Quasi-poisson glm and calculating a qAIC and qAICc...trying to modilfy Bolker et al. 2009 function to work for a glm model

Sorry about re-posting this, it never went out to the mailing list when I
posted this to r-help forum on Nabble and was pending for a few days, now
that I am subscribe to the mailing list I hope that this goes out:

I've been a viewer of this forum for a while and it has helped out a lot,
but this is my first time posting something.

I am running glm models for richness and abundances.  For example, my beetle
richness is overdispersed:

qcc.overdispersion.test(beetle.richness)

Overdispersion test Obs.Var/Theor.Var Statistic   p-value
       poisson data          2.628131  23.65318 0.0048847

So, I am running a simple glm with my distribution as quasipoisson

glm.richness1<-glm(beetle.richness~log.area, family = quasipoisson)

Now I want to calculate a qAIC and qAICc.  I was trying to modify the
equation that I found in Bolker et al 2009 supplemental material:

QAICc <- function(mod, scale, QAICc=TRUE){
        LL <- logLik(mod)

        ll <- as.numeric(LL)
        df <- attr(LL, "df")
        n <- length(mod$y)   #used $ to replace @ to make a S3 object
        if(QAICc)
                qaic = as.numeric( -2*ll/scale + 2*df +
2*df*(df+1)/(n-df-1))
        else qaic =as.numeric( -2*ll/scale + 2*df)
        qaic
}

The only problem is that I have no idea how to scale it.  In Bolker at al.
2009 it is scaled to "phi":

phi = lme4:::sigma(model)

But I am not running a mixed model and I cannot run the qAICc function
without scaling it.  I am comparing models to each other trying to find the
best model for both landscape land use land cover data and patch variables.
 How would I set the scale if I run this function?

QAICc(glm.richness1, scale = ?)

Should I set the scale to the square root of the deviance?  phi =
sqrt(glm.richness1$deviance)

Your help is much appreciated.

Regards,
Jason