Commit 41e9c9a0 authored by Kunstler Georges's avatar Kunstler Georges
Browse files

process intra all

parent 63b3a6db
......@@ -637,7 +637,7 @@ pred.res.ae$pred <- -pred.res.ae$pred
pred.res.ae$lwr <- -pred.res.ae$lwr
pred.res.ae$upr <- -pred.res.ae$upr
if(!intra.TF){
if(!intra.TF){
pred.res.a0 <- easyPredCI.param(list.res, type = 'alpha0', new.data, alpha, alpha_0)
pred.res.a0$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
......@@ -650,7 +650,7 @@ pred.res.a0$upr <- -pred.res.a0$upr
return(rbind(pred.res.max, pred.res.ar, pred.res.ae, pred.res.al, pred.res.a0))
}
#intra
if(intra.TF){
if(intra.TF){
pred.res.a0.intra <- easyPredCI.param(list.res, type = 'alpha0', new.data, alpha, alpha_0 = 'sumBn.intra')
pred.res.a0.intra$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
......@@ -694,9 +694,9 @@ pred.res.rho$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
length.out = 100) - list.var[[1]][['ql.o']]
pred.res.rho$param.type <- 'rho'
pred.res.rho$pred <- 1- exp(pred.res.rho$pred)
pred.res.rho$lwr <- 1 - exp(pred.res.rho$lwr)
pred.res.rho$upr <- 1 - exp(pred.res.rho$upr)
pred.res.rho$pred <- 1- exp(-pred.res.rho$pred)
pred.res.rho$lwr <- 1 - exp(-pred.res.rho$lwr)
pred.res.rho$upr <- 1 - exp(-pred.res.rho$upr)
new.data.GiGj <- fun.generate.pred.param.kikj.dat( list.sd = list.res$list.sd,
Tf.low = list.var[[1]][['ql']],
......@@ -728,9 +728,9 @@ pred.res.rho$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
length.out = 100) - list.var[[1]][['ql.o']]
pred.res.rho$param.type <- 'rho'
pred.res.rho$pred <- 1 - exp(pred.res.rho$pred)
pred.res.rho$lwr <- 1 - exp(pred.res.rho$lwr)
pred.res.rho$upr <- 1 - exp(pred.res.rho$upr)
pred.res.rho$pred <- 1 - exp(-pred.res.rho$pred)
pred.res.rho$lwr <- 1 - exp(-pred.res.rho$lwr)
pred.res.rho$upr <- 1 - exp(-pred.res.rho$upr)
new.data.GiGj <- fun.generate.pred.param.kikj.dat( list.sd = list.res$list.sd,
......@@ -1035,48 +1035,48 @@ for (i in traits[traits.print]){
extract.param <- function(trait, list.res,
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
param.vec = c("logD", "Tf","sumBn", "sumTnBn",
"sumTfBn", "sumTnTfBn.abs")){
list.temp <- list.res[[paste("simple_", trait ,
"_", model,
sep = '')]]$lmer.summary
param.mean <- list.temp$fixed.coeff.E[param.vec]
"sumTfBn", "sumTnTfBn.abs"),
data.type = 'simple'){
list.temp <- list.res[[paste0(data.type, "_", trait ,
"_", model)]]$lmer.summary
param.mean <- list.temp$fixed.coeff.E[param.vec]
return(param.mean)
}
extract.param.sd <- function(trait, list.res,
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
param.vec = c("logD", "Tf","sumBn", "sumTnBn",
"sumTfBn", "sumTnTfBn.abs")){
list.temp <- list.res[[paste("simple_", trait ,
"_", model,
sep = '')]]$lmer.summary
param.mean <- list.temp$fixed.coeff.Std.Error
names(param.mean) <- names(list.temp$fixed.coeff.E)
return(param.mean[param.vec])
"sumTfBn", "sumTnTfBn.abs"),
data.type = 'simple'){
list.temp <- list.res[[paste0(data.type, "_", trait ,
"_", model)]]$lmer.summary
param.sd <- list.temp$fixed.coeff.Std.Error
names(param.sd) <- names(list.temp$fixed.coeff.E)
return(param.sd[param.vec])
}
extract.R2c <- function(trait, list.res,
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species'){
list.temp <- list.res[[paste("simple_", trait ,
"_", model,
sep = '')]]$lmer.summary
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
data.type = 'simple'){
list.temp <- list.res[[paste0(data.type,"_", trait ,
"_", model)]]$lmer.summary
return(list.temp$R2c)
}
extract.R2m <- function(trait, list.res,
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species'){
list.temp <- list.res[[paste("simple_", trait ,
"_", model,
sep = '')]]$lmer.summary
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
data.type = 'simple'){
list.temp <- list.res[[paste0(data.type,"_", trait ,
"_", model)]]$lmer.summary
return(list.temp$R2m)
}
extract.AIC <- function(trait, list.res,
