Choosing best modelling practices I am modelling some behavioural data in R to determine the best parameters for explaining foraging success of marine animals. I have both fine scale parameters relating to the direct individual (e.g. dive depth, trip length) and broad-scale parameters (e.g. temperature, wind, chlorophyll a).
To avoid overfitting, I currently have 2 models, one modelling fine-scale and one modelling broad-scale predictor variables and I am using model averaging to determine the best set of predictors for each model.
My question is: Can I use the broad and the fine-scale models as initial testing of the best parameters to include, and then put all the best parameters together in a single model? I've tried this out and it provides a good model with a higher predictive capacity but would this be considered bad practice? I haven't seen this done in other work (and certainly never done it myself).
Thanks!

This method of two separate models to fight overfitting is unknown to me, and I also don't really see how it should succeed. But since you have good results, you should give it a try.
Nevertheless, I would have started with a model that contains all parameters (fine and large scale). Then, there are many methods for both selecting the appropriate parameters (random forest variable importance, ARD (e.g. relevance vector machines), spike and slab, and many more, e.g. here and here), and for fighting overfitting (e.g. regularization, Bayesian methods, or appropriate information criteria) that can be applied to this model.
