crane model RIII
This file holds the data fiddlings for the model built by Nic and Misty to explore the effects of forced renesting on the evolution of nest timing. This is based on a chick survival (conversion rate) default of 0.25.
The parent file is: RIIImerged.csv
all3<-read.csv("/users/mcpheem/Google Drive/Research _ Active/Cranes _ Google/Crane crew research/FRN simulation _ MEM/datafilesRIII/RIIImerged.csv",header=T)
names(all3)## [1] "Run" "Year"
## [3] "Population.size" "Proportion.late.nesters"
## [5] "Proportion.wild.born" "variable.input"
## [7] "File.name"levels(all3$File.name)## [1] "CP_000" "CP_025" "CP_075"
## [4] "CP_100" "default.w.CR0.25" "EN_025"
## [7] "EN_075" "NP_025" "NP_075"
## [10] "RC_00" "RC_10" "RC_15"
## [13] "RNP_025" "RNP_075"levels(all3$File.name)## [1] "CP_000" "CP_025" "CP_075"
## [4] "CP_100" "default.w.CR0.25" "EN_025"
## [7] "EN_075" "NP_025" "NP_075"
## [10] "RC_00" "RC_10" "RC_15"
## [13] "RNP_025" "RNP_075"max(all3$Year)## [1] 2115checking to make sure that each of the input files are the right size (i.e., no duplicates)
summary(all3$File.name)Building exploratory plots that show N, prop late nesters, and prop wild born in year 2115 across the different files.
I changed the order of the x-axis so that the labels made more sense.
## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated
These graphs included all populations, even those that were extremely small. The following code allows us to look at parameters in light of pops that didn’t drop below 100. For example, CP_000 had a high prop of late nesters but when we used only pops >100 it fell out.
Here we create vectors for our modifiable parameters and new data frames (the original data frame with all3 data gave a plot for RNP which the 0.5 had all3 of the other values combined (e.g., CP = 0, 0.25, 0.5…) which makes the middle box difficult to interpret.)
This code is based on the data frame successful and the graphs use 2115 data only
Collection probability (CP)
extract_cp <- function(filename) {
m <- regexec("CP_([0-9]+)", as.character(filename));
if (m[[1]][1] >= 0) {
as.integer(regmatches(filename, m)[[1]][2])/100.0;
} else {
0.5;
}
}
# all3$CP <- sapply(all3$File.name, extract_cp)
successful$CP <- sapply(successful$File.name, extract_cp)
# plot(successful$CP ~ successful$File.name, las=2, xlab="")
just_cp <- subset(successful, successful$File.name == "CP_000" | successful$File.name == "CP_025" | successful$File.name == "default.w.CR0.25" | successful$File.name == "CP_075" | successful$File.name == "CP_100")
CP.f <- factor(just_cp$CP)CP: N
boxplot(just_cp$Population.size[just_cp$Year=="2115"]~just_cp$CP[just_cp$Year=="2115"], xlab="collection probability", ylab="N", axes=F)
axis(1, at=1:4, lab=c("0.25","0.5","0.75","1.0"))
axis(2)
summary(just_cp$Population.size[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 256.0 279.5 287.0 284.2 292.8 300.0sd.cp025.N <- sd(just_cp$Population.size[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])
sd.cp025.N## [1] 11.32337error.cp025.N <- qt(0.975,
df=length(just_cp$Population.size
[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])-1)*sd.cp025.N/sqrt(length(just_cp$Population.size[just_cp$File.name=="CP_025" & just_cp$Year=="2115"]))
CI.cp025.N.left <- mean(just_cp$Population.size[just_cp$File.name=="CP_025"& just_cp$Year=="2115"])-error.cp025.N
CI.cp025.N.right <- mean(just_cp$Population.size[just_cp$File.name=="CP_025"& just_cp$Year=="2115"])+error.cp025.N
CI.cp025.N.left## [1] 280.9419CI.cp025.N.right## [1] 287.3781summary(just_cp$Population.size[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 254.0 285.0 290.5 288.6 294.0 300.0sd.cp050.N <- sd (just_cp$Population.size[just_cp$File.name=="default.w.CR0.25" &
just_cp$Year=="2115"])
sd.cp050.N## [1] 8.458205error.cp050.N <- qt(0.975,
df=length(just_cp$Population.size[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"])-1)*sd.cp050.N/sqrt(length(just_cp$Population.size[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"]))
CI.cp050.N.left <- mean(just_cp$Population.size[just_cp$File.name=="default.w.CR0.25"& just_cp$Year=="2115"])-error.cp025.N
CI.cp050.N.right <- mean(just_cp$Population.size[just_cp$File.name=="default.w.CR0.25"& just_cp$Year=="2115"])+error.cp050.N
CI.cp050.N.left## [1] 285.4219CI.cp050.N.right## [1] 291.0438summary(just_cp$Population.size[just_cp$File.name=="CP_075" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 278.0 287.0 290.0 290.2 294.8 299.0sd.cp075.N <- sd (just_cp$Population.size[just_cp$File.name=="CP_075" &
just_cp$Year=="2115"])
sd.cp075.N## [1] 5.213796error.cp075.N <- qt(0.975,
df=length(just_cp$Population.size[just_cp$File.name=="CP_075" & just_cp$Year=="2115"])-1)*sd.cp075.N/sqrt(length(just_cp$Population.size[just_cp$File.name=="CP_075" & just_cp$Year=="2115"]))
CI.cp075.N.left <- mean(just_cp$Population.size[just_cp$File.name=="CP_075"& just_cp$Year=="2115"])-error.cp025.N
CI.cp075.N.right <- mean(just_cp$Population.size[just_cp$File.name=="CP_075"& just_cp$Year=="2115"])+error.cp075.N
CI.cp075.N.left## [1] 286.9819CI.cp075.N.right## [1] 291.6817summary(just_cp$Population.size[just_cp$File.name=="CP_100" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 280 290 294 293 297 300sd.cp100.N <- sd (just_cp$Population.size[just_cp$File.name=="CP_100" &
just_cp$Year=="2115"])
sd.cp100.N## [1] 4.683841error.cp100.N <- qt(0.975,
df=length(just_cp$Population.size[just_cp$File.name=="CP_100" & just_cp$Year=="2115"])-1)*sd.cp100.N/sqrt(length(just_cp$Population.size[just_cp$File.name=="CP_100" & just_cp$Year=="2115"]))
CI.cp100.N.left <- mean(just_cp$Population.size[just_cp$File.name=="CP_100"& just_cp$Year=="2115"])-error.cp025.N
CI.cp100.N.right <- mean(just_cp$Population.size[just_cp$File.name=="CP_100"& just_cp$Year=="2115"])+error.cp100.N
CI.cp100.N.left## [1] 289.7619CI.cp100.N.right## [1] 294.3111just_cp_N.aov = aov(just_cp$Population.size[just_cp$Year == "2115"] ~ CP.f[just_cp$Year == "2115"])
summary(just_cp_N.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## CP.f[just_cp$Year == "2115"] 3 2042 680.6 10.94 1.13e-06 ***
## Residuals 196 12195 62.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_cp_N.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_cp$Population.size[just_cp$Year == "2115"] ~ CP.f[just_cp$Year == "2115"])
##
## $`CP.f[just_cp$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 4.48 0.3921122 8.567888 0.0255054
## 0.75-0.25 6.04 1.9521122 10.127888 0.0009892
## 1-0.25 8.82 4.7321122 12.907888 0.0000004
## 0.75-0.5 1.56 -2.5278878 5.647888 0.7559705
## 1-0.5 4.34 0.2521122 8.427888 0.0326644
## 1-0.75 2.78 -1.3078878 6.867888 0.2947899plot(TukeyHSD(just_cp_N.aov))
CP: prop_late
boxplot(just_cp$Proportion.late.nesters[just_cp$Year=="2115"]~just_cp$CP[just_cp$Year=="2115"], xlab="collection probability", ylab="Proportion of late nesters", axes=F)
axis(1, at=1:4, lab=c("0.25","0.5","0.75","1.0"))
axis(2)
summary(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1691 0.2764 0.3578 0.3593 0.4337 0.5433sd.cp025.late <- sd (just_cp$Proportion.late.nesters[just_cp$File.name=="CP_025" &
just_cp$Year=="2115"])
sd.cp025.late## [1] 0.0949935error.cp025.late <- qt(0.975,
df=length(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])-1)*sd.cp025.late/sqrt(length(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_025" & just_cp$Year=="2115"]))
CI.cp025.late.left <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_025"& just_cp$Year=="2115"])-error.cp025.late
CI.cp025.late.right <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_025"& just_cp$Year=="2115"])+error.cp025.late
