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Commit 5b8b2268 authored by Kunstler Georges's avatar Kunstler Georges
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fixe error in comparing null model and full model

parent 6b37eef3
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R/analysis/lmer.output-fun.R
View file @ 5b8b2268
......@@ -1261,19 +1261,51 @@ fun.layout <- function(b = border.size()){
layout(m2, widths=c(wid, b$legend.m), heights = 1)
}
fun.param.descrip <- function(seq.jitter, n.param, x.line = -0.63){
fun.param.descrip <- function(seq.jitter, n.param, x.line = -0.73, intra.TF = FALSE){
mtext("Max growth", side=2, at = n.param, cex =1.6,
line = 17.9, col = '#e41a1c')
line = 18.2, col = '#e41a1c')
lines(c(x.line, x.line),
c(n.param - abs(min(seq.jitter))-0.15, n.param + abs(min(seq.jitter))+0.15),
col = '#e41a1c',
lwd = 2.5)
mtext("Competition", side=2, at = (n.param - 1)/2 + max(seq.jitter)+abs(min(seq.jitter)), cex =1.6,
line = 17.9, col = '#377eb8')
mtext("Competition", side=2,
at = (n.param - 1)/2 + 0.5, cex =1.6,
line = 18.2, col = 'black')
lines(c(x.line, x.line),
c(1 - abs(min(seq.jitter)-0.15), n.param-1 + abs(min(seq.jitter))+0.15), col = '#377eb8',
c(1 - abs(min(seq.jitter)-0.15), n.param-1 + abs(min(seq.jitter))+0.15), col = 'black',
lwd = 2.5)
}
if (intra.TF){
y.at.1 <- 1.5
y.at.1.la <- 1
y.at.1.lb <- 2
y.at.2 <- 4
y.at.2.la <- 3
y.at.2.lb <- 5
}else{
y.at.1 <- 1
y.at.1.la <- 1
y.at.1.lb <- 1
y.at.2 <- 3
y.at.2.la <- 2
y.at.2.lb <- 4
}
mtext("Trait independant", side=2,
at = y.at.1,
cex =1.6,
line = 13, col = '#377eb8')
lines(c(x.line+0.12, x.line+0.12),
c(y.at.1.la - abs(min(seq.jitter)-0.15), y.at.1.lb + abs(min(seq.jitter))+0.15), col = '#377eb8',
lwd = 2.5)
mtext("Trait", side=2, at = y.at.2,
cex =1.6,
line = 13, col = '#a65628')
lines(c(x.line+0.12, x.line+0.12),
c(y.at.2.la - abs(min(seq.jitter)-0.15), y.at.2.lb + abs(min(seq.jitter))+0.15), col = '#a65628',
lwd = 2.5)
}
fun.legend <- function(biomes.names, biomes.c, col.vec, pch.vec){
par(mar=c(0, 0, 0, 0))
......@@ -1331,8 +1363,8 @@ plot.param.mean.and.biomes.fixed <- function(list.res,
names.bio,
intra.TF = FALSE,
...){
if(!intra.TF) x.line <- -0.63
if(intra.TF) x.line = -0.7
if(!intra.TF) x.line <- -0.72
if(intra.TF) x.line = -0.72
col.vec[2] <- col.vec[1]
biomes.c <- as.character(biomes)
Var <- "Trait indep"
......@@ -1379,7 +1411,7 @@ b <- border.size()
side = 2,
labels = param.names,
cols.vec = col.names[param.vec])
fun.param.descrip(seq.jitter,length(param.vec), x.line)
fun.param.descrip(seq.jitter,length(param.vec), x.line, intra.TF = intra.TF)
}
if(i == traits[2]){ mtext('Standardized coefficients', side=1, cex =1.5,
line = 4)}
......@@ -1530,7 +1562,7 @@ traits.exp <- list(expression(paste('Wood density (mg m', m^-3, ')')),
names(traits.exp) <- c('Wood density', 'Specific leaf area', 'Maximum height')
if(!intra.TF){
expr.p.vec <- c(expression(paste0('Trait indep ',alpha[0])),
expression(paste('Similarity ', alpha[l] %*% abs(t[f] - t[c]))),
expression(paste('Dissimilarity ', alpha[d] %*% abs(t[f] - t[c]))),
expression(paste('Competitive effect ', alpha[e] %*% t[c])),
expression(paste('Tolerance of competition ', alpha[t] %*% t[f])),
