Relationship between Sasa kurilensis distributions and climate was modelled and clarified at ca. 1-km2 spatial resolutions in eastern Honshu, Japan. Occurrence probability was then predicted under both current climate and a future climate change scenario to assess the impact of climate change. A classification tree model was used to predict the potential habitat. Five climatic factors (warmth index: WI, minimum temperature of the coldest month: TMC, summer precipitation: PRS, maximum snow water equivalent: MSW, winter rainfall: WR) were used as predictor variables, and the species distribution data obtained from Phytosociological Relevé Data Base (PRDB) was used as a response variable. Deviance-Weighted Scores (DWS) revealed that the most influential factor for the species distribution was MSW, followed by WI, PRS, WR and TMC. Predicted potential habitat was divided into “suitable habitat” and “marginal habitat”, based on the optimal threshold occurrence probability calculated from the ROC (Receiver Operating Characteristic) analysis. Climatic thresholds of potential habitat were also detected. The area of suitable habitats and marginal habitats were predicted to decrease 78.3% and 32.9% respectively, due to decrease in MSW and increase in WI.
