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13_visualize_GAM_high_pike.R
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13_visualize_GAM_high_pike.R
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#########################
##### load packages #####
#########################
library(ggplot2)
library(ggspatial)
library(gtable)
library(mgcv)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
library(foreach)
library(grid)
library(gridExtra)
#set working directory
setwd("/ma_elephant/R")
########### ########### ########### ########### ###########
########### Line Break for long axis labes #########
########### ########### ########### ########### ###########
addline_format <- function(x,...){
gsub('\\s','\n',x)
}
######################################d
### spatial dataframe for plotting ###
######################################
segments_GEE<-rgdal::readOGR(dsn= "/ma_elephant",layer= "segments_GEE")
count_hp_plot<-merge(segments_GEE, data_c, by="ID", all.x=FALSE)
count_hp_plot_p<-SpatialPointsDataFrame(count_hp_plot, count_hp_plot@data)
### points need to be a data.table therefore convert spatialpointsdataframe to dataframe
points_hp <- data.frame(count_hp_plot_p)
### polygons need to be sf object therefore convert spatialpolygonsdataframe to sf
count_hp_plot_sf<-st_as_sf(count_hp_plot)
###############################
########## COD_GAR plot ###########
#### fitted values from GAM ###
###############################
points_hp_cod_gar<-points_hp[points_hp$Site=="COD_GAR",]
count_hp_plot_sf_cod_gar<-count_hp_plot_sf[count_hp_plot_sf$Site=="COD_GAR",]
### call the plot
cod_gar_fv<-ggplot() +
labs(title="Congo Garamba")+
xlab(NA) + ylab(NA) +
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_cod_gar, aes(fill = fv ,color=fv)) +
scale_colour_gradient(low = "grey70",high = "darkred", breaks=c(0,0.5,1),
labels=c("0","0.5","1") ,limits=c(0,1),guide = "colourbar",
aesthetics = c("color","fill"),name=addline_format(c("Estimated probabilty"))) +
geom_point(data = points_hp_cod_gar[points_hp_cod_gar$COUNT_c==1,],
aes(x = coords.x1, y = coords.x2,shape = "carcs"), size = 0.25,
color="black", alpha = 0.4) +
scale_shape_manual(name="",labels = addline_format(c("Carcass observation")), values = 4)+
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))+
guides(shape = guide_legend(override.aes = list(size = 2)))
###############################
########## KEN_LAI plot ###########
#### fitted values from GAM ###
###############################
points_hp_ken_lai<-points_hp[points_hp$Site=="KEN_LAI",]
count_hp_plot_sf_ken_lai<-count_hp_plot_sf[count_hp_plot_sf$Site=="KEN_LAI",]
### call the plot
ken_lai_fv<-ggplot() +
labs(title="Kenya Laikipia")+
xlab(NA) + ylab(NA) +
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_ken_lai, aes(fill = fv ,color=fv)) +
scale_colour_gradient(low = "grey70",high = "darkred", breaks=c(0,0.5,1),
labels=c("0","0.5","1") ,limits=c(0,1),guide = "colourbar",
aesthetics = c("color","fill"),name=addline_format("Estimated probabilty")) +
geom_point(data = points_hp_ken_lai[points_hp_ken_lai$COUNT_c==1,],
aes(x = coords.x1, y = coords.x2,shape = "carcs"), size = 0.25,
color="black", alpha = 0.4) +
scale_shape_manual(name="",labels = addline_format(c("Carcass observation")), values = 4)+
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))+
guides(shape = guide_legend(override.aes = list(size = 2)))
###############################
########## KEN_TSV plot ###########
#### fitted values from GAM ###
###############################
points_hp_ken_tsv<-points_hp[points_hp$Site=="KEN_TSV",]
count_hp_plot_sf_ken_tsv<-count_hp_plot_sf[count_hp_plot_sf$Site=="KEN_TSV",]
### call the plot
ken_tsv_fv<-ggplot() +
labs(title="Kenya Tsavo")+
xlab(NA) + ylab(NA) +
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_ken_tsv, aes(fill = fv ,color=fv)) +
scale_colour_gradient(low = "grey70",high = "darkred", breaks=c(0,0.5,1),
labels=c("0","0.5","1") ,limits=c(0,1),guide = "colourbar",
aesthetics = c("color","fill"),name=addline_format("Estimated probabilty")) +
