Justification for using zero-inflated model in GLMM I am using GLMMs in R to examine the influence of various continuous predictor variables (x) on several biological counts variables (y). My response variables (n=5) each have a high number of zeros (data distribution), so I have tested the fit of various distributions (genpois, poisson, nbinom1, nbinom2, zip, zinb1, zinb2) and selected the best fit one according to the lowest AIC/LogLik value.
According to this selection criteria, three of my response variables with the highest number of zeros are best fit to the zero inflated negative binomial (zinb2) distribution. Compared to the regular NB distribution (non-zero inflated), the delta AIC is between 30-150.
My question is: must I use the ZI models for these variables considering the dAIC? I have received advice from a statistician that if dAIC is small enough between the ZI and non-ZI model, use the non-ZI model even if it is marginally worse fit since ZI models involve much more complicated modelling & interpretation. The distribution matters in this case because ZINB / NB models select a different combination of top candidate models when testing my predictors.
Thank you for any clarification!

Use the ZINB model over the NB. Usually a "small" $\Delta$AIC is of magnitude 2, not 30-150.
The R DHARMa package is a good tool to use alongside AIC selection in generalised mixed models. It can inform you of goodness of fit, patterns in the residuals etc.
