How to code proportional data with gamm binomial model using gamm4 package?

Dear user,
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I am trying to fit an additive mixed model with proportional data as
response variable using gamm4 package. My proportional data are composed of
1) the number of time an individual was observed at location i (=presence),
and 2) the number of time each location i was visited (=total). In my data
set, I also have one random variable (=bloc) and a set of continuous
independent variables that could explain part of the response variable
variability. 
Unfortunately, I have trouble to specify the response variable in the
model. When I try to code proportional dataas "cbind(presence,total)", I
don't have access to the "gam" object in the summary() function, while I
can get the "mer" object which is, fortunately, similar to that from
glmer() model... When I try to code proportional data directly as
"proportion" ([0,1]) where proportion = presence/total, I can get all what
I want except that model parameters and AIC values does not match the
previous one anymore. I guess that I am doing something wrong here... 
My question is how to code proportional response variables to get the "gam"
object in the summary() function and that gives consistant results of AIC,
parameter estimates, etc...? 
My R code is below. It is a simple version where I included only one
independent variable and no smooth term yet but in my real data set, I will
use several independent variables, potentially with smoothers. 
Any help would be really appreciated,
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Many thanks,
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Julien Beguin
 Laval University
 Ph.D. candidate


 ----------------------------------------------- R code
---------------------------------------- #################
 # simulated data#
 #################
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set.seed(1001)
 presence <- round(runif(50,0,15))
 total <- round(runif(50,50,70))
 prop <- presence/total
 bloc <- rep(c(1,2,3,4,5), each=10)
 x1 <- rnorm(50, 20, 5)
 mydata <- as.data.frame(cbind(presence, total, prop, bloc, x1))
 mydata$bloc <- as.factor(mydata$bloc)
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#############################################################
 # gamm model with response variable as cbind(presence,total)#
 #############################################################

mod1 <- gamm4(cbind(presence,total)~ x1,
random=~(1|bloc),family=binomial(link = "logit"), data=mydata)
summary(mod1$gam)

Error in object$y - object$fitted.values : non-conformable arrays

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summary(mod1$mer)

AIC?? BIC logLik deviance

 > 150.7 156.4 -72.33??? 144.7
 >
 >Fixed effects:
 >???????????? Estimate Std. Error z value Pr(>|z|)???
 >X(Intercept) -1.54827??? 0.21700? -7.135? 9.7e-13 ***
 >Xx1????????? -0.02927??? 0.01110? -2.637? 0.00837 **

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mod2 <- gamm4(prop~ x1, random=~(1|bloc),family=binomial(link = "logit"),
data=mydata)

 summary(mod2$gam)

Parametric coefficients:
 >??????????? Estimate Std. Error z value Pr(>|z|)
 >(Intercept) -1.35978??? 1.68946? -0.805??? 0.421
 >x1????????? -0.03236??? 0.08600? -0.376??? 0.707

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summary(mod2$mer)

AIC?? BIC logLik deviance

 > 8.983 14.72 -1.492??? 2.983

Fixed effects:
 >???????????? Estimate Std. Error z value Pr(>|z|)
 >X(Intercept) -1.35978??? 1.68946? -0.805??? 0.421
 >Xx1????????? -0.03236??? 0.08600? -0.376??? 0.707

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mod3 <- glmer(cbind(presence,total)~ x1 + (1|bloc), family=binomial(link =
"logit"), data=mydata, REML=FALSE) summary(mod3)

AIC?? BIC logLik deviance

 > 150.7 156.4 -72.33??? 144.7

Fixed effects:
 >??????????? Estimate Std. Error z value Pr(>|z|)???
 >(Intercept) -1.54827??? 0.21700? -7.135? 9.7e-13 ***
 >x1????????? -0.02927??? 0.01110? -2.637? 0.00837 **

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mod4 <- glmer(prop~ x1 + (1|bloc), family=binomial(link = "logit"),
data=mydata, REML=FALSE) summary(mod4)

AIC?? BIC logLik deviance

 > 8.983 14.72 -1.492??? 2.983

Fixed effects:
 >??????????? Estimate Std. Error z value Pr(>|z|)
 >(Intercept) -1.35978??? 1.68946? -0.805??? 0.421
 >x1????????? -0.03236??? 0.08600? -0.376??? 0.707