Commit fee1f548 authored by Kunstler Georges's avatar Kunstler Georges
Browse files

progress on revision two

parent 9797c8f3
......@@ -305,6 +305,8 @@ fun.generate.pred.param.kikj.dat <- function(list.sd, Tf.low,
mean.sumBn <- 1#max(seq.sumBn)
print(mean.sumBn)
seq.Tf <- seq(from = Tf.low, to = Tf.high, length.out = N.pred)
if(!intra.TF){
df <- data.frame('logG' = rep(0 , N.pred),
'logD' = rep(D.mean, N.pred),
......@@ -341,6 +343,7 @@ fun.generate.pred.param.rho.dat <- function(list.sd, Tf.low,
intra.TF = FALSE){
Tf.mean <- 0
D.mean <- 0
print(list.sd)
sd_sumBn.intra <- list.sd$sd.sumBn.intra
sd_sumBn.inter <- list.sd$sd.sumBn.inter
sd_sumBn <- list.sd$sd.sumBn
......@@ -354,22 +357,22 @@ fun.generate.pred.param.rho.dat <- function(list.sd, Tf.low,
df <- data.frame('logG' = rep(0 , N.pred),
'logD' = rep(D.mean, N.pred),
'Tf' =seq.Tf,
'sumBn' = rep(mean.sumBn, N.pred)/sd_sumBn,
'sumTfBn' = seq.Tf*mean.sumBn/sd_sumTfBn,
'sumTnBn' = seq.Tf*mean.sumBn/sd_sumTnBn,
'sumBn' = rep(mean.sumBn, N.pred),
'sumTfBn' = (seq.Tf-Tf.low)*mean.sumBn,
'sumTnBn' = rep(Tf.low, N.pred)*mean.sumBn,
'sumTnTfBn.abs' = abs(seq.Tf-Tf.low)*
mean.sumBn/sd_sumTnTfBn.abs)
mean.sumBn)
}
if(intra.TF){
df <- data.frame('logG' = rep(0 , N.pred),
'logD' = rep(D.mean, N.pred),
'Tf' =seq.Tf,
'sumBn.intra' = -rep(mean.sumBn, N.pred)/sd_sumBn.intra,
'sumBn.inter' = rep(mean.sumBn, N.pred)/sd_sumBn.inter,
'sumTfBn' = seq.Tf*mean.sumBn/sd_sumTfBn,
'sumTnBn' = seq.Tf*mean.sumBn/sd_sumTnBn,
'sumBn.intra' = -rep(mean.sumBn, N.pred),
'sumBn.inter' = rep(mean.sumBn, N.pred),
'sumTfBn' = (seq.Tf-Tf.low)*mean.sumBn,
'sumTnBn' = rep(Tf.low, N.pred)*mean.sumBn,
'sumTnTfBn.abs' = abs(seq.Tf-Tf.low)*
mean.sumBn/sd_sumTnTfBn.abs)
mean.sumBn)
}
if(MAT.MAP.TF){
df$MAT <- rep(1,nrow(df))
......@@ -525,6 +528,9 @@ easyPredCI.param <- function(list.res, type, newdata, alpha=0.05,alpha_0 = 'sumB
alphal = c(alpha_0, "sumTnTfBn.abs"),
alpha0 = alpha_0)
X[, !colnames(X) %in% sel.keep] <- 0
if (type == 'alpha0'){
X[, colnames(X) == alpha_0] <- 1
}
pred <- X %*% beta
pred.se <- sqrt(diag(X %*% V %*% t(X))) ## std errors of predictions
## inverse-link (logistic) function: could also use plogis()
......@@ -563,10 +569,12 @@ easyPredCI.stabl <- function(list.res, type, newdata, alpha=0.05) {
newdata)
sel.keep <- switch(type ,
kikj = c('Tf', 'sumTfBn'),
GiGj.intra = c('Tf', 'sumTfBn', 'sumBn.intra','sumBn.inter','sumTnTfBn.abs'),
GiGj.intra = c('Tf', 'sumTfBn', 'sumBn.intra',
'sumBn.inter','sumTnTfBn.abs'),
GiGj = c('Tf', 'sumTfBn', 'sumTnTfBn.abs'),
rho = c('sumBn','sumTnTfBn.abs'),
rho.intra = c('sumBn.intra','sumBn.inter','sumTnTfBn.abs'))
rho.intra = c('sumBn.intra','sumBn.inter',
'sumTnTfBn.abs'))
X[, !colnames(X) %in% sel.keep] <- 0
pred <- X %*% beta
pred.se <- sqrt(diag(X %*% V %*% t(X))) ## std errors of predictions
......@@ -694,9 +702,9 @@ 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$pred <- (-pred.res.rho$pred)
pred.res.rho$lwr <- (-pred.res.rho$lwr)
pred.res.rho$upr <- (-pred.res.rho$upr)
new.data.GiGj <- fun.generate.pred.param.kikj.dat( list.sd = list.res$list.sd,
Tf.low = list.var[[1]][['ql']],
......@@ -710,9 +718,9 @@ pred.res.GiGj$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
length.out = 100) - list.var[[1]][['ql.o']]
