# Appendix 4 R script. Vigilance behavior

```
# First we need to load the packages
library(behaviouR)
library(ggpubr)
## Part 1: Barnacle goose vigilance Then we load in our goose data
data("BarnacleGooseData")
# Note you will want to collect your own data and load into R using read.csv
# BarnacleGooseData <- read.csv('FULL FILE PATH TO YOUR DATASHEET.csv')
### Part 1a: Surveillance behavior Scatterplot of total number of 'head up' in our
### data
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TotalHeadsUp") + ylab("Surveillance rate")
# Now let's add a trend line to see if there is a relationship between flock size
# and the total number of 'head up' behaviors
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TotalHeadsUp", add = "reg.line") +
ylab("Surveillance rate")
# Let's see if there were any differences between you and your partner
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TotalHeadsUp", add = "reg.line",
facet.by = "Partner", cor.coef = T) + ylab("Surveillance rate")
# Let's plot you and your partner's data in different colors.
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TotalHeadsUp", add = "reg.line",
facet.by = "Partner", color = "Partner", palette = c("black", "blue"), cor.coef = T) +
ylab("Surveillance rate")
# *Question 1*: Were there any major differences between you and your partner?
# Now we will do model selection using Akaike information criterion (AIC). First
# we create a null model and then we create a model with flock size as a
# predictor of total number of heads up
SurveillanceNullModel <- glm(TotalHeadsUp ~ (1/Partner), family = poisson, data = BarnacleGooseData)
SurveillanceModel <- glm(TotalHeadsUp ~ FlockSize + (1/Partner), family = poisson,
data = BarnacleGooseData)
# Then we compare the models using AIC
bbmle::AICtab(SurveillanceNullModel, SurveillanceModel)
# *Question 2*: If the null model is ranked higher than the model with flock size
# as a predictor. How do we interpret this finding?
### Part 1b: Time vigilant (sec/min) Now we will look at the relationship between
### the duration (calculated as seconds per minute) that the geese were vigilant as
### a function of flock size.
# Scatterplot of time vigilant (sec/min) as a function of group size
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TimeSecHeadUp") + ylab("time vigilant (sec/min)")
# Scatterplot of duration of vigilance behavior in our data with a trend line.
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TimeSecHeadUp", add = "reg.line") +
ylab("time vigilant (sec/min)")
# Let's see if there were any differences between you and your partner. We will
# also add the command 'cor.coef = T' which will give us the correlation
# coefficient (R) along with an associated p-value.
# NOTE: The data here are simulated so your plots should look different
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TimeSecHeadUp", add = "reg.line",
facet.by = "Partner", cor.coef = T) + ylab("time vigilant (sec/min)")
# Let's plot you and your partner's data in different colors.
ggscatter(data = BarnacleGooseData, x = "FlockSize", y = "TimeSecHeadUp", add = "reg.line",
facet.by = "Partner", color = "Partner", cor.coef = T, palette = c("black", "blue")) +
ylab("time vigilant (sec/min)")
# As before we will create a null model and then a model with flock size as a
# predictor and compare them using AIC. We will not use a Poisson distribution
# here because our outcome variable is continuous.
# This is our null model
VigilanceNullModel <- lme4::lmer(TimeSecHeadUp ~ (1 | Partner), data = BarnacleGooseData)
# This is our model with flock size as a predictor duration of vigilance
VigilanceModel <- lme4::lmer(TimeSecHeadUp ~ FlockSize + (1 | Partner), data = BarnacleGooseData)
# Now we compare the models using AIC
bbmle::AICtab(VigilanceNullModel, VigilanceModel)
