This dataset has all the relevant variables used to investigate 1) if the order of the exploration test had an effect on overall exploration score, 2) to find out if there was a significant difference between dominance ranking position and weight to each response variable (exploration score, plasma prolactin, fGCM, fAM, and fPM concentrations), 3) to check the correlation between endocrine related response variables.

Code/Software

The dataset is located in one file named 'Manuscript Data.csv', column are described below:

'Colony' = Naked mole-rat (NMR) colony name (7 colonies used)

'Exploration Score' = exploration score from each individual from PCA analysis

'ID' = Individual identification for each NMR in each colony

'Personality_Order' = the order in which NMR individuals were added back into their natal colony

'Tag' = unique individual tag number

'Prolactin' = prolactin concentrations for each individual in each colony in ng/mL

'DOB' = Date of birth for individual when available (NA indicate when data on date of birth of individual was unavailable)

'Age' = Age in days for each individual at the time of sampling

'Mass' = Mass in g for each individual

'fAM' = faecal androgen metabolite concentrations in ug/g DW (dry weight)

'fPM' = faecal progestagen metabolite concentrations in ug/g DW

'fGCM' = faecal glucocorticoid metabolite concentrations in ug/g DW NA in fAM, fPM, fGCM indicates that no faecal sample was collected from the individual)

'Sex' = sex of each individual

'CBI' = Clutton-Brock index for each individual in each colony

'Cat Rank' = the ranking position for each individual in the colony where Queen = breeding female, BM = breeding male, HRM = high ranking male, HRF = high ranking female, MRM = middle ranking male, MRF = middle ranking female, LRM = low ranking male, LRF low ranking female

'Rank' = ranking position for each individual in each colony from first (top) to last (bottom).

I used the R program with the R studio interface (version 3.6.1). The data was uploaded into the program using the code below:

Manuscript_Data <- read.csv('Manuscript Data.csv')

To find out if the exploration test order had an effect on order of test I used the code below:

First I uploaded the relevant packages:
Library(lme4)
Library(car)
Library(MuMIN)

Then I ran a linear mixed effects model using colony as a fixed effect, Exploration_Score as the response variable, and Personality_Order as the predictor. Then I evaluated the results of the model using Anova and r.squaredGLMM.

Model1- lmer(Exploration_Score ~ Personality_Order + (1|Colony), data = Manuscript_Data, na.action = na.exclude)
Anova(Model1)
r.squaredGLMM(Model1)

Then I investigated normality of the mode residuals using the code below:
residuals_model1<-resid(Model1)
qqPlot(residuals_model1)
leveneTest(residuals_model1 ~ Manuscript_Data$Personality_Order)
shapiro.test(residuals_model1)

To find out if weight and dominance ranking position had an effect on each response variable. A linear model was used with the below code. Please note the sexes were analysed separately, thus the code below was used to separate the sexes.
Females<- subset(Manuscript_Data, Sex == "Female")
Males<- subset(Manuscript_Data, Sex == "Male")

The linear model had Cat_Rank and Weight as predictor variables, and the Response variables investigated were: Exploration Score, Prolactin, fAM, fPM, and fGCMs. Each response variable had its on model below:

Model1- lm(Response variable ~ Cat_Rank +Weight), data = Males/Females, na.action = na.exclude)
Anova(Model1)
r.squaredGLMM(Model1)

Then I investigated normality of the mode residuals using the code below:
residuals_model1<-resid(Model1)
qqPlot(residuals_model1)
leveneTest(residuals_model1 ~ Males/Females$Cat_Rank)
shapiro.test(residuals_model1)

A pairwise t-test was used posthoc to find out which rank categories were different to each other.N
pairwise.t.test(Females/Males$Response_Variable, Females/Males $Cat_Rank, p.adjust.method = "holm")

Finally, to test if there was a correlation between the response variable a pearsons correlation was used using the code below:

cor.test(Manuscript_Data$Response_variable(e.g. fGCM), Manuscript_Data$Response_variable(e.g. fAM))