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 kunstler committed Nov 21, 2014 1 %Title : Functional traits have globally consistent effects on plant competition  kunstler committed Dec 20, 2014 2 3 4 5 6 7 8 %Authors: Georges Kunstler; David A Coomes; Daniel Falster; Francis Hui; Rob Kooyman; Daniel Laughlin Lourens Poorter; Mark Vanderwel; Ghislain Vieilledent; Joe Wright; Masahiro Aiba; John Caspersen; Sylvie Gourlet-Fleury; Marc Hanewinkel; Bruno Herault; Hiroko Kurokawa; Yusuke Onoda; Maria Uriarte; Sarah Richardson; Paloma Ruiz; I-Fang Sun; Goran Ståhl; Nathan Swenson; Jill Thompson; Miguel Zavala; Hongcheng Zeng; Jess Zimmerman; Niklaus E Zimmermann; and Mark Westoby.  kunstler committed Nov 10, 2014 9 10   kunstler committed Dec 16, 2014 11 # Summary paragraph outline (max 200 words)  kunstler committed Nov 10, 2014 12   kunstler committed Dec 16, 2014 13 Competition is a very important type of ecological interaction, especially in terrestrial vegetation where plants greatly modify the local environment for each other. Competition has influence on growth of individuals and survival, on how species mixtures will change over time into the future, and on community composition. However firm generalizations have yet to be established about outcomes of competition among tree species. Here we show how key species traits have consistent influences on growth and competition. The data sources are very large, including national forest inventories monitoring growth in sometimes millions of trees, and we here synthesize across a global set of such inventories plus also several large forest-monitoring plots. Some traits have strong effect on the growth rate of the species. Then traits in part determine the tolerance to competition and the impact of the competitor. A notable generalization is that trait values that favour tolerance to competition also render species slow growing in absence of competition. There is also a small but detectable benefit in reducing competition from trait-dissimilarity between focal plant and its competitors. The trait-based picture that emerges is much simpler and more general than a quantification of competition coefficients between each pair of species, which is intractable at the global scale. Our results demonstrate that traits may be used to predict competitive interactions between forest tree species at a large scale. We anticipate that our results can have profound influence on trait model of community assembly.  kunstler committed Nov 10, 2014 14   kunstler committed Jan 20, 2015 15 # Main text  kunstler committed Dec 16, 2014 16   kunstler committed Dec 20, 2014 17 **(MAX 1500 words till the end of Methods = 1494)**  kunstler committed Dec 16, 2014 18   kunstler committed Dec 18, 2014 19 Competition is a fundamental type of interaction in ecological communities. Each individual modify their neighbouring environment and thus influences the performance of neighbouring individuals [@keddy_competition_2001]. Competition influences species composition and its changes over time. Maybe competition is especially important for vegetation on land because most vegetation types have high enough cover that shading and water and nutrient depletion are conspicuous. There have thus been a very large number of studies on competition among plants [@goldberg_patterns_1992], but firm generalizations have yet to be established about its outcomes.  kunstler committed Dec 16, 2014 20   kunstler committed Jan 20, 2015 21 When competition is described as interactions between pairs of species (as it traditionally has been), the number of different interactions to be measured grows explosively with the number of species ($N^2$), and becomes quickly intractable. Also this species-pair approach does not lead naturally to generalization across forests on different continents with different composition. Here we quantify competition between trees (in the sense of influence of neighbours on growth of a focal tree) within a framework, which is novel in two important ways: (i) competition is modelled as a function of traits rather than of species and (ii) we partition how traits drive the outcome of competition in four different key processes (Fig. \ref{ilustr}). Competition can select trait values that are the most competitive. This competitive advantage of trait values can arise because (1) there are correlated with higher potential growth (in absence of competition) [@wright_functional_2010], (2) they are correlated with a higher tolerance to competition [@goldberg_competitive_1996], or (3) they are correlated with higher competitive impact [@gaudet_comparative_1988]. In contrast, competition can promotes the coexistence of a mixture of traits values, if (4) competition decrease with increasing dissimilarity of the traits of the competitors and the focal tree [@macarthur_limiting_1967]. These four processes are likely to be connected to the key traits used to describe plant strategies[@hillerislambers_rethinking_2012; @lasky_trait-mediated_2014], however there is no agreement on their relative contributions to each of these processes and whether the magnitude and direction of these effects are conserved across large scale.  