I am trying to sort out the unique effect of various environmental predictors on species occurrence (presence/absence data). I have been running
glm models in R with
family=binomial. Most of my variables have very highly significant P values, but I have a large data set (~8000 data points), so this is maybe not so surprising. I have also been calculating the percentage deviance explained using the
BiodiversityR package. Some of my very significant variables based on the P value have very low explained deviance (1% or so).
Is there a rule of thumb for saying a predictor is not significant based on the deviance explained? Any other tests I should be including? I wanted to look at each variable on its own, and then test out specific interactions, but only if the variable was "important" in a model where it was the only predictor.