CI.cp025.late.left## [1] 0.3322756CI.cp025.late.right## [1] 0.3862693summary(just_cp$Proportion.late.nesters[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1111 0.2998 0.3431 0.3433 0.4045 0.4777sd.cp050.late <- sd (just_cp$Proportion.late.nesters[just_cp$File.name=="default.w.CR0.25" &
just_cp$Year=="2115"])
sd.cp050.late## [1] 0.08282624error.cp050.late <- qt(0.975,
df=length(just_cp$Proportion.late.nesters[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"])-1)*sd.cp050.late/sqrt(length(just_cp$Proportion.late.nesters[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"]))
CI.cp050.late.left <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="default.w.CR0.25"& just_cp$Year=="2115"])-error.cp050.late
CI.cp050.late.right <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="default.w.CR0.25"& just_cp$Year=="2115"])+error.cp050.late
CI.cp050.late.left## [1] 0.3197148CI.cp050.late.right## [1] 0.3667927summary(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_075" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1297 0.2500 0.3060 0.3105 0.3767 0.5302sd.cp075.late <- sd (just_cp$Proportion.late.nesters[just_cp$File.name=="CP_075" &
just_cp$Year=="2115"])
sd.cp075.late## [1] 0.08960268error.cp075.late <- qt(0.975,
df=length(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_075" & just_cp$Year=="2115"])-1)*sd.cp075.late/sqrt(length(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_075" & just_cp$Year=="2115"]))
CI.cp075.late.left <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_075"& just_cp$Year=="2115"])-error.cp075.late
CI.cp075.late.right <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_075"& just_cp$Year=="2115"])+error.cp075.late
CI.cp075.late.left## [1] 0.2850431CI.cp075.late.right## [1] 0.3359727summary(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_100" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1376 0.2418 0.2938 0.2931 0.3468 0.4497sd.cp100.late <- sd (just_cp$Proportion.late.nesters[just_cp$File.name=="CP_100" &
just_cp$Year=="2115"])
sd.cp100.late## [1] 0.07509609error.cp100.late <- qt(0.975,
df=length(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_100" & just_cp$Year=="2115"])-1)*sd.cp100.late/sqrt(length(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_100" & just_cp$Year=="2115"]))
CI.cp100.late.left <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_100"& just_cp$Year=="2115"])-error.cp100.late
CI.cp100.late.right <- mean(just_cp$Proportion.late.nesters[just_cp$File.name=="CP_100"& just_cp$Year=="2115"])+error.cp100.late
CI.cp100.late.left## [1] 0.2717152CI.cp100.late.right## [1] 0.3143993just_cp_late.aov = aov(just_cp$Proportion.late.nesters[just_cp$Year == "2115"] ~ CP.f[just_cp$Year == "2115"])
summary(just_cp_late.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## CP.f[just_cp$Year == "2115"] 3 0.1364 0.04548 6.156 0.000509 ***
## Residuals 196 1.4480 0.00739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_cp_late.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_cp$Proportion.late.nesters[just_cp$Year == "2115"] ~ CP.f[just_cp$Year == "2115"])
##
## $`CP.f[just_cp$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 -0.01601865 -0.06056332 0.028526020 0.7878042
## 0.75-0.25 -0.04876459 -0.09330926 -0.004219921 0.0257282
## 1-0.25 -0.06621516 -0.11075983 -0.021670495 0.0009073
## 0.75-0.5 -0.03274594 -0.07729061 0.011798729 0.2293434
## 1-0.5 -0.05019652 -0.09474118 -0.005651846 0.0202596
## 1-0.75 -0.01745057 -0.06199524 0.027094095 0.7407923plot(TukeyHSD(just_cp_late.aov))
CP: prop_wild
boxplot(just_cp$Proportion.wild.born[just_cp$Year=="2115"]~just_cp$CP[just_cp$Year=="2115"], xlab="collection probability", ylab="proportion of wild born", axes=F)
axis(1, at=1:4, lab=c("0.25","0.5","0.75","1.0"))
axis(2)
summary(just_cp$Proportion.wild.born[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5604 0.5894 0.6136 0.6108 0.6331 0.6712sd.cp025.wild <- sd (just_cp$Proportion.wild.born[just_cp$File.name=="CP_025" &
just_cp$Year=="2115"])
sd.cp025.wild## [1] 0.02811591error.cp025.wild <- qt(0.975,
df=length(just_cp$Proportion.wild.born[just_cp$File.name=="CP_025" & just_cp$Year=="2115"])-1)*sd.cp025.wild/sqrt(length(just_cp$Proportion.wild.born[just_cp$File.name=="CP_025" & just_cp$Year=="2115"]))
CI.cp025.wild.left <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="CP_025"& just_cp$Year=="2115"])-error.cp025.wild
CI.cp025.wild.right <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="CP_025"& just_cp$Year=="2115"])+error.cp025.wild
CI.cp025.wild.left## [1] 0.6027743CI.cp025.wild.right## [1] 0.6187552summary(just_cp$Proportion.wild.born[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5739 0.6375 0.6511 0.6496 0.6653 0.7081sd.cp050.wild <- sd (just_cp$Proportion.wild.born[just_cp$File.name=="default.w.CR0.25" &
just_cp$Year=="2115"])
sd.cp050.wild## [1] 0.0277858error.cp050.wild <- qt(0.975,
df=length(just_cp$Proportion.wild.born[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"])-1)*sd.cp050.wild/sqrt(length(just_cp$Proportion.wild.born[just_cp$File.name=="default.w.CR0.25" & just_cp$Year=="2115"]))
CI.cp050.wild.left <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="default.w.CR0.25"& just_cp$Year=="2115"])-error.cp050.wild
CI.cp050.wild.right <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="default.w.CR0.25"& just_cp$Year=="2115"])+error.cp050.wild
CI.cp050.wild.left## [1] 0.6417381CI.cp050.wild.right## [1] 0.6575313summary(just_cp$Proportion.wild.born[just_cp$File.name=="CP_075" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6516 0.6739 0.6863 0.6908 0.7045 0.7483sd.cp075.wild <- sd (just_cp$Proportion.wild.born[just_cp$File.name=="CP_075" &
just_cp$Year=="2115"])
sd.cp075.wild## [1] 0.02098683error.cp075.wild <- qt(0.975,
df=length(just_cp$Proportion.wild.born[just_cp$File.name=="CP_075" & just_cp$Year=="2115"])-1)*sd.cp075.wild/sqrt(length(just_cp$Proportion.wild.born[just_cp$File.name=="CP_075" & just_cp$Year=="2115"]))
CI.cp075.wild.left <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="CP_075"& just_cp$Year=="2115"])-error.cp075.wild
CI.cp075.wild.right <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="CP_075"& just_cp$Year=="2115"])+error.cp075.wild
CI.cp075.wild.left## [1] 0.6848332CI.cp075.wild.right## [1] 0.696762summary(just_cp$Proportion.wild.born[just_cp$File.name=="CP_100" & just_cp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6567 0.6939 0.7030 0.7068 0.7214 0.7752sd.cp100.wild <- sd (just_cp$Proportion.wild.born[just_cp$File.name=="CP_100" &
just_cp$Year=="2115"])
sd.cp100.wild## [1] 0.02129832error.cp100.wild <- qt(0.975,
df=length(just_cp$Proportion.wild.born[just_cp$File.name=="CP_100" & just_cp$Year=="2115"])-1)*sd.cp100.wild/sqrt(length(just_cp$Proportion.wild.born[just_cp$File.name=="CP_100" & just_cp$Year=="2115"]))
CI.cp100.wild.left <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="CP_100"& just_cp$Year=="2115"])-error.cp100.wild
CI.cp100.wild.right <- mean(just_cp$Proportion.wild.born[just_cp$File.name=="CP_100"& just_cp$Year=="2115"])+error.cp100.wild
CI.cp100.wild.left## [1] 0.7007157CI.cp100.wild.right## [1] 0.7128216just_cp_wild.aov = aov(just_cp$Proportion.wild.born[just_cp$Year == "2115"] ~ CP.f[just_cp$Year == "2115"])
summary(just_cp_wild.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## CP.f[just_cp$Year == "2115"] 3 0.2793 0.09311 151.6 <2e-16 ***
## Residuals 196 0.1204 0.00061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_cp_wild.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_cp$Proportion.wild.born[just_cp$Year == "2115"] ~ CP.f[just_cp$Year == "2115"])
##
## $`CP.f[just_cp$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 0.03886993 0.026026791 0.05171307 0.000000
## 0.75-0.25 0.08003279 0.067189654 0.09287594 0.000000
## 1-0.25 0.09600388 0.083160741 0.10884702 0.000000
## 0.75-0.5 0.04116286 0.028319722 0.05400600 0.000000
## 1-0.5 0.05713395 0.044290809 0.06997709 0.000000
## 1-0.75 0.01597109 0.003127946 0.02881423 0.008053plot(TukeyHSD(just_cp_wild.aov))
Prop early nesters (EN)
