Commit 678ccb8b authored by Kunstler Georges's avatar Kunstler Georges
Browse files

process all ecocode intra

parent 41e9c9a0
......@@ -969,7 +969,10 @@ plot.param <- function(list.res,
col.names = fun.col.param(),
data.type = "simple",
add.param.descrip.TF = TRUE,
intra.TF = FALSE,
...){
if(!intra.TF) x.line <- -0.63
if(intra.TF) x.line = -0.7
m <- matrix(c(1:3), 1, 3)
big.m <- 2.8
small.m <- 0.42
......@@ -1008,14 +1011,8 @@ for (i in traits[traits.print]){
labels = param.names,
cols.vec = col.names[param.vec])
if(add.param.descrip.TF){
mtext("Max growth", side=2, at = 4.95, cex =1.6,
line = 16.9, col = '#e41a1c')
lines(c(-0.5, -0.5), c(4.5, 5.28), col = '#e41a1c',
lwd = 2.5)
mtext("Competition", side=2, at = 2.5, cex =1.6,
line = 16.9, col = '#377eb8')
lines(c(-0.5, -0.5), c(0.82, 4.28), col = '#377eb8',
lwd = 2.5)
fun.param.descrip(c(-0.1,0.1),
length(param.vec), x.line)
}
}
if(i == traits[2]){
......
......@@ -5,16 +5,16 @@ We developed the equation of $\alpha_{c,f} = \alpha_{0,f,intra} \, CON + \alpha_
\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_s \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,het} = \sum_{i \neq f} {B_{i,c}}$,
$B_{i,het} = \sum_{c \neq f} {B_{i,c}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$,
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the index $p4 and $s$ respectively for plot and data set were not include for the sake of simplicity.
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the index $p$ and $s$ respectively for plot and data set were not include for sake of simplicity.
## Derivation of $\rho$ for a Lotka-Volterra model based on Godoy \& Levine[@Godoy-2014]
......@@ -33,7 +33,7 @@ The criteria for invasion of species $i$ in resident community of species $j$ is
Thus if $\frac{\alpha_{ij}}{\alpha'_{jj}} <1$ invasion $i$ in $j$ is possible (same approach for $j$ in $i$).
Stable coexistence for species $i$ in regard to speciesand $j$ thus requires
Stable coexistence for species $i$ in regard to speciesand $j$ thus requires
$\frac{\alpha'_{ij}}{\alpha'_{jj}}$ and $\frac{\alpha'_{ji}}{\alpha'_{i}}$ to be smaller than 1. Chesson[@Chesson-2012] then defioned the average
stabilising niche
overlap between species $i$ and $j$ as:
......@@ -293,7 +293,10 @@ Two main data type were used: national forest inventories data -- NFI, large per
\newpage
# Supplementary results
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with no differences in $\alpha_0$ for intra- and interspecific competition.** Standardized regression coefficients for growth models, fitted separately for each trait (points: mean estimates and lines: 95\% confidence intervals). Black points and lines represent global estimates and coloured points and lines represent the biome level estimates. The parameter estimates represent: effect of focal tree's trait value on maximum growth ($m_1$), the competitive effect independent of traits ($\alpha_{0}$), the effect of competitor trait values on their competitive effect ($\alpha_e$) (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree's trait value on its tolerance of competition($\alpha_t$) (positive values indicate that greater trait values result in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a stronger reduction of the growth of the focal tree). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_TP.pdf)
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with random structure in the parameter including the data set and the Koppen-Geiger ecoregion.** Standardized regression coefficients for growth models, fitted separately for each trait (points: mean estimates and lines: 95\% confidence intervals). Black points and lines represent global estimates and coloured points and lines represent the biome level estimates. The parameter estimates represent: effect of focal tree's trait value on maximum growth ($m_1$), the competitive effect independent of traits of conspecific ($\alpha_{0 \, intra}$) and heterospecific ($\alpha_{0 \, inter}$), the effect of competitor trait values on their competitive effect ($\alpha_e$) (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree's trait value on its tolerance of competition($\alpha_t$) (positive values indicate that greater trait values result in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a stronger reduction of the growth of the focal tree). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_ecocode_TP.pdf)
THIS NEED TO BE UPDATED WITH INTRA INTER
......@@ -306,7 +309,7 @@ THIS NEED TO BE UPDATED WITH INTRA INTER
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key driver of tree growth[@Stephenson-2014; @Enquist-1999]. Then there was a consistent negative effect of the total basal area of conspecific and heterospecific neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height >= 10cm diameter breast height) agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than to the adult stage [@Callaway-1997]. The variation of $\alpha_{0 \, intra} \, \& \, \alpha_{0 \, inter}$ between biomes is limited with large overlap of their confidences intervals.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
SLA was positively correlated with maximum basal area growth only in three biomes and with a weak effect. Previous studies have reported a strong positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]) and young seedling relative growth rate (REF TO ADD). Studies[@Poorter-2008; @Wright-2010] on adult tree have however generally reported weak and marginal correlation between SLA and maximum growth. Our results support this pattern. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@Niinemets-2010].
