Import data

library(ggplot2)
library(ggimage)
library(png)
library(grid)
library(viridis)
#background image
img <- 'bckgd.png'
#importing raw data
data.all <- read.csv("Delhey_JAV02222_data.csv")
#importing backtransformed contrast values for patch name model
regions.all <- read.csv("TableS1.csv")
#importing backtransformed contrast values for patch name model
patches.all <- read.csv("TableS2.csv") 

Male V-type contrast

#subset to correct models and data
patches <- subset(patches.all, model=="contrast males V-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast males V-type ~ plumage region")
data.all$variable <- data.all$mean.cont.v.male 
regions$letters <- toupper(regions$letters)
p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(2.35, 2.81))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness males")
)
fig1a <- ggbackground(p, img)
fig1b <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
fig1a

fig1b

saving images

ggsave(plot=fig1a, filename='fig1a.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1b, filename='fig1b.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)

Contrast females V-type

#subset to correct models and data
patches <- subset(patches.all, model=="contrast females V-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast females V-type ~ plumage region")
data.all$variable <- data.all$mean.cont.v.female 
regions$letters <- toupper(regions$letters)
p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(2.35, 2.81))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness females")
)
fig1c <- ggbackground(p, img)
fig1d <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
fig1c

fig1d

ggsave(plot=fig1c, filename='fig1c.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1d, filename='fig1d.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)

Sexual dichromatism V-type

#subset to correct models and data
patches <- subset(patches.all, model=="sexual dichromatism V-type ~ plumage patch")
regions <- subset(regions.all, model=="sexual dichromatism V-type ~ plumage region")
data.all$variable <- data.all$sex.dich.v 
regions$letters <- toupper(regions$letters)
p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis()
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Sexual dichromatism")
)
fig1e <- ggbackground(p, img)
fig1f <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.01)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast females/males (jnd)', breaks=c(1,2,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
fig1e

fig1f

ggsave(plot=fig1e, filename='fig1e.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1f, filename='fig1f.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)

U-type visual sensitivities

Male contrast

#subset to correct models and data
patches <- subset(patches.all, model=="contrast males U-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast males U-type ~ plumage region")
data.all$variable <- data.all$mean.cont.u.male 
regions$letters <- toupper(regions$letters)
p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(3.1, 3.71))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness males")
)
fig1a.u <- ggbackground(p, img)
fig1b.u <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
fig1a.u

fig1b.u

ggsave(plot=fig1a.u, filename='fig1a.u.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1b.u, filename='fig1b.u.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)

Contrast females U-type

#subset to correct models and data
patches <- subset(patches.all, model=="contrast females U-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast females U-type ~ plumage region")
data.all$variable <- data.all$mean.cont.u.female 
regions$letters <- toupper(regions$letters)
p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(3.1, 3.71))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness females")
)
fig1c.u <- ggbackground(p, img)
fig1d.u <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
fig1c.u

fig1d.u

ggsave(plot=fig1c.u, filename='fig1c.u.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1d.u, filename='fig1d.u.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)

sexual dichromatism

#subset to correct models and data
patches <- subset(patches.all, model=="sexual dichromatism U-type ~ plumage patch")
regions <- subset(regions.all, model=="sexual dichromatism U-type ~ plumage region")
data.all$variable <- data.all$sex.dich.u 
regions$letters <- toupper(regions$letters)
p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +scale_colour_viridis()
   +geom_text(colour='red')
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Sexual dichromatism")
)
fig1e.u <- ggbackground(p, img)
fig1f.u <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.01)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast females/males (jnd)', breaks=c(1,2,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
fig1e.u

fig1f.u

ggsave(plot=fig1e.u, filename='fig1e.u.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1f.u, filename='fig1f.u.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)
---
title: "Plots"
output: html_notebook
---
#Import data
```{r}
library(ggplot2)
library(ggimage)
library(png)
library(grid)
library(viridis)

#background image
img <- 'bckgd.png'

#importing raw data
data.all <- read.csv("Delhey_JAV02222_data.csv")

#importing backtransformed contrast values for patch name model
regions.all <- read.csv("TableS1.csv")

#importing backtransformed contrast values for patch name model
patches.all <- read.csv("TableS2.csv") 
```



#Male V-type contrast


```{r}

#subset to correct models and data
patches <- subset(patches.all, model=="contrast males V-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast males V-type ~ plumage region")

data.all$variable <- data.all$mean.cont.v.male 

regions$letters <- toupper(regions$letters)

p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(2.35, 2.81))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness males")
)

fig1a <- ggbackground(p, img)


fig1b <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)

```


```{r}
fig1a
```

```{r}
fig1b
```


#saving images
```{r}
ggsave(plot=fig1a, filename='fig1a.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1b, filename='fig1b.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)
```



#Contrast females V-type
```{r}

#subset to correct models and data
patches <- subset(patches.all, model=="contrast females V-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast females V-type ~ plumage region")

data.all$variable <- data.all$mean.cont.v.female 

regions$letters <- toupper(regions$letters)

p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(2.35, 2.81))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness females")
)

fig1c <- ggbackground(p, img)


fig1d <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")

)


```

```{r}
fig1c
```

```{r}
fig1d
```


```{r}
ggsave(plot=fig1c, filename='fig1c.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1d, filename='fig1d.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)
```