extract_en <- function(filename) {
m <- regexec("EN_([0-9]+)", as.character(filename));
if (m[[1]][1] >= 0) {
as.integer(regmatches(filename, m)[[1]][2])/100.0;
} else {
0.5;
}
}
successful$EN <- sapply(successful$File.name, extract_en)
# plot(successful$EN ~ successful$File.name, las=2, xlab="")
just_en <- subset(successful, successful$File.name == "EN_025" | successful$File.name == "default.w.CR0.25" | successful$File.name == "EN_075")
EN.f <- factor(just_en$EN)EN: N
boxplot(just_en$Population.size[just_en$Year=="2115"]~just_en$EN[just_en$Year=="2115"], axes=F, xlab = "proportion of early nesters", ylab="N")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_en$Population.size[just_en$File.name=="EN_025" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 275.0 287.0 291.0 290.7 295.8 300.0sd.en025.N <- sd (just_en$Population.size[just_en$File.name=="EN_025" &
just_en$Year=="2115"])
sd.en025.N## [1] 6.031381error.en025.N <- qt(0.975,
df=length(just_en$Population.size[just_en$File.name=="EN_025" & just_en$Year=="2115"])-1)*sd.en025.N/sqrt(length(just_en$Population.size[just_en$File.name=="EN_025" & just_en$Year=="2115"]))
CI.en025.N.left <- mean(just_en$Population.size[just_en$File.name=="EN_025"& just_en$Year=="2115"])-error.en025.N
CI.en025.N.right <- mean(just_en$Population.size[just_en$File.name=="EN_025"& just_en$Year=="2115"])+error.en025.N
CI.en025.N.left## [1] 288.9859CI.en025.N.right## [1] 292.4141summary(just_en$Population.size[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 254.0 285.0 290.5 288.6 294.0 300.0sd.en050.N <- sd (just_en$Population.size[just_en$File.name=="default.w.CR0.25" &
just_en$Year=="2115"])
sd.en050.N## [1] 8.458205error.en050.N <- qt(0.975,
df=length(just_en$Population.size[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"])-1)*sd.en050.N/sqrt(length(just_en$Population.size[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"]))
CI.en050.N.left <- mean(just_en$Population.size[just_en$File.name=="default.w.CR0.25"& just_en$Year=="2115"])-error.en050.N
CI.en050.N.right <- mean(just_en$Population.size[just_en$File.name=="default.w.CR0.25"& just_en$Year=="2115"])+error.en050.N
CI.en050.N.left## [1] 286.2362CI.en050.N.right## [1] 291.0438summary(just_en$Population.size[just_en$File.name=="EN_075" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 277.0 284.0 291.5 290.3 296.8 300.0sd.en075.N <- sd (just_en$Population.size[just_en$File.name=="EN_075" &
just_en$Year=="2115"])
sd.en075.N## [1] 6.877381error.en075.N <- qt(0.975,
df=length(just_en$Population.size[just_en$File.name=="EN_075" & just_en$Year=="2115"])-1)*sd.en075.N/sqrt(length(just_en$Population.size[just_en$File.name=="EN_075" & just_en$Year=="2115"]))
CI.en075.N.left <- mean(just_en$Population.size[just_en$File.name=="EN_075"& just_en$Year=="2115"])-error.en075.N
CI.en075.N.right <- mean(just_en$Population.size[just_en$File.name=="EN_075"& just_en$Year=="2115"])+error.en075.N
CI.en075.N.left## [1] 288.3055CI.en075.N.right## [1] 292.2145just_en_N.aov = aov(just_en$Population.size[just_en$Year == "2115"] ~ EN.f[just_en$Year == "2115"])
summary(just_en_N.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## EN.f[just_en$Year == "2115"] 2 118 58.85 1.137 0.323
## Residuals 147 7606 51.74TukeyHSD(just_en_N.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_en$Population.size[just_en$Year == "2115"] ~ EN.f[just_en$Year == "2115"])
##
## $`EN.f[just_en$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 -2.06 -5.466157 1.346157 0.3271969
## 0.75-0.25 -0.44 -3.846157 2.966157 0.9497596
## 0.75-0.5 1.62 -1.786157 5.026157 0.4995741plot(TukeyHSD(just_en_N.aov))
EN: late
boxplot(just_en$Proportion.late.nesters[just_en$Year=="2115"]~just_en$EN[just_en$Year=="2115"], axes=F, xlab = "proportion of early nesters", ylab="proportion of late nesters")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_en$Proportion.late.nesters[just_en$File.name=="EN_025" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2400 0.3633 0.4139 0.4215 0.4605 0.5705sd.en025.late <- sd (just_en$Proportion.late.nesters[just_en$File.name=="EN_025" &
just_en$Year=="2115"])
sd.en025.late## [1] 0.07453999error.en025.late <- qt(0.975,
df=length(just_en$Proportion.late.nesters[just_en$File.name=="EN_025" & just_en$Year=="2115"])-1)*sd.en025.late/sqrt(length(just_en$Proportion.late.nesters[just_en$File.name=="EN_025" & just_en$Year=="2115"]))
CI.en025.late.left <- mean(just_en$Proportion.late.nesters[just_en$File.name=="EN_025"& just_en$Year=="2115"])-error.en025.late
CI.en025.late.right <- mean(just_en$Proportion.late.nesters[just_en$File.name=="EN_025"& just_en$Year=="2115"])+error.en025.late
CI.en025.late.left## [1] 0.4003416CI.en025.late.right## [1] 0.4427097summary(just_en$Proportion.late.nesters[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1111 0.2998 0.3431 0.3433 0.4045 0.4777sd.en050.late <- sd (just_en$Proportion.late.nesters[just_en$File.name=="default.w.CR0.25" &
just_en$Year=="2115"])
sd.en050.late## [1] 0.08282624error.en050.late <- qt(0.975,
df=length(just_en$Proportion.late.nesters[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"])-1)*sd.en050.late/sqrt(length(just_en$Proportion.late.nesters[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"]))
CI.en050.late.left <- mean(just_en$Proportion.late.nesters[just_en$File.name=="default.w.CR0.25"& just_en$Year=="2115"])-error.en050.late
CI.en050.late.right <- mean(just_en$Proportion.late.nesters[just_en$File.name=="default.w.CR0.25"& just_en$Year=="2115"])+error.en050.late
CI.en050.late.left## [1] 0.3197148CI.en050.late.right## [1] 0.3667927summary(just_en$Proportion.late.nesters[just_en$File.name=="EN_075" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.06333 0.13840 0.21940 0.21430 0.27320 0.42010sd.en075.late <- sd (just_en$Proportion.late.nesters[just_en$File.name=="EN_075" &
just_en$Year=="2115"])
sd.en075.late## [1] 0.08642984error.en075.late <- qt(0.975,
df=length(just_en$Proportion.late.nesters[just_en$File.name=="EN_075" & just_en$Year=="2115"])-1)*sd.en075.late/sqrt(length(just_en$Proportion.late.nesters[just_en$File.name=="EN_075" & just_en$Year=="2115"]))
CI.en075.late.left <- mean(just_en$Proportion.late.nesters[just_en$File.name=="EN_075"& just_en$Year=="2115"])-error.en075.late
CI.en075.late.right <- mean(just_en$Proportion.late.nesters[just_en$File.name=="EN_075"& just_en$Year=="2115"])+error.en075.late
CI.en075.late.left## [1] 0.1897005CI.en075.late.right## [1] 0.2388266just_en_late.aov = aov(just_en$Proportion.late.nesters[just_en$Year == "2115"] ~ EN.f[just_en$Year == "2115"])
summary(just_en_late.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## EN.f[just_en$Year == "2115"] 2 1.0954 0.5477 82.62 <2e-16 ***
## Residuals 147 0.9744 0.0066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_en_late.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_en$Proportion.late.nesters[just_en$Year == "2115"] ~ EN.f[just_en$Year == "2115"])
##
## $`EN.f[just_en$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 -0.07827189 -0.1168263 -0.03971747 1.12e-05
## 0.75-0.25 -0.20726212 -0.2458165 -0.16870770 0.00e+00
## 0.75-0.5 -0.12899024 -0.1675447 -0.09043582 0.00e+00plot(TukeyHSD(just_en_late.aov))
EN: wild
