Win-stay, lose-switch and public information strategies for patch-fidelity of a songbird with
rare extra-pair paternity
Andrew J. Campomizzi, Department of Wildlife and Fisheries Sciences, Texas A&M University,
College Station, TX, 77843-2258, USA
Michael L. Morrison, Department of Wildlife and Fisheries Sciences, Texas A&M University,
College Station, TX, 77843-2258, USA
J. Andrew DeWoody, Forestry and Natural Resources, Purdue University, West Lafayette, IN
47907-2033, USA
Shannon L. Farrell, Department of Wildlife and Fisheries Sciences, Texas A&M University,
College Station, TX, 77843-2258, USA
R. Neal Wilkins, Institute of Renewable Natural Resources, Texas A&M University, College
Station, TX, 77843-2260, USA
Corresponding author: Andrew J. Campomizzi, 1500 Research Parkway, Suite 110, 2260
TAMU, College Station, TX 77843-2260, USA. Email: acampomizzi@tamu.edu, phone:
973.752.2390
R code for data analysis
#analysis of white-eyed vireo patch fidelity
#data were collected in oak-juniper woodland on 4 private ranches in coryell county, texas from
#2008-2010
#set working directory
setwd("D:/Publication/ScientificReports/SiteFidelity/Data/")
#load data
fidelity.data=read.table("wevi.paternity_2011.11.16.txt", header=TRUE, colClasses=c("factor",
"factor", "numeric", "factor", "numeric"))
#check data structure
str(fidelity.data)
#subset data
#for males
sub.m=subset(fidelity.data, fidelity.data$sex==0)
str(sub.m)
#for females
sub.f=subset(fidelity.data, fidelity.data$sex==1)
str(sub.f)
##############################################################################
#calculate descriptive statistics
#subset data for males showing patch fidelity
sub.m.fidelity=subset(sub.m, sub.m$patch.fidelity==1)
str(sub.m.fidelity)
#calculate median number of offspring for males showing patch fidelity
median(sub.m.fidelity$neighbor.fledglings)
#4.0
median(sub.m.fidelity$personal.fledglings)
#0.0
#subset data for males not showing patch fidelity
sub.m.nofidelity=subset(sub.m, sub.m$patch.fidelity==0)
str(sub.m.nofidelity)
#calculate median number of offspring for males not showing patch fidelity
median(sub.m.nofidelity$neighbor.fledglings)
#4.0
median(sub.m.nofidelity$personal.fledglings)
#0.0
#subset data for females showing patch fidelity
sub.f.fidelity=subset(sub.f, sub.f$patch.fidelity==1)
str(sub.f.fidelity)
#calculate median number of offspring for females showing patch fidelity
median(sub.f.fidelity$neighbor.fledglings)
#4.0
median(sub.f.fidelity$personal.fledglings)
#2.0
#subset data for females not showing patch fidelity
sub.f.nofidelity=subset(sub.f, sub.f$patch.fidelity==0)
str(sub.f.nofidelity)
#calculate median number of offspring for females not showing patch fidelity
median(sub.f.nofidelity$neighbor.fledglings)
#2.5
median(sub.f.nofidelity$personal.fledglings)
#0.5
##############################################################################
#exploratory plots
#boxplot of number of offspring in adjacent territories and number with social mate for males
#and females that did and did not show patch fidelity
#plot males, number of adjacent offspring
tiff("Fig1.tif", width = 7, height = 7, units = "in", bg = "white", compression = "lzw", res = 600)
par(mfrow=c(2, 2))
boxplot(sub.m$neighbor.fledglings ~ sub.m$patch.fidelity, bty="l", las=1, col="gray",
main="Male", ylim=c(0, 12), xlab="Patch fidelity", names = c("No", "Yes"), ylab="Number of
adjacent offspring")
#plot females number of offspring with a social mate
boxplot(sub.f$neighbor.fledglings ~ sub.f$patch.fidelity, bty="l", las=1, col="gray",
main="Female", ylim=c(0, 12), xlab="Patch fidelity", names = c("No", "Yes"), ylab="")
