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\documentclass[a4paper,11pt]{article}
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\usepackage{lmodern}
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% use upquote if available, for straight quotes in verbatim environments
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\usepackage[numbers,super,sort&compress]{natbib}
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\usepackage{graphicx}
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            pdfauthor={Kunstler},
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            pdftitle={Plant functional traits have globally consistent effects on competition},
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\usepackage{fancyhdr}
\pagestyle{fancy}
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\rhead{Traits and trees competition}
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\title{Plant functional traits have globally consistent effects on competition}

\author[1,2,3]{Georges Kunstler}
\author[3]{Daniel Falster}
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\author[4]{David A. Coomes}
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\author[5]{Francis Hui}
\author[3,6]{Robert M. Kooyman}
\author[7]{Daniel C. Laughlin}
\author[8]{Lourens Poorter}
\author[9]{Mark Vanderwel}
\author[10]{Ghislain Vieilledent}
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\author[11]{S. Joseph Wright}
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\author[12]{Masahiro Aiba}
\author[13,14]{Christopher Baraloto}
\author[15]{John Caspersen}
\author[16]{J. Hans C. Cornelissen}
\author[10]{Sylvie Gourlet-Fleury}
\author[17,18]{Marc Hanewinkel}
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\author[19]{Bruno Herault}
\author[20,21]{Jens Kattge}
\author[12,22]{Hiroko Kurokawa}
\author[23]{Yusuke Onoda}
\author[24,25]{Josep Peñuelas}
\author[26]{Hendrik Poorter}
\author[27]{Maria Uriarte}
\author[28]{Sarah Richardson}
\author[29,30]{Paloma Ruiz-Benito}
\author[31]{I-Fang Sun}
\author[32]{Göran Ståhl}
\author[33]{Nathan G. Swenson}
\author[34,35]{Jill Thompson}
\author[32]{Bertil Westerlund}
\author[36,21]{Christian Wirth}
\author[30]{Miguel A. Zavala}
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\author[15]{Hongcheng Zeng}
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\author[35]{Jess K. Zimmerman}
\author[37]{Niklaus E. Zimmermann}
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\author[3]{Mark Westoby}

\affil[1]{Irstea, UR EMGR, 2 rue de la Papeterie BP-76, F-38402, St-Martin-d'Hères, France. \\ \url{georges.kunstler@irstea.fr}}
\affil[2]{Univ. Grenoble Alpes, F-38402 Grenoble, France.}
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\affil[3]{Department of Biological Sciences, Macquarie University NSW 2109, Australia.}
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\affil[4]{Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK.}
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\affil[5]{Mathematical Sciences Institute, The Australian National University, Canberra, Australia.}
\affil[6]{National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney, NSW, Australia.}
\affil[7]{Environmental Research Institute, School of Science, University of Waikato, Hamilton, New Zealand.}
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\affil[8]{Forest Ecology and Forest Management Group, Wageningen University, Wageningen, The Netherlands.}
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\affil[9]{Department of Biology, University of Regina, 3737 Wascana Pkwy, Regina, SK, S4S 0A2, Canada.}
\affil[10]{Cirad, UPR BSEF, F-34398 Montpellier, France.}
\affil[11]{Smithsonian Tropical Research Institute, Apartado 0843–03092, Balboa, Republic of Panama.}
\affil[12]{Graduate School of Life Sciences, Tohoku University, Sendai 980-8578, Japan.}
\affil[13]{INRA, UMR Ecologie des Forêts de Guyane, BP 709, 97387 Kourou Cedex, France.}
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\affil[14]{International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, USA.}
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\affil[15]{Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, Ontario, M5S 3B3, Canada.}
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\affil[16]{Systems Ecology, Department of Ecological Science, VU University, Amsterdam, 1081 HV, The Netherlands.}
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\affil[17]{Swiss Federal Research Inst. WSL, Forest Resources and Management Unit, CH-8903 Birmensdorf, Switzerland.}
\affil[18]{University of Freiburg, Chair of Forestry Economics and Planning, D-79106 Freiburg, Germany.}
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\affil[19]{Cirad, UMR Ecologie des Forêts de Guyane, Campus Agronomique, BP 701, 97387 Kourou, France.}
\affil[20]{Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745 Jena, Germany.}
\affil[21]{German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e 04103 Leipzig, Germany.}
\affil[22]{Forestry and Forest Products Research Institute, Tsukuba, 305-8687 Japan (current address).}
\affil[23]{Graduate School of Agriculture, Kyoto University, Kyoto, Japan.}
\affil[24]{CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del
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  Vallès 08193, Catalonia, Spain.}
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\affil[25]{CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain.}
\affil[26]{Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany.}
\affil[27]{Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027, United States of America.}
\affil[28]{Landcare Research, PO Box 40, Lincoln 7640, New Zealand.}
\affil[29]{Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Stirling, UK.}
\affil[30]{Forest Ecology and Restoration Group, Department of Life Sciences, Science Building, University of Alcala, Campus Universitario, 28805 Alcalá de Henares (Madrid), Spain.}
\affil[31]{Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien 97401, Taiwan.}
\affil[32]{Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd, Umeå, Sweden.}
\affil[33]{Department of Biology, University of Maryland, College Park, Maryland, United States of America.}
\affil[34]{Centre for Ecology and Hydrology−Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB United Kingdom.}
\affil[35]{Department of Environmental Sciences, University of Puerto Rico, Río Piedras Campus P.O. Box 70377 San Juan, Puerto Rico 00936-8377, USA.}
\affil[36]{Institute for Systematic, Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21 04103 Leipzig, Germany.}
\affil[37]{Swiss Federal Research Inst. WSL, Landscape Dynamics Unit, CH-8903 Birmensdorf, Switzerland.}
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\date{}


\begin{document}
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\maketitle
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\section{Summary paragraph outline (245/max 300 but rather 200)}\label{summary-paragraph-outline}
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Phenotypic traits and their associated trade-offs are thought to play an
important role in community assembly and thus in maintaining species
diversity. Although traits have been shown to have globally consistent
effects on individual plant physiological
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functions\citep{Westoby-2002, Wright-2004, Chave-2009},
it remains unclear how these effects scale-up to
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determine competitive interactions -- key drivers of
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community assembly in terrestrial
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vegetation\citep{Keddy-1989}. Here we use growth data from
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more than 3 million trees in more than 140000 plots across the world to
show how three key functional traits -- wood density, specific leaf area
and maximum height -- consistently influence competitive interactions.
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High maximum growth of a focal tree was correlated with low wood
density in all biomes and with high specific leaf area in three biomes. At the same time, low wood density
was correlated with both a low ability to tolerate competition and a
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low competitive effect, and high
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specific leaf area with a low competitive effect. We thus demonstrate at a global scale that
traits generate trade-offs between performances with \emph{vs.} without
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competition, a fundamental ingredient in the classic hypothesis
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that coexistence of plant species is enabled via differentiation in
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successional strategy\citep{Rees-2001}. In contrast, after accounting for
trait independent difference in intra- \textit{vs.} inter-specific interactions, we
found evidence only of a weak trait similarity effect for maximum
height, through which
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neighbouring plants suffered more from competition when their trait values
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were more similar. Our
trait-based approach to modelling competition reveals key generalisations
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across the forest ecosystems of the globe.

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\section{Main text (1582/max 1500)}\label{main-text}
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Phenotypic traits are considered fundamental drivers of community
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assembly and thus species diversity \citep{Westoby-2002, Adler-2013}. The effects of traits on individual
plant physiologies and functions are increasingly understood, and have been shown to be underpinned
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by well-known and globally consistent trade-offs
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\citep{Westoby-2002, Wright-2004, Chave-2009}.
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For instance, traits such as wood density and specific leaf area capture
trade-offs between the construction cost and longevity or strength of
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wood and leaf tissues\citep{Wright-2004, Chave-2009}.
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In contrast, we still have extremely limited understanding of how trait-based trade-offs translate into
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competitive interactions between species,
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particularly for long-lived forest ecosystems. Competition is a key
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filter through which ecological and evolutionary success is
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determined\citep{Keddy-1989}. A long-standing hypothesis is that competition is stronger when two species have
similar trait values\citep{MacArthur-1967} (trait-similarity). The few
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studies\citep{Uriarte-2010, Kunstler-2012, HilleRisLambers-2012, Lasky-2014, Kraft-2015, Mayfield-2010}
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that have explored links between traits and competition have, however, shown
that these links may be more complex, as particular trait values may
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confer competitive advantage independently from the trait-similarity process\citep{Mayfield-2010, Kunstler-2012, Kraft-2014}. This
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distinction is fundamental to understand species coexistence and the
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local mixture of traits. If neighbourhood competition is driven
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mainly by trait similarity, this will favour a wide spread of trait
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values at local scale. In contrast, if neighbourhood interactions are mainly
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driven by the competitive advantage associated with particular trait
values, those trait values should be strongly selected at the local
scale, with coexistence operating at larger spatial or temporal
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scales\citep{Mayfield-2010, Adler-2013}. However,
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thus far, empirical investigations are limited to a few, isolated
locations, restricting our ability to find general mechanisms that link traits and competition in the main vegetation types
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of the world.

