Hi!
I am struggling with some data - hoping to find some answers. My main question is regarding the output when using the rarefaction function, rarefy(). I have a dataset of six locations (in rows) and 45 species of insects (in columns), and I want to express the diversity of insects in these six locations and clarify the differences between locations. Pretty straightforward I guess. I have rarefied the data by this code: rarefy(rarefactionlocation, 5, se=TRUE, MARGIN=1) And get an output like this: [,1] [,2] [,3] [,4] [,5] [,6] S 3.3946786 3.6740240 3.8372812 4.0785534 3.8305276 3.7252981 se 0.9639723 0.9112285 0.8641984 0.8093713 0.8859364 0.9109916 attr(,"Subsample") [1] 5 I am also able to get a nice curve of the data, showing the six locations in separate lines, though I have been unable to distinguish lines with different colours - yet. I did this by: raremax<-min(rowSums(rarefactionlocation)) rarecurve(rarefactionlocation, step = 5, sample = raremax, col = TRUE, cex = 0.6) A colleague of mine have used the rarefaction function by Jenna Jacobs (I could not get it to work), and she received an output in an excel file with SE and approximate confidence interval for each 5 individuals for each location. By these she could find if there were significant differences between the different locations. My question is: How can I implement the SE from the output to calculate the differences (significance) between locations? Should I implement it into a new code? Or would another function be more appropriate in this case? I have also been reading about specaccum and accumpcomp, but rarefaction seems to be right for my data and for what I want to achieve... I just don’t know how I can tell the differences between locations. Thanks! Stine [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology