ModelingPoten,alClima,cTreelineofGreatBasin
BristleconePineintheSnakeMountainRange,Nevada,USA
JamisM.Bruening1,TylerJ.Tran1,AndrewG.Bunn1,MaFhewW.Salzer2,StuartB.Weiss3
1DepartmentofEnvironmentalSciences,WesternWashingtonUniversity,
2LaboratoryofTreeRingResearch,UniversityofArizona,3CreeksideCenterforEarthObserva,on,MenloPark,CA
Introduc)on
GreatBasinBristleconepine(Pinuslongaeva)isavaluableclimate
proxy because of its mul,-millennial ring width series which aid
paleoclima,c temperature reconstruc,ons1,2. Recent work has
shown trees growing nearest to treeline (~ 70m) are most
correlated with growing season temperatures, and are thus the
best candidates for building temperature reconstruc,ons3,4. Our
analysis a1empts to iden)fy the main parameters constraining
bristlecone pine growth at the highest eleva)ons, and derive a
climate-based model that predicts this species’ poten)al treeline
posi)onasafunc)onofclima)cvariablesintheSnakeRange,NV.
WheelerPeak
andclima)ctreeline
PaulsenandKörner(2014)presentaglobalmodelpredic,ngtreelinetowithin~70meters.Indoingso,theydefinethreevariablesthat
determinetheposi,onofallclimatelimitedtreelines:athresholdtemperature(DTMIN)thatabovewhichallowsfortreegrowthandcell
division;aminimumgrowingseasonlength(LGS)definedbyalldayswithameandailytemperature<DTMIN;andaminimumseasonal
meantemperature(SMT),themeantemperatureacrossalldayswithinthegrowingseason.ThebestfitdeterminedDTMIN=0.9°C,LGS
≥94days,andSMT≥6.4°C.Whilemaintainingimpressiveaccuracyattheglobalscale,theseparametersrepresenttheaverageofall
treelineformingspeciesusedbyPaulsenandKörner(2014).Wepresentasimilar,fine-scalemodellimitedtoGreatBasinbristlecone
pineintheSnakeRange,NV,andprovideevidencesugges)ngtheaboveparametersareverydifferentforthisspecies.
Project
Workflow
TopoClimate
Modeling
Ecotone
Digi,zing
Aggregateintomonthlyvalues:Tmin,
Tmean,Tmax,diurnaltemp.range,
growingdegreehrs>5°Cand<-5°C
Treeline
Variables
Classifica,on
Modeling
UsingGoogleEarthwedigi,zedthreetargetecotonesonWheelerPeak;analpinezoneabovethetreelineboundary,atreelinezone
thatincludestheupperreachesofclosedforestandpocketsoftreesgrowingabove,andasubalpinezoneofclosedforestbelowthe
treelinezone.Wethenusedthetopoclimatemodelingoutputs,andLGSandSMTvariablesaspredic,velayersinaclassifica,onmodel
(n=885,100points/km2),calculatedinRusingthe‘Rpart’package.Webuilttwopreliminarymodelstodeterminethemostpredic,ve
variables:thefirstusingallvariablesfromthetopoclimatemodeling(72intotal,6setsofmonthlyresolvedmetrics),andasecondusing
onlytheLGSandSMTvariablessimilartoPaulsenandKörner’s(2014)globalmodel.Basedonvariableimportancescoresandclima,c
treelinetheory,webuiltafinalmodelusingbothLGSandSMTvariables,andonlythethreemostpredic,vetopoclimatevariables:July
growingdegreehoursabove5°C,andJulyandSeptemberaveragetemperatures.
Alpine
Clima)cTreelineForma)onTheory
49temperaturesensorsrecordhourly
temperaturesforoneyearabove3000
mincomplex,mountainousterrain
Classifica)onModeling
Modeltemperaturesasa
func,onoftopography,
predictacrosslandscape
UseGoogleEarthtodigi)ze3ecotones
basedonKorner’sconven,ons:Subalpine,
Treeline,Alpine
Treeline
Subalpine
EmployKörner’stheorytocalculate
growingseasonlength(LGS)andmean
temperature(SMT)fromTmean
Buildclassifica)onmodelthatpredicts
ecotonesabove3000mfrommodeled
temperaturesandLGSandSMTvariables
ThermochroniBuFonsrecordedhourlytemperaturesat49loca,onswithina1km2areanearthepeakofMountWashingtonforoneyear
(9/2013 – 10/2014). We aggregated the data and calculated six different monthly resolved metrics: Minimum, average and maximum
temperatures;averagediurnaltemperaturerange; growingdegreehoursabove5°C;andgrowingdegreehoursbelow-5°C).Wethen
usedaLASSOregressioninRtomodelthesemetricsasafunc,onoftopography,andpredictedthevaluesacrossallareasabove3000
meterswithintheSnakeRange.Thetopographicvariablesusedinclude:slope,eastnessandsouthnessindices,monthlyradia,onvalues
April–November,topographicconvergenceindex,andtopographicposi,onindicesatvaryingscales(radius=50–1000meters).
TopoClimateModelsR2
Predicted
Fine-scaleTopoclimateModeling
alpine
alpine 177
treeline 29
subalpine 95
Producererror 41%
Actual
treeline
56
87
136
51%
•
•
TopoClimateModelsRMSE
•
subalpine Consumererror
1
24%
26
39%
278
45%
9%
Kappa=0.46
Results
Treelineecotonemodeledwith50%accuracy
Impliesclima,ctreelinesinfluencedby:length
of the growing season and temperature
throughout, especially mid summer growing
degreehours
Provides evidence suppor,ng Paulsen and
Körner’s(2014)clima,ctreelinetheory
°C
References
1.
Salzer,M.W.,M.K.Hughes,A.G.Bunn,andK.F.Kipfmueller.2009.Recentunprecedentedtree-ringgrowthinbristleconepineatthehighesteleva,onsandpossiblecauses.Proceedingsof
theNa,onalAcademyofSciences106:20348-20353.
2.
Salzer,M.W.,A.G.Bunn,N.E.Graham,andM.K.Hughes.2013.Fivemillenniaofpaleotemperaturefromtree-ringsintheGreatBasin,USA.ClimateDynamics42:1517-1526.
3.
Salzer,M.W.,E.R.Larson,A.G.Bunn,andM.K.Hughes.2014.Changingclimateresponseinnear-treelinebristleconepinewitheleva,onandaspect.EnvironmentalResearchLeFers.9.
4.
Bunn,A.G.,M.K.Hughes,andM.W.Salzer.2011.Topographicallymodifiedtree-ringchronologiesasapoten,almeanstoimprovepaleoclimateinference.Clima,cChange105:627-634.
5.
Paulsen,J.&C.Körner.2014.Aclimate-basedmodeltopredictpoten,altreelineposi,onglobally.Alp.Bt.124(1),1-12.
Acknowledgements:Wegratefullyacknowledge
supportfromtheNa,onalScienceFounda,on’s
P2C2Program(GrantATM-1203749).