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NewPhytologistSupportingInformation
Articletitle:Evaluatingthekineticbasisofplantgrowthfromorganstoecosystems
Authors:SeanT.Michaletz(michaletz@email.arizona.edu)
Articleacceptancedate:18December2017
ThefollowingSupportingInformationisavailableforthisarticle:
Fig.S1Atypicaltemperatureresponsecurveforabiologicalrate.
Fig.S2ModifiedArrheniusplotsofSharpe‐Schoolfieldmodelfits(withEhasafreeparameter)
tophotosynthesistemperatureresponsedata.
Fig.S3ModifiedArrheniusplotsofSharpe‐Schoolfieldmodelfits(withEhasafixedparameter)
tophotosynthesistemperatureresponsedata.
Fig.S4Treegrowthratesacrossabroadairtemperaturegradient.
Fig.S5Distributionsofintraspecificoptimaltemperaturesforphotosynthesis,estimatedfrom
Eqn(2)withEhasafreeparameter.
Fig.S6Distributionsofintraspecificoptimaltemperaturesforphotosynthesis,estimatedfrom
Eqn(2)withEhasafixedparameter.
TableS1Listoftaxa,numberofcurvespertaxon,andprimarysourcesforphotosynthesis
temperatureresponsedatausedinanalyses.
TableS2Listofgrowthvariables,temperaturevariables,samplesizes,andprimarysourcesfor
growthanalyses.
MethodsS1Descriptionofdataandmethodsusedforanalyses.
NotesS1RcodeforfittingtheSharpe‐Schoolfieldmodel(TPCfitting_stm.R).

Fig.S1Atypicaltemperatureresponsecurveforabiologicalrate.Ingeneral,biologicalratesB
increasewithtemperatureTtoamaximumvalueB
opt
atanoptimaltemperatureT
opt
,andthen
decreasewithtemperatureabovethisoptimum.TheeffectiveactivationenergyEandthe
enzymeinactivationparameterE
h
(bothnonlinearinthisspace)influencetheincreasingand
decreasingportionsofthecurve,respectively,asformalizedinEqn(2).Datapointsarenet
photosynthesis(µmolm
‐2
s
‐1
)ofJuniperusmonosperma(Michaletzetal.,2016b),andsolidline
isafittedSharpe‐Schoolfieldmodel(Eqn(2)).

Fig.S2ModifiedArrheniusplotsofSharpe‐Schoolfieldmodel(Eqn(2))fits(withE
h
asafree
parameter)tophotosynthesistemperatureresponsedatafor(a)anindividualJuniperus
monospermaleafcluster(E=0.22eV,quasir
2
=0.996)and(b)119leavesfrom32species(E=
0.03to3.06eV,quasir
2
=0.672to1.000).RelationshipsarepresentedasmodifiedArrhenius
plotsthatlinearizeassimilationratesrelativetoleaftemperature1/kT,yieldingalinearslopeof
‐Eonthedecreasingportionofthecurve.DatausedinanalysesaredescribedinTableS1.
Fig.S3ModifiedArrheniusplotsofSharpe‐Schoolfieldmodel(Eqn(2))fits(withE
h
asafixed
parameter)tophotosynthesistemperatureresponsedatafor(a)anindividualJuniperus
monospermaleafcluster(E=0.24eV,quasir
2
=0.993)and(b)119leavesfrom32species(E=
1.98x10
‐8
to2.07eV,quasir
2
=0.146to0.999).Relationshipsarepresentedasmodified
Arrheniusplotsthatlinearizeassimilationratesrelativetoleaftemperature1/kT,yieldinga
linearslopeof‐Eonthedecreasingportionofthecurve.Datausedinanalysesaredescribedin
TableS1.

Fig.S4Treegrowthratesacrossabroadairtemperaturegradient(N=210).DatafromFig.2b
replottedwithwithin‐sitemeasurementstreatedasindependentsamplesratherthansite
means.ThesamegeneralresultsareobservedhereandinFig.2b.Treegrowthratewas
invariantwithairtemperature(P=0.373,r2=0.004).ThefittedE=0.09has95%CI=‐0.11to
0.28thatexcludehypothesizedvaluesof0.32eV(Allenetal.,2005)and0.65eVforrespiration
(Gilloolyetal.,2001).ThisisamodifiedArrheniusplotthatlinearizestherelationshipbetween
growthandtemperaturetoyieldaslope–Ethatisequalinmagnitudebutoppositeindirection
totheactivationenergyE(Eqns(1)and(S1)).Temperatureisquantifiedas1/kT(eV‐1),wherek
(8.617x10‐5eVK‐1)isBoltzmann’sconstantandT(K)isairtemperature.Treegrowthratesare
expressedasmass‐adjustedrates,3/4 /
1
EkT
BM B e

.Thisrelationshipisobtainedvia
rearrangementof3/4 /
1
EkT
BBMe
,whichisinturnobtainedbyunpackingthenormalization
constantB0inEqn(1)torevealtheinfluenceofbiomassM,whereB1isabiomass‐independent
normalizationconstant.DatafromEnquistetal.(2007).

