Ecosystemandunderstorywaterandenergy exchangeforamature,naturallyregeneratedpine flatwoodsforestinnorthFlorida1 ThomasL.Powell,GregoryStarr,KennethL.Clark,TimothyA.Martin,and HenryL.GholzAbstract: EddycovariancewasusedtomeasureenergyfluxesfromJuly2000Â–June2002abovethetreecanopyand abovetheunderstoryinamature,naturallyregeneratedslashpine( Pinuselliottii Engelm.var. elliottii )Â–longleafpine ( Pinuspalustris Mill.)flatwoodsforest.Understorylatentenergy( E )andsensibleheat( H )fluxesaccountedfor45% and55%ofwhole-ecosystemfluxes,respectively,withstrongseasonalvariationintheproportionof E attributableto theunderstory.Thepartitioningofnetradiation( Rnet)to E and H alsochangedseasonally,withhalf-hourlymean ecosystem H inthewinterpeakingat175WÂ·mÂ–2,almosttwiceaslargeas E .Incontrast,half-hourlyecosystem E and H remainedalmostequalthroughoutthedayinJulyandAugust,withmeanmiddaypeaksofapproximately200 WÂ·mÂ–2.Maximumhourlyevapotranspiration(ET)inthemonthsofJulyandAugustwas0.32and0.29mmÂ·hÂ–1for 2000and2001,respectively.Foravarietyofenvironmentalconditions,meandailyETwasapproximately2.7mmin thesummerand1.3mminthewinter.AnnualETforthefirstyearwas832mm,or87%ofannualprecipitation (956mm).Althoughleafareaindexwashigherinthesecondyear,annualETwasonly676mm,whichisconsider ablylowerthanthatofthepreviousyear,butitstillaccountedforapproximatelythesameproportion(84%)ofthe muchlowerannualprecipitation(811mm).Canopyconductancedeclinedassoilsdried,changingpatternsofpartitioningof Rnetto E . RÃ©sumÃ©: LamÃ©thodedecovariancedesturbulencesaÃ©tÃ©utilisÃ©epourmesurerlesfluxdÂ’Ã©nergiedejuillet2000Ã juin2002au-dessusducouvertforestieretau-dessusdelavÃ©gÃ©tationdusous-boisdÂ’uneforÃªtmaturedÂ’originenaturelle,Ã©tabliesurterrainplatetcomposÃ©edepindÂ’Elliotttypique( Pinuselliottii Engelm.var. elliottii )etdepindes marais( Pinuspalustris Mill.).Lesfluxdechaleurlatente( E )etsensible( H )delavÃ©gÃ©tationdusous-boisreprÃ©sentaientrespectivement45et55%desfluxdetoutlÂ’Ã©cosystÃ¨meetdefortesvariationssaisonniÃ¨resdanslaproportion de E ontÃ©tÃ©causÃ©esparlavÃ©gÃ©tationdusous-bois.LarÃ©partitiondurayonnementnet( Rnet)entre E et H aaussi variÃ©selonlessaisonsavecdesvaleursmi-horairesmoyennesde H delÂ’Ã©cosystÃ¨meatteignantunmaximumde175 WÂ·mÂ–2pendantlÂ’hiver,soitpresqueledoublede E .Aucontraire,lesvaleursmi-horairesde E et H delÂ’Ã©cosystÃ¨me sontdemeurÃ©espresqueÃ©galestoutaulongdesjournÃ©esenjuilletetaoÃ»tavecdesvaleursmaximalesdemi-journÃ©e dÂ’environ200WÂ·mÂ–2.LÂ’Ã©vapotranspiration(ET)horairemaximaleaucoursdesmoisdejuilletetaoÃ»taatteintrespec tivement0,32et0,29mmÂ·hÂ–1pourlesannÃ©es2000et2001.Pourtouteunegammedeconditionsenvironnementales,la moyennejournaliÃ¨redeETÃ©taitdÂ’environ2,7mmpendantlÂ’Ã©tÃ©et1,3mmpendantlÂ’hiver.LavaleurannuelledeET pourlapremiÃ¨reannÃ©eÃ©taitde832mm,soit87%desprÃ©cipitationsannuelles(956mm).MÃªmesilavaleurde lÂ’indicedesurfacefoliaireÃ©taitsupÃ©rieurependantladeuxiÃ¨meannÃ©e,lavaleurannuelledeETaatteintseulement 676mm,cequiestconsidÃ©rablementplusfaiblequelavaleurdelÂ’annÃ©eprÃ©cÃ©dente.CettevaleurcorrespondenvironÃ lamÃªmeproportion(84%)desprÃ©cipitationsannuelles(811mm)de2001quiontÃ©tÃ©beaucoupplusfaiblesquÂ’en 2000.LaconductanceducouvertforestieradiminuÃ©aveclÂ’assÃ¨chementdusol,cequiachangÃ©lepatronderÃ©parti tionde Rnetsousformede E . [TraduitparlaRÃ©daction] Powelletal.1580Can.J.For.Res. 35 :1568Â–1580(2005)doi:10.1139/X05-075Â©2005NRCCanada1568 Received20June2004.Accepted18March2005.PublishedontheNRCResearchPressWebsiteathttp://cjfr.nrc.caon 10August2005. T.L.Powell,2G.Starr,K.L.Clark,3T.A.Martin,4andH.L.Gholz.5SchoolofForestResourcesandConservation,Universityof Florida,P.O.Box110410,Gainesville,FL32611,USA.1JournalSeriesR-10682oftheFloridaAgriculturalExperimentStation,Gainesville,Florida.2Presentaddress:SmithsonianEnvironmentalResearchCenter,MailCode:DYN-3,KennedySpaceCenter,FL32899,USA.3Presentaddress:USDAForestService,NortheasternResearchStation,11CampusBoulevard,Ste.200,NewtownSquare,PA 19073,USA.4Correspondingauthor(e-mail:firstname.lastname@example.org).5Presentaddress:DivisionofEnvironmentalBiology,NationalScienceFoundation,4201WilsonBoulevard,Arlington,VA22230, USA.
IntroductionPineflatwoodsareawidespreadecosystemtypeonthe southeasternUSCoastalPlain,occupyingapproximately 5.35Ã—106ha(Smithetal.2001).Flatwoodsareparticularly importantinFlorida,wheretheyaccountforabouthalfof theterrestriallandscape,inamixtureofabouttwo-thirds pineuplandsandone-thirdforestedwetlands(Myersand Ewel1990).PriortoEuropeansettlement,theseecosystems werecharacterizedbyopen-canopyforestsofmixedlongleaf pine( Pinuspalustris Mill.)andslashpine( Pinuselliottii Engelm.var. elliottii ),withlow,butdenseshrubunderstories (AbrahamsonandHartnett1990).Overthelastcentury,the structureofthepineuplandshaschangedconsiderably,and muchofthenaturallyoccurringpineforestshavebeenre placedbyartificiallyregeneratedplantationsofslashpine, andtoalesserextentloblollypine( Pinustaeda L.).These plantationsaremanagedwithvaryingdegreesofsilvicultural intensity(Brown1996).Plantationsdifferinstructureand functionfromtheirnaturallyregeneratedpredecessorsina numberofways.Comparedwithmostnaturallyregenerated forests,pineplantationshavehigherstemdensity,higher treecanopyleafareaindex(LAI),lowerunderstoryLAI, smallermeantreesize,andasmallerrangeoftreesizes andtreeages.Inaddition,thelow-intensity,high-frequency firesthat characterizedpresettlementforestsareessentially excludedfrompineplantations.Theeffectsofthesecontrastingstructuralandfunctionalattributesontheenergy balanceandwaterfluxesofthelandscapearenotknown, althoughmanypublicandsomenonindustrialprivateforest landownersintheregionareincreasinglyturningtonatural regeneration anduneven-agedsilvicultureontheirlands (Owen2002). Variationsinwaterfluxandenergybalanceacrossspace andtimeareafunctionofinteractionsamongenvironmental conditions,ecosystemcharacteristics,andlandmanagement regimes.Acomplexmosaicoflanduseswithinlandscapes inducesenergygradientsthatinfluencelocalandregional waterdynamics(Baldocchietal.1996).Evaluatingtheef fectsofeven-agedforestmanagementonecosystemandre gionalwaterbudgetsonthesoutheasternCoastalPlainhas beenthefocalpointofconsiderableresearch(Riekerk1982, 1989;GolkinandEwel1984;Liu1996;Liuetal.1998; Martin2000;GholzandClark2002).Thisresearchhasshown thatevapotranspiration(ET)isthelargestcomponentofthe flatwoodswaterbalanceandaccountsfor60%Â–90%ofannual precipitation.Furthermore,ETplaysalargeroleinsurface andgroundwaterdynamicsbecauseshiftsintheratiobetween ETandprecipitationcanchangetheamountofprecipitation thatendsupingroundwaterstorageorstreamflow(Allen1982). Thegoalofthisstudywastoquantifyecosystemwater andenergybudgetsforamature,naturallyregeneratedpine flatwoodsecosysteminnorth-centralFlorida,andtounder standthekeyenvironmentalvariablesthatdeterminethese budgets.Theresearchhadthreeprimaryobjectives:(1)to quantifythewaterbudgetforthisforestovera2-yearperiod; (2)todeterminethedominantenvironmentalfactorscontrolling theseasonalpartitioningofnetradiation( Rnet)intothe main energybudgetcomponentsoflatentenergyflux( E )andsen sibleheatflux( H );and(3)todeterminetheroleofunderstory vegetationinecosystemexchangesofwaterandenergy.StudysiteandmethodsThisstudywasconductedontheUniversityofFloridaÂ’s AustinCaryMemorialForest(ACMF),15kmnortheastof Gainesville,AlachuaCounty,Florida,USA(29Â°44 N, 82Â°09 30 W).Theelevationofthesiteis50mandtheto pographyisflat.Thestudystand(Table1)wasanaturally regenerated,41-ha,mixedslashpineandlongleafpinestand (64%and36%ofthestandbasalarea,respectively).Priorto statepurchasein1936,theforesthadbeenselectivelyharvestedfortimberandotherwiseutilizedasgrazingland.A currentmanagementobjectiveisultimatelytorestoreelementsof foreststructureandfunctiontypicalpriortoEuropean settlement,includinglarge,widelyspacedtrees;avigorous understoryplantcommunity;andfrequent,low-intensityfires. Thestandwasthinnedin1991,removing27%ofthebasal area,andthemostrecentprescribedfireoccurred4years priortothisstudy.Thestandiswellstratified,witha22.1m tall,relativelyopentreecanopy15mabovea1.5mtall understory.Treeagesrangedfrom20to80yearsin2001, withameanageof60years.Theunderstorywaswelldevel oped,consistingofnaturallyoccurringnativespeciesdomi natedbysaw palmetto( Serenoarepens (Bartr.)Small),gallberry ( Ilexglabra (L.)Gray),waxmyrtle( Myricacerifera L.),and wiregrass( Aristidastricta Michx). ThesoilsofthissitearepoorlydrainedUlticAlaquods thathaveadiscontinuousspodichorizon(30Â–60cmdepth) andadeeperargillichorizon(100Â–140cmdepth,Gastonetal. 1990).Duringthestudyperiod,thewatertablerangedfrom 1.3mdepthinSeptember2000tonearl y3min depthin July2000andJune2001(Fig.2E),withtheaverageconsider ablydeeperthanthelong-termaverageof1Â–2m(Abrahamson andHartnett1990). Long-term(1961Â–2004)meanannualprecipitationrecorded 5kmfromthesitewas1259mm,with52%fallingfrom JunetoSeptember(NationalClimaticDataCenter2004). Annualprecipitationduringthestudyperiodwas956and 811mmduringthefirstandsecondyears,respectively (Fig.1A). Long-termmeanmaximumandminimumtem peraturesforthemonthsofJanuaryandJulywere19.0Â°C and6.1Â°C,and32.6Â°Cand21.8Â°C,respectively(National ClimaticDataCenter2004).Duringthestudy,meanmaximum andminimumtemperaturesforJanuaryandJulywere17.6Â°C and5.6Â°C,and31.8Â°Cand22.9Â°C,respectively(Fig.1B).Â©2005NRCCanadaPowelletal. 1569 Standarea41ha(95%pin euplands,5%cypress wetlands) Stemdensity325Â±36haÂ–1 Canopyheight22.1Â±0.55m TreeDBH(2001)25.7Â±1.17cm Treebasalarea(2001)18.0Â±0.95m2Â·haÂ–1 TreecanopyLAIa2000Â–2001:summer,3.4;winter,2.6 2001Â–2002:summer,3.8;winter,2.6 UnderstoryLAIaJan.2001: Serenoa ,0.7; Ilex ,0.2 Feb.2002: Serenoa ,1.0; Ilex ,0.2 Note: Valuesaremean Â± 1standarderror.aAll-sidedleafareaindex. Table1. Characteristicsofthenaturallyregenerated Pinus palustris Â– Pinuselliottii standintheAustinCaryMemorialForest.
Energyfluxmeasurements Netfluxesoflatentheat( E )andsensibleheat( H )were measuredusingstandardeddy-covariancemethods(e.g.,Clark etal.1999,2004). E wasestimatedas  Ewc =awhere aisthedensityofair, w istheinstantaneousdevia tionofverticalwindspeedfromthemeanverticalwind speed( www =ÂŠ ), c istheinstantaneousdeviationofwater vaporconcentrationfromthemeanwatervaporconcentra tion( ccc =ÂŠ ),andtheoverbardenotesatimeaveraged, 200-srunningmean. A30-mwalk-upscaffoldingtowerwaserectedinthecen terofthestand,extendin g8mabovemeancanopyheight.A mobile3-mantennatowerwasalsoerectedtomeasureaboveunderstoryfluxes( Euand Hu).Weassumedthat Euand Hufluxestransferredacrossahorizontalplaneexistingbetween thebottomofthecanopyandtheunderstory(Baldocchiand Vogel1996,1997).Theunderstorytowerwasmovedbe tweenthreefixedlocations(30Â–40mfromthetower)to dampenlocaleffectsofunderstoryheterogeneity,anddata werepooledforanalysis.Windspeedwasmeasuredwith3dimensionalsonicanemometersmounte d2mabovethetop ofthecanopytoweran d1mabovetheunderstorytower (WindmasterPro,GillInstrumentsLtd.,Lymington,UK). Closed-pathinfraredgasanalyzers(IRGA)(LI-6262,LICOR,Inc.,Lincoln,Nebraska)wereusedtomeasureH2O concentrationsat10Hz.Thegassampleswerepumpedat 6.0LÂ·minÂ–1fromtheanemometerstotheIRGAsthrough 4mmi.d.TeflonÂ®coatedtubes.Meangassamplelagtimes betweentheanemometersandtheIRGAswereapproximately 10and12sforthecanopyandunderstorytowers, respec tively.Nitrogenwasrunatarateof0.10LÂ·minÂ–1througha desiccantandintotheIRGAreferencecellstoestablisha water-freebaseline.TheIRGAswerecalibratedevery2to 3daysusingadewpointgenerator(LI-610,LI-COR,Inc.). Fluxcalculationsoftwarewasusedtorotatethehorizontal windvelocitiestoobtainturbulencestatisticsperpendicularÂ©2005NRCCanada1570 Can.J.For.Res.Vol.35,2005 0 10 20 30 0 20 40 60 80 100 120 07/0010/0001/0104/0107/0110/0101/0204/0207/02 0.00 0.05 0.10 0.15 0.20 0.25A Bmax. min.CWeeklyprecipitation(mm)MeanweeklyT(C)air oSoilwatercontent (at10cm,v/v)Fig.1. (A)Weeklyprecipitation,(B)maximumandminimumairtemperature,and(C)volumetricsoilwatercontentat10cmdepth forthetwomeasurementyears.
