[R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers

[Gavin Simpson]

I've removed R-Help from this now...

On Thu, 2012-05-10 at 10:13 +0000, Jari Oksanen wrote:
> On 10/05/2012, at 11:45 AM, Gavin Simpson wrote:
<snip />
> > As you provide little or no context I'll explain what envfit() does etc.
> > 
> > The idea goes back a long way (!) and is in my 1995 edition of Jongman
> > et al Data Analysis in Community and Landscape Ecology (Cambridge
> > University Press) though most likely was in 1987 version too. See
> > Section 5.4 of the Ordination chapter by Ter Braak in that book.
> > 
> > The idea is to find the direction (in the k-dimensional ordination
> > space) that has maximal correlation with an external variable.
> 
> 
> Hello,

<snip />

> Then about Bray-Curtis. The referee may be correct when writing that
> the fitted vectors are not directly related to Bray-Curtis. You fit
> the vectors to the NMDS ordination, and that is a non-linear mapping
> from Bray-Curtis to the metric ordination space.  There are two points
> here: non-linearity and stress. Because of these, it is not strictly
> about B-C. Of course, the referee is wrong when writing about NMDS
> axes: the fitted vector has nothing to do with axes (unless you rotate
> your axis parallel to the fitted vector which you can do). The NMDS is
> based on Bray-Curtis, but it is not the same, and the vector fitting
> is based on NMDS. So why not write that is about NMDS? Why to insist
> on Bray-Curtis which is only in the background?

Right, agreed. The analysis is one step removed from the B-C but the
point of doing the nMDS was to find a low-d mapping of these B-C
distances so in the sense that *if* the mapping is a good one then we
can talk about correlations between "distances" between sites and the
environmental variables. Whilst it might be strictly more correct to
talk about this from the point of view of the nMDS the implication is
that for significant envfit()s there is a significant linear correlation
between the environmental variable(s) and the approximate ranked
distances between samples.

I mean, if all we talk about is the nMDS who cares? it is the
implications of this for the system under study that are of interest.

That said, B-C is just one of many ways to think of distance so to my
mind I wouldn't even talk about the B-C distance either; the interest is
in differences between sites/samples. The relevance of B-C or some other
coefficient only comes in when considering if they are a good descriptor
of the "distance" between samples for the variables you are considering.

Cheers,

G

> Cheers, Jari Oksanen
> 

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