pred.res.GiGj$param.type <- 'GiGj'
pred.res.GiGj$pred <- exp(pred.res.GiGj$pred)
pred.res.GiGj$lwr <- exp(pred.res.GiGj$lwr)
pred.res.GiGj$upr <- exp(pred.res.GiGj$upr)
pred.res.GiGj$pred <- (pred.res.GiGj$pred)
pred.res.GiGj$lwr <- (pred.res.GiGj$lwr)
pred.res.GiGj$upr <- (pred.res.GiGj$upr)
}
......@@ -728,9 +736,9 @@ pred.res.rho$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 <- (-pred.res.rho$pred)
pred.res.rho$lwr <- (-pred.res.rho$lwr)
pred.res.rho$upr <- (-pred.res.rho$upr)
new.data.GiGj <- fun.generate.pred.param.kikj.dat( list.sd = list.res$list.sd,
......@@ -744,9 +752,9 @@ pred.res.GiGj$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
length.out = 100) - list.var[[1]][['ql.o']]
pred.res.GiGj$param.type <- 'GiGj'
pred.res.GiGj$pred <- exp(pred.res.GiGj$pred)
pred.res.GiGj$lwr <- exp(pred.res.GiGj$lwr)
pred.res.GiGj$upr <- exp(pred.res.GiGj$upr)
pred.res.GiGj$pred <- (pred.res.GiGj$pred)
pred.res.GiGj$lwr <- (pred.res.GiGj$lwr)
pred.res.GiGj$upr <- (pred.res.GiGj$upr)
}
new.data.kikj <- fun.generate.pred.param.kikj.dat( list.sd = list.res$list.sd,
......@@ -761,9 +769,9 @@ pred.res.kikj$Tf <- seq(from = list.var[[1]][['ql.o']],
to = list.var[[1]][['qh.o']],
length.out = 100) - list.var[[1]][['ql.o']]
pred.res.kikj$param.type <- 'kikj'
pred.res.kikj$pred <- exp(pred.res.kikj$pred)
pred.res.kikj$lwr <- exp(pred.res.kikj$lwr)
pred.res.kikj$upr <- exp(pred.res.kikj$upr)
pred.res.kikj$pred <- (pred.res.kikj$pred)
pred.res.kikj$lwr <- (pred.res.kikj$lwr)
pred.res.kikj$upr <- (pred.res.kikj$upr)
return(rbind(pred.res.rho, pred.res.kikj, pred.res.GiGj))
}
......@@ -1288,8 +1296,8 @@ fun.param.descrip <- function(seq.jitter, n.param, x.line = -0.73, intra.TF =
y.at.2 <- 3
y.at.2.la <- 2
y.at.2.lb <- 4
}
}
mtext("Trait independent", side=2,
at = y.at.1,
cex =1.6,
......@@ -1371,6 +1379,8 @@ Var <- "Trait indep"
intra <- "intra"
fun.layout()
b <- border.size()
traits_letters <- c('a', 'b', 'c')
names(traits_letters) <- c('Wood.density', 'SLA', 'Max.height')
##################################
## model fixed biomes
for (i in traits){
......@@ -1533,8 +1543,8 @@ fun.plot.all.param <- function(list.res,
intra.TF = FALSE,
ylim.list = list(maxG = c(-0.75, 0.75), alphae = c(-0.02, 0.009),
alphar = c(-0.013, 0.013), alphal = c(-0.017, 0.007),
alpha0 = c(0.003, 0.016), alpha0.intra = c(0.003, 0.028),
alpha0.inter = c(0.003, 0.028))
alpha0 = c(0.003, 0.016), alpha0.intra = c(0.025, 0.32),
alpha0.inter = c(0.025, 0.32))
){
traits <- c('Wood.density', 'SLA', 'Max.height')
......@@ -1587,6 +1597,13 @@ names.param <- c("Tf","sumTnBn",
names(names.param) <- c('maxG', 'alphae', 'alphar', 'alphal', 'alpha0.inter', 'alpha0.intra')
first.p <- 'alpha0.intra'
}
traits_letters <- matrix(letters[1:(3*(length(names(expr.p.vec))-1))],
nrow = length(names(expr.p.vec)) - 1, ncol = 3)
traits_letters <- rbind(traits_letters[1,], traits_letters)
colnames(traits_letters) <- c('Wood density', 'Specific leaf area', 'Maximum height')
rownames(traits_letters) <- names(expr.p.vec)
for (t in c('Wood density', 'Specific leaf area', 'Maximum height')){
for (p in names(expr.p.vec)){
df.t <- data.param[data.param$traits == t, ]
......@@ -1595,7 +1612,8 @@ par(mai = fun.mai.plot.param(t, p, big.m, small.m, first.p = first.p, last.p = '
if(t == 'Wood density'){