tothelocalstreamlineusingReynoldsdetrending(200-s constant,Katuletal.1999).Netscalarfluxeswereaveraged athalf-hourintervals.Fluxeswerecollectivelycorrectedfor attenuationofthegasconcentrationsinthesamplingtube, thenonidealfrequencyresponseoftheLI-6262,andsensor separationlossusingtransferfunctions(Moncrieffetal.1997). Thedatawerescreenedtoeliminatespuriousorincom pletehalf-hourlydataresultingfromsystemmalfunctionor environmentaldisturbance.Half-hourvalueswereeliminated iftheymetanyofthefollowingcriteria:(1)10-Hzdatawere incompletebecauseofinterruptionsforcalibrationormain tenance,(2)datawerecollectedduringrainevents,(3)data werecollectedwhenatmosphericconditionswerestable,lead ingtopoorcouplingbetweencanopyairandatmosphericair (wedefinedstableconditionsashalfhourswithfrictionve locity( u *)<0.2mÂ·sÂ–1(Gouldenetal.1996;Clarketal. 1999,2004)),or(4)10-Hzdataexhibitedunusuallyhighor unusuallylowvariation.Intotal,ecosystemfluxeswere measured~70%ofthetimeandaboutone-thirdofthose datawereeliminatedbythescreeningcriteria.Therewasno apparenttemporalbiasinthedistributionofscreeneddata. Waterandenergybudgetcalculations Interception( I )wascalculatedbysubtractingthroughfall (TF)andstemflow(SF)fromprecipitationandwasmeasuredonaneventbasisperiodicallyinthesummerandwintertoincludeseasonalvariationinLAI.TFwascollectedin twent y1mlong(808cm2)troughsthatwereslightlyangled anddrainedbyfunnelsinto4-Lcontainers.TFdepthwas determinedbydividingthevolumeofwatercollectedin eachtroughbythecollectionarea.SFwasestimatedas1% ofprecipitation( P )whentheeventwas>2.5mm(Allenand Gholz1996),otherwiseSFwasassumedtobenegligible.InterceptionforrainfalleventsinwhichTFwasnotmeasured wasestimatedfromaregressionof(TF+SF)on P . AnnualETwasestimatedbysumminghalf-hourlyabovecanopyeddycovarianceecosystemlatentheatflux( Ee) values.Forhalfhourswithmissingdata, Eewasestimated bysubstituting Rnet,vaporpressuredeficit(VPD),aerody namicconductance( ga),andcanopyconductance( gc)into thePenmanÂ–Monteithequation(Monteith1965). Rnetand VPDwereobtainedfromtheweatherstation,and gawas calculatedas ga= u *2/ uz(Kelliher1993;BaldocchiandVogel 1997),where uzishorizontalwindspeedatthesonicane mometerheight(30m).Ifsonicanemometerdatawerenot availableforatimeperiod,themedianobserved gavalue wasused.Amodelwasusedtosimulate gcfrommeasured RnetandVPDdata(Jarvis1976;Martinetal.1997):  ()() gfRfcnetVPD =Ã— where gcisthepredictedbulkcanopyconductance,and Âƒ( Rnet)isafunctionrelating gmaxto Rnet,where gmaxisde finedasthehypothetical gcunderagiven Rnetwhenthereis noreductionin gcduetoVPD.Avaporpressuredeficit function, f (VPD),derivedfromboundarylineanalysis(Martin etal.1997)wasthenusedtoreduce gmaxandproducethefi nalestimateof gc.Weparameterizedthemodelwithadata setofmeasuredbulk gccalculatedbysubstitutingmeteoro logicaldata, ga,and EeintotheinvertedPenmanÂ–Monteith equation(BaldocchiandVogel1997;Martinetal.1997).To keepthe gcmodelsimpleandparsimonious,wedesignedthe modeltoonlycapturetypicalvariationin gc,andtherefore eliminatedunusuallyhighorlow gcvaluesfromthemodel parameterizationdatasetusingthefollowingcriteria: (1)parameterizationdatawererestrictedtothosehalfhours when Eewaswithin50%of Eepredictedfromtheregres sionbetween Eeand Rnet(40%ofparameterizationdata); (2) gcvalues>0.012mÂ·sÂ–1wereeliminated(<4%ofpara meterizationdata);(3)datawerescreenedwhen Rnet<200 WÂ·mÂ–2and gmax>0.002mÂ·sÂ–1(<1%ofparameterization data).Whenthegap-fillingmodelwascomparedwithavali dationeddy-covariancedataset( n =829randomlyselected halfhours)thatwasnotusedformodelparameterization,re sidualsshowedslightunderpredictionatveryhighVPD (>2.5kPa)andverylowVPD(<0.5kPa),butthemeasured andmodeled E sumswerewithin10%ofeachother. Whenthecanopywaswet,thePenmanÂ–Monteithequation wasreducedtothePenmanequation(Penman1948)byre moving gcfromtheequation,assumingwateronlyevaporated fromexteriorsurfacesuntilthecanopydried.Forgap-filling Eeduringrainevents,thePenmanmodelwasusedforhalf hoursthathadprecipitationandsubsequenthalfhoursuntil thePenmanmodelevaporatedthedepthof I .Once I was evaporated,themodelincorporatingthePenmanÂ–Monteith equationandeq.2resumed. Tofacilitateanalysisofseasonalvariationin Eeandecosystemsensibleheatflux( He),wecreatedstandardized monthlyvaluesofthesevariables.Weperformedmonthly regressionsof Eeand Heversus Rnet,andweusedthepredictionsat Rnet=500WÂ·mÂ–2asthestandardizedvalues.To estimatetherelativeimportanceof gcand gaforcontrolling evapotranspiration,thedecouplingcoefficient wascalculatedasdescribedbyJarvisandMcNaughton(1986). Biomassandleafareaestimates A50mÃ—50minv entoryplotwasrandomlylocatedin eachoffourquadrantsaroundthetower.Theinventoryplots wereusedformeasurementsoftreediameterandheight, canopyandunderstoryLAI,andthroughfall.LAIwascalcu latedfromannuallitterfallcollectedmonthlyfromten1-m2littertrapsrandomlylocatedwithintheinventoryplotsfol lowingthemethoddescribedinMartinandJokela(2004). Litterfallwasdriedfor3daysat70Â°Cinanoven,andnee dleswereseparatedandweighedtothenearest0.1g.The needlefallyearwasdefinedasMarch1throughFebruary28, sincenewneedleflushingbeginsinMarch.Aneedlefall curvewasintegratedwithaneedle-accretioncurvecalcu latedfromalogisticequation(Gholzetal.1991;Dougherty etal.1995)toestimateseasonalchangesincanopyfoliage biomass.Multiplicationbytheappropriatespecificleafareas ofeachcohortproducedareaequivalents(Dalla-Teaand Jokela1991;Gholzetal.1991). Allometricequationswereusedtodetermineall-sidedLAI ofunderstorysawpalmettofrommeasurementsoffrond bladelengthandrachislengthmadeina2 5mÃ—25msub plotatthecenterofeachinventoryplot(Gholzetal.1999). GallberryLAIwasestimatedusingmeasurementsoftotal stemheight(centimetres)ofindividualplantsmadeintwenty 1-m2subplotsdistributedrandomlyincensusplots,usinga previouslyderivedequationbasedondestructivesampling:Â©2005NRCCanadaPowelletal. 1571
leafarea(cm2/plant)=(totalheightÃ—6.652)Â–110.2( n = 30,H.L.Gholz,unpublisheddata). Meteorologicalandsoilmeasurements Meteorologicaldatawerecontinuouslycollectedduring thestudyperiodbyscanningstandardmeteorologicalsen sorsevery5minandthenaveragingeveryhalfhourwitha datalogger(EasyloggerEL824-GP,OmnidataInternational, Ogden,Utah).Sensorsweremountedontopofthetowerfor measuringincidentsolarradiation,photosyntheticallyactive photonfluxdensity(LI-200andLI-190,respectively,LICOR, Inc.),netradiation(Q7,RadiationandEnergyBalance Systems,Inc.,Seattle,Washington),windspeedanddirec tion(3001-5,R.M.YoungCompany,TraverseCity,Michi gan),temperatureandrelativehumidity(HMP45C,Vaisala, Inc.,Helsinki,Finland),andprecipitation(tippingbucket, SierraMisco,Inc.,Berkeley,California).Watertabledepth wasmeasured15mfromtheinstrumenttowerwithaStevenÂ’s waterdepthgage(F-68,LeupoldandStevens,Inc.,Beaverton, Oregon).Volumetricsoilmoisturecontent, ,andsoiltem perature, Ts,weremonitoredwithcombinationthermistorÂ– dielectriccapacitanceprobes(Hydra,StevensVitel,Inc., Chantilly,Virginia).Ecosystemsoilheatflux( G )wasesti matedbyaveragingsoilheatfluxmeasurementscalculated fromthreesoilheatfluxplates(HFT-3.1,RadiationandEn ergyBalanceSystems,Inc.)buried10cmbelowthesoilsur faceinthreeseparatelocations ,8mfromthetower.Thesoil heatfluxforeachsensorwascalculatedasthesumofsoil heatfluxmeasuredatsensordepth(10cm)andtheenergy storedinthesoilabovethesensor, S ( G = G10cm+ S ).Soil bulkdensityof0.72gÂ·cmÂ–3(Schmalzeretal.2001)anda soilheatcapacityof840JÂ·kgÂ–1Â·KÂ–1wereusedwithlocal half-hourlymeasurementsof Tsand tocalculate S (HFT3.1technicalreference). Statisticalanalysis RegressionanalysisusingSigmaPlot5.0RegressionWizard (SPSSScience,Chicago,Illinois)andtheGLMprocedureofÂ©2005NRCCanada1572 Can.J.For.Res.Vol.35,2005 Fig.2. Monthlyvaluesofecosystemlatentenergy( Ee)(A)andsensibleheat( He)(B)fluxesstandardizedatanetradiation( Rnet)of 500WÂ·mÂ–2,derivedfrommonthlyregressionsof Eeand Heagainst Rnet;meanmonthlymidday(1000Â–1400hours)valuesofthe decouplingcoefficient(C),monthlyneedlefall(D),andwatertabledepth(E).
SAS8.1(SASInc.,Cary,NorthCarolina)wasconductedto explorerelationshipsbetween E and H andenvironmental variables.Analysisofcovariancewithindicatorvariables wasusedtodetectdifferencesbetweenseasonalregressions. Censusplotplantmeasurementdatawereanalyzedusing Statistica(StatsSoft,Inc.,Tulsa,Oklahoma)withthenonparametricKruskalÂ–Wallistesttodeterminewhetherthere weresignificantinteractions( p =0.05)betweenplotsandindividualsampleswithintheplots(Stevens1996).Nosignificantploteffectswerefound,soallplotdatawerepooled.ResultsMeteorologicaldata Duringthestudy,FloridawasexperiencingaÂ“100-year droughtÂ”,withtotalprecipitationof956and811mmduring thefirstandsecondyears,respectively,withapproximately 60%fallingduringJulytoOctobereachyear(Fig.1A). Therewere86dayswhen P >1mminthefirstyearand 78dayswhen P >1mmduringthesecondyear.Volumetric soilwatercontent( )at10cmdepthvariedwidely,from fieldcapacity(25%forthesandysoilsatthissite)toaslow as3.5%afterlongperiodswithlowprecipitation(Fig.1C). Thelow valuesof3.5%areequivalenttosoilmatricpoten tialofapproximatelyÂ–0.78MPa,basedonsoilmoisturere leasecurvesgeneratedforlocalSpodosols(H.L.Gholz unpublisheddata). Needlefallandleafareadynamics Inadditiontothenormalfallpulseofneedlefall(Gholzet al.1991),therewereprematureneedlefallpulsesinMayand Juneof2000and2001,where50%oftheannualneedlefall occurred(Fig.2D).CanopyLAI(all-sided)hadaseasonal trendwithpeakvaluesbetween3.4and4.0inAugustand minimumvaluesbetween2.2and2.6inMarch.Understory LAIwas0.9inJanuary2001and1.2inJanuary2002(Ta ble1). Exchangesofmomentumandenergy Therelationshipbetweenfrictionvelocity( u *,metresper second)andhorizontalwindspeed( uz,metrespersecond) waslinear( u *=0.241 uzÂ–0.086; R2=0.72, n =11997). Thisrelationshipincludeddatafromallwinddirectionsand indicatesthateddiespenetratedtheopencanopyinarelativelyuniformmanner.Netradiationwaslinearlyrelatedto incidentsolarradiation, Rg( Rnet=0.77 RgÂ–14.2; R2=0.98, n =4373).Ameanalbedoof0.23wascalculatedfromthe complementoftheslopeofthisrelationship.Acomparison betweenthesumof Ee, He,and G and Rnetrevealedthatthe annualenergybalanceatthissiteclosedtowithin20%(Ta ble2,Fig.3). Ecosystem E waslinearlyrelatedto Rnetandaccounted forapproximately30%of Rnet(Table2,Fig.4A).Therela tionshipbetween Eeand Rnetwassignificantlydifferentbe tweenthe2years(slope p =0.04,intercept p <0.0001). Ecosystemsensibleheatflux( He)wasalsolinearlyrelated to Rnet,and,onaverage,accountedforabout40%of Rnet. Thisrelationshipdidnotvarybetweenthetwomeasurement years(Table2,Fig.4B). Bimodalpatternswereobservedeachyearintimeseries of Eeand Hestandardizedto Rnet=500WÂ·mÂ–2(Figs.2A and2B).Aminorpeakinstandardized Eeoccurredin April,andamajorpeakoccurredinSeptemberofeachyear. AminortroughoccurredinJuneandJuly2001,andamajor troughoccurredeachJanuary.TheSeptemberpeakscoin cidedwithmaximumLAIineachyear,whileeachtrough wasassociatedwithamajorpulseoflitterfall(Fig.2D). Trendsinstandardized Heweregenerallycomplementaryto variationinstandardized Ee(Fig.2B). Thepartitioningof Rnetto EevariedwithbothVPDand soilmoisture(Fig.5).AsVPDincreasedfrom0.00to 0.75kPa, theproportionof Rnetdissipatedas Eeincreasedlin early fromabout17%to30%.Underdrysoilconditions( at10cmdepth<6%,soilwaterpotential<Â–0.45MPa), Ee/ Rnetremainedsteadyat0.30untilVPDreachedÂ©2005NRCCanadaPowelletal. 1573 Equation R2p value n Above-canopyenergybalance,October2000Â–May2002( Ee+ He+ G )=0.80 RnetÂ–17.00.83<0.00016962 Above-canopylatentenergyflux 2000Â–2001 Ee=0.29 Rnet+17.50.51<0.00014021 September2000 Ee=0.42 Rnet+13.70.65<0.0001278 January2001 Ee=0.18 Rnet+12.20.42<0.0001340 2001Â–2002 Ee=0.29 Rnet+6.10.59<0.00014666 September2001 Ee=0.34 Rnet+14.10.64<0.0001374 January2002 Ee=0.26 Rnet+5.70.58<0.0001311 Above-canopysensibleheatflux,2000Â–2002 He=0.45 RnetÂ–19.00.81<0.00018757 Understoryfluxes 2000Â–2001a Eu=0.51 Ee+14.20.62<0.00012102 Hu=0.55 HeÂ–7.20.81<0.00012102 2001Â–2002a Eu=0.43 Ee+19.40.52<0.00011553 Hu=0.45 HeÂ–3.90.77<0.00011582 Note: Allunitsarewattspersquaremetre.SeptemberandJanuaryareshowntorepresentthehighestandlowestmonthsassociatedwiththeenergy componentsofthesystem.aPooledacrossthreeunderstorytowerlocations. Table2. Energybudgetequationsandaboveandbelowcanopyfluxequationsfornetradiation( Rnet),latentenergy( E ),sensibleheat ( H ),andsoilheatflux( G ).
2.0kPa,atwhich pointtheratiobegandeclining.Underwet soilconditions, Ee/ Rnetincreasedlinearlyto0.40ataVPD of1.25kPaand thenremainedrelativelyconstant.Weekly non-light-limited gc(photosyntheticallyactivephotonflux density>1500 Âµ molÂ·mÂ–2Â·sÂ–1)waspositivelybutweaklycorrelatedwith ( gc=0.003+0.0316 ; R2=0.21). Diurnalpatternsofecosystemandunderstory E and H in wintermonths(DecemberandJanuary)andsummermonths (JulyandAugust)highlightedseasonalshiftsinenergypartitioning(Fig.6).InDecemberandJanuary, Hedominatedecosystemenergybalance,withmeanmiddayvaluesof175WÂ·mÂ–2, almosttwiceaslargeas Ee(Fig.6A).Incontrast,mean Eeand Heremainedalmostequalthroughoutthedayin JulyandAugust(Fig.6B).Understoryenergypartitioning wasmuchlessvariableoverseasons,with Euand Hure mainingapproximatelyequalthroughoutthedayinboth summerandwintermonths(Fig.6). Understory(understoryvegetationpluslitterlayerandsoil) latentenergy( Eu)andsensibleheat( Hu)fluxeswereasig nificant proportionof Eeand He(Figs.6and7).Onanan nual basis, Euand Huaccountedfor45%Â–55%ofwholeecosystemfluxes(Table2).Maximumhalf-hourly Euand Huwereapproximately200and300WÂ·mÂ–2,respectively. Therewasamajorcontrastintheproportionof Eeac countedforby Euinmidsummer(JulyandAugust)versus midwinter(DecemberandJanuary).Inmidwinter Eudomi nated Ee,withunderstorylatentheatlossaccountingfor 75%toalmost100%ofecosystemwaterlossatsomepoints duringthediurnalcycle(Fig.6A).Duringmidsummer, Euaccountedforabout50%of Eethroughoutmostoftheday (Fig.6B).Theproportionof Heaccountedforby Hure mainedsteadythroughouttheyear(Fig.6). ConductancesandETmodeling Weusedascreenedsubsetoftheeddy-covariancedatato investigateÂ“typicalÂ”variationincanopyconductance( gc) withenvironmentaldrivers,andtocreateamodelforfilling inmissingvaluesfromtheeddy-covariancetimeseries(Ta ble3).Aerodynamicconductance( ga)inthisdatasetranged from0.02to0.33mÂ·sÂ–1andhadaweaklinearrelationship with uz(Table3). Canopyconductance( gc)washighlyvariable,butdeclined nonlinearlyfrom0.048to0.001mÂ·sÂ–1withincreasingVPD (Table3).Maximumcanopyconductance,thevalueof gcthatwouldbeexpectedwithoutVPDlimitations,increased linearlywith Rnet(Table3).Evapotranspirationestimatedby substitutingthemodeled gcvaluesintothePenmanÂ–Monteith equationagreedwellwitheddy-covarianceresultsforavali dationdatasubset( R2=0.61, p <0.0001, n =1657).Asen sitivityanalysisrevealedthatannualETestimatesvariedby lessthan2%whenthegap-fillingmodelparameters(eq.2) wereindividuallychangedbyÂ±10%.Â©2005NRCCanada1574 Can.J.For.Res.Vol.35,2005 0100R(WÂ·m)net Â–2200300400500600700800900 0 100 200 300 400 500 600 700 800 900 E+H+G (WÂ·m 2 )Â–1:1Fig.3. AnnualenergybalancethefortheUniversityofFloridaÂ’s AustinCaryMemorialForestduringOctober2000Â–May2002. Forclarity,a15%randomsampleofthedatapointsisplotted. SeeTable2forcorrespondingequationsandstatistics.Rnet 0100200300400500600700800900 0 100 200 300 400 500 600 700 800 900 2000-2001 2001-2002 yr1 yr2 0100200300400 500 600700800900 0 100 200 300 400 500 600 700 800 900 1:1 1:1A B(WÂ·mÂ–2) Rnet(WÂ·mÂ–2)H(WÂ·m)eÂ–2 Ee(WÂ·m)Â–2500Fig.4. (A)Therelationshipbetweennetradiation( Rnet)andeco systemlatentenergyflux( Ee)for2000Â–2001(yr1)and2001Â– 2002(yr2).Theslopesandinterceptsweresignificantlydifferent betweenyears.(B)Therelationshipbetween Rnetandecosystem sensibleheat( He)foralldatapooledoverbothyears.Therewas nosignificantdifferenceinthisrelationshipbetweenyears.For clarity,a10%randomsampleofthedatapointsisplotted.See Table2forcorrespondingequations.
