Commit 2a0742ee authored by kunstler's avatar kunstler
Browse files

start recompute BA intra BA inter for revision

parent a45b528a
......@@ -1169,10 +1169,10 @@ require(dplyr)
# Layout
m <- matrix(c(1:12), 4, 3)
wid <- c(big.m, 0 , small.m) +
rep((6-big.m-small.m)/3, each= 3)
hei <- c(small.m, 0, 0 , big.m) +
rep((10-big.m-small.m)/4, each= 4)
wid <- c(big.m+0.1, small.m+0.1 , 2*small.m+0.1) +
rep((6-big.m-3*small.m-0.3)/3, each= 3)
hei <- c(small.m+0.1, 0.2, 0.2 , big.m) +
rep((10-big.m-small.m- 0.5)/4, each= 4)
layout(m, heights=hei, widths= wid )
......@@ -1190,36 +1190,36 @@ for (t in c('Wood density', 'Specific leaf area', 'Maximum height')){
df.t <- data.param[data.param$traits == t, ]
if(t == 'Wood density'){
if(p == 'alphal'){
par(mai=c(0.1, big.m,small.m,0))
par(mai=c(0.1, big.m,small.m,0.1))
}else{
if(p == 'maxG'){
par(mai=c(big.m, big.m,0.1,0))
par(mai=c(big.m, big.m,0.1,0.1))
}else{
par(mai=c(0.1, big.m,0.1,0))
par(mai=c(0.1, big.m,0.1,0.1))
}
}
}
if(t == 'Specific leaf area'){
if(p == 'alphal'){
par(mai=c(0.1, 0,small.m,0))
par(mai=c(0.1, small.m,small.m,0.1))
}else{
if(p == 'maxG'){
par(mai=c(big.m, 0,0.1,0))
par(mai=c(big.m, small.m,0.1,0.1))
}else{
par(mai=c(0.1, 0,0.1,0))
par(mai=c(0.1, small.m,0.1,0.1))
}
}
}
if(t == 'Maximum height'){
if(p == 'alphal'){
par(mai=c(0.1, 0,small.m,small.m))
par(mai=c(0.1, small.m,small.m,small.m))
}else{
if(p == 'maxG'){
par(mai=c(big.m, 0,0.1,small.m))
par(mai=c(big.m, small.m,0.1,small.m))
}else{
par(mai=c(0.1, 0,0.1,small.m))
par(mai=c(0.1, small.m,0.1,small.m))
}
}
}
......@@ -1243,7 +1243,7 @@ if(p == 'maxG'){
if(t == 'Specific leaf area'){
if(p == 'maxG'){
fun.plot.param.tf(df = df.t,
param.sel = p, yaxt = 'n',
param.sel = p,
xlab = expression(paste('Specific leaf area (m', m^2, ' m', g^-1, ')')),
col.param = col.vec[names.param[p]],
expr.param = expr.p.vec[p], add.ylab.TF = FALSE, cex.lab = 1.1, cex.axis =0.85, cex = 1)
......@@ -1251,7 +1251,6 @@ if(p == 'maxG'){
fun.plot.param.tf(df = df.t,
param.sel = p,
xaxt= 'n',xlab = NA,
yaxt = 'n',
col.param = col.vec[names.param[p]],
expr.param = expr.p.vec[p], add.ylab.TF = FALSE, cex.lab = 1.1, cex.axis =0.85, cex = 1)
}
......@@ -1260,7 +1259,7 @@ if(p == 'maxG'){
if(t == 'Maximum height'){
if(p == 'maxG'){
fun.plot.param.tf(df = df.t,
param.sel = p, yaxt = 'n',
param.sel = p,
xlab = expression(paste('Maximum height (m)')),
col.param = col.vec[names.param[p]],
expr.param = expr.p.vec[p], add.ylab.TF = FALSE, cex.lab = 1.1, cex.axis =0.85, cex = 1)
......@@ -1268,7 +1267,7 @@ if(p == 'maxG'){
fun.plot.param.tf(df = df.t,
param.sel = p,
xlab = NA,
xaxt= 'n', yaxt = 'n',
xaxt= 'n',
col.param = col.vec[names.param[p]],
expr.param = expr.p.vec[p], add.ylab.TF = FALSE, cex.lab = 1.1, cex.axis =0.85, cex = 1)
}
......
......@@ -305,8 +305,8 @@ require(dplyr)
fun.CWM.abs.trait <- function(trait, data){
trait <- paste(trait, 'mean', sep = '.')
