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Copy pathinteractive_effect_rcp45_habitat.R
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interactive_effect_rcp45_habitat.R
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library(rgeos)
library(raster)
library(rgdal)
library(sp)
library(rJava)
library(readxl)
library(ggplot2)
library(devtools)
library(rgeos)
library(xlsx)
library(dplyr)
library(tmap)
library(dismo)
library(ggpubr)
library(RColorBrewer)
library(cowplot)
library(mapview)
#INTERACTIVE EFECT ON SPECIES USE FOREST ONLY
#change in species richness for species use forest only
#import list of species
species.list <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/vnbats_habitat_traits.xlsx", sheetName = 'Sheet1')
species.list <- species.list%>%select(Scientific_name,Forest,Shrubland,Grassland,Wetlands,Caves,Artificial,Diet.Plant,Diet.Invertebrate)
species.list <- species.list%>%filter(Artificial=='FALSE'&Shrubland=='FALSE'&Grassland=='FALSE')
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
extent.asia.sr <- extent(11000000, 13000000, 900000, 2600000)
#current species richness
sr.current <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," current.tif"))*vn.raster
})
batsr.current <- overlay(stack(sr.current),fun=sum)
writeRaster(batsr.current,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_only.tif',format='GTiff',overwrite=T)
#species richness for individual GCMs
#combined model
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sr.2050.rcp45.both <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," 2050 rcp45 both ",gcm,".tif"))*vn.raster
})
batsr.2050.rcp45.both <- overlay(stack(sr.2050.rcp45.both),fun=sum)
writeRaster(batsr.2050.rcp45.both, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',gcm,'_forest_only.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#climate change only model
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sr.2050.rcp45.cl <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," 2050 rcp45 cl ",gcm,".tif"))*vn.raster
})
batsr.2050.rcp45.cl <- overlay(stack(sr.2050.rcp45.cl),fun=sum)
writeRaster(batsr.2050.rcp45.cl, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',gcm,'_forest_only.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#consensus future prediction rcp45
#combined model
gcm <- c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')
sr.2050.both <- lapply(gcm, function(x){
raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',x,'_forest_only.tif'))
})
#climate model
sr.2050.cl <- lapply(gcm, function(x){
raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',x,'_forest_only.tif'))
})
batsr.2050.both.median <- overlay(stack(sr.2050.both), fun=median)
batsr.2050.cl.median <- overlay(stack(sr.2050.cl), fun=median)
#land-cover change only models
sr.2050.rcp45.lc <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," 2050 rcp45 lc.tif"))*vn.raster
})
batsr.lc.rcp45 <- overlay(stack(sr.2050.rcp45.lc),fun=sum)
writeRaster(batsr.2050.both.median,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_median_forest_only.tif',format='GTiff',overwrite=T)
writeRaster(batsr.2050.cl.median,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_median_forest_only.tif',format='GTiff',overwrite=T)
writeRaster(batsr.lc.rcp45,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_only.tif',format='GTiff',overwrite=T)
#bar charts change in species richness
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
extent.asia.sr <- extent(11000000, 13000000, 900000, 2600000)
batsr.current <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_only.tif')
batsr.lc.rcp45 <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_only.tif')
batsr.lc.rcp45.current <- (batsr.lc.rcp45 - batsr.current)/batsr.current*100
pro.lc.rcp45 <- c(cellStats(batsr.lc.rcp45.current>=30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current < 30 & batsr.lc.rcp45.current > 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current == 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current < 0 & batsr.lc.rcp45.current > -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current <= -30,'sum')/cellStats(vn.raster,'sum')*100)
#effect of combined change
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
batsr.both.rcp45 <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',gcm,'_forest_only.tif'))
batsr.both.rcp45.current <- (batsr.both.rcp45 - batsr.current)/batsr.current*100
pro.both.rcp45 <- c(cellStats(batsr.both.rcp45.current>=30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current < 30 & batsr.both.rcp45.current > 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current == 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current < 0 & batsr.both.rcp45.current > -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current <= -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current < 0, 'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current > 0, 'sum')/cellStats(vn.raster,'sum')*100)
assign(paste0('pro.both.rcp45.',gcm),pro.both.rcp45)
print(gcm)
}
pro.both.all <- data.frame(pro.both.rcp45.ac,pro.both.rcp45.bc,pro.both.rcp45.ca,pro.both.rcp45.cm,pro.both.rcp45.cn,
pro.both.rcp45.cs,pro.both.rcp45.gf,pro.both.rcp45.gi,pro.both.rcp45.ip,pro.both.rcp45.mi)
#effect of climate change only
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
batsr.cl.rcp45 <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',gcm,'_forest_only.tif'))
batsr.cl.rcp45.current <- (batsr.cl.rcp45 - batsr.current)/batsr.current*100
pro.cl.rcp45 <- c(cellStats(batsr.cl.rcp45.current>=30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current < 30 & batsr.cl.rcp45.current > 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current == 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current < 0 & batsr.cl.rcp45.current > -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current <= -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current < 0, 'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current > 0, 'sum')/cellStats(vn.raster,'sum')*100)
assign(paste0('pro.cl.rcp45.',gcm),pro.cl.rcp45)
print(gcm)
}
pro.cl.all <- data.frame(pro.cl.rcp45.ac, pro.cl.rcp45.bc, pro.cl.rcp45.ca, pro.cl.rcp45.cm, pro.cl.rcp45.cn,
pro.cl.rcp45.cs, pro.cl.rcp45.gf, pro.cl.rcp45.gi, pro.cl.rcp45.ip, pro.cl.rcp45.mi)
pro.both.median <- apply(pro.both.all,1,median)
pro.both.min <- apply(pro.both.all,1,min)
pro.both.max <- apply(pro.both.all,1,max)
pro.cl.median <- apply(pro.cl.all,1,median)
pro.cl.min <- apply(pro.cl.all,1,min)
pro.cl.max <- apply(pro.cl.all,1,max)
class <- c('>30','0~30','0','-30~0','<-30')
sum.both.sr <- data.frame(class,pro.both.median[1:5],pro.both.min[1:5], pro.both.max[1:5])
sum.cl.sr <- data.frame(class,pro.cl.median[1:5], pro.cl.min[1:5], pro.cl.max[1:5])
sum.lc.sr <- data.frame(class,pro.lc.rcp45)
sum.both.sr$class <- factor(sum.both.sr$class, levels = c('<-30','-30~0','0','0~30','>30'))
sum.cl.sr$class <- factor(sum.cl.sr$class, levels = c('<-30','-30~0','0','0~30','>30'))
sum.lc.sr$class <- factor(sum.lc.sr$class, levels = c('<-30','-30~0','0','0~30','>30'))
write.xlsx(sum.both.sr,file = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_both_rcp45_forest_speciallist.xlsx')
write.xlsx(sum.cl.sr,file = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_cl_rcp45_forest_speciallist.xlsx')
write.xlsx(sum.lc.sr,file = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_lc_rcp45_forest_speciallist.xlsx')
#plot maps species richness change
batsr.current <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_only.tif')
batsr.2050.both <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_median_forest_only.tif')
batsr.2050.cl <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_median_forest_only.tif')
batsr.2050.lc <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_only.tif')