Themeanvalueofthedecouplingcoefficient varied monthlythroughoutthestudyperiod,rangingbetween0.07 and0.22(Fig.2C),indicatingstrongcouplingorstomatal controloftranspiration.Variationin followedfluctuations inthewatertabledepth(Fig.2E).Monthlymean wasposi tivelybutweaklycorrelatedwith at10cmdepth( =0.05+ 0.966 ; R2=0.23,datanotshown). Precipitation,evapotranspiration,andcanopy interception Totalprecipitation( P )was956and811mmduringthe firstandsecondyearsofthestudy,respectively,withap proximately60%fallingfromJulytoOctobereachyear. Annualevapotranspiration(ET)wasestimatedtobe832and 676mmforthe2years.Throughfall(TF)wasmeasured duringthefirstyearandthesumofTFplusstemflow(SF) wasstronglyrelatedto P (TF+SF=0.82 P Â–0.1897; R2= 0.99, p <0.0001).Thisrelationshipcanalsobeexpressedin termsofcanopyinterceptionloss( I =0.1761 P +0.1897; R2=Â©2005NRCCanadaPowelletal. 1575 Vapor Pressure Deficit (kPa) 0.00.51.01.52.02.53.03.5 E/Rnet0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50SWC < 6% SWC > 6%Fig.5. Relationshipbetweenecosystemlatentenergyflux( Ee) normalizedbynetradiation( Rnet)andvaporpressuredeficitfor drysoilconditions(volumetricsoilwatercontent(SWC)<6%) andmoistsoilcondition(SWC>6%). Energy Flux-50 0 50 100 150 200 250 EuHu EeHeTime of Day060008001000120014001600180020002200Energy Flux (WÂ·m)Â–2(WÂ·m)Â–2-50 0 50 100 150 200 250A BFig.6. Diurnalcourseofecosystemandunderstoryenergyflux componentsfor(A)midwintermonths(DecemberandJanuary) and(B)midsummermonths(JulyandAugust). -50050100150200250300350400450500 -50 0 50 100 150 200 250 300 350 Nov.2000-June2001 July2001-May2002 -50050100150200250300350400450500 -50 0 50 100 150 200 250 300 350 Nov.2000-June2001 July2001-May2002 1:11 21:11 2 A BH(WÂ·m)u Â–2E(WÂ·m) u Â–2 E(WÂ·m)e Â–2H(WÂ·m)e Â–2Fig.7. Proportionofecosystemlatentenergy( Ee)andsensible heat( He)accountedforbytheunderstorylatentenergy( Eu) (A)andsensibleheat( Hu)(B)forNovember2000Â–June2001 (1,closedsymbols)andJuly2001Â–May2002(2,opensym bols).Forclarity,a20%randomsampleofthedatapointsis plotted.SeeTable2forcorrespondingequationsandstatistics.
0.86, p <0.0001),wheretheinterceptrepresentsthecanopy storagecapacityinmillimetres.Annual I forthisstandwas estimatedtobe205mmforyear1and,byapplyingthe samerelationship,177mmforyear2. Overthecourseofthestudy,meandailyETwasapproxi mately2.7and1.3 mmÂ·dayÂ–1duringsummer(JulyÂ–September) andwinter(DecemberÂ–February),respectively.Meanhourly ETpeakedat0.32mmÂ·hÂ–1duringthesummerof2000and 0.29mmÂ·hÂ–1duringthesummerof2001.Themeandiurnal courseofETduringthesummerofthesecondyearwas asymmetrical,withadistinctmidafternoondepression.The diurnalcourseofETduringthewinterofbothyearswas symmetrical,occurringovera12-hperiodandpeakingat 0.23mmÂ·hÂ–1at1300hours.WeeklyETtrackedseasonal fluctuationin RnetmorecloselythandidLAIorenvironmental conditions.Theredidnotappeartobeawatertablethreshold belowwhichETratesweremarkedlyreduced,yetwhen E wasnormalizedby Rnet,itdidtendtodecreaseovertimeas thewatertablecontinuedtodeclineoverthestudyperiod (Fig.2).DiscussionDuringthestudy,FloridawasexperiencingaÂ“100-year droughtÂ”,withannualprecipitationfallingabout30%below long-termaverages.Typically,pinesintheseecosystemsshed mostoftheirneedlesinthemidtolatefall,withamuch smallerneedlefallpeakinMaytoJune(e.g.,Dalla-Teaand Jokela1991;Gholzetal.1991).However,uptohalfofthe needlefalloccurredinMayÂ–Juneduringthisstudy,withonly aboutaquarterduringthenormalfallperiod.Thepremature needlefallcausedsummerLAItobelowerthanusualand theseasonalrangetobeconsequentlydampened(about30% comparedwith40%instudiesfromwetteryears,Gholzet al.1991).Within-yearvariationinETandenergyexchange forotherlocalecosystemsarecloselylinkedtoLAI(Liu 1998;Liuetal.1998;GholzandClark2002),whichsug geststhatseasonalfluctuationsovertimeinforestsinthere gionmaybehighastheyexperienceandthenrecoverfrom periodicdroughts. Overthestudyperiod,meanalbedowas0.23,whichisat thelowendoftherangereportedforotherconiferforests (Jarvisetal.1976;BaldocchiandVogel1996).Thisvalueis higherthanthe0.18reportedfornearbyclosed-canopyplan tations(GholzandClark2002).Infact,itisclosertothatof anearby2-year-oldstand(GholzandClark2002),probably becauseoftherelativelylowcanopyLAIandgreaterradia tioninterceptionbyunderstoryvegetation(whichissimilar tothatregrowingintheunderstoryofthe2-year-oldstand). Thedegreeofenergybudgetclosureisonewaytoassess theaccuracyofcomponentmeasurementsinenergy-balance studies.Ifmeasurementsaretakenwithouterror,thesumof theenergydissipationterms( Ee, He,and G )shouldequal netenergyinput( Rnet),ifitisassumedthatenergystoragein canopyairorbiomassisnegligible.Onaverage,summed measurementsof Ee, He,and G inourstudywereabout 20%lowerthan Rnet(Table2,Fig.3).Similardepartures fromfullenergybudgetclosurewerefoundinanaturallyre generated,semi-aridponderosapine( Pinusponderosa )for estinOregon(Anthonietal.1999),whichalsohasanopen canopy.Whilethe20%departurefromclosureisofsimilar magnitudetothepotentialerrorofmeasurementforQ7 Rnetsensors(Â±20%,Twineetal.2000),itisalsopossiblethatthe positionofthe Rnetsensor ,8mabovethemeancanopy height,maynothavebeenhighenoughtoobtainarepresentativespatialcharacterizationof Rnetfortheentirefluxfootprintarea.Additionalerrorinourhourlyfluxestimatesmay haveoccurredbecausewedidnotmeasureheatstoragein thecanopyairorbiomass,whichcanapproach100WÂ·mÂ–2undersomemiddayconditions(BaldocchiandVogel1997). Comparisonsof E and H betweenthisecosystemand otherpineforestssuggestsimilaritiesinthepartitioningof netradiationacrossgreatlycontrastingenvironments.For example,aponderosapineforestinOregon(Anthonietal. 1999),amaritimepine( Pinuspinaster )forestinPortugal (Berbigieretal.1996),aScotspine( Pinussylvestris )forest inSiberia(Kelliheretal.1998),andaborealjackpine ( Pinusbanksiana )forestinSaskatchewan(Baldocchiand Vogel1996)allutilized27%Â–36%of Rnetfor Ee,whichis similartoboththenaturallyregeneratedstandinthisstudy aswellastheadjacentpineplantations(Table4).Therela tivelylowpartitioningofavailableenergyto Eeistypical ofpineforests,whichgenerallyhavelowLAIandlowleaflevelstomatalconductance,incontrasttomanytemperate broad-leavedforests,whichtendtohavehigherLAIand leaf-levelstomatalconductance.Asaresult,manytemperate broadleafforestspartition70%ormoreof Rnetto Ee(BaldocchiandVogel1996;Schmidetal.2000;Wilsonet al.2002). ComparingthelocalFloridapinestandswithcontrasting managementhistoriesindicatesthattheACMFstandandthe plantationsbothutilizedsimilaramountsof Rnetfor Ee.Atan Rnetof500WÂ·mÂ–2, Eeinthecurrentstudywas158WÂ·mÂ–2(averagedoverthe2years)comparedwith164WÂ·mÂ–2in surroundingplantationstands(GholzandClark2002).How ever,smallerseasonaldifferencesin Eewereobservedfor theACMFincomparisonwiththeclosed-canopyplantations. Forexample,summertime E (normalizedto Rnet=500WÂ·mÂ–2)Â©2005NRCCanada1576 Can.J.For.Res.Vol.35,2005 Equation R2p value n Aerodynamicconductance ga=0.033 uzÂ–0.0610.22<0.00014599 VPDfunctionaÂƒ(VPD)=e(Â–0.7727VPD)Â—Â—301 RnetfunctionÂƒ( Rnet)=3Ã—10Â–5Rnet+0.00210.5<0.00012933 aVPD,vaporpressuredeficit; Rnet,netradiation.Thisfunctionwasfittedthroughthetop10%ofthedataset todefinetheupperboundaryratherthantodescribethemeanresponse,andthereforeregressionstatisticsare notreported. Table3. Equationsdefiningtherelationshipbetweenaerodynamicconductance( ga)andhorizon talwindspeed( uz),andfunctionsformodelingbulkcanopyconductance( gc),wherepredicted (() gffRcnetVPD) =Ã— .
intheplantationwas40%greaterthanwintertime E ,while thesummertime E fortheACMFwasonlyabout25% greaterthanwintertime E .ThesmallerrangefortheACMF waslikelyduetodroughteffectson gc,whichwouldhave beenaffectedbyreductionsinbothLAIandleaf-level stomatalconductance.Seasonaldifferencesinstandardized Hefortheplantations(11%)weresimilartotheseasonal rangefortheACMF(12%Â–23%). Therelativelyfewstudiesthathavequantifiedunderstory energybalancehaveshownsubstantialvariationintheproportionofecosystemenergyfluxcontributedbytheunderstory(Table4;seealsoreviewbyBlackandKelliher1989). Asmightbeexpected,forestswithsparseornounderstory tendtohaveasmallercontributionof Euto Eethanforests withwell-developedunderstoryvegetation.Forexample,of theDouglas-fir( Pseudotsugamenziesii )standsreviewedby BlackandKelliher(1989),thosewithÂ“sparseÂ”ornounder storyvegetationhad EEueratiosrangingfrom0.03to 0.21,versus0.30to0.65forstandswithunderstorypro jectedLAIof1.0to3.0.Thestandinthepresentstudy wouldprobablyfallintothelattercategory,withunderstory all-sidedLAIof0.9to1.2,and EEueofabout0.50.To someextent,therelationships amongoverstory LAI,under storyLAI,and EEuemaybehomeostatic.Forinstance, Roberts(1983)suggestedthatsimilaritiesinannualevapo transpiration amongforeststandsinEuropewithwidelyvary ingov erstoryLAImaybe partially attributableto compensatory waterlossfromunderstory vegetationinstands withlower overstoryLAI. The EEueratiointhecurrentstudychangedseason ally,with Eucontributingasmuchas85%ofecosystem E inthewinterand50%Â–60%inthesummer(Fig.6).Studies inotheropen-canopypineforestshavealsoshownseasonal variationin EEue.The EEueratiointheponderosa pineforeststudiedbyLawetal.(2000)declinedfrom0.44in springto0.22Â–0.33insummer.Theyattributedthischangeto decreasedsoilevaporationfromthesparselyvegetatedunder storyas droughtdeveloped.Incontrast,BlackandKelliher (1989)citedseveralstudiesin Pinus and Pseudotsuga for eststhatdemonstratedincreasesin EEuewithdecreasing soilwateravailability(e.g.,Tanetal.1978;Robertsetal. 1980;Kelliheretal.1986).BlackandKelliher(1989)attributedthisphenomenontodifferencesbetweenoverstoryand understorycoupling.Theyhypothesizedthatthewell-coupled forestoverstories,with approaching0.0,havestrong stomatalcontrolof E andthusshowreduced E undersoil oratmosphericdroughtconditionsthatinducestomatalclosure.Incontrast,theypostulatethatunderstoryvegetationin thesestandstendedtohavehigher becauseofitsshort statureandexposuretolowwindspeeds,whichresultedin Eubeingmoreresponsiveto RnetthantoVPD(Jarvisand McNaughton1986;Martinetal.2001). Wedidnotestimate forourunderstorybecauseofthe additionalcomplexitiesandassumptionsassociatedwiththose calculations,butitdoesnotappearthat EEueinourstand respondstodroughtinthesamewayasitdoesinthestands describedinBlackandKelliher(1989).Wehypothesizethat thedeclinein EEuefromwintertosummerinthecurrent studyresultedprimarilyfromincreasedoverstory E associ atedwithelevatedLAIduringthesummer,ratherthanfrom anydifferencesbetweenoverstoryandunderstoryresponse to causedbydifferencesbetween overstory and understory .Thefactthatoverstory treeLAIwas correlated withmean monthlymidday Ee( r =0.44, p =0.0481)butnotwith Eu( r =0.12, p =0.6059)supportsthishypothesis. Thecanopywater-holdingcapacityof0.19mmforthe ACMFstandwasmuchlowerthanthe0.42Â–0.50mmfor closed-canopyplantations(Liu1998;GholzandClark2002), reflectingthelowerLAIofthisstand.However,canopyin terceptionwas20%ofannualprecipitationattheACMFin bothyears,whereasinterceptionwasonly8%ofprecipita tionfortheplantationstands(GholzandClark2002).The highinterceptionbutlowerwater-holdingcapacityforthe naturalstandisprobablyrelatedtothemuchhigherannual precipitationduringtheGholzandClarkstudy.However,it isalsolikelythatwaterinterceptedduringindividualrain eventsevaporatedmorerapidlyfromthemoreexposedsur facesofthemoreopenstructuredACMFcanopy.Â©2005NRCCanadaPowelletal. 1577 Foresttype LAI (all-sided)StemsÂ·haÂ–1 Ee(%of Rnet) Eu(%of Ee) MeanET (mmÂ·dayÂ–1)Reference Floridapineflatwoods Naturallyregenerated,summer3.43253350Â–602.7Thisstudy Naturallyregenerated,winter2.63252870Â–851.3Thisstudy Pinuselliottii plantation Rotation-aged,summer6.6130038Â—3.6GholzandClark2002 Rotation-aged,winter4.0130027Â—2.0GholzandClark2002 Pinustaeda plantation,wintera6.91150Â—Â—1.8Martin2000 Pinusbanksiana 4.018753610Â–400.5Â–2.5BaldocchiandVogel 1996,1997 Pinusponderosa 3.2620Â—44(earlyspring), 22Â–33(summer) ~1.6Â–1.7Anthonietal.1999;Law etal.2000 Pinuspinaster Â—30031152.1Berbigieretal.1996 Siberian Pinussylvestris 4.42903054Â—Kelliheretal.1998 Temperatedeciduous9.8Â—775Â— BaldocchiandVogel1996 Note: Â“Â—Â”indicatesthatdataarenotavailable.LAI,leafareaindex, Ee,ecosystemlatentenergyflux; Eu,understorylatentenergyflux; Rnet,netra diation,ET,evapotranspiration.aSapflowmeasurementsonoverstoryonly. Table4. Watervaporexchangeforpresentstudyandotherselectedsites.
