Commit 5f18d2cc authored by Kunstler Georges's avatar Kunstler Georges
Browse files

last changes of figures for proofs

parent edf421d1
......@@ -1244,7 +1244,7 @@ for (i in traits){
side = 2,
labels = param.names,
cols.vec = col.names[param.vec],
cex.axis = 2.1)
cex.axis = 3)
if(add.param.descrip.TF == 2){
mtext("Max growth", side=2, at = 4.95, cex =1.6,
line = 17.9, col = '#e41a1c')
......@@ -1280,34 +1280,40 @@ fun.legend(legend.pos, biomes.names, biomes.c, col.vec, pch.vec)
# MERGE FIG 2 and 3
border.size <- function(){
big.m <- 3.0
big.m <- 3.5
small.m <- 0
legend.m <- 1.9
legend.m <- 2.2
return(list(big.m = big.m,
small.m = small.m,
legend.m = legend.m) )
}
fun.layout <- function(b = border.size()){
wid <- c(b$big.m ,0, b$small.m) +
rep((14-b$big.m-b$small.m-b$legend.m)/3, each= 3)
m2 <- matrix(c(1:4), 1, 4)
layout(m2, widths=c(wid, b$legend.m), heights = 1)
par(mfrow = c(1,4), mar = c(0, 0, 0, 0), oma = c(6, 30, 6, 0), xpd = NA)
## wid <- c(b$big.m ,0, b$small.m) +
## rep((14-b$big.m-b$small.m-b$legend.m)/3, each= 3)
## m2 <- matrix(c(1:4), 1, 4)
## layout(m2, widths=c(wid, b$legend.m), heights = 1)
}
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 = 18.2, col = '#e41a1c')
fun.param.descrip <- function(seq.jitter, n.param, x.line = -0.73,
intra.TF = FALSE){
mtext("Max growth", side=2, at = n.param, cex =2.4,
line = 23, 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),
c(n.param - abs(min(seq.jitter))-0.15,
n.param + abs(min(seq.jitter))+0.15),
col = '#e41a1c',
lwd = 2.5)
lwd = 3)
mtext("Competition", side=2,
at = (n.param - 1)/2 + 0.5, cex =1.6,
line = 18.2, col = 'black')
at = (n.param - 1)/2 + 0.5, cex =2.4,
line = 23, 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 = 'black',
lwd = 2.5)
c(1 - abs(min(seq.jitter)-0.15),
n.param-1 + abs(min(seq.jitter))+0.15), col = 'black',
lwd = 3)
if (intra.TF){
y.at.1 <- 1.5
y.at.1.la <- 1
......@@ -1326,17 +1332,19 @@ fun.param.descrip <- function(seq.jitter, n.param, x.line = -0.73, intra.TF =
mtext("Trait independent", side=2,
at = y.at.1,
cex =1.6,
line = 13, col = '#377eb8')
cex =2.6,
line = 18, 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)
c(y.at.1.la - abs(min(seq.jitter)-0.15),
y.at.1.lb + abs(min(seq.jitter))+0.15), col = '#377eb8',
lwd = 3)
mtext("Trait dependent", side=2, at = y.at.2,
cex =1.6,
line = 13, col = '#a65628')
cex =2.6,
line = 18, 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)
c(y.at.2.la - abs(min(seq.jitter)-0.15),
y.at.2.lb + abs(min(seq.jitter))+0.15), col = '#a65628',
lwd = 3)
}
......@@ -1360,8 +1368,9 @@ fun.legend <- function(biomes.names, biomes.c, col.vec, pch.vec){
segments(rep(0.40, 1), min.y+(length(biomes.c)+1-1)*step.y,
rep(0.60, 1), min.y+(length(biomes.c)+1-1)*step.y,
lty =1, lwd = 2,col = 'black')
text(rep(0.5,length(biomes.c)+1), min.y-0.04+((length(biomes.c)+1):1-1)*step.y,
labels = c('global', biomes.names[biomes.c]), cex= 1.8)