boxplot(just_en$Proportion.wild.born[just_en$Year=="2115"]~just_en$EN[just_en$Year=="2115"], axes=F, xlab = "proportion of early nesters", ylab="proportion of wild born")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_en$Proportion.wild.born[just_en$File.name=="EN_025" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6170 0.6497 0.6717 0.6673 0.6852 0.7099sd.en025.wild <- sd (just_en$Proportion.wild.born[just_en$File.name=="EN_025" &
just_en$Year=="2115"])
sd.en025.wild## [1] 0.02430266error.en025.wild <- qt(0.975,
df=length(just_en$Proportion.wild.born[just_en$File.name=="EN_025" & just_en$Year=="2115"])-1)*sd.en025.wild/sqrt(length(just_en$Proportion.wild.born[just_en$File.name=="EN_025" & just_en$Year=="2115"]))
CI.en025.wild.left <- mean(just_en$Proportion.wild.born[just_en$File.name=="EN_025"& just_en$Year=="2115"])-error.en025.wild
CI.en025.wild.right <- mean(just_en$Proportion.wild.born[just_en$File.name=="EN_025"& just_en$Year=="2115"])+error.en025.wild
CI.en025.wild.left## [1] 0.6603867CI.en025.wild.right## [1] 0.6742001summary(just_en$Proportion.wild.born[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5739 0.6375 0.6511 0.6496 0.6653 0.7081sd.en050.wild <- sd (just_en$Proportion.wild.born[just_en$File.name=="default.w.CR0.25" &
just_en$Year=="2115"])
sd.en050.wild## [1] 0.0277858error.en050.wild <- qt(0.975,
df=length(just_en$Proportion.wild.born[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"])-1)*sd.en050.wild/sqrt(length(just_en$Proportion.wild.born[just_en$File.name=="default.w.CR0.25" & just_en$Year=="2115"]))
CI.en050.wild.left <- mean(just_en$Proportion.wild.born[just_en$File.name=="default.w.CR0.25"& just_en$Year=="2115"])-error.en050.wild
CI.en050.wild.right <- mean(just_en$Proportion.wild.born[just_en$File.name=="default.w.CR0.25"& just_en$Year=="2115"])+error.en050.wild
CI.en050.wild.left## [1] 0.6417381CI.en050.wild.right## [1] 0.6575313summary(just_en$Proportion.wild.born[just_en$File.name=="EN_075" & just_en$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6028 0.6355 0.6477 0.6479 0.6619 0.6929sd.en075.wild <- sd (just_en$Proportion.wild.born[just_en$File.name=="EN_075" &
just_en$Year=="2115"])
sd.en075.wild## [1] 0.02231972error.en075.wild <- qt(0.975,
df=length(just_en$Proportion.wild.born[just_en$File.name=="EN_075" & just_en$Year=="2115"])-1)*sd.en075.wild/sqrt(length(just_en$Proportion.wild.born[just_en$File.name=="EN_075" & just_en$Year=="2115"]))
CI.en075.wild.left <- mean(just_en$Proportion.wild.born[just_en$File.name=="EN_075"& just_en$Year=="2115"])-error.en075.wild
CI.en075.wild.right <- mean(just_en$Proportion.wild.born[just_en$File.name=="EN_075"& just_en$Year=="2115"])+error.en075.wild
CI.en075.wild.left## [1] 0.6415412CI.en075.wild.right## [1] 0.6542276just_en_wild.aov = aov(just_en$Proportion.wild.born[just_en$Year == "2115"] ~ EN.f[just_en$Year == "2115"])
summary(just_en_wild.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## EN.f[just_en$Year == "2115"] 2 0.01153 0.005763 9.292 0.000159 ***
## Residuals 147 0.09118 0.000620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_en_wild.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_en$Proportion.wild.born[just_en$Year == "2115"] ~ EN.f[just_en$Year == "2115"])
##
## $`EN.f[just_en$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 -0.017658686 -0.02945237 -0.005865007 0.0015198
## 0.75-0.25 -0.019409017 -0.03120270 -0.007615337 0.0004321
## 0.75-0.5 -0.001750331 -0.01354401 0.010043349 0.9342343plot(TukeyHSD(just_en_wild.aov))
Release count (RC)
extract_rc <- function(filename) {
m <- regexec("RC_([0-9]+)", as.character(filename));
if (m[[1]][1] >= 0) {
as.integer(regmatches(filename, m)[[1]][2]);
} else {
6;
}
}
# all3$RC <- sapply(all3$File.name, extract_rc)
successful$RC <- sapply(successful$File.name, extract_rc)
# plot(successful$RC ~ successful$File.name, las=2, xlab="")
just_rc <- subset(successful, successful$File.name == "RC_00" | successful$File.name == "RC_05" | successful$File.name == "default.w.CR0.25" | successful$File.name == "RC_10" | successful$File.name == "RC_15")
RC.f <- factor(just_rc$RC)RC: N
boxplot(just_rc$Population.size[just_rc$Year=="2115"]~just_rc$RC[just_rc$Year=="2115"], axes=F, xlab = "release count", ylab = "N")
axis(1, at=1:4, lab=c("0","6","10", "15"))
axis(2)
summary(just_rc$Population.size[just_rc$File.name=="RC_00" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 102 102 102 102 102 102sd.rc00.N <- sd (just_rc$Population.size[just_rc$File.name=="RC_00" &
just_rc$Year=="2115"])
sd.rc00.N## [1] NAerror.rc00.N <- qt(0.975,
df=length(just_rc$Population.size[just_rc$File.name=="RC_00" & just_rc$Year=="2115"])-1)*sd.rc00.N/sqrt(length(just_rc$Population.size[just_rc$File.name=="RC_00" & just_rc$Year=="2115"]))## Warning in qt(0.975, df = length(just_rc$Population.size[just_rc$File.name
## == : NaNs producedCI.rc00.N.left <- mean(just_rc$Population.size[just_rc$File.name=="RC_00"& just_rc$Year=="2115"])-error.rc00.N
CI.rc00.N.right <- mean(just_rc$Population.size[just_rc$File.name=="RC_00"& just_rc$Year=="2115"])+error.rc00.N
CI.rc00.N.left## [1] NaNCI.rc00.N.right## [1] NaNsummary(just_rc$Population.size[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 254.0 285.0 290.5 288.6 294.0 300.0sd.rc06.N <- sd (just_rc$Population.size[just_rc$File.name=="default.w.CR0.25" &
just_rc$Year=="2115"])
sd.rc06.N## [1] 8.458205error.rc06.N <- qt(0.975,
df=length(just_rc$Population.size[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"])-1)*sd.rc06.N/sqrt(length(just_rc$Population.size[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"]))
CI.rc06.N.left <- mean(just_rc$Population.size[just_rc$File.name=="default.w.CR0.25"& just_rc$Year=="2115"])-error.rc06.N
CI.rc06.N.right <- mean(just_rc$Population.size[just_rc$File.name=="default.w.CR0.25"& just_rc$Year=="2115"])+error.rc06.N
CI.rc06.N.left## [1] 286.2362CI.rc06.N.right## [1] 291.0438summary(just_rc$Population.size[just_rc$File.name=="RC_10" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 280.0 289.0 292.0 291.9 296.0 300.0sd.rc10.N <- sd (just_rc$Population.size[just_rc$File.name=="RC_10" &
just_rc$Year=="2115"])
sd.rc10.N## [1] 5.23493error.rc10.N <- qt(0.975,
df=length(just_rc$Population.size[just_rc$File.name=="RC_10" & just_rc$Year=="2115"])-1)*sd.rc10.N/sqrt(length(just_rc$Population.size[just_rc$File.name=="RC_10" & just_rc$Year=="2115"]))
CI.rc10.N.left <- mean(just_rc$Population.size[just_rc$File.name=="RC_10"& just_rc$Year=="2115"])-error.rc10.N
CI.rc10.N.right <- mean(just_rc$Population.size[just_rc$File.name=="RC_10"& just_rc$Year=="2115"])+error.rc10.N
CI.rc10.N.left## [1] 290.4522CI.rc10.N.right## [1] 293.4278summary(just_rc$Population.size[just_rc$File.name=="RC_15" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 281.0 289.2 292.0 292.0 296.0 300.0sd.rc15.N <- sd (just_rc$Population.size[just_rc$File.name=="RC_15" &
just_rc$Year=="2115"])
sd.rc15.N## [1] 4.742169error.rc15.N <- qt(0.975,
df=length(just_rc$Population.size[just_rc$File.name=="RC_15" & just_rc$Year=="2115"])-1)*sd.rc15.N/sqrt(length(just_rc$Population.size[just_rc$File.name=="RC_15" & just_rc$Year=="2115"]))
CI.rc15.N.left <- mean(just_rc$Population.size[just_rc$File.name=="RC_15"& just_rc$Year=="2115"])-error.rc15.N
CI.rc15.N.right <- mean(just_rc$Population.size[just_rc$File.name=="RC_15"& just_rc$Year=="2115"])+error.rc15.N
CI.rc15.N.left## [1] 290.6923CI.rc15.N.right## [1] 293.3877just_rc_N.aov = aov(just_rc$Population.size[just_rc$Year == "2115"] ~ RC.f[just_rc$Year == "2115"])
summary(just_rc_N.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## RC.f[just_rc$Year == "2115"] 3 35811 11937 294.9 <2e-16 ***