SR.ORG8287 <- CRS('+proj=cea +lon_0=0 +lat_ts=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs')
vn <- spTransform(getData('GADM', country='VN', level=0), SR.ORG8287)
paracell.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/paracel islands.shp'), SR.ORG8287)
spratly.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/spratly islands.shp'), SR.ORG8287)
# pas <- readOGR('F:/Working/2018/PhD_research/SDM output R/Map/vn_pas.shp')
batsr.both.current <- (batsr.2050.both - batsr.current)/batsr.current*100
batsr.cl.current <- (batsr.2050.cl - batsr.current)/batsr.current*100
batsr.lc.current <- (batsr.2050.lc - batsr.current)/batsr.current*100
plot.current<- tm_shape(batsr.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(0,2,4,6,8,10,15,20,25,30,35,40,50,65), palette = 'Greens', style = 'fixed',
# tm_raster('layer',breaks = c(0,2,4,6,8,10,12,14,16,18,20,25,30,35,40,45,50,55,60,65,70), palette = 'Blues', style = 'fixed',
title = 'Species richness')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'dark gray', alpha = 0, lwd = 0.5)+
tm_layout(legend.outside = F, title = 'Species richness',title.position = c('left','top'),title.size = 1,
legend.text.size = 0.5, legend.title.size = 1,legend.position = c(0,0.3), frame = F, legend.show = F)
plot.srchange.rcp45.both <- tm_shape(batsr.both.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(-Inf,-30,-0.0001,0.0001,30,Inf),palette = c('red','orange','grey70','lightblue','blue'),
title = 'SR change (%)',labels = c('< -30','-30~0','0','0~30','> 30'))+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'dark gray', alpha = 0, lwd = 0.5)+
tm_layout(legend.outside = F, title = '(b) Combined change', title.size = 1, legend.position = c(0, 0.3),
title.position = c('left','top'), frame = F, legend.text.size = 0.8, legend.title.size = 2, legend.show = F)
plot.srchange.rcp45.cl <- tm_shape(batsr.cl.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(-Inf,-25,-0.0001,0.0001,25,Inf),palette = c('red','orange','grey70','lightblue','blue'),
title = '% SR change')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'gray', alpha = 0, lwd = 0.5)+
tm_layout(title = '(c) Climate change only', title.size = 1,title.position = c('left','top'), frame = F,legend.show = F)
plot.srchange.rcp45.lc <- tm_shape(batsr.lc.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(-Inf,-25,-0.0001,0.0001,25,Inf),palette = c('red','orange','grey70','lightblue','blue'),
title = '% SR change')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'dark gray', alpha = 0, lwd = 0.5)+
tm_layout(title = '(d) Land cover change only', title.size = 1, title.position = c('left','top'), frame = F, legend.show = F)
figure <- tmap_arrange(plot.current,plot.srchange.rcp45.both,plot.srchange.rcp45.cl,plot.srchange.rcp45.lc, nrow = 1)
tmap_save(figure, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/current and srchange rcp45_forest_only.tiff',
units = 'cm', width = 24, height = 11, dpi=300)
#additive species richness for each GCMs
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sp.2050.rcp45.add <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/",sp," 2050 rcp45 additive ",gcm,".tif"))*vn.raster
})
batsr.2050.rcp45 <- overlay(stack(sp.2050.rcp45.add),fun=sum)
writeRaster(batsr.2050.rcp45, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_add_',gcm,'_forest_only.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#generate median additive species richness
gcm <- c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')
sr.2050.add <- lapply(gcm, function(x){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_add_",x,"_forest_only.tif"))
})
batsr.2050.add.median <- overlay(stack(sr.2050.add), fun=median)
writeRaster(batsr.2050.add.median, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_add_median_forest_only.tif', format="GTiff", overwrite=T)
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
extent.asia.sr <- extent(11000000, 13000000, 900000, 2600000)
lc.current <- crop(raster('F:/Working/2018/PhD_research/enviromental_variables/land-cover/MODISLandcover_2010_srorg8287.tif'),extent.asia.sr)*vn.raster
lc.2050.rcp45 <- crop(raster('F:/Working/2018/PhD_research/enviromental_variables/land-cover/MODISLandcover_2050_rcp45_srorg8287.tif'),extent.asia.sr)*vn.raster
area.withlcchange <- lc.2050.rcp45 != lc.current
batsr.current <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_only.tif')
batsr.lc.rcp45 <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_only.tif')
#classify interaction effects
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
batsr.2050.cl <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',gcm,'_forest_only.tif'))
batsr.2050.both <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',gcm,'_forest_only.tif'))
batsr.2050.add <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_add_',gcm,'_forest_only.tif'))
sr.interaction <- batsr.current*0
#two negative and negative neutral
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both < batsr.2050.add] <- 1
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both > batsr.2050.add & batsr.2050.both <= batsr.current] <- 4
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both > batsr.current] <- 5
#two positive and positive neutral
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both < batsr.current] <- 1
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both >= batsr.current & batsr.2050.both < batsr.2050.add] <- 2
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both > batsr.2050.add] <- 5
#opposite
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both < min(batsr.2050.cl, batsr.lc.rcp45)] <- 1
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both >= min(batsr.2050.cl, batsr.lc.rcp45) & batsr.2050.both < batsr.2050.add] <- 2
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both > batsr.2050.add & batsr.2050.both <= max(batsr.2050.cl, batsr.lc.rcp45)] <- 4
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both > max(batsr.2050.cl, batsr.lc.rcp45)] <- 5
#double neutral
sr.interaction[batsr.2050.cl == batsr.current & batsr.lc.rcp45 == batsr.current &
batsr.2050.both < batsr.2050.add] <- 1
sr.interaction[batsr.2050.cl == batsr.current & batsr.lc.rcp45 == batsr.current &
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[batsr.2050.cl == batsr.current & batsr.lc.rcp45 == batsr.current &
batsr.2050.both > batsr.2050.add] <- 5
writeRaster(sr.interaction, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/interaction_types_rcp45_',gcm,'_forest_only.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#compute proportion of interaction types within areas with land-cover change only
extent.asia.sr <- extent(11000000, 13000000, 900000, 2600000)
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
lc.current <- crop(raster('F:/Working/2018/PhD_research/enviromental_variables/land-cover/MODISLandcover_2010_srorg8287.tif'),extent.asia.sr)*vn.raster
lc.2050.rcp45 <- crop(raster('F:/Working/2018/PhD_research/enviromental_variables/land-cover/MODISLandcover_2050_rcp45_srorg8287.tif'),extent.asia.sr)*vn.raster
area.withlcchange <- lc.2050.rcp45 != lc.current
area.withlcchange.area <- cellStats(area.withlcchange, 'sum')
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sr.interaction <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/interaction_types_rcp45_',gcm,'_forest_only.tif'))*area.withlcchange
pro.interaction <- c(cellStats(sr.interaction==1,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==2,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==3,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==4,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==5,'sum')/area.withlcchange.area*100)
assign(paste0('pro.interaction.',gcm), pro.interaction)
print(gcm)
}
pro.interaction.all <- data.frame(pro.interaction.ac, pro.interaction.bc, pro.interaction.ca, pro.interaction.cm, pro.interaction.cn,
