Figure 3

Comparison among selected models ranked by mean absolute error (MAE; m) of linear distance between the observed upper limit and the predicted upper limit. For the top five models, the model description specifies the development algorithm [e.g., Random Forest (RF) or logistic regression (LR)], the stopping rule (SR) and its number (1, 2, or 3), and the type of training data [occurrence (O) or habitat (H)] used. In addition to showing the MAE for the top five models, two additional models are included, the Fransen et al.¹³ model, and a 20% slope cut off, where the lowest point on the network with a slope greater than or equal to 20% becomes the upper limit point. The model with the smallest MAE is called UPRLIMET.