EvapotranspirationhasbeenestimatedfornumerousFlorida flatwoodsecosystemsusingarangeoftechniques,including weighinglysimeters,massbalancemodels,sapfluxgauges, soilmoisturechanges,andmicrometeorology(Allenetal. 1982;Riekerk1982,1985,1989;GolkinandEwel1984;Liu 1996;Liuetal.1998;GholzandClark2002).Allenetal. (1982)usedthemassbalanceequationincomputersimula tionsofETforwatershedsinsouthFloridathatwereatleast 50%pineandestimatedETtobe70%ofannualprecipitation. Usingweighinglysimeters,Riekerk(1982)foundthatan nualETaccountedfor60%Â–90%ofprecipitationforyoung slashpinestands.Usingaprocessbasedmodel,Liu(1996) estimatedannualETtobe783Â–876mmÂ·yearÂ–1for1991Â–1993 fornearbyslashpinestandsthathadsimilarmaximumcanopy LAIvalues(2.6)andsomewhathigherstocking(544stemsÂ·haÂ–1) ascomparedwiththeACMFforest.Onadailybasisinthe winterof1997Â–1998,sapfluxdensitymeasurementsofa youngmanagedflatwoodsplantationrevealedthatforidentical climateconditions,loblollypinetranspired1.8 mmÂ·dayÂ–1,while slashpinetranspired1.5mmÂ·dayÂ–1(Martin 2000).Therefore, theACMFannualETestimatesof676Â–832mmÂ·yearÂ–1and dailyETestimatesof1.3Â–2.7mmÂ·dayÂ–1fromthisstudy comparewellwithpreviouspineflatwoodsETestimates madeatvarious spatial andtemporalscalesandwithvery contrastingmethodologies. Althoughpreviousresearchinpineflatwoodsforestsin thisregionhassuggestedthatsoiloratmosphericdrought seldomimpactstreephysiologicalfunction(Nearyetal.1990; McMurtrieetal.1994;Teskeyetal.1994),thepresentstudy tookplaceintworemarkablydryyears,andtheimpactson ecosystemfunctionwereevident.Theeffectsofdroughton energypartitioningweremanifestedinseveralways.First, reducedcanopyconductanceinresponsetoelevatedVPD (presumablycausedprimarilybystomatalclosure)resulted inmoderationofthepartitioningofradiationto E asVPD increased,evenwhensoilswererelativelymoist(Fig.5). Thisresponsewasmodifiedassoilsdriedbelow6%volu metricw atercontent,with ERnetactuallydecliningasVPD increasedabove1.75kPa(Fig.5).Thiswasaconsequence ofbothanoveralldeclinein gcwithdryingsoilandaproba bleincreaseinthesensitivityof gctoVPD(Ewersetal. 2001).Finally,monthlypatternsofradiation-normalized E appearedtobesensitivetoshort-termreductionsinLAIas sociatedwithdrought-inducedneedlefallpulses(Fig.2). Whiletheseresponsesarenotunusualintermsofgeneral forestdroughteffects(e.g.,Irvineetal.1998;Ewersetal. 2001),theyareoutoftheordinaryfortheseflatwoodseco systems,wheresoilwateravailabilityisseldomanissuebe causeofthepresenceofawatertablewithi n1mofthe surface(AbrahamsonandHartnett1990;Teskeyetal.1994). Itislikelythattherecedingwatertableduringthecourseof thisstudy(Fig.2)inducedorenhancedphysiologicalre sponsestoVPDanddryingsurfacesoilinthisforest. The decouplingcoefficientvariedmonthlyandappeared tobecloselytiedtothedepthofthewatertable(Fig.2).The declinein gcassoilsdriedwasaprimarydriverofmonthly variationin (datanotshown). isessentiallyanindexof theratio gc/ ga(JarvisandMcNaughton1986).Since gahad nodiscernibleseasonaltrends,variationin gcwasthemain determinantofchangesin inourstudy.Despitenoticeable monthlyvariationin ,eventhehighestvalueswerewell withintherangesreportedforconiferforests,wherelow gcandhigh gatendstobethenorm,leadingtostrongcontrol ofETby gcandVPD(JarvisandMcNaughton1986;Martin etal.2001). Whilevariationinannualprecipitationprecludesstrictcom parisonofthe magnitudeofannualETbetweentheACMF standandsurroundingplantations,theproportionsofprecip itationconsumedbythepinestandsintheregionaresimilar: 80%Â–86%fortheACMFandthe2-year-oldstand,and 92%Â–113%fortheclosed-canopyplantations(Gholzand Clark2002).Inotherwords,annualETforthevariousFlorida pinesystemsseemstofluctuatewithprecipitation,while maintainingarelativelyconstantET/ P .Thiscontrastswitha ponderosapineforestinOregonwithannualprecipitationof 595and188mmduring2years,whichmaintainedsimilar annualETof430and400mm,butwheretheET/ P ratios were0.72and2.12(Anthonietal.1999).Oneinterpretation isthattheFloridapinesavoiddroughtbyclosingtheirstomata toregulatewaterloss,whereastheponderosapinetolerate droughtsandmaintainETbyaccessingalternativewater sourceswithdeeptaproots(Anthonietal.1999). Inconclusion,thisnaturallyregeneratedpineflatwoods ecosystempartitionsnetradiationinto E and H inpropor tionssimilartothoseofotherconiferforests.Localsilvi cultural practicesaffectcanopystructureandhencethetransfer ofmomentumfromtheatmosphereandalbedo,andthepartitioningofenergybetween E and H .However,silviculture andstandagedonotseemtoaffectannualETasmuchas dofluctuationsinannualprecipitation.Clearly,the understoryintheopen-canopystandinthepresentstudy playsamore significantroleinecosystemhydrologybecause ofthegreaterunderstoryLAIandenhancedpenetrationofradiationthrough theoverstorycanopy.Forthisreason,a priority forfutureresearchshouldbeabetterunderstandingofthe hydrologicconsequencesofthefrequent,low-intensityfires thatcharacterizeflatwoodsmanagedundernonintensive silviculturalregimes,andwhichresultintheremovaland regrowthoftheunderstoryduringeachburningcycle.AcknowledgementsThisresearchwassupportedbytheBiologicalandEnvi ronmentalResearchProgram,USDepartmentofEnergy, throughtheSoutheastRegionalCenteroftheNationalInsti tutefor GlobalEnvironmentalChange;theNASALandCover andLandUseChangeProgram;andtheUniversityofFlorida SchoolofForestResourcesandConservation.Wealsothank RyanAtwood,JoseLuisHierro,JenniferStaiger,Jennifer Jacobs,andJulieGravesfortheircontributionstothiswork.ReferencesAbrahamson,W.G.,andHartnett,D.C.1990.Pineflatwoodsand dryprairies. 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THE IMPACT OF FERTILIZATION AND THROUGHFALL REDUCTION ON PINUS TAEDA WATER RELATIONS AND GROWTH By MAXWELL WIGHTMAN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2014
Â© 2014 Maxwell Wightman
To my parents
4 ACKNOWLEDGMENTS I would first like to express my deepest gratitude to my advisor Dr. Timothy Martin for his unconditional support and patience in guiding me through my graduate education. His mentoring and advice has helped me to grow n ot only as a research scientist, but as also a person. I am also pleased to thank Dr. Eric Jokela whose guidance was essential to my education in the ecology and management of southern pine forests. Thanks are also due to Dr. Wendell Cropper who has helped me to understand the complex world of ecological modeling. I owe a special thanks to Dr. Carlos Gonzalez whose friendship and mentoring was essential to the development of this document. This project would not have been possible without the technical and moral support of many other individuals including: E. Ward, A. Noorments, J.C. Domec, S. Gezan, C. Drum, B. Caudill, J. McCafferty, J. Cucinella, G. Lokuta, B. Ruffin, A. Milligan, B. Gottloeb, A. Garcia, P. Subedi, A. Fields, S. Walton, and J. Ireland. I am very grateful to the funding provided by The Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) and the University of Florida Institute of Food and Agricultural Sciences, both of whom made my graduate studies possibl e. PINEMAP is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #2011 68002 30185. I am also very grateful for Foley Timber and Land Company for allowing this study to be conducted on their property and for providing technical support. Many thanks go to my friends, especially Dave, Skylar, Grant, and Jake, for their camaraderie through my graduate studies. I would especially like to thank my parents Corinne and Phil and my siblings Megan and Micah for their support.
5 TABLE OF CONTENTS page ACKNOWLEDGM ENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURE S ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 LITERATURE REVIEW ................................ ................................ .............................. 11 Development of Southern Pine Plantation Management ................................ ........ 11 Climate Change and the Southeast Unites States ................................ .................. 16 Carbon, Water, and Southeastern Forests ................................ ............................. 19 Plant Water Relations and Southern Pines ................................ ............................. 23 Physiological Principles in Predicting Growth ................................ ......................... 26 2 RESPONSE OF LOBLOLLY PINE TO THROUGHFALL REDUCTION AND FERTILIZER APPLICATION ................................ ................................ ................... 28 Introduction ................................ ................................ ................................ ............. 28 Methods ................................ ................................ ................................ .................. 31 Study Design ................................ ................................ ................................ .... 31 Throughfall Exclusion D esign ................................ ................................ ........... 32 Meteorological and Soil M easurements ................................ ............................ 33 Leaf Area Index and Specific Needle Area ................................ ....................... 35 Sap flow and Transpiration ................................ ................................ ............... 36 Stomatal Conductance ................................ ................................ ..................... 39 Hydraulic Conductance ................................ ................................ .................... 40 Statistical Analysis ................................ ................................ ............................ 41 Results ................................ ................................ ................................ .................... 41 Soil Bulk Density, T exture, and M oisture ................................ .......................... 41 Leaf Area Index, Litterfall, and G rowth ................................ ............................. 42 Whole tree Hydraulic C onductance ................................ ................................ .. 43 Radial Variation in Sap Flow ................................ ................................ ............ 44 Transpiration ................................ ................................ ................................ .... 44 Canopy Conductance ................................ ................................ ....................... 45 Discussion ................................ ................................ ................................ .............. 45 3 CONCLUSIONS AND FUTURE RESEARCH NEEDS ................................ ............... 61
6 LIST OF REFERENCES ................................ ................................ ............................... 64 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 76
7 LIST OF TABLES Table page 2 1 Mean pre treatment basal area of plots within each block during the winter of 2011 2012 with associated standard errors ................................ ........................ 54 2 2 Minimum, maximum, and mean DBH of sap flow measurement trees during the winter of 2012 2013 ................................ ................................ ...................... 54 2 3 Mean aboveground net primary productivity (ANPP), basal area growth, projected leaf area index (LAI), and litt erfall ................................ ....................... 54 2 4 Water use and stomatal sensitivity to vapor pressure deficit .............................. 55 2 5 Reported values of stomatal conductance at vapor pressure deficit = 1 kPa (G Cref 2 1 ) and stomatal conductance sensitivity to vapor pressure deficit ( 2 1 1 ) ................................ ................................ ....... 56
8 LIST OF FIGURES Figure page 2 1 Example of a throughfall exclusion structure ................................ ...................... 57 2 2 Time series of water relations parameters, leaf area index, and meteorological data ................................ ................................ ............................ 58 2 3 Mean whole tree hydraulic conductivity (K S ) for each measurement date from October 2012 (Month = 10) to November 2013 (Month = 9) ............................... 59 2 4 Sap flow rates at 2 4 cm and 4 6 cm depths expressed as a percentage of sap flow at 0 2 cm depth ................................ ................................ .................... 59 2 5 Values of stomatal conductance calculated at vapor pressure deficits of 2, 5, 10, 20, 30, and 40 mbar ................................ ................................ ..................... 60
9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science THE IMPACT OF FERTILIZATION AND THROUGHFALL REDUCTION ON PINUS TAEDA WATER RELATIONS AND GROWTH Maxwell Wightman August 2014 Chair: Tim Martin Major: Forest Resources and Conservation Loblolly pine ( Pinus taeda L. ) forests are a dominant forest type in the southeastern United States, and are of great ecologic and economic value. The impact of fertilization on the growth and water relations of loblolly pine has been investigated by numerous studies; however the effe ct of drought is not well understood. Drought is of particular interest due to the potential for climate change to alter soil water availability. In this study we investigated the impact of fertilization and a 30% reduction in throughfall and on loblolly p ine productivity, transpiration, hydraulic conductance, and stomatal conductance sensitivity to vapor pressure deficit. The study was installed in a ten year old loblolly pine plantation on a somewhat poorly drained site in northern Florida. Throughfall reduction did not impact forest productivity or water relations. This lack of response was attributed to abundant rainfall and the ability of trees to access the shallow water table at this site. Fertilization increased basal area production by 0.6 m 2 ha 1 yr 1 and maximum leaf area index by 0.5 m 2 m 2 , but did not affect whole tree hydraulic conductance or the sensitivity of stomatal conductance to vapor pressure deficit. During the spring, when leaf area and vapor pressure deficit were high, the
10 fertilize r only treatment increased monthly transpiration by 17% when compared to the control. This relationship, however, was not significant during the rest of the year. This study suggest that a 30% reduction in throughfall does not affect the growth or water re lations of loblolly pine forests on somewhat poorly drained sites.
11 CHAPTER 1 LITERATURE REVIEW Development of Southern Pine Plantation Management Over the last 70 years the productivity and extent of pine plantations have dramatically increased in the southeast United States (U.S.). From 1952 to 2000 the area of pine plantations in the South increased from 728,434 hectares containing 18.6 million m 3 of timber to 13 million hectares containing 677 million m 3 of timber (USDA 1988; Wear and Greis 2002). L oblolly pine ( Pinus taeda L. ) accounts for 84% of all seedlings planted in the southeast ern (Schultz 1997; McKeand et al. 2003). Since the 1950s the productivity of these plantations has increased three fold , while rotation lengths have decreased by more than 50% due to advances in silv icultural practices and genetic selections (Johnsen et al. 2001; Fox et al. 2007). Due to these advances, the management of loblol ly pine and slash pine (Pinus elliottii) plantations now commonly include elements of mechanical and chemical site preparation, fertilization, herbaceous weed control, and deployment of genetically improved planting stock (Jokela et al. 2010). Although all of these practices have been shown to increase southern pines growth , the relative benefit of any particular practice varies across the region due to differences in site conditions. In the 1950s, when southern pine plantation management was expanding , sit e preparation was not common due to the cost (Fox et al. 2007). The importance of site preparation, however, was highlighted by observations that many plantations established on cutover forests failed while plantations established on old agricultural field s were successful. This difference in survival was attributed to sites established on old fields having reduced hardwood competition, increased fertility, and more favorable
12 soil physical properties due to previous agricultural management (Fox et al. 2007) . These observations, and management experiments (Gent et al. 1986; McKee and Wilhite 1986; Kyle et al. 2005; Fox et al. 2007), have led to site preparation practices that increase site fertility, improve soil physical properties, and reduce hardwood compe tition becoming common in southern pine plantation managem ent. For example, in the lower Coastal P lain, where seasonal high water tables and flooding can limit productivity, bedding has become a common practice and has been shown to significantly increase volume production ( Gent et al . 1986; McKee and Wilhite 1986; Kyle et al. 2005). The use of fertilization and weed control treatments have also significantly increased volume production in these systems (Fox et al. 2007). The productivity of most southern pine plantations are limited by the availabi lity of essential elements , especially phosphorous (P) and nitrogen (N) ( Pritchett and Swinford 1961; Jokela and Stearns Smith 1993; Hynynen et al. 1998; Amateis et al. 2000). Consequently, over 400,000 ha are fe rtilized with P and N in the Southeast each year (Albaugh et al. 2007). Although P and N are the most common nutrients applied to these systems, micronutrients such as copper and manganese have also been shown to limit growth on certain sites (Jokela et al . 1991; Vogel and Jokela 2011). Due to these nutrient limitations, the application of fertilizer has been show n to drastically increase the productivity and leaf area index (LAI) of southern pines on a range of sites throughout the Southeast (Albaugh et al . 1998; Martin and Jokela 2004; Kyle et al. 2005; Fox et al. 2007). This increase in productivity is likely the result of numerous physiological responses to increased nutrient availability.