kunstler committed Dec 16, 2014 22   kunstler committed Dec 18, 2014 23 Here we dissect how three key traits[@westoby_plant_2002; @chave_towards_2009] (maximum height, wood density and specific leaf area - *SLA*) affect these four processes involved in competition between trees using neighbouring modeling approach[@uriarte_neighborhood_2004]. We compiled data of growth along side local abundance of their competitor for more than 7 million trees representing more than 2500 species covering all the major biomes of the earth (Fig. \ref{res2}b). We analysed how the potential growth of each individual tree was reduced by the local abundance of its competitors. Our analysis accounts for the trait of both the focal tree and its competitors estimating the trait effect for each of the processes presented in Fig. \ref{ilustr}.  kunstler committed Dec 16, 2014 24   kunstler committed Dec 27, 2014 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Across all biomes we found that strongest drivers of individual growth was first-ranked the local abundance of competitors; and second ranked the direct influence of the focal plant’s traits on its growth (Fig. \ref{res1} Extended data Table D1). We detected only negative effect of the abundance of competitors showing that competition was predominant. Among the three traits wood density had the strongest direct effect, followed by maximum height whereas SLA had no detectable effect (Fig. \ref{res1}). Then our results show the influence of neighbour traits on their competitive impact, and of focal species traits on tolerance of competition (Fig. \ref{res1}). Taken together these two effects are in the range of half or quarter as big as the direct trait effect (Extend data Table D1), down to zero influence depending on the trait (Fig \ref{res1}). Finally, there is a small but consistent effect whereby the wider is the absolute trait separation between focal and neighbour species, the weaker is competitive suppression of growth (Fig. \ref{res1}). This may arise because of negative density dependence arising for species with similar trait because, for instance, a higher load of herbivores or pathogens [@bagchi_pathogens_2014] or less efficient use of resources (such a less efficient light use[@sapijanskas_tropical_2014] or less efficient litter recylcing[@sapijanskas_beyond_2013]). An analysis using a multiple-traits distance rather than a single trait distance didn't show different pattern (extended data Figure **\color{red}?[^todo]**). Analyses allowing for different effect between biomes did no show strong evidence for any particular biome behaving consistently differently from the others (Fig. \ref{res3}). The exception is the temperate biomes where SLA showed much stronger effect, probably ought to the dominance of deciduous species in this biome (Fig. \ref{res2}).  kunstler committed Dec 18, 2014 47 48   kunstler committed Jan 20, 2015 49 [^todo]: **\color{red}Still running.**  kunstler committed Dec 16, 2014 50 51 52 53 54  The direction of the traits effect agree well with the existing literature. High wood density was lined with slow potential growth rate but high tolerance to competition, in agreement with shade-tolerant  kunstler committed Dec 18, 2014 55  species having high wood density[@wright_functional_2010]. High wood density also resulted in a  kunstler committed Jan 20, 2015 56  higher competitive impact, that may be related to deeper crown  kunstler committed Dec 18, 2014 57  [@poorter_architecture_2006; @aiba_architectural_2009]. The lack of direct effect of *SLA* on maximum growth (but with a positive tendency)  kunstler committed Dec 20, 2014 58  agree well with the weak correlation previously reported for adult  kunstler committed Dec 18, 2014 59  trees [@wright_functional_2010]. Increasing *SLA* was also weakly  kunstler committed Jan 20, 2015 60  related to decreased competitive impact and no effect or possible  kunstler committed Dec 16, 2014 61 62 63 64 65 66  weakly decreased competitive tolerance, which agree with previous study reporting a weak negative correlation betwen *SLA* and shade tolerance[@wright_functional_2010]. Finally, maximum height was positively related to maximum growth as previously reported[@wright_functional_2010]. Species with small maximum height were also much more tolerant to competition than taller species, in  kunstler committed Dec 20, 2014 67  line with the idea that sub-canopy tree are more shade-tolerant. For  kunstler committed Jan 20, 2015 68  wood density and *SLA* the link with competitive impact and  kunstler committed Dec 20, 2014 69 70  competitive response was opposed, in agreement with a coordinated selection under which trait value that confer high competitive  kunstler committed Jan 20, 2015 71  impact also confer high competitive tolerance (a competitive  kunstler committed Dec 16, 2014 72 73  hierachy[@kunstler_competitive_2012; @mayfield_opposing_2010]). This was not the case for maximum height because short species were more  kunstler committed Jan 20, 2015 74  tolerant to competition but had a lower competitive impact. This  kunstler committed Dec 20, 2014 75  match well the observation of the persistence of sub-canopy species  kunstler committed Dec 16, 2014 76 77 78 79  under a close cover of tall tree and the stratification theory of species coexistence[@kohyama_stratification_2009]. Finally our study show that trait values that favour tolerance to competition also render species slow growing in absence of  kunstler committed Jan 20, 2015 80  competition. Our results just demonstrate that the wood density based trade-off (Fig. \ref{res3}) between fast growth in the absence of competition and lower tolerance to competition is a global phenomenon common to all forested biomes. For the two other traits their effect on competition was not sufficient to counteract the effect on maximum growth. This important because this is one of the most  kunstler committed Dec 16, 2014 81 82 83  classical process proposed to explain species coexistence in forest [@rees_long-term_2001].  