text(rep(0.5,length(biomes.c)+1),
min.y-0.04+((length(biomes.c)+1):1-1)*step.y,
labels = c('global', biomes.names[biomes.c]), cex= 2.8)
}
fun.par.mai <- function(i, traits, b){
......@@ -1397,8 +1406,8 @@ plot.param.mean.and.biomes.fixed <- function(list.res,
names.bio,
intra.TF = FALSE,
...){
if(!intra.TF) x.line <- -0.72
if(intra.TF) x.line = -0.72
if(!intra.TF) x.line <- -0.91
if(intra.TF) x.line = -0.91
col.vec[2] <- col.vec[1]
biomes.c <- as.character(biomes)
Var <- "Trait indep"
......@@ -1422,19 +1431,20 @@ names(traits_letters) <- c('Wood.density', 'SLA', 'Max.height')
names(param.std.m) <- names(list.temp.m$fixed.coeff.E)
param.std.m <- param.std.m[param.vec]
fun.par.mai(i, traits, b)
# fun.par.mai(i, traits, b)
## LOOP OVER VAR
for (n.vars in seq_len(length(param.vec))){
list.fixed <- fun.get.fixed.biomes(param.vec[n.vars], list.temp,
biomes.vec = biomes)
param.mean <- list.fixed$fixed.biomes
if (!param.vec[n.vars] %in% c("Tf", "sumTfBn")) param.mean <- -param.mean
if (!param.vec[n.vars] %in% c("Tf", "sumTfBn"))
param.mean <- -param.mean
param.std <- list.fixed$fixed.biomes.std
seq.jitter <- seq(25, -25, length.out = length(biomes)+1)/120
if(n.vars == 1){
plot(c(param.mean.m[n.vars], param.mean), seq.jitter+n.vars,
yaxt = 'n', xlab = NA, ylab = NA,
pch = c(15, pch.vec[biomes.c]) , cex = 2,
pch = c(15, pch.vec[biomes.c]) , cex = 2.2,
cex.axis = 1.5, cex.lab = 1.7,
ylim = range(1-0.21, length(param.vec)+0.21),
col = c('black', col.vec[biomes.c]), ...)
......@@ -1450,28 +1460,29 @@ names(traits_letters) <- c('Wood.density', 'SLA', 'Max.height')
fun.axis.one.by.one,
side = 2,
labels = param.names,
cols.vec = col.names[param.vec])
fun.param.descrip(seq.jitter,length(param.vec), x.line, intra.TF = intra.TF)
cols.vec = col.names[param.vec], cex.axis = 3)
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,
if(i == traits[2]){ mtext('Standardized coefficients', side=1, cex =2.25,
line = 4)}
}
if(n.vars != 1){
points(c(param.mean.m[n.vars], param.mean), seq.jitter+n.vars,
col = c('black', col.vec[biomes.c]),
pch = c(15, pch.vec[biomes.c]), cex = 2)
pch = c(15, pch.vec[biomes.c]), cex = 2.1)
}
fun.plot.error.bar.horiz(param.mean,
seq.jitter[-1]+n.vars,
param.std, col = col.vec[biomes.c], lwd= 1.2)
param.std, col = col.vec[biomes.c], lwd= 2.5)
}
points(param.mean.m, seq.jitter[1]+1:n.vars,
pch = 15, cex = 2.5)
fun.plot.error.bar.horiz(param.mean.m,
seq.jitter[1]+1:n.vars,
param.std.m, col = 'black', lwd= 2)
mtext(traits.names[i], side=3, cex =1.5, line = 1)
param.std.m, col = 'black', lwd= 3.7)
mtext(traits.names[i], side=3, cex =2.25, line = 1)
}
fun.legend( biomes.names, biomes.c, col.vec, pch.vec)
......
......@@ -38,7 +38,6 @@ GetClimate <-function(lats,lons) {
plot.mat <- numeric(length(tiles))
plot.map <- numeric(length(tiles))
#download each of the tiles
for (i.tile in unique.tiles) {
temp.tile <- getData('worldclim',
......
