Variance components in a binomial mixed model

Howdy,
I have a question sort of related to Ben's "differing variances within
different random effects levels".
I have a data set consisting of 23 mice, 11 Treated and 12 controls. The
mice are bred to have
a neurodengerative disease, and their neuromuscular connections degrade over
time. So the animals were sacrificed and
a left and right leg muscle were removed and sectioned, fixed on slides and
stained appropriately. There were 12 slides
per a muscle, and 6 sections per slide. Two slides per side were chosen, and
in all sections on the slide the total number of neuro-muscular
junctions, and the number innervated was counted. Oh and within each muscle,
there were 5 compartments, so counts are by muscle compartment.
So I have a binomial response, and for fixed effects I have Sex +
Treatment*Musclecompartment
and random (1|Animal/Side/Slide/Section) So the model is

gm1 <- glmer(cbind(Innervated.count,Total.count-Innervated.count) ~ Sex +
Treatment*Muscle.Compartment+(1|animal.ID/Side/Slide/Section), data=dat,
family = binomial)

So my questions are about interpretation of the variance components in a
binomial model.
1) the variance component for slide is 0, is that because there are only 2
slides
     or is something else going on, there is an estimated variance for side,
and when I last
     counted there were only 2 of those as well :)

2) do the variances/covariances have a similar interpretation to the
Gaussian case
     for which in an lmm everything is easier to understand? Meaning that
the error term is
    binomal, glm part, but the random effects are gaussian, so i am looking
at the variances
    from a mixture model, or is that just the integrated variance over all
levels.