I am starting an analysis, for which I have a binomial response variable (species relative abundance) and continuous predictors (habitat variables). I have done some data exploration, and there is reason to expect some non-linear relationships and spatial autocorrelation. I am starting with exploratory GAMs, and plan to use GLMs but will likely need to try GLMM to account for random effects (year, month, site). I want to follow the Burnham and Anderson (2002) approach to model selection, based on an a priori set of models. I know enough about the system in order to make a set of models (which variables to include, which interactions to include), however I do not know which types of models will be "best" (most parsimonious).
So my question is: Since I can not compare between different types of models (GAM, GLM, GLMM) using this approach, and the approach advocates against stepwise selection and unnecessary data-dredging, at what point is it acceptable to play around with variations of one model? Can I try several variations on my global model, using different methods to account for autocorrelation, random effects, overdispersion, etc, and then use the best as a basis for my subset of models? Or should I just choose what I think will be the best, and then after model selection can try different options on the best model (or group of models)?