Hi Gian. Just throwing my two cents. I guess it is a case of collinearity. Perhaps, besides the Kruskal Wallis test, a bivariate correlation coefficient might be better for evaluation. And as you better know, it is better to check for the VIF. However, I think not all statistically significant correlations necessarily matter (as a small correlation might become statistically significant in a large sample). The ones that have a great effect size are dangerous. So could you also elaborate on the VIF of the variables pertaining to soil types and the variable elevation, as well as the Spearman coefficients between soil types and elevation?

I think it would be the best to always check for the collinearity between every and all involved variables, and then find and exclude the culprits. If you only assess the collinearity between 2 independent variables and exclude your model, your model might still be affected by other hidden collinearity cases. I the best method might be to modeling all the independent variables and then assessing their VIFs. Also the assessment of the correlation matrix between all the independent variables is another good approach that can highlight potential culprits.If it can, what would be the more sound approach: to retain just one of the two? Further, shall I have to repeat the same 'screening' for all the other continuous IVs?

How to know which variable to keep? I don't know a definitive answer for this. What I know is that finding the optimum model might sometimes take up to one month. I mean, probably, it is not that simple to say which variable to keep and which to exclude. In any case, you should try to keep the variable that is theoretically more important in your model. Moreover, you can add and remove variables and see the -2 log likelihood. Then you can compare the -2 log likelihoods to see if the model has improved or worsened by removing a variable, or not? Also you can use LRT tests to compare these -2 log likelihood values, statistically.

If both of those two highly correlated independent variables are theoretically essential, a suggestion might be to conduct two similar regression models, each with one of those two independent variables and excluding the other one.

This is a question of mine too and I would appreciate any update on the way to deal with such a situation (which variable to exclude?).