zero values generalized mixed model

Hi all, I?ve got a question concerning a generalized mixed model
where some levels only have zero values. I will try to explain my
setup: I am testing location preference of ant-associated beetles
(15 species) in ant nests. Therefore I made six-chamber nests with
6 identical connected pots. Ant workers stored all their brood
(standardized volumes) in one chamber. This brood chamber also
contained most workers. My aim is to test whether some species are
attracted or repulsed from those dense brood chambers. Later, I
want to link this location preference with other traits of the
myrmecophiles (e.g. degree of parasitism). I used a generalized
(binomial) mixed model with presence (score 1) / absence (score 0)
in the brood chamber as dependent variable. A model without
intercept and an offset of (logit(1/6) was used to directly test
the deviation of the observed proportions from the expected
proportion by random distribution (1/6). Species = 15 species of
myrmecophiles, replicate = experiment was run 15 times. Model: 
modelbrood<-glmer(BROOD_CHAMBER~offset(datasetlocation$baseline)
-1+SOORT+(1|REPLICATE)+(1|ID), binomial(link=logit),
data=datasetlocation) So far, everything runs fine. However two
species were never observed in the brood chambers, even with more
than 50 individuals tested in total. So  they show a clear aversion
of the brood chambers. However, when tested, SE are extremely large
for those species, and consequently P values of 0.8 are given. So
apparently the model struggles with levels of a factor with only
zero values. When I change one zero to one in both species, the SE
are much more reliable.

So my question is there a way to handle zero values in generalized
mixed models?

Thanks a lot,