......@@ -183,29 +183,29 @@
\author[11]{S. Joseph Wright}
\author[12]{Masahiro Aiba}
\author[13,14]{Christopher Baraloto}
\author[15]{John Caspersen}
\author[16]{J. Hans C. Cornelissen}
\author[15,16]{John Caspersen}
\author[17]{J. Hans C. Cornelissen}
\author[10]{Sylvie Gourlet-Fleury}
\author[17,18]{Marc Hanewinkel}
\author[19]{Bruno Herault}
\author[20,21]{Jens Kattge}
\author[12,22]{Hiroko Kurokawa}
\author[23]{Yusuke Onoda}
\author[24,25]{Josep Peñuelas}
\author[26]{Hendrik Poorter}
\author[27]{Maria Uriarte}
\author[28]{Sarah Richardson}
\author[29,30]{Paloma Ruiz-Benito}
\author[31]{I-Fang Sun}
\author[32]{Göran Ståhl}
\author[33]{Nathan G. Swenson}
\author[34,35]{Jill Thompson}
\author[32]{Bertil Westerlund}
\author[36,21]{Christian Wirth}
\author[30]{Miguel A. Zavala}
\author[18,19]{Marc Hanewinkel}
\author[20]{Bruno Herault}
\author[21,22]{Jens Kattge}
\author[12,23]{Hiroko Kurokawa}
\author[24]{Yusuke Onoda}
\author[25,26]{Josep Peñuelas}
\author[27]{Hendrik Poorter}
\author[28]{Maria Uriarte}
\author[29]{Sarah Richardson}
\author[30,31]{Paloma Ruiz-Benito}
\author[32]{I-Fang Sun}
\author[33]{Göran Ståhl}
\author[34]{Nathan G. Swenson}
\author[35,36]{Jill Thompson}
\author[33]{Bertil Westerlund}
\author[37,22]{Christian Wirth}
\author[31]{Miguel A. Zavala}
\author[15]{Hongcheng Zeng}
\author[35]{Jess K. Zimmerman}
\author[37]{Niklaus E. Zimmermann}
\author[36]{Jess K. Zimmerman}
\author[16]{Niklaus E. Zimmermann}
\author[3]{Mark Westoby}
\affil[1]{Irstea, UR EMGR, 2 rue de la Papeterie BP-76, F-38402, St-Martin-d'Hères, France \\ \url{georges.kunstler@irstea.fr}}
......@@ -223,29 +223,28 @@
\affil[13]{INRA, UMR Ecologie des Forêts de Guyane, BP 709, 97387 Kourou Cedex, France}
\affil[14]{International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, USA}
\affil[15]{Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, Ontario, M5S 3B3, Canada}
\affil[16]{Systems Ecology, Department of Ecological Science, VU University, Amsterdam, 1081 HV, The Netherlands}
\affil[17]{Swiss Federal Research Inst. WSL, Forest Resources and Management Unit, CH-8903 Birmensdorf, Switzerland}
\affil[18]{University of Freiburg, Chair of Forestry Economics and Planning, D-79106 Freiburg, Germany}
\affil[19]{Cirad, UMR Ecologie des Forêts de Guyane, Campus Agronomique, BP 701, 97387 Kourou, France}
\affil[20]{Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745 Jena, Germany}
\affil[21]{German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e 04103 Leipzig, Germany}
\affil[22]{Forestry and Forest Products Research Institute, Tsukuba, 305-8687 Japan (current address)}
\affil[23]{Graduate School of Agriculture, Kyoto University, Kyoto, Japan}
\affil[24]{CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del
\affil[16]{Swiss Federal Research Inst. WSL, Landscape Dynamics Unit, CH-8903 Birmensdorf, Switzerland}
\affil[17]{Systems Ecology, Department of Ecological Science, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands}
\affil[19]{University of Freiburg, Chair of Forestry Economics and Planning, D-79106 Freiburg, Germany}
\affil[20]{Cirad, UMR Ecologie des Forêts de Guyane, Campus Agronomique, BP 701, 97387 Kourou, France}
\affil[21]{Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745 Jena, Germany}
\affil[22]{German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e 04103 Leipzig, Germany}
\affil[23]{Forestry and Forest Products Research Institute, Tsukuba, 305-8687 Japan (current address)}
\affil[24]{Graduate School of Agriculture, Kyoto University, Kyoto, Japan}
\affil[25]{CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del
Vallès 08193, Catalonia, Spain}
\affil[25]{CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain}
\affil[26]{Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany}
\affil[27]{Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027, United States of America}
\affil[28]{Landcare Research, PO Box 40, Lincoln 7640, New Zealand}
\affil[29]{Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Stirling, UK}
\affil[30]{Forest Ecology and Restoration Group, Department of Life Sciences, Science Building, University of Alcala, Campus Universitario, 28805 Alcalá de Henares (Madrid), Spain}
\affil[31]{Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien 97401, Taiwan}
\affil[32]{Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd, Umeå, Sweden}
\affil[33]{Department of Biology, University of Maryland, College Park, Maryland, United States of America}
