AIC ranking and ML vs REML using glmer

Hello, I am quite new to mixed models and hoping to get some advice on the
following:


I want to rank mixed effects models with and without different predictors
using AIC.  The fixed effects are the predictors that will change, with one
random effect staying constant in all models. The models are not nested.  I
understand that  it's not possible to compare models with different fixed
effects using REML.  Consequently,  I should be selecting ML in order to
rank the models using AICs.  However, my response variable is poisson
distributed, so it has been suggested that I use a generalized linear model
where I can select family=poisson.  I found that glmer  in the lme4 package
allows me to have a Generalized Linear Mixed Effects Model.

However, glmer does not provide the option to choose between REML and ML.
 I've been trying to figure out what it is using in this case.  When I run
the following model, it states that it has fit the model using the Laplace
approximation. In Bolker et al. (2008), it is mentioned that for this
approach, one must distinguish between ML and REML.  In this case, is the
package using ML?  More importantly, is it acceptable for me to be using
AIC to rank my various models when they have been fit with this
approximation?

model1<-glmer(ABUND~Type*DistfromRoad+(1|SiteID), family = poisson,
data=AB)

I would also like to generate a null model with only my random effect, and
no fixed effects, in order to compare it with my other models.  The package
appears to run with the following code, but there appears to be conflicting
advice about whether or not this is a reasonable thing to ask of a mixed
model package.  Is there any reason to question the output that I get from
this:
nullmodel<-glmer(ABUND~(1|SiteID), family = poisson, data=AB)

 I have it on good authority that a few of the statements in Bolker (2008)
are out of date or slightly incorrect :-) .