perc.BA <- data[['BA.w']]/sum(data[['BA.w']])
res <- apply(perc.BA*abs(outer(data[[trait]], data[[trait]], '-')), 2, sum)
BA <- data[['BA.w']]
res <- apply(BA*abs(outer(data[[trait]], data[[trait]], '-')), 2, sum)
return(res)
}
......@@ -316,9 +316,9 @@ fun.CWM.abs.traits.multi <- function(traits, data){
if(any(apply(is.na(data[, ts]), MARGIN = 2, sum)>0)){
res <- rep(NA, nrow(data))
}else{
perc.BA <- data[['BA.w']]/sum(data[['BA.w']])
BA <- data[['BA.w']]
dist.mat <- as.matrix(dist(data[, ts], diag = TRUE, upper = TRUE))
res <- apply(perc.BA*dist.mat, 2, sum)
res <- apply(BA*dist.mat, 2, sum)
}
return(res)
}
......@@ -344,7 +344,7 @@ fun.CWM.Tn <- function(data){
require(dplyr)
# comput CWM and perc
data.plot<- group_by(data, plot.c) %>%
summarise(
dplyr::summarise(
BATOT = sum(BA.w),
count = n(),
Leaf.N.CWM.fill = sum(BA.w*Leaf.N.mean),
......@@ -378,10 +378,19 @@ require(dplyr)
dplyr::select(-count) %>% ungroup()
data <- left_join(data, data.plot, by = 'plot.c')
## BAintra
data <- mutate(data, plot.sp = paste(plot.c, sp))
data.plot.sp<- mutate(data, plot.sp = paste(plot.c, sp)) %>% group_by(plot.sp) %>%
dplyr::summarise(BAintra = sum(BA.w)) %>% ungroup()
data <- left_join(data, data.plot.sp, by = 'plot.sp') %>% dplyr::select(-plot.sp)
## remove BA obs tree
data <- data %>%
mutate(
BATOT = BATOT - BA.w,
BAintra = BAintra - BA.w,
Leaf.N.CWM.fill = (Leaf.N.CWM.fill - BA.w*Leaf.N.mean)/BATOT,
Leaf.N.CWM.fill = ifelse(BATOT == 0, 0, Leaf.N.CWM.fill),
SLA.CWM.fill = (SLA.CWM.fill - BA.w*SLA.mean)/BATOT,
......@@ -421,12 +430,26 @@ fun.traits.focal <- function(data){
NA,
Seed.mass.mean)
) %>%
select(-Leaf.N.mean, -SLA.mean,
dplyr::select(-Leaf.N.mean, -SLA.mean,
-Wood.density.mean, -Max.height.mean,
-Seed.mass.mean)
return(data)
}
fun.rescal.abs.CWM <- function(data){
data <- mutate(data,
Leaf.N.abs.CWM.fill = Leaf.N.abs.CWM.fill/BATOT,
SLA.abs.CWM.fill = SLA.abs.CWM.fill/BATOT,
Seed.mass.abs.CWM.fill = Seed.mass.abs.CWM.fill/BATOT,
Wood.density.abs.CWM.fill = Wood.density.abs.CWM.fill/BATOT,
Max.height.abs.CWM.fill = Max.height.abs.CWM.fill/BATOT,
Multi1.abs.CWM.fill = Multi1.abs.CWM.fill/BATOT,
Multi2.abs.CWM.fill = Multi2.abs.CWM.fill/BATOT
)
return(data)
}
fun.CWM.to.NA <- function(data){
data <- mutate(data,
Leaf.N.abs.CWM.fill = ifelse(is.na(Leaf.N.focal), NA,
......@@ -474,7 +497,7 @@ fun.CWM.traits.all.plot.census.dplyr <- function(data,data.TRAITS){
# merge traits
data.TRAITS <- fun.fill.missing.traits(data.TRAITS)
data <- left_join(data,
select(data.TRAITS, sp,
dplyr::select(data.TRAITS, sp,
Leaf.N.mean, Leaf.N.genus,
Seed.mass.mean, Seed.mass.genus,
SLA.mean, SLA.genus,
......@@ -487,9 +510,10 @@ fun.CWM.traits.all.plot.census.dplyr <- function(data,data.TRAITS){
# compute CWM abs
data.CWM.abs <- data %>% group_by(plot.c) %>%
do(fun.CWM.abs.all(.)) %>%
ungroup() %>% select(-plot.c)
ungroup() %>% dplyr::select(-plot.c)
data <- left_join(data, data.CWM.abs, by = 'obs.id') %>%
select(-weights, -plot.c)
dplyr::select(-weights, -plot.c)
data <- fun.rescal.abs.CWM(data)
# set trait to NA for species with missing species
data <- fun.traits.focal(data)
data <- fun.CWM.to.NA(data)
......
......@@ -90,13 +90,17 @@ fun.compute.CWM.test.plot <- function(samp.id, samp.plot, data, data.TRAITS){
data[["plot"]] ==samp.plot])
sp.a.f <- factor(data[["sp"]])[data[["obs.id"]]!=samp.id &
data[["plot"]] ==samp.plot]
sp.id <- factor(data[["sp"]])[data[["obs.id"]]==samp.id]
BA.n <- tapply(BA.a, INDEX = sp.a.f, FUN = sum, na.rm = TRUE)
BA.n.no0 <- BA.n[!is.na(BA.n)]
sp.n <- as.character(names(BA.n.no0))
mat.t <- fun.trait.format(trait = "Wood.density",
traits.data = data.TRAITS,
vec.sp = sp.n)
res.fill <- sum(BA.n.no0*mat.t[, 3])/sum(BA.n.no0)
mat.t.id <- fun.trait.format(trait = "Wood.density",
traits.data = data.TRAITS,
vec.sp = sp.id)
res.fill <- sum(BA.n.no0*abs(mat.t.id[,3] - mat.t[, 3]))/sum(BA.n.no0)
return(res.fill)
}
......@@ -134,14 +138,13 @@ fun.test.CWM.plot <- function(data.t, data.TRAITS, data.tree) {
res.fill <- fun.compute.CWM.test.plot(samp.id, samp.plot, data.tree.t,
data.TRAITS)
test.cwm.fill <- all.equal(res.fill,
data[["Wood.density.CWM.fill"]][
data[["Wood.density.abs.CWM.fill"]][
data[["obs.id"]]==samp.id])
res <- all(c(test.focal, test.cwm.fill) == TRUE)
if (is.na(res.fill)) res <- NA
}
return(res)
}
#################################
## test function XY type data set
......@@ -185,11 +188,15 @@ fun.compute.CWM.trait.test <- function(samp.id, data, Rlim, data.TRAITS){
select = c("x", "y"))),
diam = data[["D"]], sp = data[["sp"]], Rlim = Rlim)
sp.n <- as.character(names(BA.n)[BA.n>0])
sp.id <- as.character(data[["sp"]][data[["obs.id"]] == samp.id])
BA.n <- BA.n[BA.n>0]
mat.t <- fun.trait.format(trait = "Wood.density",
traits.data = data.TRAITS,
vec.sp = sp.n)
res.fill <- sum(BA.n * mat.t[, 3])/sum(BA.n)
mat.t.id <- fun.trait.format(trait = "Wood.density",
traits.data = data.TRAITS,
vec.sp = sp.id)
res.fill <- sum(BA.n * abs(mat.t.id[,3] - mat.t[, 3]))/sum(BA.n)
return(res.fill)
}
......@@ -224,7 +231,7 @@ if(!is.na(data.TRAITS[data.TRAITS[["sp"]]==samp.sp, "Wood.density.mean"]) &
res.fill <- fun.compute.CWM.trait.test(samp.id, data.tree.d,
Rlim, data.TRAITS)
test.cwm.fill <- all.equal(res.fill,
data[["Wood.density.CWM.fill"]][data[["obs.id"]]==samp.id])
data[["Wood.density.abs.CWM.fill"]][data[["obs.id"]]==samp.id])
res <- all(c(test.focal, test.cwm.fill)==TRUE)
if(is.na(res.fill)) res <- NA
}
......
