Soil chemical variables improve models of understory plant species distributions

Aim
To determine the importance of soil variables relative to more commonly used topo-climatic or remotely sensed variables in species distribution models (SDMs) for understory plants.
 
Location
White Mountain National Forest, New Hampshire, U.S.A.
 
Methods
We fit models for presence of 41 forest understory plant species across 158 plots using soil, topographic, and spectral predictors to determine the relative contribution of different predictor types. We determined (a) if the potential importance of soil variables is greater than generally described in SDM literature, (b) which predictors are most important, and (c) if a standard subset of predictors can be used to effectively model all species.
 
Results
Models containing all three predictor types performed best. Soil and topographic variables had comparable importance; spectral variables were of lesser importance. The best predictor variable was B horizon carbon to nitrogen ratio (B C:N), followed by topographic position index,...

**研究目标**：明确林下植物物种分布模型（Species Distribution Models, SDMs）中，土壤变量相较于更为常用的地形气候变量或遥感变量的重要性程度。

**研究区域**：美国新罕布什尔州怀特山国家森林公园（White Mountain National Forest）。

**研究方法**：针对158个样地中的41种森林林下植物的物种出现数据，我们采用土壤、地形及光谱预测变量构建模型，以明确不同类型预测变量的相对贡献度。我们旨在解答三个核心问题：(a) 土壤变量的潜在重要性是否高于物种分布模型相关文献中的普遍认知；(b) 哪些预测变量的重要性最高；(c) 是否可通过一组标准化的预测变量子集有效实现所有物种的分布建模。

**研究结果**：同时包含三类预测变量的模型表现最优。土壤变量与地形变量的重要性相当，而光谱变量的重要性相对较低。表现最佳的预测变量为B层土壤碳氮比（B horizon carbon to nitrogen ratio, B C:N），其次为地形位置指数……