Why does mean decrease accuracy and mean decrease gini give me different variables? Which is more important? I don't understand why Mean Decrease Accuracy and Mean Gini Decrease give very different variables, when they both are supposed to show top important variables for a random forest.
Here is an example from my random forest, where I am training my model to use taxonomy information to predict maternal versus no maternal care in my data.
Here is the top 10 variable output:

So according to this, if I had to pick the top 3 important plant families for my RFC would it be Anacardiaceae, Moraceae, and Rosaceae according to MDA or Anacardiaceae, Fagaceae, and Vitaceae according to MGD? It is unclear to me why they wouldn't be in the same order - wouldn't the top three features that would decrease accuracy if removed also be the best for creating pure node splits?