id,citation
1,"Kooyman, R.M. and Westoby, M. (2009) Costs of height gain in rainforest saplings: main stem scaling, functional traits and strategy variation across 75 species. Annals of Botany 104: 987-993."
2,"Kooyman, R.M., Rossetto, M., Allen, C. and Cornwell, W. (2012) Australian tropical and sub-tropical rainforest: phylogeny, functional biogeography and environmental gradients. Biotropica 44: 668-679."
3,"Condit, R. (1998). Tropical forest census plots. Springer, Berlin, Germany."
4,"Condit, R., Engelbrecht, B.M.J., Pino, D., Perez, R., Turner, B.L., (2013). Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110: 5064-5068."
5,"Yakushima Forest Environment Conservation Center, Ishihara, M.I., Suzuki, S.N., Nakamura, M., Enoki, T., Fujiwara, A., Hiura, T., Homma, K., Hoshino, D., Hoshizaki, K., Ida, H., Ishida, K., Itoh, A., Kaneko, T., Kubota, K., Kuraji, K., Kuramoto, S., Makita, A., Masaki, T., Namikawa, K., Niiyama, K., Noguchi, M., Nomiya, H., Ohkubo, T., Saito, S., Sakai, T., Sakimoto, M., Sakio, H., Shibano, H., Sugita, H., Suzuki, M., Takashima, A., Tanaka, N., Tashiro, N., Tokuchi, N., Yoshida, T., Yoshida, Y., (2011). Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecological Research 26: 1007-1008. "
6,"Thompson, J., N. Brokaw, J. K. Zimmerman, R. B. Waide, E. M. Everham III, D. J. Lodge, C. M. Taylor, D. GarciaMontiel, and M. Fluet. (2002). Land use history, environment, and tree composition in a tropical forest. Ecological Applications 12: 1344-1363."
7,"Ouadraogo, D.-Y., Mortier, F., Gourlet-Fleury, S., Freycon, V., and Picard, N. (2013). Slow-growing species cope best with drought: evidence from long-term measurements in a tropical semi-deciduous moist forest of Central Africa. Journal of Ecology 101: 1459-1470."
8,"Gourlet-Fleury, S., V. Rossi, M. Rejou-Mechain, V. Freycon, A. Fayolle, L. Saint-Andr, G. Cornu, J. Gerard, J. M. Sarrailh, and O. Flores. (2011). Environmental Filtering of Dense-Wooded Species Controls above-Ground Biomass Stored in African Moist Forests. Journal of Ecology 99: 981-90."
9,"Lasky, J.R., Sun, I., Su, S.-H., Chen, Z.-S., and Keitt, T.H. (2013). Trait-mediated effects of environmental filtering on tree community dynamics. Journal of Ecology 101: 722-733."
10,"Herault, B., Bachelot, B., Poorter, L., Rossi, V., Bongers, F., Chave, J., Paine, C.E., Wagner, F., and Baraloto, C. (2011). Functional traits shape ontogenetic growth trajectories of rain forest tree species. Journal of Ecology 99: 1431-1440."
11,"Herault, B., Ouallet, J., Blanc, L., Wagner, F., and Baraloto, C. (2010). Growth responses of neotropical trees to logging gaps. Journal of Applied Ecology 47: 821-831."
12,"IFN. (2011). Les resultats issus des campagnes d'inventaire 2006, 2007, 2008, 2009, 2010 et 2011. Inventaire Forestier National, Nogent-sur-Vernisson, FR."
13,http://inventaire-forestier.ign.fr/spip/spip.php?rubrique153
14,"Villaescusa, R. & Diaz, R. (1998) Segundo Inventario Forestal Nacional (1986-1996), Ministerio de Medio Ambiente, ICONA, Madrid."
15,"Villanueva, J.A. (2004) Tercer Inventario Forestal Nacional (1997-2007). Comunidad de Madrid. Ministerio de Medio Ambiente, Madrid."
16,http://www.magrama.gob.es/es/desarrollo-rural/temas/politica-forestal/inventario-cartografia/inventario-forestal-nacional/default.aspx
17,http://www.lfi.ch/index-en.php
18,"Fridman, J., and Stahl, G. (2001). A three-step approach for modelling tree mortality in Swedish forests. Scandinavian Journal of Forest Research 16: 455-466."
19,http://www.fia.fs.fed.us/tools-data/
20,"Wiser, S.K., Bellingham, P.J. & Burrows, L.E. (2001) Managing biodiversity information: development of New Zealand's National Vegetation Survey databank. New Zealand Journal of Ecology, 25: 1-17."
