Plant functional traits have globally consistent effects on competition

TLDR: Traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies.

Abstract: Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits--wood density, specific leaf area and maximum height--consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.

Figures

Figure 3: Variation of maximum growth, competitive effects and competitive tolerance with wood density and specific leaf area predicted by global traits models. Variation of maximum growth (m1 tf ), tolerance of competition (αt tf ) and competitive effect (αe tc) parameters with wood density (first column) and specific leaf area (second column). The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with α0 or m0).

Table 2 for trait coverage). Where available we used data collected locally (references for the local trait data used in this analysis27,36–39); otherwise we sourced data from the TRY trait data base40140 (references for the data extracted from the TRY database used in this analysis36,41–110). Local data were available for most tropical sites and species (see Supplementary Methods). Several of the NFI data sets also provided tree height measurements, from which we computed a species’ maximum height as the 99% quantile of observed values (for France, US, Spain, Switzerland). For Sweden we used the estimate from the French data set and for Canada we used the estimate from the US data set.145 Otherwise, we extracted height measurements from the TRY database. We were not able to account for trait variability within species.

Figure 2: Trait-dependent and trait-independent effects on maximum growth and competition across the globe and their variation among biomes. Standardized regression coefficients for growth models, fitted separately for each trait (points: mean estimates and lines: 95% confidence intervals). Black points and lines represent global estimates and coloured points and lines represent the biome level estimates. The parameter estimates represent: effect of focal tree’s trait value on maximum growth m1, the effect of competitor trait values on their competitive effect αe (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree’s trait value on its tolerance of competition αt (positive values indicate that greater trait values result in greater tolerance of competition), the effect on competition of trait dissimilarity between the focal tree and its competitors αd (negative values indicate that higher trait dissimilarity leads to a lower reduction of the growth of the focal tree), and the trait-independent competitive effect of conspecific α0 intra and heterospecific α0 inter. Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig 1a. for biomes definitions).

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Article (refereed) - postprint
Kunstler, Georges; Falster, Daniel; Coomes, David A.; Hui, Francis; Kooyman,
Robert M.; Laughlin, Daniel C.; Poorter, Lourens; Vanderwel, Mark;
Vieilledent, Ghislain; Wright, S. Joseph; Aiba, Masahiro; Baraloto,
Christopher; Caspersen, John; Cornelissen, J. Hans C.; Gourlet-Fleury, Sylvie;
Hanewinkel, Marc; Herault, Bruno; Kattge, Jens; Kurokawa, Hiroko; Onoda,
Yusuke; Peñuelas, Josep; Poorter, Hendrik; Uriarte, Maria; Richardson, Sarah;
Ruiz-Benito, Paloma; Sun, I-Fang; Ståhl, Göran; Swenson, Nathan G.;
Thompson, Jill; Westerlund, Bertil; Wirth, Christian; Zavala, Miguel A.; Zeng,
Hongcheng; Zimmerman, Jess K.; Zimmermann, Niklaus E.; Westoby, Mark.
2016. Plant functional traits have globally consistent effects on
competition. Nature, 529 (7585). 204-207.
https://doi.org/10.1038/nature16476
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Traitsandtreescompetition
Plantfunctionaltraitshavegloballyconsistenteffectsoncompetition
GeorgesKunstler
1,2,3
,DanielFalster
3
,DavidA.Coomes
4
,FrancisHui
5
,RobertM.
Kooyman
3,6
,DanielC.Laughlin
7
,LourensPoorter
8
,MarkVanderwel
9
,GhislainVieilledent
10
,
S.JosephWright
11
,MasahiroAiba
12
,ChristopherBaraloto
13,14
,JohnCaspersen
15
,J.HansC.
Cornelissen
16
,SylvieGourlet-Fleury
10
,MarcHanewinkel
17,18
,BrunoHerault
19
,Jens5
Kattge
20,21
,HirokoKurokawa
12,22
,YusukeOnoda
23
,JosepPeñuelas
24,25
,HendrikPoorter
26
,
MariaUriarte
27
,SarahRichardson
28
,PalomaRuiz-Benito
29,30
,I-FangSun
31
,GöranStåhl
32
,
NathanG.Swenson
33
,JillThompson
34,35
,BertilWesterlund
32
,ChristianWirth
36,21
,MiguelA.
Zavala
30
,HongchengZeng
15
,JessK.Zimmerman
35
,NiklausE.Zimmermann
37
,andMark
Westoby
3
10
1
Irstea,UREMGR,2ruedelaPapeterieBP-76,F-38402,St-Martin-d’Hères,France,
georges.kunstler@irstea.fr
2
Univ.GrenobleAlpes,F-38402Grenoble,France
3
DepartmentofBiologicalSciences,MacquarieUniversityNSW2109,Australia
4
ForestEcologyandConservationGroup,DepartmentofPlantSciences,Universityof15
Cambridge,CambridgeCB23EA,UK
5
MathematicalSciencesInstitute,AustralianNationalUniversity,Canberra,Australia
6
NationalHerbariumofNewSouthWales,RoyalBotanicGardensandDomainTrust,Sydney,
NSW,Australia
7
EnvironmentalResearchInstitute,SchoolofScience,UniversityofWaikato,Hamilton,New20
Zealand
8
ForestEcologyandForestManagementGroup,WageningenUniversity,Wageningen,The
Netherlands
9
DepartmentofBiology,UniversityofRegina,3737WascanaPkwy,Regina,SK,S4S0A2,
Canada25
10
Cirad,UPRBSEF,F-34398Montpellier,France
11
SmithsonianTropicalResearchInstitute,Apartado0843–03092,Balboa,Republicof
Panama
12
GraduateSchoolofLifeSciences,TohokuUniversity,Sendai980-8578,Japan
13
INRA,UMREcologiedesForêtsdeGuyane,BP709,97387KourouCedex,France30
14
InternationalCenterforTropicalBotany,DepartmentofBiologicalSciences,Florida
InternationalUniversity,Miami,FL,USA
15
FacultyofForestry,UniversityofToronto,33WillcocksStreet,Toronto,Ontario,M5S3B3,
Canada
1
Accepted manuscript

