Deriving field-based species sensitivity distributions (f-SSDs) from stacked species distribution models (S-SDMs).

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.