Using Observations as Random Effect in GLMM?

I've been looking recently at animal count data that I've modeled
as Poisson with an observation level random effect, and have
worried a bit about such issues.

The observation level random effects model and the over-dispersion 
model add variances on different scales -- for the observation level
random effects random effects model the added variance is 
proportional to the square of the Poisson mean, whereas for the
over-dispersion model it is proportional to the mean. (These 
comments assume small additional error; but they do delineate
the broad ballparks in which the two models operate.   The glmer() 
function is making its own very specific assumptions about the 
scale on which to add the additional normal error.

The models are thus pretty much equivalent only if the range of 
expected values is small.  It would be useful to have more flexibility,
at the observation level at least, in the modelling of the extra-Poisson
error.  Among the various packages that handle GLMMs, do any of
them offer such flexibility, maybe allowing e.g. a quasi-Poisson error? 

(Sure, there are issues about how legit quasi-Poisson errors are.  
I expect however someone will sometime work out how to give them 
full theoretical respectability, and they will duly be admitted to the part 
of the statistical pantheon allocated to those models that are thus 
theoretically respectable.)