Poisson for percentage data if values are low? I have percentage data for diet per area (example here.....)
I have no data on the individuals contributing to this diet, only for the population as a whole for each area. 

I want to assess whether the size of the area significantly affects the percentage of grains in the diet (so a GLM with area as the explanatory variable, and % grains as the response, ignoring all other diet items for now..)
I just want to check which distribution these data would fall under...I fitted the model using a Poisson distribution as, although %, all the values are fairly low (47 is the highest for the whole dataset). It was overdispersed, so I then tried a negative binomial which seemed to correct the problem. 
Does this sound like the correct approach? 
 A: These percentages look like continuous proportions, like the percentage of fat in milk, and as such shouldn't normally be analyzed with count-data models like the Poisson. 
[In addition, the Poisson is unbounded on the right. I simply wouldn't use the Poisson on this problem at all.]
A more typical model for such proportions would be the beta. It's bounded in (0,1) and is often used for such continuous proportions. Search here (and elsewhere) on beta regression, for example. 
Many packages offer beta regression in some form, or something that would be suitable for continuous proportions. You might looks at a transformation (either to linearize the mean and weight for the variance function or to stablize the variance and use a nonlinear model), but I'd see if you could do beta regression or something else suited for modelling continuous proportions first.
In some cases*, you might get away with a quasi-binomial model for the proportion since it has the same mean-variance relationship as the beta; the logit link (or one of the other typical links for the binomial) may also make sense for your problem. You may have to do some small amount of fiddling to get it to work (the $n$ will essentially be arbitrary, but you have a compensating scale factor in the dispersion parameter).
*(I'd at least want proportions not too close to 0 or 1 and probably largish sample sizes as well)
