I am running mixed effects models with percentage data (Variable: total cavities used from a maximum number of 104 provided).

I run my model using a gaussian distribution approach. AIC=-258, my conditional and marginal pseudo-R squares were 0.33 and 0.11 respectively (very good!). I realized that I should model it using a binomial distribution because I have percentages. Now, the results are pretty similar, but my AIC=2386 is worse and pseudo-R squares diminished a lot (0.07 conditional and 0.02 marginal).

Is this saying that the gaussian approach fits the data better and therefore I should use it preferentially? How could I justify it?

I am running mixed effects models with percentage data.

I run my model using a gaussian distribution approach. AIC=-258, my conditional and marginal pseudo-R squares were 0.33 and 0.11 respectively (very good!). I realized that I should model it using a binomial distribution because I have percentages. Now, the results are pretty similar, but my AIC=2386 is worse and pseudo-R squares diminished a lot (0.07 conditional and 0.02 marginal).

Is this saying that the gaussian approach fits the data better and therefore I should use it preferentially? How could I justify it?