Breeding males, but not females, of <i>Fukomys</i> mole-rats use stronger bites to defend reproductive monopoly. <i>Fukomys</i>

This dataset supports the analyses and conclusions presented in the above-titled manuscript. It includes <i>in vivo</i> bite force measurements, morphological data, and associated sample metadata from four cooperatively breeding species of African mole-rats (genus <i>Fukomys</i>). These data provide the empirical foundation for testing hypotheses about sex- and status-specific differences in bite force and its morphological correlates in relation to intraspecific competition and reproductive roles.<br><br>The supplementary materials include two structured datasets and one annotated R script. These resources allow full replication of the analyses and figures presented in the manuscript and ensure compliance with the Nature Portfolio’s data transparency standards.<br><b>1. Dataset: Dataset_biteforce.txt</b><br>Description:<br>This tab-delimited text file contains the primary dataset used to analyze <i>in vivo</i> bite force (BF) in 404 adult individuals of four species of African mole-rats from the genus <i>Fukomys</i>: <i>F. anselli</i> (treated taxonomically as <i>F. micklemi</i>), <i>F. darlingi</i>, <i>F. mechowii</i>, and <i>F. damarensis</i>. Each row corresponds to one individual.<br><br>Key variables include:<br><br>Column Name Description<br>IDchip Unique chip or specimen ID<br>animal_id Animal identifier code<br>species Nominal species (e.g., <i>F. anselli</i>, <i>F. darlingi</i>)<br>species1 Taxonomic grouping used in analysis (e.g., <i>F. micklemi</i> includes <i>F. anselli</i>)<br>status Breeding status: B (breeder) or N (non-breeder)<br>sex Sex of the individual: M or F<br>statussex Combined breeding status and sex (e.g., BM, BF, NM, NF)<br>weight Body mass in grams<br>Head_Width Head width (mm), measured between zygomatic arches<br>Incisor_width Upper incisor width (mm), measured at widest point<br>bfmaxn Maximum <i>in vivo</i> bite force (N)<br>The file also includes binary classification columns used in modeling:<br><br>FNonbreeders, FBreeders, MNonbreeders, MBreeders, FMBreeders: dummy variables for model specification.<br><b>2. Dataset: Dataset_headincisorwidth.txt</b>Description:<br>This tab-delimited file is an extended version of the above dataset. It includes the same variables as Dataset_biteforce_review.txt, but is specifically structured and filtered for detailed analysis of head width and incisor width variation across breeding status and sex.<br><br>It contains slightly more rows due to inclusion of additional individuals who may have been excluded from bite force analyses (e.g., due to refusal to bite) but were still measured morphologically.<br><br>Use case:<br>Used to analyze structural traits—head width and upper incisor width—and their scaling with body mass across sex and breeding status categories.<br><br><b>3. Script: BiteForce_rscrip.txt</b><br>Description:<br>This R script contains the complete analysis workflow used in the manuscript. It is written in R and uses the brms package for Bayesian multilevel modeling, along with ggplot2 and related visualization packages.<br><br>The script includes:<br><br>Data loading and preprocessing (e.g., log-transformation, standardization)<br>Calculation of species-specific summaries for bite force, head width, and incisor width<br>Visualization of trait–body mass relationships across species and groups<br>Bayesian model fitting for:<br>Bite force (BF)<br>Head width (HW)<br>Incisor width (IW)<br>Model comparisons (WAIC and LOO-CV)<br>Posterior predictive checks<br>Back-transformation of standardized estimates to original measurement scales<br>Visualization of conditional effects and pairwise contrasts<br>Species-specific models (e.g., <i>F. micklemi </i>subset analysis)<br>Dependencies:brms, ggplot2, marginaleffects, emmeans, tidybayes, dplyr, patchwork, bayesplot, ggdist, loo, ggpubr, ggeffects<br>Reproducibility Note:The script is organized for full reproducibility. All figures and results reported in the manuscript (Figures 1–4, Tables 1–3, S1–S7) can be reproduced from this script using the provided datasets. The modeling follows a clearly documented structure using well-defined priors and diagnostics.