Fig.S5Distributionsofintraspecificoptimaltemperaturesforphotosynthesis,estimatedfrom
Eqn(2)withEhasafreeparameter.Optimaltemperaturesforphotosynthesisvarysubstantially
withinandamongspecies,reflectingacclimationandlocaladaptationofphotosynthetictraits
tosite‐averagedleafoperatingtemperatures.Themeanoptimaltemperatureacrossall
samplesis25.66˚C.Thickblacklinescorrespondtomedians,lowerandupperhinges
correspondtofirstandthirdquartiles,respectively,lowerandupperwhiskerscorrespondto
smallestandlargestvaluesthatdonotexceed1.5timestheinterquartilerangeofthehinges,
respectively,andpointscorrespondtooutliersbeyondtheendofthewhiskers.
Fig.S6Distributionsofintraspecificoptimaltemperaturesforphotosynthesis,estimatedfrom
Eqn(2)withEhasafixedparameter.Optimaltemperaturesforphotosynthesisvary
substantiallywithinandamongspecies,reflectingacclimationandlocaladaptationof
photosynthetictraitstosite‐averagedleafoperatingtemperatures.Themeanoptimal
temperatureacrossallsamplesis26.21˚C.Thickblacklinescorrespondtomedians,lowerand
upperhingescorrespondtofirstandthirdquartiles,respectively,lowerandupperwhiskers
correspondtosmallestandlargestvaluesthatdonotexceed1.5timestheinterquartilerange
ofthehinges,respectively,andpointscorrespondtooutliersbeyondtheendofthewhiskers.
TableS1Listoftaxa,numberofcurvespertaxon,andprimarysourcesforphotosynthesis
temperatureresponsedatausedinanalyses
TaxonNumberofcurvesPrimarysource
Acersaccharum1Gundersonetal.(2000)
Artemisiatridentata5Michaletzetal.(2016b)
Atriplexdioica1Sageetal.(2011)
Atriplexglabriuscula1Osmondetal.(1980)
Balsamorhizasagittata2Michaletzetal.(2015)
Carexduriuscula3Monsonetal.(1983)
Carexhelleri1Sageetal.(2011)
Chamerionangustifolium3Michaletzetal.(2016b)
Delphiniumbarbeyi1Michaletzetal.(2016b)
Helianthusannuus1Pauletal.(1990)
Helianthusmollis1Zhouetal.(2007)
Juniperusmonosperma31Michaletzetal.(2016b)
Larixdecidua4Tranquillinietal.(1986)
Larreadivaricata7Mooneyetal.(1978)
Ligusticumporteri4Michaletzetal.(2016b)
Piceaengelmannii1Huxmanetal.(2003)
Piceamariana3Way&Sage(2008a)(2008b)
Pinuscontorta1Huxmanetal.(2003)
Pinusedulis21Michaletzetal.(2016b)
Pinussylvestris1Wangetal.(1996)
Plantagolanceolata4Atkinetal.(2006)
Potentillagracilis3Michaletzetal.(2016b)
Prosopisvelutina6Barron‐Gaffordetal.(2012)
Solanumlycopersicum2Yamorietal.(2010)
Solanumtuberosum2Yamorietal.(2010)
Valerianaoccidentalis3Michaletzetal.(2016b)
Veratrumcalifornicum6Michaletzetal.(2016b)

TableS2Listofgrowthvariables,temperaturevariables,samplesizes,andprimarysourcesfor
growthanalyses
Levelof
organization
Growthvariable
Temperature
variable
Sample
size
Primarysource
OrganRootmeristemcelldoubling
rate(h‐1)
Celltemperature
(˚C)
60Körner(2003a)

IndividualMass‐adjustedindividual
treegrowthrate(kg1/4mo‐1)
Growingseason
airtemperature
(˚C)
210Enquistetal.(2007)

EcosystemAdjustednetprimary
production(kgm‐2mo‐1)
Growingseason
airtemperature
(˚C)
138Michaletzetal.(2014;2016a)

MethodsS1Descriptionofdataandmethodsusedforanalyses.
PhotosynthesistemperatureresponsedataandSharpe‐Schoolfieldfittingprocedures
PhotosynthesistemperatureresponsecurveswereobtainedfromMichaletzetal.
(2016b).Allcurveswereunimodal(e.g.seeFig.S1)andcomprisedatameasuredinColorado
andNewMexico,aswellascompiledfromtheliterature.Furtherdetailsonmeasurement
methodologiesareavailableintheprimarysources.Atotalof184curvesfrom32specieswere
available.Taxa,numberofcurvespertaxa,andprimarysourcesaresummarizedinTableS1.
ThesetemperatureresponsedatawereusedtoestimateactivationenergiesEfornet
photosynthesis.
VariousapproachescanbeusedtoestimateactivationenergiesEfromunimodal
temperatureresponsedata.Forexample,theBoltzmann‐Arrheniusmodel(Eqn(1))canbefit
separatelytotheincreasinganddecreasingportionsofthecurve,yieldingseparateestimates
ofEforeachportion(Knies&Kingsolver,2010;Delletal.,2011).However,suchestimatescan
bestronglybiasedbythetemperaturerangesusedtodefinetheincreasinganddecreasing
portionsofthecurve(Knies&Kingsolver,2010;Pawaretal.,2016).Thus,inthispaper,
activationenergiesEfornetphotosynthesiswereestimatedusingSharpe‐Schoolfieldmodelfits
(Sharpe&DeMichele,1977;Schoolfieldetal.,1981).Thisapproachcanhelpreducebiasand
variationinestimatesofEascomparedwiththeBoltzmann‐Arrheniusapproachdescribed
above(Pawaretal.,2016).Followingrecentmethodology,Eqn(2)wasfittocurvesforwhich
photosynthesiswasmeasuredataminimumoffivetemperaturesspanningarangeofatleast5
˚C(Delletal.,2011;Pawaretal.,2016),yieldingatotalof119fittedcurvesfrom27species
(FigsS2,S3).FittingwasaccomplishedusingLevenberg‐Marquardtnonlinearregressionwith
Gaussianrandomstartingvalues,followedbyAICcmodelselectionfrom100fitsforeachcurve.
ModelfittingwasconductedinthestatisticalsoftwareRusingamodifiedversionofcode
providedbyTonyDell,SamraatPawar,andSofiaSal(seeNotesS1).
Eqn(2)hasbeensuggestedtobeover‐parameterizedforsomephotosynthesisdatasets
thatarerelativelylimitedinthenumberofobservationsorrangeoftemperatures(Harleyetal.,
1992;Dreyeretal.,2001;Medlynetal.,2002).Thus,IalsoconductedasecondsetofSharpe‐
SchoolfieldmodelfitsusingafixedvalueofEh=200kJmol‐1=2.073eV.Thisvaluehasbeen
usedinmanypreviousstudies(Farquharetal.,1980;Medlynetal.,2002;Slot&Winter,2017;
Stinzianoetal.,Inpress),andoriginatesfromdataforJmaxinHordeumvulgare(Nolan&Smillie,
1976).However,estimatesofEhforJmaxareavailableforothertaxa(Dreyeretal.,2001;
Leuning,2002;Warren&Dreyer,2006;Galmésetal.,2015),andtheseestimatesshowthatof
Ehvariesmorethan8‐foldacrosstaxa(Dreyeretal.,2001).Itisthusunclearhowappropriateit
istoapplyasinglefixedvalueofEhforJmaxfromasinglespeciestonetphotosynthesisdatafor
diversetaxa(asinTableS1).Additionally,thedatausedherewereallunimodalwitharelatively
largenumberofobservationsandrangeoftemperatures(e.g.FigsS1‐S3).Itmaybeforthese
reasonsthattheSharpe‐Schoolfieldmodelprovidedvastlybetterfitstodata(TableS1)whenEh
wastakenasafreeparameter(quasir2=0.672to1.000)ratherthanafixedparameter(quasir2
=0.146to0.999).
PerhapsabetteralternativetoafixedEhistouseafreeEhandrejectfittedcurvesbased
onstandarderrorsofestimatesforeachparameter(cf.Pawaretal.,2016).Nonetheless,
evaluationofSharpe‐Schoolfieldfittingproceduresisbeyondthescopeofthispaper,andthe
abovetwoapproachesarepresentedinordertohighlightthesensitivityofEtoSharpe‐
Schoolfieldfittingprocedures.
PlantgrowthdataandBoltzmann‐Arrheniusfittingprocedures
Plantgrowthdatawerecompiledandanalyzedforthreelevelsofbiological
organization:organs,individuals,andecosystems.Rootorgangrowthdata(Fig.2a)were
obtainedfromFigure3ofKörner(2003b),whichisacompilationofdatafromapproximately50
primarysources.Dataaredivisionratesofrootmeristemcells.Forlandplants,rootmeristem
cellsprovideoneofthemostaccurateandresolvedmeasuresofinsitugrowthrates,forthree
reasons.First,ratesofcelldivisionaretightlycoupledtoratesofcelldifferentiation,whenmost
biomassgrowthoccurs(Körner,2003a;Körner,2012).Second,rootoperativetemperaturesare
inthermalequilibriumwithsoil,sotheyarerelativelystraightforwardtocontrolandquantify
(unlikeabovegroundorgans;Michaletzetal.,2015;Michaletzetal.,2016b).Third,cellsizeis
essentiallyconstant,whicheliminatesconfoundingeffectsofbiomass.Datawereextracted
fromFigure3usingthesoftwareDataThiefIIIv1.7.Dataweregivenascelldoublingtimes(h),
whichwerecalculatedasthestatisticalmeansacrossallcellswithinameristematicregion.The
inverseofcelldoublingtimeswasusedtocalculatethecellgrowthrates(h‐1)thatwereusedin
Fig.2a.
Individualgrowthdata(Fig.2b)wereobtainedfromEnquistetal.(2007).Datacomprise
mass‐adjustedtreegrowthratesthatcontrolforvariationintreesizeandgrowingseason
length(moyr‐1),whichisneededtoproperlyevaluatetemperatureeffectsonplantmetabolic
kinetics(Michaletzetal.,Inpress).Growthratescorrespondtototal(above‐plusbelowground)
biomassgrowth.Mass‐adjustedrates3/4
B
M(kg1/4mo‐1)aregivenby3/4 /
1
EkT
BM B e