kunstler committed Dec 18, 2014 84 The globally consistent link between competition effect on growth and traits that we report is promising to simplify the complex interaction governing forest communities. Our results also demonstrates that most assumptions about the link between traits and competition that are use to tease out community assembly are too simplistic[@adler_trait-based_2013]. Analysis for other fitness component (survival and recruitment) are now need to be able to scale up these short-term interactions to population dynamics impacts on traits composition.  kunstler committed Dec 16, 2014 85 86 87 88   kunstler committed Nov 21, 2014 89   kunstler committed Dec 16, 2014 90 # Methods summary  kunstler committed Nov 21, 2014 91   kunstler committed Dec 16, 2014 92 To examine the link between competition and traits we first compiled  kunstler committed Dec 18, 2014 93 forest plot data from both national forest inventories and long-term  kunstler committed Dec 16, 2014 94 permanent plots from 14 countries covering all the major biomes of the  kunstler committed Dec 20, 2014 95 world (Fig. 1b, see extended methods Table M1 \& M2). We restricted our analysis to  kunstler committed Dec 16, 2014 96 97 tree with trunk diameter $>= 10cm$ to have a common minimum size across all data set. Second, we extracted traits mean per species (not  kunstler committed Dec 18, 2014 98 accounting for intra-specific variability) from either a global  kunstler committed Dec 16, 2014 99 ([TRY](http://www.try-db.org/) data base[@kattge_try_2011] or local  kunstler committed Dec 20, 2014 100 data base (extended Table 1), for three key traits: wood density, SLA,  kunstler committed Dec 16, 2014 101 102 103 104 105 106 and Maximum height. Third, we computed the local abundance (measured as the basal area - $m^2/ha$) of competitors per species in the neighborhood of each tree. The neighborhood was defined as a 15m radius around the focal tree in large plot with xy coordinates of all trees or as the whole plot for national forest inventory data that are based on small plot (the plot size correspond to a circular plot  kunstler committed Dec 18, 2014 107 108 ranging from 10 to 25m in radius). Fourth, for each traits, we computed for each individual, four crowding indices that where representing (i) the overall crowding  kunstler committed Dec 16, 2014 109 110 111 112 irrespective of the species trait (as the sum of basal area of local competitors), (ii) the overall crowding times the trait of focal species, representing how traits affect tolerance to competition, (iii) the mean traits of the competitor weighted by their abundance  kunstler committed Jan 20, 2015 113 (basal area), representing how traits affect the competitive impact,  kunstler committed Dec 16, 2014 114 115 and (iv) the mean of the trait absolute distance between the focal tree and the competitor species weighted per their abundance (basal  kunstler committed Jan 20, 2015 116 area), representing how the trait similarity affect competition. Finally, for each trait, we fitted a model estimating how  kunstler committed Dec 16, 2014 117 118 individuals tree basal area growth was affected by the focal species traits (direct traits effect) and by these four crowding indexes,  kunstler committed Dec 18, 2014 119 while accounting for size effect. To  kunstler committed Dec 16, 2014 120 121 facilitate comparison between parameters and traits all traits and variable were standaridised to a SD of one. We report these  kunstler committed Dec 18, 2014 122 standardized parameters for each traits. Two models were fitted, (i)  kunstler committed Dec 16, 2014 123 124 we included biomes as a random effect in each parameters to estimate the overall effect across all biomes and (ii) we included biomes as a  kunstler committed Dec 18, 2014 125 fixed effect to analyse the difference between biomes.  kunstler committed Nov 21, 2014 126   kunstler committed Dec 20, 2014 127 **Supplementary Information** is available in the online version of the paper.  kunstler committed Nov 21, 2014 128   kunstler committed Dec 16, 2014 129 130 131 132 133 134 **Acknowledgements** We are thankful that people whose long term commitment allowed the established and maintained the forest plots and their associated databases, granted us access to forest inventory data and long term forest plots of NVS, Japan, Spain, France, Switzerland, Sweden, US, Canada (for the following provinces: Québec, ...), CTFS plots (BCI, Fushan and Luquillo) and Cirad permanent plots (Paracou, M'Baiki). GK was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Program (Demo- traits project, no. 299340).  