16
SystemsEcology,DepartmentofEcologicalScience,VUUniversity,Amsterdam,1081HV,35
TheNetherlands
17
SwissFederalResearchInst.WSL,ForestResourcesandManagementUnit,CH-8903
Birmensdorf,Switzerland
18
UniversityofFreiburg,ChairofForestryEconomicsandPlanning,D-79106Freiburg,
Germany40
19
Cirad,UMREcologiedesForêtsdeGuyane,CampusAgronomique,BP701,97387Kourou,
France
20
MaxPlanckInstituteforBiogeochemistry,HansKnöllStr.10,07745Jena,Germany
21
GermanCentreforIntegrativeBiodiversityResearch(iDiv),Halle-Jena-Leipzig,Deutscher
Platz5e04103Leipzig,Germany45
22
ForestryandForestProductsResearchInstitute,Tsukuba,305-8687Japan(currentaddress)
23
GraduateSchoolofAgriculture,KyotoUniversity,Kyoto,Japan
24
CSIC,GlobalEcologyUnitCREAF-CSIC-UAB,CerdanyoladelVallès08193,Catalonia,
Spain
25
CREAF,CerdanyoladelVallès,08193Barcelona,Catalonia,Spain50
26
PlantSciences(IBG-2),ForschungszentrumJülichGmbH,D-52425Jülich,Germany
27
DepartmentofEcology,EvolutionandEnvironmentalBiology,ColumbiaUniversity,New
York,NY10027,UnitedStatesofAmerica
28
LandcareResearch,POBox40,Lincoln7640,NewZealand
29
BiologicalandEnvironmentalSciences,SchoolofNaturalSciences,UniversityofStirling,55
FK94LA,Stirling,UK
30
ForestEcologyandRestorationGroup,DepartmentofLifeSciences,ScienceBuilding,
UniversityofAlcala,CampusUniversitario,28805AlcaládeHenares(Madrid),Spain
31
DepartmentofNaturalResourcesandEnvironmentalStudies,NationalDongHwa
University,Hualien97401,Taiwan60
32
DepartmentofForestResourceManagement,SwedishUniversityofAgriculturalSciences
(SLU),Skogsmarksgränd,Umeå,Sweden
33
DepartmentofBiology,UniversityofMaryland,CollegePark,Maryland,UnitedStatesof
America
34
CentreforEcologyandHydrology−Edinburgh,BushEstate,Penicuik,MidlothianEH2665
0QBUnitedKingdom
35
DepartmentofEnvironmentalSciences,UniversityofPuertoRico,RíoPiedrasCampusP.O.
Box70377SanJuan,PuertoRico00936-8377,USA
36
InstituteforSystematic,BotanyandFunctionalBiodiversity,UniversityofLeipzig,
Johannisallee2104103Leipzig,Germany70
37
SwissFederalResearchInst.WSL,LandscapeDynamicsUnit,CH-8903Birmensdorf,
Switzerland
2
Accepted manuscript

