[R-sig-eco] Reducing spatial autocorrelation

[Matthew Landis]

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
>