pro.interaction.cs, pro.interaction.gf, pro.interaction.gi, pro.interaction.ip, pro.interaction.mi)
pro.interaction.median <- apply(pro.interaction.all,1,median)
pro.interaction.min <- apply(pro.interaction.all,1,min)
pro.interaction.max <- apply(pro.interaction.all,1,max)
interaction <- c('-S','+A','AD','-A','+S')
sum <- data.frame(interaction, pro.interaction.median)
sum.both.sr <- read_excel('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_both_rcp45_forest_speciallist.xlsx')[2:5]
sum.cl.sr <- read_excel('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_cl_rcp45_forest_speciallist.xlsx')[2:5]
sum.lc.sr <- read_excel('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_lc_rcp45_forest_speciallist.xlsx')[2:3]
names(sum.both.sr) <- c('class','pro.both.median','pro.both.min','pro.both.max')
names(sum.cl.sr) <- c('class','pro.cl.median','pro.cl.min','pro.cl.max')
figure.bar.both <- sum.both.sr%>%ggplot(aes(x=factor(class,levels = c('<-30','-30~0','0','0~30','>30')),y=pro.both.median[1:5],fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue','0'='grey70','-30~0'='orange','<-30'='red'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,95)+
geom_errorbar(aes(ymin=pro.both.min[1:5], ymax=pro.both.max[1:5]), width=.2,position=position_dodge(.9))+
theme(legend.position = 'none', axis.text = element_text(size = 12),title = element_text(size = 14))
figure.bar.cl <- sum.cl.sr%>%ggplot(aes(x=factor(class,levels = c('<-30','-30~0','0','0~30','>30')),y=pro.cl.median[1:5],fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue','0'='grey70','-30~0'='orange','<-30'='red'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,95)+
geom_errorbar(aes(ymin=pro.cl.min[1:5], ymax=pro.cl.max[1:5]), width=.2,position=position_dodge(.9))+
theme(legend.position = 'none',axis.text = element_text(size = 12), title = element_text(size = 14))
figure.bar.lc <- sum.lc.sr%>%ggplot(aes(x=factor(class,levels = c('<-30','-30~0','0','0~30','>30')),y=pro.lc.rcp45[1:5],fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue','0'='grey70','-30~0'='orange','<-30'='red'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,95)+
theme(legend.position = 'none',axis.text = element_text(size = 12), title = element_text(size = 14))
figure.interaction <- ggplot(sum, aes(fill=interaction, y=pro.interaction.median, x=factor(interaction,levels = c('-S','+A','AD','-A','+S')))) +
geom_bar(position="stack", stat="identity")+ ylim(0,95)+ theme_classic()+
scale_fill_manual('',values = c('-S'='purple4','+A'='purple','AD'='grey70', '-A'='aquamarine','+S'='aquamarine4'))+
annotate("text", label = "", size = 4, x = 3, y = 70)+
xlab('')+ylab('')+labs(title = '')+
geom_errorbar(aes(ymin=pro.interaction.min, ymax=pro.interaction.max), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14), legend.position = 'none',
axis.text = element_text(size = 12))
figure <- ggarrange(figure.bar.both,figure.bar.cl,figure.bar.lc,figure.interaction,nrow = 1)
ggsave(figure, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/srchange_interaction_bar_rcp45_forest_only.tiff',
width = 30, height = 8, units = 'cm', dpi = 300)
#Plot interaction maps
SR.ORG8287 <- CRS('+proj=cea +lon_0=0 +lat_ts=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs')
vn <- spTransform(getData('GADM', country='VN', level=0), SR.ORG8287)
paracell.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/paracel islands.shp'), SR.ORG8287)
spratly.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/spratly islands.shp'), SR.ORG8287)
# pas <- readOGR('F:/Working/2018/PhD_research/SDM output R/Map/vn_pas.shp')
gcm <- c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')
sr.interaction <- lapply(gcm, function(x){
raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/interaction_types_rcp45_',x,'_forest_only.tif'))
})
select.major <- function(x){
modal(x, na.rm=T, freq=F, ties='random')
}
a <- sr.interaction.ensemble==0
sr.interaction.ensemble <- overlay(stack(sr.interaction), fun=select.major)#select major interaction types across GCMs
figure.map <- tm_shape(sr.interaction.ensemble,bbox = c(11070729, 943456.3,12186081, 2517140)) +
tm_raster('layer', breaks = c(-0.5,0.5,1.5,2.5,3.5,4.5,5.5),palette = c('white','purple4','purple','grey70','aquamarine','aquamarine4'),
labels = c("","-S", "+A", "AD","-A","+S"),title = '')+
tm_shape(vn)+tm_polygons(col = 'white', alpha = 0, border.col = 'grey')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_layout(title = '',title.position = c(0,0.95),legend.show = F, frame = F)
tmap_save(figure.map, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/interaction_sr_rcp45_forest_only.tiff',width = 8, height = 10, units = 'cm',dpi = 300)
#compare impact of climate & lc change on distribution range
species.list <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/vnbats_habitat_traits.xlsx", sheetName = 'Sheet1')
species.list <- species.list%>%select(Scientific_name,Forest,Shrubland,Grassland,Wetlands,Caves,Artificial,Diet.Plant,Diet.Invertebrate)
species.list <- species.list%>%filter(Artificial=='FALSE'&Shrubland=='FALSE'&Grassland=='FALSE')
area.sum <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/area sum rcp45.xlsx", sheetName = 'Sheet1')
area.sum <- area.sum%>%filter(Scientific_name%in%species.list$Scientific_name)
area.sum <- area.sum%>%mutate(area.change.rcp45.lc = (area.2050.rcp45.lc - area.current)/area.current*100)
no.species.increase30.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc >= 30))/nrow(area.sum)*100
no.species.increase0.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc < 30 & area.change.rcp45.lc>0))/nrow(area.sum)*100
no.species.decrease0.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc <= 0 &area.change.rcp45.lc > -30))/nrow(area.sum)*100
no.species.decrease30.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc <= -30 & area.2050.rcp45.lc > 0))/nrow(area.sum)*100
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
area.sum[,'area.change.rcp45.both'] <- (area.sum[,paste0('area.2050.rcp45.both.',gcm)] - area.sum[,"area.current"])/area.sum[,"area.current"]*100
area.sum[,'area.change.rcp45.cl'] <- (area.sum[,paste0('area.2050.rcp45.cl.',gcm)] - area.sum[,"area.current"])/area.sum[,"area.current"]*100
area.sum[,'area.2050.rcp45.both'] <- area.sum[,paste0('area.2050.rcp45.both.',gcm)]
area.sum[,'area.2050.rcp45.cl'] <- area.sum[,paste0('area.2050.rcp45.cl.',gcm)]
no.species.increase30.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both>= 30))/nrow(area.sum)*100
no.species.increase0.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both< 30 & area.change.rcp45.both >0))/nrow(area.sum)*100
no.species.decrease0.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both <= 0 & area.change.rcp45.both > -30))/nrow(area.sum)*100
no.species.decrease30.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both <= -30))/nrow(area.sum)*100
no.species.increase30.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl>= 30))/nrow(area.sum)*100
no.species.increase0.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl< 30 & area.change.rcp45.cl>0))/nrow(area.sum)*100
no.species.decrease0.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl <= 0 & area.change.rcp45.cl > -30))/nrow(area.sum)*100
no.species.decrease30.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl <= -30 & area.2050.rcp45.cl > 0))/nrow(area.sum)*100
no.species.rcp45 <- c(no.species.increase30.rcp45.both, no.species.increase0.rcp45.both,no.species.decrease0.rcp45.both,no.species.decrease30.rcp45.both,
no.species.increase30.rcp45.cl, no.species.increase0.rcp45.cl,no.species.decrease0.rcp45.cl, no.species.decrease30.rcp45.cl,
no.species.increase30.rcp45.lc, no.species.increase0.rcp45.lc,no.species.decrease0.rcp45.lc,no.species.decrease30.rcp45.lc)
assign(paste0('no.rcp45.',gcm), no.species.rcp45)
