Model output
To analyse the output of the simulation model, we ran linear models of
defaunation parameters and species traits (predictor variables) against
‘defaunation response’ (response variable;
log10-transformed to account for extreme skew). We
assessed a global model as well as models representing all subsets of
predictor variables, and selected the model with the lowest BIC value as
our top ‘additive model’ using the MuMIn package (Barton 2019) in
R (R Core Team 2018). We used BIC rather than AIC because it more
strongly penalizes complex models, and so is better for identifying a
few variables with strong effect (Burnham & Anderson 2004). We also
created ‘interaction models’ to assess whether the inclusion of
statistical interaction terms between main-effect variables in the
additive model improved model fit.
For the additive model and each interaction model, we used therelaimpo package (Grömping 2006) in R to calculate the relative
importance of each variable, measured as the contribution of each
variable to the model’s R2, following the
Lindeman-Merenda-Gold (Lindeman et al. 1980) method.