Traitsandtreescompetition
Summaryparagraphoutline
Phenotypictraitsandtheirassociatedtrade-offshavebeenshowntohavegloballyconsistenteffects
onindividualplantphysiologicalfunctions
1–3
,butithasremainedunclearhowtheseeffectsscaleup75
toinfluencecompetition–akeydriverofcommunityassemblyinterrestrialvegetation
4
.Hereweuse
growthdata,frommorethan3milliontreesinmorethan140000plotsacrosstheworld,toshowhow
threekeyfunctionaltraits–wooddensity,specificleafareaandmaximumheight–consistentlyinflu-
encecompetitiveinteractions.Fastmaximumgrowthofaspecieswascorrelatednegativelywithits
wooddensityinallbiomesandpositivelywithitsspecificleafareainmostbiomes.Lowwooddensity80
wasalsocorrelatedwithalowabilitytotoleratecompetitionandalowcompetitiveimpactonneigh-
bours(competitiveeffect),whilehighspecificleafareawascorrelatedwithalowcompetitiveeffect.
Thus,traitsgeneratetrade-offsbetweenperformancewithvs.withoutcompetition,afundamental
ingredientintheclassicalhypothesisthatcoexistenceofplantspeciesisenabledviadifferentiation
intheirsuccessionalstrategies
5
.Competitionwithinspecieswasstrongerthanbetweenspecies,but85
anincreaseintraitdissimilaritybetweenspecieshadlittleinfluenceinweakeningcompetition.No
benefitofdissimilaritywasdetectedforspecificleafareaandwooddensityandonlyaweakbenefitfor
maximumheight.Ourtrait-basedapproachtomodellingcompetitionmakesgeneralisationpossible
acrosstheforestecosystemsoftheglobeandtheirhighlydiversespeciescomposition.
Maintext90
Phenotypictraitsareconsideredfundamentaldriversofcommunityassemblyandthusspeciesdiver-
sity
1,6
.Theeffectsoftraitsonindividualplantphysiologiesandfunctionsareincreasinglyunderstood,
andhavebeenshowntobeunderpinnedbywell-knownandgloballyconsistenttrade-offs
1–3
. Forin-
stance,traitssuchaswooddensityandspecificleafareacapturetrade-offsbetweentheconstruction
costandlongevityorstrengthofwoodandleaftissues
2,3
.Incontrast,westillhavelimitedunder-95
standingofhowsuchtrait-basedtrade-offstranslateintocompetitiveinteractionsbetweenspecies,
particularlyforlong-livedorganismssuchastrees.Competitionisakeyfilterthroughwhicheco-
logicalandevolutionarysuccessisdetermined
4
.Along-standinghypothesisisthattheintensityof
competitiondecreasesastwospeciesdivergeintraitvalues
7
(traitdissimilarity).Thefewstudies
8–13
thathaveexploredlinksbetweentraitsandcompetitionhaveshownthatlinkagesweremorecomplex100
thanthis,asparticulartraitvaluesmayalsoconfercompetitiveadvantageindependentlyfromtrait
dissimilarity
9,13,14
.Thisdistinctionisfundamentalforspeciescoexistenceandthelocalmixtureof
traits.Ifneighbourhoodcompetitionisdrivenmainlybytraitdissimilarity,thiswillfavourawide
spreadoftraitvaluesatalocalscale.Incontrast,ifneighbourhoodinteractionsaremainlydriven
bythe competitiveadvantageassociatedwithparticulartraitvalues, thosetraitvaluesshouldbe105
stronglyselectedatthelocalscale,withcoexistenceoperatingatlargerspatialortemporalscales
6,13
.
Empiricalinvestigationshavebeenlimitedsofartoafewparticularlocations,restrictingourability
3
Accepted manuscript