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species'){
list.temp <- list.res[[paste("simple_", trait ,
"_", model,
sep = '')]]$lmer.summary
model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
data.type = 'simple'){
list.temp <- list.res[[paste0(data.type,"_", trait ,
"_", model)]]$lmer.summary
return(list.temp$AIC)
}
......@@ -1546,7 +1546,7 @@ first.p <- 'alpha0'
if(intra.TF){
expr.p.vec <- c(expression(paste('Trait indep ', alpha['0 intra/inter'])),
expression(paste('Trait indep ', alpha['0 intra/inter'])),
expression(paste('Trait indep ', alpha['0 intra/inter'])),
expression(paste('Similarity ', alpha[l] %*% abs(t[f] - t[c]))),
expression(paste('Competitive effect ', alpha[e] %*% t[c])),
expression(paste('Tolerance of competition ', alpha[t] %*% t[f])),
......@@ -1584,7 +1584,7 @@ if(p == 'maxG'){
col.vec = col.vec,
expr.param = expr.p.vec[p], cex.lab = 1.1, cex.axis =0.85, cex = 1,
ylim = ylim.list[[p]])
}
}
}
}
......@@ -1613,7 +1613,7 @@ if(p == 'maxG'){
expr.param = expr.p.vec[p], add.ylab.TF = FALSE,
cex.lab = 1.1, cex.axis =0.85, cex = 1,
ylim = ylim.list[[p]])
}
}
}
}
......@@ -1677,7 +1677,7 @@ first.p <- 'maxG'
if(intra.TF){
expr.p.vec <- c(expression(paste('Trait indep ', alpha['0 intra/inter'])),
expression(paste('Trait indep ', alpha['0 intra/inter'])),
expression(paste('Trait indep ', alpha['0 intra/inter'])),
expression(paste('Similarity ', alpha[l] %*% abs(t[f] - t[c]))),
expression(paste('Competitive effect ', alpha[e] %*% t[c])),
expression(paste('Tolerance of competition ', alpha[t] %*% t[f])),
......@@ -1773,10 +1773,10 @@ polygon(c(df.t[, 'Tf'],
c(df.t[, 'upr'],
rev(df.t[, 'lwr'])),
col = add.alpha(col.vec[names.param[p]], 0.5), border = NA)
}
}
if(add.ylab.TF){
if(p != 'alpha0.intra'){
if(p != 'alpha0.intra'){
mtext(expr.param,
side=2, cex =cex,
line = 2.5, col = col.vec[names.param[p]])
......@@ -1922,7 +1922,7 @@ layout(m, heights=hei, widths= wid )
names(expr.p.vec) <- c('rho', 'kikj', 'GiGj')
names.param <- c("rho","kikj", 'GiGj')
names(names.param) <- c('rho', 'kikj', 'GiGj')
col.vec <- c('green', 'red', 'blue')
col.vec <- c('#018571', '#a6611a', '#dfc27d')
names(col.vec) <- c("rho","kikj", 'GiGj')
for (t in c('Wood density', 'Specific leaf area', 'Maximum height')){
for (p in c('rho', 'kikj', 'GiGj')){
......@@ -1970,7 +1970,7 @@ if(t == 'Maximum height'){
if(t == 'Wood density'){
if(p == 'GiGj'){
fun.plot.param.tf(df = df.t,
p = p,
p = p,names.param = names.param,
ylim = range(filter(df.t,
param.type == 'GiGj')%>% select(upr,lwr)),
xlab = expression(paste(Delta, ' Wood density (mg m', m^-3, ')')),
......@@ -1978,7 +1978,7 @@ if(p == 'GiGj'){
expr.param = expr.p.vec[p], cex.lab = 1.1, cex.axis =0.85, cex = 1)
}else{
fun.plot.param.tf(df = df.t,
p = p,
p = p, names.param = names.param,
ylim = range(filter(df.t,
param.type == p)%>% select(upr,lwr)),
xaxt= 'n',xlab = NA,
......@@ -2010,7 +2010,7 @@ if(p == 'GiGj'){
if(t == 'Maximum height'){
if(p == 'GiGj'){
fun.plot.param.tf(df = df.t,
param.sel = p,names.param = names.param,
p = p,names.param = names.param,
xlab = expression(paste(Delta, ' Maximum height (m)')),
ylim = range(filter(df.t,
param.type == 'GiGj')%>% select(upr,lwr)),
......@@ -2018,7 +2018,7 @@ if(p == 'GiGj'){
expr.param = expr.p.vec[p], add.ylab.TF = FALSE, cex.lab = 1.1, cex.axis =0.85, cex = 1)
}else{
fun.plot.param.tf(df = df.t,
param.sel = p,names.param = names.param,
p = p,names.param = names.param,
xlab = NA,
xaxt= 'n',
ylim = range(filter(df.t,
......@@ -2068,7 +2068,7 @@ layout(m, heights=hei, widths= wid )
names(expr.p.vec) <- c('rho', 'kikj', 'GiGj')
names.param <- c("rho","kikj", 'GiGj')
names(names.param) <- c('rho', 'kikj', 'GiGj')
col.vec <- c('green', 'red', 'blue')
col.vec <- c('#018571', '#a6611a', '#dfc27d')
names(col.vec) <- c("rho","kikj", 'GiGj')
for (t in c('Wood density', 'Specific leaf area', 'Maximum height')){
for (p in c('rho')){
......