print(gcm)
}
pro <- data.frame(rbind(no.rcp45.ac, no.rcp45.bc, no.rcp45.ca, no.rcp45.cm, no.rcp45.cn,
no.rcp45.cs, no.rcp45.gf, no.rcp45.gi, no.rcp45.ip, no.rcp45.mi))
pro <- pro%>%mutate(in.both = X1 + X2, de.both = X3 + X4, in.cl = X5+ X6, de.cl = X7 + X8, in.lc = X9+ X10, de.lc = X11 + X12)
pro.median <- apply(pro,2,median)
pro.min <- apply(pro,2,min)
pro.max <- apply(pro,2,max)
class <- c('>30','0~30',' -30~0','< -30')
sum.both <- data.frame(class, pro.median[1:4], pro.min[1:4], pro.max[1:4])
sum.cl <- data.frame(class, pro.median[5:8], pro.min[5:8], pro.max[5.8])
sum.lc <- data.frame(class, pro.median[9:12])
sum.both$class <- factor(sum.both$class, levels = c('< -30',' -30~0','0~30','>30'))
pro.median
pro.min
pro.max
#interaction effects on distribution range size
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
area.sum <- read_xlsx('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/area sum rcp45.xlsx')
area.sum <- area.sum%>%filter(Scientific_name%in%species.list$Scientific_name)
area.sum <- area.sum%>%select(Scientific_name,area.current, paste0('area.2050.rcp45.cl.',gcm), area.2050.rcp45.lc, paste0('area.2050.rcp45.both.', gcm),paste0('area.2050.rcp45.add.', gcm))
colnames(area.sum) <- c('Scientific_name','area.current', 'area.2050.cl', 'area.2050.lc', 'area.2050.both', 'area.2050.add')
area.sum <- area.sum%>%mutate(interaction = 0)
#two negative and negative neutral
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both < area.2050.add,1))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both == area.2050.add,3))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both > area.2050.add & area.2050.both <= area.current,4))
area.sum <- area.sum%>%mutate(interaction = replace(interaction,area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both > area.current,5))
#two positive and positive neutral
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both < area.current,1))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both >= area.current& area.2050.both<area.2050.add,2))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both == area.2050.add,3))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both > area.2050.add,5))
#opposite
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both < min(area.2050.cl, area.2050.lc),1))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both >= min(area.2050.cl, area.2050.lc)&area.2050.both < area.2050.add,2))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both ==area.2050.add,3))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both >area.2050.add & area.2050.both<= max(area.2050.cl, area.2050.lc),4))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both > max(area.2050.cl, area.2050.lc),5))
interaction.sp <- c(area.sum$interaction)
assign(paste0('interaction.',gcm), interaction.sp)
# area.sum <- area.sum%>%mutate(paste0('interaction.',gcm) == interaction)
pro.range.interaction <- c(nrow(area.sum%>%filter(interaction == 1))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 2))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 3))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 4))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 5))/nrow(area.sum)*100)
assign(paste0('pro.range.interaction.', gcm), pro.range.interaction)
print(gcm)
}
pro.range.interaction.all <- data.frame(pro.range.interaction.ac,pro.range.interaction.bc,pro.range.interaction.ca,pro.range.interaction.cm,pro.range.interaction.cn,
pro.range.interaction.cs,pro.range.interaction.gf,pro.range.interaction.gi,pro.range.interaction.ip,pro.range.interaction.mi)
pro.range.interaction.median <- apply(pro.range.interaction.all,1,median)
pro.range.interaction.min <- apply(pro.range.interaction.all,1,min)
pro.range.interaction.max <- apply(pro.range.interaction.all,1,max)
class <- c('-S','+A','AD','-A','+S')
sum.range.interaction <- data.frame(class,pro.range.interaction.median,pro.range.interaction.min, pro.range.interaction.max)
sum.range.interaction$class <- factor(sum.range.interaction$class, levels = c('-S','+A','AD','-A','+S'))
figure.both <- sum.both%>%ggplot(aes(x=factor(class, levels = c('< -30',' -30~0','0~30','>30')),y=pro.median.1.4.,fill=class)) +
geom_bar(stat = 'identity')+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue',' -30~0'='orange','< -30'='red'))+
scale_x_discrete(limits=c('< -30',' -30~0','0~30','>30'))+ theme_classic()+ ylim(0,100)+
labs(x='', y='', title = '')+
geom_errorbar(aes(ymin=pro.min[1:4], ymax=pro.max[1:4]), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14),axis.text = element_text(size = 14),
plot.title = element_text(hjust = 0),legend.position = 'none')
figure.cl <- sum.cl%>%ggplot(aes(x=factor(class, levels = c('< -30',' -30~0','0~30','>30')),y=pro.median.5.8.,fill=class)) +
geom_bar(stat = 'identity')+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue',' -30~0'='orange','< -30'='red'))+
scale_x_discrete(limits=c('< -30',' -30~0','0~30','>30'))+ theme_classic()+ ylim(0,100)+
labs(x='', y='', title = '')+
geom_errorbar(aes(ymin=pro.min[5:8], ymax=pro.max[5:8]), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14), axis.text = element_text(size = 14),
plot.title = element_text(hjust = 0),legend.position = 'none')
figure.lc <- sum.lc%>%ggplot(aes(x=factor(class, levels = c('< -30',' -30~0','0~30','>30')),
y=pro.median.9.12.,fill=class)) +
geom_bar(stat = 'identity')+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue',' -30~0'='orange','< -30'='red'))+
scale_x_discrete(limits=c('< -30',' -30~0','0~30','>30'))+ theme_classic()+ ylim(0,100)+
labs(x='', y='', title = '')+
theme(text = element_text(size = 14),axis.text = element_text(size = 14),
plot.title = element_text(hjust = 0),legend.position = 'none')
figure.interaction <- sum.range.interaction%>%ggplot(aes(x=factor(class,levels = c('-S','+A','AD','-A','+S')),y=pro.range.interaction.median,fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('-S'='purple4','+A'='purple','AD'='grey70', '-A'='aquamarine','+S'='aquamarine4'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,100)+
geom_errorbar(aes(ymin=pro.range.interaction.min, ymax=pro.range.interaction.max), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14), legend.position = 'none', axis.text = element_text(size = 14))
figure <- ggarrange(figure.both, figure.cl, figure.lc,figure.interaction,nrow = 1)
ggsave(figure, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/rangesize_change_interaction_rcp45_forest_speciallist.tiff',
units = 'cm', width = 28, height = 8, dpi = 300)
#INTERACTIVE EFECT ON SPECIES USE FOREST ONLY
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
species.list <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/vnbats_habitat_traits.xlsx", sheetName = 'Sheet1')
species.list <- species.list%>%select(Scientific_name,Forest,Shrubland,Grassland,Wetlands,Caves,Artificial,Diet.Plant,Diet.Invertebrate)
species.list <- species.list%>%filter(Artificial=='TRUE'|Shrubland=='TRUE'|Grassland=='TRUE')
#current species richness
sr.current <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," current.tif"))*vn.raster
})
batsr.current <- overlay(stack(sr.current),fun=sum)
writeRaster(batsr.current, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_others.tif', format="GTiff", overwrite=T)
#species richness for individual GCMs
#combined model
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sr.2050.rcp45.both <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," 2050 rcp45 both ",gcm,".tif"))*vn.raster
})
batsr.2050.rcp45.both <- overlay(stack(sr.2050.rcp45.both),fun=sum)
writeRaster(batsr.2050.rcp45.both, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',gcm,'_forest_others.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#climate change only model
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sr.2050.rcp45.cl <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," 2050 rcp45 cl ",gcm,".tif"))*vn.raster