if(p == 'maxG'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = traits.exp[[t]],
labels.x = TRUE, labels.y = TRUE,
col.vec = col.vec,
......@@ -1604,14 +1622,16 @@ if(p == 'maxG'){
}else{
if(p == 'alpha0.inter'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = NA,
labels.x = FALSE, labels.y = FALSE,
col.vec = col.vec,
expr.param = NA, cex.lab = 1.1, cex.axis =0.85, cex = 1, add = TRUE, add.ylab.TF = FALSE)
}else{
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = NA,
labels.x = FALSE, labels.y = TRUE,
col.vec = col.vec,
......@@ -1624,7 +1644,8 @@ if(p == 'maxG'){
if(t %in% c('Specific leaf area', 'Maximum height')){
if(p == 'maxG'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = traits.exp[[t]],
labels.x = TRUE, labels.y = FALSE,
col.vec = col.vec,
......@@ -1634,7 +1655,8 @@ if(p == 'maxG'){
}else{
if(p == 'alpha0.inter'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = NA,
labels.x = FALSE, labels.y = FALSE,
col.vec = col.vec,
......@@ -1642,7 +1664,8 @@ if(p == 'maxG'){
cex = 1, add =TRUE)
}else{
fun.plot.param.tf(df = df.t,
p = p,names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = NA,
labels.x = FALSE, labels.y = FALSE,
col.vec = col.vec,
......@@ -1725,6 +1748,10 @@ names.param <- c("Tf","sumTnBn",
names(names.param) <- c('maxG', 'alphae', 'alphar', 'alphal', 'alpha0.inter', 'alpha0.intra')
first.p <- 'maxG'
}
traits_letters <- matrix(letters[1:6], nrow = 3, ncol = 2)
colnames(traits_letters) <- c('Wood density', 'Specific leaf area')
rownames(traits_letters) <- c('maxG', 'alphar', 'alphae')
for (t in c('Wood density', 'Specific leaf area')){
for (p in c( 'maxG', 'alphar', 'alphae')){
df.t <- data.param[data.param$traits == t, ]
......@@ -1733,7 +1760,8 @@ par(mai = fun.mai.plot.param(t, p, big.m, small.m, first.p = first.p, last.p = '
if(t == 'Wood density'){
if(p == 'alphae'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = traits.exp[[t]],
labels.x = TRUE, labels.y = TRUE,
col.vec = col.vec,
......@@ -1741,7 +1769,8 @@ if(p == 'alphae'){
cex.axis =0.85, cex = 1, ylim = ylim.list[[p]])
}else{
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = NA,
labels.x = FALSE, labels.y = TRUE,
col.vec = col.vec,
......@@ -1750,10 +1779,11 @@ if(p == 'alphae'){
}
}
if(t %in% c('Specific leaf area', 'Maximum height')){
if(t %in% c('Specific leaf area')){
if(p == 'alphae'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = traits.exp[[t]],
labels.x = TRUE, labels.y = FALSE,
col.vec = col.vec,
......@@ -1762,7 +1792,8 @@ if(p == 'alphae'){
ylim = ylim.list[[p]])
}else{
fun.plot.param.tf(df = df.t,
p = p,names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
xlab = NA,
labels.x = FALSE, labels.y = FALSE,
col.vec = col.vec,
......@@ -1789,8 +1820,8 @@ add.alpha <- function(col, alpha=0.5){
rgb(x[1], x[2], x[3], alpha=alpha))
}
fun.plot.param.tf <- function(df, p, xlab, labels.x, labels.y,
col.vec, expr.param,names.param,
fun.plot.param.tf <- function(df, p, t, xlab, labels.x, labels.y,
col.vec, expr.param,names.param, traits_letters,
add.ylab.TF = TRUE,
cex.lab = 1.3, cex.axis = 1,
cex = 1.3, add = FALSE, ...){
......@@ -1802,6 +1833,9 @@ if(!add){
xlab = xlab, ylab = NA,
lwd = 3, cex.lab = cex.lab, cex.axis = cex.axis,
col = col.vec[names.param[p]], type = 'l',...)
