# Interpretation of standardized (z-score rescaled) linear model coefficients

I have analyzed some data on vegetation change as a function of change in soil parameters. I compared a dataset from 2001 with a dataset from 2018 (fully balanced).

To investigate the change in vegetation (expressed as Bray-Curtis dissimilarity indice) as a function of change in soil parameters (just 2018 minus 2001 value) i ran several models. When comparing using AIC I ended up with a model of 9 soil parameters explaning vegetation change.

For visualizing the effect of different soil parameters i wanted to keep it simple and use a forest plot, but it turned out to be paradoxal. I saw in several ecological papers how researchers presented the results of their models using forest plots and found it an elegant and seemingly simple way to present results. I needed to scale my covariates however, because as R stated: "the predictor variables are on very different scales". The model looks fine, but how do interpret the results of my model with scaled variables?

Below is the result of the linear mixed model using a forest plot to visualize results.

Model: Vegetation change expressed as dissimilarity commposition between 2001 and 2018~change in total N + change in total P + change in PO4 (etc.) + (1|block/plot) +(1|Year) • An exact answer would depend on how you rescaled your variables (did you standardize i.e. subtract the mean and divide by standard deviation?). Could you add that information. Also, this answer may be useful background for you: stats.stackexchange.com/questions/29781/… – mkt May 13 at 8:36
• I used Z-score (scale function in R) to normalize the dataset. Thanks for the link! – Tom van Heusden May 13 at 9:11
• I answered this but on reflection I think I might be misinterpreting your independent variable. Is it the change in vegetation dissimilarity index from 2001 to 2018? It might be useful to define your model more clearly so that the answers can address your question more precisely. – mkt May 13 at 9:38
• My dependant variable is vegetation change. My independant variables are the covariates (as i was in the believe these are the same in this case). – Tom van Heusden May 13 at 10:30