Here we quantify the links between traits and competition, measured as
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the influence of neighbouring trees on growth of a focal tree. Our framework is
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novel in two important ways: (i) competition is analysed at an
unprecedented scale covering all the major forest biomes on Earth (Fig.
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\ref{ilustr}a), and (ii) the influence of traits on competition is
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partitioned among four fundamental mechanisms (Fig. \ref{ilustr}b,c) as
follows. A competitive advantage for some trait values compared to
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others can arise through: (1) permitting faster maximum growth in absence
of competition\citep{Wright-2010}; (2) exerting a stronger
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competitive
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effect\citep{Goldberg-1996, Gaudet-1988},
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meaning that competitor species possessing those traits suppress more strongly the
growth of their neighbours; or (3) permitting a better tolerance of
competition (or competitive `response' in
Goldberg\citep{Goldberg-1996}), so that growth
of species possessing those traits are less affected by competition
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from neighbours. Finally, (4) competition can promote trait
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diversification, if decreasing trait similarity between species
reduce inter-specific competition compare to intra-specific competition \citep{MacArthur-1967}. Here we dissect how these
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four mechanisms are connected to three key traits that describe plant
strategies
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worldwide\citep{Westoby-2002, Wright-2004, Chave-2009}. These traits are
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wood density (an indicator of a trade-off between stem construction cost
and strength), specific leaf area (SLA, an indicator of a trade-off
between leaf construction cost and leaf longevity), and maximum height
(an indicator of a trade-off between access to light and early
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reproduction). We analyse basal area growth (annual increase in the
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area of the cross section of tree trunk at 1.3 m height) of more than 3
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million trees from more than 2500
species, in all the major forested biomes of the earth (Fig. \ref{ilustr}). Species' mean trait values were extracted from the global TRY
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data base\citep{Kattge-2011, Niinemets-2001} and local
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data bases (see Methods). We analysed how basal area growth of each
individual tree was reduced by the local abundance of competitors in its local
neighbourhood\citep{Uriarte-2004} (measured as the sum of
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basal areas of competitors in \(m^2/ha\)), accounting for traits of both
the focal tree and its competitors. This analysis allowed effect sizes
to be estimated for each of the four pathways (Fig. \ref{ilustr}c).

