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isolation_graph_code.R
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library(ggplot2)
library(viridis)
library(extrafont)
library(patchwork)
library(ggnewscale)
Z = c(10,20,30,40,50,60,70,80,90,100)
R = 1:30
Method = c("Betweenness", "Degree", "Random")
Time = 1:1820
# WITH DFTD ---------------------------------------------------
NData = setNames(data.frame(matrix(ncol = 5, nrow = 0)), c("Method","Z","PopMedian", "InfMedian", "GenDiversity"))
MData = setNames(data.frame(matrix(ncol = 8, nrow = 0)), c("Method","Z","MeanPop", "PopSE", "MeanInf", "InfSE", "MeanDivers", "DiversSE"))
for(M in Method){
for(C in Z){
for(D in R){
if(file.exists(paste0(M,"_rep_",D,"_step_",C,".rda")) == T){
load(paste0(M,"_rep_",D,"_step_",C,".rda"))
totinf = rep(0,1820)
for(r in 1:477){
totinf = totinf + inflist[[r]]
}
if(totinf[1040] > 0){
diversetotal = c()
altdivers = c()
for(t in Time){
diversetotal[t] = 0
for(v in 1:477){
if(diverselist[[v]][t] == 1){altdivers[t] = 0}
else{altdivers[t] = diverselist[[v]][t]}
diversetotal[t] = diversetotal[t] + altdivers[t]
}
}
islanddiversity = diversetotal / 477
infpops = 0
for(i in 1:477){
if(sum(inflist[[i]]) > 0){
infpops = infpops + 1
}
}
infpops2 = infpops / 477
NData3 = setNames(data.frame(matrix(ncol = 5, nrow = 1)), c("Method","Z","PopMedian", "InfMedian", "GenDiversity"))
NData3$Method[1] = M
NData3$Z[1] = C
NData3$PopMedian[1] = NData2$MedianPop
NData3$InfMedian[1] = infpops2
NData3$GenDiversity[1] = median(tail(islanddiversity, 520))
NData = rbind(NData, NData3)
} else{}
}
else{}
}
}
}
for(M in Method){
for(C in Z){
MData2 = setNames(data.frame(matrix(ncol = 8, nrow = 1)), c("Method","Z","MeanPop", "PopSD", "MeanInf", "InfSD", "MeanDivers", "DiversSD"))
MData2$Z[1] = C
MData2$Method[1] = M
MData2$MeanPop[1] = mean(NData[NData$Z== C & NData$Method == M,]$PopMedian, rm.na = T)
MData2$PopSE[1] = sd(NData[NData$Z== C & NData$Method == M,]$PopMedian)/sqrt(nrow(NData[NData$Z== C & NData$Method == M,]))
MData2$MeanInf[1] = mean(NData[NData$Z== C & NData$Method == M,]$InfMedian, rm.na = T)
MData2$InfSE[1] = sd(NData[NData$Z== C & NData$Method == M,]$InfMedian)/sqrt(nrow(NData[NData$Z== C & NData$Method == M,]))
MData2$MeanDivers[1] = mean(NData[NData$Z== C & NData$Method == M,]$GenDiversity, rm.na = T)
MData2$DiversSE[1] = sd(NData[NData$Z== C & NData$Method == M,]$GenDiversity)/sqrt(nrow(NData[NData$Z== C & NData$Method == M,]))
MData = rbind(MData, MData2)
}
}
#NO DFTD ---------------------------------------------------------------
NDataNoDFTD = setNames(data.frame(matrix(ncol = 5, nrow = 0)), c("Method","Z","PopMedian", "InfMedian", "GenDiversity"))
MDataNoDFTD = setNames(data.frame(matrix(ncol = 8, nrow = 0)), c("Method","Z","MeanPop", "PopSE", "MeanInf", "InfSE", "MeanDivers", "DiversSE"))
for(M in Method){
for(C in Z){
for(D in R){
if(file.exists(paste0(M,"_noDFTD_rep_",D,"_step_",C,".rda")) == T){
load(paste0(M,"_noDFTD_rep_",D,"_step_",C,".rda"))
totinf = rep(0,1820)
for(r in 1:477){