y.max <- par("usr")[4] - (par("usr")[4] - par("usr")[3])*0.05
x.min <- par("usr")[1] + (par("usr")[2] - par("usr")[1])*0.05
text(x.min, y.max , traits_letters[p, t], cex = 1.5, font = 2)
axis(1, labels = labels.x)
axis(2, labels = labels.y)
polygon(c(df.t[, 'Tf'],
......@@ -2106,7 +2140,7 @@ require(dplyr)
layout(m, heights=hei, widths= wid )
expr.p.vec <- c(expression(1-rho),
expr.p.vec <- c(expression(inter~italic(vs.)~intra),
expression(kappa[i]/kappa[j]),
expression(G[i]/G[j]))
names(expr.p.vec) <- c('rho', 'kikj', 'GiGj')
......@@ -2114,6 +2148,13 @@ names.param <- c("rho","kikj", 'GiGj')
names(names.param) <- c('rho', 'kikj', 'GiGj')
col.vec <- c('#018571', '#a6611a', '#dfc27d')
names(col.vec) <- c("rho","kikj", 'GiGj')
traits_letters <- matrix(letters[1:3],
nrow = 1, ncol = 3)
colnames(traits_letters) <- c('Wood density', 'Specific leaf area', 'Maximum height')
rownames(traits_letters) <- 'rho'
for (t in c('Wood density', 'Specific leaf area', 'Maximum height')){
for (p in c('rho')){
df.t <- data.param[data.param$traits == t, ]
......@@ -2135,7 +2176,8 @@ if(t == 'Maximum height'){
if(t == 'Wood density'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
ylim = range(filter(df.t,
param.type == p)%>% select(upr,lwr)),
labels.x = TRUE, labels.y = TRUE,
......@@ -2146,7 +2188,8 @@ if(t == 'Wood density'){
if(t == 'Specific leaf area'){
fun.plot.param.tf(df = df.t,
p= p, names.param = names.param,
p= p, t = t, names.param = names.param,
traits_letters = traits_letters,
ylim = range(filter(df.t,
param.type == p)%>% select(upr,lwr)),
xlab = expression(paste(Delta, ' Specific leaf area (m', m^2, ' m', g^-1, ')')),
......@@ -2157,7 +2200,8 @@ if(t == 'Specific leaf area'){
if(t == 'Maximum height'){
fun.plot.param.tf(df = df.t,
p = p, names.param = names.param,
p = p, t = t, names.param = names.param,
traits_letters = traits_letters,
ylim = range(filter(df.t,
param.type == p)%>% select(upr,lwr)),
labels.x = TRUE, labels.y = TRUE,
......
......@@ -19,15 +19,16 @@ world.map.all.sites <- function(data,add.legend=FALSE,
cols = fun.col.pch.set()$col.vec){
sets <- unique(data$set)
type.d <- c(rep('nfi', 8), rep('lpp', 6))
names(type.d) <- sets
# new map
world.map(NA, NA)
for(set in sets){
i <- data$set ==set
cex = 0.4
if(sum(i) <100)
cex=1.5
cex = 0.3
if(type.d[set] == 'lpp')
cex=1.1
world.map(data[i, "Lon"], data[i, "Lat"], col = cols[[set]],
add=TRUE, cex=cex)
}
......
Data set name,Country,Data type,Plot size,Diameter at breast height threshold,Number of plots,Traits,Source trait data,Evidence of disturbances and succession dynamics,References,Contact of person in charge of data formatting,Comments Panama,Panama,LPP,1 to 50 ha,1 cm,42,"Wood density, SLA, and Maximum height",local,Gap disturbances are common in the large 50ha BCI plot [see @Young-1991; @Hubbell-1999; @Lobo-2014]. Hubbell et al.[@Hubbell-1999] estimated that less than 30% of the plot experienced no disturbance over a 13-year period.,"3,4,25","Plot data: R. Condit (conditr@gmail.com), Trait data: J. Wright (wrightj@si.edu)",The data used include both the 50 ha plot of BCI and the network of 1 ha plots from Condit et al. (2013). The two first censuses of BCI plot were excluded. Japan,Japan,LPP,0.35 to 1.05 ha,2.39 cm,16,"Wood density, SLA, and Maximum height",local,"The network of plot comprise 50% of old growth forest, 17% of old secondary forest and 33% of young secondary forest.",5,"Plot data: M. I. Ishihara (moni1000f_networkcenter@fsc.hokudai.ac.jp), Trait data: Y Onoda (yusuke.onoda@gmail.com)", Luquillo,Puerto Rico,LPP,16 ha,1 cm,1,"Wood density, SLA, and Maximum height",local,"The plot has been struck by hurricanes in 1989 and in 1998[@Uriarte-2009]. In addition, two-third of the plot is a secondary forest on land previously used for agriculture and logging[@Uriarte-2009].","6, 23","Plot data: J. Thompson (jiom@ceh.ac.uk) and J. Zimmerman (esskz@ites.upr.edu), Trait data: N. Swenson (swensonn@msu.edu )", M'Baiki,Central African Republic,LPP,4 ha,10 cm,10,Wood density and SLA,local,The plot network was established with three levels of harvesting and unharvested control [@Gourlet-Fleury-2013].,"7,8",G. Vieilledent (ghislain.vieilledent@cirad.fr), Fushan,Taiwan,LPP,25 ha,1 cm,1,Wood density and SLA,local,"Fushan experienced several Typhoon disturbances in 1994 with tree fall events, the main effect was trees defoliation[@Lin-2011].",9,I-F. Sun (ifsun@mail.ndhu.edu.tw), Paracou,French Guiana,LPP,6.25 ha,10 cm,15,Wood density and SLA,local,The plot network was established with three levels of harvesting and unharvested control (Herault et al. 2010).,"10,11,24","Plot data: B. Herault (bruno.herault@cirad.fr), Trait data: C. Baraloto (Chris.Baraloto@ecofog.gf)", France,France,NFI,0.017 to 0.07 ha,7.5 