Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 551024
Title Modeling the Sensitivity of Aquatic Macroinvertebrates to Chemicals Using Traits
Author(s) Berg, Sanne J.P. Van Den; Baveco, Hans; Butler, Emma; Laender, Frederik De; Focks, Andreas; Franco, Antonio; Rendal, Cecilie; Brink, Paul J. Van Den
Source Environmental Science and Technology 53 (2019)10. - ISSN 0013-936X - p. 6025 - 6034.
DOI https://doi.org/10.1021/acs.est.9b00893
Department(s) Animal Breeding & Genomics
WIMEK
Environmental Risk Assessment
Aquatic Ecology and Water Quality Management
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract

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.

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