Data from: Higher dominance rank is associated with lower glucocorticoids in wild female baboons: A rank metric comparison
Levy, Emily et al. (2020), Data from: Higher dominance rank is associated with lower glucocorticoids in wild female baboons: A rank metric comparison, Dryad, Dataset, https://doi.org/10.5061/dryad.4j0zpc88p
In vertebrates, glucocorticoid secretion occurs in response to energetic and psychosocial stressors that trigger the hypothalamic-pituitary-adrenal (HPA) axis. Measuring glucocorticoid concentrations can therefore shed light on the stressors associated with different social and environmental variables, including dominance rank. Using 14,172 fecal samples from 237 wild female baboons, we test the hypothesis that high-ranking females experience fewer psychosocial and/or energetic stressors than lower-ranking females. We predicted that high-ranking females would have lower fecal glucocorticoid (fGC) concentrations than low-ranking females. Because dominance rank can be measured in multiple ways, we employ an information theoretic approach to compare 5 different measures of rank as predictors of fGC concentrations: ordinal rank; proportional rank; Elo rating; and two approaches to categorical ranking (alpha vs non-alpha and high-middle-low).
Our hypothesis was supported, but it was also too simplistic. We found that alpha females exhibited substantially lower fGCs than other females (typical reduction = 8.2%). If we used proportional rank instead of alpha- versus non-alpha status in the model, we observed a weak effect of rank such that fGCs rose 4.2% from the highest- to lowest-ranking female in the hierarchy. Models using ordinal rank, Elo rating, or high-middle-low categories alone failed to explain variation in female fGCs. Our findings shed new light on the association between dominance rank and the stress response, the competitive landscape of female baboons as compared to males, and the assumptions inherent in a researcher's choice of rank metric.
The 3 attached files are:
- The raw data from which all statistical analyses were derived
- A spreadsheet of model results (calculated using the raw data) that facilitates reproducting Figure 2 in the manuscript
- A script to recreate all ananlyses in R. If you are not familiar with R, this file will still be useful as a readme file if you look at the comments (lines starting with a #)
Missing values: Because we did not have hybrid scores for all individuals, missing hybrid score values are expected.