expression(paste('Maximum growth ', m[1] %*% t[f])))
......@@ -1544,7 +1576,7 @@ 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('Similarity ', alpha[l] %*% abs(t[f] - t[c]))),
expression(paste('Dissimilarity ', alpha[d] %*% abs(t[f] - t[c]))),
expression(paste('Competitive effect ', alpha[e] %*% t[c])),
expression(paste('Tolerance of competition ', alpha[t] %*% t[f])),
expression(paste('Maximum growth ', m[1] %*% t[f])))
......@@ -1661,7 +1693,7 @@ traits.exp <- list(expression(paste('Wood density (mg m', m^-3, ')')),
names(traits.exp) <- c('Wood density', 'Specific leaf area', 'Maximum height')
if(!intra.TF){
expr.p.vec <- c(expression(paste0('Trait indep ',alpha[0])),
expression(paste('Similarity ', alpha[l] %*% abs(t[f] - t[c]))),
expression(paste('Dissimilarity ', alpha[d] %*% abs(t[f] - t[c]))),
expression(paste('Competitive effect ', alpha[e] %*% t[c])),
expression(paste('Tolerance of competition ', alpha[t] %*% t[f])),
expression(paste('Maximum growth ', m[1] %*% t[f])))
......@@ -1675,7 +1707,7 @@ 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('Similarity ', alpha[l] %*% abs(t[f] - t[c]))),
expression(paste('Dissimilarity ', alpha[d] %*% abs(t[f] - t[c]))),
expression(paste('Competitive effect ', alpha[e] %*% t[c])),
expression(paste('Tolerance of competition ', alpha[t] %*% t[f])),
expression(paste('Maximum growth ', m[1] %*% t[f])))
......
R/analysis/lmer.run.R
View file @ 5b8b2268
......@@ -206,9 +206,6 @@ load.data.for.lmer <- function(trait, data.type,
}
}
}
if(data.type != 'all.census') {
df <- select.one.census.per.plot(df)
}
list.sd <- get.sd.lmer(df, trait, min.obs = 10)
print('sd ok')
res <- format.data.for.lmer(df, trait,
......@@ -316,6 +313,9 @@ load.and.save.data.for.lmer <- function(trait,
fname <- 'data.all.no.log.all.census.rds'
data.tree.tot <- readRDS(file.path(base.dir, fname))
df <- select.data.trait(data.tree.tot, trait)
if(data.type != 'all.census') {
df <- select.one.census.per.plot(df)
}
saveRDS(df,file = file.path(base.dir,paste('data', trait, data.type, 'rds',
sep = '.')))
}
......
R/analysis/model.lmer/model.lmer.LOGLIN.MAT.MAP.intra.r.set.fixed.biomes.species.R
View file @ 5b8b2268
load.model <- function () {
list(name="lmer.LOGLIN.MAT.MAP.intra.r.set.fixed.biomes.species",
var.BLUP = 'set.id',
lmer.formula.tree.id=formula("logG~1+biomes.id+MAT+MAP+logD+sumBn.inter+sumBn.inter:biomes.id+sumBn.intra+sumBn.intra:biomes.id +(1+logD||species.id)+(1|plot.id)+(1+sumBn.inter+sumBn.intra||set.id)"))
lmer.formula.tree.id=formula("logG~1+biomes.id+MAT+MAP+logD+sumBn.inter+sumBn.inter:biomes.id+sumBn.intra+sumBn.intra:biomes.id +(1+logD+sumBn.inter+sumBn.intra||species.id) +(1|plot.id)+(1+sumBn.inter+sumBn.intra||set.id)"))
}
R/analysis/model.lmer/model.lmer.LOGLIN.MAT.MAP.intra.r.set.species.R
View file @ 5b8b2268
load.model <- function () {
list(name="lmer.LOGLIN.MAT.MAP.intra.r.set.species",
var.BLUP = 'set.id',
lmer.formula.tree.id=formula("logG~1+logD+sumBn.inter+MAT+MAP+sumBn.intra+(1+logD||species.id)+(1|plot.id)+(1+sumBn.inter+sumBn.intra||set.id)"))
lmer.formula.tree.id=formula("logG~1+logD+sumBn.inter+MAT+MAP+sumBn.intra+(1+logD+sumBn.inter+sumBn.intra||species.id)+(1|plot.id)+(1+sumBn.inter+sumBn.intra||set.id)"))
}
......
