Alpine climate and soils heterogeneity data and simulation results

Data for 8 climate variables from CHELSA and 11 soils variables from ISRIC (uppermost layer only) were used to produce metrics of environmental heterogeneity. The first two axes were used to plot the points for each of the 23 regions in 2D PCA (PC-ORD v7) space. Between 5 and 15 points that outlined the area occupied by the points were selected to create a polygon, and the shoelace algorithm was applied to them to calculate the area. The data show the two PCA axes split out for each region, the points selected for the shoelace calculation, and the area. In some cases where the points were in two distinct clusters the areas were measured separately and summed. The complete CHELSA data are available at Dryad: http://dx.doi.org/doi:10.5061/dryad.kd1d4. The ISRIC data are available at https://www.isric.org/explore/isric-soil-data-hub under ISRIC’s own applicable access categories: CC-BY-NC and CC-BY, and their policies are described at https://www.isric.org/about/data-policy The data from CHELSA and ISRIC are not included here. Only the results of the PCA that used those data are in this submission to Dryad. A simulation model was developed in NetLogo for computational experiments. The treatment parameters were environmental heterogeneity and area, and 10 random number seeds were used to create replications. The output was regional species richness, i.e., the number of species extant on the grid at equilibrium. The explanation of the observed regional richness was shared by area and heterogeneity.

本研究采用CHELSA数据集的8个气候变量与ISRIC数据集的11个土壤变量（仅取最上层土壤数据），以计算环境异质性指标。基于主成分分析（Principal Component Analysis, PCA）的前两个主轴，在二维PCA空间（PC-ORD v7软件）中绘制23个区域的对应点位。选取5至15个能够勾勒出点位分布范围的点构建多边形，并应用鞋带算法（shoelace algorithm）计算该多边形的面积。本数据集包含各区域拆分后的两个PCA轴数据、用于鞋带算法计算的选取点位，以及对应测算得到的面积值。针对点位分为两个明显集群的案例，将分别测算两个集群的面积后求和。完整的CHELSA数据集可于Dryad学术数据仓储获取，链接为：http://dx.doi.org/doi:10.5061/dryad.kd1d4。ISRIC数据集可于https://www.isric.org/explore/isric-soil-data-hub获取，需遵循其适用授权类别：知识共享署名-非商业性使用（CC-BY-NC）与知识共享署名（CC-BY），其数据政策详见https://www.isric.org/about/data-policy。本次提交至Dryad的数据未包含CHELSA与ISRIC原始数据集，仅包含基于上述原始数据生成的PCA分析结果。本研究在NetLogo中开发了仿真模型以开展计算实验，实验的处理参数为环境异质性与区域面积，采用10组随机数种子设置重复实验组。模型输出结果为区域物种丰富度，即平衡状态下研究网格中存活的物种数量。观测得到的区域物种丰富度由区域面积与环境异质性共同解释。