totinf = totinf + inflist[[r]]
}
diversetotal = c()
altdivers = c()
for(t in Time){
diversetotal[t] = 0
for(v in 1:477){
if(diverselist[[v]][t] == 1){altdivers[t] = 0}
else{altdivers[t] = diverselist[[v]][t]}
diversetotal[t] = diversetotal[t] + altdivers[t]
}
}
islanddiversity = diversetotal / 477
infpops = 0
for(i in 1:477){
if(sum(inflist[[i]]) > 0){
infpops = infpops + 1
}
}
infpops2 = infpops / 477
NData4 = setNames(data.frame(matrix(ncol = 5, nrow = 1)), c("Method","Z","PopMedian", "InfMedian", "GenDiversity"))
NData4$Method[1] = M
NData4$Z[1] = C
NData4$PopMedian[1] = NData2$MedianPop
NData4$InfMedian[1] = infpops2
NData4$GenDiversity[1] = median(tail(islanddiversity, 520))
NDataNoDFTD = rbind(NDataNoDFTD, NData4)
}
else{}
}
}
}
for(M in Method){
for(C in Z){
MData2 = setNames(data.frame(matrix(ncol = 8, nrow = 1)), c("Method","Z","MeanPop", "PopSD", "MeanInf", "InfSD", "MeanDivers", "DiversSD"))
MData2$Z[1] = C
MData2$Method[1] = M
MData2$MeanPop[1] = mean(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]$PopMedian, rm.na = T)
MData2$PopSE[1] = sd(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]$PopMedian)/sqrt(nrow(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]))
MData2$MeanInf[1] = mean(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]$InfMedian, rm.na = T)
MData2$InfSE[1] = sd(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]$InfMedian)/sqrt(nrow(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]))
MData2$MeanDivers[1] = mean(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]$GenDiversity, rm.na = T)
MData2$DiversSE[1] = sd(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]$GenDiversity)/sqrt(nrow(NDataNoDFTD[NDataNoDFTD$Z== C & NDataNoDFTD$Method == M,]))
MDataNoDFTD = rbind(MDataNoDFTD, MData2)
}
}
#NON ISOLATED ----------------------------------------------------------
NDataNonIsolated = setNames(data.frame(matrix(ncol = 2, nrow = 0)), c("PopMedian", "InfMedian"))
MDataNonIsolated = setNames(data.frame(matrix(ncol = 4, nrow = 1)), c("MeanPop", "PopSE", "MeanInf", "InfSE"))
for(q in 1:30){
if(file.exists(paste0("100_Year_Density_Disp_0.009_Contact_0.1_BP_0.4_rep_",q,".rda"))){
print(q)
load(paste0("100_Year_Density_Disp_0.009_Contact_0.1_BP_0.4_rep_",q,".rda"))
totinf = rep(0,1820)
for(r in 1:477){
totinf = totinf + inflist[[r]]
}
infpops = 0
for(i in 1:477){
if(sum(inflist[[i]]) > 0){
infpops = infpops + 1
}
}
infpops2 = infpops / 477
NData3 = setNames(data.frame(matrix(ncol = 2, nrow = 1)), c("PopMedian", "InfMedian"))
NData3$PopMedian[1] = NData2$MedianPop
NData3$InfMedian[1] = infpops2
NDataNonIsolated = rbind(NDataNonIsolated, NData3)
}
}
MDataNonIsolated$MeanPop = mean(NDataNonIsolated$PopMedian[NDataNonIsolated$InfMedian>0.1])
MDataNonIsolated$PopSE = sd(NDataNonIsolated$PopMedian[NDataNonIsolated$InfMedian>0.1])/sqrt(nrow(NDataNonIsolated[NDataNonIsolated$InfMedian>0.1,]))
MDataNonIsolated$MeanInf = mean(NDataNonIsolated$InfMedian[NDataNonIsolated$InfMedian>0.1])