......
......@@ -5,16 +5,16 @@ We developed the equation of $\alpha_{c,f} = \alpha_{0,f,intra} \, CON + \alpha_
\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_s \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,het} = \sum_{i \neq f} {B_{i,c}}$,
$B_{i,het} = \sum_{c \neq f} {B_{i,c}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c}}$,
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the index $p4 and $s$ respectively for plot and data set were not include for the sake of simplicity.
and $N_i$ is the number of species in the local neighbourhood of the tree $i$. Note that the index $p$ and $s$ respectively for plot and data set were not include for sake of simplicity.
## Derivation of $\rho$ for a Lotka-Volterra model based on Godoy \& Levine[@Godoy-2014]
......@@ -33,7 +33,7 @@ The criteria for invasion of species $i$ in resident community of species $j$ is
Thus if $\frac{\alpha_{ij}}{\alpha'_{jj}} <1$ invasion $i$ in $j$ is possible (same approach for $j$ in $i$).
Stable coexistence for species $i$ in regard to speciesand $j$ thus requires
Stable coexistence for species $i$ in regard to speciesand $j$ thus requires
$\frac{\alpha'_{ij}}{\alpha'_{jj}}$ and $\frac{\alpha'_{ji}}{\alpha'_{i}}$ to be smaller than 1. Chesson[@Chesson-2012] then defioned the average
stabilising niche
overlap between species $i$ and $j$ as:
......@@ -62,7 +62,7 @@ refs$citation <- iconv(refs$citation, "ISO_8859-2", "UTF-8")
replace_refs <- function(x){
ids <- as.numeric(unlist(strsplit(x,",")))
if(length(ids>0))
ret <- paste0("\n\t- ", refs$citation[match(ids, refs$id)],
ret <- paste0("\n\t- ", refs$citation[match(ids, refs$id)],
collapse="")
else
ret <- ""
......@@ -92,10 +92,13 @@ writeLines(unlist(list.t[dat[["Country"]]]))
\newpage
# Supplementary results
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with random structure in the parameter including the data set and the Koppen-Geiger ecoregion.** Standardized regression coefficients for growth models, fitted separately for each trait (points: mean estimates and lines: 95\% confidence intervals). Black points and lines represent global estimates and coloured points and lines represent the biome level estimates. The parameter estimates represent: effect of focal tree's trait value on maximum growth ($m_1$), the competitive effect independent of traits of conspecific ($\alpha_{0 \, intra}$) and heterospecific ($\alpha_{0 \, inter}$), the effect of competitor trait values on their competitive effect ($\alpha_e$) (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree's trait value on its tolerance of competition($\alpha_t$) (positive values indicate that greater trait values result in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a stronger reduction of the growth of the focal tree). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_ecocode_TP.pdf)
THIS NEED TO BE UPDATED WITH INTRA INTER
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with no differences in $\alpha_0$ for intra- and interspecific competition.** Standardized regression coefficients for growth models, fitted separately for each trait (points: mean estimates and lines: 95\% confidence intervals). Black points and lines represent global estimates and coloured points and lines represent the biome level estimates. The parameter estimates represent: effect of focal tree's trait value on maximum growth ($m_1$), the competitive effect independent of traits ($\alpha_{0}$), the effect of competitor trait values on their competitive effect ($\alpha_e$) (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree's trait value on its tolerance of competition($\alpha_t$) (positive values indicate that greater trait values result in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a stronger reduction of the growth of the focal tree). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig 1a. for biomes definitions).](../../figs/figres12_TP.pdf)
![**Global trait effects and trait-independent effects on maximum growth and competition and their variation among biomes for a model with random structure in the parameter including the data set and the Koppen-Geiger ecoregion.