geom_point(data = points_hp_ken_tsv[points_hp_ken_tsv$COUNT_c==1,],
aes(x = coords.x1, y = coords.x2,shape = "carcs"), size = 0.25,
color="black", alpha = 0.4) +
scale_shape_manual(name="",labels = addline_format(c("Carcass observation")), values = 4)+
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))+
guides(shape = guide_legend(override.aes = list(size = 2)))
###################################
########## TCD_ZAK plot ###########
#### fitted values from GAM #######
###################################
points_hp_tcd_zak<-points_hp[points_hp$Site=="TCD_ZAK",]
count_hp_plot_sf_tcd_zak<-count_hp_plot_sf[count_hp_plot_sf$Site=="TCD_ZAK",]
### call the plot
tcd_zak_fv<-ggplot() +
labs(title="Chad Zakouma")+
xlab(NA) + ylab(NA) +
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_tcd_zak, aes(fill = fv ,color=fv)) +
scale_colour_gradient(low = "grey70",high = "darkred", breaks=c(0,0.5,1),
labels=c("0","0.5","1") ,limits=c(0,1),guide = "colourbar",
aesthetics = c("color","fill"),name=addline_format("Estimated probabilty")) +
geom_point(data = points_hp_tcd_zak[points_hp_tcd_zak$COUNT_c==1,],
aes(x = coords.x1, y = coords.x2,shape = "carcs"), size = 0.25,
color="black", alpha = 0.4) +
scale_shape_manual(name="",labels = addline_format(c("Carcass observation")), values = 4)+
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))+
guides(shape = guide_legend(override.aes = list(size = 2)))
###################################
########## XWA_TBC plot ###########
#### fitted values from GAM #######
###################################
points_hp_xwa_tbc<-points_hp[points_hp$Site=="XWA_TBC",]
count_hp_plot_sf_xwa_tbc<-count_hp_plot_sf[count_hp_plot_sf$Site=="XWA_TBC",]
### call the plot
xwa_tbc_fv<-ggplot() +
labs(title="Burkina Faso, Niger,Benin Pendjari-Complex")+
xlab(NA) + ylab(NA) +
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_xwa_tbc, aes(fill = fv ,color=fv)) +
scale_colour_gradient(low = "grey70",high = "darkred", breaks=c(0,0.5,1),
labels=c("0","0.5","1") ,limits=c(0,1),guide = "colourbar",
aesthetics = c("color","fill"),name=addline_format("Estimated probabilty")) +
geom_point(data = points_hp_xwa_tbc[points_hp_xwa_tbc$COUNT_c==1,],
aes(x = coords.x1, y = coords.x2,shape = "carcs"), size = 0.25,
color="black", alpha = 0.4) +
scale_shape_manual(name="",labels = addline_format(c("Carcass observation")), values = 4)+
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))+
guides(shape = guide_legend(override.aes = list(size = 2)))
######## combine maps ##########
grid.arrange(cod_gar_fv,ken_lai_fv,ncol=1)
grid.arrange( ken_tsv_fv,tcd_zak_fv, ncol=1)
xwa_tbc_fv
################################
###########################
### residuals in space ####
###########################
#cod_gar
cod_gar_res<-ggplot() +
xlab("Longitude") + ylab("Latitude") +
labs(title="Congo Garamba")+
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_cod_gar, aes(fill = res ,color= res)) +
scale_colour_gradient2(low = "red",mid="grey70",high = "blue",
breaks=c(-1,0,1),labels=c("-1","0","1") ,limits=c(-1,1),
guide = "colourbar", aesthetics = c("color","fill"),name="Residuals") +
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))
#ken_lai
ken_lai_res<-ggplot() +
xlab("Longitude") + ylab("Latitude") +
labs(title="Kenya Laikipia")+
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_ken_lai, aes(fill = res ,color= res)) +
scale_colour_gradient2(low = "red",mid="grey70",high = "blue", breaks=c(-1,0,1),
labels=c("-1","0","1") ,limits=c(-1,1),guide = "colourbar",
aesthetics = c("color","fill"),name="Residuals") +
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))
#ken_tsv
ken_tsv_res<-ggplot() +
xlab("Longitude") + ylab("Latitude") +
labs(title="Kenya Tsavo")+
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_ken_tsv, aes(fill = res ,color= res)) +
scale_colour_gradient2(low = "red",mid="grey70",high = "blue", breaks=c(-1,0,1),
labels=c("-1","0","1") ,limits=c(-1,1),guide = "colourbar",
aesthetics = c("color","fill"),name="Residuals") +
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))
#tcd_zak
tcd_zak_res<-ggplot() +
