I recently undertook to analyze a relatively simple data set.

It includes estimates of tree species composition for several sites within a forest (for example Poplar 0.7, white ash 0.2, sugar maple 0.1)

And it includes ground surveyed data of these same areas of the forest.

The goal is to assess the accuracy of the original estimates.

I originally thought to use - forgive me I have little training in stats - a chi-squared test of goodness of fit. But I have my doubts about this, since that test seems to be designed for cases in which the expected values are frequencies. Now I understand that my data is dealing with frequencies (frequencies of species occurring), but to me each site represents a single case, and the areas are limited. It is senseless to say that an estimate for one species off by 15% has a high probability of approaching the 'limit' if enough trees were measured, and so the fit is still good.. If you expect there to be 80% of one species and there is only 65% your expectation was incorrect, and that seems significant. Also, the chi-squared tests, as far as I understand, can only be performed for one site at a time, and so don't capture any larger trends in accuracy or inaccuracy.

Are their any tests that better fit this problem? I did run an RDA using covariates to 'pair' the sites, and it gave me a p value of 0.0001, but I'm not sure if that's the most appropriate test.

Thank you very much for reading and considering. Forgive my eccentric use of terms.