13 The increases in LAI associated with fertilization, increase the capacity of the canopy to intercept solar ra diation and therefore increase aggregate photosynthesis ( Vose 1988; Albaugh et al. 1998; Martin and Jokela 2004). As a result a strong correlation between foliage biomass and stemwood production has been observe d in loblolly pine (Teskey et al. 1987). Although fertilization increases the capacity of the canopy to undergo photosynthesis the response of leaf level net photosynthesis (A net ) is unclear for loblolly pine. Fertilization has consistently been shown to increases the foliar nit rogen concentration ([N]) of loblolly pine (Murthy et al. 1996; Albaugh et al. 2004; Martin and Jokela 2004; Choi et al. 2005). Although increases in A net with increased foliar [N] have been observed in numerous species (Brix 1971; Evans 1989; Sinclair and Horie 1989; Mitchell and Hinckley 1993; Bond et al. 1999), this relationship is not entirely clear for loblolly pine (Martin and Jokela 2004). Conflicting results are abundant in the literature with numerous studies reporting a pos itive relationship between foliar [N] and A net (Tjoelker and Luxmoore 1991; Green and Mitchell 1992; Murthy et al. 1996b; Samuelson 2000; Lai et al. 2002) while many others did not observe any effect (Samuelson 1998; Tang et al. 1999; Samuelson et al. 2001 ; Munger et al. 2003; Martin and Jokela 2004). These differences in the response of A net to foliar [N] may be due to the differences in the inherent fertility of the various study sites. A critical foliar [N] of 1.1% has been reported for loblolly pine (Al len 1987). Many of the unfertilized trees in studies that reported increases in A net with increases in foliar [N] had foliar [N] below this critical value while the o pposite was true for studies that did not observe a difference. Gough et al. (2004) observ ed a significant correlation between foliar [N] and A net within the range of measured foliar [N] (0.8 1.6%) ; however , this relationship was not
14 significant for values above 1.2% foliar [N]. These results suggest that fertilization may increase A net on site s where nutrient limitation causes foliar [N] to be below the critical value, but may not affect A net on sites where critical values of foliar [N] are maintained. Due to the modest and often insignificant increases in A net with fertilization, it is likely that the observed increases in productivity are the result of increases in LAI rather than physiological adjustments at the leaf level. Another contributing factor may be that fertilization might decrease root carbon allocation relative to aboveground carbon allocation; however , few studies have investigated this effect (Haynes and Gower 1995; Albaugh et al. 1998). Competition with hardwood and herbaceous species was a significant obstacle in the development of southern pine plantation forestry. The inc orporation of herbicide treatments in the management of southern pine plantations has increased growth by 100% or more (Fox et al. 2007). Understory elimination not only decreases competition for resources, but also allows for a shift in resource allocatio n from belowground to aboveground, thereby increasing stemwood growth (Shan et al. 2001). The benefit of herbaceous weed control on the productivity of southern pine plantations is well documented (Borders and Bailey 2001; Amishev and Fox 2006; Jokela et a l. 2010). For a variety of sites in Georgia, Borders and Bailey (2001) observed that the impact of herbaceous weed control on loblolly pine productivity was significantly greater than the impact of fertilization. In contrast, Jokela et al. (2010) observed that the impact of fertilization on loblolly pine productivity was greater than that of herbaceous weed control on sites in Florid a. These differences could be due to differences in site conditions. In both studies the benefit of herbaceous weed control an d fertilization
15 treatments was greater if applied together than if either treatment was applied individually. The productivity of southern pine plantations has increased dramatically due to the development of genetically superior genotypes and an increased understanding of how non local seed sources perform off site. Extensive tree breeding efforts for loblolly cycle were producing seed (Li et al. 1999). Plantations establishe d with these second generation genotypes had 17 30% more volume per hectare at time of harvest than plantations established from wild seed sources (Li et al. 1999). These plantations not only exhibited higher volume production, but also had lower rates of fusiform rust ( Cronartium fusiforme ) infection and improved wood quality (Li et al. 1999). These from different regions differ in a number of traits including: growth rate, st em straightness, fusiform rust resistance, cold tolerance, and drought tolerance (Lambeth et al. 2005). In general, loblolly pine genotypes in the eastern portion of the range have higher growth rates, lower drought tolerance, and lower fusiform rust resis tance than genotypes from the western portion of its range (Lambeth et al. 2005). This information was not only useful in tree breeding efforts, but also assisted land managers in selecting which seed sources to deploy in different areas. In the early 190 radius of a given site was recommended as these genotypes performed well under local information from well con ducted provenance and progeny trails. H owever , once this
16 information became available land managers were able to make informed decision s on the use of non local seed sources (Lambeth et al. 2005). Private landowners in the Southeast have since used non loc al seed sources in operational plantings for numerous reasons including: increased fusiform rust resistance, increased growth rate, and increased drought resistance (Lambeth et al. 2005). For example, in the 1970s Weyerhaeuser Company planted North Carolin a genotypes on various sites throughout Arkansas and Oklahoma in order to increase the productivity of l oblolly pine plantations in that region (Lam beth et al. 1984). Although these genotype s were observed to grow faster than local seed sources, they also exhibited reduced drought resistance a nd mortality rates on sites having a soil moisture deficit greater than 32 cm (Lambeth et al. 1984). As a result this company began to limit the use of the North Carolina genotype to sites with soil moisture deficits b elow 32 cm. This example illustrates the potential gains in the informed use of non local seed sources. Climate Change and the Southeast Unites States The concentration of carbon dioxide (CO 2 ) and other greenhouse gases (GHGs) phere have increased markedly due to the combustion of fossil fuels and land use changes. The concentration of atmospheric CO 2 has increased from 280 parts per million (ppm) to over 400 ppm since the start of the Industrial Revolution and continues to rise 1 (Johnsen et al. 2001; Siegenthaler et al. 2005; Bala 2013). Is likely that atmospheric CO 2 concentration will continue to rise as world energy demand is expected to increase 60% by 2030 and a vast majority of this d emand will be met through the use of fossil fuels (Bessou et al. profound impacts on the global climate since these gases trap exiting solar radiation.
17 Although there is some un certainly in the magnitude and rate at which climate change will occur, numerous models have been developed to address these issues at both the global and regional scale. Atmosphere Ocean General Circulation Models require assumptions of future GHG emissio n rates in order to assess potential impacts of climate change. In 2000, a special report on emission scenarios (SRES) was developed for the Intergovernmental Panel on Climate Change (IPCC) (Nakicenovic and Swart 2000). The IPCC was formed by the World Met eorological Organization and the United Nations Environmental Programme in 1988 to assess potential impacts of climate change. Although the original SRES publication outline d four potential emissions scenarios, the A2 and B1 scenarios have become the most renewable energy, and a relatively slow declines in fertility rates. The B1 scenario, on the other hand, increased international cooperation, timely and effective use of renewable energies, and a more rapid decline in fertility rates. In 2013 the National Oceanic and Atmospheric Administra tion released a technical report (NESDIS 142 2) that outlined potential changes in the climate of the U.S. under the A2 and B1 emission scenarios. This report was created to assist in the development of the U.S. National Climate Assessment which is current ly being prepared in accordance with the Global Change Research Act of 1990 (Kunkel et al. 2013). Th e NOAA NESDIS 142 2 report used fifteen Atmosphere Ocean General Circulation Models to predict the impact of increased atmospheric GHG concentrations
18 on the climate of the U.S. by region. The reported changes in climate represent the mean output of these models as there tends to be considerable model to model variability. Under both the A2 and B1 scenarios, significant changes in temperature and prec ipitation are reported for the s outheast ern U.S. The southeast ern U.S. will likely experience an increase in mean annual temperature of 2.3 to 3.6Â°C by 2070 2099 under the B1 and A2 emission scenarios, respectively (Kunkel et al. 2013). There is considerable variation in the output of individual models for this predicte d change in temperature with estimates ranging from 1.4 to 2.9Â°C for the B1 scenario and from 2.2 to 5.3Â°C for the A2 scenario. Although predicted temperature changes by 2070 2099 vary drastically under the B1 a nd A2 scenarios, there is less of a differenc e between the two scenarios for temperature changes by 2021 2050 (1.3 vs 1.6Â°C). This highlights the importance of future GHG emissions on the magnitude of future temperature changes. Climate change will not only affect mean annual temperature but will als o alter temperature extremes and the number of frost free d ays. Predictions for changes in annual prec ipitation by 2070 2099 for the s outheast U.S. are much less certain , ranging from a 12% decrease to a 9% increase for the B1 scenario and a 23% decrease to an 11% increase for the A2 scenario (Kunkel et al. 2013). The median of the multi model output was a 4% increase for both the B1 and A2 scenario. The largest changes in precipitation are predicted to occur during the summer months (June, July, and Augus t). Changes in summertime precipitation range from a 24% decrease to an 18% increase under the B1 scenario and a 50% decrease to a 24% increase under the A2 scenario. Climate change will also influence the frequency of
19 extreme rainfall events and under the A2 scenario the number of days with more than 50 mm of rainfall is predicted to increase by 32% by 2040 2079. Changes in temperature and pre cipitation patterns across the S outheast have the potential to impact the water supply of the region. In 2013 a Na tional Climate Assessment Report was developed for the southeast ern U.S. to inform the 2014 National Climate Assessment on the potential social, economic and environmental impacts of climate change . Among the many findings of this report was that the water supply of the region would become significantly stressed by 2050 due to several factors (Sun et al. 2013). The increases in future temperatures across the region will reportedly increase water loss through increas ed evapotranspiration (ET) which could decrease total stream flow, groundwater recharge, and regional water supplies. Under conditions of increased ET it is likely that water withdrawals for irrigation will increase in order to maintain a robust agricultural economy and maintain food prices. Ir rigation has already expanded substantially in many southeastern states over the last 40 years. Predicted increases in energy demand will also have profound impacts on the water supply of the Southeast as power production by nucle ar, coal, gas, and hydropo wer represents the largest overall use of water in the region. Carbon, Water, and Southeastern Forests The terrestrial biosphere is an important pool of carbon (C) storing approximately 2000 Pg of carbon , approximately 60% of which is contained in forests (Winjum et al. 1992; McKinley et al. 2011). Each year terrestrial ecosystems offset approximately 30% of anthropogenic carbon emissions by removing 3 Pg of carbon from the atmosphere through net growth (McKinley et al. 2011; Canadell and Raupach 2008). For ests cover
20 30% of the planet and store twice as much carbon as is contained in the atmosphere (FAO 2006; Canadell and Raupach 2008 ). H istoric conversion s of forest land to non forest land has released about 200 Pg of carbon and continue to release 1 2 Pg o f carbon each year ( Canadell and Raupach 2008; McKinley et al. 2011). The potential of forest to reduce atmospheric CO 2 concentration through increased sequestration has gained attention due to concerns over climate change. Forest s have the potential to mitigate anthropogenic carbon emissions though: i) reforestation and afforestation, ii) increased forest carbon density, iii) expanded use of forest products, and iv) reduced deforestation and degradation (Canadell and Raupach 2008). The conversion of non forest land into forest has great potential to mitigate carbon emissions. Although competing land use, such as agriculture and pasture land, are often more economically attractive than forest management, afforestation could outcompete non forest land uses if carbon markets were to be established. McKinley et al. (2011) estimated that if carbon markets were to be established at $18, $55, $100, and $183 per Mg C, then afforestation would mitigate 1, 37, 119, and 225 Tg C per year in the U.S., respectively. Th e carbon sequestered in current forest land also could be increased through altered forest management practices including increased harvest interval, increasing growth rate, and establishing preserves (McKinley et al. 2011). With carbon prices set at $18, $55, $100, and $183 per Mg C the use of these forest management practices are projected to increase the carbon sequestered in current forest land in the U.S. by 29, 60, 86, and 105 Tg C per year, respectively (McKinley et al. 2011). The timber harvested fr om these forests also play s an important role in mitigating carbon emissions by storing carbon in long lived wood products such as
21 furniture, flooring, and lumber. The substitution of carbon intensive building materials, such as concrete and steel, with wo od products can also significantly reduce carbon emissions. For example, the construction of concrete floors produces more than four times the GHG emissions of flooring constructed from wood products (Malmsheimer et al. 2008). Since deforestation currently releases significant amounts of carbon to the atmosphere, reducing the rate of deforestation will help avoid further increases in atmospheric GHG concentrations. If deforestation rates were to decrease by 50% by 2050 and countries stopped deforestation on ce reaching 50% of their current forest area, an emissions equivalent of 50 Pg C would be avoided (Gullison et al. 2007). This the potential to avoid large amounts of potential emissions (Canadell and Raupach 2008). The U.S. has been estimated to store 110,900 Tg of carbon , with 18.9% contained within the Southeast (Han et al. 2007). Of the 21, 102 Tg of carbon stored in the S outheast, 78% is in soils, 21% is in fores t biomass, and <1% is in crop and pasture biomass (Han et al. 2007). Approximately 50% of the Southeast is currently forested 1 (Johnsen et al. 2001b; Han et al. 2007). This represents 13% of regional GHG 1 ) , and an additional 9.3% of regional emissions could be sequestered in these forests through policy changes and best land management practices (Han et al. 2007). Not only are these forests important sources of carbon storage, buy th ey also play an important role in regulating water quality and yield.
22 Water draining from undisturbed foreste d landscapes is generally of high quality, especially when compared to landscapes dominated by agriculture, urbanization, or industry (Ice et al. 1 997; Shepard et al. 2004; Schoenholtz 2004, Schoonover et al. 2005). Forested watersheds provide clean drinking water and aquatic habitat for native species (Schoenholtz 2004). Forest management activities such as timber harvest, road construction, and sit e preparation can impact water quality. The most significant effect of forestry activities on water quality is potential increases in stream sediment loads (Schoenholtz 2004). Increased sediment loads can increase turbidity, alter the physical structure of streams, and degrade water quality as sediment typically carries nutrients and anthropogenic chemicals (Schoenholtz 2004). The use of best management practices (BMPs), however, can effectively minimize these impacts (Anderson and Lockaby 2011). Increases in sedimentation from timber harvesting are also short lived and infrequent (Schoenholtz 2004). In the southeast ern U.S. the use of BMPs, especially the implementation of streamside management zones, have bee n shown to effectively minimize stream sediment loads ( Anderson and Lockaby 2011). Streamside management zones also protect aquatic habitats by providing shade for streams , which maintains water temperatures and dissolved oxygen content near natural levels (Schoenholtz 2004). The relationship between w ater yield and forest cover has long been understood. Hibbert (1966) reviewed 39 studies available at the time and concluded that the reduction or removal of forest stands increases water yield and reforestation decreased water yield (Jackson et al. 2004). This relationship is driven by the impact of forests on landscape evapotranspiration (ET) rates. Water yield supplies both surface and
23 groundwater resources and is defined as the difference between precipitation (PPT) and ET (McLaughlin et al. 2013). With in the U.S. , ET is greatest in the Southeast , averaging 70% of PPT (Hanson et al. 1991). The ET of mature intensively managed pine plantations in this region, however, have been shown to exhibit ET levels above 90% of PPT (Gholz and Clark 2002; Sun et al. 2010). Evapotranspiration levels are typically lower in naturally regenerated pine stands with a range of 75 85% of PPT reported for stands in Florida (Powell et al. 2005; Bracho et al. 2008). Clear cutting a forest drastically reduces ET and has been show n to increases stream flow (Bosch and Hewlett 1982; Jackson et al. 2004). A rise in the water table following timber harvest has also been observed in the Southeast, especially in the coastal plain (Williams and Lipscomb 1981; Sun et al. 2001; Bliss and Co merford 2002; Lu et al. 2009). Changes in ET can have profound effects on water yield since this is the dominant form of ecosystem water loss. For example, a reduction in ET from 90% to 80% of PPT would double water yield from 10% to 20% (McLaughlin et al. 2013). The balance of land cover types, especially forested land, across the Southeast , would have important impacts on future water yields due to differences in the ET of various land cover types and sensitivity of water yield to ET. Plant Water Relation s and Southern Pines The movement of water from the bulk soil to the foliage of plants is governed by the principles of the cohesion tension theory. This theory was first proposed 120 years ago by Dixon and Joly (1894) and, although there has been some de bate, is now considered to accurately describe the movement of water through plants (Tyree 1997; Tyree and Zimmermann 2002 ; Angeles et al. 2004 ). Under the cohesion tension theory, water moves through plants in a metastable state under tension. The movemen t of
24 water is driven by the evaporation of water from the walls of cells within the substomatal chamber (in leafs). This process is driven by large difference in the water potential within these cell walls and the water potential of surrounding air. As wat er evaporates the curvature of water menisci in cell wall microfibrils increases and creates surface tension such that the water potential of surrounding regions is lowered. The tension created through this process is transmitted through a continuous water column , running through the entire plant , which ultimately drives the movement of water. Water in xylem conduits is referred to as metastable when xylem pressures (P X ) are below the vapor pressure of water since, under normal conditions, this water should evaporate. This metastable state is maintained by hydrogen bonding between individual water molecules (cohesion) and between water molecules and the cell walls of xylem conduits (adhesion). Although interfaces between air and water may exist in the column of water running through a plant, the small diameter of bordered pits between cell walls and surface tension of water prevent the passage of air. However, if P X becomes sufficiently negative air bubbles can overcome the surfac e tension of water and be pul led into xylem conduits. When this occurs, the water within the xylem conduit evaporates and the water column is broken. This process is known as cavitation. Cavitated xylem conduits are unable to transport liquid water and increase the resistance of the v ascular system to the flow of water. The inverse of this resistance is generally referred to as hydraulic conductance (K S ). The magnitude of P X is determined S L ). Although plants S L can be regulated through stomatal conductance (Tyree 2003).
25 Under conditions of water stress, which may be caused by a reduction in soil water availability or increases in vapor pressure deficit (D), plants tend to close their stomata. This reduces the risk of xylem cavitation by limiting dramatic reductions in P X (Sperry and Ikeda 1997; Sperry 2000; Tyree 2003). Stomatal conductance (G C ) is L at which st omatal closure occurs is closely related to the water potential at which xylem cavitation becomes significant (Cochard et al. 1996; Sperry and Ikeda 1997; Tyree and Zimmermann 2002). The P X at which cavitation occurs is determined by the morphology of xyle m bordered pit membranes . In angiosperms, xylem vulnerability to cavitation is determined by the size of openings between microfibrils running across b ordered pits. This is due to the pressure potential required to overcome the surface tension of water dec reasing with increasing pores size (Tyree and Sperry 1989). In conifers, bordered pit membranes contain a valve like structure known as the torus margo , which protect s xylem from adjacent cavitated cells by closing off bordered pit s . In these species, vuln erability to cavitation is determined by the strength of the torus margo structure (Domec et al. 2006). The P X at which K S 50 ) is a common measure of vulnerability to cavi tation and varies widely among 50 tend to be more negative in plants that have evolved in dry environments and less negative in plants that evolved in wet environments. For example, plants in Mediterranean and 50 less than 4 MPa , while plants in tropical rainforest hav 50 of about 1 MPa (BrÃ©da et al. 2006). Changes in D and soil water availability have significant impacts on the G C and K S of loblolly pine (Sperry 2000; Domec et al. 2009; Gonzalez Benecke and Martin
26 2010). Vapor pressure deficit is the most important environmental variable to which stomata respond (Domec et al. 2009). The G C of loblolly pine has been observed to respond dynamically with changes in D by numerous studies ; however , the sensitivity of this response varies widely (Ewers et al. 200 1 b ; Samuelson and Stokes 2006; Samuelson et al. 2008; Gonzalez Benecke and Martin 2010). The K S of loblolly pine is sensitive to changes in soil moisture largely due to dynamic changes in the K S of roots (Domec et al. 2009). Loblolly roots have been shown to lose 25 45% of conductivity at soil water potentials of 0.5 to 0.75 MPa , and at a water potential of 1.5 MPa a 50 80% loss in conductivity has been observed (Ewers et al. 2000; Hacke et al. 2000). Gonzalez 50 of 0.9 MPa for loblolly pine and observed a more than 3 fold increase in K S in response to irrigation. These results suggest that soil water availability is important in determining the K S of loblolly pine. Physiological Principles in Predicting Gro wth The Physiological Principles in Predicting Growth 3 PG model uses physiological process data, statistical growth and yield models, and easily obtained weather and site conditions to simulate the growth and yield of forest stands (Landsberg and Waring 1 997). When properly calibrated, t he model has been shown to accurately predict forest productivity under varying environmental conditions in a variety of forest s throughout the W orld (Coops and Waring 2001a; Coops and Waring 2001b; Tickle et al. 2001). The 3 PG model is able to predict forest productivity under a variety of management and environmental conditions making it an especially useful tool for predicting the impact of climate change scenarios on loblolly pine stands (Landsberg et al. 2001). The ca rbon gain of forests is determined in 3 PG through the use of multiple growth modifiers on estimates of canopy quantum efficiency (Landsberg and Sands
27 C ) represents carbon gain per unit intercepted radiation. The 3 PG mod el requires a species Cx which C when no factors are limiting growth and atmospheric CO 2 is at 350 C for a given time step is estimated Cx . Growth modifiers are functions that describe the C and include: temperature, number of frost days, site fertility, site salinity, atmospheric CO 2 concentration , and . The value of is a function of eithe r soil water content or vapor pressure deficit and is determined C has been calculated from these growth C and radiation. Due to this structure, the abilit y of 3 PG to accurately predict the growth of a given species is dependent on having quality estimates for the impacts of growth modifiers on carbon gain .
