add leaftype

R/analysis/lmer.nolog.all.output.R

@@ -27,9 +27,3 @@ names(out) <- lapply(lapply(files,files.details.all),
 ### remove missing
 out <- out[!unlist(lapply(out,FUN=function(x) is.null(x$lmer.summary)))]
 saveRDS(out,file='output/list.lmer.out.nolog.all.rds')
-names.param <- unique(unlist(lapply(out,function(list.res)
-                            names(list.res$lmer.summary$fixed.coeff.E))))
-DF.results <- do.call("rbind",lapply(out, fun.format.in.data.frame,
-                                     names.param = names.param))
-DF.results$id <- paste(DF.results$set, DF.results$ecocode, sep=".")
-saveRDS(DF.results,file='output/DF.lmer.nolog.all.rds')

R/find.trait/BCI.R

@@ -98,7 +98,12 @@ data.cat.extract$deciduous[data.cat.extract$deciduous %in% c('E') &
 data.cat.extract$Pheno.T[!is.na(data.cat.extract$deciduous)] <-
     data.cat.extract$deciduous[!is.na(data.cat.extract$deciduous)]
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
+
+## fix pheno for species with issue
+
+data.traits[data.traits$Latin_name %in% c('Vasconcellea cauliflora'),
+            'Phylo.group'] <- 'Angiosperm'
 
 
 

R/find.trait/Canada.R

@@ -54,7 +54,7 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Betula spp.','Crataegus spp
                                                     'Tilia spp.','Ulmus spp.'),'Pheno.T'] <- 'D'
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
 
 ### 
 write.csv(data.traits,file="output/formatted/Canada/traits.csv",row.names = FALSE)

R/find.trait/France.R

@@ -64,7 +64,7 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Pinus nigra spp. salzmannii
 
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
 
 ### 
 write.csv(data.traits,file="output/formatted/France/traits.csv",row.names = FALSE)

R/find.trait/Fushan.R

@@ -14,7 +14,7 @@ species.clean <- data.frame(sp=data.tree[!duplicated(data.tree[["sp"]]),"sp"],
 ################ MASSAGE TRAIT DATA Obtain maximum height per species from data.trait no sd
 ################ available as we have only one observation for species
 data.trait <- read.table("data/raw/Fushan/fs_trait_Kunstler.txt", header = T, 
-    sep = "\t")
+    sep = "\t",stringsAsFactors = F)
 colnames(data.trait) <- c("sp", "sla", "wd", "seedmassmg", "meanN", "maxheightm")
 data.trait <- merge(data.trait, data.frame(sp = species.clean$sp, Latin_name= species.clean$Latin_name, 
 	stringsAsFactors = F), by = "sp")
@@ -61,7 +61,7 @@ data.cat.extract[data.cat.extract$Latin_name %in%  c('Machilus zuihoensis',
 'Symplocos sonoharae','Cyclobalanopsis sessilifolia',
 'Eriobotrya deflexa','Neolitsea konishii'),"Pheno.T"] <- 'EV'
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
 
 
 

R/find.trait/Japan.R

@@ -58,7 +58,13 @@ data.cat.extract <- fun.change.factor.angio.try(data.cat.extract)
 data.cat.extract <- fun.fill.pheno.try.with.zanne(data.cat.extract)
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
+
+## fix pheno for species with issue
+
+data.traits[data.traits$Latin_name %in% c('Benthamidia japonica','Tutcheria virgata'),
+            'Phylo.group'] <- 'Angiosperm'
+
 
 ### TODO ADD GENUS MEAN FOR HEIGHT IF SPECIES IS MISSING
 write.csv(data.traits,file="output/formatted/Japan/traits.csv",row.names = FALSE)

R/find.trait/Luquillo.R

@@ -97,7 +97,13 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Cecropia schreberiana',
 data.cat.extract[data.cat.extract$Latin_name %in% c('Chione venosa') ,'Pheno.T']<-  'D_EV'
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
+
+## fix pheno for species with issue
+
+data.traits[data.traits$Latin_name %in% c('Beilschmedia pendula'),
+            'Phylo.group'] <- 'Angiosperm'
+
 
