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null models with continuous abundance data

Dear Etienne,

You can try the Chris Hennig's prablus package which have a parametric
bootstrap based null-model where clumpedness of occurrences or
abundances (this might allow continuous data, too) is estimated from
the site-species matrix and used in the null-model generation. But
here, the sum of the matrix will vary randomly.

But if you have environmental covariates, you might try something more
parametric. For example the simulate.rda or simulate.cca functions in
the vegan package, or fit multivariate LM for nested models (i.e.
intercept only, and with other covariates) and compare AIC's, or use
the simulate.lm to get random numbers based on the fitted model. This
way you can base you desired statistic on the simulated data sets, and
you know explicitly what is the model (plus it is good for continuous
data that you have). By using the null-model approach, you implicitly
have a model by defining constraints for the permutations, and
p-values are probabilities of the data given the constraints (null
hypothesis), and not probability of the null hypothesis given the data
(what people usually really want).

Cheers,

Peter

P?ter S?lymos
Alberta Biodiversity Monitoring Institute
Department of Biological Sciences
CW 405, Biological Sciences Bldg
University of Alberta
Edmonton, Alberta, T6G 2E9, Canada
Phone: 780.492.8534
Fax: 780.492.7635
On Wed, Jan 6, 2010 at 2:18 AM, Carsten Dormann <carsten.dormann at ufz.de> wrote: