Modeling the Sensitivity of Aquatic Macroinvertebrates to Chemicals Using Traits.

In this study, a trait-based macroinvertebrate sensitivity modeling tool is presented that provides two main outcomes: (1) it constructs a macroinvertebrate sensitivity ranking and, subsequently, a predictive trait model for each one of a diverse set of predefined Modes of Action (MOAs) and (2) it reveals data gaps and restrictions, helping with the direction of future research. Besides revealing taxonomic patterns of species sensitivity, we find that there was not one genus, family, or class which was most sensitive to all MOAs and that common test taxa were often not the most sensitive at all. Traits like life cycle duration and feeding mode were identified as important in explaining species sensitivity. For 71% of the species, no or incomplete trait data were available, making the lack of trait data the main obstacle in model construction. Research focus should therefore be on completing trait databases and enhancing them with finer morphological traits, focusing on the toxicodynamics of the chemical (e.g., target site distribution). Further improved sensitivity models can help with the creation of ecological scenarios by predicting the sensitivity of untested species. Through this development, our approach can help reduce animal testing and contribute toward a new predictive ecotoxicology framework.