[R-sig-eco] Which R package for Second-Stage nMDS ?

[Pierre THIRIET]

Dear useRs,

I want to perform 2nd stage nMDS, as described in Clarke, K.R., et al 
(2006). Exploring interactions by second-stage community analyses. 
Journal of Experimental Marine Biology and Ecology 338, 179-192. See 
Abstract below

Do you know a package in R for that ? Or would you have home-made 
scripts, at least a function for computing the distance matrix of 
pair-wise correlations among dissimilarity matrices ?

Thank you,

Pierre


Abstract of Clarke et al 2006 :

Many biological data sets, from field observations and manipulative 
experiments, involve crossed factor designs, analysed in a univariate 
context by higher-way analyses of variance which partition out ‘main’ 
and ‘interaction’ effects. Indeed, tests for significance of 
interactions among factors, such as differing Before–After responses at 
Control and Impact sites, are the basis of the widely used BACI strategy 
for detecting impacts in the environment. There are difficulties, 
however, in generalising simple univariate definitions of interaction, 
from classic linear models, to the robust, non-parametric multivariate 
methods that are commonly required in handling assemblage data. The size 
of an interaction term, and even its existence at all, depends crucially 
on the measurement scale, so it is fundamentally a parametric construct. 
Despite this, certain forms of interaction can be examined using 
non-parametric methods, namely those evidenced by changing assemblage 
patterns over many time periods, for replicate sites from different 
experimental conditions (types of ‘Beyond BACI’ design) – or changing 
multivariate structure over space, at many observed times. *Second-stage 
MDS, which can be thought of as an MDS plot of the pairwise similarities 
between MDS plots (e.g. of assemblage time trajectories), can be used to 
illustrate such interactions, and they can be formally tested by 
second-stage ANOSIM permutation tests. Similarities between 
(first-stage) multivariate patterns are assessed by rank-based matrix 
correlations, preserving the fully non-parametric approach common in 
marine community studies. *The method is exemplified using time-series 
data on corals from Thailand, macrobenthos from Tees Bay, UK, and 
macroalgae from a complex recolonisation experiment carried out in the 
Ligurian Sea, Italy. The latter data set is also used to demonstrate how 
the analysis copes straightforwardly with certain repeated-measures designs.


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