DOI: 10.5061/dryad.18931zd37
title: The dissection of a despotic society: exploration, dominance and hormonal traits

description: Naked mole-rats (Heterocephalus glaber) live in large colonies with one
 breeding female (queen), one to three breeding males, and the remainder
 are non-reproductive subordinates. We investigated how the dominance
 ranking position in naked mole-rats differs with exploration (the
 propensity to explore a novel environment), and endocrine related markers.
 Faecal progestagen metabolite (fPM), glucocorticoid metabolite (fGCM),
 androgen metabolite (fAM) metabolite, and plasma prolactin concentrations
 were quantified in breeding, high, middle and low ranked females and
 males. There were no significant differences between the dominance ranking
 positions and exploration behaviour. Interestingly, the queens and
 high-ranking females had higher fGCM and fAM concentrations compared to
 middle and low ranked females; possibly due to an increased possibility of
 conflict with each other. The queen had significantly higher fPM
 concentrations than all other ranked females since she is responsible for
 procreation. In the males, the breeding males had higher fGCM
 concentrations compared to high and low ranked males. However, breeding
 males and middle-ranking males had overall higher prolactin levels than
 all other ranked males and these individuals may indirectly contribute to
 greater cooperative care and burrow maintenance. We suggest that
 high-ranking females exhibit higher fGCM and fAM concentrations possibly
 due to competition with the queen.
description: Microsoft Excel/Numbers
description: # The dissection of a despotic society: exploration, dominance and
 hormonal traits 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 &lt;-
 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&lt;-resid(Model1)
 qqPlot(residuals_model1) leveneTest(residuals_model1 ~
 Manuscript_Data$Personality_Order) shapiro.test(residuals_model1) To find
 out if weight and dominance ranking position had an effect on each
 response variable. A linear model was used with the below code. Please
 note the sexes were analysed separately, thus the code below was used to
 separate the sexes. Females&lt;- subset(Manuscript_Data, Sex ==
 "Female") Males&lt;- subset(Manuscript_Data, Sex ==
 "Male") The linear model had Cat_Rank and Weight as predictor
 variables, and the Response variables investigated were: Exploration
 Score, Prolactin, fAM, fPM, and fGCMs. Each response variable had its on
 model below: Model1- lm(Response variable ~ Cat_Rank +Weight), data =
 Males/Females, na.action = na.exclude) Anova(Model1) r.squaredGLMM(Model1)
 Then I investigated normality of the mode residuals using the code below:
 residuals_model1&lt;-resid(Model1) qqPlot(residuals_model1)
 leveneTest(residuals_model1 ~ Males/Females$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))