21,https://nvs.landcareresearch.co.nz/
23,"Swenson, N.G., J.C. Stegen, S.J. Davies, D.L. Erickson, J. Forero-Montana, A.H. Hurlbert, W.J. Kress, J. Thompson, M. Uriarte, S.J. Wright and J.K. Zimmerman. (2012). Temporal turnover in the composition of tropical tree communities: functional determinism and phylogenetic stochasticity. Ecology 93: 490-499."
24,"Baraloto, C, P.C.E. Timothy, L. Poorter, J. Beauchene, D. Bonal, AM Domenach, B. Hrault, S. Patio, JC Roggy, and Jerome Chave. (2010). Decoupled Leaf and Stem Economics in Rain Forest Trees. Ecology Letters 13: 1338-47."
25,"Wright, S.J., Kitajima, K., Kraft, N.J.B., Reich, P.B., Wright, I.J., Bunker, D.E., Condit, R., Dalling, J.W., Davies, S.J., Daz, S., Engelbrecht, B.M.J., Harms, K.E., Hubbell, S.P., Marks, C.O., Ruiz-Jaen, M.C., Salvador, C.M. & Zanne, A.E. (2010) Functional traits and the growth-mortality trade-off in tropical trees. Ecology 91: 3664-3674."
id,citation 1,"Kooyman, R.M. and Westoby, M. (2009) Costs of height gain in rainforest saplings: main stem scaling, functional traits and strategy variation across 75 species. Annals of Botany 104: 987-993." 2,"Kooyman, R.M., Rossetto, M., Allen, C. and Cornwell, W. (2012) Australian tropical and sub-tropical rainforest: phylogeny, functional biogeography and environmental gradients. Biotropica 44: 668-679." 3,"Condit, R. (1998). Tropical forest census plots. Springer, Berlin, Germany." 4,"Condit, R., Engelbrecht, B.M.J., Pino, D., Perez, R., Turner, B.L., (2013). Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110: 5064-5068." 5,"Yakushima Forest Environment Conservation Center, Ishihara, M.I., Suzuki, S.N., Nakamura, M., Enoki, T., Fujiwara, A., Hiura, T., Homma, K., Hoshino, D., Hoshizaki, K., Ida, H., Ishida, K., Itoh, A., Kaneko, T., Kubota, K., Kuraji, K., Kuramoto, S., Makita, A., Masaki, T., Namikawa, K., Niiyama, K., Noguchi, M., Nomiya, H., Ohkubo, T., Saito, S., Sakai, T., Sakimoto, M., Sakio, H., Shibano, H., Sugita, H., Suzuki, M., Takashima, A., Tanaka, N., Tashiro, N., Tokuchi, N., Yoshida, T., Yoshida, Y., (2011). Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecological Research 26: 1007-1008. " 6,"Thompson, J., N. Brokaw, J. K. Zimmerman, R. B. Waide, E. M. Everham III, D. J. Lodge, C. M. Taylor, D. GarciaMontiel, and M. Fluet. (2002). Land use history, environment, and tree composition in a tropical forest. Ecological Applications 12: 1344-1363." 7,"Ouadraogo, D.-Y., Mortier, F., Gourlet-Fleury, S., Freycon, V., and Picard, N. (2013). Slow-growing species cope best with drought: evidence from long-term measurements in a tropical semi-deciduous moist forest of Central Africa. Journal of Ecology 101: 1459-1470." 8,"Gourlet-Fleury, S., V. Rossi, M. Rejou-Mechain, V. Freycon, A. Fayolle, L. Saint-Andr_, G. Cornu, J. Gerard, J. M. Sarrailh, and O. Flores. (2011). Environmental Filtering of Dense-Wooded Species Controls above-Ground Biomass Stored in African Moist Forests. Journal of Ecology 99: 981-90." 9,"Lasky, J.R., Sun, I., Su, S.-H., Chen, Z.-S., and Keitt, T.H. (2013). Trait-mediated effects of environmental filtering on tree community dynamics. Journal of Ecology 101: 722-733." 10,"Herault, B., Bachelot, B., Poorter, L., Rossi, V., Bongers, F., Chave, J., Paine, C.E., Wagner, F., and Baraloto, C. (2011). Functional traits shape ontogenetic growth trajectories of rain forest tree species. Journal of Ecology 99: 1431-1440." 11,"Herault, B., Ouallet, J., Blanc, L., Wagner, F., and Baraloto, C. (2010). Growth responses of neotropical trees to logging gaps. Journal of Applied Ecology 47: 821-831." 12,"IFN. (2011). Les resultats issus des campagnes d'inventaire 2006, 2007, 2008, 2009, 2010 et 2011. Inventaire Forestier National, Nogent-sur-Vernisson, FR." 13,http://inventaire-forestier.ign.fr/spip/spip.php?rubrique153 14,"Villaescusa, R. & Diaz, R. (1998) Segundo Inventario Forestal Nacional (1986-1996), Ministerio de Medio Ambiente, ICONA, Madrid." 15,"Villanueva, J.A. (2004) Tercer Inventario Forestal Nacional (1997-2007). Comunidad de Madrid. Ministerio de Medio Ambiente, Madrid." 16,http://www.magrama.gob.es/es/desarrollo-rural/temas/politica-forestal/inventario-cartografia/inventario-forestal-nacional/default.aspx 17,http://www.lfi.ch/index-en.php 18,"Fridman, J., and Stahl, G. (2001). A three-step approach for modelling tree mortality in Swedish forests. Scandinavian Journal of Forest Research 16: 455-466." 19,http://www.fia.fs.fed.us/tools-data/ 20,"Wiser, S.K., Bellingham, P.J. & Burrows, L.E. (2001) Managing biodiversity information: development of New Zealand's National Vegetation Survey databank. New Zealand Journal of Ecology, 25: 1-17." 21,https://nvs.landcareresearch.co.nz/ 23,"Swenson, N.G., J.C. Stegen, S.J. Davies, D.L. Erickson, J. Forero-Montana, A.H. Hurlbert, W.J. Kress, J. Thompson, M. Uriarte, S.J. Wright and J.K. Zimmerman. (2012). Temporal turnover in the composition of tropical tree communities: functional determinism and phylogenetic stochasticity. Ecology 93: 490-499." 24,"Baraloto, C, P.C.E. Timothy, L. Poorter, J. Beauchene, D. Bonal, AM Domenach, B. H_rault, S. Patio, JC Roggy, and Jerome Chave. (2010). Decoupled Leaf and Stem Economics in Rain Forest Trees. Ecology Letters 13: 1338-47." 25,"Wright, S.J., Kitajima, K., Kraft, N.J.B., Reich, P.B., Wright, I.J., Bunker, D.E., Condit, R., Dalling, J.W., Davies, S.J., Daz, S., Engelbrecht, B.M.J., Harms, K.E., Hubbell, S.P., Marks, C.O., Ruiz-Jaen, M.C., Salvador, C.M. & Zanne, A.E. (2010) Functional traits and the growth-mortality trade-off in tropical trees. Ecology 91: 3664-3674." 26,"Brandli, U.-B. (Red.) 2010: Schweizerisches Landesforstinventar. Ergebnisse der dritten Erhebung 2004-2006. Birmensdorf, Eidgenossische Forschungsanstalt fur Wald, Schnee und Landschaft WSL. Bern, Bundesamt fur Umwelt, BAFU. 312 S."
