I have percent coverage data for plant species in 1mx1m quadrats at 8 sites with various sampling efforts. My coverage data includes non integers, is there a best practice for creating a rarefaction curve with non integer data like this, in R specifically? I am currently researching the package rareMNtests, am I on the right track?
Hello Andrea; I have no statistical qualifications but a little personal experiment comes to mind. Round your % cover values to the nearest whole %, I bet that is precise to the level of the method of measurement. Will values in that form be accepted by the R package? I have used a similar routine in other applications and the referees didn't balk. Best regards, Jim Des Lauriers
Hi, I wanna be sincer but I just have a litlle notion for R but I am used to do it by PAST statistical software. I can help you if know this software then
You pose an interesting situation. Rarefaction is meant to work for count data, as you are sampling individuals from a population and using these counts to estimate species richness. Thus it makes little sense to rarefy data that uses coverage as a measure of abundance, even if you round to integer numbers (mathematically it works, but the result can be misleading). Firstly you are accounting for one or many individuals (depending on the plant species) with a given percent cover, and secondly you are bound to a maximum value (100) for each species. Consequently, rarefying coverage data makes little sense and is somewhat meaningless. An alternative approach you can take is rarefying with presence/absence data. Not sure if the package you mention allows that, but you can check Anne Chao's iNEXT. Not only you can estimate number of species with p/a data, you can also estimate with different Hill numbers, giving you a much wider understanding of diversity. Hope this helps.
If you know your plants well, you could convert percent data to counts (number of individuals), or use presence/absence conversion
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