Across all biomes the strongest driver of individual growth was the
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local abundance of neighbours,
irrespective of their traits (positive values of $\alpha_{0 intra}$
and $\alpha_{0 inter}$ indicate that these neighbours had competitive rather than
facilitative effects Fig. \ref{res1}). The main effects of traits were that some trait values led
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to a competitive advantage compared to others through two main
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mechanisms. First, traits of the focal species had direct influences on its
maximum growth (\emph{i.e.} in the absence of competition -- $m_1$, see Fig.
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\ref{res1} and Extended data Table 3), with the fastest growing species
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having low wood density and high SLA, but the confidence interval
intercepted zero for this last trait (Fig. \ref{res1}). This is in agreement with previous studies\citep{Wright-2010} of
adult trees reporting strong link between maximum growth and wood
density but a weak correlation for SLA. Second,
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some trait values were associated with species having stronger
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competitive effects, or better tolerance of competition (Extended
data Table 3; Fig.
\ref{res1}). High wood density was
correlated with higher tolerance of competition from neighbours and stronger
competitive effect on their neighbours, whereas low SLA was only
correlated to a higher competitive effect. This is in agreement with studies reporting that high wood density species are
more shade-tolerant\citep{Wright-2010}, and have larger crowns (in
depth and radius)\citep{Poorter-2006a, Aiba-2009} thus potential
a higher light interception (further detail in Supplementary Discussion). The link between
SLA and competitive effect is in agreement with the observation that the
shorter leaf life span associated with high SLA results in low standing
leaf area and low light interception\citep{Niinemets-2010}. We found
no correlation of maximum height with any mechanisms linked to
competitive advantage. This trait is, however, generally considered closely connected with
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light interception, a key advantage in
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competition\citep{Mayfield-2010}, but it is possible that maximum
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height effect appears only when considering long-term
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population levels outcome of competition rather than its short-term effect
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on basal area growth\citep{Adams-2007}.
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Finally, after accounting for differences in trait independent
intraspecific competition \textit{vs.} interspecific competition, we found
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very weak evidences of trait similarity effect on competitive
interactions. Only maximum height similarity between focal and
neighbour species led to a weak increase in competitive suppression of
tree growth (Fig. \ref{res1}). The ratios of inter-specific over intra-specific competition between
pairs of species -- a key indicator of stabilising mechanisms --  was thus
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only weakly related to traits differences (see Extended data Fig 3). `Trait similarity' effect has generally been considered as the key mechanism
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by which traits affect competition, but has rarely been confirmed with
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field data\citep{Mayfield-2010}. Our analysis shows at
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global scale that this process is very weak. The underlying mechanisms
explaining the higher trait independent intraspecific \textit{vs.}
interspecific competition and the small maximum height similarity
effect are unknown, but could include less efficient light
capture because of less complementarity in architectural
niche\citep{Sapijanskas-2014, Jucker-2015}, or higher loads of specialised
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pathogens\citep{Bagchi-2014} between conspecific or species with similar traits. This highlight that other traits, for instance traits more directly related to natural enemies,
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may show stronger trait
similarity effect.
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Analyses that allowed for different effects among biomes did not show
strong evidence for any particular biome behaving consistently
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differently from the others (Fig. \ref{res1}). This surprising lack of context
dependence in the traits effects, may results from the predominant role of
competition for light in all forests of the world (further
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details in Supplementary Discussion).
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Our global study supports the hypothesis that trait values
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favouring high tolerance of competition or high competitive effects also
render species slow growing in the absence of competition across all
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forested biomes (Fig. \ref{res3}). This trait-based trade-off is a key ingredient in the classical model for successional
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coexistence of species in forests, where fast-growing species are more abundant
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in early successional stages where competitors are absent, and are
later replaced by slow-growing species in late successional stages where
competitors are abundant\citep{Rees-2001}. As human or
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natural disturbances are conspicuous in all forests analysed, such successional
dynamics is likely to be on going in all these sites (see
data description in Supplementary Methods). These trade-offs was presents for wood
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density, as high wood density was associated with slow potential
growth rate but high tolerance to competition and strong competitive effect
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(Fig. \ref{res3}). Similar pattern was present, but less clear, for SLA. High SLA was
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correlated with a low competitive effect and marginally correlated with a fast maximum
growth (confidence intervals do not intercepted zero only in three
biomes, see Fig. \ref{res1} and \ref{res3}). In addition, the long
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term impact of SLA is unclear, because long-term outcomes of competition at the population level may be less
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influenced by competitive effect than the tolerance of competition\citep{Goldberg-1996}.
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Coordination between trait values conferring high competitive effect and
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trait values conferring high tolerance of competition has generally
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been expected\citep{Goldberg-1996, Kunstler-2012}, but rarely
documented\citep{Goldberg-1996, Wang-2010}.
We found evidences for such coordination for wood density with the
same direction for its competitive effect and tolerance of competition
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parameters (Fig. \ref{res1}).
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The globally consistent links that we report here between traits and
competition have considerable promise to predict the complex species
interactions governing forest communities across different vegetation types and
different continents of the globe. A challenge for the future is to
analyse how traits determine the competitive outcomes at the
population level by analysing all key demographic rates and life
history stages of trees. This would allow to explore population level
consequences of traits effects on competition, such as the stable coexistence of species with diverse
trait. The analysis presented
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here already
demonstrates, that trait similarity is not the major determinant of local
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scale competition impact on trees growth for these three traits. In
contrast, if forest disturbances create a mosaic of successional
stages, this could favour different compromises along the trait-based trade-off in performance with \textit{vs.} without
competition, and thus promotes
coexistence of species with diverse trait.
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\textbf{Supplementary Information} is available in the online version of
the paper.

\textbf{Acknowledgements} We are grateful to researchers whose long-term
commitments to establish and maintain the forest plots and their
associated databases use in this study, and those who granted us access
- forest inventories and permanent plots of New Zealand, Spain
(MAGRAMA), France, Switzerland, Sweden, US, Canada (for the following
provinces: Quebec, Ontario, Saskatchewan, Manitoba, New Brunswick, and
Newfoundland and Labrador), CTFS (BCI, Fushan and Luquillo), Cirad
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 (Paracou), Cirad, MEFCP, and ICRA (M'Baïki) and Japan. GK was supported by a Marie Curie
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International Outgoing Fellowship within the 7th European Community
Framework Program (Demo-traits project, no. 299340). The working group
that initiated this synthesis was supported by Macquarie University and
by Australian Research Council through a fellowship to MW.

