-
Notifications
You must be signed in to change notification settings - Fork 0
/
03_SpeciesComposition_plot.R
237 lines (179 loc) · 8.86 KB
/
03_SpeciesComposition_plot.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
# libraries ---------------------------------------------------------------
library(raster)
library(tidyverse)
library(fuzzySim)
library(circlize)
library(RColorBrewer)
library(wesanderson)
library(dendextend)
library(sf)
library(gplots)
library(ggplot2)
library(data.table)
library(foreach)
# data --------------------------------------------------------------------
# 1. Presence of species in cells and biomes
spOcc_geo<-readRDS("outputs/02_Species_grid_id_biomes_df.rds")
# Convert into a normal dataframe
spOcc_geo_df <- as_tibble(spOcc_geo)
## Number of cells per species in each biome
cells_in_sp<-spOcc_geo_df %>%
group_by(scrubbed_species_binomial,Biomes) %>%
summarise(N_cells=n_distinct(grid_id)) %>%
group_by(scrubbed_species_binomial) %>%
mutate(Total_cells=sum(N_cells), prop_cells=N_cells/sum(N_cells)) %>%
mutate(max_prop=max(prop_cells))
cells_in_sp$Biomes<-recode(cells_in_sp$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")
# 2. Species list for each biome ------------------------------------------
cells_in_sp$Species <- cells_in_sp$scrubbed_species_binomial
# 2.1 Total numbr of species
Total_sp_list<-tapply(cells_in_sp$Species,cells_in_sp$Biomes,unique)
# 2.2 Species with highest proportion of their ranges in each biome
Wides_sp<-cells_in_sp %>%
dplyr::filter(prop_cells==max_prop)
Wides_sp_list<-tapply(Wides_sp$Species,Wides_sp$Biomes,unique)
# 2.3 Endemics for each biome
Endemics_sp<-cells_in_sp %>%
dplyr::filter(prop_cells==1)
Endemics_sp_list<-tapply(Endemics_sp$Species,Endemics_sp$Biomes,unique)
# 2.4 Proportion of endemics in each biome
total_n<-unlist(lapply(Total_sp_list,length))
endemics_n<-unlist(lapply(Endemics_sp_list,length))
prop_endemics<-round(endemics_n/total_n,3)*100
# 3. Create similarity matrix ---------------------------------------------
## Create a loop to calculate the similarity (number of species shared among biomes)
biome_richness<-Total_sp_list
spSimilarity<-foreach(i=1:length(biome_richness), .combine='cbind') %:%
foreach(j=1:length(biome_richness), .combine='c') %do% {
length(intersect(biome_richness[[i]],biome_richness[[j]]))
}
colnames(spSimilarity)<-names(biome_richness)
rownames(spSimilarity)<-names(biome_richness)
## Double check that the numbers are correct
#diag(spSimilarity)==unlist(lapply(biome_richness, n_distinct))
#spSimilarity/diag(spSimilarity)
# 4. Chordplot of similarities --------------------------------------------
# 4.1 Species composition among biomes using all the species
col=c(wes_palette("Darjeeling1",6,type="continuous"),
wes_palette("Cavalcanti1",5,type="continuous"))
diag(spSimilarity)<-0
indx<-match(colnames(spSimilarity),names(prop_endemics))
colnames(spSimilarity)<-paste(colnames(spSimilarity),", ", prop_endemics[indx],"%", sep="")
rownames(spSimilarity)<-colnames(spSimilarity)
pdf("./figs/species_composition/Total_similarity_biomes_withEndemics.pdf")
par(mar=c(0, 0, 0, 0))
chordDiagram(spSimilarity, annotationTrack = "grid", preAllocateTracks = 1, grid.col =col,symmetric = TRUE,
column.col = col)
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
circos.text(mean(xlim), ylim[1] + .2, sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5), cex=0.7)
circos.axis(h = "top", labels.cex = 0.5, major.tick.percentage = 0.2, sector.index = sector.name, track.index = 2)
}, bg.border = NA)
dev.off()
# 4.2 Species composition using Dominant species (those that occupy most of their ranges in each biome)
# This is to give some direction of shared species among biome
spSimilarity_Wides<-foreach(i=1:length(Wides_sp_list), .combine='rbind') %:%
foreach(j=1:length(Total_sp_list), .combine='rbind') %do% {
N_sp=length(intersect(Wides_sp_list[[i]],Total_sp_list[[j]]))