% Supplementary Information
# Supplementary methods
We developed the equation of $\alpha_{c,f} = \alpha_{0,f} - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c-t_f \vert$ along with the basal area of each competitive species in the competition index to show the parameters are directly related to community weighted means of the different traits variables as:
We developed the equation of $\alpha_{c,f} = \alpha_{0,f,intra} \, CON + \alpha_{0,f,inter} \ (1-CON) - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c-t_f \vert$ along with the basal area of each competitive species in the competition index to show the parameters are directly related to community weighted means of the different traits variables as:
\begin{equation} \label{alphaBA}
\sum_{c=1}^{N_i} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f} \, B_{i,tot} - \alpha_t \, t_f \, B_{i,tot} + \alpha_e \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\sum_{c=1}^{N_i} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f,intra} \, B_{i,f} + \alpha_{0,f,inter} \, B_{i,het}- \alpha_t \, t_f \, B_{i,tot} + \alpha_e \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,tot} = \sum_{c=1}^{N_i} {B_{i,c,p,s}}$,
$B_{i,het} = \sum_{i \neq f} {B_{i,c}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c,p,s}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c,p,s}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$,
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the index $p4 and $s$ respectively for plot and data set were not include for the sake of simplicity.
## Derivation of $\rho$ for a Lotka-Volterra model based on Godoy \& Levine[@Godoy-2014]
Chesson[@Chesson-2012] proposed to estimate the stabilising niche difference, based on the per capita growth rate of rare invader in the population of a resident species. Godoy \& Levine[@Godoy-2014] used this method on an annual plant population model. This approach can be explained using teh Lotka-Volterra model, defined as:
\begin{equation}
\frac{dN_i}{dt} = N_i \times r_i \times (1 - \alpha'_{ii} N_i -
\alpha'_{ij} N_j)
\end{equation}
The criteria for invasion of species $i$ in resident community of species $j$ is (at equilibrium population of single resident $j$ is $\overline{N_j} = \frac{1}{\alpha'_{jj}}$):
\begin{equation}
(1 - \frac{\alpha_{ij}}{\alpha'_{jj}})
\end{equation}
Thus if $\frac{\alpha_{ij}}{\alpha'_{jj}} <1$ invasion $i$ in $j$ is possible (same approach for $j$ in $i$).
Stable coexistence for species $i$ in regard to speciesand $j$ thus requires
$\frac{\alpha'_{ij}}{\alpha'_{jj}}$ and $\frac{\alpha'_{ji}}{\alpha'_{i}}$ to be smaller than 1. Chesson[@Chesson-2012] then defioned the average
stabilising niche
overlap between species $i$ and $j$ as:
\begin{equation}
\rho = \sqrt{\frac{\alpha'_{ij} \alpha'_{ji}}{\alpha'_{jj} \alpha'_{ii}}}
\end{equation}
and $N_i$ is the number of species in the local neighbourhood of the tree $i$.
## Details on data sets used
......@@ -80,7 +107,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 10
- Traits: Wood density and SLA
- Source trait data: local
- Evidences of disturbances and succession dynamics: "The plot network was established with three levels of harvesting and one control [@Ouedraogo-2013]."
- Evidences of disturbances and succession dynamics: "The plot network was established with three levels of harvesting and unharvested control [@Gourlet-Fleury-2013]."
- Contact of person in charge of data formatting: G. Vieilledent (ghislain.vieilledent@cirad.fr)
- Comments:
- References:
......@@ -113,7 +140,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 15
- Traits: Wood density and SLA
- Source trait data: local
- Evidences of disturbances and succession dynamics: "The plot network was established with three levels of harvesting and one control (Herault et al. 2010)."
- Evidences of disturbances and succession dynamics: "The plot network was established with three levels of harvesting and unharvested control (Herault et al. 2010)."
- Contact of person in charge of data formatting: Plot data: B. Herault (bruno.herault@cirad.fr), Traits data: C. Baraloto (Chris.Baraloto@ecofog.gf)
- Comments:
- References:
......@@ -131,7 +158,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 41503
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidences of disturbances and succession dynamics: "French forests monitored by the French National Forest Inventory experience several types of natural disturbances (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Evidences of disturbances and succession dynamics: "French forests monitored by the French National Forest Inventory experience several types of natural disturbances[@Seidl-2014] (such as wind, forest fire, and insects attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Contact of person in charge of data formatting: G. Kunstler (georges.kunstler@gmail.com)
- Comments: The French NFI is based on temporary plot, but 5 years tree radial growth is estimated with short core. All trees with dbh > 7.5 cm, > 22.5 cm and > 37.5 cm were measured within a radius of 6 m, 9 m and 15 m, respectively. Plots are distributed over forest ecosystems on a 1-km 2 cell grid
- References:
......@@ -148,7 +175,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 49855
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidences of disturbances and succession dynamics: "Spanish forests monitored by the Spanish National Forest Inventory experience several types of natural disturbances (such as wind, forest fire, and bark beetles) and harvesting. No data are available on the age structure of the plots."
- Evidences of disturbances and succession dynamics: "Spanish forests monitored by the Spanish National Forest Inventory experience several types of natural disturbances[@Seidl-2014] (such as wind, forest fire, and insects attacks) and harvesting. No data are available on the age structure of the plots."
