As a beginner to MDS, here is my thought process:
Given a data set of environmental factors that may effect a certain sites, when I run a PCA on each site I get a list of principal components. If I want to understand which environmental factors are driving each of the principal components, ie:
PCA1 <- Mostly Oxygen and Salinity driven PCA2 <- Mostly Temperature driven
I look at the loadings of the PCA results to find out which of the original environmental factors are weighting each of the principal components.
When I run a NMMDS on a similar set of data, my result is a plot in which the sites are grouped so that more dissimilar sites are further apart. While I can make an educated guess as to what is causing the grouping in an MSD plot, is there something analogous to the loadings of a PCA that explains which environmental variables may be playing the largest roles in the resulting plot? Or a way to quantify the input of each environmental variable to the final structure of the plot?