I am a student who is performing a meta-analysis and want to select the best regression model (in terms of AICc values) that explain what are the explanatory variables that can explain better the negative impacts of nitrogen fertilization on plant biodiversity. The idea then is to always keep nitrogen amounts as explanatory variable ("mods") and then include other 7 (continuous) variables (like precipitation or temperature) and categorical variables (e.g. type of nitrogen fertilizer) in the model and to see how the nitrogen fertilization's term changes the slope.
1)What is exactly the role of interaction (obtained with "*" in the moderators in a lm or rma function?). How should I use it? Is it true that I should exclude from the model the other explanatory variables that shows an high significance in the interaction term of my model results?
2)Would you raccomand to just rely on model selection based on the 'glmulti' function? Or it would be better to play with the explanatory variables by myself? Although it is not my case, I know that glmulti might drop out the variable that I always need to keep included in my model (amount of nitrogen fertilization).
I hope my question is not too broad.