- Contact of person in charge of data formatting: M. Zavala (madezavala@gmail.com)
- Comments: Each SFI plot included four concentric circular sub-plots of 5, 10, 15 and 25-m radius. In these sub-plots, adult trees were sampled when diameter at breast height (d.b.h.) was 7.5-12.4 cm, 12.5-22.4 cm, 22.5-42.5 cm and >= 42.5 cm, respectively.
- References:
......@@ -166,7 +193,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 2665
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidences of disturbances and succession dynamics: "Swiss forests monitored by the Swiss National Forest Inventory experience several types of natural disturbances (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Evidences of disturbances and succession dynamics: "Swiss forests monitored by the Swiss National Forest Inventory experience several types of natural disturbances (such as wind, forest fire, fungi and instects attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Contact of person in charge of data formatting: M. Hanewinkel & N. E. Zimmermann (niklaus.zimmermann@wsl.ch)
- Comments: All trees with dbh > 12 cm and > 36 cm were measured within a radius of 7.98 m and 12.62 m, respectively.
- References:
......@@ -183,7 +210,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 22904
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidences of disturbances and succession dynamics: "Swedish forests monitored by the Swedish National Forest Inventory experience several types of natural disturbances (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Evidences of disturbances and succession dynamics: "Swedish forests monitored by the Swedish National Forest Inventory experience several types of natural disturbances[@Seidl-2014] (such as wind, forest fire, and insects attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Contact of person in charge of data formatting: G. Stahl (Goran.Stahl@slu.se)
- Comments: All trees with dbh > 10 cm, were measured on circular plots of 10 m radius.
- References:
......@@ -199,7 +226,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 97434
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidences of disturbances and succession dynamics: "US forests monitored by the FIA experience several types of natural disturbances (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Evidences of disturbances and succession dynamics: "US forests monitored by the FIA experience several types of natural disturbances (such as wind, forest fire, fungi and insects attacks) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Contact of person in charge of data formatting: M. Vanderwel (Mark.Vanderwel@uregina.ca)
- Comments: FIA data are made up of cluster of 4 subplots of size 0.017 ha for tree dbh > 1.72 cm and nested in each subplot sapling plots of 0.0014 ha for trees dbh > 2.54 cm. The data of the four subplot were lumped together.
- References:
......@@ -215,7 +242,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 15019
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidences of disturbances and succession dynamics: "Canadian forests monitored by the regional forest monitoring programs experience several types of natural disturbances (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Evidences of disturbances and succession dynamics: "Canadian forests monitored by the regional forest monitoring programs experience several types of natural disturbances (such as wind, forest fire, fungi and insects attacks) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below)."
- Contact of person in charge of data formatting: J. Caspersen (john.caspersen@utoronto.ca)
- Comments: The protocol is variable between Provinces. A large proportion of data is from the Quebec province and the plot are 10 m in radius in this Province.
- References:
......@@ -230,7 +257,7 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Number of plots: 1415
- Traits: Wood density, SLA, and Maximum height
- Source trait data: local
- Evidences of disturbances and succession dynamics: "New Zealand forests are experiencing disturbance by earthquake, landslide, storm and volcanic eruptions. According to Holdaway et al.[@Holdaway-2014] having been disturbed during their measurement interval."
- Evidences of disturbances and succession dynamics: "New Zealand forests are experiencing disturbance by earthquake, landslide, storm, volcanic eruptions other types. According to Holdaway et al.[@Holdaway-2014] having been disturbed during their measurement interval."
- Contact of person in charge of data formatting: D. Laughlin (d.laughlin@waikato.ac.nz)
- Comments: Plots are 20 x 20 m.
- References:
......@@ -254,6 +281,8 @@ Two main data type were used: national forest inventories data -- NFI, large per
- Kooyman, R.M. and Westoby, M. (2009) Costs of height gain in rainforest saplings: main stem scaling, functional traits and strategy variation across 75 species. Annals of Botany 104: 987-993.
- Kooyman, R.M., Rossetto, M., Allen, C. and Cornwell, W. (2012) Australian tropical and sub-tropical rainforest: phylogeny, functional biogeography and environmental gradients. Biotropica 44: 668-679.
\newpage
## Age distribution for Europe and North America.
......@@ -261,20 +290,29 @@ Two main data type were used: national forest inventories data -- NFI, large per
![Age distribution of forest area in 20-year age class for North America (USA and Canada), estimated by Pan et al.[@Pan-2011]. The last class plotted at 150 years is for age > 140 years.](../../figs/age_na.pdf)
\newpage
# Supplementary results
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with random structure in the parameter including the data set and the Koppen-Geiger ecoregion.** 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 ($m_1$), 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 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 in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a 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 plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_ecocode_TP.pdf)
THIS NEED TO BE UPDATED WITH INTRA INTER
\newpage
# Supplementary discussion
## Trait effects and potential mechanisms
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key driver of tree growth[@Stephenson-2014; @Enquist-1999]. Then there was a consistent negative effect of the total basal area of neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height >= 10cm diameter breast height) agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than to the adult stage [@Callaway-1997]. The variation of $\alpha_0$ between biomes is limited with large overlap of their confidences intervals.
