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07_Hypervolumes_per_cell.R
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07_Hypervolumes_per_cell.R
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# libraries ---------------------------------------------------------------
library(hypervolume)
library(tidyverse)
library(foreach)
library(raster)
# Functions ---------------------------------------------------------------
source("./functions/BIEN2.0_RangeMaps_functions.R")
source("./functions/Biomes_hypervolumes_fun.R")
# data --------------------------------------------------------------------
# 1. Trait data frame
Traits_phylo<-read.csv("./data/processed/traits_ALLMB_lambda.csv")
# 2. Values of distinctiveness and Restrictedness for species per biome
Biome_Di_Ri<-read.csv("./outputs/Biome_Di_Ri_phylo.csv", row.names = 1)
# 5. Presence matrix of species
spPresence<-read.csv("./data/base/BIEN_2_Ranges/presence100km.csv")
names(spPresence) = c("Species","Y","X")
TraitSpecies <- unique(Traits_phylo$species)
spMatrix_sub <- splistToMatrix(spPresence,TraitSpecies)
## Remove cells without species
indx<-which(rowSums(spMatrix_sub)!=0)
spMatrix_sub<-spMatrix_sub[indx,]
# Data manipulation -------------------------------------------------------
# 1. Merging data frames
Traits_Biome_Di_Ri<-merge(Biome_Di_Ri,Traits_phylo)
## 3. Rename and order biomes
Traits_Biome_Di_Ri$Biome<-recode(Traits_Biome_Di_Ri$Biome,Moist_Forest="Moist",
Savannas="Savannas",
Tropical_Grasslands="Trop_Grass",
Dry_Forest="Dry",
Xeric_Woodlands="Xeric",
Mediterranean_Woodlands="Mediterranean",
Temperate_Grasslands="Temp_Grass",
Temperate_Mixed="Temp_Mixed",
Coniferous_Forests="Coniferous",
Taiga="Taiga",
Tundra="Tundra")
Traits_Biome_Di_Ri$Biome<-factor(Traits_Biome_Di_Ri$Biome,
levels=c("Moist","Savannas","Trop_Grass",
"Dry","Xeric","Mediterranean",
"Temp_Grass","Temp_Mixed","Coniferous",
"Taiga","Tundra"))
# Calculate hypervolumes -------------------------------
# Transforming and Scaling variables
Traits_Biome_Di_Ri$logseed_mass<-log(Traits_Biome_Di_Ri$Seed_mass)
Traits_Biome_Di_Ri$logHeight<-log(Traits_Biome_Di_Ri$Height)
Traits_Biome_Di_Ri$logWoodDensity<-log(Traits_Biome_Di_Ri$Wood_density)
Traits_Biome_Di_Ri$sqrtSLA<-sqrt(Traits_Biome_Di_Ri$SLA)
#Selecting and Scalling variables
Traits_Biome_Di_Ri<-
Traits_Biome_Di_Ri %>%
mutate(Scaled_logSeed_mass=as.numeric(scale(logseed_mass)),
Scaled_logHeight=as.numeric((logHeight)),
Scaled_SLA=as.numeric(scale(sqrtSLA)),
Scaled_logWood_density=as.numeric(scale(logWoodDensity)),
Scaled_Leaf_N=as.numeric(scale(Leaf_N)),
Scaled_Leaf_P=as.numeric(scale(Leaf_P))
)
Trait_df<-
Traits_Biome_Di_Ri %>%
dplyr::select(species,contains("Scaled"))
## Hypervolumes for each biomes cells using hypervolume box algorithm
cell_biomes_df<-readRDS("./outputs/spPresence_biomes_all.rds")
cell_biomes<-cell_biomes_df %>%
filter(Species%in%Trait_df$species)
cell_biomes <-tapply(cell_biomes$cells, cell_biomes$biomes, unique)
cells_names<-as.character(as.vector(unlist(cell_biomes)))
cells_names <- paste("Cell", cells_names, sep="_")
hypervolume_per_cell <- function(cells_list, pb_matrix, trait_df){
count <- 0
foreach(i = cells_list, .combine = rbind)%do%{
print(i)
count <- count + 1
x<-pb_matrix[i,]
print(paste("Processing",count, "out of ",length(cells_list)))
sp_names<-names(x[x > 0 & !is.na(x)])
if (length(sp_names)>1){
res<- tryCatch({
cell_hyper<-trait_df %>%
filter(species%in%sp_names) %>%
dplyr::select(contains("Scaled")) %>%
hypervolume_box(kde.bandwidth=0.5)
cell_hyper@Volume
},
