Commit aa205e1c authored by Kunstler Georges's avatar Kunstler Georges
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output for all new ecocode

parent 5be64c3e
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 one control [@Ouedraogo-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 one 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 (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below).""","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 (such as wind, forest fire, and bark beetles) 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, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below).""","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 (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below).""",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, and bark beetles) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below).""",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, and bark beetles) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below).""",,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 and volcanic eruptions. 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
\ No newline at end of file
Data set name,Country,Data type,Plot size,Diameter at breast height threshold,Number of plots,Traits,Source trait data,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 one control [@Ouedraogo-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 one 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 (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below).""","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 (such as wind, forest fire, and bark beetles) 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, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below).""","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 (such as wind, forest fire, and bark beetles) and harvesting. The age structure reconstructed by Vilen et al.[@Vilen-2012] shows that young forests represents a significant percentage of the forested area (see age distribution below).""",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, and bark beetles) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below).""",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, and bark beetles) and harvesting. The age structure reconstructed by Pan et al.[@Pan-2011] shows that young forests represents a significant percentage of the forested area (see age distribution below).""",,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 and volcanic eruptions. 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
\ No newline at end of file
......
......@@ -60,7 +60,7 @@ writeLines(unlist(list.t[dat[["Country"]]]))
![Age distribution of forest area in 20-year age class for North America (USA and Canada), estimated by Pan et al.[@Pan-2011]. The last class plotted at 150 years is for age > 140 years.](../../figs/age_na.pdf)
# Supplementary discussion
## Trait effects and potential mechanisms
......
......@@ -123,11 +123,13 @@ The effect of a focal species' trait value, \(t_f\), on its
maximum growth was include as:
\begin{equation} \label{Gmax}
\log{G_{\textrm{max} \, f,p,s}} = m_{0} + m_1 \, t_f + \varepsilon_{G_{\textrm{max}}, f} + \varepsilon_{G_{\textrm{max}}, p} + \varepsilon_{G_{\textrm{max}}, s}.
\log{G_{\textrm{max} \, f,p,s}} = m_{0} + m_1 \, t_f + m_2 \, MAT +
m_2 \, MAP + \varepsilon_{G_{\textrm{max}}, f} + \varepsilon_{G_{\textrm{max}}, p} + \varepsilon_{G_{\textrm{max}}, s}.
\end{equation}
Here \(m_0\) is the average maximum growth, \(m_1\) gives the effect of
the focal species trait, and \(\varepsilon_{G_{\textrm{max}}, f}\),
the focal species trait, $m_2$ and $m_3$ of mean annual temperature
$MAT$ and sum of annual precipitation $MAP$ respectively, and \(\varepsilon_{G_{\textrm{max}}, f}\),
\(\varepsilon_{G_{\textrm{max}}, p}\), \(\varepsilon_{G_{\textrm{max}}, s}\)
are normally distributed random effect for species \(f\), plot or
quadrat \(p\) (see below), and data set \(s\) {[}where
......@@ -182,6 +184,31 @@ where:
{[}$\varepsilon_{\alpha_s,s} \sim \mathcal{N} (0,\sigma_{\alpha_s})${]}.
\end{itemize}
To explore whether the trait similarity effect $\alpha_s$ was driven
only by different competitive effect between conspecific and
heterospecific, we also explored an alternative model where the traits
similarity was computed only for heterospecific and distinct trait
independent parameter $alpha_0$ were estimated for intraspecific and
interspecific competition. Intra-specific competition was modelled as:
\begin{equation}
\alpha_{f,f} = \alpha_{0,intra,f} + \alpha_t \, t_f + \alpha_e \, t_f
\end{equation}
where \(\alpha_{0,intra,f}\) is the intraspecific trait independent
competition for the focal species \(f\) (with the same random
structure as above), and the other parameters have the same definition as in the previous model.
And inter-specific competition was modelled as:
\begin{equation}
\alpha_{f,c} = \alpha_{0,inter,f} + \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c - t_f \vert
\end{equation}
where \(\alpha_{0,inter,f}\) is the interspecific trait independent
competition for the focal species \(f\) (with the same random
structure as above), and the other parameters have the same definition as in the previous model.
