Type of input data (proportions or log-ratios) for compositional analysis I am comparing tree species use vs. availability within bird territories. I am using the R compana() function which uses Aebischer et al. (1993) method (package adehabitatHS) to determine if there are significant differences in proportional availability and proportional use of different tree species. 
I am trying to determine if the input to the program needs to be proportions (of both use and availability) or log-ratios of use and availability.
Additionally, I am also trying to figure out whether or not the compana code for compositional analysis automatically replaces missing values in the available matrix by using the weighted mean $\lambda$ when you specify test="randomization", which means that randomisation tests are performed for both the habitat ranking and the test of habitat selection (check out CRAN website for more information). I just can’t figure out if compana does the replacement of zeros in the available matrix automatically or if you need to do something else with the code.
 A: The input should be proportions. One data set with used proportions and one with available. Try looking at the example data from Aebischer et al., 1993 supplied by writing:
## load data and display it
data(pheasant)
pheasant

It uses the weighted mean λ method, described in Aebischer et al., 1993 paper, to replace zeros in the available matrix automatically. You can convince yourself of this by running the example from the R help on this method, and changing test to test="randomization" :
#############################
## Pheasant dataset: first
## example in Aebischer et al.

data(pheasant)

## Second order habitat selection
## Selection of home range within the
## study area (example of parametric test)
pheana2 <- compana(pheasant$mcp, pheasant$studyarea,
                   test = "randomization")
pheana2

Then change some of the availability data (pheasant$studyarea) to 0, and run it again.
You could also take a look at the code by just writing the function without "(  )" like this:
compana

As stated in the examples from R it would be a good idea to first perform an eigenanalysis of selection ratios, preliminary to the use of compositional analysis. To the test if the underlying hypotheses for compositional analysis are correct. E.g the compositional analysis method rely on the following hypotheses:  (i) independence between animals, and (ii) all animals are selecting habitat in the same way. See R help for more information:
?compana
?eisera

I have tried something similar, but ended up pooling the trees in categories, as there where to many 0 in the availability data, and I had 13 different tree species.