## Residuals 147 5950 40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_rc_N.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_rc$Population.size[just_rc$Year == "2115"] ~ RC.f[just_rc$Year == "2115"])
##
## $`RC.f[just_rc$Year == "2115"]`
## diff lwr upr p adj
## 6-0 186.64 169.942203979 203.337796 0.0000000
## 10-0 189.94 173.242203979 206.637796 0.0000000
## 15-0 190.04 173.342203979 206.737796 0.0000000
## 10-6 3.30 -0.006656341 6.606656 0.0506734
## 15-6 3.40 0.093343659 6.706656 0.0413299
## 15-10 0.10 -3.206656341 3.406656 0.9998252plot(TukeyHSD(just_rc_N.aov))
RC: late
boxplot(just_rc$Proportion.late.nesters[just_rc$Year=="2115"]~just_rc$RC[just_rc$Year=="2115"], axes=F, xlab = "release count", ylab = "proportion late nesters")
axis(1, at=1:4, lab=c("0","6","10", "15"))
axis(2)
summary(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_00" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9314 0.9314 0.9314 0.9314 0.9314 0.9314sd.rc00.late <- sd (just_rc$Proportion.late.nesters[just_rc$File.name=="RC_00" &
just_rc$Year=="2115"])
sd.rc00.late## [1] NAerror.rc00.late <- qt(0.975,
df=length(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_00" & just_rc$Year=="2115"])-1)*sd.rc00.late/sqrt(length(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_00" & just_rc$Year=="2115"]))## Warning in qt(0.975, df = length(just_rc$Proportion.late.nesters[just_rc
## $File.name == : NaNs producedCI.rc00.late.left <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_00"& just_rc$Year=="2115"])-error.rc00.late
CI.rc00.late.right <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_00"& just_rc$Year=="2115"])+error.rc00.late
CI.rc00.late.left## [1] NaNCI.rc00.late.right## [1] NaNsummary(just_rc$Proportion.late.nesters[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1111 0.2998 0.3431 0.3433 0.4045 0.4777sd.rc06.late <- sd (just_rc$Proportion.late.nesters[just_rc$File.name=="default.w.CR0.25" &
just_rc$Year=="2115"])
sd.rc06.late## [1] 0.08282624error.rc06.late <- qt(0.975,
df=length(just_rc$Proportion.late.nesters[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"])-1)*sd.rc06.late/sqrt(length(just_rc$Proportion.late.nesters[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"]))
CI.rc06.late.left <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="default.w.CR0.25"& just_rc$Year=="2115"])-error.rc06.late
CI.rc06.late.right <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="default.w.CR0.25"& just_rc$Year=="2115"])+error.rc06.late
CI.rc06.late.left## [1] 0.3197148CI.rc06.late.right## [1] 0.3667927summary(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_10" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1279 0.2599 0.2889 0.2880 0.3263 0.4765sd.rc10.late <- sd (just_rc$Proportion.late.nesters[just_rc$File.name=="RC_10" &
just_rc$Year=="2115"])
sd.rc10.late## [1] 0.0644555error.rc10.late <- qt(0.975,
df=length(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_10" & just_rc$Year=="2115"])-1)*sd.rc10.late/sqrt(length(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_10" & just_rc$Year=="2115"]))
CI.rc10.late.left <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_10"& just_rc$Year=="2115"])-error.rc10.late
CI.rc10.late.right <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_10"& just_rc$Year=="2115"])+error.rc10.late
CI.rc10.late.left## [1] 0.2696403CI.rc10.late.right## [1] 0.3062764summary(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_15" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.03082 0.17520 0.20480 0.20960 0.25280 0.30740sd.rc15.late <- sd (just_rc$Proportion.late.nesters[just_rc$File.name=="RC_15" &
just_rc$Year=="2115"])
sd.rc15.late## [1] 0.05725961error.rc15.late <- qt(0.975,
df=length(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_15" & just_rc$Year=="2115"])-1)*sd.rc15.late/sqrt(length(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_15" & just_rc$Year=="2115"]))
CI.rc15.late.left <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_15"& just_rc$Year=="2115"])-error.rc15.late
CI.rc15.late.right <- mean(just_rc$Proportion.late.nesters[just_rc$File.name=="RC_15"& just_rc$Year=="2115"])+error.rc15.late
CI.rc15.late.left## [1] 0.1933189CI.rc15.late.right## [1] 0.2258649just_rc_late.aov = aov(just_rc$Proportion.late.nesters[just_rc$Year == "2115"] ~ RC.f[just_rc$Year == "2115"])
summary(just_rc_late.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## RC.f[just_rc$Year == "2115"] 3 0.8722 0.29073 61.02 <2e-16 ***
## Residuals 147 0.7004 0.00476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_rc_late.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_rc$Proportion.late.nesters[just_rc$Year == "2115"] ~ RC.f[just_rc$Year == "2115"])
##
## $`RC.f[just_rc$Year == "2115"]`
## diff lwr upr p adj
## 6-0 -0.58811876 -0.76927629 -0.40696122 0.0000000
## 10-0 -0.64341421 -0.82457174 -0.46225668 0.0000000
## 15-0 -0.72178061 -0.90293814 -0.54062307 0.0000000
## 10-6 -0.05529545 -0.09116999 -0.01942092 0.0005626
## 15-6 -0.13366185 -0.16953639 -0.09778731 0.0000000
## 15-10 -0.07836640 -0.11424093 -0.04249186 0.0000004plot(TukeyHSD(just_rc_late.aov))
RC: wild
boxplot(just_rc$Proportion.wild.born[just_rc$Year=="2115"]~just_rc$RC[just_rc$Year=="2115"], axes=F, xlab = "release count", ylab = "proportion wild born")
axis(1, at=1:4, lab=c("0","6","10", "15"))
axis(2)
summary(just_rc$Proportion.wild.born[just_rc$File.name=="RC_00" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1sd.rc00.wild <- sd (just_rc$Proportion.wild.born[just_rc$File.name=="RC_00" &
just_rc$Year=="2115"])
sd.rc00.wild## [1] NAerror.rc00.wild <- qt(0.975,
df=length(just_rc$Proportion.wild.born[just_rc$File.name=="RC_00" & just_rc$Year=="2115"])-1)*sd.rc00.wild/sqrt(length(just_rc$Proportion.wild.born[just_rc$File.name=="RC_00" & just_rc$Year=="2115"]))## Warning in qt(0.975, df = length(just_rc$Proportion.wild.born[just_rc
## $File.name == : NaNs producedCI.rc00.wild.left <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="RC_00"& just_rc$Year=="2115"])-error.rc00.wild
CI.rc00.wild.right <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="RC_00"& just_rc$Year=="2115"])+error.rc00.wild
CI.rc00.wild.left## [1] NaNCI.rc00.wild.right## [1] NaNsummary(just_rc$Proportion.wild.born[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5739 0.6375 0.6511 0.6496 0.6653 0.7081sd.rc06.wild <- sd (just_rc$Proportion.wild.born[just_rc$File.name=="default.w.CR0.25" &
just_rc$Year=="2115"])
sd.rc06.wild## [1] 0.0277858error.rc06.wild <- qt(0.975,
df=length(just_rc$Proportion.wild.born[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"])-1)*sd.rc06.wild/sqrt(length(just_rc$Proportion.wild.born[just_rc$File.name=="default.w.CR0.25" & just_rc$Year=="2115"]))
CI.rc06.wild.left <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="default.w.CR0.25"& just_rc$Year=="2115"])-error.rc06.wild
CI.rc06.wild.right <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="default.w.CR0.25"& just_rc$Year=="2115"])+error.rc06.wild
CI.rc06.wild.left## [1] 0.6417381CI.rc06.wild.right## [1] 0.6575313summary(just_rc$Proportion.wild.born[just_rc$File.name=="RC_10" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4710 0.5214 0.5378 0.5382 0.5570 0.5898sd.rc10.wild <- sd (just_rc$Proportion.wild.born[just_rc$File.name=="RC_10" &
just_rc$Year=="2115"])
sd.rc10.wild## [1] 0.02719695error.rc10.wild <- qt(0.975,
df=length(just_rc$Proportion.wild.born[just_rc$File.name=="RC_10" & just_rc$Year=="2115"])-1)*sd.rc10.wild/sqrt(length(just_rc$Proportion.wild.born[just_rc$File.name=="RC_10" & just_rc$Year=="2115"]))
CI.rc10.wild.left <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="RC_10"& just_rc$Year=="2115"])-error.rc10.wild
CI.rc10.wild.right <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="RC_10"& just_rc$Year=="2115"])+error.rc10.wild
CI.rc10.wild.left## [1] 0.5304617CI.rc10.wild.right## [1] 0.5459203summary(just_rc$Proportion.wild.born[just_rc$File.name=="RC_15" & just_rc$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3514 0.4010 0.4122 0.4151 0.4372 0.4744sd.rc15.wild <- sd (just_rc$Proportion.wild.born[just_rc$File.name=="RC_15" &
just_rc$Year=="2115"])
sd.rc15.wild## [1] 0.0262669error.rc15.wild <- qt(0.975,
df=length(just_rc$Proportion.wild.born[just_rc$File.name=="RC_15" & just_rc$Year=="2115"])-1)*sd.rc15.wild/sqrt(length(just_rc$Proportion.wild.born[just_rc$File.name=="RC_15" & just_rc$Year=="2115"]))
CI.rc15.wild.left <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="RC_15"& just_rc$Year=="2115"])-error.rc15.wild
CI.rc15.wild.right <- mean(just_rc$Proportion.wild.born[just_rc$File.name=="RC_15"& just_rc$Year=="2115"])+error.rc15.wild
CI.rc15.wild.left## [1] 0.4076446CI.rc15.wild.right## [1] 0.4225745just_rc_wild.aov = aov(just_rc$Proportion.wild.born[just_rc$Year == "2115"] ~ RC.f[just_rc$Year == "2115"])
summary(just_rc_wild.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## RC.f[just_rc$Year == "2115"] 3 1.5916 0.5305 722.9 <2e-16 ***
## Residuals 147 0.1079 0.0007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_rc_wild.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_rc$Proportion.wild.born[just_rc$Year == "2115"] ~ RC.f[just_rc$Year == "2115"])
##
## $`RC.f[just_rc$Year == "2115"]`
## diff lwr upr p adj
## 6-0 -0.3503653 -0.4214647 -0.27926592 0
## 10-0 -0.4618090 -0.5329084 -0.39070963 0
## 15-0 -0.5848905 -0.6559898 -0.51379108 0
## 10-6 -0.1114437 -0.1255235 -0.09736394 0
## 15-6 -0.2345252 -0.2486049 -0.22044539 0
## 15-10 -0.1230815 -0.1371612 -0.10900168 0plot(TukeyHSD(just_rc_wild.aov))
Nesting probability (NP)
extract_np <- function(filename) {
m <- regexec("NP_([0-9]+)", as.character(filename));
if (m[[1]][1] >= 0) {
as.integer(regmatches(filename, m)[[1]][2])/100.0;
} else {
0.5;
}
}
successful$NP <- sapply(successful$File.name, extract_np)
# plot(successful$NP ~ successful$File.name, las=2, xlab="")
just_np <- subset(successful, successful$File.name == "NP_025" | successful$File.name == "default.w.CR0.25" | successful$File.name == "NP_075")
NP.f <- factor(just_np$NP)NP: N
boxplot(just_np$Population.size[just_np$Year=="2115"]~just_np$NP[just_np$Year=="2115"], axes=F, xlab = "nesting probability", ylab = "N")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_np$Population.size[just_np$File.name=="NP_025" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 152.0 175.0 181.5 181.3 188.0 210.0sd.np025.N <- sd (just_np$Population.size[just_np$File.name=="NP_025" &
just_np$Year=="2115"])
sd.np025.N## [1] 12.43039error.np025.N <- qt(0.975,
df=length(just_np$Population.size[just_np$File.name=="NP_025" & just_np$Year=="2115"])-1)*sd.np025.N/sqrt(length(just_np$Population.size[just_np$File.name=="NP_025" & just_np$Year=="2115"]))
CI.np025.N.left <- mean(just_np$Population.size[just_np$File.name=="NP_025"& just_np$Year=="2115"])-error.np025.N
CI.np025.N.right <- mean(just_np$Population.size[just_np$File.name=="NP_025"& just_np$Year=="2115"])+error.np025.N
CI.np025.N.left## [1] 177.8073CI.np025.N.right## [1] 184.8727summary(just_np$Population.size[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 254.0 285.0 290.5 288.6 294.0 300.0sd.np050.N <- sd (just_np$Population.size[just_np$File.name=="default.w.CR0.25" &
just_np$Year=="2115"])
sd.np050.N## [1] 8.458205error.np050.N <- qt(0.975,
df=length(just_np$Population.size[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"])-1)*sd.np050.N/sqrt(length(just_np$Population.size[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"]))
CI.np050.N.left <- mean(just_np$Population.size[just_np$File.name=="default.w.CR0.25"& just_np$Year=="2115"])-error.np050.N
CI.np050.N.right <- mean(just_np$Population.size[just_np$File.name=="default.w.CR0.25"& just_np$Year=="2115"])+error.np050.N
CI.np050.N.left## [1] 286.2362CI.np050.N.right## [1] 291.0438summary(just_np$Population.size[just_np$File.name=="NP_075" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 279.0 288.0 292.0 291.8 295.8 299.0sd.np075.N <- sd (just_np$Population.size[just_np$File.name=="NP_075" &
just_np$Year=="2115"])
sd.np075.N## [1] 5.06162error.np075.N <- qt(0.975,
df=length(just_np$Population.size[just_np$File.name=="NP_075" & just_np$Year=="2115"])-1)*sd.np075.N/sqrt(length(just_np$Population.size[just_np$File.name=="NP_075" & just_np$Year=="2115"]))
CI.np075.N.left <- mean(just_np$Population.size[just_np$File.name=="NP_075"& just_np$Year=="2115"])-error.np075.N
CI.np075.N.right <- mean(just_np$Population.size[just_np$File.name=="NP_075"& just_np$Year=="2115"])+error.np075.N
CI.np075.N.left## [1] 290.3815CI.np075.N.right## [1] 293.2585just_np_N.aov = aov(just_np$Population.size[just_np$Year == "2115"] ~ NP.f[just_np$Year == "2115"])
summary(just_np_N.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## NP.f[just_np$Year == "2115"] 2 395487 197744 2357 <2e-16 ***
## Residuals 147 12332 84
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_np_N.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_np$Population.size[just_np$Year == "2115"] ~ NP.f[just_np$Year == "2115"])
##
## $`NP.f[just_np$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 107.30 102.962738 111.637262 0.0000000
## 0.75-0.25 110.48 106.142738 114.817262 0.0000000
## 0.75-0.5 3.18 -1.157262 7.517262 0.1953281plot(TukeyHSD(just_np_N.aov))
NP: late
boxplot(just_np$Proportion.late.nesters[just_np$Year=="2115"]~just_np$NP[just_np$Year=="2115"], axes=F, xlab = "nesting probability", ylab = "proportion of late nesters")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_np$Proportion.late.nesters[just_np$File.name=="NP_025" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1154 0.1917 0.2333 0.2395 0.2863 0.3901sd.np025.late <- sd (just_np$Proportion.late.nesters[just_np$File.name=="NP_025" &
just_np$Year=="2115"])
sd.np025.late## [1] 0.06005491error.np025.late <- qt(0.975,
df=length(just_np$Proportion.late.nesters[just_np$File.name=="NP_025" & just_np$Year=="2115"])-1)*sd.np025.late/sqrt(length(just_np$Proportion.late.nesters[just_np$File.name=="NP_025" & just_np$Year=="2115"]))
CI.np025.late.left <- mean(just_np$Proportion.late.nesters[just_np$File.name=="NP_025"& just_np$Year=="2115"])-error.np025.late
CI.np025.late.right <- mean(just_np$Proportion.late.nesters[just_np$File.name=="NP_025"& just_np$Year=="2115"])+error.np025.late
CI.np025.late.left## [1] 0.2223901CI.np025.late.right## [1] 0.2565249summary(just_np$Proportion.late.nesters[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1111 0.2998 0.3431 0.3433 0.4045 0.4777sd.np050.late <- sd (just_np$Proportion.late.nesters[just_np$File.name=="default.w.CR0.25" &
just_np$Year=="2115"])
sd.np050.late## [1] 0.08282624error.np050.late <- qt(0.975,
df=length(just_np$Proportion.late.nesters[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"])-1)*sd.np050.late/sqrt(length(just_np$Proportion.late.nesters[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"]))
CI.np050.late.left <- mean(just_np$Proportion.late.nesters[just_np$File.name=="default.w.CR0.25"& just_np$Year=="2115"])-error.np050.late
CI.np050.late.right <- mean(just_np$Proportion.late.nesters[just_np$File.name=="default.w.CR0.25"& just_np$Year=="2115"])+error.np050.late
CI.np050.late.left## [1] 0.3197148CI.np050.late.right## [1] 0.3667927summary(just_np$Proportion.late.nesters[just_np$File.name=="NP_075" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1649 0.3205 0.3755 0.3744 0.4288 0.6276sd.np075.late <- sd (just_np$Proportion.late.nesters[just_np$File.name=="NP_075" &
just_np$Year=="2115"])