** Standardized regression coefficients for growth models, fitted separately for each trait (points: mean estimates and lines: 95\% confidence intervals). Black points and lines represent global estimates and coloured points and lines represent the biome level estimates. The parameter estimates represent: effect of focal tree's trait value on maximum growth ($m_1$), the competitive effect independent of traits of conspecific ($\alpha_{0 \, intra}$) and heterospecific ($\alpha_{0 \, inter}$), the effect of competitor trait values on their competitive effect ($\alpha_e$) (positive values indicate that higher trait values lead to a stronger reduction in growth of the focal tree), the effect of the focal tree's trait value on its tolerance of competition($\alpha_t$) (positive values indicate that greater trait values result in greater tolerance of competition), and the effect on competition of trait similarity between the focal tree and its competitors ($\alpha_s$) (negative values indicate that higher trait similarity leads to a stronger reduction of the growth of the focal tree). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few plots were available (see Fig 1a. for biomes definitions).](../../figs/figres1_ecocode_TP_intra.pdf)
THIS NEED TO BE UPDATED WITH BIOMES ESTIMATES
\newpage
......@@ -105,7 +108,7 @@ THIS NEED TO BE UPDATED WITH INTRA INTER
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key driver of tree growth[@Stephenson-2014; @Enquist-1999]. Then there was a consistent negative effect of the total basal area of conspecific and heterospecific neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height >= 10cm diameter breast height) agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than to the adult stage [@Callaway-1997]. The variation of $\alpha_{0 \, intra} \, \& \, \alpha_{0 \, inter}$ between biomes is limited with large overlap of their confidences intervals.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
In terms of traits effects, wood density (WD) was strongly negatively associated with maximum growth, which is in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@Nock-2009; @Wright-2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth low wood density species will have higher basal area increments than species with high wood density[@Enquist-1999]. Other advantages of low wood density may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an direct consequence. A countervailing advantage for high wood density species was their better tolerance of competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@Chave-2009; @Nock-2009; @Wright-2010]. This has generally been related to the higher survival associated with high wood density[@Kraft-2010] via resistance to mechanical damage, herbivores and pathogens[@Chave-2009; @Kraft-2010]. Yet this may also be related to lower maintenance respiration[@Larjavaara-2010]. For growth, lower respiration may lead to a direct advantage in deep shade, but this relationship might also arise through correlated selection for high survival and high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effects. This might possibly have been mediated by larger crowns (both in depth and radius)[@Poorter-2006a; @Aiba-2009], casting a deeper shade.
SLA was positively correlated with maximum basal area growth only in three biomes and with a weak effect. Previous studies have reported a strong positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]) and young seedling relative growth rate (REF TO ADD). Studies[@Poorter-2008; @Wright-2010] on adult tree have however generally reported weak and marginal correlation between SLA and maximum growth. Our results support this pattern. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@Niinemets-2010].
......
......@@ -104,12 +104,8 @@ pandoc.table(cor.mat,
##+ ComputeTable_Effectsize, echo = FALSE, results = 'hide', message=FALSE
list.all.results.0 <-
readRDS.root('output/list.lmer.out.all.NA.simple.set.rds')
list.all.results <-
readRDS.root('output/list.lmer.out.all.NA.intra.set.rds')
#TODO UPDATE NULL MODEL FOR INTRA
mat.param <- do.call('cbind',
lapply(c('Wood.density', 'SLA', 'Max.height'),
......@@ -120,8 +116,10 @@ mat.param <- do.call('cbind',
"sumTnBn","sumTfBn", "sumTnTfBn.abs"),
data.type = 'intra'))
mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD", "Tf", "sumTfBn"),] <-
-mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD", "Tf", "sumTfBn"),]
mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD",
"Tf", "sumTfBn"),] <-
-mat.param[!row.names(mat.param) %in% c("(Intercept)", "logD",
"Tf", "sumTfBn"),]
mat.param.sd <- do.call('cbind',
lapply(c('Wood.density', 'SLA', 'Max.height'),
......@@ -149,8 +147,8 @@ mat.AIC <- do.call('cbind',
data.type = 'intra'))
mat.AIC.0 <- do.call('cbind',
lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.AIC, list.res = list.all.results.0,
model = 'lmer.LOGLIN.r.set.species'))
extract.AIC, list.res = list.all.results,
model = 'lmer.LOGLIN.MAT.MAP.intra.r.set.species'))
bold.index <- which(((mat.param - 1.96*mat.param.sd) >0 & mat.param > 0) |
((mat.param + 1.96*mat.param.sd) <0 & mat.param <0),
......