xlab("Longitude") + ylab("Latitude") +
labs(title="Chad Zakouma")+
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_tcd_zak, aes(fill = res ,color= res)) +
scale_colour_gradient2(low = "red",mid="grey70",high = "blue", breaks=c(-1,0,1),
labels=c("-1","0","1") ,limits=c(-1,1),guide = "colourbar",
aesthetics = c("color","fill"),name="Residuals") +
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))
# xwa_tbc
xwa_tbc_res<-ggplot() +
xlab("Longitude") + ylab("Latitude") +
labs(title="Burkina Faso, Niger, Benin, Pendjari-Complex")+
annotation_scale(location = "tr", width_hint = 0.25) +
annotation_north_arrow(location = "tr", which_north = "true",
pad_x = unit(0.25, "in"), pad_y = unit(0.25, "in"),
style = north_arrow_fancy_orienteering) +
geom_sf(data = count_hp_plot_sf_xwa_tbc, aes(fill = res ,color= res)) +
scale_colour_gradient2(low = "red",mid="grey70",high = "blue", breaks=c(-1,0,1),
labels=c("-1","0","1") ,limits=c(-1,1),guide = "colourbar",
aesthetics = c("color","fill"),name="Residuals") +
theme(panel.background = element_rect(fill="white"),axis.text=element_blank(),
axis.title=element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
axis.ticks=element_blank(),
legend.title=element_text(size=15))
######## combine maps ##########
grid.arrange(cod_gar_res,ken_lai_res, ncol=1)
grid.arrange( ken_tsv_res, tcd_zak_res, ncol=1 )
xwa_tbc_res
#######################################
############ Effect plots #############
#######################################
###################################
### get family to backtransform ###
###################################
fam<-family(carc_mod_hp)
ilink<-fam$linkinv
##############################
##### get unscaled data ######
##############################
efdat<-read.table("/ma_elephant/paper+lit/R/predictors_car/yxtable_norm2.csv",header=TRUE,sep=",")
efdat$SQRT_DR<-(efdat$SQRT_DR)^2
efdat$SQRT_BS<-(efdat$SQRT_BS)^2
efdat$SQRT_LE<-(efdat$SQRT_LE)^2
efdat$SQRT_TC300<-(efdat$SQRT_TC300)^2
efdat$SQRT_PD<-(efdat$SQRT_PD)^2
efdat$SQRT_SL<-(efdat$SQRT_SL)^2
efdat$SQRT_DW<-(efdat$SQRT_DW)^2
efdat$LC<-as.factor(efdat$LC)
efdat<-merge(efdat, TR, by="ID")
efdat$LOG_TR<-exp(efdat$LOG_TR)
efdat_hp<-efdat[efdat$Site %in% c("COD_GAR","KEN_LAI","KEN_TSV","TCD_ZAK","XWA_TBC"),]
efdat_hp$PIKE[efdat_hp$Site=="COD_GAR"]<-mean(year2014[rownames(year2014) %in% "GAR",])
efdat_hp$PIKE[efdat_hp$Site=="KEN_LAI"]<-mean(year2014[rownames(year2014) %in% "SBR",])
efdat_hp$PIKE[efdat_hp$Site=="KEN_TSV"]<-mean(year2014[rownames(year2014) %in% "TSV",])
efdat_hp$PIKE[efdat_hp$Site=="TCD_ZAK"]<-mean(year2014[rownames(year2014) %in% "ZAK",])
efdat_hp$PIKE[efdat_hp$Site=="XWA_TBC"]<-mean(year2014[rownames(year2014) %in% "PDJ",])
########################
##### MODE FUN #########
########################
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
###############################################################################################
######## Probabilty of living elephants (P) [LE] #####################
###############################################################################################
#create new data.frame with mean/median obs for predictions
nwdtleh<-data.frame(DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$VC),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=seq(from= min(data_c$SQRT_LE), to=max(data_c$SQRT_LE), length.out = 200),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
LE_fith<-predict(carc_mod_hp, newdata=nwdtleh, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
LEmh<-data.frame(fv=ilink(LE_fith$fit), lowCI=NA,upCI=NA,x=NA)
### 0.05 CI
LEmh$lowCI<-ilink(LE_fith$fit-(2*LE_fith$se.fit))
### 0.95 CI
LEmh$upCI<-ilink(LE_fith$fit+(2*LE_fith$se.fit))
### get unscaled x axis
LEmh$x<-seq(from= min(efdat_hp$SQRT_LE), to=max(efdat_hp$SQRT_LE), length.out = 200)
#### marginal ggplot
LEploth<- ggplot(data=LEmh)+
ylim(0, 1)+
xlab("Estimated probability of living elephants (P)")+
ylab("")+
geom_ribbon(aes(x=LEmh$x, ymax=LEmh$upCI, ymin=LEmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=LEmh$x, y=LEmh$fv),col="grey10")+
