Mixed model and negative binomial distribution

Dear list: this is a continuation of a previous question. To recap,after
input from several listserv members, I have been using glmmADMB to model
snag (dead tree) densities before, after, and 5 years after forest (factor
variable 'time'). (I apologize for disregarding the advice to use 'BUGS'
approaches -- I didn't have $ for new books or time for assimilation of new
paradigm). The data sampling was stratified by 'Ecozone' ('East' densities
are much lower than 'West'). Because there are many plots with no snags, I
evaluated use of Poisson vs. Negative binomial distribution by finding mean
& variance for each combination of time & Ecozone. AIC for the NB
distribution was vastly smaller (more favorable) than for the Poisson.
Variance tended to be much higher prior to treatment (because a few plots
have large numbers of snags and loggers are instructed to knock most of
these down and simply retain a few).

My statement is glmmadmb(snag~time+Ecozone + (1|SettingID), data=.,
family="nbinom1"). ('SettingID' is a plot identifier).

So, I have these observations/questions. First, when I experiment with
'family="poisson", AIC for the glmm is only 2 AIC units greater than for the
negative binomial model, yet with AIC estimated for individual cells, AIC
Poisson >> AIC NegBinom. Any idea why this might be? (I've tried
zero-inflation for poisson & NB, doesn't seem to make much difference)

It's tricky to interpret the log-transformed coefficients and associated
confidence intervals, but they don't seem to reflect the decrease in snags
after treatment that I see when I examine the cell means (e.g., mean East
density at time1 (pre-treatment) is 1.7, dropping to 0.7 at time2 (after
treatment).) Here's part of the summary statement:

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)     -0.764      0.359   -2.13   0.0334 * 
time2             -0.588    0.186   -3.16   0.0016 **
time3            -0.604     0.189   -3.20   0.0014 **
EcozoneW     1.249          0.487    2.57   0.0103 *