cm,41503,"Wood density, SLA, and Maximum height",TRY,"French forests monitored by the French National Forest Inventory experience several types of natural disturbances[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).","12,13",G. Kunstler (georges.kunstler@gmail.com),"The French NFI is based on temporary plots, but 5 years tree radial growth is estimated with a 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 1x1-km grid" Spain,Spain,NFI,0.0078 to 0.19 ha,7.5 cm,49855,"Wood density, SLA, and Maximum height",TRY,"Spanish forests monitored by the Spanish National Forest Inventory experience several types of natural disturbances[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. No data are available on the age structure of the plots.","14,15,16",M. Zavala (madezavala@gmail.com),"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." Swiss,Switzerland,NFI,0.02 to 0.05 ha,12 cm,2665,"Wood density, SLA, and Maximum height",TRY,"Swiss forests monitored by the Swiss National Forest Inventory experience several types of natural disturbances (such as wind, forest fire, fungi and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).","17,26",M. Hanewinkel & N. E. Zimmermann (niklaus.zimmermann@wsl.ch),"All trees with dbh > 12 cm and > 36 cm were measured within a radius of 7.98 m and 12.62 m, respectively." Sweden,Sweden,NFI,0.0019 to 0.0314 ha,5 cm,22904,"Wood density, SLA, and Maximum height",TRY,"Swedish forests monitored by the Swedish National Forest Inventory experience several types of natural disturbances[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).",18,G. Stahl (Goran.Stahl@slu.se),All trees with dbh > 10 cm were measured on circular plots of 10 m radius. US,USA,NFI,0.0014 to 0.017 ha,2.54 cm,97434,"Wood density, SLA, and Maximum height",TRY,"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).",19,M. Vanderwel (Mark.Vanderwel@uregina.ca),FIA data are made up of clusters of 4 subplots of size 0.017 ha for tree dbh > 1.72 cm and nested within each subplot sampling plots of 0.0014 ha for trees dbh > 2.54 cm. The data for the four subplots were pooled Canada,Canada,NFI,0.02 to 0.18 ha,2 cm,15019,"Wood density, SLA, and Maximum height",TRY,"Canadian forests monitored by the regional forest monitoring programs experience several types of natural disturbances (such as wind, forest fire, fungi and insect attacks) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represent a significant percentage of the forested area (see age distribution below).",,J. Caspersen (john.caspersen@utoronto.ca),The protocol is variable between Provinces. A large proportion of data is from the Quebec province and the plots are 10 m in radius in this Province. NZ,New Zealand,NFI,0.04 ha,3 cm,1415,"Wood density, SLA, and Maximum height",local,"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.","20,21",D. Laughlin (d.laughlin@waikato.ac.nz),Plots are 20 x 20 m. NSW,Australia,NFI,0.075 to 0.36 ha,10 cm,30,"Wood density, and Maximum height",local,The plot network was initially established in the 60s with different levels of selection harvesting[@Kariuki-2006].,"1,2",R. M. Kooyman (robert@ecodingo.com.au) for plot and trait data,Permanents plots established by the NSW Department of State Forests or by RMK
\ No newline at end of file
Data set name,Country,Data type,Plot size,Diameter at breast height threshold,Number of plots,Traits,Source trait data,Evidence of disturbances and succession dynamics,References,Contact of person in charge of data formatting,Comments Panama,Panama,LPP,1 to 50 ha,1 cm,42,"Wood density, SLA, and Maximum height",local,Gap disturbances are common in the large 50ha BCI plot [see @Young-1991; @Hubbell-1999; @Lobo-2014]. Hubbell et al.[@Hubbell-1999] estimated that less than 30% of the plot experienced no disturbance over a 13-year period.,"3,4,25","Plot data: R. Condit (conditr@gmail.com), Trait data: J. Wright (wrightj@si.edu)",The data used include both the 50 ha plot of BCI and the network of 1 ha plots from Condit et al. (2013). The two first censuses of BCI plot were excluded. Japan,Japan,LPP,0.35 to 1.05 ha,2.39 cm,16,"Wood density, SLA, and Maximum height",local,"The network of plot comprise 50% of old growth forests, 17% of old secondary forests and 33% of young secondary forests.",5,"Plot data: M. I. Ishihara (moni1000f_networkcenter@ fsc.hokudai.ac.jp), Trait data: Y Onoda (yusuke.onoda@gmail.com)", Luquillo,Puerto Rico,LPP,16 ha,1 cm,1,"Wood density, SLA, and Maximum height",local,"The plot has been struck by hurricanes in 1989 and in 1998[@Uriarte-2009]. In addition, two-third of the plot is a secondary forest on land previously used for agriculture and logging[@Uriarte-2009].","6, 23","Plot data: J. Thompson (jiom@ceh.ac.uk) and