General modelling procedures
We fitted linear mixed-effect models using a Bayesian framework implemented in R v3.3 (R Core Team 2018) with the package MCMCglmm (Hadfield 2010). We ran 1,100,000 iterations per model, from which we discarded the initial 100,000 (burn-in period). Each chain was sampled at an interval of 500 iterations, which resulted in low autocorrelation (<0.05) among thinned samples. Posterior modes, 95% credible intervals (CI) and (co)variances were estimated across the thinned samples for the fixed and random effects. Fixed-effect priors were normally distributed and diffuse with a mean of zero and a large variance (100). We explored the sensitivity of the variance-covariance matrix to the choice of prior. See Appendix S1 for prior details on each of the analyses. Mean values of the posterior distributions were robust to different relatively uninformative priors. However, the width and mode of the posterior distribution for the animal model was susceptible to prior choice. We thus decided to present the animal model results estimated with restricted maximum likelihood framework using the package ASreml-R v.4.
Results