geom_line(aes(x=LEmh$x, y=LEmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=LEmh$x, y=LEmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$SQRT_LE), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
LEploth
#################################################################
####### Distance to nearest Settlement (km) [DC] #################
#################################################################
#create new data.frame with mean/median obs for predictions
nwdtdchp<-data.frame( DC=seq(from= min(data_c$DC), to=max(data_c$DC), length.out = 200),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$SQRT_BS),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
DC_fit_hp<-predict(carc_mod_hp, newdata=nwdtdchp, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
DCmh<-data.frame(fv=ilink(DC_fit_hp$fit), lowCI=NA,upCI=NA, x=NA)
### 0.05 CI
DCmh$lowCI<-ilink(DC_fit_hp$fit-(2*DC_fit_hp$se.fit))
### 0.95 CI
DCmh$upCI<-ilink(DC_fit_hp$fit+(2*DC_fit_hp$se.fit))
### get unscaled x axis
DCmh$x<-seq(from= min(efdat_hp$DC)/1000, to=max(efdat_hp$DC)/1000, length.out = 200)
#### effect ggplot
DCplothp<- ggplot(data=DCmh)+
ylim(0, 1)+
xlab("Distance to nearest settlement (km)")+
ylab("Estimated probability")+
geom_ribbon(aes(x=DCmh$x, ymax=DCmh$upCI, ymin=DCmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=DCmh$x, y=DCmh$fv),col="grey10")+
geom_line(aes(x=DCmh$x, y=DCmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=DCmh$x, y=DCmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=(efdat_hp$DC/1000)), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
DCplothp
##############################################
####### Elevation [m a.s.l.] (EL) ############
##############################################
#create new data.frame with mean/median obs for predictions
nwdtelhp<-data.frame( DC=mean(data_c$DC),
EL=seq(from= min(data_c$EL), to=max(data_c$EL), length.out = 200),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$SQRT_BS),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
EL_fit_hp<-predict(carc_mod_hp, newdata=nwdtelhp, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
ELmp<-data.frame(fv=ilink(EL_fit_hp$fit), lowCI=NA,upCI=NA, x=NA)
### 0.05 CI
ELmp$lowCI<-ilink(EL_fit_hp$fit-(2*EL_fit_hp$se.fit))
### 0.95 CI
ELmp$upCI<-ilink(EL_fit_hp$fit+(2*EL_fit_hp$se.fit))
### get unscaled x axis
ELmp$x<-seq(from= min(efdat_hp$EL), to=max(efdat_hp$EL), length.out = 200)
#### effect ggplot
ELploth<- ggplot(data=ELmp)+
ylim(0, 1)+
xlab("Elevation (m a.s.l.)")+
ylab("Estimated probability")+
geom_ribbon(aes(x=ELmp$x, ymax=ELmp$upCI, ymin=ELmp$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=ELmp$x, y=ELmp$fv),col="grey10")+
geom_line(aes(x=ELmp$x, y=ELmp$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=ELmp$x, y=ELmp$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$EL), col="grey30", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
ELploth
##########################################################################################
######## Normalized Difference Vegetation Index (NDVI) ###################################
##########################################################################################
#create new data.frame with mean obs for predictions
nwdtndvih<-data.frame(DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=seq(from= min(data_c$NDVI), to=max(data_c$NDVI), length.out = 200),
T=mean(data_c$T),
TD=mean(data_c$TD),
TP4M=mean(data_c$TP4M),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$SQRT_BS),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
NDVI_fith<-predict(carc_mod_hp, newdata=nwdtndvih, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
NDVImh<-data.frame(fv=ilink(NDVI_fith$fit), lowCI=NA,upCI=NA,x=NA)
### 0.05 CI
NDVImh$lowCI<-ilink(NDVI_fith$fit-(2*NDVI_fith$se.fit))
### 0.95 CI
NDVImh$upCI<-ilink(NDVI_fith$fit+(2*NDVI_fith$se.fit))
### get unscaled x axis
NDVImh$x<-seq(from= min(efdat_hp$NDVI), to=max(efdat_hp$NDVI), length.out = 200)
#### effect ggplot
NDVIploth<- ggplot(data=NDVImh)+
ylim(0, 1)+
xlab("NDVI")+
ylab("")+