28 CHAPTER 2 RESPONSE OF LOBLOLLY PINE TO THROUGH FALL REDUCTION AND FERTILIZER APPLICATION Intro duction Loblolly pine (Pinus taeda) accounts for 84% of all seedlings planted in the McKeand et al. 2003). Within its native range, which extends from Texas eastward to Florida and northward to Delaware, there are approximately 12 million hectares of loblolly pine plantations ( Schultz 1997; Smith et al. 2009). These plantations have tremendous ecological and economic value. Southeastern forests sequester 76 Tg of carbo n each year which represents 13% of regional greenhouse gas emissions (Han et al. 2007). They also play an important role in regulating water quality and yield (Anderson and Lockaby 2011; McLaughlin et al. 2013). Loblolly pine forests provide habitat for a diversity of plants and wildlife including game species such as turkey and white tailed deer (Palmer et al. 1993; Schultz 1997; Jones et al. 2009). The hunting of such game species is an important recreational activity in the region and provides over $5 b illion to the southeastern economy each year (Southwick 1994). Forestry is also vital to the economy of the South providing 5.5% of the jobs and 7.5% of the industrial output (Wear and Greis 2002). The productivity of loblolly pine plantations is often lim ited by the availability of essential elements , especially phosphorus and nitrogen (Fox et al. 2007). As a result, the application of fertilizer to these systems has become a common practice and has been shown to increase stemwood production and leaf area index (LAI) on many sites (Albaugh et al. 1998; Albaugh et al. 2007; Jokela et al. 2010). The increases in LAI associated with fertilizer application also increase water use (Ewers et al. 1999; Ewers
29 et al. 2000; Samuelson et al. 2008). The water relations of these forests is of particular interest due to predictions that climate change will increase water supply stress in the southeastern U.S. and their role in regulating watershed yield (McLaughlin et al. 2013; Sun et al. 2013). The concentration of carbo n dioxide (CO 2 ) and other greenhouse gases (GHG) and land use changes. The concentration of atmospheric CO 2 has increased from 280 parts per million (ppm) to over 400 p pm since the start of the Industrial Revolution atmosphere will have profound effects on local climates. Climate change simulations using the assumptions of the Intergover nmental Panel on Climate Change A2 scenario, which represents a largely business as usual future, suggest that the southeastern U.S. would experience an increase in av erage temperature of 2.5 to 4.7Â°C by 2085 (Kunkel et al. 2013) . Estimates for changes in precipitation for this region are much less certain and range from a 22% reduction to a 9% increase in summertim e (June, July, August) precipitation by 2070 2099 (Kunkel et al. 2013). Increased average temperature and potential decreases in precipitation w ould decrease soil water availability and increase vapor pressure deficits (D). These changes could influence the carbon gain and water use of loblolly pine plantations due to the sensitivity of stomatal conductance to these variables. To undergo photosynt hesis plants must display a large surface of fully hydrated cells to obtain carb on dioxide from the surrounding air. As a result, higher plants transpire approximately 100 to 1,000 water molecules per molecule of carbon
30 assimilated (Maseda and FernÃ¡ndez 20 06). This places a large demand on soil water resources with nearly all the water entering plant roots being lost through stomata. Under conditions of water stress, which may be caused by a reduction in soil water availability or increase in D, plants tend to close their stomata. This reduces the risk of xylem cavitation by limiting dramatic reduc tions in xylem water potential (Sperry and Ikeda 1997; Sperry 2000; Tyree 2003). Cavitated vascular tissue is unable to transport liquid water and increases the re sistance of the vascular system to the flow of water. The inverse of this resistance is generally referred to as hydraulic conductance. In plants, both stomatal conductance and hydr aulic conductance can limit the rate of gas exchange and in turn carbon gai n ( Tyree 2003; Bracho et al. 2012). Several studies have investigated the impact of genetics, fertilization, and irrigation on the growth and water relations of loblolly pine (Ewers et al. 2000; Samuelson et al. 2008; Gonzalez Benecke and Martin 2010; Aspi nwall et al. 2011), but few have investigated the influence of drought on these factors (Tang et al. 2004). Given the economic and ecologic importance of loblolly pine forests, it is important to understand how potential changes in precipitation will impac t their growth and water relations. The overall objectives of this study were to investigate the influence of throughfall reduction and fertilization on loblolly pine: i) productivity, transpiration, and whole tree hydraulic conductance, ii) sensitivity of stomatal conductance to D, and iii) sap flow radial variation. To meet these objectives a replicated throughfall reduction by fertilization experiment was installed in a loblolly pine plantation in northern Florida .
31 M ethods Study Design This study took place at the Pine Integrated Network: Education, Mit igation, and Adaptation Project ( PINEMAP , www.pinemap.org ) throughfall by fertilization study in Taylor County, Florida. The study design is replicated at sites in G eorgia, Oklahoma, and Virginia and together they constitute the third tier of the PINEMAP monitoring network. For a detailed description of the PINEMAP Tier 3 network see Will et al. in review. The site is located on a Melvina Moriah Lutterloh soil complex which is characterized as a somewhat poorly drained fine sand (Soil Survey Staff 2012). The site was planted in the winter of 2003 2004 with a seed orchard mix of St. Joe Timber Company open pollinated stock at a spacing of approximately 1.7 x 3.4 m . The study design is a randomized complete block containing two levels of fertilization and throughfall reduction in a 2x2 factorial arrangemen t, with four replicate blocks. Plots treated with fertilizer received 224N, 28P, 56K (kg/ha ) and a micronutrient blen d while plots treated with throughfall reduction received a 30% exclusion of incoming throughfall. A 30% reduction in throughfall was selected a s this represented the extreme for predicted changes in drought severity within the United States at the time th e study was established (Karl et al. 2009) . The factorial design results in f our individual treatment types. The sixteen plots were organized into four blocks to reduce the variability of plot basal area within a block (Table 2 1). The Treat ment types were : Control (C): ambient throughfall, no fertilizer Fertilization (F): ambien t throughfall, fertilization Throughfall Reduction (R): 30% throughfall reduction , no fertilizer Fertilization and Throughfall Reduction (FR) : 30% throughfall reduction , fertilization.
32 All measurements were taken in 14.6 x 16.8 m measurement plots. The m ean number of trees within measurement plots was 47. Measurement plots were surrounded by a 6 m treated buffer on all sides, followed by an additional 9.1 m buffer around the treatment plot . The gross area of the measurement and treated plots were 0.025 ha and 0.047 ha, respectively. All p 1 of 1 of glyphosate to remove c ompeting vegetation prior to treatments being applied in April of 2012. An inventory of all trees in measurem ent plots was conducted in the dormant season of 2011 2012 to assess baseline conditions. Additional inventories were conducted in the dormant season of 2012 2013 and 2013 2014 to assess the impact of treatments on stand growth. Inventories include d measur ements of dia meter at breast height (DBH, 1.37 m ) and total tree height. The sapwood area of all trees was estimating using an allometric equation relating sapwood area to DBH as reported in Gonzalez Benecke and Martin ( 2010) . In the original publication the equation was misreported as the s ign of the intercept was incorrect. The corrected equation is: where A S is stem sapwood area in m 2 and DBH is expressed in mm ( Gonzalez Benecke , personal communication). Throughfall Exclusion D esign Throughfall exclusion troughs were installed parallel to pine rows and were constructed using decay resistant lumber and Poly Scrim 12. Poly Scrim 12 is a 0.3 mm extrusion laminate with 2 layers of U.V. stabilized coextruded polyethylene and a high strength cord grid. Each throughfall exclusion structure contained two 0.5 m wide troughs separated by a 0.4 m opening (Figure 2 1) . The opening between the exclusion
33 troughs was designed to allow some throughfall to access the area beneath each trough in order to minimize s oil moisture heterogeneity. Exclusions structures we re approximately 0.9 m in he ight at the low end and 1.4 m on the high end. PVC pipes and polyethylene drainage pipes were installed at the low end to remove the excluded throughfall from the gross plot ar ea. The structures were installed such that 30% of the ground area was covered by troughs . Meteorological and Soil M easurements A weather station was installed on a tower above the stand in August of 2012 to measure photosynthetic photon flux density (PPF D) (Kipp & Zonon PQS1 par quantum), temperature and vapor pressure deficit (D) (Campbell Scientific HMP45AC), and precipitation (Texas Electronics TE525MM L). All sensors were measured every minute and half hour averages were collected on data loggers. S oil volumetric water content was measured monthly from November 2013 to April 2014 using a Tektronix 1502B Metallic Time Domain Reflectometer . Measurements were taken on tungsten inert gas (TIG) welding rods of varying lengths installed vertically into the soil. In each plot three pairs of TIG rods (30 cm, 60 cm, and 84 cm lengths) were installed at two locations. One set of TIG rods was installed in the tree row and another set between tree rows. In plots containing throughfall exclusions the set of TIG ro ds between the tree rows was placed directly under an exclusion trough. The location of the TIG rods in the tree row was randomly selected and the set of TIG rods between the trees rows were installed adjacent to this location . Measurements were converted to volumetric water content using the Topp equation (Topp et al. 1980) . The volumetric water content of the 30 30 60 ) and 60 84 cm 6 0 84 ) soil regions was calculated as the difference of each reading from the reading of
34 the next shallowest soil depth, assuming that the original reading represented the weighted average of the entire measured soil profile: 0 60 is the volumetric water content of the 0 0 84 is the volumetric water content of the 0 84 cm soil region. The volumetric water content of the top 20 cm of soil ( 20 ) was measured monthly from April 2013 to December 2013 using a Campbell Scientific CS620 water content sensor. Measurements were taken in the tree row, a quarter of the distance between tree rows, and half of the dist ance between tree rows. For plots with throughfall exclusion structures this represented readings in the tree row, at the edge of an exclusion trough and directly under a trough. Additional CWS655 Campbell Scientific soil moisture probes were installed vertically at a depth of 2 m in all of th e plots in block 2. Measurements were taken every two minutes and half hour averages were stored on data loggers. Soil bulk density and texture was measured in the top 20 cm of soil in the winter of 2012 2013. Soil bulk density was measured u sing a soil b ulk density sampler of known volume (150 cm 3 ) . Soil samples were collected for the 0 10 cm and 10 20 cm depths at three locations in each of the 16 plots. One sample was taken in the tree row, one was taken a quarter of the distance between tree rows, and one was taken half of the dist ance between tree rows. Soil samples were weighed after being oven dried at 105Â°C for 48 hours . Bulk density was then calculated as soil weight divided by soil
35 volume. To measure soil texture, samples were collected from eight random locations in each plot for each depth (0 10 cm and 10 20 cm). The eight samples were then randomly split into two groups for each depth and mixed, resulting in two samples per depth per plot. All samples where then homogenized, sieved to 2 mm, and root s and organic matter set aside. Forty grams of soil was taken from each sample for texture analysis using the hydrometer method out lined by Gee and Bauder (1986) . Leaf Area Index and Specific Needle A rea Litterfall was collected approximately eve ry four weeks from June 2012 to January 2014. Each plot contained 12, 0.5 m 2 litterfall traps. After collection any non needle material was removed from the sample and the remaining needles were oven dried for at least one week at 70Â°C and weighed. Leaf ar ea index for each collection period was calculated as described in Martin and Jokela ( 2004) . Because the Martin and Jokela (2004) approach requires two years of litterfall data to calculate LAI for any particular date (a full year of litterfall for the previous phenological year, and for the subsequent phenological year), we were not able to directly calculate LAI for the first few months of our record or for the last few months of our record. To adjust for this, we previous year was identical to that starting in June 2012. We did the same for the year following our l ast litterfall collection in January 2014, by assuming that litterfall after that point was identical to that in the last year of our litterfall record. Specific needle area was measured on 5 trees per plot. Ten fascicles were collected from each sample tr ee on March 13, 2014. Needles were cut to 10 cm and the radius of each needle in each of the 10 fascicles per tree was measured. For each individual needle the radius was measured on the two flat sides and then averaged. All
36 needles were then dried at 65Â°C for four days and weighed. Specific needle area was calculated as the ratio of the surface area of all needles measured for a given tree divided by the dry weight of all the measured needles. Aboveground net primary productivity (ANPP) for the 2013 growin g season was calculated as in Martin and Jokela (2004). Wood biomass increment was calculated using allometric equations ( Gonzalez Benecke et al. in press) and the biomass of foliage produced in 2012 (collected in 2013) was assumed to be equal to the bioma ss of foliage produced in 2013. Sap F low and Transpiration Sap flux density (J S 2 s 1 ) was measured in the stem xylem on a subset of five trees per plot (80 trees in total) throughout 2013 using Granier style heat dissipation sensors (Granier 1985; Granier 1987) . Each sensor consisted of a lower reference probe and an upper heated probe. Both probes were 20 mm in length with a T type thermocouple (copper constant) inserted at 10 mm. The upper probe contained a heating element which was continuously heated at a constant power of 0.2 watts. The lower probe was left unheated to measure the ambient temperature of xylem tissue and t o act as a reference. The temperature difference ( T) between these probes was measured every minute and half hour averages were stored on data loggers. Values of T were converted to J S using the empirical calibration developed by Granier ( 1985) and confirmed by Clearwater et al. ( 1999) : where T is the instantaneous temperature difference between the reference and heated probes and T m is the maximum temperature difference . Values of T m were
37 determined on nights when VPD was below 0.2 kPa for at least two hours, as there is little to no sap flow during these periods. Measurement trees were selected in . This technique skews the selection of measurement trees toward larger diameter trees which tend to dominate stand transpiration. A summary of the minimum, maximum, and mean DBH of sap flow measurem ent trees at the start of 2013 is given in Table 2 2. All sensors were inserted into the north side of the tree and covered with reflective insulation to minimize the influence of solar radiation on the temperature of the stemwood. To account for radial v ariation in J s , the measurement trees closest to the quadratic mean diameter of 8 plots (2 per treatment) were outfitted with specialized probes to measure J s at the 2 4 cm and 4 6 cm depths from February, 2014 to April, 2014. These probes were constructed using longer needles of the same gauge and material as the needles used in 20 mm probes. The outer 20 mm of the heated probes were outfitted with a heating element and the remaining portion of the needle was left unheated. Thermocouples were inserted at 1 0 mm from the end of each probe. In 5 trees the 2 4 cm specialized radial variation probes were tested against the typical 20 mm probes inserted into the 2 4 cm region of xylem. No differences were detected in sap flow measured using the two methods (p = 0 .447). An equation was developed to correct estimates of J S measured in the outer 2 cm of sapwood to account for radial variation using the Q T method described in Gonzalez Benecke and Martin 2010 . For trees outfitted with radial variation probes, corrected half hourly estimates of transpira tion (E RAD ) were calculated by multiplying the J S measured
38 in each region of sapwood (0 2 cms, 2 4 cms, and > 4 cms) by the corresponding sapwood area of each region. Uncorrected transpiration estimates (E 20 ) were then calculated by multiplying the entire sapwood area by the value of J S measured in the outer 2 cm of sapwood. A linear regression model was then developed to predict corrected transpiration estimates ( E COR ) from E 20 , D, and PPFD:  E COR = 20 T his equation accounted for 99% of the variation in E COR . The variables D and PPFD were included in this model as they have been shown to affect the radial pattern of sap flow in loblolly pine ( Gonzalez Benecke and Martin 2010 ) After accounting for radia l variation, transpiration for each measurement tree was calculated by multiplying the corrected J S estimates by total sapwood area. Plot level transpiration estimates were calculated by using the proportion of total sapwood area of measurement trees in a plot to the total sapwood area of all trees within a plot. By using the quantiles of total method in selecting measurement trees, it was assumed that the summed transpiration estimates for the 5 measurement trees within a plot were representative of the gr eater plot area. Gaps that occurred in the J S dataset due to sensor error and power failure were filled using a variety of models. Regression models were constructed for each individual measurement tree relating the J S of each tree to the overall mean J S of all trees wi thin a given treatment. The R 2 of these models ranged from 0.87 to 0.99 with a mean of 0.96. If a data gap existed for any individual tree while sensors in other trees of the same treatment were functioning properly, values of J S were filled using these regression equations. For the entire data set of all 80 trees, 21% of the J S data points were filled in
39 this manner. If all of the sensors at the site were inoperable, due to power failure, then values of J S were filled from a nonlinear functi on which predicted J S fro m meteorological data. The R 2 of these models ranged from 0.86 to 0.95 with a mean of 0.92 . F or the e ntire data set of all 80 trees, 14% of the J S data points were filled in this manner. Power failure occurred at the site during th e entire month of December and values of J S were not calculated. For this month daily estimates of transpiration (E Day , mm) were filled using a model to predict E Day from meteorological data. The mean R 2 of these models was 0.82. Stomatal Conductance Canopy stomatal conductance (G C ) was calculated using the simplified version of the Penman Monteith equation reported in Samuelson et al. 2007 : where is the latent heat of vaporization (2465 1 ), is the psychometric constant 1 3 ), c p 1 K 1 ), E L 2 s 1 ), and D is vapor pressure deficit (Pa). Transpiration per unit leaf area was calculated by dividing canopy transpiration s 1 ) by projected leaf area index. Values of G C were calculated only when D > 0.6 kPa due to errors associated with calculations of G C at low D (Ewers and Oren 2000) . The sensitivity of G C to changes in D was described by fitting two different models previously developed to describe this rel ationship. The first model is reported in Ewers et al. ( 2001 a ):
40 where G Cref is the maximum G C in 2 s 1 boundary line of the relationship between G C and ln D (kPa). The second model is used in the Phys iological Princi ples in Predicting Growth model (3 PG) to describing the sensitivity of G C to changes in D: where MaxCond is the maximum G C 1 at D = 0 and CoeffCond defines stomatal response to D in mb 1 ( Landsberg and Waring 1997 ; Gonzalez Benecke et a l. 2014) . Both of the models were fit using the quantile regression procedure in SAS 9.3 with a quantile threshold of 0.98 (SAS Inc., Cary, NC, USA) (Gonzalez Benecke et al. 2014) . Hydraulic Conductance Whole tree sapwood specific hydraulic conductance ( Ks m 2 s 1 MPa 1 ) was calculated for individual trees as the slope of the linear regression of J S and leaf water L ) (Wullschleger et al. 1998) L were taken on shoot tips every two to three months using a portable pressure chamber (PMS 1000, MPS Instrument Co., Corvallis, OR, USA). Two of the four blocks were sampled on each measurement date due to time limitations. Samples were taken from four sap flow trees per plot from pre dawn to late afternoon on each measurement day. Measurements were taken at approximately 2 hour intervals resulting in four to five measurements being completed for each tree on each day. Shoot tips were collected using a pol e pruner and were stored in plastic bags with wet paper towels in order to minimize desiccation. All measurements were completed within 3 minutes of shoot excision. The hydraulic conductance of each tree was then calculated and used to produce plot level a verages for statistical analysis.
