COMPARISON OF PREDICTOR SELECTION PROCEDURES IN SPECIES DISTRIBUTION MODELING: A CASE STUDY OF Fagus hayatae

ABSTRACT Selecting predictors for species distribution models (SDMs) is a major challenge. In this study, we evaluated a comprehensive set of 62 environmental predictors that may be related to the occurrence of Fagus hayatae. We modeled F. hayatae as a case study to compare model performance through different environmental predictor subsets according to three selection procedures, namely correlation coefficients between predictors, contribution level of predictors, and expert choice of biologically relevant predictors. The three selection procedures provided satisfactory results with high performance using about 6-10 valid predictors but had their respective limitations. Consequently, we suggest a synthetic strtegy of predictor selection. Accordingly, the first step was identifying and eliminating ineffective variables with nonidentifiability by using bivariate scatterplots. Next, calculate the correlation coefficients between other candidate predictors. Finally, comprehensively select the applicable environmental predictors with lower correlation coefficient on the basis of highly contribution level and expert knowledge for SDM of target species.