Improving cross-species extrapolation of chemical sensitivity

Abstract

In this study, a trait-based macroinvertebrate sensitivity modelling 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 pre-defined 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 traits 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 towards a new predictive ecotoxicology framework.