#Sexual dichromatism V-type
```{r}

#subset to correct models and data
patches <- subset(patches.all, model=="sexual dichromatism V-type ~ plumage patch")
regions <- subset(regions.all, model=="sexual dichromatism V-type ~ plumage region")

data.all$variable <- data.all$sex.dich.v 

regions$letters <- toupper(regions$letters)

p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis()
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Sexual dichromatism")
)

fig1e <- ggbackground(p, img)


fig1f <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.01)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast females/males (jnd)', breaks=c(1,2,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)


```

```{r}
fig1e
```

```{r}
fig1f
```


```{r}
ggsave(plot=fig1e, filename='fig1e.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1f, filename='fig1f.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)
```





##U-type visual sensitivities


#Male  contrast


```{r}

#subset to correct models and data
patches <- subset(patches.all, model=="contrast males U-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast males U-type ~ plumage region")

data.all$variable <- data.all$mean.cont.u.male 

regions$letters <- toupper(regions$letters)

p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(3.1, 3.71))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness males")
)





fig1a.u <- ggbackground(p, img)


fig1b.u <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")
)
```


```{r}
fig1a.u
```

```{r}
fig1b.u
```



```{r}
ggsave(plot=fig1a.u, filename='fig1a.u.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1b.u, filename='fig1b.u.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)
```



#Contrast females U-type

```{r}

#subset to correct models and data
patches <- subset(patches.all, model=="contrast females U-type ~ plumage patch")
regions <- subset(regions.all, model=="contrast females U-type ~ plumage region")

data.all$variable <- data.all$mean.cont.u.female 

regions$letters <- toupper(regions$letters)

p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +geom_text(colour='red')
   +scale_colour_viridis(limits=c(3.1, 3.71))
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Conspicuousness females")
)




fig1c.u <- ggbackground(p, img)


fig1d.u <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=10^variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=ci.lb-0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.5)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.65)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.5)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast against background (jnd)', breaks=c(1,2,3,4,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")

)


```

```{r}
fig1c.u
```
```{r}
fig1d.u
```

```{r}
ggsave(plot=fig1c.u, filename='fig1c.u.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1d.u, filename='fig1d.u.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)

```

#sexual dichromatism
```{r}

#subset to correct models and data
patches <- subset(patches.all, model=="sexual dichromatism U-type ~ plumage patch")
regions <- subset(regions.all, model=="sexual dichromatism U-type ~ plumage region")

data.all$variable <- data.all$sex.dich.u 

regions$letters <- toupper(regions$letters)

p=(ggplot(data=patches, aes(x=coord.x+0.25, y=coord.y+0.3, colour=post.mean, label=as.character(patch.name)))
   +geom_point(size=12)
   +scale_colour_viridis()
   +geom_text(colour='red')
   +theme_void()
   +theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())
   +theme(panel.grid.major.x = element_blank(),panel.grid.minor.x = element_blank())
   +theme(axis.line=element_blank())
   +labs(colour="contrast (jnd)")
   +scale_x_continuous(limits=c(2,7))
   +scale_y_continuous(limits=c(2,6))
   +ggtitle("Sexual dichromatism")
)


fig1e.u <- ggbackground(p, img)


fig1f.u <- (ggplot(data=patches, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T),
                          y=post.mean, label=letters ))
+geom_jitter(data=data.all, aes(x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T), y=variable), inherit.aes=F, colour='dark grey', alpha=0.3)
+geom_point(size=4)
+geom_errorbar( aes(ymin=ci.lb, ymax=ci.ub), width=0)
+geom_text(aes(y=0.2, x=factor(patch.name, levels=c('fr','cr','ch',"th",'dn',"ubk","lbk","ru","dtp","dtd","ws","wp","vn","ubr","lbr","be","ve"), ordered=T)), angle=45)
+geom_errorbar(data=regions, aes(x=c(8.5,15.5,2.5), ymin=ci.lb, ymax=ci.ub), width=0, size=1, inherit.aes=F, colour='red')
+geom_point(data=regions, aes(x=c(8.5,15.5,2.5), y=post.mean), alpha=1, size=5, shape=18, inherit.aes=F, colour='red')
+geom_vline(xintercept=4.5)
+geom_vline(xintercept=12.5)
+geom_text(aes(label=c("head"), x=c(2.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[3], x=c(2.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("dorsal"), x=c(8.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[1], x=c(8.5), y=c(0.01)), inherit.aes=F, colour='red')
+geom_text(aes(label=c("ventral"), x=c(15.5), y=c(0.02)), inherit.aes=F, colour='red')
+geom_text(aes(label=regions$letters[2], x=c(15.5), y=c(0.01)), inherit.aes=F, colour='red')
+scale_y_log10(name='contrast females/males (jnd)', breaks=c(1,2,5,10,20,30))
+scale_x_discrete(name='patch name')
+theme_bw()
+theme(legend.position = "none")

)


```

```{r}
fig1e.u
```

```{r}
fig1f.u
```



```{r}
ggsave(plot=fig1e.u, filename='fig1e.u.png', device = 'png', width = 20, height=10, units = 'cm', dpi=600) 
ggsave(plot=fig1f.u, filename='fig1f.u.png', device = 'png', width = 13, height=7, units = 'cm', dpi=600)


```