\ No newline at end of file
......
Data set name,Country,Data type,Plot size,Dbh threshold,Number of plots,Traits,Source trait data,References,Contact of person in charge of data formatting,Comments
Panama,Panama,LPP,1 to 50 ha,1 cm,42,"Wood density, SLA, Maximum height, and Seed mass",local,"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, Maximum height, and Seed mass",local,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, Maximum height, and Seed mass",local,"6, 23","Plot data: 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, SLA, and Seed mass",local,"7,8",G. Vieilledent (ghislain.vieilledent@cirad.fr),
Fushan,Taiwan,LPP,25 ha,1 cm,1,"Wood density, SLA, and Seed mass",local,9,I-F. Sun (ifsun@mail.ndhu.edu.tw),
Paracou,French Guiana,LPP,6.25 ha,10 cm,15,"Wood density, SLA, and Seed mass",local,"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, Maximum height, and Seed mass",TRY,"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, Maximum height, and Seed mass",TRY,"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, Maximum height, and Seed mass",TRY,17,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, Maximum height, and Seed mass",TRY,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, Maximum height, and Seed mass",TRY,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, Maximum height, and Seed mass",TRY,,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, Maximum height, and Seed mass",local,"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, Maximum height, and Seed mass",local,"1,2",R. M. Kooyman (robert@ecodingo.com.au),Permanents plots established by the NSW Department of State Forests or by RMK
Data set name,Country,Data type,Plot size,Diameter at breast height threshold,Number of plots,Traits,Source trait data,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,"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,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,"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,"7,8",G. Vieilledent (ghislain.vieilledent@cirad.fr), Fushan,Taiwan,LPP,25 ha,1 cm,1,Wood density and SLA,local,9,I-F. Sun (ifsun@mail.ndhu.edu.tw), Paracou,French Guiana,LPP,6.25 ha,10 cm,15,Wood density and SLA,local,"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,"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,"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,"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,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,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,,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,"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,"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
......
......@@ -28,7 +28,6 @@ extended_data.pdf: extended_data.R include.tex references.bib
Rscript -e "library(sowsear); sowsear('extended_data.R', 'Rmd')"
Rscript -e "library(knitr); knit('extended_data.Rmd', output = 'extended_data.md')"
pandoc extended_data.md --csl=nature.csl --filter pandoc-citeproc --bibliography=references.bib --standalone --template=include.tex --variable mainfont="Times New Roman" --variable sansfont=Arial --variable fontsize=12pt --latex-engine=xelatex -o $@
rm extended_data.Rmd extended_data.md
SupplMat.pdf: Suppl_Mat.Rmd include.tex references.bib
Rscript -e "library(knitr); knit('Suppl_Mat.Rmd', output = 'SupplMat.md')"
......
This diff is collapsed.
......@@ -3,20 +3,22 @@
# Supplementary methods
We developed the equation of $\alpha_{c,f} = \alpha_{0,f} - \alpha_t \, t_f + \alpha_e \, t_c + \alpha_s \, \vert t_c-t_f \vert$ along with the basal area of each competitive species in the competition index to show the parameters are directly related to community weighted means of the different traits variables as:
\begin{equation} \label{alphaBA}
\sum_{c=1}^{N_p} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f} \, B_{i,tot} - \alpha_t \, t_f \, B_{i,tot} + \alpha_e \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\sum_{c=1}^{N_i} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f} \, B_{i,tot} - \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:
$B_{i,tot} = \sum_{c=1}^{N_p} {B_{i,c,p,s}}$,
$B_{i,tot} = \sum_{c=1}^{N_i} {B_{i,c,p,s}}$,
$B_{i,t_c} = \sum_{c=1}^{N_p} {t_c \times B_{i,c,p,s}}$,
$B_{i,t_c} = \sum_{c=1}^{N_i} {t_c \times B_{i,c,p,s}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_p} {\vert t_c - t_f \vert \times B_{i,c,p,s}}$,
$B_{i,\vert t_c - t_f \vert} = \sum_{c=1}^{N_i} {\vert t_c - t_f \vert \times B_{i,c,p,s}}$,
and $N_p$ is the number of species on the plot $p$.
and $N_i$ is the number of species in the local neighbourhood of the tree $i$.
