In the random forest model, we used the same set of response and explanatory variables, as well as the same covariates. In each run of the random forest analysis, we generated 1,000 regression trees. The model performance was evaluated by the proportion of variance explained by the model. We evaluated the relative importance of each explanatory variable based on the increase in the mean squared error when the variable was permutated.