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key driver of tree growth[@Stephenson-2014; @Enquist-1999]. Then there was a consistent negative effect of the total basal area of conspecific and heterospecific neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height >= 10cm diameter breast height) agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than to the adult stage [@Callaway-1997]. The variation of $\alpha_{0 \, intra} \, \& \, \alpha_{0 \, inter}$ between biomes is limited with large overlap of their confidences intervals.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects, especially in tropical forest where the confidence interval did not include zero. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
SLA was positively correlated with maximum basal area growth (growth without competition). This agrees well with previous studies that reported a positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]). As in previous studies[@Poorter-2008; @Wright-2010], this direct effect of SLA was smaller than the effect size of wood density and had wider confidence intervals. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@Niinemets-2010].
SLA was positively correlated with maximum basal area growth only in three biomes and with a weak effect. Previous studies have reported a strong positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]) and young seedling relative growth rate (REF TO ADD). Studies[@Poorter-2008; @Wright-2010] on adult tree have however generally reported weak and marginal correlation between SLA and maximum growth. Our results support this pattern. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@Niinemets-2010].
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@Poorter-2006a; @Poorter-2008; @Wright-2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might select for slower growth in long-lived plants[@Poorter-2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@Poorter-2006a]. There was however a tendency for species with tall maximum height to have stronger competitive effects (though with wider confidence intervals intercepting zero). This might be explained by greater light interception from taller trees.
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@Poorter-2006a; @Poorter-2008; @Wright-2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might select for slower growth in long-lived plants[@Poorter-2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@Poorter-2006a]. There was however a tendency for species with tall maximum height to have stronger competitive effects (though with wider confidence intervals intercepting zero). This might be explained by greater light interception from taller trees. These small effect of maximum height are probably explained by the fact that our analysis focus on short-term effect on tree growth. Size-structure population models[@Adams-2007] have in contrast shown that maximum height is a key drivers of the long-term competitive success in term of population growth rate.
Our results raise the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and tolerance are the two central elements of a species' competitive ability[@Goldberg-1991]. One may expect that because of intra-specific competition, species with strong competitive effects should have evolved a high tolerance to competition. However, in agreement with previous studies[@Goldberg-1990; @Goldberg-1991; @Wang-2010], we found little evidence for such coordination. There was only such a tendency for wood density and SLA. High wood density conferred better competitive tolerance and also stronger competitive effects, but with wide confidence intervals intercepting zero for the latter. High SLA conferred stronger competitive effects and higher tolerance of competition, but with wide confidence intervals intercepting zero for the latter. For maximum height, as explained above, there was a tendency for short maximum height to lead to high tolerance of competition but to low competitive effects. This is interesting because a trade-off between competitive tolerance and maximum height has been proposed as a fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@Kohyama-2009]. Finally the lack of support for coordination between tolerance and effects is important because it means that competitive interactions are not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_t$ as in @Kunstler-2012; @Kraft-2014; @Lasky-2014). Traits of competitors alone or of focal plants alone may convey more information than the trait hierarchy. These processes of traits linked to either competitive effects or competitive tolerance, nevertheless, still lead to some trait values having an advantage in competitive interactions.
Our results raise the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and tolerance are the two central elements of a species' competitive ability[@Goldberg-1991]. One may expect that because of intra-specific competition, species with strong competitive effects should have evolved a high tolerance to competition. We found clear evidence for such coordination for wood density, and only weak evidence for SLA. High wood density conferred better competitive tolerance and also stronger competitive effects. High SLA conferred stronger competitive effects and higher tolerance of competition, but with wide confidence intervals intercepting zero for the latter. For maximum height, as explained above, there was a tendency for short maximum height to lead to high tolerance of competition but to low competitive effects. This is interesting because a trade-off between competitive tolerance and maximum height has been proposed as a fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@Kohyama-2009]. The mixed results on the coordination between tolerance and effects are important because they mean that competitive interactions are not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_t$ as in @Kunstler-2012; @Kraft-2014; @Lasky-2014). Traits of competitors alone or of focal plants alone may convey more information than the trait hierarchy. These processes of traits linked to either competitive effects or competitive tolerance, nevertheless, still lead to some trait values having an advantage in competitive interactions.
Given that the effect sizes we report for effects of traits on competitive interactions are modest, the question arises whether the three traits available to us (wood density, SLA, and maximum height) were the best candidates. It is possible that traits more directly related to mechanisms of competition -- for instance for competition for light, the leaf area index of the competitors or the light compensation point at leaf or whole-plant level -- may be more powerful. It is also possible that traits measured at the individual level rather than as species averages might strengthen the predictive power of our analysis[@Kraft-2014].
......@@ -282,7 +320,7 @@ Given that the effect sizes we report for effects of traits on competitive inter
## Variations between biomes
Overall, most results were rather consistent across biomes (Fig 2 main text), but some exceptions deserve comments.