df<-data.frame(from=names(Wides_sp_list)[i],
to=names(Total_sp_list)[j],
Sp_shared=N_sp)
df
}
spSimilarity_ma<-matrix(data = spSimilarity_Wides$Sp_shared, nrow = n_distinct(spSimilarity_Wides$from), ncol = n_distinct(spSimilarity_Wides$to), byrow = FALSE,
dimnames = NULL)
rownames(spSimilarity_ma)<-unique(spSimilarity_Wides$from)
colnames(spSimilarity_ma)<-unique(spSimilarity_Wides$to)
biome_order<-c("Moist","Savannas","Dry",
"Xeric","Trop_Grass",
"Coniferous","Temp_Mixed","Temp_Grass",
"Mediterranean","Taiga","Tundra")
spSimilarity_ma<-spSimilarity_ma[biome_order,biome_order]
## Print file to include into the supplementary information
diag(spSimilarity_ma)<-endemics_n[biome_order]
write.csv(spSimilarity_ma,"./supp_info/Shared_species_matrix.csv")
diag(spSimilarity_ma)<-0
Wides_sp_total<-unlist(lapply(Wides_sp_list,length))
## Proportion of widespread species
total_n<-unlist(lapply(Total_sp_list,length))
Wides_sp_total<-unlist(lapply(Wides_sp_list,length))
prop_widespread<-round(Wides_sp_total/total_n,2)*100
colnames(spSimilarity_ma)<-paste(colnames(spSimilarity_ma),", ", prop_widespread[biome_order],"%", sep="")
rownames(spSimilarity_ma)<-colnames(spSimilarity_ma)
pdf("./figs/03_Total_similarity_biomes_DominantSp_Occ.pdf",width = 8, height = 8)
par(mar=c(0, 0, 0, 0))
chordDiagram(spSimilarity_ma,column.col = col,
grid.col =col, directional = -1,
direction.type = c("diffHeight", "arrows"),link.largest.ontop=TRUE,
annotationTrack = c("grid"),link.arr.length = 0.2,link.arr.type = "big.arrow",
preAllocateTracks = 1)
circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
sector.name = get.cell.meta.data("sector.index")
circos.text(mean(xlim), ylim[1], sector.name, facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5), cex=0.8)
}, bg.border = NA)
dev.off()
# 5. Species dissimilarity among biomes --------------------------------------
Similarity_sp_biomes<-function(sp_list){
biome_similarity <- foreach(i = 1:length(sp_list), .combine='rbind') %:%
foreach(j = 1:length(Total_sp_list), .combine='rbind') %do% {
sp_intersection = length(intersect(sp_list[[i]],sp_list[[j]]))
biome1 <- length(sp_list[[i]])
biome2 <- length(sp_list[[j]])
sorensen <- 2 * sp_intersection/(biome1 + biome2)
sorensen_df<-data.frame(sorensen=sorensen,
from=names(sp_list)[i],
to=names(sp_list)[j])
sorensen_df
}
Similarity_ma<-matrix(data = biome_similarity$sorensen,
nrow = n_distinct(biome_similarity$from),
ncol = n_distinct(biome_similarity$to), byrow = FALSE,
dimnames = NULL)
rownames(Similarity_ma)<-unique(biome_similarity$from)
colnames(Similarity_ma)<-unique(biome_similarity$to)
Similarity_ma
}
## Dissimilarity all species
Total_similarity<-Similarity_sp_biomes(Total_sp_list)
fit_total_sim <-hclust(as.dist(1-Total_similarity))
labels(fit_total_sim)<-c("Trop_Grass", "Moist","Savannas", "Dry","Xeric",
"Taiga","Tundra","Mediterranean","Coniferous","Temp_Grass","Temp_Mixed")
dend_total<-
fit_total_sim %>%
as.dendrogram() %>%
color_branches(1,col=wes_palette("Cavalcanti1")[2]) %>%
dendextend::set("branches_lwd", 4) %>%
dendextend::set("labels_cex", 1.5)
circlize_dendrogram(dend_total,dend_track_height = 0.7,labels_track_height = 0.2)
### Heatmaps
my_palette <- colorRampPalette(c(wes_palette("Cavalcanti1")[2],
wes_palette("Cavalcanti1")[c(2,4)],
"white"))(n = 100)
my_palette <-rev(colorRampPalette(c('#ffffcc','#c2e699','#78c679','#31a354','#006837','#006837'))(n = 100))
my_palette <-rev(colorRampPalette(c('#ffffcc','#c2e699','#78c679','#31a354','#006837','#006837'))(n = 100))
col_breaks<-seq(0,1,by=0.01)
pdf("./figs/03_species_composition_heatmap.pdf", width = 10)
heatmap.2(as.matrix(1-Total_similarity), symm = TRUE,
distfun = function(x) as.dist(x),dendrogram = "both",margins = c(12,10),
revC = TRUE,
cexRow=1.5,cexCol=1.5,
trace = "none", density.info = "none",keysize = 1.3,
key.title = "",
key.xlab = "",
col=my_palette,
breaks=col_breaks)
dev.off()