The dissection of a despotic society: exploration, dominance and hormonal traits
Data files
Mar 27, 2024 version files 13.89 KB
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Manuscript_Data.csv
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README.md
Abstract
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.
README: 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 <- 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))
Usage notes
Microsoft Excel/Numbers