\affil[34]{Centre for Ecology and Hydrology−Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB United Kingdom}
\affil[35]{Department of Environmental Sciences, University of Puerto Rico, Río Piedras Campus P.O. Box 70377 San Juan, Puerto Rico 00936-8377, USA}
\affil[36]{Institute for Systematic, Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21 04103 Leipzig, Germany}
\affil[37]{Swiss Federal Research Inst. WSL, Landscape Dynamics Unit, CH-8903 Birmensdorf, Switzerland}
\affil[26]{CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain}
\affil[27]{Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany}
\affil[28]{Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027, United States of America}
\affil[29]{Landcare Research, PO Box 40, Lincoln 7640, New Zealand}
\affil[30]{Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, FK9 4LA, Stirling, UK}
\affil[31]{Forest Ecology and Restoration Group, Department of Life Sciences, Science Building, University of Alcala, Campus Universitario, 28805 Alcalá de Henares (Madrid), Spain}
\affil[32]{Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien 97401, Taiwan}
\affil[33]{Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd, Umeå, Sweden}
\affil[34]{Department of Biology, University of Maryland, College Park, Maryland, United States of America}
\affil[35]{Centre for Ecology and Hydrology−Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB United Kingdom}
\affil[36]{Department of Environmental Sciences, University of Puerto Rico, Río Piedras Campus P.O. Box 70377 San Juan, Puerto Rico 00936-8377, USA}
\affil[37]{Institute for Systematic, Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21 04103 Leipzig, Germany}
\date{}
......@@ -733,7 +732,7 @@ reductions due to competition from individuals growing in the local
neighbourhood (see definition below). Specifically, we assumed a relationship of the form
\begin{equation} \label{G1}
G_{i,f,p,s,t} = G_{\textrm{max} \, f,p,s} \, D_{i,f,p,s,t}^{\gamma_f} \, \exp\left(\sum_{c=1}^{N_i} {-\alpha_{f,c} B_{i,c,p,s}}\right),
G_{i,f,p,s,t} = G_{\textrm{max}_{f,p,s}} \, D_{i,f,p,s,t}^{\gamma_f} \, \exp\left(\sum_{c=1}^{N_i} {-\alpha_{f,c} B_{i,c,p,s}}\right),
\end{equation}
where:
\begin{itemize}
......@@ -743,7 +742,7 @@ where:
growth and diameter at breast height of individual \(i\) from species
\(f\), plot or quadrat (see below) \(p\), data set \(s\), and census $t$,
\item
\(G_{\textrm{max} \, f,p,s}\) is the maximum basal area growth for species \(f\) on plot or quadrat \(p\) in data set \(s\), i.e.~in
\(G_{\textrm{max}_{f,p,s}}\) is the maximum basal area growth for species \(f\) on plot or quadrat \(p\) in data set \(s\), i.e.~in
absence of competition,
\item
\(\gamma_f\) determines the rate at which growth changes with size for
......@@ -778,8 +777,8 @@ Log-transformation of equ. \ref{G1} leads to a linearised model of the
form
\begin{equation} \label{logG1}
\log{G_{i,f,p,s,t}} = \log{G_{\textrm{max} \, f,p,s}} + \gamma_f \,
\log{D_{i,f,p,s,t}} + \sum_{c=1}^{N_i} {-\alpha_{f,c} B_{i,c,p,s}} \,
\log(G_{i,f,p,s,t}) = \log(G_{\textrm{max}_{f,p,s}}) + \gamma_f \,
\log(D_{i,f,p,s,t}) + \sum_{c=1}^{N_i} {-\alpha_{f,c} B_{i,c,p,s}} \,
.
\end{equation}
......@@ -788,7 +787,7 @@ The effect of a focal species' trait value, \(t_f\), on its
maximum growth was included as:
\begin{equation} \label{Gmax}
\log{G_{\textrm{max} \, f,p,s}} = m_{0} + m_1 \, t_f + m_2 \, \textrm{MAT} +
\log(G_{\textrm{max}_{f,p,s}}) = m_{0} + m_1 \, t_f + m_2 \, \textrm{MAT} +
m_3 \, \textrm{MAP} + \varepsilon_{G_{\textrm{max}}, f} +
\varepsilon_{G_{\textrm{max}}, p} + \varepsilon_{G_{\textrm{max}}, s}
\, .
......@@ -800,16 +799,16 @@ $\textrm{MAT}$ and sum of annual precipitation $\textrm{MAP}$ respectively, and
\(\varepsilon_{G_{\textrm{max}}, p}\), \(\varepsilon_{G_{\textrm{max}}, s}\)
are normally distributed random effects for species \(f\), plot or
quadrat \(p\) (see below), and data set \(s\) {[}where
\(\varepsilon_{G_{\textrm{max}, f}} \sim \mathcal{N} (0,\sigma_{G_{\textrm{max}, f}})\);
\(\varepsilon_{G_{\textrm{max}, p}} \sim \mathcal{N} (0,\sigma_{G_{\textrm{max}, p}})\)
\(\varepsilon_{G_{\textrm{max}}, f} \sim \mathcal{N} (0,\sigma_{G_{\textrm{max}}, f})\);
\(\varepsilon_{G_{\textrm{max}}, p} \sim \mathcal{N} (0,\sigma_{G_{\textrm{max}}, p})\)
and
\(\varepsilon_{G_{\textrm{max}, s}} \sim \mathcal{N} (0,\sigma_{G_{\textrm{max}, s}})\){]}.
