diff --git a/plot_SpeciesComposition.R b/plot_SpeciesComposition.R
index 9eb0043..a298c93 100644
--- a/plot_SpeciesComposition.R
+++ b/plot_SpeciesComposition.R
@@ -113,105 +113,6 @@ chordDiagram(spSimilarity_1, grid.col =col,symmetric = TRUE,
 dev.off()
 
 
-# Plot with species with traits -------------------------------------------
-trait_BIEN<-read.csv("./data/processed/BIEN_trait_GrowthForm.csv")
-trait_BIEN$scrubbed_species_binomial<-gsub(" ","_",trait_BIEN$scrubbed_species_binomial)
-
-spPresence_sub<-spPresence[spPresence$Species%in%trait_BIEN$scrubbed_species_binomial,]
-spPresence_sub<-na.omit(spPresence_sub)
-
-
-# Species list
-biome_richness_sub<-foreach(i=1:length(biome_poly))  %do% {
-  
-  cells_tmp<-unlist(cellFromPolygon(r_Total_Rich,biome_poly[[i]]))
-  sp_list_tmp<-unique(spPresence_sub$Species[spPresence_sub$cells%in%cells_tmp])
-  
-}
-names(biome_richness_sub)<-names(biome_poly)
-
-
-## Create similarity matrix
-## Create a loop to calculate the similarity (number of species shared among biomes)
-spSimilarity_sub<-foreach(i=1:length(biome_richness_sub), .combine='cbind') %:%
-  foreach(j=1:length(biome_richness_sub), .combine='c') %do% {
-    length(intersect(biome_richness_sub[[i]],biome_richness_sub[[j]]))
-  }
-
-colnames(spSimilarity_sub)<-names(biome_richness_sub)
-rownames(spSimilarity_sub)<-names(biome_richness_sub)
-
-
-col=c(wes_palette("Darjeeling",6,type="continuous"),
-      wes_palette("Cavalcanti",6,type="continuous"))
-
-spSimilarity_sub1<-spSimilarity_sub
-diag(spSimilarity_sub1)<-0
-
-colnames(spSimilarity_sub1)<-c("moist","tropical.mixed",
-                            "savanna","grasslands",
-                            "dry","xeric","MED",
-                            "TEMP","CONI","PRA","TA","TU")
-
-rownames(spSimilarity_sub1)<-colnames(spSimilarity_sub1)
-
-
-pdf("./figs/OnlyTrait_similarity_biomes.pdf")
-chordDiagram(spSimilarity_sub1, grid.col =col,symmetric = TRUE,link.sort = TRUE,
-             column.col = col)
-dev.off()
-
-
-
-# Plot species with growth form -------------------------------------------
-Growth_form<-read.table("data/base/GrowthForm_Final.txt", header = TRUE)
-Growth_form$SPECIES_STD<-gsub(" ","_",Growth_form$SPECIES_STD)
-
-spPresence_sub<-spPresence[spPresence$Species%in%Growth_form$SPECIES_STD,]
-spPresence_sub<-na.omit(spPresence_sub)
-
-
-# Species list
-biome_richness_sub<-foreach(i=1:length(biome_poly))  %do% {
-  
-  cells_tmp<-unlist(cellFromPolygon(r_Total_Rich,biome_poly[[i]]))
-  sp_list_tmp<-unique(spPresence_sub$Species[spPresence_sub$cells%in%cells_tmp])
-  
-}
-names(biome_richness_sub)<-names(biome_poly)
-
-
-## Create similarity matrix
-## Create a loop to calculate the similarity (number of species shared among biomes)
-spSimilarity_sub<-foreach(i=1:length(biome_richness_sub), .combine='cbind') %:%
-  foreach(j=1:length(biome_richness_sub), .combine='c') %do% {
-    length(intersect(biome_richness_sub[[i]],biome_richness_sub[[j]]))
-  }
-
-colnames(spSimilarity_sub)<-names(biome_richness_sub)
-rownames(spSimilarity_sub)<-names(biome_richness_sub)
-
-
-col=c(wes_palette("Darjeeling",6,type="continuous"),
-      wes_palette("Cavalcanti",6,type="continuous"))
-
-spSimilarity_sub1<-spSimilarity_sub
-diag(spSimilarity_sub1)<-0
-
-colnames(spSimilarity_sub1)<-c("moist","tropical.mixed",
-                               "savanna","grasslands",
-                               "dry","xeric","MED",
-                               "TEMP","CONI","PRA","TA","TU")
-
-rownames(spSimilarity_sub1)<-colnames(spSimilarity_sub1)
-
-
-pdf("./figs/OnlyGrowth_similarity_biomes.pdf")
-chordDiagram(spSimilarity_sub1, grid.col =col,symmetric = TRUE,link.sort = TRUE,
-             column.col = col)
-dev.off()
-
-