I am studying the effect of forest structure on recruitment. One of the variables is species richness defined as the number of species.
The aim is to quantify the effect of species richness on recruitment by using a glmm. But I want to avoid obtaining a variable which reflects an elevational gradient instead of species richness. The correlation between elevation and species richness makes this difficult.
The model also includes precipitation and temperature-related variables. Degree day sum, water balance and soil information to be more precise. Is this sufficient to account for the elevation? Or how do I know if my precipitation and temperature variables are good enough? I am unsure if I should scale the variable for species richness by elevation.
So far I use z-transformation for every 500 m of elevation.
But I couldn't find any literature on similar problems. Is there a general way to normalize/standardize a variable that is correlated with another variable?