[R-sig-eco] Reducing spatial autocorrelation

[Matthew Landis]

That's a really great paper, but if memory serves, it focuses on 
univariate regression models.  Useful in this context for exploring the 
responses of a single species at a time, instead of a multivariate 
approach considering multiple species simultaneously.

By the way, I have the author as Dormann.

M

Marcelino de la Cruz wrote:
> 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
>>>
>>> _______________________________________________
>>> R-sig-ecology mailing list
>>> R-sig-ecology at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>>>
>>>       
>>
>> --
>> Martín 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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