(Brown
&Sibly,2012;Whiteetal.,2012),whereB(kgmo‐1)istheseasonalgrowthrate(Michaletzet
al.,Inpress),M(kg)isthetotaltreebiomass,andthe3/4scalingexponentisbasedon
extensiveempiricalandtheoreticalsupport(Brown&Sibly,2012).Sincemultiplegrowthrate
datawereavailableforsomesitesinthisdataset,theyweretakenasthemean±1standard
errorinFig.2b(althoughthesamegeneralresultswereobtainedwhentheseweretreatedas
independentsamples;Fig.S4).Airtemperaturedataarebasedonmonthlyaverageair
temperaturesduringthegrowingseason(includingdayandnight).Growingseasonair
temperatureisthemonthlyaverageairtemperatureduringthegrowingseasonmonths
(includingdayandnight).Datawerecalculatedfromsitelatitudeandlongitudeandagridded
globalclimatedataset(Newetal.,2002).Thisdatasetinterpolatesweatherstationdata,so
thesetemperaturescorrespondtoweatherstationstandardsandnotplantoperative
temperatures.Growingseasonmonthswereestimatedfromairtemperature,precipitation,
andpotentialevapotranspirationdataasdescribedinKerkhoffetal.(2005).
Ecosystemproductiondata(Fig.2c)wereobtainedfromMichaletzetal.(2016a),which
isasubsetofdatacompiledbyMichaletzetal.(2014).Dataaremonthlynetprimary
production(NPP;kgm‐2mo‐1)ratescalculatedoverthegrowingseason(moyr‐1).Fig.2cisa
partialregressionplotthatshowsthecorrectrelationship(slopeandvariance)betweennet
primaryproductionandtemperaturewhilecontrollingfortheinfluenceofbiomass,age,and
precipitation.Inthisanalysis,samplesfromthesamelatitudeandlongitudethatshare
temperatureandprecipitationdataallhaveuniquedataforageand/orstandbiomass,andare
thustreatedasindependentsamples.Airtemperaturedataarebasedonmonthlyaverageair
temperaturesduringthegrowingseason(includingdayandnight).Growingseasonair
temperatureisthemonthlyaverageairtemperatureduringthegrowingseasonmonths
(includingdayandnight).Datawerecalculatedfromsitelatitudeandlongitudeandagridded
globalclimatedataset(Newetal.,2002).Thisdatasetinterpolatesweatherstationdata,so
thesetemperaturescorrespondtostandardweatherstationmeasurementsandnotplant
operativetemperatures.Growingseasonmonthswereestimatedfromairtemperature,
precipitation,andpotentialevapotranspirationdataasdescribedinMichaletzetal.(2014).
Sinceorgan,individual,andecosystem‐levelgrowthdatacorrespondtotemperatures
belowthoseoptimalforplantmetabolism(FigsS5,S6;Delletal.,2011;Slot&Winter,2017),
activationenergiesEwereestimatedusingBoltzmann‐Arrheniusmodelfits.Specifically,Eqn(1)
wasloge‐transformedtogivethegrowthrateBas