})
batsr.2050.rcp45.cl <- overlay(stack(sr.2050.rcp45.cl),fun=sum)
writeRaster(batsr.2050.rcp45.cl, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',gcm,'_forest_others.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#land-cover change only models
sr.2050.rcp45.lc <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/output_maps/",sp," 2050 rcp45 lc.tif"))*vn.raster
})
batsr.lc.rcp45 <- overlay(stack(sr.2050.rcp45.lc),fun=sum)
#consensus future prediction rcp45
gcm <- c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')
sr.2050.both <- lapply(gcm, function(x){
raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',x,'_forest_others.tif'))
})
sr.2050.cl <- lapply(gcm, function(x){
raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',x,'_forest_others.tif'))
})
batsr.2050.both.median <- overlay(stack(sr.2050.both), fun=median)
batsr.2050.cl.median <- overlay(stack(sr.2050.cl), fun=median)
writeRaster(batsr.2050.both.median,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_median_forest_others.tif',format='GTiff',overwrite=T)
writeRaster(batsr.2050.cl.median,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_median_forest_others.tif',format='GTiff',overwrite=T)
writeRaster(batsr.lc.rcp45,filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_others.tif',format='GTiff',overwrite=T)
#Plot map richness and richness change
batsr.current <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_others.tif')
batsr.lc.rcp45 <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_others.tif')
batsr.lc.rcp45.current <- (batsr.lc.rcp45 - batsr.current)/batsr.current*100
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
pro.lc.rcp45 <- c(cellStats(batsr.lc.rcp45.current>=30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current < 30 & batsr.lc.rcp45.current > 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current == 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current < 0 & batsr.lc.rcp45.current > -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.lc.rcp45.current <= -30,'sum')/cellStats(vn.raster,'sum')*100)
#effects of combined change
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
batsr.both.rcp45 <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',gcm,'_forest_others.tif'))
batsr.both.rcp45.current <- (batsr.both.rcp45 - batsr.current)/batsr.current*100
pro.both.rcp45 <- c(cellStats(batsr.both.rcp45.current>=30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current < 30 & batsr.both.rcp45.current > 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current == 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current < 0 & batsr.both.rcp45.current > -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current <= -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current < 0, 'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.both.rcp45.current > 0, 'sum')/cellStats(vn.raster,'sum')*100)
assign(paste0('pro.both.rcp45.',gcm),pro.both.rcp45)
print(gcm)
}
pro.both.all <- data.frame(pro.both.rcp45.ac,pro.both.rcp45.bc,pro.both.rcp45.ca,pro.both.rcp45.cm,pro.both.rcp45.cn,
pro.both.rcp45.cs,pro.both.rcp45.gf,pro.both.rcp45.gi,pro.both.rcp45.ip,pro.both.rcp45.mi)
#effect of climate change only
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
batsr.cl.rcp45 <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',gcm,'_forest_others.tif'))
batsr.cl.rcp45.current <- (batsr.cl.rcp45 - batsr.current)/batsr.current*100
pro.cl.rcp45 <- c(cellStats(batsr.cl.rcp45.current>=30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current < 30 & batsr.cl.rcp45.current > 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current == 0,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current < 0 & batsr.cl.rcp45.current > -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current <= -30,'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current < 0, 'sum')/cellStats(vn.raster,'sum')*100,
cellStats(batsr.cl.rcp45.current > 0, 'sum')/cellStats(vn.raster,'sum')*100)
assign(paste0('pro.cl.rcp45.',gcm),pro.cl.rcp45)
print(gcm)
}
pro.cl.all <- data.frame(pro.cl.rcp45.ac, pro.cl.rcp45.bc, pro.cl.rcp45.ca, pro.cl.rcp45.cm, pro.cl.rcp45.cn,
pro.cl.rcp45.cs, pro.cl.rcp45.gf, pro.cl.rcp45.gi, pro.cl.rcp45.ip, pro.cl.rcp45.mi)
pro.both.median <- apply(pro.both.all,1,median)
pro.both.min <- apply(pro.both.all,1,min)
pro.both.max <- apply(pro.both.all,1,max)
pro.cl.median <- apply(pro.cl.all,1,median)
pro.cl.min <- apply(pro.cl.all,1,min)
pro.cl.max <- apply(pro.cl.all,1,max)
class <- c('>30','0~30','0','-30~0','<-30')
sum.both.sr <- data.frame(class,pro.both.median[1:5],pro.both.min[1:5], pro.both.max[1:5])
sum.cl.sr <- data.frame(class,pro.cl.median[1:5], pro.cl.min[1:5], pro.cl.max[1:5])
sum.lc.sr <- data.frame(class,pro.lc.rcp45)
sum.both.sr$class <- factor(sum.both.sr$class, levels = c('<-30','-30~0','0','0~30','>30'))
sum.cl.sr$class <- factor(sum.cl.sr$class, levels = c('<-30','-30~0','0','0~30','>30'))
sum.lc.sr$class <- factor(sum.lc.sr$class, levels = c('<-30','-30~0','0','0~30','>30'))
write.xlsx(sum.both.sr,'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_both_rcp45_habitat_generalist.xlsx')
write.xlsx(sum.cl.sr,'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_cl_rcp45_habitat_generalist.xlsx')
write.xlsx(sum.lc.sr,'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_lc_rcp45_habitat_generalist.xlsx')
#plot maps species richness change
batsr.current <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_others.tif')
batsr.2050.both <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_median_forest_others.tif')
batsr.2050.cl <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_median_forest_others.tif')
batsr.2050.lc <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_others.tif')
SR.ORG8287 <- CRS('+proj=cea +lon_0=0 +lat_ts=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs')
vn <- spTransform(getData('GADM', country='VN', level=0), SR.ORG8287)
paracell.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/paracel islands.shp'), SR.ORG8287)
spratly.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/spratly islands.shp'), SR.ORG8287)
# pas <- readOGR('F:/Working/2018/PhD_research/SDM output R/Map/vn_pas.shp')
batsr.both.current <- (batsr.2050.both - batsr.current)/batsr.current*100
batsr.cl.current <- (batsr.2050.cl - batsr.current)/batsr.current*100
batsr.lc.current <- (batsr.2050.lc - batsr.current)/batsr.current*100
plot.current<- tm_shape(batsr.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(0,2,4,6,8,10,15,20,25,30,35,40,50,65), palette = 'Greens', style = 'fixed',
# tm_raster('layer',breaks = c(0,2,4,6,8,10,12,14,16,18,20,25,30,35,40,45,50,55,60,65,70), palette = 'Blues', style = 'fixed',
title = 'Species richness')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'dark gray', alpha = 0, lwd = 0.5)+
tm_layout(legend.outside = F, title = 'Species richness',title.position = c('left','top'),title.size = 1,
legend.text.size = 0.5, legend.title.size = 1,legend.position = c(0,0.3), frame = F, legend.show = F)
plot.srchange.rcp45.both <- tm_shape(batsr.both.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(-Inf,-30,-0.0001,0.0001,30,Inf),palette = c('red','orange','grey70','lightblue','blue'),
title = 'SR change (%)',labels = c('< -30','-30~0','0','0~30','> 30'))+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'dark gray', alpha = 0, lwd = 0.5)+
tm_layout(legend.outside = F, title = '(b) Combined change', title.size = 1, legend.position = c(0, 0.3),
title.position = c('left','top'), frame = F, legend.text.size = 0.8, legend.title.size = 2, legend.show = F)
plot.srchange.rcp45.cl <- tm_shape(batsr.cl.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(-Inf,-25,-0.0001,0.0001,25,Inf),palette = c('red','orange','grey70','lightblue','blue'),