 ### 
 write.csv(data.traits,file="output/formatted/Luquillo/traits.csv",row.names = FALSE)

R/find.trait/Mbaiki.R

@@ -254,7 +254,7 @@ data.cat.extract <- fun.change.factor.angio.try(data.cat.extract)
 data.cat.extract <- fun.fill.pheno.try.with.zanne(data.cat.extract)
 
 data.traits <- merge(data.traits,
-                     data.cat.extract[,c("sp","Phylo.group","Pheno.T")],
+                     data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],
                      by="sp")
 
 #=============================================================

R/find.trait/NSW.R

@@ -49,7 +49,13 @@ data.cat.extract <- fun.change.factor.angio.try(data.cat.extract)
 data.cat.extract <- fun.fill.pheno.try.with.zanne(data.cat.extract)
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
+
+## fix pheno for species with issue
+
+data.traits[data.traits$Latin_name %in% c('Cyclophylum longipetalum'),
+            'Phylo.group'] <- 'Angiosperm'
+
 
 
 ### TODO ADD GENUS MEAN FOR HEIGHT IF SPECIES IS MISSING

R/find.trait/NVS.R

@@ -72,7 +72,7 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Aristotelia serrata',
 'Fuchsia procumbens','Muehlenbeckia australis','Muehlenbeckia complexa',
 'Olearia fimbriata','Urtica ferox'),'Pheno.T'] <- 'D_EV'
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
 
 ### TODO ADD GENUS MEAN FOR HEIGHT IF SPECIES IS MISSING
 write.csv(data.traits,file="output/formatted/NVS/traits.csv",row.names = FALSE)

R/find.trait/Paracou.R

@@ -344,6 +344,10 @@ data.traits2 <- fun.extract.format.sp.traits.TRY(sp=species.clean2[["sp"]],
                                                 sp.syno.table=species.clean2,
                                                 data=data.TRY.std)
 
+# for familly
+
+species.clean3 <- species.clean[paste('sp',species.clean$sp,sep='.') %in% species.clean2$sp,]
+
 ## TODO NEED TO COMBINE BOTH
 data.traits <- fun.combine.nontry.and.try(
                            trait = c('Leaf.N', 'SLA', 'Seed.mass',
@@ -371,7 +375,8 @@ data.cat.extract <- fun.fill.pheno.try.with.zanne(data.cat.extract)
 
 data.traits <- merge(data.traits,data.cat.extract[, c("sp",
                                                       "Phylo.group",
-                                                      "Pheno.T")],
+                                                      "Pheno.T",
+                                                      'LeafType.T')],
                      by="sp")
 
 
@@ -394,7 +399,15 @@ data.traits$Pheno.T2 <-  NULL
 ## compute perc of traits cover per species
 print(sapply(c('Leaf.N','SLA','Seed.mass','Wood.density','Max.height'),
              fun.compute.perc.cover.one.trait,data.traits))
-       
+
+## Fix missing angio
+Taxo.Zanne <- read.csv("data/raw/ZanneNature/Spermatophyta_Genera.csv",
+         stringsAsFactors=FALSE)
+
+data.traits$Phylo.group[is.na(data.traits$Phylo.group)][
+                        species.clean3$Famille[is.na(data.traits$Phylo.group)] %in%
+                        Taxo.Zanne[Taxo.Zanne$Angiospermae=='Angiospermae','family']] <- 'Angiosperm'
+
 # write.table(data.genus,file="data.genus.txt",sep="\t",row.names=FALSE)
 write.csv(data.traits,file="./output/formatted/Paracou/traits.csv",
           row.names=FALSE)

R/find.trait/Spain.R

@@ -62,7 +62,20 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Juniperus turbinata',
 
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group",
+                                                     "Pheno.T",'LeafType.T')],
+                     by="sp")
+
+## fill missing phylo missing value
+
+data.traits[data.traits$Latin_name %in% c('Otros pinos'),
+            'Phylo.group'] <- 'Gymnosperm'
+data.traits[data.traits$Latin_name %in% c('Otros eucaliptos',
+                                          'Otros arboles ripicolas',
+                                          'Otras laurisilvas',
+                                          'Otras frondosas'),
+            'Phylo.group'] <- 'Angiosperm'
+
 