## Details on sites
## Details on data sets used
Two main data type were used: national forest inventories data -- NFI, large permanent plots data -- LPP.
```{r kable, echo = FALSE, results="asis"}
library(plyr)
......@@ -54,23 +56,23 @@ writeLines(unlist(list.t[dat[["Country"]]]))
## Trait effects and potential mechanisms
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 dbh), agrees well with the idea that facilitation processes are generally limited to the regeneration phase rather than at the adult stage [@Callaway-1997]. The variation of $\alpha_0$ between biomes is limited with large overlap of their confidences intervals.
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 term of traits effects, Wood density (WD) was strongly negatively associated with maximum growth, 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 a low wood density species will have a higher basal area increment than a high wood density species[@Enquist-1999]. Other advantages of light wood may include higher xylem conductivity[@Chave-2009], though for angiosperms this is a correlated trait rather than an automatic consequence. A countervailing advantage for high wood density species was their better tolerance to 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], but may also be connected to a lower maintenance respiration[@Larjavaara-2010]. For growth, the lower respiration may lead to a direct advantage in deep shade, but the correlation might also arise through correlated selection for high survival rate and for high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effect, especially in tropical forest where the confidence interval did not span 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, 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.
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 nitrogen and phosphorus concentration, 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].
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].
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@Poorter-2006a; @Poorter-2008; @Wright-2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might be expected to select for slower growth in long-lived plants[@Poorter-2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@Poorter-2006a]. There was however a tendency for species with tall maximum height to have stronger competitive effect (though with wider confidence intervals intercepting zero), that might be explained by greater light interception from taller trees.
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@Poorter-2006a; @Poorter-2008; @Wright-2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might select for slower growth in long-lived plants[@Poorter-2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@Poorter-2006a]. There was however a tendency for species with tall maximum height to have stronger competitive effects (though with wider confidence intervals intercepting zero). This might be explained by greater light interception from taller trees.
Our results raised the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and tolerance are the two central elements of the species competitive ability[@Goldberg-1991]. One may expect that because of intra-specific competition, species with strong competitive effect should have evolved a high tolerance to competition. However, in agreement with previous studies[@Goldberg-1990; @Goldberg-1991; @Wang-2010], we found little evidence for such coordination. There was only such a tendency for wood density and SLA. High wood density conferred better competitive tolerance and also stronger competitive effect, but with wide confidence intervals intercepting zero for the later. High SLA conferred stronger competitive effect and higher tolerance of competition, but with wide confidence intervals intercepting zero for the later. For maximum height as explained above there was a tendency for short maximum height to lead to high tolerance to competition but to low competitive effect. This is interesting because a trade-off between competitive tolerance and maximum height has been proposed as a fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@Kohyama-2009]. Finally the lack of support for coordination between tolerance and effect is important because it means that competitive interaction is not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_t$ as in @Kunstler-2012; @Kraft-2014; @Lasky-2014). Traits of competitors alone or of focal plants alone may convey more information than the trait hierarchy. These processes of traits linked to either competitive effect or competitive tolerance, nevertheless, still leads to some trait values having an advantage in competitive interactions.
Our results raise the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and tolerance are the two central elements of a species' competitive ability[@Goldberg-1991]. One may expect that because of intra-specific competition, species with strong competitive effects should have evolved a high tolerance to competition. However, in agreement with previous studies[@Goldberg-1990; @Goldberg-1991; @Wang-2010], we found little evidence for such coordination. There was only such a tendency for wood density and SLA. High wood density conferred better competitive tolerance and also stronger competitive effects, but with wide confidence intervals intercepting zero for the latter. High SLA conferred stronger competitive effects and higher tolerance of competition, but with wide confidence intervals intercepting zero for the latter. For maximum height, as explained above, there was a tendency for short maximum height to lead to high tolerance of competition but to low competitive effects. This is interesting because a trade-off between competitive tolerance and maximum height has been proposed as a fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@Kohyama-2009]. Finally the lack of support for coordination between tolerance and effects is important because it means that competitive interactions are not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_t$ as in @Kunstler-2012; @Kraft-2014; @Lasky-2014). Traits of competitors alone or of focal plants alone may convey more information than the trait hierarchy. These processes of traits linked to either competitive effects or competitive tolerance, nevertheless, still lead to some trait values having an advantage in competitive interactions.
Given that the effect sizes we report for effects of traits on competitive interaction are modest, the question arises whether the three traits available to us (wood density, SLA, and maximum height) were the best candidates. It is possible that traits more directly related to mechanisms of competition -- for instance for competition for light, the leaf area index of the competitors or the compensation point at leaf or whole-plant level -- may be more powerful. It is also possible that if traits measured at the individual level were available, rather than species averages, this might strengthen predictive power[@Kraft-2014].
Given that the effect sizes we report for effects of traits on competitive interactions are modest, the question arises whether the three traits available to us (wood density, SLA, and maximum height) were the best candidates. It is possible that traits more directly related to mechanisms of competition -- for instance for competition for light, the leaf area index of the competitors or the light compensation point at leaf or whole-plant level -- may be more powerful. It is also possible that traits measured at the individual level rather than as species averages might strengthen the predictive power of our analysis[@Kraft-2014].
## Variations between biomes
Overall most results were rather consistent across biomes (Fig 2 main text), but some exceptions deserve comment.