geom_ribbon(aes(x=NDVImh$x, ymax=NDVImh$upCI, ymin=NDVImh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=NDVImh$x, y=NDVImh$fv),col="grey10")+
geom_line(aes(x=NDVImh$x, y=NDVImh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=NDVImh$x, y=NDVImh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$NDVI), col="grey30", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
NDVIploth
##########################################################################################
######## annual mean surface air temperature (°C) [T] ####################################
##########################################################################################
#create new data.frame with mean/median obs for predictions
nwdtth<-data.frame( DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=seq(from= min(data_c$T), to=max(data_c$T), length.out = 200),
TD=mean(data_c$TD),
TP4M=mean(data_c$TP4M),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$SQRT_BS),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
T_fith<-predict(carc_mod_hp, newdata=nwdtth, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
Tmh<-data.frame(fv=ilink(T_fith$fit), lowCI=NA,upCI=NA,x=NA)
### 0.05 CI
Tmh$lowCI<-ilink(T_fith$fit-(2*T_fith$se.fit))
### 0.95 CI
Tmh$upCI<-ilink(T_fith$fit+(2*T_fith$se.fit))
### get unscaled x axis
Tmh$x<-seq(from= min(efdat_hp$T), to=max(efdat_hp$T), length.out = 200)
#### effect ggplot
Tploth<- ggplot(data=Tmh)+
ylim(0, 1)+
xlab("Annual mean surface air temperature (°C) ")+
ylab("")+
geom_ribbon(aes(x=Tmh$x, ymax=Tmh$upCI, ymin=Tmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=Tmh$x, y=Tmh$fv),col="grey10")+
geom_line(aes(x=Tmh$x, y=Tmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=Tmh$x, y=Tmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$T), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
Tploth
####################################################################
######## Total annual precipitation (mm) [TPA] #####################
####################################################################
#create new data.frame with mean/median obs for predictions
nwdttpah<-data.frame( DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TD=mean(data_c$TD),
TP4M=mean(data_c$TP4M),
TPA=seq(from= min(data_c$TPA), to=max(data_c$TPA), length.out = 200),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$SQRT_BS),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
TPA_fith<-predict(carc_mod_hp, newdata=nwdttpah, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
TPAmh<-data.frame(fv=ilink(TPA_fith$fit), lowCI=NA,upCI=NA, x=NA)
### 0.05 CI
TPAmh$lowCI<-ilink(TPA_fith$fit-(2*TPA_fith$se.fit))
### 0.95 CI
TPAmh$upCI<-ilink(TPA_fith$fit+(2*TPA_fith$se.fit))
### get unscaled x axis
TPAmh$x<-seq(from= min(efdat_hp$TPA), to=max(efdat_hp$TPA), length.out = 200)
#### effect ggplot
TPAploth<- ggplot(data=TPAmh)+
ylim(0, 1)+
xlab("Total annual precipitation (mm)")+
ylab("")+
geom_ribbon(aes(x=TPAmh$x, ymax=TPAmh$upCI, ymin=TPAmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=TPAmh$x, y=TPAmh$fv),col="grey10")+
geom_line(aes(x=TPAmh$x, y=TPAmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=TPAmh$x, y=TPAmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$TPA), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
TPAploth
#############################################################################################
######## Percentage of ground covered by herbaceous vegetation [%] (VC) #####################
#############################################################################################
#create new data.frame with mean/median obs for predictions
nwdtvch<-data.frame( DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TPA=mean(data_c$TPA),
VC=seq(from= min(data_c$VC), to=max(data_c$VC), length.out = 200),
SQRT_BS=mean(data_c$SQRT_BS),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
VC_fith<-predict(carc_mod_hp, newdata=nwdtvch, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
VCmh<-data.frame(fv=ilink(VC_fith$fit), lowCI=NA,upCI=NA,X=NA)
### 0.05 CI
VCmh$lowCI<-ilink(VC_fith$fit-(2*VC_fith$se.fit))