kunstler committed Nov 21, 2014 135   kunstler committed Dec 16, 2014 136 137 138 **Author contributions** G.K. conceived the study with feedback from M.W and D.F. G. K., M. W and ... wrote the manuscript. G.K. devised the main analytical approach, wrote the computer code and performed analyses.  kunstler committed Nov 21, 2014 139   kunstler committed Dec 16, 2014 140 **Author information**  Georges Kunstler committed Nov 10, 2014 141   kunstler committed Dec 16, 2014 142 \newpage  kunstler committed Nov 10, 2014 143   kunstler committed Dec 16, 2014 144 # FIGURES & TABLES  Georges Kunstler committed Nov 10, 2014 145   kunstler committed Jan 20, 2015 146 147 148   kunstler committed Jan 26, 2015 149 ![**Assessing competitive interactions at global scale.** **a,** Precipitation-temperature space occupied by the natural forest communities studied (NFI data : national forest Inventories, LPP data : large permanent plots) . Biomes follow the definition of Whittaker [@ricklefs_economy_2001]:  kunstler committed Jan 20, 2015 150 151 152 153 154 155 156 157 158 159  1, tundra; 2, taiga; 3, woodland/shrubland; 4, temperate forest; 5, temperate rainforest; 6, desert; 7, tropical seasonal forest; 8, tropical rainforest. **b,** Sampled patches vary in the density of competitors from species $c$ around individuals from a focal species $f$. For each tree we record the stem diameter $D_i$ across multiple censuses, and link this with records for the traits of the focal and competitor species, $t_f$ and $t_c$ respectively. **c,** We use a neighbourhood modelling framework to model the effects of the traits and size of the focal tree, and the total basal area ($B_c$) and traits of competitor species on growth of the focal tree. These  kunstler committed Jan 26, 2015 160 161  effects can be broken down into those influencing maximum growth rate (red) and those influencing reduction in growth per unit basal area of competitor (blue), $\alpha_{c,f}$. Trait value of focal tree ($t_f$) can influences the tree growth without competition (maximum growth, $m_1$). The trait value of the focal tree can influences its tolerance to competition ($t_f \, \alpha_r$) and the trait values of the competitors can influence their competitive impact ($t_c \, \alpha_i$). Finally, the trait similarity between the focal tree and its competitors can influences competitive interactions ($\alpha_s \, \vert t_c-t_f \vert$) (see extended methods). The parameters $m_1, \gamma, \alpha_0, \alpha_i, \alpha_r$ and $\alpha_s$ are fitted from data. \label{ilustr2}](image/fig1e.pdf)  kunstler committed Jan 20, 2015 162 163 164   kunstler committed Jan 20, 2015 165 \newpage  kunstler committed Jan 20, 2015 166   kunstler committed Jan 20, 2015 167 168  ![**Traits effects on maximum growth and competition.** Standardized regression coefficients of the growth models in function of the trait of the focal tree and its competitors fitted separately for each traits (Wood density, *SLA*, and maximum height). The parameters estimate represents the traits effect explained in the Fig 1c.: the direct trait effect on maximum growth of the focal tree, the competition trait independent, the trait of the competitors effect on their competitive impact, the trait of the focal tree effect on its competitive response, and trait similarity between the focal tree and its competitors effect on competition. The points represent the mean estimate and the line the 95\% confidence interval. \label{res1}](../../figs/figressp1b.pdf)  Georges Kunstler committed Nov 10, 2014 169   kunstler committed Dec 16, 2014 170 \newpage  Georges Kunstler committed Nov 10, 2014 171 172   kunstler committed Jan 20, 2015 173 ![**Variation of traits effects on maximum growth and competition between biomes.** Standardized regression coefficients of the growth models in function of the trait of the focal tree and its competitors fitted separately for each traits as in Fig. 2, but with a different estimates for each biomes (see Fig 1a. for the definitions of the biomes). **NEED TO UPDATE WITH FIGURE WITH FIXED BIOMES EFFECT**. \label{res2}](../../figs/figresspBLUP1.pdf)  Georges Kunstler committed Nov 10, 2014 174   kunstler committed Jan 20, 2015 175 \newpage  Georges Kunstler committed Nov 10, 2014 176   kunstler committed Jan 20, 2015 177 ![Trade off between growth without competition and growth with competition underpinned by Maximum height, SLA (specific leaf area), and wood density (**I need to update the name of the traits**). The growth of a focal tree with low or high trait value (respectively 5 and 95\% quantile) in function of the local basal area of competitors with trait value resulting in a high competitive impact. The shaded area represents the 95% confidence interval of the prediction. \label{res3}](../../figs/figres3.pdf)  Georges Kunstler committed Nov 10, 2014 178   kunstler committed Nov 21, 2014 179 180   kunstler committed Jan 20, 2015 181   kunstler committed Nov 21, 2014 182   kunstler committed Jan 20, 2015 183 184 185 186 187 188   Georges Kunstler committed Nov 10, 2014 189 190 191   kunstler committed Nov 10, 2014 192   kunstler committed Dec 16, 2014 193 # References  kunstler committed Nov 21, 2014 194