Taiwania

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 × 50 m2 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.

本研究旨在开发一种统计方法，以生成高分辨率的历史和未来气候数据集，用于模拟气候变化下山区植物分布范围及其范围变化。研究采用了台湾气候变化预测信息与适应知识平台（TCCIP）和气象站提供的两个气候数据集，分别构建了两个具有50 × 50 m²空间分辨率的气候历史数据集。这两个历史气候数据集在北台湾（NTWN）呈现了相似的温度模式，但降水模式则有所不同。当使用这两个气候数据集应用随机森林（RF）模型时，RF预测了沿着山脊的自然草原的相似分布范围；而应用真实缺失数据时，RF预测了受限的分布范围。将这两个历史气候数据集与代表四种未来气候情景的气候变量相对变化相结合。基于未来气候数据集的RF方法预测了在本世纪中叶和末叶自然草原的栖息地丧失，无论数据集和四种升温情景如何。鉴于NTWN的垂直限制，在气候变化下，自然草原几乎没有机会追踪其气候需求向更高海拔迁移。由统计方法生成的高分辨率历史和未来气候数据集对于物种分布模型来说，有助于预测山区物种的潜在分布范围，并可用来考察物种在气候变化下的分布范围变化。基于高分辨率气候数据集的模型性能可能更好地表达了山区物种的气候需求及其精确的气候生态位。