Hi all,
I have a data set of species data in different sites but instead of
abundances, the presence has been converted to a rank score site-species
matrix. The ranks are calculated based on max-scores that have been log
transformed ln(x+1).
0 = absent and 5 = highest abundance for that species relative to other
sites and other species.
I would like to carry out multivariate analyses on this data such as PCA /
PCoA RDA/Variation partitioning.
I assume no transformation is needed for this data since they are already
ranks.
My questions are :
*1) What is the best dissimilarity measure to use for this rank data?*
I read that Gower's distance *gowdis()* in {FD} or *daisy()* in {cluster}
may be good choices?
*2) Is it appropriate to conduct RDA on rank data? *
Is an there a better alternative?
As a bonus question - can I treat these data the same as I would abundance
data in more advanced analyses such as beta-diversity analyses (e.g.*
betadisper()
in {vegan} ) *or spatial eigenvector mapping (e.g. *dbMEM / AEM in
{adespatial}* )?
Many thanks
Tania
Tania Bird MSc
PhD Student: Coastal dune biodiversity conservation in Nizzanim LTER
Dept. of Geography & Environmental Development,
Ben Gurion University, Beer Sheva
https://www.linkedin.com/in/taniabird
[[alternative HTML version deleted]]
_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