#add title to top
mtext("(a) Public information", side = 3, line=3, at=0, font=2)
#plot males, number of fledglings with social mate
boxplot(sub.m$personal.fledglings ~ sub.m$patch.fidelity, las=1, col="gray", ylim=c(0, 4),
xlab="Patch fidelity", names=c("No", "Yes"), ylab="Number of offspring with social mate")
#plot females, number of offspring with social mate
boxplot(sub.f$personal.fledglings ~ sub.f$patch.fidelity, las=1, col="gray", ylim=c(0, 4),
xlab="Patch fidelity", names=c("No", "Yes"), ylab="")
#add title to bottom
mtext("(b) Win-stay, lose-switch", side = 3, line=2, at=0, font=2)
dev.off()
##############################################################################
#check correlations between predictor variables
hist(fidelity.data$personal.fledglings)
hist(fidelity.data$neighbor.fledglings)
plot(fidelity.data$personal.fledglings, fidelity.data$neighbor.fledglings)
attach(fidelity.data)
lines(lowess(personal.fledglings, neighbor.fledglings), lwd=2, col="gray40", lty="dashed")
detach(fidelity.data)
#test for correlation using spearman's
cor1=cor.test(fidelity.data$personal.fledglings, fidelity.data$neighbor.fledglings,
alternative="two.sided", method = "spearman")
cor1
##############################################################################
#fit and evaluate models for males
#model 1 - public information strategy - is patch fidelity associated with number of conspecific
#offspring in adjacent territories
glm1.m=glm(formula=patch.fidelity ~ 1 + neighbor.fledglings, data=sub.m,
family=binomial(link="logit"))
summary(glm1.m)
#model 2 - win-stay, lose-switch - is site fidelity associated with number of offspring with social
#mate
glm2.m=glm(formula=patch.fidelity ~ 1 + as.numeric(personal.fledglings), data=sub.m,
family=binomial(link="logit"))
summary(glm2.m)
#model 0 - model with intercept only - suggesting neither strategy used for patch fidelity
glm01.m=glm(formula=patch.fidelity ~ 1, data=sub.m, family=binomial(link="logit"))
summary(glm01.m)
###########################################
#compare fit of 3 models for males
#calculate aicc for glm1 for males
#set k, number of parameters in model
k=length(glm1.m$coefficients)
#set n, sample size
n=33
aicc.glm1.m=glm1.m$aic+(2*k*(k+1)/(n-k-1))
#47.42407
#calculate aicc for glm01 for males
k=length(glm01.m$coefficients)
n=33
aicc.glm01.m=glm01.m$aic+(2*k*(k+1)/(n-k-1))
#=45.39083
#calculate aicc for glm2 for males
k=length(glm2.m$coefficients)
n=33
aicc.glm2.m=glm2.m$aic+(2*k*(k+1)/(n-k-1))
#=45.97311
#calculate delta aicc
#model glm01.m has the lowest aicc for males
#calculate delta aicc for glm1.m
delta.aicc1.m=aicc.glm1.m-aicc.glm01.m
#=2.033245
#calculate delta aicc for glm2.m
delta.aicc2.m=aicc.glm2.m-aicc.glm01.m
#=0.5822788
#########################################
#calculate model likelihood
#for glm1.m
l1.m=exp(-1/2*abs(delta.aicc1.m))
#for glm01.m
l01.m=exp(-1/2*0)
#for glm2.m
l2.m=exp(-1/2*abs(delta.aicc2.m))
#sum likelihoods for males
suml.m=sum(l1.m, l01.m, l2.m)
#########################################
#calculate model weights
#for glm1.m
w1.m=l1.m/suml.m
#for glm01.m
w01.m=l01.m/suml.m
#for glm2.m
w2.m=l2.m/suml.m
##############################################################################
#fit and evaluate models for females
#model 1 - public information strategy - is patch fidelity associated with number of conspecific
#offspring in adjacent territories
glm1.f=glm(formula=patch.fidelity ~ 1 + neighbor.fledglings, data=sub.f,
family=binomial(link="logit"))
summary(glm1.f)
#model 2 - win-stay, lose-switch - is site fidelity associated with number of offspring with social
