Dear all,

I make habitat suitability models for animal species. The purpose of my
research is to investigate the accuracy of different models.

I clearly have a nested design:

- accuracy_measure -> response variable
- 2 model types (model_type) -> fixed effect
- 230 species (species) -> random effect
- 10 replicates/species (replicate) -> random effect
- 10 subreplicate/replicate -> observation

So I have: 10*10*230 observations/model, identified as
speciesID_replicate_subreplicate (species_ID ranging 1:230, replicate 1:10
and subreplicate 1:10)

One could think about such mixed-effect model:

my.model<-lme(fixed = accuracy_measure~model_type, random =
~1|species/replicate)

I do not expand here into model simplification nor if it is best to use lme
or lmer, YET... but here are more conceptual questions

1) my replicates & subreplicates are paired in the sense that they come
from the same split of the data. As an example for species X, the 20
observations of replicate1 of model A and B (10 for A and 10 for B) are
linked by a same data split which is likely to influence my accuracy
measure. In the same way the 2 observations replicate1_subreplicate2 of
model A and B (1 for A and 1 for B) are also linked. Is there a way to
introduce such pairing in a mixed effect model?

2) several continuous covariates, attributes of species (number of points
for modelization, size in km2 of the range) may influence the measures of
accuracy and I may be interested in investigating those effects. How could
I include a covariate in such model? How should I strucutre it given that
there are species covariates (high order of nesting)?

I hope that my questions are relevant!
Thank you very much in advance for your help!


Nathan

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