Taylor,1992;MorenoGutiérrez et al., 2012). It is possible that species‐specific carbon‐water tradeoffs could buffer plants from short‐term environmen-tal fluctuations, or itmay limit acclimation and resilience tomorepersistentenvironmental change.Futurework thatexplicitly testsspeciesmechanistic responses to long‐termenvironmentalchangeingeneral, and their IMS responses inparticular,will becritical inaddressing the generality of these results for other dryland plantspecies.

A common plastic response to water limitation is stomatalclosureandreducedgrowthoftenassociatedwithmoreenrichedδ13CandΔ18O(Ellsworthetal.,2017)andreducedleafarea(Anyia&Herzog,2004;Edwards,Ewers,McClung,Lou,&Weinig,2012).Although all four species reduced growth under dry conditions(Figure5b)bydecreasingtotalleafareaandheightandallocatingmorebiomasstorootsrelativetoleavesandstems(FigureS5),theylargelymaintainedsimilarIMS.Thishighlightsthathighmetabolicefficiencydoesnothavetocomeattheexpenseofslowergrowth(Cernusak et al., 2007), and that species can reduce growth viadifferentialbiomassallocationratherthanreducingleafmetabo-lismperse.Indeed,althoughspecieshadlowergrowthunderdryconditions,theyappeartohavecompensatedbyproducingfewerleavesthataremoreefficientviaup‐regulatingcarboxylation(i.e.,largerleafnitrogencontentand/orVcmax).Thisisconsistentwithotherstudiesthatreportlowergrowthinresponsetowaterlimita-tionaccompaniedbyincreasingleafnitrogen(Edwardsetal.,2012)andnochanges inδ13C (Johnson&Bassett,1991).Moreover,A. verticillatahadlargerIMSunderdryconditions,achievedbypro-portionally lowerδ13C relative toΔ18O.Lowerδ13Cunderwater

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limitationisnottypicallyexpected,buthasalsobeenreportedforbristlegrasses (Ellsworthet al., 2017).Plasticity inbiomass, leafnutrientallocationandstomatalbehaviourallfocusresourcesintowater‐use,eitherbyincreasingrootbiomasstoobtainwaterorbyconservingwaterviastomatalclosureandreducingleafarea.ThiscouldindicatethatmetabolicdemandforCO2ismorefixedwithina species, but that water dynamics are more plastic and drivethe within‐species response across different water availabilities(Gilbert,Zwieniecki,&Holbrook,2011).

4.3 | Evolution of plant ecophysiological strategies

Acrossspecies,thosethatweremoremetabolicallyefficientattheleaf‐levelweregenerallyfromdrylands,hadsmallerleaves,andgrewmoreslowly,likelyconservingwaterattheexpenseoffastgrowth.This is consistentwith predictions of leaf economic and fast‐slowgrowthstrategytheories,whichpredictslowergrowthratesunderresource limitations (Reich, 2014; Wright et al., 2004). However,within‐species responses were less consistent with these predic-tions, as plants under water limitation did grow more slowly butwerenomoremetabolically inefficientatthe leaf levelthanthoseunderwatersaturatedconditions(Figure5).Moreover,bothamongandwithinspeciestraitcombinationswerelargelyinconsistentwithleaf economic and fast‐slow growth strategy predictions, namely,thatfastergrowingplantsinourstudyhadrelativelylowerleafni-trogen, less efficient carboxylation and no substantial differencesin leafsize, structureor ratesofphotosynthesis relative toslowergrowingplants(FiguresS3–S6).

Our results on the relationship between leaf economic traitsand IMS highlight some of the challenges in comparing and cor-relatingtraitsthatcrossscalesandlevelsofplantorganisation(i.e.,whole plant vs. specific organs, area vs.mass) (Lloyd, Bloomfield,Domingues, & Farquhar, 2013). In addition, across‐clade compari-sons,whichareahallmarkofleafeconomicspectrumstudies(Reich,2014;Wrightetal.,2005,2004),mayconflatecoarse‐grainedstrat-egy shifts with mechanistic shifts that occur as new species areformed within a clade. Given that IMS integrates both area andmass‐based traits, this isaclearadvantageofapplying IMS,aloneoralongsideotherecologicalstrategyapproaches.AlthoughIMSisaleaf‐levelstrategyandmaybelimitedinitsabilitytopredictplantgrowthper se, it has thedistinct advantageof being an endpointintegrationofmultipleanatomicalandphysiologicalcharactersthattogetherdefineaplant’sstrategytobalancemetaboliccarbongainandwaterloss.

Effortstounderstandanddescribehowgeneralcategorisationof plant characteristics describe strategies for success in differ-ent environments has progressed fromGrime’s conceptualmodel(Grime, 1977) to more explicitly mechanistic and predictive ap-proaches(Chapin,Autumn,&Pugnaire,1993;Reich,2014;Wrightetal.,2004).Muchofthefocushasbeenonleafanatomicalcharactersandinstantaneousgasexchangerates,butevidenceforthegener-alityofpredictedtraitcombinationsisdecidedlymixed.Thisisper-hapsinpartbecausecertaintraitcombinationsthatareexpressed

acrossspecies,presumablyasaresultofnaturalselection,maydifferfromtraitcombinationsthatdefineplasticresponsestomoreshort‐term environmental changes. Similarly, there are differential traitcombinationsthatcanarriveatasimilargrowthormetabolicrate.Weoffer the IMS framework tobetter understand themechanis-ticbasisofaplant’sstrategytobalancemetaboliccarbongainandwater lossatthe leaf‐level.Althoughnotexplicitlycorrelatedwithgrowthrates,aplant’sIMSappearswellmatchedtoenvironmentalconditions.Whenconsideredalongsidecurrentlyestablishedstrat-egytheories,andespeciallywithinaphylogeneticcontext,variationinIMSamongandwithinspeciesmayshedlightoncurrentlyunre-solvedquestionsrelatingtoevolutionandecologyofplantecophys-iologicalstrategies.

ACKNOWLEDG EMENTS

WethankAmyHastingsandPatriciaJonesforhelpwithseedger-minationandgrowthprotocols.WearegratefultoKimSparks,JohnPollack, Sylvia Prehmus, Brynne Merkeley, Anna Daytz, MichaelRoddy,JuliaSealockandVictorAndreevforhelpinthelabandtech-nicalassistance,andtoKelseyJensen,BenJohnsonandFionaSoperfor helpful feedback on the manuscript. Shannon Straub is grate-fully acknowledged for permission to use previously unpublishedplastomesequences.ThisworkwassupportedbyNSFDEBawards1457510/1457473toM.F.andShannonStraubandNSFIOS‐1645256toA.A.A.Theauthorshavenoconflictsofinteresttodeclare.

AUTHORS’ CONTRIBUTIONS

E.M.G.,A.A.A.andJ.P.S.conceivedoftheresearchidea;M.F.pro-duced the phylogeny; E.M.G. collected and analysed the data;E.M.G.,A.A.A.andJ.P.S.wrotethemanuscript,allauthorsdiscussedtheresultsandcontributedtothefinalmanuscript.

DATA ACCE SSIBILIT Y

Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.203pf67(Goud,Sparks,Fishbein,&Agrawal,2019).

ORCID

Ellie M. Goud https://orcid.org/0000‐0001‐5494‐5884

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How to cite this article:GoudEM,SparksJP,FishbeinM,AgrawalAA.Integratedmetabolicstrategy:Aframeworkforpredictingtheevolutionofcarbon‐watertradeoffswithinplantclades.J Ecol. 2019;107:1633–1644. https://doi.org/10.1111/1365‐2745.13204