Traitsandtreescompetition
tofindgeneralmechanismsthatlinktraitsandcompetitioninthemainvegetationtypesoftheworld.
Herewequantifythelinksbetweentraitsandcompetition,measuredastheinfluenceofneighbouring
treesongrowthofafocaltree.Ourframeworkisnovelintwoimportantways:(i)competitionis110
analysedatanunprecedentedscalecoveringallthemajorforestbiomesonEarth(Fig.1a), and
(ii)theinfluenceoftraitsoncompetitionispartitionedamongfourfundamentalmechanisms(Fig.
1b,c)asfollows.Acompetitiveadvantagefortreeswithsometraitvaluescomparedtootherscan
arisethrough:(1)permittingfastermaximumgrowthintheabsenceofcompetition
15
;(2)exerting
astrongercompetitiveeffect
16,17
,meaningthatcompetitorspeciespossessingthosetraitssuppress115
morestronglythegrowthoftheirneighbours;or(3)permittingabettertoleranceofcompetition(or
competitive‘response’inGoldberg
16
),meaningthatgrowthofspeciespossessingthosetraitsisless
affectedbycompetitionfromneighbours.Finally,(4)competitioncanpromotetraitdiversification,if
increasingtraitdissimilaritybetweenspeciesreducesinterspecificcompetitioncomparedtointraspe-
cificcompetition
7
.Hereweshowhowthesefourmechanismsareconnectedtothreekeytraitsthat120
describeplantstrategiesworldwide
1–3
.Thesetraitsarewooddensity(anindicatorofatrade-offin
stemsbetweengrowthandstrength), specificleafarea(SLA,anindicatorofatrade-offinleaves
betweencheapconstructioncostandleaflongevity),andmaximumheight(anindicatorofatrade-off
betweensustainedaccesstolightandearlyreproduction).Weanalysebasalareagrowth(annual
increaseintheareaofthecrosssectionoftreetrunkat1.3mheight)ofmorethan3milliontrees125
frommorethan2500species,acrossallmajorforestedbiomesoftheearth(Fig.1).Speciesmean
traitvalueswereextractedfromlocaldatabasesandtheglobalTRYdatabase
18,19
(seeMethods).
Weanalysedhowbasalareagrowthofeachindividualtreewasreducedbytheabundanceofcompeti-
torsinitslocalneighbourhood
20
(measuredasthesumofbasalareasofcompetitorsinm
2
ha
−1
),
accountingfortraitsofboththefocaltreeanditscompetitors.Thisanalysisallowedeffectsizesto130
beestimatedforeachofthefourmechanismsoutlinedabove(Fig.1c).
Acrossallbiomesthestrongestdriverofindividualgrowthwasthetotalabundanceofneighbours,
irrespectiveoftheirtraits(parametersα
0intra
andα
0inter
inFig.2).Valueswerestronglypositive,in-
dicatingneighbourshadcompetitiveratherthanfacilitativeeffect.Themaineffectsoftraitswerethat
sometraitvaluesledtoacompetitiveadvantagecomparedtoothersthroughtwomainmechanisms.135
First,traitsofthefocalspecieshaddirectinfluencesonitsmaximumgrowth–i.e.intheabsence
ofcompetition–(parameterm
1
inFig. 2andExtendedDataTable3).Thefastestgrowingspecies
hadlowwooddensityandhighSLA,thoughtheconfidenceintervalinterceptedzerointwooutof
fivebiomesforSLA(Fig. 2).Thisisinagreementwithpreviousstudies
15,21
ofadulttreesreporting
astronglinkbetweenmaximumgrowthandwooddensitybutaweakerlinkforSLA.Second,some140
traitvalueswereassociatedwithspecieshavingstrongercompetitiveeffects,orbettertoleranceof
competition(Fig.2;ExtendedDataTable3).Highwooddensitywascorrelatedwithbettertolerance
ofcompetitionfromneighboursandwithastrongercompetitiveeffectuponneighbours,whereaslow
SLAwascorrelatedonlywithastrongercompetitiveeffect.Thisagreeswithstudiesreportingthat
highwooddensityspeciesaremoreshade-tolerant
15
andhavedeeperandwidercrowns
22,23
,hence145
4
Accepted manuscript

Frequently asked questions

Q1. What are the contributions in "Plant functional traits have globally consistent effects on competition" ?

