Hi Class,My data are number of species per site (specie richness). Regarding the sensitivity of the species richness to the spatial scale I believe and I am pretty sure that I can assume that all species react at the same way to the spatial scale since I am working only with forest in which ecological process such as dispersal should be quite similar between plant species.Moreover, species richness is expected to be influenced by two main aspects of spatial heterogeneity: 1) the variability ofenvironmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal.
Any suggestions and discussions are well accepted.
Thanks for replies,
Regards,Ludovico

                                                                                                                                
Ludovico
Frate

PhD student (University of Molise - Italy)
Environmetrics Lab
http://www.distat.unimol.it/STAT/environmetrica/organico/collaboratori/ludovico-frate-1
Department of Biosciences and Territory - DiBT
Università del Molise.
Contrada Fonte
Lappone, 
86090 -  Pesche (IS)
ITALIA.
Cel: ++39
3333767557
Fax: ++39 (0874) 404123
E-mail ludovico.frate@unimol.it
ludovicofrate@hotmail.it


> From: r-sig-ecology-request@r-project.org
> Subject: R-sig-ecology Digest, Vol 71, Issue 14
> To: r-sig-ecology@r-project.org
> Date: Sun, 23 Feb 2014 12:00:01 +0100
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> Today's Topics:
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>    1. Re: R-sig-ecology Digest, Vol 71, Issue 13: SDM-model
>       evaluation (via AUC or AIC) (Claas Damken)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Sat, 22 Feb 2014 12:15:16 +0000
> From: Claas Damken <c.damken@auckland.ac.nz>
> To: "r-sig-ecology@r-project.org" <r-sig-ecology@r-project.org>
> Subject: Re: [R-sig-eco] R-sig-ecology Digest, Vol 71, Issue 13:
> 	SDM-model evaluation (via AUC or AIC)
> Message-ID:
> 	<083E7BEE32A3304994EF5EC4D46962607B0D40D7@uxcn10-tdc02.UoA.auckland.ac.nz>
> 	
> Content-Type: text/plain; charset="us-ascii"
> 
> Hi Ludovico,
> 
> I have my doubts that AUC is suitable for count data analysis, since its based on the ROC, a measure of how well a parameter can distinguish between two diagnostic groups (i.e., presence vs. absence). But for count data, say 123 species at site a and 321 species at site b, most if not all model settings will fail to hit exactly those numbers. YOu could try and calculate the AUC for one of your models, but I would guess it would be lower than 0.5, but for the wrong reasons.
> 
> AICc and AICc weights are a better way to select model parameters, and Nagelkerkes N2 will give you additional information about the overal fit of your models. However, model selection via AIC or any of the other ICs is not straightfoward either and - FAR MORE IMPORTANT than choosing a statistical method - your data are "number of species per site" and not simply count data for one species? For the latter, an ecological parameter at a certain spatial scale might explain the number of individuals at site x very well, but if you are dealing with many species, is it ecologically speaking reasonable to assume that all species react to the same ecological parameters at the same spatial scale? Not sure what data you are working with, but for many systems, your results will be fairly predictable (not in a positive way).
> 
> Hope that helps
> 
> Cheers,
> 
> Claas
> 
> Claas Damken
> Postdoctoral Fellow
> Institute for Biodiversity and Environmental Research
> Jalan Tungku Link, Gadong BE1410
> Universiti Brunei Darussalam
> Brunei Darussalam
> 
> 
> 
> ________________________________________
> 
>    1. Re: SDM-model evaluation (Martin Weiser)
>    2. Re: SDM-model evaluation (Frederico V. Faleiro)
> 
> Message: 1
> Date: Fri, 21 Feb 2014 13:31:32 +0100
> From: Martin Weiser <weiser2@natur.cuni.cz>
> To: Ludovico Frate <ludovicofrate@hotmail.it>
> Cc: "r-sig-ecology@r-project.org" <r-sig-ecology@r-project.org>
> Subject: Re: [R-sig-eco] SDM-model evaluation
> Message-ID: <1392985892.22618.85.camel@zouzel.natur.cuni.cz>
> Content-Type: text/plain; charset="UTF-8"
> 
> Hi,
> 
> I am not sure why AUC should be better than, say, AICc - second order
> Akaike criterion and AIC weights (comfortably calculated by AICcModavg
> package) or Nagelkerke's R2 in this case. Would anybody be so kind and
> explain this for me?
> I just want to make warning: do not put strong (or any) emphasis to
> p-values of partial tests.
> 
> Best,
> Martin
> 
>    Ludovico Frate p??e v ?t 20. 02. 2014 v 14:19 +0100:
> > Hi all,I am trying to built a species distribution model with count data (Species Richness) using a GLM. My independent variable are computed at multiple-scales. In order to select which is the best scale for my model, I first calculate a bivariate model for each of the predictor for each scale. So, I know that one possible way to select the best predictor is the use of the AUC, but I don't know if is possible to use the AUC for count data. I have read about presence/absence data (for example in the package DISMO, function evaluate) but never for count data! Any suggestions?Thank you!
> >
> >
> > Ludovico
> > Frate
> >
> > PhD student (University of Molise - Italy)
> > Environmetrics Lab
> > http://www.distat.unimol.it/STAT/environmetrica/organico/collaboratori/ludovico-frate-1
> > Department of Biosciences and Territory - DiBT
> > Universit del Molise.
> > Contrada Fonte
> > Lappone,
> > 86090 -  Pesche (IS)
> > ITALIA.
> > Cel: ++39
> > 3333767557
> > Fax: ++39 (0874) 404123
> > E-mail ludovico.frate@unimol.it
> > ludovicofrate@hotmail.it
> >
> >       [[alternative HTML version deleted]]
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