 # write
 write.csv(data.traits,file="output/formatted/Spain/traits.csv",row.names = FALSE)

R/find.trait/Sweden.R

@@ -102,7 +102,16 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Abies ssp',
        'Picea ssp other than P.abies') ,'Pheno.T']<-  'EV'
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
+
+## fill missing phylo missing value
+
+data.traits[data.traits$Latin_name %in% c('Conifers other than pinus; larix; picea and abies'),
+            'Phylo.group'] <- 'Gymnosperm'
+data.traits[data.traits$Latin_name %in% c('Other broadleaved'),
+            'Phylo.group'] <- 'Angiosperm'
+
+
 
 ### writing
 write.csv(data.traits,file="output/formatted/Sweden/traits.csv",row.names = FALSE)

R/find.trait/Swiss.R

@@ -92,7 +92,7 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Abies spec.',
        'Picea spec.','Thuja spec.','Sequoiadendron') ,'Pheno.T']<-  'EV'
 
 
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
 
 
 ### write

R/find.trait/US.R

@@ -6,8 +6,10 @@ source("R/find.trait/trait-fun.R")
 ### read species names
 data.tree <- read.csv("output/formatted/US/tree.csv", stringsAsFactors = FALSE)
 species.clean <- data.frame(sp=data.tree[!duplicated(data.tree[["sp"]]),"sp"],
-                             Latin_name=data.tree[!duplicated(data.tree[["sp"]]),"sp.name"],
-                             Latin_name_syn=data.tree[!duplicated(data.tree[["sp"]]),"sp.name"],
+                             Latin_name=data.tree[!duplicated(data.tree[["sp"]])
+                                 ,"sp.name"],
+                             Latin_name_syn=data.tree[
+                                 !duplicated(data.tree[["sp"]]),"sp.name"],
                              stringsAsFactors =FALSE)
 rm(data.tree)
 
@@ -82,10 +84,20 @@ data.cat.extract[data.cat.extract$Latin_name %in% c('Abies spp.','Tsuga spp.','M
                         'Cecropia schreberiana','Cestrum laurifolium',
                         'Cupania triquetra','Coffea liberica','Myrsine cubana',
                         ' Petitia domingensis','Prunus occidentalis',
-                        'Quercus hypoleucoides','Senna siamea') ,'Pheno.T']<-  'EV'
+                        'Quercus hypoleucoides','Senna siamea') ,
+                 'Pheno.T']<-  'EV'
 data.cat.extract[data.cat.extract$Latin_name %in% c('Annona reticulata',
-                                                    'Cinnamomum elongatum') ,'Pheno.T']<-  'D_EV'
-data.traits <- merge(data.traits,data.cat.extract[,c("sp","Phylo.group","Pheno.T")],by="sp")
+                                                    'Cinnamomum elongatum') ,
+                 'Pheno.T']<-  'D_EV'
+data.traits <- merge(data.traits,
+                     data.cat.extract[,c("sp","Phylo.group",
+                                         "Pheno.T",'LeafType.T')],by="sp")
+
+## fix pheno for species with issue
+
+data.traits[data.traits$Latin_name %in% c('Family Arecaceae not listed above',
+                                          'Poitea florida'),
+            'Phylo.group'] <- 'Angiosperm'
 
 # Write
 write.csv(data.traits,file="output/formatted/US/traits.csv",row.names = FALSE)

R/find.trait/test.traits-fun.R

@@ -101,3 +101,18 @@ fun.test.set <- function(set,filedir) {
      cat(set,"OK \n")
   return(data.frame(set=rep(set,nrow(get.traits)),get.traits))
   }
+
+
+
+fun.ellipsoid.hull <- function(set,x,y,sets.v,col.vec){
+require(cluster)
+xy <- cbind((x[sets.v==set]),(y[sets.v==set]))
+xy <- xy[!is.na(xy[,1]) & !is.na(xy[,2]),]
+xy <- xy[xy[,1]>quantile(xy[,1],0.1) &xy[,1]<quantile(xy[,1],0.9) &
+         xy[,2]>quantile(xy[,2],0.1) &xy[,2]<quantile(xy[,2],0.9),]
+
+if (nrow(xy)>10){
+   exy <- ellipsoidhull(xy)
+   lines(predict(exy),col=col.vec[set])
+   }
+}