MDataNonIsolated$InfSE = sd(NDataNonIsolated$InfMedian[NDataNonIsolated$InfMedian>0.1])/sqrt(nrow(NDataNonIsolated[NDataNonIsolated$InfMedian>0.1,]))
#GRAPHS --------------------------------------------------------------------
g1 = ggplot(data = MData, aes(Z,MeanPop)) +
geom_point(data = MData, aes(x = Z, y = MeanPop, colour = Method, group = Method)) +
geom_line(data = MData,aes(x = Z, y = MeanPop, colour = Method, group = Method),size = 1)+ xlim(0,100) +
geom_errorbar(data = MData,aes(ymin=MeanPop-PopSE, ymax=MeanPop+PopSE, colour = Method), width=2) +
scale_color_viridis_d() + scale_y_continuous(label=comma) +
ggtitle("A") +
new_scale_color() +
geom_point(data = MDataNoDFTD, aes(x = Z, y = MeanPop, colour = Method, group = Method)) +
geom_line(data = MDataNoDFTD, aes(x = Z, y = MeanPop, colour = Method, group = Method),size = 1)+
geom_errorbar(data = MDataNoDFTD, aes(ymin=MeanPop-PopSE, ymax=MeanPop+PopSE, colour = Method), width=2) +
scale_color_grey() +
theme_bw() +
ylab("Metapopulation size") +
geom_hline(yintercept = MDataNonIsolated$MeanPop, linetype = "dashed", colour= "grey") +
theme(text=element_text(family="Calibri"), legend.position="none", axis.title.x = element_blank())
#geom_text(aes(70, MDataNonIsolated$MeanPop, label = "Non-isolated mean"), colour = "grey", vjust = -0.5) +
g2 = ggplot(data = MData, aes(x = Z, y = MeanInf, colour = Method, group = Method)) +
#geom_text(aes(70, MDataNonIsolated$MeanInf, label = "Non-isolated mean"), colour = "grey", vjust = -0.5) +
geom_point() + geom_line(size = 1) +
geom_errorbar(aes(ymin=MeanInf-InfSE, ymax=MeanInf+InfSE, colour = Method), width=2) +
theme_bw() + scale_color_viridis_d() + xlim(0,100) +
ylab("Proportion of Patches Infected") + xlab("No. patches isolated") +
geom_hline(yintercept = MDataNonIsolated$MeanInf, linetype = "dashed", colour= "grey") +
ggtitle("B") +
theme(text=element_text(family="Calibri"), legend.position="none")
g3 = ggplot(data = MData, aes(Z,MeanDivers)) +
geom_point(data = MData, aes(x = Z, y = MeanDivers, colour = Method, group = Method)) +
geom_line(data = MData,aes(x = Z, y = MeanDivers, colour = Method, group = Method),size = 1)+ xlim(0,100) +
geom_errorbar(data = MData,aes(ymin=MeanDivers-DiversSE, ymax=MeanDivers+DiversSE, colour = Method), width=2) +
scale_color_viridis_d("With DFTD") +
new_scale_color() +
geom_point(data = MDataNoDFTD, aes(x = Z, y = MeanDivers, colour = Method, group = Method)) +
geom_line(data = MDataNoDFTD, aes(x = Z, y = MeanDivers, colour = Method, group = Method),size = 1)+
geom_errorbar(data = MDataNoDFTD, aes(ymin=MeanDivers-DiversSE, ymax=MeanDivers+DiversSE, colour = Method), width=2) +
scale_color_grey("Without DFTD") +
theme_bw() +
ylab("Mean Within-Population Genetic Variation") +
ggtitle("C") +
#geom_hline(yintercept = MDataNonIsolated$MeanDivers, linetype = "dashed", colour= "grey") +
#geom_text(aes(70, MDataNonIsolated$MeanDivers, label = "Non-isolated mean"), colour = "grey", vjust = -0.5) +
theme(text=element_text(family="Calibri"), axis.title.x = element_blank())
p = g1 + g2 + g3
ggsave("figure_6_square.tiff", p, dpi = 300)