R/analysis/model.lmer/model.lmer.LOGLIN.MAT.MAP.r.set.fixed.biomes.species.R
View file @ 5b8b2268
load.model <- function () {
list(name="lmer.LOGLIN.MAT.MAP.r.set.fixed.biomes.species",
var.BLUP = 'set.id',
lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+MAT+MAP+biomes.id+logD+sumBn+sumBn:biomes.id +(logD-1|species.id) +(sumBn-1|set.id)"))
lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+MAT+MAP+biomes.id+logD+sumBn+sumBn:biomes.id +(1+logD+sumBn.inter+sumBn.intra||species.id) +(sumBn-1|set.id)"))
}
R/analysis/model.lmer/model.lmer.LOGLIN.MAT.MAP.r.set.species.R
View file @ 5b8b2268
load.model <- function () {
list(name="lmer.LOGLIN.MAT.MAP.r.set.species",
var.BLUP = 'set.id',
lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+MAT+MAP+logD+sumBn +(logD-1|species.id) +(sumBn-1|set.id)"))
lmer.formula.tree.id=formula("logG~1+(1|plot.id)+MAT+MAP+logD+sumBn +(1+logD+sumBn||species.id) +(1+sumBn||set.id)"))
}
......
R/analysis/model.lmer/model.lmer.LOGLIN.r.set.fixed.biomes.species.R
View file @ 5b8b2268
load.model <- function () {
list(name="lmer.LOGLIN.r.set.fixed.biomes.species",
var.BLUP = 'set.id',
lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+biomes.id+logD+sumBn+sumBn:biomes.id +(logD-1|species.id) +(sumBn-1|set.id)"))
lmer.formula.tree.id=formula("logG~1+(1|plot.id)+biomes.id+logD+sumBn+sumBn:biomes.id +(1+logD+sumBn||species.id)x +(1 + sumBn||set.id)"))
}
R/analysis/model.lmer/model.lmer.LOGLIN.r.set.species.R
View file @ 5b8b2268
load.model <- function () {
list(name="lmer.LOGLIN.r.set.species",
var.BLUP = 'set.id',
lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+logD+sumBn +(logD-1|species.id) +(sumBn-1|set.id)"))
lmer.formula.tree.id=formula("logG~1+(1|plot.id)+logD+sumBn +(1+logD+sumBn||species.id) +(1+sumBn||set.id)"))
}
......
data/metadata/sites/sites_description.csv
View file @ 5b8b2268
Data set name,Country,Data type,Plot size,Diameter at breast height threshold,Number of plots,Traits,Source trait data,Evidences 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), Traits 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 census 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), Traits 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), Traits 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), Traits 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 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).""","12,13",G. Kunstler (georges.kunstler@gmail.com),"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"
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 insects 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 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).""","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 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).""",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 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.
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 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).""",,J. Caspersen (john.caspersen@utoronto.ca),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.
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 level of selection harvesting[@Kariuki-2006].,"1,2",R. M. Kooyman (robert@ecodingo.com.au),Permanents plots established by the NSW Department of State Forests or by RMK
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
......
