A couple of general ideas. First, there can be some utility in converting your continuous data into something categorical using your favorite data transformation. It becomes several times easier to handle these relationships with standard microbiome testing. A statistician or someone familiar with your field and measurements could probably best help you work with this.
Analysis wise, I always start with diversity analyses because I assume my feature-based (ASV/OTU/genus/gene/etc) analysis will be underpowered.
So, there, I would look at correlation with alpha diversity, or even an alpha diversity regression (this is in
q2-longitudinal or your favorite regression package).
In beta diversity, i would tend toward an adonis test for continous data, which addresses the amount of variation explained, a mantel test, which shows a univariate correlation, or the permanova implementation here. These live in the
Once you’ve established relationships on a whole community level, then you could go to feature-based analysis. There, I might again start with your categorical data and explore some fo the common techniques here. But, you could also try something like Gneiss or Songbird, both of which take continous data.