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\textbf{Author contributions} GK and MW conceived the study and led --with help form DF-- a
workshop to develop this analysis with the participation of DAC, FH,
RMK, DCL, LP, MV, GV, and SJW. GK wrote the manuscript with key inputs form all workshop participants and help form all authors. GK, DF and FH wrote the computer code and processed the data. GK devised the main analytical approach and performed analyses with assistance from DF for the figures.
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GK, DAC, DF, FH, RMK, DCL, MV, GV, SJW, MA, CB, JC, JHCC, SGF, MH, BH,
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JK, HK, YO, JP, HP, MU, SR, PRB, IFS, GS, NS, JT, BW, CW, MAZ, HZ, JZ,
NEZ collected and processed the raw data.

\textbf{Author information}
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The authors declare no competing financial interests.
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\newpage

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\section{FIGURES}\label{figures}
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\begin{figure}[htbp]
\centering
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\includegraphics{image/fig1f_intra.pdf}
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\caption{\textbf{Assessing competitive interactions at global scale.}
\textbf{a,} Precipitation-temperature space occupied by each data set
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(NFI -- national forest inventories data, LPP -- large permanent
plots data). For data with multiple plots, the range of climatic condition is
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represented by an ellipse covering 98\% of the plots. Biomes are: 1,
tundra; 2, taiga; 3, mediterranean; 4, temperate forest; 5, temperate
rainforest; 6, desert; 7, tropical seasonal forest; 8, tropical
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rainforest (as defined by Ricklefs\citep{Ricklefs-2001}). \textbf{b,}
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Sampled patches vary in the abundance of competitors from species $c$
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around individuals of focal species $f$. \textbf{c,} We modelled how
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trait values of the focal tree ($t_f$), and the abundance (measured as
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the sum of their basal areas) and traits values of competitor species ($t_c$)
influence basal area growth of the focal tree. Species maximum growth (red) was
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influenced by trait of the focal tree ($m_0 + m_1 \, t_f$, with $m_0$
maximum growth independent of trait). Reduction
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in growth per unit basal area of competitor (blue, $-\alpha_{c,f}$) was
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modelled as the sum of growth reduction independent of trait by conspecific
($\alpha_{0 \, intra}$) and heterospecific ($\alpha_{0 \, inter}$), the effect of competitor traits ($t_c$) on their
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competitive effect ($\alpha_e$), the effect of the focal tree's traits
($t_f$) on its tolerance of competition ($\alpha_t$), and the effect
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of trait similarity between the focal tree and its competitors
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($\vert t_c-t_f \vert$) on competition ($\alpha_s$). The parameters $m_0, m_1, \alpha_{0 \, intra}, \alpha_{0 \, inter}, \alpha_e, \alpha_t$ and
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$\alpha_s$ are fitted from data using maximum likelihood method.
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\label{ilustr}}
\end{figure}

\newpage

\begin{figure}[htbp]
\centering
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\includegraphics{../../figs/figres12_TP_intra.pdf}
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\caption{\textbf{Global trait effects and trait-independent effects on
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maximum growth and competition and their variation among biomes.}
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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
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and coloured points and lines represent the biome level estimates. The
parameter estimates represent: effect of focal tree's trait value on maximum
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growth \(m_1\), the competitive effect independent of traits of conspecific
$\alpha_{0 \, intra}$ and heterospecific $\alpha_{0 \, inter}$, the effect of competitor trait values on their competitive
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effect \(\alpha_e\) (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
\(\alpha_t\) (positive values indicate that greater trait values result
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in greater tolerance of competition), and the effect on competition of
trait similarity between the focal tree and its competitors \(\alpha_s\)
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(negative values indicate that higher trait similarity leads to a
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stronger reduction of the growth of the focal tree). 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
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plots were available (see Fig 1a. for biomes definitions).
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\label{res1}}
\end{figure}

\newpage

\begin{figure}[htbp]
\centering
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\includegraphics{../../figs/figres4t_TP_intra.pdf}
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\caption{\textbf{Trade offs between maximum growth and competitive
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effects or competitive tolerance parameters of wood density and specific
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leaf area predicted by the global traits models.} Variation of maximum growth
(\(m_1 \, t_f\)), tolerance of competition (\(\alpha_t \, t_f\)) and
competitive effect ($\alpha_e \, t_c$)
parameters with wood density (first column) and specific leaf area
(second column). The shaded
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area represents the 95\% confidence interval of the prediction
(including uncertainty associated with \(\alpha_0\) or \(m_0\)).
\label{res3}}
\end{figure}

\newpage

\newpage
\clearpage
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\section{References}\label{references-max-30}
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\bibliographystyle{naturemag}
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\bibliography{references}
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\end{document}