# Can I fit a dbRDA model on a dataset that has the same continuous explanatory variable measurement in different samples?

Can I fit a dbRDA on the following dataset:

CommunityData.distance <- matrix(c(0.58,0,0,0,0,0,0.26,0.20,0,0,0,0,0.64,0.21,0.22,0,0,0,0.18,0.40,0.91,0.78,0,0,0.37,0.23,0.68,0.91,0.25,0,0.86,0.25,0.92, 0.85,0.74,0.36), nrow=6, ncol=6, byrow = TRUE)

Envdata <- data.frame(Plot = c(1, 1, 2, 2, 3, 3), Temperature = c(0.5, 0.5, 1, 1, 2.5, 2.5), Species = c(A, B, A, B, A, B))

dbRDA <- capscale(CommunityData.distance <- Species + Temperature, data=Envdata)



I am primarily concerned about the repeated measure of temperature for two different samples.

You can't include plot and temperature in the model as explanatory effects nor can you control for one variable and then try to estimate the effect of the other. plot and temperature are the same data, just rescaled. You could force differences say by treating temperature as linear but including plot as a factor as the factor representation of plot (by two additional variables on top of the intercept compared with one additional parameter if you treat temperature as linear).
You should now probably use the dbrda() in {vegan} to fit this model, in place of capscale().