My problem is the statistical analysis of community data with gradients of environmental data.
I have a table of species occurrences (from metabarcoding) per sample site and measurements of environmental parameters (like pH, nutrient concentration, ...). We did all our samples in technical triplicates. That is, we took each sample three times and ran it trough our complete lab process.
Now these replicates are obviously not statistically independent from each other so I have to take that into account when doing statistical test on the data.
I want to test if the environmental data we measured explain the variance of the metabarcoding data. For this I have so far considered Canonical Correspondence Analysis and PERMANOVA. If I understand what I read correctly, in a linear model I would have to include a "random effect" of the replicate. As far as I can see I can not include such an effect in the before mentioned tests.
I found in a FAQ of the vegan package the statement that this is not possible (at least in vegan) and as a work around they suggest to use partial CCA or include a "strata argument" in the PERMANOVA. I think this is not possible to use in my case, because this would imply that the effect of the replicate number is consistent over the sample sites.
The only other idea I have dome up with so fat is to average over the replicates, but I would rather find a different solution.
Any ideas or pointers would be greatly appreciated.