J. Zimmerman (esskz@ites.upr.edu), Trait data: N. Swenson (swensonn@msu.edu )", M'Baiki,Central African Republic,LPP,4 ha,10 cm,10,Wood density and SLA,local,The plot network was established with three levels of harvesting and an unharvested control [@Gourlet-Fleury-2013].,"7,8",G. Vieilledent (ghislain.vieilledent@cirad.fr), Fushan,Taiwan,LPP,25 ha,1 cm,1,Wood density and SLA,local,Fushan experienced several Typhoon disturbances in 1994 with tree fall events. The main disturbance effect was trees defoliation[@Lin-2011].,9,I-F. Sun (ifsun@mail.ndhu.edu.tw), Paracou,French Guiana,LPP,6.25 ha,10 cm,15,Wood density and SLA,local,The plot network was established with three levels of harvesting and unharvested control[@Herault-2011].,"10,11,24","Plot data: B. Herault (bruno.herault@cirad.fr), Trait data: C. Baraloto (Chris.Baraloto@ecofog.gf)", France,France,NFI,0.017 to 0.07 ha,7.5 cm,41503,"Wood density, SLA, and Maximum height",TRY,"French forests monitored by the French National Forest Inventory experienced several types of natural disturbance[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).","12,13",G. Kunstler (georges.kunstler@gmail.com),"The French NFI is based on temporary plots, but 5 years tree radial growth is estimated with a 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 1x1-km grid" Spain,Spain,NFI,0.0078 to 0.19 ha,7.5 cm,49855,"Wood density, SLA, and Maximum height",TRY,"Spanish forests monitored by the Spanish National Forest Inventory experienced several types of natural disturbance[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. No data are available on the age structure of the plots.","14,15,16",M. Zavala (madezavala@gmail.com),"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." Swiss,Switzerland,NFI,0.02 to 0.05 ha,12 cm,2665,"Wood density, SLA, and Maximum height",TRY,"Swiss forests monitored by the Swiss National Forest Inventory experienced several types of natural disturbance (such as wind, forest fire, fungi and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).","17,26",M. Hanewinkel & N. E. Zimmermann (niklaus.zimmermann@wsl.ch),"All trees with dbh > 12 cm and > 36 cm were measured within a radius of 7.98 m and 12.62 m, respectively." Sweden,Sweden,NFI,0.0019 to 0.0314 ha,5 cm,22904,"Wood density, SLA, and Maximum height",TRY,"Swedish forests monitored by the Swedish National Forest Inventory experienced several types of natural disturbance[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).",18,G. Stahl (Goran.Stahl@slu.se),All trees with dbh > 10 cm were measured on circular plots of 10 m radius. US,USA,NFI,0.0014 to 0.017 ha,2.54 cm,97434,"Wood density, SLA, and Maximum height",TRY,"US forests monitored by the FIA experienced several types of natural disturbance (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).",19,M. Vanderwel (Mark.Vanderwel@uregina.ca),FIA data are made up of clusters of 4 subplots of size 0.017 ha for tree dbh > 1.72 cm and nested within each subplot sampling plots of 0.0014 ha for trees dbh > 2.54 cm. The data for the four subplots were pooled Canada,Canada,NFI,0.02 to 0.18 ha,2 cm,15019,"Wood density, SLA, and Maximum height",TRY,"Canadian forests monitored by the regional forest monitoring programs experienced several types of natural disturbance (such as wind, forest fire, fungi and insect attacks) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represent a significant percentage of the forested area (see age distribution below).",,J. Caspersen (john.caspersen@utoronto.ca),"Provinces included are Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Quebec and Saskatchewan. The protocol is variable between Provinces. A large proportion of data is from the Quebec province and the plots are 10 m in radius in this Province." NZ,New Zealand,NFI,0.04 ha,3 cm,1415,"Wood density, SLA, and Maximum height",local,"New Zealand forests are experiencing disturbance by earthquake, landslide, storm, volcanic eruptions, and other types. According to Holdaway et al.[@Holdaway-2014] the disturbance return interval on the plots is 63 years.","20,21",D. Laughlin (d.laughlin@waikato.ac.nz),Plots are 20 x 20 m. NSW,Australia,NFI,0.075 to 0.36 ha,10 cm,30,"Wood density, and Maximum height",local,The plot network was initially established in the 1960s with different levels of selection harvesting[@Kariuki-2006].,"1,2",R. M. Kooyman (robert@ecodingo.com.au) for plot and trait data,Permanents plots established by the NSW Department of State Forests or by RMK
\ No newline at end of file
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......@@ -24,11 +24,21 @@ extended_method.docx: extended_method.tex include.tex references.bib
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......