error = function(cond){
message("Species with the same trait values")
return(NA)
})
}else{
res = NA
}
tmp_df <- data.frame(cell = i, vol = res)
tmp_df
}
}
## Running the function
system.time(
hyper_cells_box <- hypervolume_per_cell(cells_list = cells_names,
pb_matrix = spMatrix_sub,
trait_df = Trait_df)
)
write_rds(hyper_cells_box, "./outputs/07_hypervol_cell_SDM_BW0.5.rds")
##
write_rds(hyper_cells_box, "./outputs/07_hypervol_cell_SDM_501_end_box.rds")
hyper_cells_box_1<-readRDS("outputs/07_hypervol_cell_SDM_501_end_box.rds")
hyper_cells_box_2<-readRDS("outputs/07_hypervol_cell_SDM_1:500_box.rds")
hyper_cells_box<- rbind(hyper_cells_box_2,hyper_cells_box_1)
hyper_cells_box$cell<-as.numeric(gsub("Cell_","",hyper_cells_box$cell))
indx<-match(hyper_cells_box$cell,cell_biomes_df$cells)
hyper_cells_box$biomes<-cell_biomes_df$biomes[indx]
hyper_cells_box$biomes<-factor(hyper_cells_box$biomes,
levels=c("Moist_Forest","Savannas","Tropical_Grasslands",
"Dry_Forest","Xeric_Woodlands","Mediterranean_Woodlands",
"Temperate_Grasslands","Temperate_Mixed","Coniferous_Forests",
"Taiga","Tundra"))
hyper_cells_box$biomes<-recode(hyper_cells_box$biomes,Moist_Forest="Moist",
Savannas="Savannas",
Tropical_Grasslands="Trop_Grass",
Dry_Forest="Dry",
Xeric_Woodlands="Xeric",
Mediterranean_Woodlands="Mediterranean",
Temperate_Grasslands="Temp_Grass",
Temperate_Mixed="Temp_Mixed",
Coniferous_Forests="Coniferous",
Taiga="Taiga",
Tundra="Tundra")
library(wesanderson)
pdf("./figs/07_Hypervolume_cells_SDM_box.pdf", width=10)
ggplot(data=hyper_cells_box,aes(x=biomes,y=vol)) +
geom_boxplot()+
geom_jitter(alpha=0.5,color=wes_palette("Cavalcanti1")[4])+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlab("")+ylab(expression(paste("SD"^"6")))
dev.off()
# Add richness info
spMatrix_sub_copy <-spMatrix_sub
spMatrix_sub_copy[which(spMatrix_sub_copy>0)]<-1
cell_richness <- rowSums(spMatrix_sub_copy)
names(cell_richness) <- gsub("Cell_", "", names(cell_richness))
indx<-match(hyper_cells_box$cell,names(cell_richness))
hyper_cells_box$Richness<-cell_richness[indx]
library(ggpmisc)
library(ggpubr)
biomes_to_plot <- c("Dry","Xeric","Mediterranean")
hyper_cells_box$logRich <- log(hyper_cells_box$Richness)
tmp_df <- hyper_cells_box %>%
filter(biomes%in%biomes_to_plot & logRich > 4)
png("./figs/07_Hypervolume_cells_SDM_box_DMX.png", width=700)
ggscatterhist(data = tmp_df, x = "logRich", y = "vol",
color = "biomes", size = 3, alpha = 0.6,
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
margin.plot = "boxplot",
ggtheme = theme_bw())
dev.off()
## Moist, Temperate, and Taiga
biomes_to_plot <- c("Moist","Temp_Mixed","Taiga")
tmp_df <- hyper_cells_box %>%
filter(biomes%in%biomes_to_plot & logRich > 4)
png("./figs/07_Hypervolume_cells_SDM_box_MoisTempTaig.png", width=700)
ggscatterhist(data = tmp_df, x = "logRich", y = "vol",
color = "biomes", size = 3, alpha = 0.6,
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
margin.plot = "boxplot",
ggtheme = theme_bw())
dev.off()
###
### Taking only the 10% of the cells per each biomes
cell_biomes_df<-readRDS("./outputs/spPresence_biomes_all.rds")
cell_biomes<-cell_biomes_df %>%
filter(Species%in%Trait_df$species)
cell_biomes <-tapply(cell_biomes$cells, cell_biomes$biomes, unique)
### Taking only the 20% of the cells per each biomes
Random_cells<-
lapply(cell_biomes,
function(x)
sample(x,length(x)*.20)
)
cells_names<-as.character(as.vector(unlist(Random_cells)))
cells_names <- paste("Cell", cells_names, sep="_")
Tmp<-NULL
count <- 0
system.time(
for (i in cells_names)
{
print(i)