docs/paper/SupplMat.md
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docs/paper/Suppl_Mat.Rmd
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docs/paper/extended_data.R
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......@@ -97,8 +97,7 @@ pandoc.table(cor.mat,
## # Model results
## ![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 $m_0$). $\alpha_{0 \, intra}$ and $\alpha_{0 \, inter}$, which do not vary with traits are also represented with their associated confidence intervals.](../../figs/
## figres4b_TP_intra.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 dissimilarity ($|t_f - t_c| \, \alpha_d$ ($t_c$ 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 $m_0$). $\alpha_{0 \, intra}$ and $\alpha_{0 \, inter}$, which do not vary with traits are also represented with their associated confidence intervals.](../../figs/figres4b_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 (see Methods), where $\rho$ is a measure of niche overlap between a pair of species. If inter-specific competition is equal or greater than intra-specific competition $1- \rho \leqslant 0$, and there is no stabilising processes. If inter-specific competition is smaller than intra-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 and not on population growth, this can not be taken as a direct indication of coexistence.](../../figs/rho_set_TP_intra.pdf)
......@@ -113,21 +112,22 @@ 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",
param.vec = c("(Intercept)", "logD","Tf", 'MAT', 'MAP',
"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"),]
"Tf", 'MAT', 'MAP', "sumTfBn"),] <-
-mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD", "Tf",
'MAT', 'MAP',
"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",
param.vec = c("(Intercept)", "logD", "Tf",'MAT', 'MAP',
"sumBn.intra","sumBn.inter",
"sumTnBn","sumTfBn", "sumTnTfBn.abs"),
data.type = 'intra'))
......@@ -166,14 +166,14 @@ mat.param <- rbind(mat.param.mean.sd,
round(mat.AIC- apply(rbind(mat.AIC,mat.AIC.0), MARGIN = 2, min), 0),
round(mat.AIC.0- apply(rbind(mat.AIC,mat.AIC.0), MARGIN = 2, min), 0))
colnames(mat.param) <- c('Wood density', 'SLA', 'Maximum height')
row.names(mat.param) <- c('$m_0$', '$\\gamma$', '$m_1$',
row.names(mat.param) <- c('$m_0$', '$\\gamma$', '$m_1$', '$m_2$','$m_3$',
'$\\alpha_{0 \\, intra}$','$\\alpha_{0 \\, inter}$',
'$\\alpha_e$', '$\\alpha_t$',
'$\\alpha_s$', '$R^2_m$*', '$R^2_c$*',
'$\\alpha_d$', '$R^2_m$*', '$R^2_c$*',
'$\\Delta$ AIC', '$\\Delta$ AIC no trait')
##+ Table2_Effectsize, echo = FALSE, results='asis', message=FALSE
pandoc.table(mat.param[c(1,3,2,4:12), ], caption = "Standardized parameters estimates and standard error (in bracket) estimated for each trait, $R^2$* of models and $\\Delta$ AIC of the model and of a model with no trait effect. Best model have a $\\Delta$ AIC of zero. See section Method for explanation of parameters",
pandoc.table(mat.param[c(1,3:5,2,6:14), ], caption = "Standardized parameters estimates and standard error (in bracket) estimated for each trait, $R^2$* of models and $\\Delta$ AIC of the model and of a model with no trait effect. Best model have a $\\Delta$ AIC of zero. See section Method for explanation of parameters",
digits = 3, justify = c('left', rep('right', 3)),
emphasize.strong.cells = bold.index, split.tables = 200)
......
docs/paper/extended_method.tex
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docs/paper/paper.tex
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docs/paper/references.bib
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......@@ -1231,6 +1231,19 @@
}
@article{Shipley-2006,
title = {Net assimilation rate, specific leaf area and leaf mass ratio: which is most closely correlated with relative growth rate? A meta-analysis},
volume = {20},
shorttitle = {Net assimilation rate, specific leaf area and leaf mass ratio},
number = {4},
journal = {Functional Ecology},
author = {Shipley, B.},
year = {2006},
pages = {565--574}
}
@article{Seidl-2014,
title = {Increasing forest disturbances in Europe and their impact on carbon storage},
volume = {4},
......@@ -1397,6 +1410,19 @@
Volume = {25}
}
@article{Wright-2001,
title = {Understanding seedling growth relationships through specific leaf area and leaf nitrogen concentration: generalisations across growth forms and growth irradiance},
volume = {127},
shorttitle = {Understanding seedling growth relationships through specific leaf area and leaf nitrogen concentration},
number = {1},
journal = {Oecologia},
author = {Wright, Ian J. and Westoby, Mark},
year = {2001},
pages = {21--29}
}
@Article{Wright-2007,
Title = {Relationships among ecologically important dimensions of plant trait variation in seven Neotropical forests},
Author = {Wright, Ian J and Ackerly, David D and Bongers, Frans and Harms, Kyle E and Ibarra-Manriquez, Guillermo and Martinez-Ramos, Miguel and Mazer, Susan J and Muller-Landau, Helene C and Paz, Horacio and Pitman, Nigel {CA}},
......
launch.cluster/launch_all_data.lmer.bash
View file @ 5b8b2268
......@@ -4,7 +4,7 @@ export LD_LIBRARY_PATH=/usr/lib64/R/library
mkdir -p Rscript_temp
for trait in "'SLA'" "'Leaf.N'" "'Wood.density'" "'Max.height'" "'Seed.mass'"; do
for trait in "'SLA'" "'Wood.density'" "'Max.height'"; do
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); load.and.save.data.for.lmer($trait);print('done')\"" > Rscript_temp/data1$trait.sh
qsub Rscript_temp/data1$trait.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "data1$trait" -q opt32G -j oe
......
