Dear friends,

I found this thread very useful, so I wanted to apport something, Corrado,
you asked for some references about PCNM, here is what i found:

Borcard, D. and Legendre, P. 2002. All-scale spatial analysis of
ecological data by means of principal coordinates of neighbour
matrices. Ecological Modelling 153: 51-68.

Borcard, D., P. Legendre, Avois-Jacquet, C. & Tuomisto, H. 2004.
Dissecting the spatial structures of ecologial data at all scales.
Ecology 85(7): 1826-1832.

I hope will help you.

2009/10/14 Corrado <ct529@york.ac.uk>

> Dear Matthew,
>
> thanks for your kind answer!
>
> The first approach you describe is the one I have been looking at until
> now.
>
> I am puzzled about the second one: I do not really understand it. What
> model
> are you talking about, when you say "incorporate the spatial variation in
> the
> model"? At the moment I have no model, just the data and I am trying to
> reduce
> autocorrelation before analysing the data.
>
> Do you have any good reference (articles or books) about the approach you
> mention?
>
> Thanks in advance
>
>
> On Wednesday 14 October 2009 13:11:04 Matthew Landis wrote:
> > Corrado:
> >
> > The simplest way would be to take a subset of sites to maximize the
> > distance between them.  Say, choose 400 sites evenly spread over the
> > study area.  That would minimize autocorrelation to the greatest extent
> > possible, but you would be throwing away data.
> >
> > The second thing you could try would be to incorporate the spatial
> > variation in the model to control for it.  This way you can also study
> > the autocorrelation, see what spatial scales it is operating and what it
> > looks like and try to learn something from it.  Legendre, Borcard, Dray
> > and colleagues have developed some really interesting ways of dealing
> > with multivariate data and decomposing the variance into spatial
> > component vs. explanatory variables.  I believe it is called PCNM and
> > can be found in the spacemakeR package (don't think it is on CRAN - have
> > to do a google search).
> >
> > Good luck!
> >
> > Matthew Landis
> >
> > Corrado wrote:
> > > Dear friends,
> > >
> > > I have a large matrix of species (first 1100 columns) and environmental
> > > variables (last 36 columns) for approx 2000 sites.
> > >
> > > The distance between sites varies. Some sites are near to each other,
> > > others are far.
> > >
> > > I would like to select a subset of N sites (for example: 400 sites)
> with
> > > the minimum spatial autocorrelation. The aim is to obtain a significant
> > > number of sites to carry out some statistical analysis, but with
> spatial
> > > autocorrelation significantly reduced.
> > >
> > > Is there a procedure to do so in R? How would you approach the problem?
> > >
> > > The aim of the "reduction" is to then work on dissimilarities between
> > > sites that have the lowest possible spatial autocorrelation.
> > >
> > > Thanks
>
>
>
> --
> Corrado Topi
>
> Global Climate Change & Biodiversity Indicators
> Area 18,Department of Biology
> University of York, York, YO10 5YW, UK
> Phone: + 44 (0) 1904 328645, E-mail: ct529@york.ac.uk
>
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>



-- 
Martín Alejandro Piazzon de Haro
PhD Student

IMEDEA (CSIC)
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Spain.
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