Commit 8bf80732 authored by Kunstler Georges's avatar Kunstler Georges
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......@@ -5,11 +5,11 @@ Luquillo,Puerto Rico,LPP,16 ha,1 cm,1,"Wood density, SLA, and Maximum height",lo
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 (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.
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
......@@ -73,7 +73,7 @@ writeLines(unlist(list.t[dat[["Country"]]]))
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 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 $\alpha_0$ 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, especially in tropical forest where the confidence interval did not include zero. 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 (growth without competition). This agrees well with previous studies that reported a positive correlation between SLA and gas exchange (the 'leaf economic spectrum'[@Wright-2004]). As in previous studies[@Poorter-2008; @Wright-2010], this direct effect of SLA was smaller than the effect size of wood density and had wider confidence intervals. 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].
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......@@ -9,8 +9,17 @@
Year = {2007},
Number = {2},
Pages = {135--45},
Volume = {10},
Keywords = {Adaptation, Physiological,Ecosystem,Models, Biological,Plants,Plants: anatomy \& histology,Population Dynamics,Species Specificity}
Volume = {10}
}
@article{Adams-2007,
title = {Understanding height-structured competition in forests: is there an {R}* for light?},
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Title = {lme4: Linear mixed-effects models using {S4} classes},
Author = {Bates, Douglas and Maechler, Martin and Bolker, Ben},
Year = {2014},
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Author = {Callaway, Ragan M. and Walker, Lawrence R.},
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Year = {2006},
Number = {6},
Pages = {1465--71},
Volume = {87},
Keywords = {Ecosystem,Models, Biological,Plant Components, Aerial,Plant Components, Aerial: genetics,Plants,Plants: genetics,Quantitative Trait, Heritable}
Volume = {87}
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Year = {2012},
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Keywords = {2012,community assembly,competition,ecology letters,environmental filtering,functional similarity,netic relatedness,niche similarity,phyloge-,plant interaction,traits hierarchy}
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......
......@@ -11,17 +11,17 @@ source("R/analysis/lmer.run.R")
## 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.koppen.results.nolog.all.rds",
list.file.name = 'list.lmer.out.all.NA.simple.set.null.rds',
models = c(model.files.lmer.Tf.0b),
traits = c("SLA", "Wood.density", "Max.height"))
## format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
## list.file.name = 'list.lmer.out.all.NA.simple.set.rds',
## models = c(model.files.lmer.Tf.0, model.files.lmer.Tf.0b,
## model.files.lmer.Tf.1, model.files.lmer.Tf.3),
## list.file.name = 'list.lmer.out.all.NA.simple.set.null.rds',
## models = c(model.files.lmer.Tf.0b),
## traits = c("SLA", "Wood.density", "Max.height"))
format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
list.file.name = 'list.lmer.out.all.NA.simple.set.rds',
models = c(model.files.lmer.Tf.0, model.files.lmer.Tf.0b,
model.files.lmer.Tf.1, model.files.lmer.Tf.3),
traits = c("SLA", "Wood.density", "Max.height"))
## format.all.output.lmer(file.name = "NA.koppen.results.nolog.all.rds",
## list.file.name = 'list.lmer.out.all.NA.intra.set.rds',
## models = c(model.files.lmer.Tf.intra.1,
......
......@@ -61,7 +61,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.set , data.type = "simple",
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(-0.30, 0.35))
dev.off()
......@@ -83,8 +83,8 @@ 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(-0.28, 0.31),
add.param.descrip.TF =2)
xlim = c(-0.30, 0.35),
intra.TF =TRUE)
dev.off()
......@@ -106,7 +106,7 @@ plot.param.mean.and.biomes.fixed(list.all.results.set , data.type = "simple",
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(-0.30, 0.35))
dev.off()
......@@ -128,8 +128,8 @@ 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(-0.28, 0.31),
add.param.descrip.TF =2)
xlim = c(-0.30, 0.35),
intra.TF = TRUE)
dev.off()
......@@ -174,7 +174,7 @@ 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(-0.30, 0.30))
dev.off()
......@@ -192,13 +192,23 @@ fun.plot.wd.sl.param(list.all.results.set,
MAT.MAP.TF = TRUE)
dev.off()
pdf('figs/figres4_TP.pdf', width = 8, height = 7)
fun.plot.wd.sl.param(list.all.results.set,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
MAT.MAP.TF = TRUE)
pdf('figs/figres4_TP_intra.pdf', width = 8, height = 7)
fun.plot.wd.sl.param(list.all.results.intra,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
MAT.MAP.TF = TRUE, intra.TF = TRUE,
data.type ='intra')
dev.off()
pdf('figs/figres4t_TP_intra.pdf', width = 8, height = 7)
fun.plot.trade.param(list.all.results.intra,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
MAT.MAP.TF = TRUE, intra.TF = TRUE,
data.type ='intra')
dev.off()
pdf('figs/figres4b.pdf', width = 8, height = 11)
fun.plot.all.param(list.all.results.set)
dev.off()
......@@ -206,13 +216,19 @@ dev.off()
pdf('figs/figres4b_TP.pdf', width = 8, height = 11)
fun.plot.all.param(list.all.results.set,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
MAT.MAP.TF = TRUE)
MAT.MAP.TF = TRUE,
ylim.list = list(maxG = c(-0.75, 0.75), alphae = c(-0.02, 0.009),
alphar = c(-0.013, 0.013), alphal = c(-0.028, 0.007),
alpha0 = c(0.003, 0.016), alpha0.intra = c(0.003, 0.028),
alpha0.inter = c(0.003, 0.028))
)
dev.off()
pdf('figs/figres4b_TP_intra.pdf', width = 8, height = 11)
fun.plot.all.param(list.all.results.intra,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
MAT.MAP.TF = TRUE, intra.TF = TRUE)
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
MAT.MAP.TF = TRUE, intra.TF = TRUE,
data.type ='intra')
dev.off()
pdf('figs/stabl_set.pdf')
......@@ -221,20 +237,20 @@ fun.plot.all.stabl(list.all.results.set,
MAT.MAP.TF = FALSE)
dev.off()
pdf('figs/stabl_set_intra.pdf')
pdf('figs/stabl_set_TP_intra.pdf')
fun.plot.all.stabl(list.all.results.intra,
model = 'lmer.LOGLIN.ER.AD.Tf.intra.r.set.species',
MAT.MAP.TF = FALSE, intra.TF = TRUE, data.type ='intra')
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
MAT.MAP.TF = TRUE, intra.TF = TRUE, data.type ='intra')
dev.off()
pdf('figs/rho_set.pdf')
pdf('figs/rho_set_TP.pdf')
fun.plot.all.rho(list.all.results.set,
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.species',
MAT.MAP.TF = FALSE)
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
MAT.MAP.TF = TRUE)
dev.off()
pdf('figs/rho_set_intra.pdf')
pdf('figs/rho_set_TP_intra.pdf')
fun.plot.all.rho(list.all.results.intra,
model = 'lmer.LOGLIN.ER.AD.Tf.intra.r.set.species',
MAT.MAP.TF = FALSE, intra.TF = TRUE, data.type ='intra')
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.intra.r.set.species',
MAT.MAP.TF = TRUE, intra.TF = TRUE, data.type ='intra')
dev.off()
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