For SLA, the sign of the tolerance of competition parameters were changing a lot among biomes (Fig. 2 main text). High SLA species tended to be more competition-tolerant (tolerance to competition parameter $\alpha_t$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical forests. These different outcomes may trace to the prevalence of deciduous species in temperate forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@Lusk-2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive impact was positive, versus negative in the other biomes (Fig 2 main text). We do not have a mechanistic explanation for this discrepancy, but observe that taiga has relatively few species, many of which are conifers where the range of wood density is narrower than in angiosperms (see Extended data Table 1).
For SLA, the sign of the tolerance of competition parameters were changing a lot among biomes (Fig. 2 main text). High SLA species tended to be more competition-tolerant (tolerance to competition parameter $\alpha_t$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical and temperate rain forests. These different outcomes may trace to the lack of deciduous species in tropical and temperate rain forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@Lusk-2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive effect was negative, versus positive in the other biomes (Fig 2 main text). We do not have a mechanistic explanation for this discrepancy, but observe that taiga has relatively few species, many of which are conifers where the range of wood density is narrower than in angiosperms (see Extended data Table 1).
# References
......
% Supplementary Information
# Supplementary methods
We developed the equation of $\alpha_{c,f} = \alpha_{0,f} - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c-t_f \vert$ along with the basal area of each competitive species in the competition index to show the parameters are directly related to community weighted means of the different traits variables as:
We developed the equation of $\alpha_{c,f} = \alpha_{0,f,intra} \, CON + \alpha_{0,f,inter} \ (1-CON) - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c-t_f \vert$ along with the basal area of each competitive species in the competition index to show the parameters are directly related to community weighted means of the different traits variables as:
\begin{equation} \label{alphaBA}
\sum_{c=1}^{N_i} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f} \, B_{i,tot} - \alpha_t \, t_f \, B_{i,tot} + \alpha_e \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\sum_{c=1}^{N_i} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f,intra} \, B_{i,f} + \alpha_{0,f,inter} \, B_{i,het}- \alpha_t \, t_f \, B_{i,tot} + \alpha_e \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,tot} = \sum_{c=1}^{N_i} {B_{i,c,p,s}}$,
$B_{i,het} = \sum_{i \neq f} {B_{i,c}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c,p,s}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c,p,s}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$,
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the index $p4 and $s$ respectively for plot and data set were not include for the sake of simplicity.
## Derivation of $\rho$ for a Lotka-Volterra model based on Godoy \& Levine[@Godoy-2014]
Chesson[@Chesson-2012] proposed to estimate the stabilising niche difference, based on the per capita growth rate of rare invader in the population of a resident species. Godoy \& Levine[@Godoy-2014] used this method on an annual plant population model. This approach can be explained using teh Lotka-Volterra model, defined as:
\begin{equation}
\frac{dN_i}{dt} = N_i \times r_i \times (1 - \alpha'_{ii} N_i -
\alpha'_{ij} N_j)
\end{equation}
The criteria for invasion of species $i$ in resident community of species $j$ is (at equilibrium population of single resident $j$ is $\overline{N_j} = \frac{1}{\alpha'_{jj}}$):
\begin{equation}
(1 - \frac{\alpha_{ij}}{\alpha'_{jj}})
\end{equation}
Thus if $\frac{\alpha_{ij}}{\alpha'_{jj}} <1$ invasion $i$ in $j$ is possible (same approach for $j$ in $i$).
Stable coexistence for species $i$ in regard to speciesand $j$ thus requires
$\frac{\alpha'_{ij}}{\alpha'_{jj}}$ and $\frac{\alpha'_{ji}}{\alpha'_{i}}$ to be smaller than 1. Chesson[@Chesson-2012] then defioned the average
stabilising niche
overlap between species $i$ and $j$ as:
\begin{equation}
\rho = \sqrt{\frac{\alpha'_{ij} \alpha'_{ji}}{\alpha'_{jj} \alpha'_{ii}}}
\end{equation}
and $N_i$ is the number of species in the local neighbourhood of the tree $i$.
## Details on data sets used
......@@ -53,6 +80,8 @@ list.t <- dlply(dat, 1, paste_name_data)
writeLines(unlist(list.t[dat[["Country"]]]))
```
\newpage
## Age distribution for Europe and North America.
......@@ -60,26 +89,29 @@ writeLines(unlist(list.t[dat[["Country"]]]))
![Age distribution of forest area in 20-year age class for North America (USA and Canada), estimated by Pan et al.[@Pan-2011]. The last class plotted at 150 years is for age > 140 years.](../../figs/age_na.pdf)
\newpage
# Supplementary results
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with random structure in the parameter including the data set and the Koppen-Geiger ecoregion.** 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 ($m_1$), the competitive effect independent of traits ($\\alpha_{0}$), the effect of competitor trait values on their competitive 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 in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\\alpha_s$) (negative values indicate that higher trait similarity leads to a 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 plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_ecocode_TP.pdf)
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with random structure in the parameter including the data set and the Koppen-Geiger ecoregion.** 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 ($m_1$), 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 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 in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a 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 plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_ecocode_TP.pdf)
THIS NEED TO BE UPDATED WITH INTRA INTER
\newpage
# Supplementary discussion
## Trait effects and potential mechanisms
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key driver of tree growth[@Stephenson-2014; @Enquist-1999]. Then there was a consistent negative effect of the total basal area of neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height >= 10cm diameter breast height) agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than to the adult stage [@Callaway-1997]. The variation of $\alpha_0$ between biomes is limited with large overlap of their confidences intervals.