% Supplementary Information
# Supplementary Methods
We developed the equation of $\alpha_{c,f} = \alpha_{0,f,intra} \, CON + \alpha_{0,f,inter} \ (1-CON) - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_d \, \vert t_c-t_f \vert$ along with the basal area of each competing species in the competition index to show the parameters are directly related to community weighted means of the different trait variables as:
\begin{equation} \label{alphaBA}
\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_d \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,het} = \sum_{c \neq f} {B_{i,c}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$,
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the indices $p$ and $s$ respectively for plot and data set are not shown here for 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 a rare invader into a population of a resident species. Godoy \& Levine[@Godoy-2014] used this method on an annual plant population model. This approach can be explained using the Lotka-Volterra model:
\begin{equation}
\frac{dN_i}{dt} = N_i \times r_i \times (1 - \alpha'_{ii} N_i -
\alpha'_{ij} N_j)
\end{equation}
The criterion for invasion of species $i$ into a resident community of species $j$ (at equilibrium population $\overline{N_j} = \frac{1}{\alpha'_{jj}}$) is:
\begin{equation}
(1 - \frac{\alpha_{ij}}{\alpha'_{jj}})
\end{equation}
Thus if $\frac{\alpha_{ij}}{\alpha'_{jj}} <1$ invasion of $i$ into $j$ is possible (and similar approach for $j$ into $i$).
Stable coexistence between species $i$ and species $j$ thus requires
$\frac{\alpha'_{ij}}{\alpha'_{jj}}$ and $\frac{\alpha'_{ji}}{\alpha'_{ii}}$ to be smaller than 1. Chesson[@Chesson-2012] then defined 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}
## Details on data sets used
......@@ -74,8 +32,8 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 16
- Traits: Wood density, SLA, and Maximum height
- Source trait data: local
- Evidence of disturbances and succession dynamics: The network of plot comprise 50% of old growth forest, 17% of old secondary forest and 33% of young secondary forest.
- Contact of person in charge of data formatting: Plot data: M. I. Ishihara (moni1000f_networkcenter@fsc.hokudai.ac.jp), Trait data: Y Onoda (yusuke.onoda@gmail.com)
- Evidence of disturbances and succession dynamics: The network of plot comprise 50% of old growth forests, 17% of old secondary forests and 33% of young secondary forests.
- Contact of person in charge of data formatting: Plot data: M. I. Ishihara (moni1000f_networkcenter@ fsc.hokudai.ac.jp), Trait data: Y Onoda (yusuke.onoda@gmail.com)
- Comments:
- References:
- Yakushima Forest Environment Conservation Center, Ishihara, M.I., Suzuki, S.N., Nakamura, M., Enoki, T., Fujiwara, A., Hiura, T., Homma, K., Hoshino, D., Hoshizaki, K., Ida, H., Ishida, K., Itoh, A., Kaneko, T., Kubota, K., Kuraji, K., Kuramoto, S., Makita, A., Masaki, T., Namikawa, K., Niiyama, K., Noguchi, M., Nomiya, H., Ohkubo, T., Saito, S., Sakai, T., Sakimoto, M., Sakio, H., Shibano, H., Sugita, H., Suzuki, M., Takashima, A., Tanaka, N., Tashiro, N., Tokuchi, N., Yoshida, T., Yoshida, Y., (2011). Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecological Research 26: 1007-1008.
......@@ -107,7 +65,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 10
- Traits: Wood density and SLA
- Source trait data: local
- Evidence of disturbances and succession dynamics: The plot network was established with three levels of harvesting and unharvested control [@Gourlet-Fleury-2013].
- Evidence of disturbances and succession dynamics: The plot network was established with three levels of harvesting and an unharvested control [@Gourlet-Fleury-2013].
- Contact of person in charge of data formatting: G. Vieilledent (ghislain.vieilledent@cirad.fr)
- Comments:
- References:
......@@ -124,7 +82,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 1
- Traits: Wood density and SLA
- Source trait data: local
- Evidence of disturbances and succession dynamics: Fushan experienced several Typhoon disturbances in 1994 with tree fall events, the main effect was trees defoliation[@Lin-2011].
- Evidence of disturbances and succession dynamics: Fushan experienced several Typhoon disturbances in 1994 with tree fall events. The main disturbance effect was trees defoliation[@Lin-2011].