......@@ -21,7 +21,7 @@
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
% % use microtype if available
% \IfFileExists{microtype.sty}{%
% \usepackage{microtype}
% \usepackage{microtype}
% \UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts
% }{}
......@@ -184,7 +184,7 @@ where:
{[}$\varepsilon_{\alpha_s,s} \sim \mathcal{N} (0,\sigma_{\alpha_s})${]}.
\end{itemize}
Estimating different $\alpha_0$ for intra- and interspecific competition allow to account for trait independant differences in interactions with conspecific or with heterospecific.
Estimating different $\alpha_0$ for intra- and interspecific competition allow to account for trait independant differences in interactions with conspecific or with heterospecific.
We also explored a simpler version of the model where only one $\alpha_0$ was include in the model of $\alpha_{c,f}$ as most previous studies have generally not make this distinction which may lead into an overestimation of the trait similarity effect. In this alternative model the equation was:
......@@ -192,6 +192,7 @@ We also explored a simpler version of the model where only one $\alpha_0$ was in
\alpha_{c,f}= \alpha_{0,f} - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c-t_f \vert
\end{equation}
This results are presented in Supplementary Results.
Eqs. \ref{logG1}-\ref{alpha} were then fitted to empirical estimates of
growth based on change in diameter between census $t$
......@@ -219,12 +220,13 @@ analysis as too few plots were available. To evaluate if our results
were robust to the random effect structure we also explored a model
with a single effect constant across biomes but with a random effect
for the parameters with both the data set and a local ecoregion using
the K{\"o}ppen-Geiger ecoregion\citep{Kriticos-2012}.
the K{\"o}ppen-Geiger ecoregion\citep{Kriticos-2012} (see
Supplementary Results).
\subsection{Estimating of strength of stabilising processes}\label{rho}
To estimate if traits effects on competition have the potential to lead to a stable coexistence of species with diverse traits values, studies\citep{Kraft-2015, Godoy-2014} have recently proposed to use the method developed by Chesson\citep{Chesson-2012} to estimate the stabilising niche difference between species which estimates the strength process favouring the establishment of a species as rare invader in an other species already established (see an example based on the Lotka-Volterra model based on Godoy \& Levine\citep{Kraft-2015, Godoy-2014} in the Supplementary Methods). This approach shows that $\rho$ defined as the ratio of geometric mean of interspecific competition over intraspecific competition ($\rho = \sqrt{\frac{\alpha'_{ij} \alpha'_{ji}}{\alpha'_{jj} \alpha'_{ii}}}$, where $\alpha'_{ij}$ represent the population level competitive effect of species $j$ on species $i$) to quantify stabilising niche overlap. Our model only estimate competition effect on the individual basal area growth, and not on the population growth, it is thus impossible to estimate the fitness of a rare invader. We can, however, compare the relative importance of interspecific to intraspecific competition using the same approach as $\rho$. The competitive effect of species $j$ on species $i$ can be defined by the inverse of the growth reduction per unit of basal area of the species $j$ on species $j$ predicted by tree basal area growth model (see equ. \ref{logG1}). According to this model $\alpha'_{ij} = \frac{1}{e^{-\alpha_{ij}}}$, with $\alpha_{ij}$ defined by equ. \ref{alpha}. $\rho$ can then be simplified based on eqn. \ref{alpha} as:
\begin{equation} \label{rho}
\rho = \sqrt{\frac{\alpha'_{ij} \alpha'_{ji}}{\alpha'_{jj} \alpha'_{ii}}} = e^{(\alpha_{0,inter} - \alpha_{0,intra} + \alpha_s \vert t_j - t_i \vert)}
\end{equation}
......
......@@ -15,7 +15,7 @@
\fi
\defaultfontfeatures{Mapping=tex-text,Scale=MatchLowercase}
\newcommand{\euro}{}
\fi
\fi
% use upquote if available, for straight quotes in verbatim environments
\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
......@@ -141,9 +141,9 @@
\begin{document}
\maketitle
\maketitle
\section{Summary paragraph outline (246/max 300 but rather 200)}\label{summary-paragraph-outline}
\section{Summary paragraph outline (245/max 300 but rather 200)}\label{summary-paragraph-outline}
Phenotypic traits and their associated trade-offs are thought to play an
important role in community assembly and thus in maintaining species
......@@ -174,7 +174,7 @@ were more similar. Our
trait-based approach to modelling competition reveals key generalisations
across the forest ecosystems of the globe.
