Step 2: Variance component analysis
Variance component analysis was used to compare the relative importance of each fixed effect included within the best performing models from Step 1. We built a Bayesian hierarchical multilevel linear model for each response variable considered in the mixed effects models, implemented through R package ‘R2Jags’ (Su & Yajima 2021), following Gelman & Hill (2007), Hector et al. (2011) and Catford et al. (2014). These models were produced by taking the structure of the best performing mixed-effects models and replicating this in JAGS (Just Another Gibbs Sampler) multilevel structure, with variable intercepts and slopes applied following Gelman & Hill (2007). Variance components were then calculated from these multilevel models and presented on a standard deviation scale to aid comparison between predictors (Gelman & Hill 2007; Hector et al. 2011).