Data for: Classification and regression with random forests as a standard method for presence-only data SDMs: A future conservation example using China tree species

This compressed file contains the following data sets from an ensemble prediction with two different methods of selecting pseudo-absence data sets (SRE, 2 degree) and eight different methods of transforming numerical prediction into binary predictions.
(1) Figure 2: Model accuracy for numerical prediction of random forests regression (RT) and classification (CT) algorithms.
(2) Figure 3: Optimal threshold and model accuracy for binary predictions produced by eight threshold-selecting methods.
(3) Figure 4: Spatial correspondence (as judged by the first axis of principal component analysis) among binary predictions produced by eight threshold approaches.
(4) Figure 5: Spatial correspondence in binary predictions (as judged by McNemar tests) for pairwise among threshold approaches.
(5) Table 1: Species range shifts predicted by classification (CT) and regression (RT) algorithms of random forests.
(6) Table S1_Ecological requirements, biological characteristics and niche properties for the 52 tree species.
(7) Table S2_Species range shifts estimated basing on numerical prediction of RT.
(8) Species distribution maps for 52 forest trees (Raw data file, Species distribution maps).
(9) Supplementary figures and tables.
(10) R codes & R functions used in the study.