\section{Main text (1555/max 1500)}\label{main-text}
\section{Main text (1582/max 1500)}\label{main-text}
Phenotypic traits are considered fundamental drivers of community
assembly and thus species diversity \citep{Westoby-2002, Adler-2013}. The effects of traits on individual
......@@ -274,7 +274,7 @@ no correlation of maximum height with any mechanisms linked to
competitive advantage. This trait is, however, generally considered closely connected with
light interception, a key advantage in
competition\citep{Mayfield-2010}, but it is possible that maximum
height effect appears only when considering long-term
height effect appears only when considering long-term
population levels outcome of competition rather than its short-term effect
on basal area growth\citep{Adams-2007}.
Finally, after accounting for differences in trait independent
......@@ -293,7 +293,7 @@ interspecific competition and the small maximum height similarity
effect are unknown, but could include less efficient light
capture because of less complementarity in architectural
niche\citep{Sapijanskas-2014, Jucker-2015}, or higher loads of specialised
pathogens\citep{Bagchi-2014} between conspecific or species with similar traits. This highlight that other traits, for instance traits more directly related to natural enemies,
pathogens\citep{Bagchi-2014} between conspecific or species with similar traits. This highlight that other traits, for instance traits more directly related to natural enemies,
may show stronger trait
similarity effect.
......@@ -302,7 +302,7 @@ strong evidence for any particular biome behaving consistently
differently from the others (Fig. \ref{res1}). This surprising lack of context
dependence in the traits effects, may results from the predominant role of
competition for light in all forests of the world (further
details in Supplementary Discussion).
details in Supplementary Discussion).
Our global study supports the hypothesis that trait values
favouring high tolerance of competition or high competitive effects also
......@@ -322,7 +322,7 @@ correlated with a low competitive effect and marginally correlated with a fast m
growth (confidence intervals do not intercepted zero only in three
biomes, see Fig. \ref{res1} and \ref{res3}). In addition, the long
term impact of SLA is unclear, because long-term outcomes of competition at the population level may be less
influenced by competitive effect than the tolerance of competition\citep{Goldberg-1996}.
influenced by competitive effect than the tolerance of competition\citep{Goldberg-1996}.
Coordination between trait values conferring high competitive effect and
trait values conferring high tolerance of competition has generally
......
......@@ -27,7 +27,7 @@ format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
models = c(model.files.lmer.Tf.intra.0,
model.files.lmer.Tf.intra.1,
model.files.lmer.Tf.intra.2,
model.files.lmer.Tf.intra.3[1]),
model.files.lmer.Tf.intra.3),
traits = c("SLA", "Wood.density", "Max.height"),
data.type = 'intra')
......
......@@ -155,27 +155,48 @@ plot.param.mean.and.biomes.fixed(list.all.results.ecocode ,
dev.off()
pdf('figs/figres12_ecocode_TP.pdf', height = 14, width = 16)
plot.param.mean.and.biomes.fixed(list.all.results.ecocode ,
data.type = "simple",
models = c('lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.ecocode.species',
'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.ecocode.fixed.biomes.species'),
pdf('figs/figres12_ecocode_TP_intra.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.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", "Tf"),
param.print = 1:5,
"sumBn.intra", "sumBn.inter", "Tf"),
param.print = 1:6,
param.names = c(expression('Trait sim '(alpha['s'])),
expression('Tolerance '(alpha['t'])),
expression('Effect '(alpha['e'])),
expression('Trait indep'(alpha[0])),
expression("Direct trait "(m[1])),
expression("Size "(gamma %*% log('D'))) ) ,
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))
dev.off()
pdf('figs/figres1_ecocode_TP_intra.pdf', height = 14, width = 16)
plot.param(list.all.results.intra ,
data.type = "intra",
model = c('lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.ecocode.species'),
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn.intra", "sumBn.inter", "Tf"),
param.print = 1:6,
param.names = c(expression('Trait sim '(alpha['s'])),
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),
intra.TF = FALSE)
dev.off()
......
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