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key driver of tree growth[@Stephenson-2014; @Enquist-1999]. Then there was a consistent negative effect of the total basal area of conspecific and heterospecific neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height >= 10cm diameter breast height) agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than to the adult stage [@Callaway-1997]. The variation of $\alpha_{0 \, intra} \, \& \, \alpha_{0 \, inter}$ between biomes is limited with large overlap of their confidences intervals.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
SLA was positively correlated with maximum basal area growth (growth without competition). This agrees well with previous studies that reported a positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]). As in previous studies[@Poorter-2008; @Wright-2010], this direct effect of SLA was smaller than the effect size of wood density and had wider confidence intervals. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@Niinemets-2010].
SLA was positively correlated with maximum basal area growth only in three biomes and with a weak effect. Previous studies have reported a strong positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]) and young seedling relative growth rate (REF TO ADD). Studies[@Poorter-2008; @Wright-2010] on adult tree have however generally reported weak and marginal correlation between SLA and maximum growth. Our results support this pattern. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@Niinemets-2010].
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@Poorter-2006a; @Poorter-2008; @Wright-2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might select for slower growth in long-lived plants[@Poorter-2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@Poorter-2006a]. There was however a tendency for species with tall maximum height to have stronger competitive effects (though with wider confidence intervals intercepting zero). This might be explained by greater light interception from taller trees.
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@Poorter-2006a; @Poorter-2008; @Wright-2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might select for slower growth in long-lived plants[@Poorter-2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@Poorter-2006a]. There was however a tendency for species with tall maximum height to have stronger competitive effects (though with wider confidence intervals intercepting zero). This might be explained by greater light interception from taller trees. These small effect of maximum height are probably explained by the fact that our analysis focus on short-term effect on tree growth. Size-structure population models[@Adams-2007] have in contrast shown that maximum height is a key drivers of the long-term competitive success in term of population growth rate.
Our results raise the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and tolerance are the two central elements of a species' competitive ability[@Goldberg-1991]. One may expect that because of intra-specific competition, species with strong competitive effects should have evolved a high tolerance to competition. However, in agreement with previous studies[@Goldberg-1990; @Goldberg-1991; @Wang-2010], we found little evidence for such coordination. There was only such a tendency for wood density and SLA. High wood density conferred better competitive tolerance and also stronger competitive effects, but with wide confidence intervals intercepting zero for the latter. High SLA conferred stronger competitive effects and higher tolerance of competition, but with wide confidence intervals intercepting zero for the latter. For maximum height, as explained above, there was a tendency for short maximum height to lead to high tolerance of competition but to low competitive effects. This is interesting because a trade-off between competitive tolerance and maximum height has been proposed as a fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@Kohyama-2009]. Finally the lack of support for coordination between tolerance and effects is important because it means that competitive interactions are not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_t$ as in @Kunstler-2012; @Kraft-2014; @Lasky-2014). Traits of competitors alone or of focal plants alone may convey more information than the trait hierarchy. These processes of traits linked to either competitive effects or competitive tolerance, nevertheless, still lead to some trait values having an advantage in competitive interactions.
Our results raise the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and tolerance are the two central elements of a species' competitive ability[@Goldberg-1991]. One may expect that because of intra-specific competition, species with strong competitive effects should have evolved a high tolerance to competition. We found clear evidence for such coordination for wood density, and only weak evidence for SLA. High wood density conferred better competitive tolerance and also stronger competitive effects. High SLA conferred stronger competitive effects and higher tolerance of competition, but with wide confidence intervals intercepting zero for the latter. For maximum height, as explained above, there was a tendency for short maximum height to lead to high tolerance of competition but to low competitive effects. This is interesting because a trade-off between competitive tolerance and maximum height has been proposed as a fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@Kohyama-2009]. The mixed results on the coordination between tolerance and effects are important because they mean that competitive interactions are not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_t$ as in @Kunstler-2012; @Kraft-2014; @Lasky-2014). Traits of competitors alone or of focal plants alone may convey more information than the trait hierarchy. These processes of traits linked to either competitive effects or competitive tolerance, nevertheless, still lead to some trait values having an advantage in competitive interactions.
Given that the effect sizes we report for effects of traits on competitive interactions are modest, the question arises whether the three traits available to us (wood density, SLA, and maximum height) were the best candidates. It is possible that traits more directly related to mechanisms of competition -- for instance for competition for light, the leaf area index of the competitors or the light compensation point at leaf or whole-plant level -- may be more powerful. It is also possible that traits measured at the individual level rather than as species averages might strengthen the predictive power of our analysis[@Kraft-2014].
......@@ -87,7 +119,7 @@ Given that the effect sizes we report for effects of traits on competitive inter
## Variations between biomes
Overall, most results were rather consistent across biomes (Fig 2 main text), but some exceptions deserve comments.