title = '% SR change')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'gray', alpha = 0, lwd = 0.5)+
tm_layout(title = '(c) Climate change only', title.size = 1,title.position = c('left','top'), frame = F,legend.show = F)
plot.srchange.rcp45.lc <- tm_shape(batsr.lc.current, bbox = c(11380000, 930000,12450000, 2517140)) +
tm_raster('layer',breaks = c(-Inf,-25,-0.0001,0.0001,25,Inf),palette = c('red','orange','grey70','lightblue','blue'),
title = '% SR change')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_shape(vn)+ tm_polygons(border.col = 'dark gray', alpha = 0, lwd = 0.5)+
tm_layout(title = '(d) Land cover change only', title.size = 1, title.position = c('left','top'), frame = F, legend.show = F)
figure <- tmap_arrange(plot.current,plot.srchange.rcp45.both,plot.srchange.rcp45.cl,plot.srchange.rcp45.lc, nrow = 1)
tmap_save(figure, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/current and srchange rcp45_forest_others.tiff',
units = 'cm', width = 24, height = 11, dpi=300)
#classify interactions
batsr.current <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_current_forest_others.tif')
batsr.lc.rcp45 <- raster('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_lc_forest_others.tif')
#additive species richness for each GCMs
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sp.2050.rcp45.add <- lapply(species.list$Scientific_name, function(sp){
raster(paste0("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/",sp," 2050 rcp45 additive ",gcm,".tif"))*vn.raster
})
batsr.2050.rcp45 <- overlay(stack(sp.2050.rcp45.add),fun=sum)
writeRaster(batsr.2050.rcp45, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_add_',gcm,'_forest_others.tif'), format="GTiff", overwrite=T)
print(gcm)
}
#classify interaction
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
batsr.2050.cl <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_cl_',gcm,'_forest_others.tif'))
batsr.2050.both <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_both_',gcm,'_forest_others.tif'))
batsr.2050.add <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/batsr_2050_rcp45_add_',gcm,'_forest_others.tif'))
sr.interaction <- batsr.current*0
#two negative and negative neutral
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both < batsr.2050.add] <- 1
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both > batsr.2050.add & batsr.2050.both <= batsr.current] <- 4
sr.interaction[batsr.2050.cl <= batsr.current & batsr.lc.rcp45 <= batsr.current & (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both > batsr.current] <- 5
#two positive and positive neutral
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both < batsr.current] <- 1
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both >= batsr.current & batsr.2050.both < batsr.2050.add] <- 2
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[batsr.2050.cl >= batsr.current & batsr.lc.rcp45 >= batsr.current& (batsr.2050.cl + batsr.lc.rcp45 != 2* batsr.current)&
batsr.2050.both > batsr.2050.add] <- 5
#opposite
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both < min(batsr.2050.cl, batsr.lc.rcp45)] <- 1
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both >= min(batsr.2050.cl, batsr.lc.rcp45) & batsr.2050.both < batsr.2050.add] <- 2
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both > batsr.2050.add & batsr.2050.both <= max(batsr.2050.cl, batsr.lc.rcp45)] <- 4
sr.interaction[(batsr.2050.cl - batsr.current)*(batsr.lc.rcp45 - batsr.current) < 0 &
batsr.2050.both > max(batsr.2050.cl, batsr.lc.rcp45)] <- 5
#double neutral
sr.interaction[batsr.2050.cl == batsr.current & batsr.lc.rcp45 == batsr.current &
batsr.2050.both < batsr.2050.add] <- 1
sr.interaction[batsr.2050.cl == batsr.current & batsr.lc.rcp45 == batsr.current &
batsr.2050.both == batsr.2050.add] <- 3
sr.interaction[batsr.2050.cl == batsr.current & batsr.lc.rcp45 == batsr.current &
batsr.2050.both > batsr.2050.add] <- 5
writeRaster(sr.interaction, filename = paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/interaction_types_rcp45_',gcm,'_forest_others.tif'), format="GTiff", overwrite=T)
print(gcm)
}
# compute proportion of interaction types within areas with land-cover change only
extent.asia.sr <- extent(11000000, 13000000, 900000, 2600000)
vn.raster <- raster('F:/Working/2018/PhD_research/SDM output R/Map/vn raster.tif') #import Vietnam
lc.current <- crop(raster('F:/Working/2018/PhD_research/enviromental_variables/land-cover/MODISLandcover_2010_srorg8287.tif'),extent.asia.sr)*vn.raster
lc.2050.rcp45 <- crop(raster('F:/Working/2018/PhD_research/enviromental_variables/land-cover/MODISLandcover_2050_rcp45_srorg8287.tif'),extent.asia.sr)*vn.raster
area.withlcchange <- lc.2050.rcp45 != lc.current
area.withlcchange.area <- cellStats(area.withlcchange, 'sum')
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
sr.interaction <- raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/interaction_types_rcp45_',gcm,'_forest_others.tif'))*area.withlcchange
pro.interaction <- c(cellStats(sr.interaction==1,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==2,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==3,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==4,'sum')/area.withlcchange.area*100,
cellStats(sr.interaction==5,'sum')/area.withlcchange.area*100)
assign(paste0('pro.interaction.',gcm), pro.interaction)
print(gcm)
}
pro.interaction.all <- data.frame(pro.interaction.ac, pro.interaction.bc, pro.interaction.ca, pro.interaction.cm, pro.interaction.cn,
pro.interaction.cs, pro.interaction.gf, pro.interaction.gi, pro.interaction.ip, pro.interaction.mi)
pro.interaction.median <- apply(pro.interaction.all,1,median)
pro.interaction.min <- apply(pro.interaction.all,1,min)
pro.interaction.max <- apply(pro.interaction.all,1,max)
interaction <- c('-S','+A','AD','-A','+S')
sum <- data.frame(interaction, pro.interaction.median)
sum.both.sr <- read_excel('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_both_rcp45_habitat_generalist.xlsx')[2:5]
sum.cl.sr <- read_excel('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_cl_rcp45_habitat_generalist.xlsx')[2:5]
sum.lc.sr <- read_excel('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/srchange_lc_rcp45_habitat_generalist.xlsx')[2:3]
names(sum.both.sr) <- c('class','pro.both.median','pro.both.min','pro.both.max')
names(sum.cl.sr) <- c('class','pro.cl.median','pro.cl.min','pro.cl.max')
figure.bar.both <- sum.both.sr%>%ggplot(aes(x=factor(class,levels = c('<-30','-30~0','0','0~30','>30')),y=pro.both.median[1:5],fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue','0'='grey70','-30~0'='orange','<-30'='red'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,95)+
geom_errorbar(aes(ymin=pro.both.min[1:5], ymax=pro.both.max[1:5]), width=.2,position=position_dodge(.9))+
theme(legend.position = 'none',
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size = 12, angle = 0, vjust = 0.5),
title = element_text(size = 14))
figure.bar.cl <- sum.cl.sr%>%ggplot(aes(x=factor(class,levels = c('<-30','-30~0','0','0~30','>30')),y=pro.cl.median[1:5],fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue','0'='grey70','-30~0'='orange','<-30'='red'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,95)+
geom_errorbar(aes(ymin=pro.cl.min[1:5], ymax=pro.cl.max[1:5]), width=.2,position=position_dodge(.9))+
theme(legend.position = 'none',
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size = 12, angle = 0, vjust = 0.5),
title = element_text(size = 14))
figure.bar.lc <- sum.lc.sr%>%ggplot(aes(x=factor(class,levels = c('<-30','-30~0','0','0~30','>30')),y=pro.lc.rcp45[1:5],fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue','0'='grey70','-30~0'='orange','<-30'='red'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,95)+
theme(legend.position = 'none',
