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Check_species_biases.R
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Check_species_biases.R
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library(rgdal)
library(raster)
library(tidyverse)
# 1. data ------------------------------------------------------------------------
# 1.1 Presence of species in cells and biomes with occurrences without filtering
spOcc_geo<-readRDS("outputs/02_Species_grid_id_biomes_df.rds")
# 1.2. Trait species dataframe
Traits_phylo<-read.csv("./data/traits/traits_ALLMB_lambda.csv")
############################################################################
# Occurrence data patterns ------------------------------------------------
############################################################################
# 2. Create total richness dataframe -----------------------------------------
# Convert into a normal dataframe
spOcc_geo_df <- as_tibble(spOcc_geo)
## Species richnnes per biomes
spMatrix_occ <- table(spOcc_geo_df$grid_id,spOcc_geo_df$scrubbed_species_binomial)
cell_N_records <- rowSums(spMatrix_occ)
undesampled_cells <- names(cell_N_records)[which(cell_N_records<100)]
ix <- which(rownames(spMatrix_occ)%in%undesampled_cells==FALSE)
spMatrix_occ <- spMatrix_occ[ix,]
spMatrix_occ[which(spMatrix_occ > 0)] <- 1
Sp_Rich_Occ <- rowSums(spMatrix_occ)
## Include the richnnes values to a dataframe
grid_richness_total <- data.frame(grid_id = names(Sp_Rich_Occ), Richness = Sp_Rich_Occ)
indx<-match(grid_richness_total$grid_id,spOcc_geo_df$grid_id)
grid_richness_total$Biomes <- spOcc_geo_df$Biomes[indx]
library(wesanderson)
grid_richness_total$Biomes<-recode(grid_richness_total$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")
grid_richness_total$Biomes<-factor(grid_richness_total$Biomes,
levels=c("Moist","Savannas","Trop_Grass",
"Dry","Xeric","Mediterranean",
"Temp_Grass","Temp_Mixed","Coniferous",
"Taiga","Tundra"))
png("./figs/Total_richnnes_occ.png", width = 700)
ggplot(data=grid_richness_total,aes(x=Biomes,y=log(Richness+1))) +
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("Richness")))
dev.off()
# 3. Richness for grids with trait values ------------------------------------
spOcc_geo_df$Species <- gsub(" ", "_", spOcc_geo_df$scrubbed_species_binomial)
spOcc_geo_traits <-
spOcc_geo_df %>%
dplyr::filter(Species%in%unique(Traits_phylo$species))
## Species richnnes per biomes
spMatrix_occ_trait <- table(spOcc_geo_traits$grid_id,spOcc_geo_traits$Species)
spMatrix_occ_trait[which(spMatrix_occ_trait > 0)] <- 1
Sp_Rich_Occ_trait<- rowSums(spMatrix_occ_trait)
## Include the richnnes values to a dataframe
grid_richness_traits <- data.frame(grid_id = names(Sp_Rich_Occ_trait), Richness = Sp_Rich_Occ_trait)
indx<-match(grid_richness_traits$grid_id,spOcc_geo_traits$grid_id)
grid_richness_traits$Biomes <- spOcc_geo_traits$Biomes[indx]
grid_richness_traits$Biomes<-recode(grid_richness_traits$Biomes,Moist_Forest="Moist",
Savannas="Savannas",
Tropical_Grasslands="Trop_Grass",
Dry_Forest="Dry",
Xeric_Woodlands="Xeric",
Mediterranean_Woodlands="Mediterranean",
Temperate_Grasslands="Temp_Grass",
Temperate_Mixed="Temp_Mixed",
Coniferous_Forests="Coniferous",
Taiga="Taiga",
Tundra="Tundra")
grid_richness_traits$Biomes<-factor(grid_richness_traits$Biomes,
levels=c("Moist","Savannas","Trop_Grass",