Only for SLA, the sign of the effect size parameters were changing a lot between biomes (Fig. 2 main text). High SLA species tended to be more competition-tolerant (tolerance to competition parameter $\alpha_t$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical forests. These different outcomes may trace to the prevalence of deciduous species in temperate forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@Lusk-2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive impact was positive, versus negative in the other biomes (Fig 2 main text). We do not have a mechanistic explanation to suggest for this discrepancy, but can observe that taiga has relatively few species many of which are conifers where the range of wood density is narrower than for angiosperms (see Extended data Table 1).
Overall, most results were rather consistent across biomes (Fig 2 main text), but some exceptions deserve comments.
For SLA, the sign of the tolerance of competition parameters were changing a lot among biomes (Fig. 2 main text). High SLA species tended to be more competition-tolerant (tolerance to competition parameter $\alpha_t$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical forests. These different outcomes may trace to the prevalence of deciduous species in temperate forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@Lusk-2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive impact was positive, versus negative in the other biomes (Fig 2 main text). We do not have a mechanistic explanation for this discrepancy, but observe that taiga has relatively few species, many of which are conifers where the range of wood density is narrower than in angiosperms (see Extended data Table 1).
# References
......
......@@ -7,9 +7,6 @@
## ![Map of the plot locations of all data sets analysed. LPP plots are represented with a large points and NFI plots with small points (The data set of Panama comprises both a 50ha plot and a network of 1ha plots).](image/worldmapB.png)
## \newpage
## ![Variation of the four parameters linking the three studied traits with maximum growth and competition - maximum growth ($t_f \, m_1$), tolerance to competition ($t_f \, \alpha_t$), competitive effect ($t_c \, \alpha_e$) and limiting similarity ($|t_f - t_c| \, \alpha_l$ ($t_c$ was fixed at the lowest value and $t_f$ varying from quantile 5 to 95\%). The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with $\alpha_0$ or $m_0$).](../../figs/figres4b.pdf)
## \newpage
......@@ -79,14 +76,33 @@ pandoc.table(dat.2[, c(1,9:11)],
source.root("R/analysis/lmer.output-fun.R")
source.root("R/analysis/lmer.run.R")
source.root("R/utils/plot.R")
library(pander)
## ## Species traits correlation
##+ Read Table_cor, echo = FALSE, results = 'hide', message=FALSE
cor.mat <- read.csv(file.path(path.root,'output',
'formatted', 'cor.mat.traits.csv'),
row.names = 1)
cor.mat <- round(cor.mat,3)
cor.mat[is.na(cor.mat)] <- ''
colnames(cor.mat) <- row.names(cor.mat) <- c("Wood density", "SLA",
"Max height")
##+ Table1_cor, echo = FALSE, results='asis', message=FALSE
pandoc.table(cor.mat,
caption = "Pairwise functional trait correlations (Pearson's r)",
digits = 3)
## \newpage
## # Model results
## ![Variation of the four parameters linking the three studied traits with maximum growth and competition - maximum growth ($t_f \, m_1$), tolerance to competition ($t_f \, \alpha_t$), competitive effect ($t_c \, \alpha_e$) and limiting similarity ($|t_f - t_c| \, \alpha_l$ ($t_c$ was fixed at the lowest value and $t_f$ varying from quantile 5 to 95\%). The shaded area represents the 95% confidence interval of the prediction (including uncertainty associated with $\alpha_0$ or $m_0$).](../../figs/figres4b.pdf)
##+ ComputeTable_Effectsize, echo = FALSE, results = 'hide', message=FALSE
list.all.results <-
readRDS.root('output/list.lmer.out.all.NA.simple.set.rds')
library(pander)
mat.param <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
......@@ -142,3 +158,6 @@ pandoc.table(mat.param[c(1,3,2,4:11), ], caption = "Standardized parameters esti
......@@ -51,7 +51,7 @@
\usepackage{fancyhdr}
\pagestyle{fancy}
\rhead{Methods}
\title{Methods (1497-without refs /max 3000 words)}
\title{Methods}
\date{}
\begin{document}
......@@ -63,12 +63,12 @@ To examine the link between competition and traits we used a
neighbourhood modelling
framework\citep{Canham-2006, Uriarte-2010, Ruger-2012, Kunstler-2012, Lasky-2014}
to model the growth of a focal tree of species \(f\) as a product of its
maximum growth rate (determined by its traits and size) together with
maximum growth (determined by its traits and size) together with
reductions due to competition from individuals growing in the local
neighbourhood. Specifically, we assumed a relationship of the form
neighbourhood (see definition below). Specifically, we assumed a relationship of the form
\begin{equation} \label{G1}
G_{i,f,p,s} = G_{\textrm{max} \, f,p,s} \, D_{i,f,p,s}^{\gamma_f} \, \exp\left(\sum_{c=1}^{N_p} {-\alpha_{c,f} 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_{c,f} B_{i,c,p,s}}\right),
\end{equation}
where:
......@@ -76,12 +76,11 @@ where:
\begin{itemize}
\itemsep1pt\parskip0pt\parsep0pt
\item
\(G_{i,f,p,s}\) and \(D_{i,f,p,s}\) are the the annual basal area
\(G_{i,f,p,s,t}\) and \(D_{i,f,p,s,t}\) are the the annual basal area
growth and diameter at breast height of individual \(i\) from species
\(f\), plot \(p\) and data set \(s\),
\(f\), plot or quadrat (see below) \(p\), data set \(s\), and census $t$,
\item
\(G_{\textrm{max} \, f,p,s}\) is the potential growth rate in basal
area growth for species \(f\) on plot \(p\) 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
......@@ -90,18 +89,23 @@ where:
\(\gamma_f = \gamma_0 + \varepsilon_{\gamma, f}\) where
\(\varepsilon_{\gamma, f} \sim \mathcal{N} (0,\sigma_{\gamma})\) -- a
normal distribution of mean 0 and standard deviation $\sigma_{\gamma}${]}
\item
\(N_p\) is the number of competitor species on plot \(p\) ,
\item
\(\alpha_{c,f}\) is the per unit basal area effect of individuals from
species \(c\) on growth of an individual in species \(f\), and
\item
\(B_{i,c,p,s}= 0.25\, \pi \, \sum_{j \neq i} w_j \, D_{j,c,p,s}^2\) is
the sum of basal area of all individuals trees \(j\) of the species
\(c\) competiting with the tree \(i\) within the plot \(p\) and data
set \(s\), where \(w_j\) is a constant based on subplot size where
tree \(j\) was measured. Note that \(B_{i,c,p,s}\) include all trees of species $c$
in the plot excepted the tree \(i\).