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size = 12),
title = element_text(size = 14))
figure.interaction <- ggplot(sum, aes(fill=interaction, y=pro.interaction.median, x=factor(interaction,levels = c('-S','+A','AD','-A','+S')))) +
geom_bar(position="stack", stat="identity")+ ylim(0,95)+ theme_classic()+
scale_fill_manual('',values = c('-S'='purple4','+A'='purple','AD'='grey70', '-A'='aquamarine','+S'='aquamarine4'))+
annotate("text", label = "", size = 4, x = 3, y = 70)+
xlab('')+ylab('')+labs(title = '')+
geom_errorbar(aes(ymin=pro.interaction.min, ymax=pro.interaction.max), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14), legend.position = 'none',
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size = 12))
figure <- ggarrange(figure.bar.both,figure.bar.cl,figure.bar.lc,figure.interaction,nrow = 1)
ggsave(figure, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/srchange_interaction_bar_rcp45_forest_others.tiff',
width = 30, height = 8, units = 'cm', dpi = 300)
#Plot interaction maps
SR.ORG8287 <- CRS('+proj=cea +lon_0=0 +lat_ts=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs')
vn <- spTransform(getData('GADM', country='VN', level=0), SR.ORG8287)
paracell.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/paracel islands.shp'), SR.ORG8287)
spratly.islands <- spTransform(readOGR('F:/Working/2018/PhD_research/SDM output R/Map/spratly islands.shp'), SR.ORG8287)
# pas <- readOGR('F:/Working/2018/PhD_research/SDM output R/Map/vn_pas.shp')
gcm <- c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')
sr.interaction <- lapply(gcm, function(x){
raster(paste0('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/output_maps/interaction_types_rcp45_',x,'_forest_others.tif'))
})
select.major <- function(x){
modal(x, na.rm=T, freq=F, ties='random')
}
sr.interaction.ensemble <- overlay(stack(sr.interaction), fun=select.major)#select major interaction types across GCMs
figure.map <- tm_shape(sr.interaction.ensemble,bbox = c(11070729, 943456.3,12186081, 2517140)) +
tm_raster('layer', breaks = c(-0.5,0.5,1.5,2.5,3.5,4.5,5.5),palette = c('white','purple4','purple','grey70','aquamarine','aquamarine4'),
labels = c("","-S", "+A", "AD","-A","+S"),title = '')+
tm_shape(vn)+tm_polygons(col = 'white', alpha = 0, border.col = 'grey')+
tm_shape(paracell.islands) + tm_lines(col = 'grey')+
tm_shape(spratly.islands) + tm_lines(col = 'grey')+
# tm_shape(pas) + tm_polygons(col = 'green', alpha = 0,border.col = 'darkgreen')+
tm_layout(title = '',title.position = c(0,0.95), frame = F)
tmap_save(figure.map, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/interaction_sr_rcp45_forest_others.tiff',width = 8, height = 10, units = 'cm',dpi = 300)
#impact of climate and land cover change on distribution range size
species.list <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/vnbats_habitat_traits.xlsx", sheetName = 'Sheet1')
species.list <- species.list%>%select(Scientific_name,Forest,Shrubland,Grassland,Wetlands,Caves,Artificial,Diet.Plant,Diet.Invertebrate)
species.list <- species.list%>%filter(Artificial=='TRUE'|Shrubland=='TRUE'|Grassland=='TRUE')
area.sum <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/area sum rcp45.xlsx", sheetName = 'Sheet1')
area.sum <- area.sum%>%filter(Scientific_name%in%species.list$Scientific_name)
area.sum <- area.sum%>%mutate(area.change.rcp45.lc = (area.2050.rcp45.lc - area.current)/area.current*100)
no.species.increase30.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc >= 30))/nrow(area.sum)*100
no.species.increase0.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc < 30 & area.change.rcp45.lc>0))/nrow(area.sum)*100
no.species.decrease0.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc <= 0 &area.change.rcp45.lc > -30))/nrow(area.sum)*100
no.species.decrease30.rcp45.lc <- nrow(area.sum%>%filter(area.change.rcp45.lc <= -30 & area.2050.rcp45.lc > 0))/nrow(area.sum)*100
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
area.sum[,'area.change.rcp45.both'] <- (area.sum[,paste0('area.2050.rcp45.both.',gcm)] - area.sum[,"area.current"])/area.sum[,"area.current"]*100
area.sum[,'area.change.rcp45.cl'] <- (area.sum[,paste0('area.2050.rcp45.cl.',gcm)] - area.sum[,"area.current"])/area.sum[,"area.current"]*100
area.sum[,'area.2050.rcp45.both'] <- area.sum[,paste0('area.2050.rcp45.both.',gcm)]
area.sum[,'area.2050.rcp45.cl'] <- area.sum[,paste0('area.2050.rcp45.cl.',gcm)]
no.species.increase30.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both>= 30))/nrow(area.sum)*100
no.species.increase0.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both< 30 & area.change.rcp45.both >0))/nrow(area.sum)*100
no.species.decrease0.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both <= 0 & area.change.rcp45.both > -30))/nrow(area.sum)*100
no.species.decrease30.rcp45.both <- nrow(area.sum%>%filter(area.change.rcp45.both <= -30))/nrow(area.sum)*100
no.species.increase30.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl>= 30))/nrow(area.sum)*100
no.species.increase0.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl< 30 & area.change.rcp45.cl>0))/nrow(area.sum)*100
no.species.decrease0.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl <= 0 & area.change.rcp45.cl > -30))/nrow(area.sum)*100
no.species.decrease30.rcp45.cl <- nrow(area.sum%>%filter(area.change.rcp45.cl <= -30 & area.2050.rcp45.cl > 0))/nrow(area.sum)*100
no.species.rcp45 <- c(no.species.increase30.rcp45.both, no.species.increase0.rcp45.both,no.species.decrease0.rcp45.both,no.species.decrease30.rcp45.both,
no.species.increase30.rcp45.cl, no.species.increase0.rcp45.cl,no.species.decrease0.rcp45.cl, no.species.decrease30.rcp45.cl,
no.species.increase30.rcp45.lc, no.species.increase0.rcp45.lc,no.species.decrease0.rcp45.lc,no.species.decrease30.rcp45.lc)
assign(paste0('no.rcp45.',gcm), no.species.rcp45)
print(gcm)
}
pro <- data.frame(rbind(no.rcp45.ac, no.rcp45.bc, no.rcp45.ca, no.rcp45.cm, no.rcp45.cn,
no.rcp45.cs, no.rcp45.gf, no.rcp45.gi, no.rcp45.ip, no.rcp45.mi))
pro <- pro%>%mutate(in.both = X1 + X2, de.both = X3 + X4, in.cl = X5+ X6, de.cl = X7 + X8, in.lc = X9+ X10, de.lc = X11 + X12)
pro.median <- apply(pro,2,median)
pro.min <- apply(pro,2,min)
pro.max <- apply(pro,2,max)
class <- c('>30','0~30',' -30~0','< -30')
sum.both <- data.frame(class, pro.median[1:4], pro.min[1:4], pro.max[1:4])
sum.cl <- data.frame(class, pro.median[5:8], pro.min[5:8], pro.max[5.8])
sum.lc <- data.frame(class, pro.median[9:12])
sum.both$class <- factor(sum.both$class, levels = c('< -30',' -30~0','0~30','>30'))
pro.median
pro.min
pro.max
#interaction effects on distribution range size
for(gcm in c('ac', 'bc','ca','cm','cn','cs','gf','gi','ip','mi')){
area.sum <- read_xlsx('F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/area sum rcp45.xlsx')
area.sum <- area.sum%>%filter(Scientific_name%in%species.list$Scientific_name)
area.sum <- area.sum%>%select(Scientific_name,area.current, paste0('area.2050.rcp45.cl.',gcm), area.2050.rcp45.lc, paste0('area.2050.rcp45.both.', gcm),paste0('area.2050.rcp45.add.', gcm))
colnames(area.sum) <- c('Scientific_name','area.current', 'area.2050.cl', 'area.2050.lc', 'area.2050.both', 'area.2050.add')
area.sum <- area.sum%>%mutate(interaction = 0)
#two negative and negative neutral
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both < area.2050.add,1))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both == area.2050.add,3))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both > area.2050.add & area.2050.both <= area.current,4))
area.sum <- area.sum%>%mutate(interaction = replace(interaction,area.2050.cl<= area.current & area.2050.lc<=area.current & area.2050.cl+area.2050.lc!=area.current &
area.2050.both > area.current,5))
#two positive and positive neutral
