What are the most crucial soil variables for predicting the distribution of mountain plant species? a comprehensive study in the Swiss Alps

Aim: To investigate the potential of a large range of soil variables to improve topo-climatic models of plant species distributions in a temperate mountain region encompassing complex relief.
Location: The western Swiss Alps.
Methods: Fitting topo-climatic models for >60 plant species across >250 sites with and without added soil predictor variables (>30). Testing included: (i) which soil variables improve plant species distribution models; (ii) whether an optimal subset of soil variables can improve models for the majority of species and habitat types; and (iii) how much variation in plant species distributions soil variables alone explain.
Results: Geochemical variables (i.e., CaO, pH and inorganic carbon) and a drainage indicator (i.e., bulk soil water content) improved the predictive abilities of the models across the large majority of alpine plant species. The improvement of the models after the addition of soil information varied strongly between plant species and habitat...

研究目的：探究在兼具复杂地形的温带山地区域中，多类土壤变量对优化植物物种分布地形气候模型（topo-climatic models）的潜力。
研究区域：瑞士西部阿尔卑斯山区。
研究方法：针对250余个样地中的60余种植物，分别构建添加与未添加30余种土壤预测变量的地形气候模型，并开展三项测试：(i) 哪些土壤变量可有效优化植物物种分布模型；(ii) 最优土壤变量子集能否提升多数物种与生境类型的模型性能；(iii) 仅依靠土壤变量即可解释的植物物种分布变异幅度。
研究结果：地球化学变量（即氧化钙（CaO）、pH值与无机碳）以及排水指标（即土壤体积含水量）可显著提升绝大多数阿尔卑斯植物物种的模型预测能力。添加土壤信息后，模型性能的提升幅度在不同植物物种与生境间存在显著差异……