41 Statistical Analysis Analysis of variance was used to analyze the effects of treatments on growth and water relations traits, including Bonferroni adjustments. (PROC MIXED; SAS Institute Inc., Cary, North Carolina) . The l inear model for the analysis was where Y ijk is the parameter value of the plot with the i th level of fertilization (0,1), the j th level of throughfall reduction (0,1), in the k th lation mean ijk 2 ) (NID, normally and independently distributed). For a given main effect a value of 0 represented not receiving a treatment while a value of 1 represented receiving the treatment. Repeated measures analysi s of variance was used to analyze time series data with an autore gressive covariance structure. Values of whole tree hydraulic conductance were scaled to the plot level by averaging the values for the four trees measured in each plot. Results Soil Bulk Den sity, Texture, and M oisture The bulk density and texture of the top 20 cm of soil was relatively consistent among treatments. The mean soil texture was 95% sand 4% silt and 2% clay. There were no differences in sand (p = 0.232) or silt (p = 0.198) content among treatments, but there were differences in clay content (p = 0.026). Soil bulk density did not differ among treatments for the 0 10 cm (p = 0.220) or 10 20 cm (p = 0.429) depths and 3 , respectively. Throughfall reduction de creased the volumetric water content of the upper 20 cm of soil 0 20 ), however this effect was not apparent at deeper depths. Values of 0 20 were reduced by a mean of 0.014 cm 3 H 2 3 soil in response to throughfall reduction
42 (p = 0.001). This estimate 0 20 measurements taken at different positions on each measurement date. When analyzed individually, throughfall reduction significantly decreased 0 20 in the tree line (p = 0.030 ), at the edge of exclusion trough s (p = 0.002), and directly under exclusions troughs (p < 0.001). The region of soil directly under e xclusion troughs also had significantly lower volumetric water content in the 0 30 cm ( 0 30 , p = <0.001) and 30 60 cm ( 3 0 60, p = 0.022) soil depths , but not the 60 84 depth ( 60 84 , p = 0.084). Within the tree r ow throughfall reduction decreased 3 0 60 0 30 60 84 (p = 0.210). Measurements of volumetric water content from the wired probes installed at 2 m were above 0. 2 cm 3 H 2 3 soil throughout the entire measurement period (November 2013 April 2014). For a similar soil Gonzalez Benecke et al. ( 2011) , reported maximum 3 H 2 3 . This, along with observation s of water table depth, suggested that during this period the water table was consistently above 2m. Numer ous studies have shown that for somewhat poorly to poorly drained sites in the lower coastal plain Flat woods, the water table typically fluctuates between 2m and the surface (McCarthy and Stone 1991; Teskey et al. 1994; Gholz and Clark 2002; Powell et al. 2008) . Leaf Area Index , L itterfall, and G rowth P rojected l eaf area index (LAI) was greater in the fertilized plots in both 2012 (p = 0.037) and 2013 (p < .001). Throughfall reduction did not significantly affect LAI. In both years LAI was at a maximum at the end of July and at a minimum at the beginning of March (Figure 2 2 ) . Maximum values of LAI for fertilized and unfertilized plots were 3.7 and 3.4 in 2012 and 4.3 and 3.7 in 2013, respectively (Table 2 3) . Total annual litterfall
43 in 2013 was significantly greater in plots treated with fertilizer (p < .001). Litte rfall rates were 6.5 Mg ha 1 yr 1 f or fertilized plots and 5.5 Mg ha 1 yr 1 for unfertilized plots (Table 2 3) . Specific needle area was unaffected by both fertilization (p = 0.465) and throughfall reduction (p = 0.693). The mean specific needl e area was 104.8 cm 2 g 1 . Basal area increment was greater in plots treated with fertilizer in both 2012 and 2013 (p = 0.007, p = 0.033) and was unaffected by throughfall reduction (p = 0.230, p = 0.576). Growth rates were higher in 2012 than in 20 13 and ranged from 4.0 to 5.0 m 2 ha 1 yr 1 and 3.4 to 4.3 m 2 ha 1 yr 1 for each year, respectively (Table 2 3) . The FR treatment had a lower absolute growth rate than the F treatment in both years, but this difference was not significant. Height growth was unaffected by the treatments and 1 . 1 yr 1 for the C, R, F and FR treatm ents respectively There was a significant F*R interaction for aboveground net primary production (ANPP) (p = 0.049) such that the ANPP of the F and FR treatments were both greater than the control (p < 0.001, p = 0.012), but only the F treatment was greate r than the R treatment (p = 0.006) (table 2 3). There was no difference between the F and FR treatments (p = 0.096). Whole Tree Hydraulic C onductance Whole tree hydraulic conductance was not affected by fertilization (p = 0.276) or throughfall reduct ion (p = 0.425). There was, however, a significant effect of measurement date on Ks (p < 0.001 ) (Figure 2 3 ). Throughout the measurement period Ks ranged from 1.63 molÂ·m 2 Â·S 1 Â·MPa 1 in A ugust of 2013 to 2.21 molÂ·m 2 Â·S 1 Â·MPa 1 in October of 2012. Similar to Ks, pre dawn water potential ( p ) was not significantly
44 affected by fertilization (p = 0.372) or throughfall redu ction (p = 0.093), but did vary p ranged from 0.34 MPa to 0.53 M Pa. Radial Variation in S ap Flow To examine the relative rates of sap flow at different depths in the xylem, w e compared daily summed sap flow per unit sapwood area (J s ) at 2 4 cm and 4 6 cm depths with daily sums in the outer 0 2 cm . Daily sums of J s decreased significan tly with depth (p < 0.001). The J s of the 2 4 cm depth was on average 87% of the 0 2 cm J s in all treatments (Figure 2 4 ). The J s of the 4 6 cm depth was 58% of the 0 2 cm J s for the C, R and FR treatments , but 76% for the F treatment. Daily J s of the 0 2 cm depth did not differ among treatments (p = 0.598). Transpiration Total transpiration for 2013 (E Y ear ) was not affected by fertilization (p=0.349) or throughfall reduction (p=0.544). Values of E Y ear 1 for the C, R, F, and FR treatments, respectively ( Table 2 4 ). Total rainfall for 2013 was 1396.7 mm resulting in annual transpiration representing 38.3%, 39.2%, 42.7%, and 39.1% of annual rainfall for the C, R, F, and FR treatments, respectively (NOAA ). Monthly transpirat ion rates ( E Month ) varied over time with higher transpiration rates occurring during the spring and summer months when LAI and VPD were high (Figure 2 2 ). There was a significant interaction between month, fertilization and throughfall reduction (p=0.033) such that in May, when VPD was at a maximum, E Month was significantly higher in the F treatment than the C (p=0.013), R (p=0.034), and FR (p = 0.029) treatments. There was no effect of treatment on leaf level transpiration (E Leaf ) during this month (p = 0. 401) and E Leaf 2 s 1 for May . In March and
45 April the E Month of the F treatment was greater than the control (p = 0.042, p = 0.029) , but not the R (p = 0.105, p = 0.063) or FR (p = 0.113, p = 0.079) treatments. Canopy Conductance The response of G C to VPD was not significantly affected the by treatment s . The mean sensitivity of G C to VPD and G Cref , was 78.1 2 s 1 ln(kPa) 1 2 s 1 , respectively. This response was consistent across a ll treatments. The 3 PG model uses parameters describing maximum G C 1 ) and stomatal response to VPD (CoeffCond, mbar 1 ). 1 and 0.059 mbar 1 , respectively. These parameters were not significant ly affected by treatment. Discussion The over 10 million hectares of loblolly pine plantations in the southeastern U.S. are of great economic and ecological importance to the region (Robert P. Schultz 1997; Wear and Greis 2002; Sm ith et al. 2009) . Since the 1950s, advancements in our understanding of the effects of various silvicultural practices, such as fertilization, on the growth of southern pines has drastically increased the productivity of these systems (Fox et al. 2007) . The potential impact of climate change on the productivity of these forests, however, is not well und erstood. This study was designed to gain insight on the impacts of fertilization and a 30% reduction in throughfall on the growth and water relations of loblolly pine. This information will improve the ability of models to predict the impacts of climate ch ange on these systems, and will assist land managers in making rational decisions in the face of a changing climate. In this study throughfall reduction had little effect on loblolly pine plantation water relations and growth on a poorly drained site in N orthern Florida. Although throughfall
46 cm 3 H 2 O cm 3 soil of the 60 84 cm soil region, even when measured directly under exclusion troughs. Values of predawn P ), which in theory reflect soil water potential, did not differ between treatments and were similar to values reported for irrigated loblolly pine stands in other studies (Samuelso n et al. 2008; Gonzalez Benecke and Martin 2010) . The water table was consistently above 2 m throughout the study period and annual rainfall was similar to the long term average for the region (1396 mm vs 1404 mm). The rooting depth of loblolly pine has consistently been shown to extend below 150 cm (Van Rees and Comerford 1986; Torreano and Morris 1998; Albaugh et al. 2006) . It is likely the ess to a high water table limited responses to throughfall reduction. Others have also noted a limited response to drought in these systems where, in most years, the water table is rarely deeper than 150 cm (Teskey et al. 1994; Bracho et al. 2012) . Treatments did not affect whole tree hydraulic conductivity (K S ), although K S did vary over time. While we did not measure organ level K S , the literature suggests that differences in K S between measurement dates may have been due to changes in root hydraulic conductivity which, in loblolly pine, has been shown to respond dynamically to changes in soil moisture (Domec et al. 2009) . The mean K S across all treatments and measurements dates was 1.84 mol Â·m 2 Â·s 1 Â·MPa 1 . For stands of a similar age and genotype, Gonzalez Benecke and Martin ( 2010) reported values of K S ranging from 0.84 mol Â·m 2 Â·s 1 Â·MPa 1 in the control treatment to 2.92 mol Â·m 2 Â·s 1 Â·MPa 1 in the irrigation treatment. Samuelson and Stokes ( 2006) found no interaction betw een weed control,
47 irrigation, and fertilization with K S in five year old loblolly pine stands. The mean K S reported by Samuelson and Stokes ( 2006) was much higher than the value reported here (3.0 vs. 1.8 mol Â·m 2 Â·s 1 Â·MPa 1 ); howe ver, the trees in this study were much younger and K S has been observed to decline with age in pines (Mencuccini and Grace 1996) . The failure of throughfall reduction to affect K S in this experiment suggests that soil water availability was not reduced to the extent that xylem cavitation was increased. P , suggests that water availability was n ot limiting in any treatment. Fertilization increased leaf area index (LAI) and basal area production. Increases in LAI are generally associated with increased productivity and a strong correlation between foliage biomass and stem wood production has been shown in loblolly pine (Teskey et al. 1987) . Minimum LAI was similar across treatments prior to study establishment, but once treatments were applied, the LAI of fertilized plots was consistently higher than untreated plots with the magnitude of difference increasing through time. Increases in LAI in response to fertilization have been observed in loblolly pine plantations growing on diverse sites throughout the southeastern U.S. (Albaugh et al. 1998; Ewers et al. 1999; Martin and Jokela 2004) . Fertilization increased basal area production by 0.8 m 2 ha 1 yr 1 in 2012 and 0.6 m 2 ha 1 yr 1 in 2013. Although increases in basal area production in response to fertilization are well documented for loblolly pine, few studies have investigated this response in stands at intermediate ages (Martin and J okela 2004 ; Fox et al. 2007; Jokela et al. 2010) . For an eight year old loblolly pine plantation, Albaugh et al. ( 1998) reported an increase of 0.7 m 2 ha 1 yr 1 for plots receiv ing a single fertilization treatment
48 in the previous year. However, due to poor site conditions, the range of basal areas reported in Albaugh et al. ( 1998) were significantly lower than those reported here (1.8 2.9 m 2 ha 1 vs. 25.5 34.4 m 2 ha 1 ). Aboveground net primary productivity (ANPP) was significantly increased by fertilizer application relative to the control. This was the result of increased foliage production and increased woody biomass increment. Sap flow per unit sapwood area (J S ) toward the pith . On average, the 2 4 cm and 4 6 cm depths had rates that were 87% and 63% of the 0 2 cm J S , respectively. Others have also observed reductions in J S with increasing depth in loblolly pine, however the magnitude and pattern of this reduction varies widely among studies. Phillips et al. ( 1996) reported that for twelve year old lo blolly pine the J S of the 2 4 cm sapwood region was 41% of the outer 0 2 cm J S . For mature loblolly pine, Ford et al. ( 2004) observed that J S was almost always greatest in the outer 2 cm of sapwood and tended to decrease with increasing depth. In contrast, Gonzalez Benecke and Martin ( 2010) did not observe a consistent pattern of radial variation and often observed maxim um rates of J S in the inner sapwood regions of loblolly pine. The reason for these varying results is not readily apparent. Observed reductions in J S with increasing sapwood depth are likely the result of many contributing factors. The K S of sapwood has be en shown to decrease with increasing depth in conifers (Spicer and Gartner 2001; Domec et al. 2005) . This decrease in K S is likely the result of changes in xylem bordered pit membranes resulting in increased resistance to flow and increased permeability to air. Mark and Crews ( 1973) showed that in Pinus engelmannii and Pinus contorta xylem bordered pit membranes were most open in the outer sapwood and became increasingly blocked and encrusted with increasing sapwood depth. The pit membrane
49 of older xylem elements has also been shown to exhibit an increased permeability to air making them more vulnerable to cavitation (Sperry et al. 1991) . The inner sapwood may also not be as well co nnected to the branches and leaves of the upper canopy, where transpiration rate is greatest, due to this region originally developing to supply branches and leaves that have since died or become shaded (Dye et al. 1991; JimÃ©nez et al. 2000) . After accounting for radial variation in sap flow, daily maximum stand level transpiration (E Max ) did not differ among treatments and ranged from 2.6 to 3.1 mm for the C and F treatments, respectively. Values of E Max were within the range reported for loblolly pine stands of various structures, ages , and genetics (Phillips and Oren 2001; Samuelson et al. 2008; Gonzalez Benecke and Martin 2010) . Variation in daily stand level transpiration (E Day ) was explained principally by total daily PPFD and, to a lesser extent, mean daily D and temperature. Gonzalez Benecke and Martin ( 2010) r eported a similar relationship between E Day , radiation, D, and temperature for control plots and also observed that the E Day of irrigated plots was explained solely by total daily radiation (R 2 = 0.98). During the spring, when VPD and LAI were high, ferti lization in absence of throughfall reduction increased monthly transpiration rate (E Month ). The E Month of the F treatment was significantly higher than all other treatments in May, when D was at a maximum, and was also significantly greater than the C trea tment during March and April. This observed increase in transpiration can be attributed to the higher levels of LAI in the F plots, as there was no difference in leaf level transpiration (E L ) during this time.
