Dear Pierre,
Thank you very much for your answer. In fact I would like to make two different analyses: one spatial and one temporal. For the spatial analysis, I will compute the
dissimilarities in the way you suggested it, using beta.pair and dbRDA. For temporal analysis of beta diversity between sites, Baselga proposed to use the function
beta.temp that produces for each site a value for beta1, beta2 and betaTotal. I have 30 sites, 10 of factor level 1, 10 of factor level 2 and 10 of factor level 3. I
thought that the best way to look at the relationships between the factor and the components of beta diversity was to make a logistic regression as mentioned
earlier: glm(cbind(beta1,beta2)~x,family=quasibinomial). However since beta1 and beta2 are non-integers I am not sure about being allowed to use binomial
regression. I would like to mention as well that using "family=binomial" I get a warning about non-integer values, whereas by using "family=quasibinomial" no such
warning appears. My model being not overdispersed, there would be no justification of using a quasi-model. But maybe somebody may have some more information
about this.
Thank you
Val?rie
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