[R-sig-eco] null models for a single species

[Penner, Johannes]

Dear Prof. Oksanen,

thank you very much for the quick reply! I will try to give more details on the problem.

The data is from a colleague and she is investigating what are the responsible factors (environmental and genetic) for selection of ponds by reproducing frogs (one species only). So far all the usual ordination (nmds, pca, cluster analysis, etc.) has provided no result. No environmental factor tested seems to be important.

Therefore, one of the hypotheses is that the frogs do not select at all and take ponds at random.

We would now like to test that statistically but we do not know how. Do you mean that by "structured model" and "structured hypothesis" or the structure of the ponds in the landscape?

Thank you very much for your help!

With kind regards
Johannes



-----Ursprüngliche Nachricht-----
Von: Jari Oksanen [mailto:jari.oksanen at oulu.fi] 
Gesendet: Dienstag, 8. März 2011 13:42
An: Penner, Johannes; r-sig-ecology at r-project.org
Betreff: Re: [R-sig-eco] null models for a single species

On 8/03/11 10:54 AM, "Penner, Johannes" <Johannes.Penner at mfn-berlin.de>
wrote:

> Dear List members,
> 
> I would like to test whether an observed occupancy of lakes in a landscape has
> occurred randomly (by chance) or not.
> 
> How can I do that? The problem is that it concerns only a single species and I
> would like to use binary data only.
> 
> At first I thought of generating null models and test the observed occupancy
> against the randomly generated one. However, this needs more than one
> species...
> 
> Any hints are highly appreciated!
> 
Johanne,

Actually many of the null models as defined in vegan would work here: you
only provide a one-column matrix. Although they work, they would not make
much sense: null models of type "r00" and "c0" would only give you random
permutation of your data (and "c0" would give you the data). Naturally, this
is one way to go: just permute your observations. For simple permutations
you can use sample() function of base R, and for constrained permutation you
can download Gavin Simpson's 'permute' package from
http://www.r-forge.r-project.org/. However, if you have a structured model
and a structured hypothesis you can do much better than have a simple
permutation. I have no idea of your hypothesis, though, and I can't help
here.

Cheers, Jari Oksanen