launch.cluster/launch_all_lmer.bash
View file @ 5b8b2268
......@@ -22,37 +22,6 @@ samplesize=$1
# "'Seed.mass'" "'Leaf.N'"
for trait in "'SLA'" "'Wood.density'" "'Max.height'" ; do
# # # ALL data 0
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.0[1], run.lmer,$trait);print('done')\"" > Rscript_temp/all0${trait}.sh
# qsub Rscript_temp/all0${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.0${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.0[2], merge.biomes.TF = TRUE, run.lmer,$trait);print('done')\"" > Rscript_temp/all02${trait}.sh
# qsub Rscript_temp/all02${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.02${trait}" -q opt32G -j oe
# # # ALL data 0b
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.0b[1], run.lmer,$trait);print('done')\"" > Rscript_temp/all0b${trait}.sh
# qsub Rscript_temp/all0b${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.0b${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.0b[2], merge.biomes.TF = TRUE, run.lmer,$trait);print('done')\"" > Rscript_temp/all02b${trait}.sh
# qsub Rscript_temp/all02b${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.02b${trait}" -q opt32G -j oe
# # # ALL data 1
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.1[1], run.lmer,$trait);print('done')\"" > Rscript_temp/allf${trait}.sh
# qsub Rscript_temp/allf${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.f${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.1[2], merge.biomes.TF = TRUE, run.lmer,$trait);print('done')\"" > Rscript_temp/allf2${trait}.sh
# qsub Rscript_temp/allf2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.f2${trait}" -q opt32G -j oe
# # # ALL data 1
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.3[1], run.lmer,$trait);print('done')\"" > Rscript_temp/allTP${trait}.sh
# qsub Rscript_temp/allTP${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.TP${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.3[2], merge.biomes.TF = TRUE, run.lmer,$trait);print('done')\"" > Rscript_temp/allTP2${trait}.sh
# qsub Rscript_temp/allTP2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.TP2${trait}" -q opt32G -j oe
# # INTRA 0
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.0[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRA0${trait}.sh
......@@ -62,44 +31,23 @@ samplesize=$1
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.0[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRA02${trait}.sh
qsub Rscript_temp/allINTRA02${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRA02${trait}" -q opt32G -j oe
# # # INTRA 1
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.1[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRA${trait}.sh
# qsub Rscript_temp/allINTRA${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRA${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.1[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRA2${trait}.sh
# qsub Rscript_temp/allINTRA2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRA2${trait}" -q opt32G -j oe
# # # INTRA 2
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB${trait}.sh
# qsub Rscript_temp/allINTRAB${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAB${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB2${trait}.sh
# qsub Rscript_temp/allINTRAB2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAB2${trait}" -q opt32G -j oe
# # # INTRA 3
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.3[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE${trait}.sh
# qsub Rscript_temp/allINTRAE${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAE${trait}" -q opt32G -j oe
# # INTRA 2
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB${trait}.sh
qsub Rscript_temp/allINTRAB${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAB${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.3[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE2${trait}.sh
# qsub Rscript_temp/allINTRAE2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAE2${trait}" -q opt32G -j oe
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.2[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAB2${trait}.sh
qsub Rscript_temp/allINTRAB2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAB2${trait}" -q opt32G -j oe
# # # # # ecocode 3
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.2[1], run.lmer,$trait, data.type = 'simple');print('done')\"" > Rscript_temp/allECO${trait}.sh
# qsub Rscript_temp/allECO${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.ECO${trait}" -q opt32G -j oe
# # INTRA 3
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.3[1], run.lmer,$trait, data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE${trait}.sh
qsub Rscript_temp/allINTRAE${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAE${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.2[2], run.lmer,merge.biomes.TF = TRUE,$trait, data.type = 'simple');print('done')\"" > Rscript_temp/allECO2${trait}.sh
# qsub Rscript_temp/allECO2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.ECO2${trait}" -q opt32G -j oe
# # # # ecocode TP
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.4[1], run.lmer,$trait, data.type = 'simple');print('done')\"" > Rscript_temp/allECOTP${trait}.sh
# qsub Rscript_temp/allECOTP${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.ECOTP${trait}" -q opt32G -j oe
echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.intra.3[2], merge.biomes.TF = TRUE, run.lmer,$trait,data.type = 'intra');print('done')\"" > Rscript_temp/allINTRAE2${trait}.sh
qsub Rscript_temp/allINTRAE2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.INTRAE2${trait}" -q opt32G -j oe
# echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); run.multiple.model.for.set.one.trait(model.files.lmer.Tf.4[2], run.lmer,merge.biomes.TF = TRUE,$trait, data.type = 'simple');print('done')\"" > Rscript_temp/allECOTP2${trait}.sh
# qsub Rscript_temp/allECOTP2${trait}.sh -d ~/trait.competition.workshop -l nodes=1:ppn=1,mem=8gb -N "lmerall2all.ECOTP2${trait}" -q opt32G -j oe
done
......