count <- count + 1
x<-spMatrix_sub[i,]
print(paste("Processing",count, "out of ",length(cells_names)))
sp_names<-names(x[x > 0 & !is.na(x)])
#if(length(sp_names)>100){
# sample_sp<-sample(sp_names,100)
#}else{
# sample_sp<-sp_names
#}
if (length(sp_names)>1){
res<- tryCatch({
cell_hyper<-Trait_df %>%
filter(species%in%sp_names) %>%
dplyr::select(contains("Scaled")) %>%
hypervolume_gaussian()
cell_hyper@Volume
},
error = function(cond){
message("Species with the same trait values")
return(NA)
})
}else{
res=NA
}
tmp_df <- data.frame(cell = i, vol = res)
Tmp<-rbind(Tmp,tmp_df)
write_rds(Tmp,"./outputs/07_Hypervolume_sp_sample_box.rds")
}
)
cell_hyper_df <- Tmp
cell_hyper_df$cell<-as.numeric(gsub("Cell_","",cell_hyper_df$cell))
indx<-match(cell_hyper_df$cell,cell_biomes_df$cells)
cell_hyper_df$biomes<-cell_biomes_df$biomes[indx]
cell_hyper_df$biomes<-factor(cell_hyper_df$biomes,
levels=c("Moist_Forest","Savannas","Tropical_Grasslands",
"Dry_Forest","Xeric_Woodlands","Mediterranean_Woodlands",
"Temperate_Grasslands","Temperate_Mixed","Coniferous_Forests",
"Taiga","Tundra"))
cell_hyper_df$biomes<-recode(cell_hyper_df$biomes,Moist_Forest="Moist",
Savannas="Savannas",
Tropical_Grasslands="Trop_Grass",
Dry_Forest="Dry",
Xeric_Woodlands="Xeric",
Mediterranean_Woodlands="Mediterranean",
Temperate_Grasslands="Temp_Grass",
Temperate_Mixed="Temp_Mixed",
Coniferous_Forests="Coniferous",
Taiga="Taiga",
Tundra="Tundra")
library(wesanderson)
pdf("./figs/07_Hypervolume_cells_spNewHypervolume_box.pdf", width=10)
ggplot(data=cell_hyper_df,aes(x=biomes,y=vol)) +
geom_boxplot()+
geom_jitter(alpha=0.5,color=wes_palette("Cavalcanti1")[4])+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlab("")+ylab(expression(paste("SD"^"6")))
dev.off()
# Add richness info
spMatrix_sub[which(spMatrix_sub>0)]<-1
cell_richness <- rowSums(spMatrix_sub)
names(cell_richness) <- gsub("Cell_", "", names(cell_richness))
indx<-match(cell_hyper_df$cell,names(cell_richness))
cell_hyper_df$Richness<-cell_richness[indx]
library(ggpmisc)
library(ggpubr)
biomes_to_plot <- c("Dry","Xeric","Mediterranean")
cell_hyper_df$logRich <- log(cell_hyper_df$Richness)
tmp_df <- cell_hyper_df %>%
filter(biomes%in%biomes_to_plot & logRich > 4)
ggscatterhist(data = tmp_df, x = "logRich", y = "vol",
color = "biomes", size = 3, alpha = 0.6,
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
margin.plot = "boxplot",
ggtheme = theme_bw())
Total_richness <- raster("./data/base/BIEN_2_Ranges/richness100km.tif")
tmp_df$TotallogRich <- log(Total_richness[tmp_df$cell])
## Loop of 50 times
### Taking only the 10% of the cells per each biomes
cell_biomes_df<-readRDS("./outputs/spPresence_biomes_all.rds")
cell_biomes<-cell_biomes_df %>%
filter(Species%in%Trait_df$species)
cell_biomes <-tapply(cell_biomes$cells, cell_biomes$biomes, unique)
for (j in 1:50){
Random_cells<-
lapply(cell_biomes,
function(x)
sample(x,length(x)*.10)
)
Random_cells<-lapply(Random_cells, function(x)paste("Cell",x,sep="_"))
cells_names<-as.vector(unlist(Random_cells))
Tmp<-NULL
for (i in cells_names)
{
x<-spMatrix_sub[i,]
print(paste("Processing",length(Tmp)))
cell_names<-names(x[x > 0 & !is.na(x)])
if(length(cell_names)>10){
sample_sp<-sample(cell_names,10)
}else{
sample_sp<-cell_names
}
if (length(cell_names)>1){
cell_hyper<-Trait_df %>%
filter(species%in%sample_sp) %>%
dplyr::select(contains("Scaled")) %>%
hypervolume_gaussian()
res<-cell_hyper@Volume
}else{
res=0
}
Tmp<-c(Tmp,res)
names(Tmp)<-cell_names
write_rds(Tmp,"outputs/Hypervolume_sp_sample_gaussian50_Itera.rds")
}
}