testing for distribution

Dear all,
 
I have a kind of a theoretical question from which I hope it might interest you and hopefully can help me a bit.
 
In order to obtain ecological (surrvey) data, I try to make a prediction about the accuracy of a sampling tool to estimate mussel density. For this reason I took a lot of samples at a certain fixed location and counted the amount of mussels in each sample. Because mussels are aggregated on the sediment, I had a lot of zero values. To estimate the sample size I used a binomial distribution and obtained the k value and the mu from the fitdistr(x,"negative binomial") (MASS).
 
The question I have is: how can I test if this distribution accurately described my (zero inflated count) data?
 
I am a bit familiar with the AIC but since I only have counts on one variable I cannot perform a GLS. 
Creating a vector with rnbinom() using the k and mu from the fitdistr() I plotted a histogram and compared it with my data, this showed that is was roughly comparable, but I want to quantify this.
 
I have a biological background not a statistical one, so I realize I can ask silly questions.
But I hope someone can give me some hints. 
 
Kind regards,
 
Jacob Capelle
 
PhD student
Wageningen Imares
The Netherlands
jacob.capelle at wur.nl <mailto:jacob.capelle at wur.nl>