R/find.trait/test.traits.R

@@ -15,23 +15,10 @@ all.traits <- do.call("rbind",aa)
 
 trait.name <- c("Leaf.N.mean","SLA.mean","Wood.density.mean","Max.height.mean")
 
-fun.ellipsoid.hull <- function(set,x,y,sets.v,col.vec){
-require(cluster)
-xy <- cbind((x[sets.v==set]),(y[sets.v==set]))
-xy <- xy[!is.na(xy[,1]) & !is.na(xy[,2]),]
-xy <- xy[xy[,1]>quantile(xy[,1],0.1) &xy[,1]<quantile(xy[,1],0.9) &
-         xy[,2]>quantile(xy[,2],0.1) &xy[,2]<quantile(xy[,2],0.9),]
-
-if (nrow(xy)>10){
-   exy <- ellipsoidhull(xy)
-   lines(predict(exy),col=col.vec[set])
-   }
-}
-
 
  
 
-pdf("figs/test.traits/traits.XY.pdf",height=9,width=6)
+pdf("figs/test.traits/traits.XY.set.pdf",height=9,width=6)
 t.min <- c(2,1,0.1,1)
 t.max <- c(60,100,1.5,100)
 par(mfrow=c(3,2))
@@ -42,16 +29,97 @@ par(mfrow=c(3,2))
                          xlab=trait.name[i],ylab=trait.name[j],
                          log="xy",xlim=c(t.min[i],t.max[i]),ylim=c(t.min[j],t.max[j]),
                          cex=0.2)
-                    lapply(sets,fun.ellipsoid.hull,
-                         all.traits[[trait.name[i]]] ,
-                         all.traits[[trait.name[j]]],
-                         all.traits[['set']],col.vec=col.vec)
+                    ## lapply(sets,fun.ellipsoid.hull,
+                    ##      all.traits[[trait.name[i]]] ,
+                    ##      all.traits[[trait.name[j]]],
+                    ##      all.traits[['set']],col.vec=col.vec)
                 }
      }
      legend("topright",  legend = sets, bty = "n", col = col.vec[sets],pch=pch.vec[sets], cex = 0.7) 
 
 dev.off()
 
