Figure 2. Variables importance for model training for Asiatic black bear.

The regularized training gain shows how much better the model distribution fits the presence data relative to a uniform distribution. “Without variable” denotes the effect of removing that single variable from the model “with only variable” denotes the results of the model when an only that variable is run; “with all variables” indicates the results of the model when all variables are run (Phillips, 2017).