Furthermore, we applied the jackknife test to identify the important factors affecting predictions. Our results showed that the highest accuracy (i.e., AUC = 0.98 and TSS = 0.93) in predicting epiphyllous liverworts was achieved by the model that combined climatic and remotely sensed vegetation variables. The satellite-derived annual mean and minimum Normalized Difference Vegetation Index (NDVI) as well as the annual mean and minimum Normalized Difference Water Index (NDWI) emerged as the most important predictors of distribution patterns of epiphyllous liverworts, while climatic variables such as precipitation in the wettest quarter and temperature of the coldest quarter were of ancillary importance.