Dear list,
I have been using the vegan package to do mds via the metaMDS function, but I
have some questions regarding the output.
1) First off about the rankindex function {vegan}: On my data I always get
values that I would consider as low, e.g. something in the range of 0.0344 as
best result (euclidean) and the mean being 0.028 over 7 other metrices. Do
results as low as this have any relevance? Are there some guidelines as to what
absolute (or relative) values one should at least obtain to make a distinction?
2) Is there a way to estimate what percentage of the variation within the data
can be explained by the mds?
3) using envfit {vegan} I get significant p-values for 5 out of 14 env.
variables/factors (which is of course very nice). However, if I do a CCA and a
ANOVA (call: anova(cca,by="terms",permu=200)) with the same environmental
values, usually only one of these same variables/factors ends up being
significant. I am aware that these are different techniques, but I always
thought that CCA was supposed to "force" the ordination on the env. vars, so
why then would I get much better p-values for the unconstrained nmds (I use 5
dimensions in the nmds)?
4) how can I interpret the relation between species and the environmental fit
in a nmds plot call? The same as sites and env. fit?
e.g.
ef=envfit(nmds,environment)
plot(ef); points(nmds, dis = "species");
Any help or links to relevant literature would be greatly appreciated.
best,
Falk
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