Paul J. van den Brink (Cancelled due to ill health)
Alterra and Wageningen University, the Netherlands
Species sensitivity distributions assume that sensitivity to toxicants within target species is random. While the SSD approach has shown to be promising, it is limited by the fact that data are sparse for most compounds, and that these data are largely based on the lethal responses of a small group of testing laboratory species. Here an alternative approach, based on the hypothesis that organisms’ sensitivity to stress is a function of their biology, and can be predicted from species traits such as morphology, life history, physiology and feeding ecology is presented.
Examples of how species traits have been used to explain the differences in sensitivity between species will be shown in this talk.
- Using data from the US EPA’s AQUIRE database, we found that 4 species traits explained 71% of the variability in sensitivity to toxicants within a group of 12 species exposed to 15 chemicals. Our results indicate that this approach is promising, but effort is needed to compile species trait information to increase the power, precision and taxonomic representativeness of this approach.
- Secondly, we mined existing data on organophosphate, carbamate and pyrethroid toxicity and mode of action and also species trait information. We linked taxon sensitivity to their traits at the family level in order to generate empirical and mechanistic hypotheses about sensitivity-trait relationships. In this way, we developed a Mode-specific sensitivity (MSS) ranking method, and tested this at the taxonomic level of family and genus. The MSS rankings were successfully linked to existing trait data in order to identify traits with predictive potential. Single traits as well as combinations of traits can be used to predict laboratory sensitivity to the substances tested, although associations were not as strong as in previous studies.
- We also explored whether and in what ways traits can be linked purposefully to mechanistic effect models to predict intrinsic sensitivity using available data on the acute sensitivity and toxicokinetics of a range of freshwater arthropods exposed to chemicals, using the insecticide chlorpyrifos as an example. The results of a quantitative linking of 7 different endpoints and 12 traits demonstrate that while quantitative links between traits and/or trait combinations and process based (toxicokinetic) model parameters could be established, the use of simple traits to predict classical sensitivity endpoints yields less insight. Future research in this area should include a quantitative linking of toxicodynamic parameter estimations and physiological traits, and requires further consideration of how mechanistic trait-process/parameter links can be used for prediction of intrinsic sensitivity across species for different substances in environmental risk assessment (ERA).