Modelling survival: exposure pattern, species sensitivity and uncertainty

Abstract

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

How to Evaluate the Quality of Toxicokinetic—Toxicodynamic Models in the Context of Environmental Risk Assessment

“…The confidence intervals on the parameters tell us how well they can be identified from this particular data set, and the joint confidence set can be used to propagate uncertainty in the model parameters to uncertainty in model predictions (for examples, see Jager and Zimmer 2012; Ashauer et al 2016). The confidence intervals on the parameters tell us how well they can be identified from this particular data set, and the joint confidence set can be used to propagate uncertainty in the model parameters to uncertainty in model predictions (for examples, see Jager and Zimmer 2012; Ashauer et al 2016).…”

“…Instead of classical sensitivity and uncertainty analysis, application of TKTD models benefits most from a robust and coherent statistical treatment in model fitting. The confidence intervals on the parameters tell us how well they can be identified from this particular data set, and the joint confidence set can be used to propagate uncertainty in the model parameters to uncertainty in model predictions (for examples, see Jager and Zimmer 2012;Ashauer et al 2016). It is important to stress that the approaches for model calibration and dealing with parameter uncertainty tell us nothing about the quality of the conceptual model.…”

“…Subsequently, we can set up additional experiments to test those predictions. These are very useful exercises to clarify the accuracy and precision of the model predictions, but they have been quite rare so far for TKTD models (for GUTS, see Nyman et al 2012;Ashauer et al 2016). However, there are several stumbling blocks that must be considered.…”

Pesticide exposure assessment for surface waters in the EU. Part 2: Determination of statistically based run‐off and drainage scenarios for Germany

“…As the TK‐TD model we used an implementation of the GUTS model (General Unified Threshold Model of Survival) by Ashauer et al . for most of the simulations.…”

“…As the TK-TD model we used an implementation of the GUTS model 26 (General Unified Threshold Model of Survival) by Ashauer et al 27 for most of the simulations. GUTS covers only lethal effects and, in practice, its substance and species specific parameters are calibrated and verified on the basis of data from toxicity tests.…”