Species Affinities and Biome Area Predictions

Data package for the review of the paper "Approaching Peak Biodiversity: Non-linear Responses of Plant Biodiversity to Climate Warming". Please refer to the README.md file for detailed explanations on the content and individual columns of the CSV, as well as the provided notebooks. Here is a brief description of the main files: Prediction GeoTIFs BiomePredictions.tif: The predicted biome for each pixel. Different bands correspond to the different SSPs and year brackets. (e.g. "ssp126_2021-2040" is the predicted biome for SSP1-2.6 in the years 2021-2040). Values are: 4 = alpine, 7 = tussock grasslands, 10 = shrublands, 14 = podocarp forest, 15 = beech forest, 16 = Mixed podocarp/beech forest. PredictionUncertainty_Shape.tif: The extrapolation distance following (Velazco et al. 2024). The cutoff used in this paper is 1. Pixels above will be classified as "novel". PredictionUncertainty_Charney.tif: The extrapolation distance following (Charney et al. 2021). This method is discussed in the paper and compared in the supplementary materials, but not used. Computed Data ClimateChange.csv: Documents the change of environmental variables per biome. SpeciesAffinity.csv: Contains the affinity values computed for a species set (or bracket), biome, year bracket and SSP. The affinity values are normalised differently for making them comparable across species and/or variables. AffinityRegressions.csv: Contains the regressions for the affinities of difderent subsets/brackets of species in "Species Affinity.csv" using bent cable regression. LandscapeAffinity.csv: Analogous to "SpeciesAffinity.csv", but the affinity values for each species across the 6 biomes are now combined into a single landscape affinity value. Landscape affinity is always computed on the raw affinity values and then normalised in the same way as described above (Species normalisation with suffix _I, variable normalisation with suffix _V, full normalisation with suffix _F). LandscapeAffinityRegressions.csv: Analogous to "AffinityRegressions.csv".本数据包用于审阅论文《趋近生物多样性峰值：植物生物多样性对气候变暖的非线性响应》（Approaching Peak Biodiversity: Non-linear Responses of Plant Biodiversity to Climate Warming）。 请参考README.md文档，以获取CSV文件的内容、各列字段说明以及配套Notebook的详细解释。以下为主要文件的简要说明： 预测结果地理栅格文件（GeoTIFF） BiomePredictions.tif：各像素点的预测生物群区。不同波段对应不同的共享社会经济路径（Shared Socioeconomic Pathways，SSP）与年代区间。例如，"ssp126_2021-2040"代表2021-2040年SSP1-2.6情景下的预测生物群区。像素值定义如下：4 = 高山植被带，7 = 丛状草原，10 = 灌丛，14 = 罗汉松科森林，15 = 山毛榉林，16 = 罗汉松-山毛榉混交林。 PredictionUncertainty_Shape.tif：遵循Velazco等人2024年的研究方法计算的外推距离。本研究采用的截断值为1，像素值大于1的区域将被归类为"新出现生境"。 PredictionUncertainty_Charney.tif：遵循Charney等人2021年的研究方法计算的外推距离。该方法仅在论文中讨论并在补充材料中进行了对比，未在本研究中实际使用。 计算得到的数据文件 ClimateChange.csv：记录了各生物群区的环境变量变化情况。 SpeciesAffinity.csv：包含针对特定物种类群（或区间）、生物群区、年代区间以及共享社会经济路径（SSP）计算得到的亲和度值。为实现不同物种类群或环境变量间的可比较性，亲和度值采用了差异化的标准化处理。 AffinityRegressions.csv：包含针对SpeciesAffinity.csv中不同物种类群/区间的亲和度值，采用弯曲电缆回归（bent cable regression）进行拟合得到的回归结果。 LandscapeAffinity.csv：与SpeciesAffinity.csv结构类似，但将每个物种在6个生物群区下的亲和度值整合为单一的景观亲和度值。景观亲和度始终基于原始亲和度值计算，并采用与前文一致的方式进行标准化处理（物种标准化后缀为_I、变量标准化后缀为_V、全量标准化后缀为_F）。 LandscapeAffinityRegressions.csv：与AffinityRegressions.csv结构类似。