Epiphyllous liverworts form a special group of bryophytes that primarily grow on leaves of understory vascular plants, occurring in constantly moist and warm evergreen forest in tropical and subtropical regions. They are very sensitive to climate change and environmental pollution. Previous studies have focused largely on microhabitat preferences of epiphyllous liverworts and demonstrated the importance of climate factors such as humidity, temperature and light. However, little is known about the relationship between distribution of epiphyllous liverworts and macro-habitat factors at broad spatial scales. Here, we predicated the distribution of epiphyllous liverworts in China based on topographic and bioclimatic variables, as well as satellite-derived vegetation indices at a 1 km spatial resolution using presence-only ecological niche models. We used the Area Under the receiver operating characteristic Curve (AUC) and True Skill Statistic (TSS) to validate the models, and then used the Wilcoxon paired test to compare model performances. 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. The significant contributions of NDVI and NDWI in defining the distribution range and spatial patterns of epiphyllous liverworts, and the strong relationship between this species and evergreen forest implies that epiphyllous liverworts may be a useful indicator for forest degradation or integrity at broad spatial scales.
- Species distribution model