Hypothetically speaking, if there was a significant difference reported, how could I determine which samples are responsible? Would it make sense to look at the reported pairwise comparisons and the associated p-values and corrected p-values (q)?
When I do my beta analysis (bray Curtis and unweighted unifrac) on the same set of samples they both report a significant p-value.
Yes, looking at the pair-wise comparisons would give you the metrics to indicate samples responsible.
However, looking at your data the replicates may be problematic. For Kruskal-Wallis, and most of the statistical tests in Qiime2, one of the key assumptions is independence across samples. Replicates have inherent dependance. You will need to work around this by creating models that account for dependance. This forum post regarding replicates should be helpful to you in treating your replicates.
The pairwise comparisons also will be helpful to you in this case for specific sample comparisons.
I hope this answer is helpful to you! Let me know if you have any other questions as you work through your analysis.
I am not sure what you mean by problematic. Do you mean they are not independent as they are in triplicate? Would you suggest the 3 replicates for each timepoint be combined? I thought I had read elsewhere that this was not advised.
I could be misinterpreting this and your explanation.