\(B_{i,c,p,s}= 0.25\, \pi \, \sum_{j \neq i} w_j \, D_{j,c,p,s,t}^2\) is
the sum of basal area of all individuals competitor trees \(j\) of the species
\(c\) within the local neighbourhood
of the tree $i$ in
plot \(p\), data
set \(s\) and census $t$, where \(w_j\) is a constant based on
neighboorhood size for tree $j$ depending on the data set (see
below). Note that \(B_{i,c,p,s}\) include all trees of species $c$
in the local neighbourhood excepted the tree
\(i\),
\item
\(N_i\) is the number of competitor species in the local
neighbourhood of focal tree $i$.
\end{itemize}
Values of \(\alpha_{c,f}> 0\) indicate competition, whereas
......@@ -111,10 +115,11 @@ Log-transformation of eq. \ref{G1} leads to a linearised model of the
form
\begin{equation} \label{logG1}
\log{G_{i,f,p,s}} = \log{G_{\textrm{max} \, f,p,s}} + \gamma_f \, \log{D_{i,f,p,s}} + \sum_{c=1}^{N_p} {-\alpha_{c,f} 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_{c,f} B_{i,c,p,s}}.
\end{equation}
To include the effect of a focal trees' traits, \(t_f\), on its growth,
To include the effect of a focal species' traits, \(t_f\), on its
maximum growth,
we let:
\begin{equation} \label{Gmax}
......@@ -122,7 +127,7 @@ we let:
\end{equation}
Here \(m_0\) is the average maximum growth, \(m_1\) gives the effect of
the focal trees trait, and \(\varepsilon_{G_{\textrm{max}}, f}\),
the focal species trait, 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
......@@ -173,9 +178,11 @@ where:
\end{itemize}
Eqs. \ref{logG1}-\ref{alpha} were then fitted to empirical estimates of
growth based on change in diameter between time $t$ and $t+1$, given by
growth based on change in diameter between census $t$
and $t+1$ (respectively at year $y_t$ and $y_{t+1}$), given by
\begin{equation} \label{logGobs} G_{i,f,p,s} = 0.25 \pi \left(D_{i,f,p,s,t+1}^2 - D_{i,f,p,s,t}^2\right).
\begin{equation} \label{logGobs} G_{i,f,p,s,t} = 0.25 \pi
\left(D_{i,f,p,s,t+1}^2 - D_{i,f,p,s,t}^2\right)/(y_{t+1} - y_t).
\end{equation}
To estimate standardised coefficients (one type of standardised effect
......@@ -183,16 +190,16 @@ size)\citep{Schielzeth-2010}, response and explanatory variables
were standardized (divided by their standard deviations) prior to
analysis. Trait and diameter were also centred to facilitate
convergence. The models were fitted using \(lmer\) in lme4\citep{Bates-2014}
with R\citep{RTeam-2014}. We fitted two versions of this model. In the first
in the R statistical environment\citep{RTeam-2014}. We fitted two versions of this model. In the first
version parameters \(m_{0}, m_1, \alpha_0,\alpha_t,\alpha_i,\alpha_s\)
were estimated as constant across all biomes. In the second version, we
repeated the same analysis as the first version but provided for
repeated the same analysis as in the first version but allowed
different fixed estimates of these parameters for each biome. This
enabled us to explore variation between biomes. Because some biomes had
few observations, we merged some biomes with similar climate. Tundra was
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 data were available.
analysis as too few plots were available.
\section{Data}\label{data}
......@@ -200,7 +207,7 @@ analysis as too few data were available.
Our main objective was to collate data sets spanning the dominant forest
biomes of the world. Data sets were included if they (i) allowed both
growth rate of individual trees and the local abundance of competitors
growth of individual trees and the local abundance of competitors
to be estimated, and (ii) had good (\textgreater{}40\%) coverage for at
least one of the traits of interest (SLA, wood density, and maximum
height).
......@@ -216,17 +223,18 @@ regions. The minimum diameter of recorded trees varied among sites from
analysis to trees greater than 10cm. Moreover, we excluded from the
analysis any plots with harvesting during the growth measurement period,
that were identified as a plantations, or overlapping a forest edge.
Finally, we selected only two consecutive census dates for each tree to
Finally, we randomly selected only two consecutive census dates per
plot or quadrat to
avoid having to account for repeated measurements, as less than a third
of the data had repeated measurements. See the Supplementary Methods and
Extended Data Table 1 for more details on the individual data sets.
Basal area growth was estimated from diameter measurements recorded
across successive time points. For the French NFI, these data were
between the two census. For the French NFI, these data were
obtained from short tree cores. For all other data sets, diameter at
breast height (\(D\)) of each individual was recorded at multiple census
dates. We excluded trees (i) with extreme positive or negative diameter
growth rates, following criteria developed at the BCI site