50 Ewers et al. ( 1999 ) also reported increased canopy transpiration (E C ) in response to fertilization, but when E C was normalized by leaf area, plots receiving only fertilizer had significantly lower E L than the control; plots receiving both fertilizer and irrigation were not differe nt. Samuelson and Stokes ( 2006) reported similar values of E L for stands treated with either irrigation or irrigation and fertilization. These results suggest that under conditions of abundant soil moisture E L is similar in ferti lized and unfertilized stands and observed differences in E C are driven by differences in LAI. Under conditions of water limitation, however, fertilization tends to reduce E L (Ewers et al. 1999; Ewers et al. 2001 b ) . Ewers et al. ( 1999 ) hypothesized that this reduction in E L is the result of needles developing under conditions of relative soil drought. This relative soil drought was attributed to increases in LAI without corresponding increases in root growth and increased interception losses. In contrast, Samuelson et al. ( 2008) repo rted higher values of E L in response to fertilization. These dissimilar results may be due to differences in the soil properties of study sites and differences in stand age, structure, and genetics. Total annual transpiration (E Year ) did not differ between treatments for 2013, despite observed differences in E Month during the spring. Values of E Year ranged from 535 mm in the C treatment to 596 mm in the F treatment, which corresponded to 38.3% and 42.7% of total rainfall, respectively. For unirrigated stand s of a similar age, Gonzalez Benecke an d Martin ( 2010) reported an E Year of 490 mm, which represented 54.3% of total rainfall. Differences between these studies are likely due to differences in total annual rainfall (1396 mm vs 902 mm). Values of E Year as high as 930 mm have been reported fo r irrigated loblolly pine stands receiving the equivalent of 3000 mm of annual
51 rainfall (Gonzalez Benecke and Martin 2010) . Samuelson and Stokes ( 2006) also observed significant increases in canopy transpiration (E C ) in response to irrigation. These results suggest that the annual transpiration of loblolly pine forests is closely tied to rainfall inputs. In contrast, Ewers et al. ( 1999 ) and Ewers et al. ( 2000) did not observe increases in E C with irrigation; however, when fertilizer and irrigation treatments were applied E C was significantly greater than if either treatment was applied individua lly. This suggests that under conditions of nutrient limitation additional water inputs may not impact E C . Fertilization, in absence of irrigation, has also been shown to increase E C despite reductions in E L (Ewers et al. 1999; Brent E. Ewers et al. 2001 b ) . This effect is the result of increases in LAI being significant enough to increase E C despite reduction in E L . The sensitivity of stomatal conductance to D was not affected by throughfall reduction or fertilization. For stands of a similar age and genetics Gonzalez Benecke and Martin ( 2010) reported similar values of G Cref range o f values of G Cref 2 5). The difference in values reported by these studies is likely due to differences in stand genetics and site conditions. The genetics of loblolly pine has been shown to affect G Cref (Gonzalez Benecke and M artin 2010; Aspinwall et al. 2011) . In Gonzalez Benecke and Martin ( 2010) irrigation was found to increase G Cref source while a Florida seed source was unaffected. This suggests that loblolly pine originating from Florida was less conservative in regulating water loss in response to changes in soil moisture. The lack of response of the northern Florida source may have
52 resulted from adaptions to abundant water supply due to a high water table and high annual precipitation. In loblolly pine, drastic reductions in G Cref have been reported in response to fertilizer additions (Ewers et al. 2000; Brent E. Ewers et al. 2001 b ) . This reduction in G Cref was likely due to the same factors as those hypothesized to reduce E L (Ewers et al. 1999) . Although a dramatic reduction in G Cref was observed in response to fertilization in Ewers et al. ( 2001 ), when both irrigation and fertilizer treatments were applied, G Cref was found to be greater than the control or irrigation treatments alone. Samuelson et al. ( 2008) also observed an increase in G Cref with fertilization during a growing season having above average rainfall. The results of these studies suggest that the interaction between genetics, soil nutrition, and soil water availability play an important role in the response of stomatal conductance to changes in D. The parameters describing the sensitivity of stomatal conductance to changes in D used in the 3 PG model (MaxCond and CoeffCond) (Bryars et al. 2013) were unaffected by either fertilizer or throughfall reduction. The value of MaxCond observed in t his study (0.006 1 ) was identical to the number reported for the parameterization of 3 PG for loblolly pine (Bryars et al. 2013) . The value of CoeffCond measured in this study ( 0.059 mbar 1 ), however, was more than twice the reported value (0.025 mbar 1 ) (Bryars et al. 2013) . To gene ralize the response of G C to D, for the purposes of parameterizing 3 PG and other models, we fit a regression line through values of G C calculated for a range of D using published values of G Cref 2 5). The function produced a n estimate of CoeffCond of 0.051 mbar 1 and a MaxCond of 0.005 1 (Figure 2 5 ) . The 95% confidence interval for these variables ranged from
53 0.045 mbar 1 to 0.063 mbar 1 for CoeffCond and from 0.005 1 to 0.006 1 for MaxCond. This analysis, along with the results from this study, suggest that the average sensitivity of stomatal conductance to changes in D is approximately twice as great as currently reflected in the published 3 PG parameters for loblolly pine (Bryars et al. 2013) . This may ha ve important consequences as the model is sensitive to this term when estimating carbon gain. In this study a 30% reduction in throughfall did not significantly affect the growth or water use of a loblolly pine plantation on a poorly drained site in the lower Coastal 20 , water use was not affected at the canopy or leaf level. This lack of responses was likely attributed to the ability of trees to meet soil moisture requirements by accessing the water table . A lack of stomatal regulation with changes in soil moisture has been observed in loblolly pine genotypes originating from Florida (Gonzalez Benecke and Martin 2010) . This lack of response may be due to these genotypes evolving with regular access to the water table which, in most years, is rarely deeper than 150 cm. If future reductions in precipitation are severe enough to lower the water table, genotypes from Florida may be ill adapted to sites where they once thrived. Under such a scenario, the application of fertilizer to these forests could exacerbate drought dress by increasing LAI and E C . Although the results of this study can not directly address these issues, the information gained from the PINEMAP tier III sites over the coming years will provide additional insight on the potential impacts of climate change on loblolly pine productivity and water relations.
54 Table 2 1. Mean pre treatment basal area of plots within each block during the winter of 2011 2012 with associated standard errors. Block Basal Area (m 2 1 ) Standard Error 1 20 0.48 2 21 0.46 3 22 0.54 4 26 0.52 Table 2 2. Minimum, maximum, and mean DBH of sap flow measurement trees during the winter of 2012 2013 for the control (C), throughfall reduction (R), fertilization (F), and throughfall reduction and fertilization (FR) treatments. DBH (cm) Treatment Minimum Maximum Mean C 10 19.1 14.9 R 8.6 19.5 15.0 F 9.6 19.3 14.8 FR 9 19.6 15.3 Table 2 3. Mean aboveground net primary productivity (ANPP), basal area growth, projected leaf area index (LAI), and litterfall for control (C), throughfall reduction (R), fertilization (F), and throughfall reduction and fertilization (FR) treatments with associated p values. Values for LAI represent monthly minimum s and maximums within a given year. For the LAI time series data, there were no treatment by month interactions. Treatment P Value C R F FR F R F*R ANPP 2013 1 1 ) 26.5 28.1 32.3 30.2 < 0.001 0.736 0.049 Basal Area Growth 2012 (m 2 1 1 ) 4.2 4.0 5.0 4.7 0.007 0.230 0.789 Basal Area Growth 2013 (m 2 1 1 ) 3.4 3.5 4.3 3.9 0.033 0.576 0.314 LAI 2012 (Projected) 2.0 3.4 1.9 3.3 1.9 3.6 1.9 3.7 0.037 0.938 0.308 LAI 2013 (Projected) 2.3 3.7 2.3 3.8 2.7 4.2 2.7 4.3 < 0.001 0.294 0.840 Litterfall 2013 1 1 ) 5.5 5.6 6.4 6.6 < 0.001 0.383 0.536
55 Table 2 4. Water use and stomatal sensitivity to vapor pressure deficit reported as m ean annual transpiration (E Year ), maximum daily transpiration (E Max ), sensitivity of stomatal conductance to vapor pressure deficit ( conductance at vapor pressure deficit = 1 kPa (G Cref ), 3 PG parameter defining maximum stomatal conductance (MaxCond), and 3 PG parameter defining stomatal sensitivity to vapor pressure deficit (CoeffCond) for control (C), throughfall reduction (R), fertilization (F), and throughfall reduction and fertilization (FR) treatments with associated p values. Treatment P value C R F FR F R F*R E Year 1 ) 535 547 596 546 0.349 0.545 0.334 E Max 1 ) 2.60 2.70 3.08 2.76 0.125 0.517 0.229 2 1 1 ) 81.05 79.32 77.08 75.07 0.504 0.709 0.888 G Cref 2 1 ) 138.14 139.28 136.67 127.70 0.521 0.684 0.610 MaxCond 1 ) 0.0064 0.0062 0.0060 0.0057 0.204 0.593 0.944 CoeffCond (mbar 1 ) 0.060 0.057 0.056 0.063 0.878 0.549 0.137
56 Table 2 5. Reported values of stomatal conductance at vapor pressure deficit = 1 kPa (G Cref 2 1 ) and stomatal conductance sensitivity to vapor pressure deficit ( 2 1 1 ) for loblolly pine stands under a variety of treatments in the southeastern U.S. Treatment Gcref m Source Irrigation x Fertilization 177.0 75.0 Ewers et al. 2000 South Carolina Seed Source: Irrigation 148.6 83.8 Gonzalez Benecke and Martin 2010 Florida Seed Source: Control 136.6 82.7 Gonzalez Benecke and Martin 2010 Florida Seed Source: Irrigation 136.0 79.7 Gonzalez Benecke and Martin 2010 Irrigation 124.8 77.9 Samuelson et al. 2008 Control 123.0 94.0 Ewers et al. 2000 Genetics: Half sib 120.6 51.5 Aspinwall et al. 2011 Genetics: Full sib 118.8 51.8 Aspinwall et al. 2011 Irrigation 117.0 94.0 Ewers et al. 2000 South Carolina Seed Source: Control 111.6 66.6 Gonzalez Benecke and Martin 2010 Irrigation x Fertilization 111.1 69.6 Samuelson et al. 2008 Genetics: Clone 106.4 48.2 Aspinwall et al. 2011 Fertilization 101.0 65.6 Samuelson et al. 2008 Control 87.2 57.3 Samuelson et al. 2008 Sandhill Site: Control 85.0 46.0 Domec et al. 2012 Coastal Site: Control 62.0 33.0 Domec et al. 2012 Piedmont Site: Control 55.0 36.0 Domec et al. 2012 Fertilization 52.0 41.0 Ewers et al. 2000 Piedmont Site: Fertilization 45.0 26.0 Domec et al. 2012 Piedmont Site: Fertilization and Elevated CO2 43.0 25.0 Domec et al. 2012 Sandhill Site: Fertilization 39.0 20.0 Domec et al. 2012
57 Figure 2 1 E xample of a throughfall exclusion structure
58 Figure 2 2 Time series of water relations parameters, leaf area index, and meteorological data. A) Total monthly transpiration rate by treatment, B) Mean monthly stomatal conductance by treatment, D) Mean monthly LAI by treatment, D) Total Monthly Photosynthetic Photon Flux Density (PPFD) and mean daylight ho ur vapor pressure deficit (D), and E) Total monthly precipitation for 2013. Treatments include control (C), throughfall reduction (R), fertilization (F), and throughf all reduction and fertilization (FR) .
59 Figure 2 3 Mean whole tree hydraulic conductivity (K S ) for each measurement date from October 2012 (Month = 10) to November 2013 (Month = 9) for control (C), throughfall reduction (R), fertilization (F), and throughfall reduction and fertilization (FR) treatments. Figure 2 4 Sap flow rates at 2 4 c m and 4 6 cm depths expressed as a percentage of sap flow at 0 2 cm depth for control (C), throughfall reduction (R), fertilization (F), and throughf all reduction and fertilization (FR) treatments . Percentage values shown above each bar; values followed b y the same letter are not significantly different (p = 0.05). 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 10 11 12 1 2 3 4 5 6 7 8 9 K S (mol H 2 2 1 1 ) 2012 Month 2013 C R F FR 88.7a 87.3a 87.8a 85.2a 58.7b 61.2b 76.0c 55.3b 0.0 20.0 40.0 60.0 80.0 100.0 C R F FR % 0f 0 2 cm Treatment 2-4 cm 4-6 cm
60 Figure 2 5 Values of stomatal conductance calculated at vapor pressure deficits of 2, 5, 10, 20, 30, and 40 mbar using a range of published values of stomatal conductance at vapor pressure deficit = 1 kPa (G Cref ) and sensitivity of stomatal conductance to vapor pres sure deficit ( exponential regression function. The reported values of G Cref and produce this graph are provided in Table 2 5. y = 0.0051e 0.051x RÂ² = 0.6505 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0 5 10 15 20 25 30 35 40 45 1 ) Vapor Pressure Deficit (mbar) Regression Function
61 CHAPTER 3 CONCLUSIONS AND FUTURE RESEARCH NEEDS This study investigated the water relation s and productivity of a ten year old loblolly pine plantation on a somewhat poorly drained site in northern Florida. A factorial combination of throughfall reduction and fertilization treatments was applied in the spring of 2012 to investigate the impact o f nutrient availability and soil moisture on loblolly pine function. Throughfall reduction did not impact on any of the measured variables. This lack of response was likely due to abundant rainfall during the study period as well as the ability of trees to access the shallow water table at the study site. Fertilizer application significantly increased basal area production and leaf area index (LAI). This response has been observed by many others and is considered to be typical (Albaugh et al. 1998; Ewers et al. 1999; Martin and Jokela 2004; Fox et al. 2007; Jokela et al. 2010). During the spring, when vapor pressure deficit (D) was high, the greater level of LAI in the fertilizer only treatment resulted in monthly transpiration rate being 17% higher than the control. These results suggest a reduction in precipitation may not affect the growth or water use of loblolly pine on poorly drained sites in the lower Coastal Plain; however, fertilizer application will likely affect these variables, especially under co nditions of high D. The sensitivity of stomatal conductance (G C ) to changes in D was found to be twice as great as currently reflected in the parameterization of the Physiological Principles in Predicting Growth Model (3 PG) for loblolly pine (Bryars et a l. 2013). A review of published values for loblolly pine sensitivity of G C to D also suggested that the current parameterization of 3 PG does not accurately reflect this relationship. This may have important consequences as the model is sensitive to this r elationship when
62 calculating canopy quantum efficiency, and in turn carbon gain. These results suggest that future efforts are required for the parameterization of 3 PG for loblolly pine. This study was conducted on one of the four throughfall by fertili zation treatments that constitute the Tier III monitoring network of the Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP). The other throughfall by fertilization studies are located in Georgia, Oklahoma, and Virginia. These study sites vary in genetics, stand age, climatic conditions, and soil properties. PINEMAP will soon be conducting an analysis on how loblolly pine at these various sites respond to changes in nutrient availability and soil moisture. This analysis will pro vide a much more detailed review of how loblolly pine across the southeastern U.S. will responded to potential changes in climate. The sensitivity of G C to changes in D of loblolly pine growing in these various regions will be of particular interest given current issues with the parameterization of this relationship in 3 PG. The PINEMAP tier III monitoring network will continue to take measurements until 2016. The response of loblolly pine to various levels of annual rainfall throughout this period will she d light on the influence of rainfall inputs to stand water use. This will be of particular interest at the site in Florida where annual rainfall has ranged from 896 mm to 2076 mm over the past thirty years (NOAA). Information on the impact of loblolly pine plantation management practices on transpiration rates is important due to predicted water supply stress in the Southeast (Sun et al. 2013). Projected increases in the amount of water used for irrigation and energy production in combination with increased evapotranspiration rates (ET) will importantly affect water supply. The increase in temperature associated with climate change will likely not only increase ET but also D. Under conditions of increased D, the
63 use of fertilizer could further increase the E T of loblolly pine plantations and therefore reduce watershed yield. In light of this, the role of loblolly pine forests in regulating watershed yield could become an important management concern; however, few studies have investigated how various silvicul ture practices, such as fertilizer application, impact the ET of loblolly pine forests. Information on the impact of changes in both forest area and forest management intensity on watershed yield would also be useful in addressing future potential water su pply stress. Whole tree hydraulic conductivity (K S ) was not impacted by throughfall reduction of fertilizer application, but did vary significantly through time. Although reductions in the K S of loblolly pine have been attributed to dynamic changes in t he conductivity of roots in response to changes in soil moisture, this study can not address the cause of observed changes in K S (Domec et al. 2009). As measurements continue to be taken at the PINEMAP throughfall by fertilization studies, it would be usef ul to have estimates of the conductivity of roots, stems, and branches in order to investigate the source of changes in K S . This study presented the results from the first year of measurements at the Florida PINEMAP Tier III site. This information wil l be used in a regional analysis on the impact of nutrient availability and soil moisture on loblolly pine function. The results of this study will also serve a baseline for future measurements at this study site.
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76 BIOGRAPHICAL SKETCH Maxwell Wightman was born in Rochester, New York in 1987. Throughout his youth, Maxwell demonstrated an affinity for the outdoors. He was an avid member of Boy Scout troop 356 which provided him with the opportunity to partake in many backpacking and canoe ing trips within the Adirondack Park. His affinity for the outdoors would lead him to seek a career in natural resource management. In 2009, Maxwell received his Bachelor of Science degree from the State University of New York College of Environmental S cience an d Forestry where he majored in forest ecosystem s cience. After his graduation, Maxwell worked as a research technician at the Adirondack Ecological Center working under the direction of Dr. Colin Beier. In 2010 , he served an AmeriCorps position at the Phoenix Charter School in Roseburg, Oregon as a native plant nursery coordinator. In 2014, Maxwell received his Master of Science Degree from the University of Florida where he majored in forest resources and conservation .