"Dry","Xeric","Mediterranean",
"Temp_Grass","Temp_Mixed","Coniferous",
"Taiga","Tundra"))
png("./figs/Trait_richnnes_occ.png", width = 700)
ggplot(data=grid_richness_traits,aes(x=Biomes,y=log(Richness+1))) +
geom_boxplot()+
geom_jitter(alpha=0.5,color=wes_palette("Cavalcanti1")[4])+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlab("")+ylab(expression(paste("Richness")))
dev.off()
############################################################################
# Spatial models distributions ---------------------------------------------
############################################################################
# 1. Data -----------------------------------------------------------------
## Since there are a lot of NAs that are on the coast I will use the dataframe used in the original analysis
spPresence_biomes <- readRDS("./data/spPresence_biomes_all.rds")
spMatrix_SDM_total <- table(spPresence_biomes$cells,spPresence_biomes$Species)
spMatrix_SDM_total[which(spMatrix_SDM_total > 0)] <- 1
Sp_Rich_SDM_total<- rowSums(spMatrix_SDM_total)
## Include the richnnes values to a dataframe
grid_richness_SDM <- data.frame(grid_id = names(Sp_Rich_SDM_total),
Richness = Sp_Rich_SDM_total)
indx<-match(grid_richness_SDM$grid_id,spPresence_biomes$cells)
grid_richness_SDM$Biomes <- spPresence_biomes$biomes[indx]
grid_richness_SDM$Biomes<-recode(grid_richness_SDM$Biomes,Moist_Forest="Moist",
Savannas="Savannas",
Tropical_Grasslands="Trop_Grass",
Dry_Forest="Dry",
Xeric_Woodlands="Xeric",
Mediterranean_Woodlands="Mediterranean",
Temperate_Grasslands="Temp_Grass",
Temperate_Mixed="Temp_Mixed",
Coniferous_Forests="Coniferous",
Taiga="Taiga",
Tundra="Tundra")
grid_richness_SDM$Biomes<-factor(grid_richness_SDM$Biomes,
levels=c("Moist","Savannas","Trop_Grass",
"Dry","Xeric","Mediterranean",
"Temp_Grass","Temp_Mixed","Coniferous",
"Taiga","Tundra"))
png("./figs/Total_richnnes_SDM.png", width = 700)
ggplot(data=grid_richness_SDM,aes(x=Biomes,y=log(Richness+1))) +
geom_boxplot()+
geom_jitter(alpha=0.5,color=wes_palette("Cavalcanti1")[4])+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlab("")+ylab(expression(paste("Richness"))) +
ylim(2.5,9)
dev.off()
### Richness counting only those species with traits
spPresence_biomes_traits <-
spPresence_biomes %>%
dplyr::filter(Species%in%unique(Traits_phylo$species))
spMatrix_SDM_trait <- table(spPresence_biomes_traits$cells,spPresence_biomes_traits$Species)
spMatrix_SDM_trait[which(spMatrix_SDM_trait > 0)] <- 1
Sp_Rich_SDM_traits<- rowSums(spMatrix_SDM_trait)
## Include the richnnes values to a dataframe
grid_richness_SDM_trait <- data.frame(grid_id = names(Sp_Rich_SDM_traits),
Richness = Sp_Rich_SDM_traits)
indx<-match(grid_richness_SDM_trait$grid_id,spPresence_biomes_traits$cells)
grid_richness_SDM_trait$Biomes <- spPresence_biomes_traits$biomes[indx]
grid_richness_SDM_trait$Biomes<-recode(grid_richness_SDM_trait$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")
grid_richness_SDM_trait$Biomes<-factor(grid_richness_SDM_trait$Biomes,
levels=c("Moist","Savannas","Trop_Grass",
"Dry","Xeric","Mediterranean",
"Temp_Grass","Temp_Mixed","Coniferous",
"Taiga","Tundra"))
png("./figs/Trait_richnnes_SDM.png", width = 700)
ggplot(data=grid_richness_SDM_trait,aes(x=Biomes,y=log(Richness+1))) +
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("Richness")))
dev.off()