0
1
ln lnBBE
kT

 (S1)
whereB0isanormalizationconstantthatimplicitlyincludeseffectsofothervariablesnot
consideredhere,kistheBoltzmannconstant(8.617x10‐5eVK‐1),andE(eV)isaneffective
activationenergythatcharacterizesthetemperature‐dependenceoftherateunder
consideration.Notethathere,theunitsofBandB0willvarydependingonthegrowthrate
underconsideration(h‐1forcells,kg1/4mo‐1forindividuals,andkgm‐2mo‐1forecosystems).
Theseloge‐scaledgrowthdatawerethenregressedovertemperature1/kT,toproduce
modifiedArrheniusplots(Fig.2)withaslope–Ethatisequalinmagnitudebutoppositein
directiontotheactivationenergyE.
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  • Dec 2017
Terrestrial net primary production (NPP) varies across global climate gradients, but the mechanisms through which climate drives this variation remain subject to debate. Specifically, it is debatable whether NPP is primarily influenced by ‘direct’ effects of climate on the kinetics of plant metabolism or ‘indirect’ effects of climate on plant size, stand biomass, stand age structure and growing season length. We clarify several issues in this debate by presenting multiple lines of evidence that support a primarily indirect influence of climate on global variation in NPP across broad geographical gradients. First, we highlight > 60 years of research that suggests leaf area, growing season length, plant biomass and/or plant age are better predictors of NPP than climate or latitude. Second, we refute recent claims that using biomass and age as predictors of NPP represents circular reasoning. Third, we illustrate why effects of climate on the kinetics of plant production must be evaluated using instantaneous (not annualized) rates of productivity. Fourth, we review recent analyses showing that the effects of biomass and age on NPP are much stronger than the effects of climate. Fifth, we present new analyses of a high-quality NPP dataset that demonstrate further that biomass, age and growing season length are better predictors of global variation in NPP than climate variables. Our results are consistent with the hypothesis that variation in NPP across global climate gradients primarily reflects the influence of climate on growing season length and stand biomass, as well as stand age, rather than the effects of temperature and precipitation on the kinetics of metabolism. However, this hypothesis should be evaluated further using larger, high-quality observational and experimental datasets spanning multiple geographical scales.
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In situ temperature relationships of biochemical and stomatal controls of photosynthesis in four lowland tropical tree species
Article
Full-text available
  • Sep 2017
Net photosynthetic carbon uptake of Panamanian lowland tropical forest species is typically optimal at 30–32°C. The processes responsible for the decrease in photosynthesis at higher temperatures are not fully understood for tropical trees. We determined temperature responses of maximum rates of RuBP-carboxylation (VCMax) and RuBP-regeneration (JMax), stomatal conductance (Gs) and respiration in the light (RLight) in situ for four lowland tropical tree species in Panama. Gs had the lowest temperature optimum (TOpt), similar to that of net photosynthesis, and photosynthesis became increasingly limited by stomatal conductance as temperature increased. JMax peaked at 34–37°C and VCMax ~2°C above that, except in the late-successional species Calophyllum longifolium, in which both peaked at ~33°C. RLight significantly increased with increasing temperature, but simulations with a photosynthesis model indicated that this had only a small effect on net photosynthesis. We found no evidence for Rubisco-activase limitation of photosynthesis. TOpt of VCMax and JMax fell within the observed in situ leaf temperature range, but our study nonetheless suggests that net photosynthesis of tropical trees is more strongly influenced by the indirect effects of high temperature—e.g., through elevated vapor pressure deficit and resulting decreases in stomatal conductance—than by direct temperature effects on photosynthetic biochemistry and respiration.
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The energetic and carbon economic origins of leaf thermoregulation
Article
Full-text available
  • Aug 2016
Leaf thermoregulation has been documented in a handful of studies, but the generality and origins of this pattern are unclear. We suggest that leaf thermoregulation is widespread in both space and time, and originates from the optimization of leaf traits to maximize leaf carbon gain across and within variable environments. Here we use global data for leaf temperatures, traits and photosynthesis to evaluate predictions from a novel theory of thermoregulation that synthesizes energy budget and carbon economics theories. Our results reveal that variation in leaf temperatures and physiological performance are tightly linked to leaf traits and carbon economics. The theory, parameterized with global averaged leaf traits and microclimate, predicts a moderate level of leaf thermoregulation across a broad air temperature gradient. These predictions are supported by independent data for diverse taxa spanning a global air temperature range of ∼60 °C. Moreover, our theory predicts that net carbon assimilation can be maximized by means of a trade-off between leaf thermal stability and photosynthetic stability. This prediction is supported by globally distributed data for leaf thermal and photosynthetic traits. Our results demonstrate that the temperatures of plant tissues, and not just air, are vital to developing more accurate Earth system models.