- Contact of person in charge of data formatting: I-F. Sun (ifsun@mail.ndhu.edu.tw)
- Comments:
- References:
......@@ -140,7 +98,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 15
- Traits: Wood density and SLA
- Source trait data: local
- Evidence of disturbances and succession dynamics: The plot network was established with three levels of harvesting and unharvested control (Herault et al. 2010).
- Evidence of disturbances and succession dynamics: The plot network was established with three levels of harvesting and unharvested control[@Herault-2011].
- Contact of person in charge of data formatting: Plot data: B. Herault (bruno.herault@cirad.fr), Trait data: C. Baraloto (Chris.Baraloto@ecofog.gf)
- Comments:
- References:
......@@ -158,7 +116,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 41503
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidence 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 insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).
- Evidence of disturbances and succession dynamics: French forests monitored by the French National Forest Inventory experienced several types of natural disturbance[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent 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 plots, but 5 years tree radial growth is estimated with a 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 1x1-km grid
- References:
......@@ -175,7 +133,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 49855
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidence 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 insect attacks) and harvesting. No data are available on the age structure of the plots.
- Evidence of disturbances and succession dynamics: Spanish forests monitored by the Spanish National Forest Inventory experienced several types of natural disturbance[@Seidl-2014] (such as wind, forest fire, and insect 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:
......@@ -193,7 +151,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 2665
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidence 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 insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).
- Evidence of disturbances and succession dynamics: Swiss forests monitored by the Swiss National Forest Inventory experienced several types of natural disturbance (such as wind, forest fire, fungi and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent 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:
......@@ -210,7 +168,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 22904
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidence 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 insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent a significant percentage of the forested area (see age distribution below).
- Evidence of disturbances and succession dynamics: Swedish forests monitored by the Swedish National Forest Inventory experienced several types of natural disturbance[@Seidl-2014] (such as wind, forest fire, and insect attacks) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represent 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:
......@@ -226,7 +184,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 97434
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidence 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).
- Evidence of disturbances and succession dynamics: US forests monitored by the FIA experienced several types of natural disturbance (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 clusters of 4 subplots of size 0.017 ha for tree dbh > 1.72 cm and nested within each subplot sampling plots of 0.0014 ha for trees dbh > 2.54 cm. The data for the four subplots were pooled
- References:
......@@ -242,9 +200,9 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 15019
- Traits: Wood density, SLA, and Maximum height
- Source trait data: TRY
- Evidence 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 insect attacks) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represent a significant percentage of the forested area (see age distribution below).
- Evidence of disturbances and succession dynamics: Canadian forests monitored by the regional forest monitoring programs experienced several types of natural disturbance (such as wind, forest fire, fungi and insect attacks) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represent 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 plots are 10 m in radius in this Province.
- Comments: Provinces included are Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Quebec and Saskatchewan. The protocol is variable between Provinces. A large proportion of data is from the Quebec province and the plots are 10 m in radius in this Province.
- References:
......@@ -257,7 +215,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 1415
- Traits: Wood density, SLA, and Maximum height
- Source trait data: local
- Evidence 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.
- Evidence of disturbances and succession dynamics: New Zealand forests are experiencing disturbance by earthquake, landslide, storm, volcanic eruptions, and other types. According to Holdaway et al.[@Holdaway-2014] the disturbance return interval on the plots is 63 years.
- Contact of person in charge of data formatting: D. Laughlin (d.laughlin@waikato.ac.nz)
- Comments: Plots are 20 x 20 m.
- References:
......@@ -274,7 +232,7 @@ Two main data types were used: national forest inventories -- NFI, large permane
- Number of plots: 30
- Traits: Wood density, and Maximum height
- Source trait data: local
- Evidence of disturbances and succession dynamics: The plot network was initially established in the 60s with different levels of selection harvesting[@Kariuki-2006].
- Evidence of disturbances and succession dynamics: The plot network was initially established in the 1960s with different levels of selection harvesting[@Kariuki-2006].
- Contact of person in charge of data formatting: R. M. Kooyman (robert@ecodingo.com.au) for plot and trait data
- Comments: Permanents plots established by the NSW Department of State Forests or by RMK
- References:
......@@ -283,23 +241,20 @@ Two main data types were used: national forest inventories -- NFI, large permane
\newpage
## Age distribution for Europe and North America.
## Forests age distribution for Europe and North America.
![Age distribution of forest area in 20-year age class for France, Switzerland and Sweden, estimated by Vilen et al.[@Vilen-2012]. The last class plotted at 150 years is for age > 140 years (except for Sweden where the last class 110 is age > 100 years).](../../figs/age_europe.pdf)
![**Age distribution of forest area in 20-year age class for France, Switzerland and Sweden, estimated by Vilen et al.[@Vilen-2012].** The last class plotted at 150 years is for age > 140 years (except for Sweden where the last class 110 is age > 100 years).](../../figs/age_europe.pdf)
![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)
![**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