Eqs. \ref{logG1}-\ref{alpha} were then fitted to empirical estimates of
growth based on change in diameter between census $t$
and $t+1$ (respectively at year $y_t$ and $y_{t+1}$), given by
......@@ -204,7 +231,11 @@ enabled us to explore variation among biomes. Because some biomes had
few observations, we merged those with biomes with similar climates. Tundra was
merged with taiga, tropical rainforest and tropical seasonal forest were
merged into tropical forest, and deserts were not included in this final
analysis as too few plots were available.
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}.
\section{Data}\label{data}
......
......@@ -388,7 +388,7 @@ $\alpha_s$ are fitted from data using maximum likelihood method.
\begin{figure}[htbp]
\centering
\includegraphics{../../figs/figres12.pdf}
\includegraphics{../../figs/figres12_TP.pdf}
\caption{\textbf{Global trait effects and trait-independent effects on
maximum growth and competition and their variation among biomes.}
Standardized regression coefficients for growth models, fitted
......
......@@ -618,6 +618,17 @@
Volume = {188}
}
@article{Kriticos-2012,
title = {{CliMond}: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling},
volume = {3},
number = {1},
journal = {Methods in Ecology and Evolution},
author = {Kriticos, Darren J. and Webber, Bruce L. and Leriche, Agathe and Ota, Noboru and Macadam, Ian and Bathols, Janice and Scott, John K.},
year = {2012},
pages = {53--64}
}
@Article{Kunstler-2012,
Title = {Competitive interactions between forest trees are driven by species' trait hierarchy, not phylogenetic or functional similarity: implications for forest community assembly.},
Author = {Kunstler, Georges and Lavergne, S{\'e}bastien and Courbaud, Beno{\^\i}t and Thuiller, Wilfried and Vieilledent, Ghislain and Zimmermann, Niklaus E and Kattge, Jens and Coomes, David A},
......
......@@ -8,7 +8,7 @@ source("R/analysis/lmer.run.R")
format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
list.file.name = 'list.lmer.out.all.NA.simple.ecocode.koppen.rds',
models = c(model.files.lmer.Tf.4),# model.files.lmer.Tf.2),
models = c(model.files.lmer.Tf.4, model.files.lmer.Tf.2),
traits = c("SLA", "Wood.density", "Max.height"))
## format.all.output.lmer(file.name = "NA.wwf.results.nolog.all.rds",
......
......@@ -17,7 +17,7 @@ list.all.results.set <-
readRDS('output/list.lmer.out.all.NA.simple.set.rds')
list.all.results.ecocode <-
readRDS('output/list.lmer.out.all.NA.simple.ecocode.rds')
readRDS('output/list.lmer.out.all.NA.simple.ecocode.koppen.rds')
list.all.results.intra <-
......
......@@ -29,6 +29,24 @@ a <- fun.hexbin.with.smooth.ggplot(data.BA.G$MAT, data.BA.G$BA.G,
b <- fun.hexbin.with.smooth.ggplot(data.BA.G$MAP, data.BA.G$BA.G,
'MAP', 'BA.G')
library(ggplot2)
levels(data.glob$set) <- c("Sweden", "New Zealand", "USA", "Canada", "Australia", "France", "Switzerland", "Spain", "Panama", "French Guiana", "Japan", "Taiwan",
"Puerto Rico", "Central African Republic")
pdf('figs/boxplot_batot_set.pdf')
p0 = ggplot(data.glob, aes(y = BATOT)) + geom_boxplot(aes(x = factor(set)), outlier.size = 0)
p1 = p0 + coord_cartesian(ylim = c(0, 125)) +ylab(expression('Total basal area' (m^2/ha))) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +xlab('data')
p1
dev.off()
pdf('figs/boxplot_batot_biomes.pdf')
p0 = ggplot(data.glob, aes(y = BATOT)) + geom_boxplot(aes(x = factor(biomes)), outlier.size = 0)
p1 = p0 + coord_cartesian(ylim = c(0, 125)) +ylab(expression('Total basal area' (m^2/ha))) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +xlab('Biomes')
p1
dev.off()
multiplot(a, b,cols=2)
dev.off()
......
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