Hi @nouse1234,
The answer to this overall question is "It depends"
.
I don't think there is a best practice, but I can offer some of my approaches and reasoning.
I think the first step is to remove your very shallow samples. I tend to set this threshhold at my rarefaction depth, but if you're only doing CoDa and not doing like alpha diversity or something, you may not have that. I think a lot of tools (DEICODE, ANCOM-BC) are going for 500 sequences/sample; my personal thershhold tends to be that 1000 is my minimum, and TBH, that's probably too low.
Throwing out the super low depth samples gets rid of zeros and gives more confidence in the remaining samples.
Before CoDA, I will also filter out low prevalence features. My tendency is to require at least 10% prevelance. Partially because I think that's a threshhold I can use to make statistical inference if I want to do prevalence testing. Whether of not you want ot combine that with a limit of detection is up to you. (In q2-feature-table, there's a function called filter-features-conditionally that I tend to use. It's not perfect for dealing wtih depth, but it very much helps.
I'll also so that empirically, I find that DEICODE handles variation in library size really well. It's not perfect, but because of the magic 
in zero handling, it works better. (Any math that is suffeciently complex is indistingishable from magic to me... linear algebra is about my threshhold, but the docs for the plugin are great if you're so inclined.)
Best,
Justine