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Plant Thermoregulation: Energetics, Trait–Environment Interactions, and Carbon Economics
Article
Full-text available
  • Oct 2015
  • TRENDS ECOL EVOL
Building a more predictive trait-based ecology requires mechanistic theory based on first principles. We present a general theoretical approach to link traits and climate. We use plant leaves to show how energy budgets (i) provide a foundation for understanding thermoregulation, (ii) explain mechanisms driving trait variation across environmental gradients, and (iii) guide selection [ 2 6 _ T D $ D I F F ] on functional traits via carbon economics. Although plants are often considered to be poikilotherms, the data suggest that they are instead limited homeotherms. Leaf functional traits that promote limited homeothermy are adaptive because homeothermy maximizes instantaneous and lifetime carbon gain. This theory provides a process-based foundation for trait–climate analyses and shows that future studies should consider plant (not only air) temperatures.
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Improving models of photosynthetic thermal acclimation: Which parameters are most important and how many should be modified?
Article
  • Oct 2017
Photosynthetic temperature acclimation could strongly affect coupled vegetation-atmosphere feedbacks in the global carbon cycle, especially as the climate warms. Thermal acclimation of photosynthesis can be modelled as changes in the parameters describing the direct effect of temperature on photosynthetic capacity (i.e. activation energy, Ea; deactivation energy, Hd; entropy parameter, ΔS) or the basal value of photosynthetic capacity (i.e. photosynthetic capacity measured at 25 °C). However, the impact of acclimating these parameters (individually or in combination) on vegetative carbon gain is relatively unexplored. Here we compare the ability of 66 photosynthetic temperature acclimation scenarios to improve the ability of a spatially explicit canopy carbon flux model, MAESTRA, to predict eddy covariance data from a loblolly pine forest. We show that: 1) incorporating seasonal temperature acclimation of basal photosynthetic capacity improves the model's ability to capture seasonal changes in carbon fluxes and outperforms acclimation of other single factors (i.e. Ea or ΔS alone); 2) multifactor scenarios of photosynthetic temperature acclimation provide minimal (if any) improvement in model performance over single factor acclimation scenarios; 3) acclimation of Ea should be restricted to the temperature ranges of the data from which the equations are derived; and 4) model performance is strongly affected by the Hd parameter. We suggest that a renewed effort be made into understanding whether basal photosynthetic capacity, Ea, Hd and ΔS co-acclimate across broad temperature ranges to determine whether and how multifactor thermal acclimation of photosynthesis occurs.
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Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits
Book
  • Jan 2012
Alpine treelines mark the low-temperature limit of tree growth and occur in mountains world-wide. Presenting a companion to his book Alpine Plant Life, Christian Körner provides a global synthesis of the treeline phenomenon from sub-arctic to equatorial latitudes and a functional explanation based on the biology of trees. The comprehensive text approaches the subject in a multi-disciplinary way by exploring forest patterns at the edge of tree life, tree morphology, anatomy, climatology and, based on this, modelling treeline position, describing reproduction and population processes, development, phenology, evolutionary aspects, as well as summarizing evidence on the physiology of carbon, water and nutrient relations, and stress physiology. It closes with an account on treelines in the past (palaeo-ecology) and a section on global change effects on treelines, now and in the future. With more than 100 illustrations, many of them in colour, the book shows alpine treelines from around the globe and offers a wealth of scientific information in the form of diagrams and tables.
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Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits
Article
  • Jan 2016
  • AM NAT
Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ∼0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.
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Linked Research

Evaluating the kinetic basis of plant growth from organs to ecosystems
Article
February 2018
Sean Michaletz