I've created a Non-metric MultiDimensional Scaling (NMDS) ordination from a Bray-Curtis dissimilarity matrix. (Starting data were basal areas of various tree species across multiple research plots).
I'd like to determine correlations of various plot-level environmental variables (e.g., soil chemistry, topography, elevation ,etc.) with my two NMDS ordination axes. Correlations between the ordination axes and environmental variables will be calculated with Pearson’s r2.
- For reference, I've chosen to do this using the cor2m() and vf() functions available in the Ecodist package (vs vegan) in R.
My question: Do I have to scale/standardize my environmental variables before calculating correlations with my NMDS ordination axes?
I ask because my variable cover multiple orders of magnitudes: some of my variables have values in the 1000s while others have values in the hundredths.
If the answer is yes, what is the appropriate method? If the answer is no, why not?