Radboud University , Institute for Water and Wetland Research, Department of Environmental Science, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands.
Environ Sci Technol. 2014 Dec 16;48(24):14464-71. doi: 10.1021/es503223k. Epub 2014 Dec 3.
Quantitative relationships between species richness and single environmental factors, also called species sensitivity distributions (SSDs), are helpful to understand and predict biodiversity patterns, identify environmental management options and set environmental quality standards. However, species richness is typically dependent on a variety of environmental factors, implying that it is not straightforward to quantify SSDs from field monitoring data. Here, we present a novel and flexible approach to solve this, based on the method of stacked species distribution modeling. First, a species distribution model (SDM) is established for each species, describing its probability of occurrence in relation to multiple environmental factors. Next, the predictions of the SDMs are stacked along the gradient of each environmental factor with the remaining environmental factors at fixed levels. By varying those fixed levels, our approach can be used to investigate how field-based SSDs for a given environmental factor change in relation to changing confounding influences, including for example optimal, typical, or extreme environmental conditions. This provides an asset in the evaluation of potential management measures to reach good ecological status.
物种丰富度与单一环境因素之间的定量关系，也称为物种敏感分布（SSD），有助于理解和预测生物多样性模式，识别环境管理选项和制定环境质量标准。然而，物种丰富度通常依赖于多种环境因素，这意味着从现场监测数据中量化 SSD 并不简单。在这里，我们提出了一种新颖灵活的方法来解决这个问题，该方法基于堆叠物种分布模型。首先，为每个物种建立一个物种分布模型（SDM），描述其在与多个环境因素相关的环境中出现的概率。接下来，SDM 的预测沿着每个环境因素的梯度堆叠，其余环境因素处于固定水平。通过改变这些固定水平，我们的方法可用于研究给定环境因素的现场 SSD 如何随变化的混杂影响而变化，包括例如最佳、典型或极端环境条件。这在评估潜在的管理措施以达到良好的生态状况方面具有重要意义。