scripts/analysis/results.fig.R
View file @ 5b8b2268
......@@ -10,7 +10,6 @@ names.biomes <- c('Desert', 'Desert', 'Woodland/shrubland', 'Temperate forest',
## load results
list.all.results.set <-
......@@ -51,7 +50,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.set , data.type = "simple",
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn", "Tf"),
param.print = 1:5,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression('Trait dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha[0])),
......@@ -72,7 +71,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.intra , data.type = "intra",
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn.intra", "sumBn.inter", "Tf"),
param.print = 1:6,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression('Trait dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha['0 intra'])),
......@@ -93,13 +92,13 @@ plot.param.mean.and.biomes.fixed(list.all.results.set , data.type = "simple",
models = c('lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.fixed.biomes.species'),
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn", "Tf"),
param.vec = c("sumBn","sumTnTfBn.abs", "sumTfBn","sumTnBn",
"Tf"),
param.print = 1:5,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression(' '(alpha[0])),
expression('Dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha[0])),
expression("Direct trait "(m[1])),
expression("Size "(gamma %*% log('D'))) ) ,
col.vec = fun.col.pch.biomes()$col.vec,
......@@ -114,14 +113,15 @@ plot.param.mean.and.biomes.fixed(list.all.results.intra , data.type = "intra",
models = c('lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.fixed.biomes.species'),
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn.intra", "sumBn.inter", "Tf"),
param.vec = c("sumBn.intra", "sumBn.inter",
"sumTnTfBn.abs", "sumTfBn","sumTnBn",
"Tf"),
param.print = 1:6,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression('Intra'(alpha['0 intra'])),
expression('Inter'(alpha['0 inter'])),
expression('Dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha['0 intra'])),
expression('Trait indep'(alpha['0 inter'])),
expression("Direct trait "(m[1])),
expression("Size "(gamma %*% log('D'))) ) ,
col.vec = fun.col.pch.biomes()$col.vec,
......@@ -142,7 +142,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.ecocode ,
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn", "Tf"),
param.print = 1:5,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression('Trait dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha[0])),
......@@ -151,7 +151,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.ecocode ,
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.28, 0.27))
xlim = c(-0.3, 0.27))
dev.off()
......@@ -161,19 +161,21 @@ plot.param.mean.and.biomes.fixed(list.all.results.intra ,
models = c('lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.ecocode.species',
'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.ecocode.fixed.biomes.species'),
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn.intra", "sumBn.inter", "Tf"),
param.vec = c("sumBn.intra", "sumBn.inter",
"sumTnTfBn.abs", "sumTfBn","sumTnBn",
"Tf"),
param.print = 1:6,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression('Intra'(alpha['0 intra'])),
expression('Inter'(alpha['0 inter'])),
expression('Dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha['0 intra'])),
expression('Trait indep'(alpha['0 inter'])),
expression("Direct trait "(m[1])) ) ,
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.30, 0.30))
xlim = c(-0.3, 0.33),
intra.TF = TRUE)
dev.off()
pdf('figs/figres1_ecocode_TP_intra.pdf', height = 14, width = 16)
......@@ -184,7 +186,7 @@ plot.param(list.all.results.intra ,
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn.intra", "sumBn.inter", "Tf"),
param.print = 1:6,
param.names = c(expression('Trait sim '(alpha['s'])),
param.names = c(expression('Trait dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha['0 intra'])),
......@@ -194,7 +196,7 @@ plot.param(list.all.results.intra ,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.30, 0.30),
intra.TF = FALSE)
intra.TF = TRUE)
dev.off()
......
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