\(\varepsilon_{G_{\textrm{max}}, s} \sim \mathcal{N} (0,\sigma_{G_{\textrm{max}}, s})\){]}.
As presented in Fig. 1, competitive
parameter $\alpha$ was modelled using an equation of the form:
\begin{equation} \label{alpha}
\alpha_{f,c}= \alpha_{0 \, \mathrm{intra}, f} \, C + \alpha_{0,\, \mathrm{inter},f} \, (1-C) - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_d \, \vert t_c-t_f \vert
\alpha_{f,c}= \alpha_{0 \, \mathrm{intra}, f} \, C + \alpha_{0 \, \mathrm{inter},f} \, (1-C) - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_d \, \vert t_c-t_f \vert
\end{equation}
where:
......@@ -839,7 +838,7 @@ where:
\(\alpha_{e}\) is the \textbf{competitive effect}, i.e.~change in
competition effect due to traits \(t_c\) of the competitor tree with a
normally distributed random effect of data set \(s\) included
{[}\(\varepsilon_{\alpha_i,s} \sim \mathcal{N} (0,\sigma_{\alpha_i})\){]}, and
{[}\(\varepsilon_{\alpha_e,s} \sim \mathcal{N} (0,\sigma_{\alpha_e})\){]}, and
\item
\(\alpha_d\) is the effect of \textbf{trait dissimilarity}, i.e.~change
in competition due to absolute distance between traits
......@@ -870,7 +869,7 @@ 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 $B_{i,het} = \sum_{c \neq f} {B_{i,c}}$, $B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$, and $B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$. $N_i$ is the number of species in the local neighbourhood
Where $B_{i,het} = \sum_{c \neq f} {B_{i,c}}$ is the sum of basal area of heterospecific competitors (het), $B_{i,tot} = B_{i,f} + B_{i,het}$ is the sum of basal area of all competitors, $B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$, and $B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$. $N_i$ is the number of species in the local neighbourhood
of the tree $i$ (note that the indices $p$ and $s$ for plot and data set are not shown here for sake of simplicity).
Estimating separate $\alpha_0$ for intra and interspecific competition
......@@ -1935,7 +1934,7 @@ plots were available (see Fig 1a. for biomes definitions).
\caption{\textbf{Variation of maximum growth, competitive
effects and competitive tolerance with wood density (\textbf{a}, \textbf{b} and \textbf{c}) and specific
leaf area (\textbf{d}, \textbf{e} and \textbf{f}) predicted by global traits models.} Variation of maximum growth
(\(m_1 \, t_f\)), tolerance of competition (\(\alpha_t \, t_f\)) and
(\(m_1 \times t_f\)), tolerance of competition (\(\alpha_t \, t_f\)) and
competitive effect ($\alpha_e \, t_c$)
parameters with wood density (first column) and specific leaf area
(second column). The shaded
......
......@@ -84,7 +84,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.set , data.type = "simple",
dev.off()
pdf('figs/figres12_TP_intra.pdf', height = 14, width = 16)
pdf('figs/figres12_TP_intra.pdf', height = 14, width = 18)
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'),
......@@ -103,10 +103,38 @@ plot.param.mean.and.biomes.fixed(list.all.results.intra , data.type = "intra",
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.30, 0.35),
xlim = c(-0.30, 0.365),
intra.TF = TRUE)
dev.off()
biomes.white <- rep('white', length.out = length(biomes.white))
names(biomes.white) <- names(fun.col.pch.biomes()$col.vec)
biomes.names.vec.w <- rep('', length.out = length(fun.biomes.names()))
names(biomes.names.vec.w) <- names(fun.biomes.names())
pdf('figs/figres12_TP_intra_m.pdf', height = 14, width = 16)
plot.param.mean.and.biomes.fixed(list.all.results.intra , data.type = "intra",
models = c('lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.fixed.biomes.species'),
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumBn.intra", "sumBn.inter",
"sumTnTfBn.abs", "sumTfBn","sumTnBn",
"Tf"),
biomes.names = biomes.names.vec.w,
param.print = 1:6,
param.names = c(expression('Intra'(alpha['0 intra'])),
expression('Inter'(alpha['0 inter'])),
expression('Dissim '(alpha['d'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression("Trait link "(m[1])),
expression("Size "(gamma %*% log('D'))) ) ,
col.vec = biomes.white,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.30, 0.35),
intra.TF = TRUE)
dev.off()
......
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