sd.np075.late## [1] 0.09426914error.np075.late <- qt(0.975,
df=length(just_np$Proportion.late.nesters[just_np$File.name=="NP_075" & just_np$Year=="2115"])-1)*sd.np075.late/sqrt(length(just_np$Proportion.late.nesters[just_np$File.name=="NP_075" & just_np$Year=="2115"]))
CI.np075.late.left <- mean(just_np$Proportion.late.nesters[just_np$File.name=="NP_075"& just_np$Year=="2115"])-error.np075.late
CI.np075.late.right <- mean(just_np$Proportion.late.nesters[just_np$File.name=="NP_075"& just_np$Year=="2115"])+error.np075.late
CI.np075.late.left## [1] 0.3476033CI.np075.late.right## [1] 0.4011853just_np_late.aov = aov(just_np$Proportion.late.nesters[just_np$Year == "2115"] ~ NP.f[just_np$Year == "2115"])
summary(just_np_late.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## NP.f[just_np$Year == "2115"] 2 0.4992 0.24959 38.69 3.17e-14 ***
## Residuals 147 0.9483 0.00645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_np_late.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_np$Proportion.late.nesters[just_np$Year == "2115"] ~ NP.f[just_np$Year == "2115"])
##
## $`NP.f[just_np$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 0.10379627 0.065762089 0.14183044 0.000000
## 0.75-0.25 0.13493681 0.096902634 0.17297099 0.000000
## 0.75-0.5 0.03114055 -0.006893631 0.06917472 0.131573plot(TukeyHSD(just_np_late.aov))
NP: wild
boxplot(just_np$Proportion.wild.born[just_np$Year=="2115"]~just_np$NP[just_np$Year=="2115"], axes=F, xlab = "nesting probability", ylab = "proportion wild born")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_np$Proportion.wild.born[just_np$File.name=="NP_025" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2917 0.3490 0.3746 0.3760 0.4054 0.4629sd.np025.wild <- sd (just_np$Proportion.wild.born[just_np$File.name=="NP_025" &
just_np$Year=="2115"])
sd.np025.wild## [1] 0.04071909error.np025.wild <- qt(0.975,
df=length(just_np$Proportion.wild.born[just_np$File.name=="NP_025" & just_np$Year=="2115"])-1)*sd.np025.wild/sqrt(length(just_np$Proportion.wild.born[just_np$File.name=="NP_025" & just_np$Year=="2115"]))
CI.np025.wild.left <- mean(just_np$Proportion.wild.born[just_np$File.name=="NP_025"& just_np$Year=="2115"])-error.np025.wild
CI.np025.wild.right <- mean(just_np$Proportion.wild.born[just_np$File.name=="NP_025"& just_np$Year=="2115"])+error.np025.wild
CI.np025.wild.left## [1] 0.3644493CI.np025.wild.right## [1] 0.3875938summary(just_np$Proportion.wild.born[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5739 0.6375 0.6511 0.6496 0.6653 0.7081sd.np050.wild <- sd (just_np$Proportion.wild.born[just_np$File.name=="default.w.CR0.25" &
just_np$Year=="2115"])
sd.np050.wild## [1] 0.0277858error.np050.wild <- qt(0.975,
df=length(just_np$Proportion.wild.born[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"])-1)*sd.np050.wild/sqrt(length(just_np$Proportion.wild.born[just_np$File.name=="default.w.CR0.25" & just_np$Year=="2115"]))
CI.np050.wild.left <- mean(just_np$Proportion.wild.born[just_np$File.name=="default.w.CR0.25"& just_np$Year=="2115"])-error.np050.wild
CI.np050.wild.right <- mean(just_np$Proportion.wild.born[just_np$File.name=="default.w.CR0.25"& just_np$Year=="2115"])+error.np050.wild
CI.np050.wild.left## [1] 0.6417381CI.np050.wild.right## [1] 0.6575313summary(just_np$Proportion.wild.born[just_np$File.name=="NP_075" & just_np$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6952 0.7215 0.7387 0.7387 0.7535 0.7823sd.np075.wild <- sd (just_np$Proportion.wild.born[just_np$File.name=="NP_075" &
just_np$Year=="2115"])
sd.np075.wild## [1] 0.02134374error.np075.wild <- qt(0.975,
df=length(just_np$Proportion.wild.born[just_np$File.name=="NP_075" & just_np$Year=="2115"])-1)*sd.np075.wild/sqrt(length(just_np$Proportion.wild.born[just_np$File.name=="NP_075" & just_np$Year=="2115"]))
CI.np075.wild.left <- mean(just_np$Proportion.wild.born[just_np$File.name=="NP_075"& just_np$Year=="2115"])-error.np075.wild
CI.np075.wild.right <- mean(just_np$Proportion.wild.born[just_np$File.name=="NP_075"& just_np$Year=="2115"])+error.np075.wild
CI.np075.wild.left## [1] 0.7326354CI.np075.wild.right## [1] 0.744767just_np_wild.aov = aov(just_np$Proportion.wild.born[just_np$Year == "2115"] ~ NP.f[just_np$Year == "2115"])
summary(just_np_wild.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## NP.f[just_np$Year == "2115"] 2 3.572 1.786 1857 <2e-16 ***
## Residuals 147 0.141 0.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_np_wild.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_np$Proportion.wild.born[just_np$Year == "2115"] ~ NP.f[just_np$Year == "2115"])
##
## $`NP.f[just_np$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 0.27361314 0.25892669 0.2882996 0
## 0.75-0.25 0.36267965 0.34799320 0.3773661 0
## 0.75-0.5 0.08906651 0.07438007 0.1037530 0plot(TukeyHSD(just_np_wild.aov))
Renesting probability (RNP)
extract_rnp <- function(filename) {
m <- regexec("RNP_([0-9]+)", as.character(filename));
if (m[[1]][1] >= 0) {
as.integer(regmatches(filename, m)[[1]][2])/100.0;
} else {
0.5;
}
}
successful$RNP <- sapply(successful$File.name, extract_rnp)
# plot(successful$RNP ~ successful$File.name, las=2, xlab="")
just_rnp <- subset(successful, successful$File.name == "RNP_025" | successful$File.name == "default.w.CR0.25" | successful$File.name == "RNP_075")
RNP.f <- factor(just_rnp$RNP)RNP: N
boxplot(just_rnp$Population.size[just_rnp$Year=="2115"]~just_rnp$RNP[just_rnp$Year=="2115"], axes=F, xlab = "renesting probability", ylab = "N")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_rnp$Population.size[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 173.0 195.0 208.0 209.6 220.0 265.0sd.rnp025.N <- sd (just_rnp$Population.size[just_rnp$File.name=="RNP_025" &
just_rnp$Year=="2115"])
sd.rnp025.N## [1] 19.36554error.rnp025.N <- qt(0.975,
df=length(just_rnp$Population.size[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"])-1)*sd.rnp025.N/sqrt(length(just_rnp$Population.size[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"]))
CI.rnp025.N.left <- mean(just_rnp$Population.size[just_rnp$File.name=="RNP_025"& just_rnp$Year=="2115"])-error.rnp025.N
CI.rnp025.N.right <- mean(just_rnp$Population.size[just_rnp$File.name=="RNP_025"& just_rnp$Year=="2115"])+error.rnp025.N
CI.rnp025.N.left## [1] 204.0764CI.rnp025.N.right## [1] 215.0836summary(just_rnp$Population.size[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 254.0 285.0 290.5 288.6 294.0 300.0sd.rnp050.N <- sd (just_rnp$Population.size[just_rnp$File.name=="default.w.CR0.25" &
just_rnp$Year=="2115"])
sd.rnp050.N## [1] 8.458205error.rnp050.N <- qt(0.975,
df=length(just_rnp$Population.size[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"])-1)*sd.rnp050.N/sqrt(length(just_rnp$Population.size[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"]))
CI.rnp050.N.left <- mean(just_rnp$Population.size[just_rnp$File.name=="default.w.CR0.25"& just_rnp$Year=="2115"])-error.rnp050.N
CI.rnp050.N.right <- mean(just_rnp$Population.size[just_rnp$File.name=="default.w.CR0.25"& just_rnp$Year=="2115"])+error.rnp050.N
CI.rnp050.N.left## [1] 286.2362CI.rnp050.N.right## [1] 291.0438summary(just_rnp$Population.size[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 277.0 290.2 294.0 292.7 296.0 300.0sd.rnp075.N <- sd (just_rnp$Population.size[just_rnp$File.name=="RNP_075" &
just_rnp$Year=="2115"])
sd.rnp075.N## [1] 5.2668error.rnp075.N <- qt(0.975,
df=length(just_rnp$Population.size[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"])-1)*sd.rnp075.N/sqrt(length(just_rnp$Population.size[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"]))
CI.rnp075.N.left <- mean(just_rnp$Population.size[just_rnp$File.name=="RNP_075"& just_rnp$Year=="2115"])-error.rnp075.N
