Abstract:Aims: Climate shapes forest types on our planet and also drives the differentiation of zonal vegetation at regional scale. A climate-based ecological model may provide an effective alternative to the traditional approach for assessing limitations, thresholds, and the potential distribution of forests. The main objective of this study is to develop such a model, with a machine-learning approach based on scale-free climate variable estimates and classified vegetation plots, to generate a fine-scale predicted vegetation map of Taiwan, a subtropical mountainous island.
Location: Taiwan.
Methods: A total of 3,824 plots from 13 climate-related forest types and 57 climatic variable estimates for each plot were used to build an individual ecological niche model for each forest type with random forest (RF). A predicted vegetation map was developed through the assemblage of RF predictions for each forest type at the spa-tial resolution of 100 m. The accuracy of the ensemble RF model was evaluated by comparing the predicted forest type with its original classification by plot.
Results: The climate environment of regions higher than 100 m above sea level in 
Taiwan was classified into potential habitats of 13 forest types by using model pre-dictions. The predicted vegetation map displays a distinct altitudinal zonation from subalpine to montane cloud forests, followed by the latitudinal differentiation of sub-tropical mountain forests in the north and tropical montane forests in the south, with an average mismatch rate of 6.59%. An elevational profile and 3D visualization dem-onstrate the excellence of the model in estimating a fine, precise, and topographically corresponding potential distribution of forests.
Conclusions: The machine-learning approach is effective for handling a large num-ber of variables and to provide accurate predictions. This study provides a statistical procedure integrating two sources of training data: (a) the locations of field sampling plots; and (b) their corresponding climate variable estimates, to predict the potential distribution of climate-related forests.