+
+pdf("figs/test.traits/traits.XY.angio.pdf",height=9,width=6)
+t.min <- c(2,1,0.1,1)
+t.max <- c(60,100,1.5,100)
+par(mfrow=c(3,2))
+     for (i in 1:3) {
+         for (j in (i+1):4) {
+                    plot(all.traits[[trait.name[i]]] ,all.traits[[trait.name[j]]],
+                         col=c('black','red')[unclass(factor(all.traits$Phylo.group=='Angiosperm'))],
+                         xlab=trait.name[i],ylab=trait.name[j],
+                         log="xy",xlim=c(t.min[i],t.max[i]),ylim=c(t.min[j],t.max[j]),
+                         cex=0.2)
+                }
+     }
+     legend("topright",  legend = unique(factor(na.exclude(all.traits$Phylo.group)=='Angiosperm')), bty = "n",
+            col =c('black','red')[unique(unclass(factor(na.exclude(all.traits$Phylo.group)=='Angiosperm')))],
+            pch=1,cex = 0.7) 
+
+dev.off()
+
+
+pdf("figs/test.traits/traits.XY.EV.pdf",height=9,width=6)
+t.min <- c(2,1,0.1,1)
+t.max <- c(60,100,1.5,100)
+par(mfrow=c(3,2))
+     for (i in 1:3) {
+         for (j in (i+1):4) {
+                    plot(all.traits[[trait.name[i]]] ,all.traits[[trait.name[j]]],
+                         col=c('black','red')[unclass(factor(all.traits$Pheno.T=='EV'))],
+                         xlab=trait.name[i],ylab=trait.name[j],
+                         log="xy",xlim=c(t.min[i],t.max[i]),ylim=c(t.min[j],t.max[j]),
+                         cex=0.2)
+                }
+     }
+     legend("topright",  legend = unique(factor(na.exclude(all.traits$Pheno.T)=='EV')), bty = "n",
+            col =c('black','red')[unique(unclass(factor(na.exclude(all.traits$Pheno.T)=='EV')))],
+            pch=1,cex = 0.7) 
+
+dev.off()
+
+
+
+## DIVIDE IN CONIF ANGIO_EV ANGIO_NOT_ EV ???
+cat <- all.traits$Phylo.group
+cat[cat=='Gymnosperm' & !is.na(cat)] <- "Gymno"
+cat[all.traits$Phylo.group=='Angiosperm' &  all.traits$Pheno.T == 'EV' &
+    !is.na(all.traits$Pheno.T ) & !is.na(all.traits$Phylo.group)] <- "Angio_EV"
+cat[all.traits$Phylo.group=='Angiosperm' &  all.traits$Pheno.T != 'EV' &
+        !is.na(all.traits$Pheno.T ) & !is.na(all.traits$Phylo.group)] <- "Angio_other"
+cat[all.traits$Phylo.group=='Angiosperm'  &
+        is.na(all.traits$Pheno.T ) & !is.na(all.traits$Phylo.group)] <- "Angio_unknown"
+sum(is.na(cat))
+
+pdf("figs/test.traits/traits.XY.cat.pdf",height=9,width=6)
+t.min <- c(2,1,0.1,1)
+t.max <- c(60,100,1.5,100)
+par(mfrow=c(3,2))
+     for (i in 1:3) {
+         for (j in (i+1):4) {
+                    plot(all.traits[[trait.name[i]]] ,all.traits[[trait.name[j]]],
+                         col=unclass(factor(cat)),
+                         xlab=trait.name[i],ylab=trait.name[j],
+                         log="xy",xlim=c(t.min[i],t.max[i]),ylim=c(t.min[j],t.max[j]),
+                         cex=0.2)
+                }
+     }
+     legend("bottomleft",  legend = unique(na.exclude(cat)), bty = "n",
+            col =unique(unclass(factor(na.exclude(cat)))),
+            pch=1,cex = 0.7) 
+
+dev.off()
+
+
+pdf("figs/test.traits/traits.boxplot.cat.pdf")
+par(mfrow=c(2,2))
+     for (i in 1:4) {
+                    boxplot(all.traits[[trait.name[i]]]~ cat,las=3)
+     }
+dev.off()
+
+
 par(mfrow=c(2,2))
 for (i in 1:4) hist(log10(all.traits[[trait.name[i]]]),main=trait.name[i])
 

R/process.data/process-fun.R

@@ -324,7 +324,7 @@ fun.data.per.ecoregion <- function(ecoregion, data.tot, site.name,
             data.merged <- fun.merged.DT(data,data.CWM,"obs.id")
             cat('merge data and CWM done',"\n")
             ##  add Phylo.group and Pheno.T to the data
-            data.merged <- merge(data.merged,data.TRAITS[,c("sp","Phylo.group","Pheno.T")],by="sp")
+            data.merged <- merge(data.merged,data.TRAITS[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
             ## write data
               if(std.traits=='local'){
                 write.csv(data.merged, file = file.path(path, "data.tree.tot.csv"),  row.names = FALSE)
@@ -382,7 +382,7 @@ for (i in unique(data$ecocode)){
               data.merged <- fun.remove.tree.in.buffer.cluster(data.merged)
               cat("dim after buffer tree removed",dim(data.merged),'vs ',dim(data.t),"\n")
               ##  add Phylo.group and Pheno.T to the data
-              data.merged <- merge(data.merged,data.TRAITS[,c("sp","Phylo.group","Pheno.T")],by="sp")
+              data.merged <- merge(data.merged,data.TRAITS[,c("sp","Phylo.group","Pheno.T",'LeafType.T')],by="sp")
 
               if(std.traits=='local'){
                 write.csv(data.merged, file = file.path(path, "data.tree.tot.csv"),  row.names = FALSE)