CI.rnp075.N.right <- mean(just_rnp$Population.size[just_rnp$File.name=="RNP_075"& just_rnp$Year=="2115"])+error.rnp075.N
CI.rnp075.N.left## [1] 291.1632CI.rnp075.N.right## [1] 294.1568just_rnp_N.aov = aov(just_rnp$Population.size[just_rnp$Year == "2115"] ~ RNP.f[just_rnp$Year == "2115"])
summary(just_rnp_N.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## RNP.f[just_rnp$Year == "2115"] 2 219482 109741 694.1 <2e-16 ***
## Residuals 147 23241 158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_rnp_N.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_rnp$Population.size[just_rnp$Year == "2115"] ~ RNP.f[just_rnp$Year == "2115"])
##
## $`RNP.f[just_rnp$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 79.06 73.1058 85.0142 0.0000000
## 0.75-0.25 83.08 77.1258 89.0342 0.0000000
## 0.75-0.5 4.02 -1.9342 9.9742 0.2494891plot(TukeyHSD(just_rnp_N.aov))
RNP: late
boxplot(just_rnp$Proportion.late.nesters[just_rnp$Year=="2115"]~just_rnp$RNP[just_rnp$Year=="2115"], axes=F, xlab = "renesting probability", ylab = "proportion late nesters")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1818 0.3350 0.3999 0.3860 0.4414 0.5556sd.rnp025.late <- sd (just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_025" &
just_rnp$Year=="2115"])
sd.rnp025.late## [1] 0.08777547error.rnp025.late <- qt(0.975,
df=length(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"])-1)*sd.rnp025.late/sqrt(length(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"]))
CI.rnp025.late.left <- mean(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_025"& just_rnp$Year=="2115"])-error.rnp025.late
CI.rnp025.late.right <- mean(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_025"& just_rnp$Year=="2115"])+error.rnp025.late
CI.rnp025.late.left## [1] 0.3610315CI.rnp025.late.right## [1] 0.4109225summary(just_rnp$Proportion.late.nesters[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1111 0.2998 0.3431 0.3433 0.4045 0.4777sd.rpn050.late <- sd (just_rnp$Proportion.late.nesters[just_rnp$File.name=="default.w.CR0.25" &
just_rnp$Year=="2115"])
sd.rpn050.late## [1] 0.08282624error.rpn050.late <- qt(0.975,
df=length(just_rnp$Proportion.late.nesters[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"])-1)*sd.rpn050.late/sqrt(length(just_rnp$Proportion.late.nesters[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"]))
CI.rpn050.late.left <- mean(just_rnp$Proportion.late.nesters[just_rnp$File.name=="default.w.CR0.25"& just_rnp$Year=="2115"])-error.rpn050.late
CI.rpn050.late.right <- mean(just_rnp$Proportion.late.nesters[just_rnp$File.name=="default.w.CR0.25"& just_rnp$Year=="2115"])+error.rpn050.late
CI.rpn050.late.left## [1] 0.3197148CI.rpn050.late.right## [1] 0.3667927summary(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1119 0.2156 0.2487 0.2590 0.3076 0.4533sd.rnp075.late <- sd (just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_075" &
just_rnp$Year=="2115"])
sd.rnp075.late## [1] 0.07658407error.rnp075.late <- qt(0.975,
df=length(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"])-1)*sd.rnp075.late/sqrt(length(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"]))
CI.rnp075.late.left <- mean(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_075"& just_rnp$Year=="2115"])-error.rnp075.late
CI.rnp075.late.right <- mean(just_rnp$Proportion.late.nesters[just_rnp$File.name=="RNP_075"& just_rnp$Year=="2115"])+error.rnp075.late
CI.rnp075.late.left## [1] 0.2372493CI.rnp075.late.right## [1] 0.2807792just_rnp_late.aov = aov(just_rnp$Proportion.late.nesters[just_rnp$Year == "2115"] ~ RNP.f[just_rnp$Year == "2115"])
summary(just_rnp_late.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## RNP.f[just_rnp$Year == "2115"] 2 0.4174 0.20868 30.64 7.52e-12 ***
## Residuals 147 1.0011 0.00681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_rnp_late.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_rnp$Proportion.late.nesters[just_rnp$Year == "2115"] ~ RNP.f[just_rnp$Year == "2115"])
##
## $`RNP.f[just_rnp$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 -0.04272319 -0.08180074 -0.003645642 0.0284273
## 0.75-0.25 -0.12696277 -0.16604032 -0.087885221 0.0000000
## 0.75-0.5 -0.08423958 -0.12331713 -0.045162031 0.0000030plot(TukeyHSD(just_rnp_late.aov))
RNP: wild
boxplot(just_rnp$Proportion.wild.born[just_rnp$Year=="2115"]~just_rnp$RNP[just_rnp$Year=="2115"], axes=F, xlab = "renesting probability", ylab = "proportion wild born")
axis(1, at=1:3, lab=c("0.25","0.5","0.75"))
axis(2)
summary(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3279 0.4114 0.4390 0.4475 0.4912 0.5925sd.rnp025.wild <- sd (just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_025" &
just_rnp$Year=="2115"])
sd.rnp025.wild## [1] 0.05759269error.rnp025.wild <- qt(0.975,
df=length(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"])-1)*sd.rnp025.wild/sqrt(length(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_025" & just_rnp$Year=="2115"]))
CI.rnp025.wild.left <- mean(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_025"& just_rnp$Year=="2115"])-error.rnp025.wild
CI.rnp025.wild.right <- mean(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_025"& just_rnp$Year=="2115"])+error.rnp025.wild
CI.rnp025.wild.left## [1] 0.4311386CI.rnp025.wild.right## [1] 0.463874summary(just_rnp$Proportion.wild.born[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5739 0.6375 0.6511 0.6496 0.6653 0.7081sd.rnp050.wild <- sd (just_rnp$Proportion.wild.born[just_rnp$File.name=="default.w.CR0.25" &
just_rnp$Year=="2115"])
sd.rnp050.wild## [1] 0.0277858error.rnp050.wild <- qt(0.975,
df=length(just_rnp$Proportion.wild.born[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"])-1)*sd.rnp050.wild/sqrt(length(just_rnp$Proportion.wild.born[just_rnp$File.name=="default.w.CR0.25" & just_rnp$Year=="2115"]))
CI.rnp050.wild.left <- mean(just_rnp$Proportion.wild.born[just_rnp$File.name=="default.w.CR0.25"& just_rnp$Year=="2115"])-error.rnp050.wild
CI.rnp050.wild.right <- mean(just_rnp$Proportion.wild.born[just_rnp$File.name=="default.w.CR0.25"& just_rnp$Year=="2115"])+error.rnp050.wild
CI.rnp050.wild.left## [1] 0.6417381CI.rnp050.wild.right## [1] 0.6575313summary(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6902 0.7158 0.7307 0.7292 0.7403 0.7692sd.rnp075.wild <- sd (just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_075" &
just_rnp$Year=="2115"])
sd.rnp075.wild## [1] 0.01876157error.rnp075.wild <- qt(0.975,
df=length(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"])-1)*sd.rnp075.wild/sqrt(length(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_075" & just_rnp$Year=="2115"]))
CI.rnp075.wild.left <- mean(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_075"& just_rnp$Year=="2115"])-error.rnp075.wild
CI.rnp075.wild.right <- mean(just_rnp$Proportion.wild.born[just_rnp$File.name=="RNP_075"& just_rnp$Year=="2115"])+error.rnp075.wild
CI.rnp075.wild.left## [1] 0.7238376CI.rnp075.wild.right## [1] 0.7345015just_rnp_wild.aov = aov(just_rnp$Proportion.wild.born[just_rnp$Year == "2115"] ~ RNP.f[just_rnp$Year == "2115"])
summary(just_rnp_wild.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## RNP.f[just_rnp$Year == "2115"] 2 2.1086 1.0543 712.2 <2e-16 ***
## Residuals 147 0.2176 0.0015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(just_rnp_wild.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = just_rnp$Proportion.wild.born[just_rnp$Year == "2115"] ~ RNP.f[just_rnp$Year == "2115"])
##
## $`RNP.f[just_rnp$Year == "2115"]`
## diff lwr upr p adj
## 0.5-0.25 0.20212841 0.18390903 0.22034779 0
## 0.75-0.25 0.28166327 0.26344389 0.29988264 0
## 0.75-0.5 0.07953486 0.06131548 0.09775423 0plot(TukeyHSD(just_rnp_wild.aov))