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.