### 0.95 CI
VCmh$upCI<-ilink(VC_fith$fit+(2*VC_fith$se.fit))
### get unscaled x axis
VCmh$x<-seq(from= min(efdat_hp$VC), to=max(efdat_hp$VC), length.out = 200)
#### effect ggplot
VCploth<- ggplot(data=VCmh)+
ylim(0, 1)+
xlab("Percentage of herbaceous vegetation")+
ylab("Estimated probability")+
geom_ribbon(aes(x=VCmh$x, ymax=VCmh$upCI, ymin=VCmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=VCmh$x, y=VCmh$fv),col="grey10")+
geom_line(aes(x=VCmh$x, y=VCmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=VCmh$x, y=VCmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$VC), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
VCploth
###############################################################################################
######## Percentage of ground covered by bare soil (%) [BS] ###################################
###############################################################################################
#create new data.frame with mean/median obs for predictions
nwdtbsh<-data.frame(DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=seq(from= min(data_c$SQRT_BS), to=max(data_c$SQRT_BS), length.out = 200),
SQRT_PD=mean(data_c$SQRT_PD),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
BS_fith<-predict(carc_mod_hp, newdata=nwdtbsh, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
BSmh<-data.frame(fv=ilink(BS_fith$fit), lowCI=NA,upCI=NA)
### 0.05 CI
BSmh$lowCI<-ilink(BS_fith$fit-(2*BS_fith$se.fit))
### 0.95 CI
BSmh$upCI<-ilink(BS_fith$fit+(2*BS_fith$se.fit))
### get unscaled x axis
BSmh$x<-seq(from= min(efdat_hp$SQRT_BS), to=max(efdat_hp$SQRT_BS), length.out = 200)
#### effect ggplot
BSploth<- ggplot(data=BSmh)+
ylim(0, 1)+
xlab("Percentage of bare soil")+
ylab("Estimated probability")+
geom_ribbon(aes(x=BSmh$x, ymax=BSmh$upCI, ymin=BSmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=BSmh$x, y=BSmh$fv),col="grey10")+
geom_line(aes(x=BSmh$x, y=BSmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=BSmh$x, y=BSmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$SQRT_BS), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
BSploth
############################################################################
######## Population density (inhabitants per km²) [PD] #####################
############################################################################
#create new data.frame with mean/median obs for predictions
nwdtpbh<-data.frame(DC=mean(data_c$DC),
EL=mean(data_c$EL),
NDVI=mean(data_c$NDVI),
T=mean(data_c$T),
TPA=mean(data_c$TPA),
VC=mean(data_c$VC),
SQRT_BS=mean(data_c$VC),
SQRT_PD=seq(from= min(data_c$SQRT_PD), to=max(data_c$SQRT_PD), length.out = 200),
SQRT_LE=mean(data_c$SQRT_LE),
SQRT_SL=mean(data_c$SQRT_SL),
SQRT_DR=mean(data_c$SQRT_DR),
SQRT_TC300=mean(data_c$SQRT_TC300),
SQRT_DW=mean(data_c$SQRT_DW),
PA=getmode(data_c$PA),
LC=getmode(data_c$LC),
PIKE=mean(data_c$PIKE),
LOG_TR=mean(data_c$LOG_TR)
)
#predict on newdata
PD_fith<-predict(carc_mod_hp, newdata=nwdtpbh, se.fit=TRUE)
#call dataframe to store fitted values, 0.05 CI and 0.95 CI
PDmh<-data.frame(fv=ilink(PD_fith$fit), lowCI=NA,upCI=NA,x=NA)
### 0.05 CI
PDmh$lowCI<-ilink(PD_fith$fit-(2*PD_fith$se.fit))
### 0.95 CI
PDmh$upCI<-ilink(PD_fith$fit+(2*PD_fith$se.fit))
### get unscaled x axis
PDmh$x<-seq(from= min(efdat_hp$SQRT_PD), to=max(efdat_hp$SQRT_PD), length.out = 200)
#### effect ggplot
PDploth<- ggplot(data=PDmh)+
ylim(0, 1)+
xlab("Population density (Inhabitants per km²)")+
ylab("")+
geom_ribbon(aes(x=PDmh$x, ymax=PDmh$upCI, ymin=PDmh$lowCI), fill="grey50", alpha=.5) +
geom_line(aes(x=PDmh$x, y=PDmh$fv),col="grey10")+
geom_line(aes(x=PDmh$x, y=PDmh$lowCI),linetype = 2, col="grey30")+
geom_line(aes(x=PDmh$x, y=PDmh$upCI),linetype = 2, col="grey30")+
geom_rug(data=efdat_hp, aes(x=efdat_hp$SQRT_PD), col="grey50", size=0.1, sides="b")+
theme(axis.text=element_text(size=18,colour="black"),
axis.title=element_text(size=16),
panel.background = element_rect(fill = 'white', colour = 'white'),
panel.border = element_rect(colour = "black", fill=NA, size=1),
aspect.ratio=1)
PDploth