Kunstler, Georges, Falster, Daniel ; Coomes, David A ; Hui, Francis ; Kooyman, Robert M ; Laughlin, Daniel C ; Poorter, Lourens ; Vanderwel, Mark ; Vieilledent, Ghislain ; Wright, S. Joseph ; Aiba, Masahiro ; Baraloto, Christopher ; Caspersen, John ; Cornelissen, J. 

Q2. What is the main reason why the trait dissimilarity effects are weak?

Trait dissimilarity effects are widely considered to be a key mechanism by which traits affect competition13, but their analysis shows at global scale that trait dissimilarity effects are weak or absent. 

Q3. What is the main effect of traits on the growth of a tree?

The main effects of traits were that some trait values led to a competitive advantage compared to others through two main mechanisms. 

Q4. What is the key ingredient in the classical model of successional coexistence in forests?

This trait-based trade-off is a key ingredient in the classical model of175 successional coexistence in forests, where fast-growing species are more abundant in early successional stages where competitors are absent or rare, and are later replaced by slow-growing species in late successional stages where competitors become more abundant5. 

Q5. What is the main hypothesis of the study?

Their global study supports the hypothesis that trait values favouring high tolerance of competition or high competitive effects also render species slow growing in the absence of competition across all forested biomes (Fig. 3). 

Q6. What is the strongest driver of individual growth in three out of five biomes?

Maximum height was weakly negatively correlated with tolerance to competition in three out of five biomes, supporting the idea that sub-canopy trees are more shade-tolerant22.150 

Q7. What is the relationship between the wood density and the effect of neighbours?

High wood density was correlated with better tolerance of competition from neighbours and with a stronger competitive effect upon neighbours, whereas low SLA was correlated only with a stronger competitive effect. 

Q8. What is the main determinant of tree growth?

Their analysis demonstrates that trait dissimilarity is not the major determinant of local-scale competitive impacts on tree growth, at least for these three traits. 

Q9. What was the strongest driver of growth in the tree?

Across all biomes the strongest driver of individual growth was the total abundance of neighbours, irrespective of their traits (parameters α0intra and α0inter in Fig. 2). 

Q10. What did the trait dissimilarity effect on competition between species?

After separating trait-independent differences between intraspecific vs. interspecific competition, trait155 dissimilarity had little effect on competition between species (Fig. 2). 

Q11. How many studies have shown that traits and competition are more complex than this?

The few studies8–13 that have explored links between traits and competition have shown that linkages were more complex100 than this, as particular trait values may also confer competitive advantage independently from trait dissimilarity9,13,14. 

Q12. What is the main reason why the results are not consistent across all the studied sites?

Human or natural disturbances are conspicuous in all the forests analysed, hence successional dynamics are likely to be present in all these sites (see Supplementary Methods). 

Q13. What is the effect of trait dissimilarity on competition between species?

The average differences in strength of interspecific vs. intraspecific competition between two species – a key indicator of processes that160 could stabilise coexistence – were thus only weakly related to trait dissimilarity (Extended Data Fig. 3). 

Q14. Who wrote the computer code and processed the raw data?

DAC, DF, FH, RMK, DCL, MV, GV, SJW, MA, CB, JC, JHCC, SGF, MH, BH, JK, HK, YO, JP, HP, MU, SR, PRB, IFS, GS, NS, JT, BW, CW, MAZ, HZ,215 JZ, NEZ collected and processed the raw data. 

Q15. How many neighbourhood interactions should be driven by the competitive advantage associated with particular trait values?

In contrast, if neighbourhood interactions are mainly driven by the competitive advantage associated with particular trait values, those trait values should be105 strongly selected at the local scale, with coexistence operating at larger spatial or temporal scales6,13. 

Q16. What is the main reason why the authors find consistency across such diverse forest types?

This lack of context dependence in trait effects may seem surprising, but reinforces that competition for light is important in most forests, and this may170 explain why the authors find consistency across such diverse forest types (further details in Supplementary Discussion).