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

Published: 15 May 2019| Version 1 | DOI: 10.17632/cymrs4s7kj.1
Lei Zhang, zhen Yu, Shirong Liu, Falk Huettmann, Pengsen Sun


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.



Artificial Intelligence, Ecological Modeling, Forestry in Global Change, Machine Learning, Niche Modelling, Forest Ecology, Habitat Conservation, Forestry Practice, Random Decision Forest