MaAsLin2 will handle potential confounders as additional covariates. If the covariate is categorical (e.g. recruitment site), then there’s an option to model the covariate as a fixed effect or a random effect. There’s some benefit to using a random effect if the covariate has >5 levels and many theoretical levels that are unsampled (for example, in the case of something like “recruitment site,” there are likely many potential sites that were NOT sampled, and so the variance explained by recruitment site will tend to be underestimated if it is treated as a fixed effect).
We recommend converting counts to relative abundance without rarefaction before running MaAsLin2.