A statistical method to generate high-resolution climate datasets for modeling plant distribution range and range shifts under climate change in mountainous areas

This study aims to develop a statistical method to generate high-resolution historical and future climate datasets for modeling plant distributions in mountainous area. Two climate datasets that were from Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP) and meteorological stations were used to construct two historical climate datasets with 50 x 50 m(2) spatial resolution, respectively. The two historical climate datasets presented similar temperature pattern but distinct precipitation patterns in northern Taiwan (NTWN). Random Forests (RF) had predicted similar distribution range of natural grassland along mountain ridge when RF were applied by the two climate datasets, whereas RF had predicted restricted distribution range when it was applied by true absence data. The two historical climate datasets were added to the relative changes of climate variables representing four future climate scenarios. RF method based on the future climate datasets predicted habitat loss of natural grassland at the mid and end of this century, regardless of climate datasets and four warming scenarios. Due to the altitudinal limits of NTWN, there is almost no chance for natural grassland to track their climatic requirements toward higher elevations under climate change. High-resolution historical and future climate datasets generated by the statistical method were useful for species distribution model to project species potential distribution range in mountainous area and were available to examine species range shifts under climate change. Model performances based on the high-resolution climate dataset may have better expressed the climatic requirements and exact climatic niches of species in mountainous areas.