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both < area.current,1))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both >= area.current& area.2050.both<area.2050.add,2))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both == area.2050.add,3))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, area.2050.cl>=area.current & area.2050.lc>=area.current & area.2050.cl+area.2050.lc!=2*area.current&
area.2050.both > area.2050.add,5))
#opposite
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both < min(area.2050.cl, area.2050.lc),1))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both >= min(area.2050.cl, area.2050.lc)&area.2050.both < area.2050.add,2))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both ==area.2050.add,3))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both >area.2050.add & area.2050.both<= max(area.2050.cl, area.2050.lc),4))
area.sum <- area.sum%>%mutate(interaction = replace(interaction, (area.2050.cl -area.current)*(area.2050.lc-area.current)< 0 &
area.2050.both > max(area.2050.cl, area.2050.lc),5))
interaction.sp <- c(area.sum$interaction)
assign(paste0('interaction.',gcm), interaction.sp)
# area.sum <- area.sum%>%mutate(paste0('interaction.',gcm) == interaction)
pro.range.interaction <- c(nrow(area.sum%>%filter(interaction == 1))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 2))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 3))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 4))/nrow(area.sum)*100,
nrow(area.sum%>%filter(interaction == 5))/nrow(area.sum)*100)
assign(paste0('pro.range.interaction.', gcm), pro.range.interaction)
print(gcm)
}
pro.range.interaction.all <- data.frame(pro.range.interaction.ac,pro.range.interaction.bc,pro.range.interaction.ca,pro.range.interaction.cm,pro.range.interaction.cn,
pro.range.interaction.cs,pro.range.interaction.gf,pro.range.interaction.gi,pro.range.interaction.ip,pro.range.interaction.mi)
pro.range.interaction.median <- apply(pro.range.interaction.all,1,median)
pro.range.interaction.min <- apply(pro.range.interaction.all,1,min)
pro.range.interaction.max <- apply(pro.range.interaction.all,1,max)
class <- c('-S','+A','AD','-A','+S')
sum.range.interaction <- data.frame(class,pro.range.interaction.median,pro.range.interaction.min, pro.range.interaction.max)
sum.range.interaction$class <- factor(sum.range.interaction$class, levels = c('-S','+A','AD','-A','+S'))
figure.both <- sum.both%>%ggplot(aes(x=factor(class, levels = c('< -30',' -30~0','0~30','>30')),y=pro.median.1.4.,fill=class)) +
geom_bar(stat = 'identity')+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue',' -30~0'='orange','< -30'='red'))+
scale_x_discrete(limits=c('< -30',' -30~0','0~30','>30'))+ theme_classic()+ ylim(0,100)+
labs(x='', y='', title = '')+
geom_errorbar(aes(ymin=pro.min[1:4], ymax=pro.max[1:4]), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14),axis.text = element_text(size = 14),
plot.title = element_text(hjust = 0),legend.position = 'none')
figure.cl <- sum.cl%>%ggplot(aes(x=factor(class, levels = c('< -30',' -30~0','0~30','>30')),y=pro.median.5.8.,fill=class)) +
geom_bar(stat = 'identity')+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue',' -30~0'='orange','< -30'='red'))+
scale_x_discrete(limits=c('< -30',' -30~0','0~30','>30'))+ theme_classic()+ ylim(0,100)+
labs(x='', y='', title = '')+
geom_errorbar(aes(ymin=pro.min[5:8], ymax=pro.max[5:8]), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14), axis.text = element_text(size = 14),
plot.title = element_text(hjust = 0),legend.position = 'none')
figure.lc <- sum.lc%>%ggplot(aes(x=factor(class, levels = c('< -30',' -30~0','0~30','>30')),
y=pro.median.9.12.,fill=class)) +
geom_bar(stat = 'identity')+
scale_fill_manual('',values = c('>30'='blue','0~30'='lightblue',' -30~0'='orange','< -30'='red'))+
scale_x_discrete(limits=c('< -30',' -30~0','0~30','>30'))+ theme_classic()+ ylim(0,100)+
labs(x='', y='', title = '')+
theme(text = element_text(size = 14),axis.text = element_text(size = 14),
plot.title = element_text(hjust = 0),legend.position = 'none')
figure.interaction <- sum.range.interaction%>%ggplot(aes(x=factor(class,levels = c('-S','+A','AD','-A','+S')),y=pro.range.interaction.median,fill=class))+
geom_bar(stat = 'identity')+ theme_classic()+
scale_fill_manual('',values = c('-S'='purple4','+A'='purple','AD'='grey70', '-A'='aquamarine','+S'='aquamarine4'))+
xlab('')+ylab('')+labs(title = '')+ ylim(0,100)+
geom_errorbar(aes(ymin=pro.range.interaction.min, ymax=pro.range.interaction.max), width=.2,position=position_dodge(.9))+
theme(text = element_text(size = 14), legend.position = 'none', axis.text = element_text(size = 14))
figure <- ggarrange(figure.both, figure.cl, figure.lc,figure.interaction,nrow = 1)
ggsave(figure, filename = 'F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/figures/rangesize_change_interaction_rcp45_habitat_generalist.tiff',
units = 'cm', width = 28, height = 8, dpi = 300)
#test the significant on the change of distribution range size
area.sum <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/interactive_effects/area sum rcp45.xlsx", sheetName = 'Sheet1')
area.sum <- area.sum%>%mutate(area.2050.both.median = 0)
area.sum <- area.sum%>%mutate(area.2050.cl.median = 0)
for(i in 1:length(area.sum$Scientific_name)){
area.sum[,'area.2050.both.median'][i] <- median(area.sum$area.2050.rcp45.both.ac[i], area.sum$area.2050.rcp45.both.bc[i], area.sum$area.2050.rcp45.both.ca[i],
area.sum$area.2050.rcp45.both.cm[i], area.sum$area.2050.rcp45.both.cn[i], area.sum$area.2050.rcp45.both.cs[i],
area.sum$area.2050.rcp45.both.gf[i], area.sum$area.2050.rcp45.both.gi[i], area.sum$area.2050.rcp45.both.ip[i],
area.sum$area.2050.rcp45.both.mi[i])
area.sum[,'area.2050.cl.median'][i] <- median(area.sum$area.2050.rcp45.cl.ac[i], area.sum$area.2050.rcp45.cl.bc[i], area.sum$area.2050.rcp45.cl.ca[i],
area.sum$area.2050.rcp45.cl.cm[i], area.sum$area.2050.rcp45.cl.cn[i], area.sum$area.2050.rcp45.cl.cs[i],
area.sum$area.2050.rcp45.cl.gf[i], area.sum$area.2050.rcp45.cl.gi[i], area.sum$area.2050.rcp45.cl.ip[i],
area.sum$area.2050.rcp45.cl.mi[i])
}
area.sum <- area.sum%>%mutate('area.change.both.median' = (area.2050.both.median-area.current)/area.current*100)
area.sum <- area.sum%>%mutate('area.change.cl.median' = (area.2050.cl.median-area.current)/area.current*100)
area.sum <- area.sum%>%mutate('area.change.lc' = (area.2050.rcp45.lc-area.current)/area.current*100)
species.list <- read.xlsx("F:/Working/2018/PhD_research/SDM output R/2_6_23_BC_revise/vnbats_habitat_traits.xlsx", sheetName = 'Sheet1')
species.list <- species.list%>%select(Scientific_name,Forest,Shrubland,Grassland,Wetlands,Caves,Artificial,Diet.Plant,Diet.Invertebrate)
area.sum <- merge(area.sum, species.list, by='Scientific_name',bt.x=T)
#check species associate with forest only and others
area.sum$forest.only <- ifelse(area.sum$Artificial==FALSE&area.sum$Shrubland==FALSE&area.sum$Grassland==FALSE,'Forest specialist','Habitat generalist')
figure.both <- area.sum%>%ggplot(aes(x= factor(forest.only, levels = c('Forest specialist', 'Habitat generalist')), y=area.change.both.median))+
stat_summary(geom = "boxplot"
fun.data = function(x) setNames(quantile(x, c(0.0, 0.25, 0.5, 0.75, 1)), c("ymin", "lower", "middle", "upper", "ymax")),
position = "dodge")+ylim(-100,200)+
theme_classic()+
labs(x='',y='Percent change in range size', title = '(a)')+
theme(text = element_text(size = 12), axis.text.x = element_text(size = 12), axis.title.y = element_text(size = 14))+
stat_compare_means(aes(label = ..p.signif..), method = 'wilcox.test',label.x = 1.5, label.y = 180,size = 10)#add Wincolson test
figure.cl <- area.sum%>%ggplot(aes(x= factor(forest.only, levels = c('Forest specialist', 'Habitat generalist')), y=area.change.cl.median))+
stat_summary(geom = "boxplot",
fun.data = function(x) setNames(quantile(x, c(0.0, 0.25, 0.5, 0.75, 1)), c("ymin", "lower", "middle", "upper", "ymax")),