# *Question 3.* How do you interpret the results of your model selection?
## Part 2: Meerkat data revisted Please upload your meerkat scan data to this
## project and delete the existing datasheet.
data("MeerkatScanData")
# Note you will want to collect your own data and load into R using read.csv
# MeerkatScanData <- read.csv('FULL FILE PATH TO YOUR DATASHEET.csv')
# As before we will turn our NA values to zero
MeerkatScanData[is.na(MeerkatScanData)] <- "0"
# We will remove the time and out of sight columns as we do not need them
MeerkatScanData <- dplyr::select(MeerkatScanData, -c(Time, OutOfSight))
# We need to reformat our data so that we can plot it
MeerkatScanDataSummaryLong <- reshape2::melt(MeerkatScanData, id.vars = c("Treatment",
"Partner"))
# Here we add more informative column names
colnames(MeerkatScanDataSummaryLong) <- c("Treatment", "Partner", "BehavioralState",
"InstancesOfBehavior")
# We need to tell R that our outcome variable is not categorical but numeric
MeerkatScanDataSummaryLong$InstancesOfBehavior <- as.numeric(MeerkatScanDataSummaryLong$InstancesOfBehavior)
# Now we plot our data
ggboxplot(MeerkatScanDataSummaryLong, x = "Treatment", y = "InstancesOfBehavior",
fill = "BehavioralState")
# **Question 4.** Based on your inspection of the boxplot, are there any major
# differnces between treatment groups?
# Now we will test to see if there were differences in vigilance behaviors across
# treatments?
# First we subset our data so that it only includes the vigilant category
MeerkatScanDataVigilantOnly <- subset(MeerkatScanDataSummaryLong, BehavioralState ==
"Vigilant")
# R can be picky about the format of data, so we use this command to tell R that
# treatment group is a factor
MeerkatScanDataVigilantOnly$Treatment <- as.factor(MeerkatScanDataVigilantOnly$Treatment)
# Here we are reordering the levels of the factors. For our model selection we
# are interested in whether we see differences from the control (no predator) and
# the predator treatments, so here we are setting the no predator group as our
# reference group.
MeerkatScanDataVigilantOnly$Treatment <- factor(MeerkatScanDataVigilantOnly$Treatment,
levels = c("NoPredator", "AerialPredator", "TerrestrialPredator"))
# Now as before we will do model selection. Note that because our outcome
# variable (instances of behavior) is in the form of count data we use a poisson
# distribution.
MeerkatVigilanceNullModel <- glm(InstancesOfBehavior ~ 1, family = poisson, data = MeerkatScanDataVigilantOnly)
MeerkatVigilanceModel <- glm(InstancesOfBehavior ~ Treatment, family = poisson, data = MeerkatScanDataVigilantOnly)
# Now we compare the models using AIC
bbmle::AICtab(MeerkatVigilanceNullModel, MeerkatVigilanceModel)
# Here we will use the summary function to look at the estimates. There is a lot
# of information here but we want to focus on the 'Estimate'. In particular we
# are interested in the estimates for 'TreatmentAerialPredator' and
# 'TreatmentTerrestrialPredator'. The estimate is showing the effect that these
# variables have on our outcome (instances of behavior), relative to our control
# (no predator). Therefore positive estimates indicate that there were more
# vigilance behaviors in aerial and terrestrial predator treatments.
summary(MeerkatVigilanceModel)
# A common way to visulize results such as these are coefficient plots. Here we
# are looking at the effect of 'TreatmentAerialPredator' and
# 'TreatmentTerrestrialPredator' relative to our control group. The reference or
# group is indicated by the vertical dashed line. So, we can interpret that
# because the coefficients are positive (and the confidence intervals don't
# overlap zero) that both terrestrial and aerial treatments lead to an increase
# in vigilant behaviors.
coefplot::coefplot(MeerkatVigilanceModel, intercept = F)
# For reasons that will not go into here, I am not a fan of p-values or null
# hypothesis significance testing. There is a nice overview if you want to learn
# more here: https://doi.org/10.1098/rsbl.2019.0174.But, the model selection
# approach will lead to the same inference as the use of a one-way anova, an
# approach that you may be more familiar with.
# Compute the analysis of variance
MeerkatAOV <- aov(InstancesOfBehavior ~ Treatment, data = MeerkatScanDataVigilantOnly)
# Here this will tell us if there are differences between groups
summary(MeerkatAOV)
# Since the ANOVA test is significant, we can compute Tukey Honest Significant
# Differences test.
TukeyHSD(MeerkatAOV)
# **Question 5.** Based on your interpretation of the model selection and the
# coefficient plots were there differences between treatment groups?
```