For SLA, the sign of the tolerance of competition parameters were changing a lot among biomes (Fig. 2 main text). High SLA species tended to be more competition-tolerant (tolerance to competition parameter $\alpha_t$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical forests. These different outcomes may trace to the prevalence of deciduous species in temperate forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@Lusk-2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive impact was positive, versus negative in the other biomes (Fig 2 main text). We do not have a mechanistic explanation for this discrepancy, but observe that taiga has relatively few species, many of which are conifers where the range of wood density is narrower than in angiosperms (see Extended data Table 1).
For SLA, the sign of the tolerance of competition parameters were changing a lot among biomes (Fig. 2 main text). High SLA species tended to be more competition-tolerant (tolerance to competition parameter $\alpha_t$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical and temperate rain forests. These different outcomes may trace to the lack of deciduous species in tropical and temperate rain forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@Lusk-2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive effect was negative, versus positive in the other biomes (Fig 2 main text). We do not have a mechanistic explanation for this discrepancy, but observe that taiga has relatively few species, many of which are conifers where the range of wood density is narrower than in angiosperms (see Extended data Table 1).
# References
......
......@@ -97,42 +97,60 @@ pandoc.table(cor.mat,
## # Model results
## ![Variation of the four parameters linking the three studied traits with maximum growth and competition - maximum growth ($t_f \, m_1$), tolerance to competition ($t_f \, \alpha_t$), competitive effect ($t_c \, \alpha_e$) and limiting similarity ($|t_f - t_c| \, \alpha_l$ ($t_c$ was fixed at the lowest value and $t_f$ varying from quantile 5 to 95\%). The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with $\alpha_0$ or $m_0$).](../../figs/figres4b.pdf)
## ![Variation of the four parameters linking wood density, specific leaf area and maximum height with maximum growth and competition - maximum growth ($t_f \, m_1$), tolerance to competition ($t_f \, \alpha_t$), competitive effect ($t_c \, \alpha_e$) and limiting similarity ($|t_f - t_c| \, \alpha_l$ ($t_c$ was fixed at the lowest value and $t_f$ varying from quantile 5 to 95\%). The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with $\alpha_0$ or $m_0$).](../../figs/figres4b.pdf)
## ![**TODO**. BLABLA.](../../figs/rho_set_TP_intra.pdf)
## ![**Stabilising effect of competition between pairs of species in function of their traits distance, predicted according to the basal area growth models fitted for wood density, specific leaf area and maximum height.** $1 -\rho$ measure the relative strengh of intra-specific competition compared to inter-specific competition (where $\rho$ is defined as $\sqrt{\frac{\alpha{ij} \alpha{ji}}{\alpha{jj} \alpha{ii}}}$), where $\rho$ is a measure of niche overlap between a pair of species. If inter-specific competition is equal or greater than inter-specific competition $1- \rho \leqslant 0$, and there is no stabilising processes. If inter-specific competition is smaller than inter-specific competition $1- \rho > 0$, and this indicates the occurence of stabilising processes resulting in stronger intra- than inter-specific competition. As the niche overlap $\rho$ is estimated only with competition effect on individual tree basal area growth, this can not be taken as a direct indication of coexistence as with population growth model.](../../figs/rho_set_TP_intra.pdf)
##+ ComputeTable_Effectsize, echo = FALSE, results = 'hide', message=FALSE
list.all.results <-
list.all.results.0 <-
readRDS.root('output/list.lmer.out.all.NA.simple.set.rds')
list.all.results.intra <-
readRDS('output/list.lmer.out.all.NA.intra.set.rds')
#TODO UPDATE FOR INTRA
mat.param <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.param, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
param.vec = c("(Intercept)", "logD", "Tf","sumBn",
"sumTnBn","sumTfBn", "sumTnTfBn.abs")))
mat.param.sd <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.param.sd, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAP.MAT.r.set.species',
param.vec = c("(Intercept)", "logD", "Tf","sumBn",
"sumTnBn","sumTfBn", "sumTnTfBn.abs")))
mat.R2c <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.R2c, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.species'))
mat.R2m <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.R2m, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species'))
mat.AIC <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.AIC, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species'))
mat.AIC.0 <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.AIC, list.res = list.all.results,
model = 'lmer.LOGLIN.r.set.species'))
list.all.results <-
readRDS.root('output/list.lmer.out.all.NA.intra.set.rds')
#TODO UPDATE NULL MODEL FOR INTRA
mat.param <- do.call('cbind',
lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.param, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
param.vec = c("(Intercept)", "logD", "Tf",
"sumBn.intra","sumBn.inter",
"sumTnBn","sumTfBn", "sumTnTfBn.abs"),
data.type = 'intra'))
mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD", "Tf", "sumTfBn"),] <-
-mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD", "Tf", "sumTfBn"),]
mat.param.sd <- do.call('cbind',
lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.param.sd, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
param.vec = c("(Intercept)", "logD", "Tf",
"sumBn.intra","sumBn.inter",
"sumTnBn","sumTfBn", "sumTnTfBn.abs"),
data.type = 'intra'))
mat.R2c <- do.call('cbind',
lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.R2c, list.res = list.all.results,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',