Reducing spatial autocorrelation

I would recomend the paper of  Dortman et al. 
(Ecography 30: 609628, 2007). This reviews  many 
available spatial statistical methods to take 
spatial autocorrelation into account in tests of 
statistical significance. From their abstract:

"Here, we describe six different statistical 
approaches to infer correlates of species? 
distributions, for both presence/absence
(binary response) and species abundance data 
(poisson or normally distributed response), while accounting for
spatial autocorrelation in model residuals: 
autocovariate regression; spatial eigenvector mapping; generalised
least squares; (conditional and simultaneous) 
autoregressive models and generalised estimating equations."

The suplementary material includes R scripts to run all the methods.

HTH

Marcelino





At 14:49 14/10/2009, Martin Alejandro Piazzon de Haro wrote:
  

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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 at 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 at york.ac.uk

--
Mart?n Alejandro Piazzon de Haro
PhD Student

IMEDEA (CSIC)
C/Miquel Marques, 21
(07190) Esporles
Mallorca, Illes balears.
Spain.
Tlf: (+34) 971611807

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