#mate
glm2.f=glm(formula=patch.fidelity ~ 1 + as.numeric(personal.fledglings), data=sub.f,
family=binomial(link="logit"))
summary(glm2.f)
#model 0 - model with intercept only - suggesting neither strategy used for patch fidelity
glm01.f=glm(formula=patch.fidelity ~ 1, data=sub.f, family=binomial(link="logit"))
summary(glm01.f)
###########################################
#compare fit of 3 models for females
#calculate aicc for glm1 for females
#set k, number of parameters in model
#calculate aicc for glm1 for females
k=length(glm1.f$coefficients)
#set n, sample size
n=17
aicc.glm1.f=glm1.f$aic+(2*k*(k+1)/(n-k-1))
#=25.11729
#calculate aicc for glm2 for females
k=length(glm2.f$coefficients)
n=17
aicc.glm2.f=glm2.f$aic+(2*k*(k+1)/(n-k-1))
#=19.33194
#calculate aicc for glm01 for females
k=length(glm01.f$coefficients)
n=17
aicc.glm01.f=glm01.f$aic+(2*k*(k+1)/(n-k-1))
#=22.86378
#calculate delta aicc
#model glm2.f has the lowest aicc for females
#calculate delta aicc for glm1.f
delta.aicc1.f=aicc.glm1.f-aicc.glm2.f
#=5.785356
#calculate delta aicc for glm01.f
delta.aicc01.f=aicc.glm01.f-aicc.glm2.f
#=3.531845
#########################################
#calculate model likelihood
#for glm1.f
l1.f=exp(-1/2*abs(delta.aicc1.f))
#for glm2.f
l2.f=exp(-1/2*0)
#for glm01.f
l01.f=exp(-1/2*abs(delta.aicc01.f))
#sum likelihoods for females
suml.f=sum(l1.f, l01.f, l2.f)
#########################################
#calculate model weights
#for glm1.f
w1.f=l1.f/suml.f
#=0.04519327
#for glm2.f
w2.f=l2.f/suml.f
#=0.8153571
#for glm01.f
w01.f=l01.f/suml.f
#=0.1394496
######################################
#make prediction for probability of female patch fidelity based on number of offspring with
#social mate because this was the best-fit model
#set range and increment of predictor variable, number of offspring with social mate
pers.fledg.num=seq(0, 4, 0.5)
#make predictions based on model
plogis.predict2f=plogis(coef(summary(glm2.f))[1,1] +
coef(summary(glm2.f))[2,1]*pers.fledg.num)
#attach data
attach(sub.f)
#make data frame for values for range of prediction
personal.num.df=data.frame(personal.fledglings=pers.fledg.num)
#predict confidence intervals
predict.cl2f=predict(glm2.f, personal.num.df, interval=("confidence"), level=0.95, type="link",
se.fit=TRUE)
#calculate 95% confidence intervals
upper.cl2f=predict.cl2f$fit + 1.96 * predict.cl2f$se.fit
lower.cl2f=predict.cl2f$fit - 1.96 * predict.cl2f$se.fit
#transform confidence intervals onto logit scale
logit.upper.cl2f=plogis(upper.cl2f)
logit.lower.cl2f=plogis(lower.cl2f)
detach(sub.f)
#########################################
#export figure 2 as tif
tiff("Figure-2(Campomizzi).tif", width = 7, height = 7, units = "in", bg = "white", compression =
"lzw", res = 600)
#plot prediction of probability of site fidelity given number of offspring with social mate, for
#females
plot(pers.fledg.num, plogis.predict2f, type="l", lwd=2, las=1, bty="l", xlim=c(0, 4), ylim=c(0.0,
1.0), main="Predicted female patch fidelity", xlab="Number of offsping with social mate",
ylab="Probability of patch fidelity")
#add 95% CI
lines(pers.fledg.num, logit.upper.cl2f, type="l", lty="dashed", lwd=2)
lines(pers.fledg.num, logit.lower.cl2f, type="l", lty="dashed", lwd=2)
#add data points, jittered for visibility because data are integers
points(sub.f$personal.fledglings + rnorm(length(sub.f$personal.fledglings), 0, 0.07),
as.numeric(sub.f$patch.fidelity) - 1, cex=1.15, col="gray60")
dev.off()
##############################################################################
#end of analysis
##############################################################################