Demographic Toxicokinetic–Toxicodynamic Modeling of Lethal Effects

Abstract

The aquatic effect assessment of chemicals is largely based on standardized measures of toxicity determined in short-term laboratory tests which are designed to reduce variability. For this purpose, uniform individuals of a species are kept under environmental and chemical exposure conditions which are as constant as possible. In nature, exposure often appears to be pulsed, effects might last longer than a few days, sensitivity might vary among different sized organisms and populations are usually size or age structured and are subject to demographic processes. To overcome this discrepancy, we tested toxicokinetic-toxicodynamic models of different complexities, including body size scaling approaches, for their ability to represent lethal effects observed for Daphnia magna exposed to triphenyltin. The consequences of the different toxicokinetic and toxicodynamic assumptions for population level responses to pulsed exposure are tested by means of an individual based model and are evaluated by confronting model predictions with population data for various pulsed exposure scenarios. We provide an example where increased model complexity reduces the uncertainty in model outputs. Furthermore, our results emphasize the importance of considering population demography in toxicokinetics and toxicodynamics for understanding and predicting potential chemical impacts at higher levels of biological organization.

Toward sustainable environmental quality: Priority research questions for Europe

“…Mechanistic effect models include toxicokinetic-toxicodynamic models and AOPs that extrapolate chemical concentrations or molecular initiating events to individual-level effects (Ankley et al 2010;Ashauer et al 2011;Ashauer and Jager 2018), dynamic energy budget models that extrapolate changes in physiological responses to vital rates (Kooijman 2010), individual-based and population models that extrapolate individual-level effects to populationlevel consequences (Forbes et al 2011;Martin et al 2013), food web models that extrapolate effects on populations to community-level consequences (Pastorok et al 2002), and ecological production functions that extrapolate from changes in biophysical structure or process to ecosystem functions driving ecosystem services (Bruins et al 2017). Recent advances include the development of good modeling practice (Grimm et al 2014); the integration of the toxicokinetic-toxicodynamic, dynamic energy budget, and individual-based model approaches (e.g., Gergs et al 2016b); and the use of scenarios and trait-based approaches to improve the general applicability of models (Van den Brink et al 2013;Rico et al 2016). In addition to approaches for extrapolating across levels of biological organization, there are emerging computational approaches for extrapolating across species based on the conservation of key biological traits and molecular processes (e.g., LaLone et al 2016;Ankley et al 2016;question 6).…”

Short‐term to long‐term extrapolation of lethal effects of an herbicide on the marine mysid shrimpAmericamysis Bahiaby use of the General Unified Threshold Model of Survival (GUTS)

“…Toxicokinetic and toxicodynamic processes are related, and therefore the dominant rate constant will reflect the slowest compensating process (elimination or damage repair) driving the overall dynamics of toxicity. The application of the SIC has been shown to provide meaningful results (Jager et al ; Nyman et al ; Gergs et al ) and was used as dose metric in the present study. In such a case, the reduced form of the GUTS model (GUTS‐RED) is employed (Jager and Ashauer, ).…”

“…In case of survival, the General Unified Threshold Model of Survival (GUTS, Jager et al ) has been used to address many unresolved questions. Examples of applications include predicting effects from time‐varying exposure (Nyman et al ; Ashauer et al ), modeling combined effects of toxicity and starvation (Nyman et al ), representing the temporal variation in toxicity of the different organisms within a population (Gergs et al ; Gabsi et al ), or investigating differences in sensitivity between species (Beaudouin et al ; Kon Kam King et al ; Gergs et al ) and life‐stages (Kulkarni et al ; Gergs et al ). In 2015, an international workshop involving scientists and representatives from regulatory authorities was held on the use of GUTS in environmental risk assessment.…”

Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment

“…Thus, DEB theory can serve as a pivotal framework for building process-based models that link molecular-, cellular-, and tissue-level responses to apical endpoints, such as survival, growth, and reproduction (Murphy et al 2018), and subsequently to those at higher levels of ecological organization (Martin et al 2013a,b;Forbes et al 2017;Gergs et al 2014Gergs et al , 2016. The life cycle of an individual is the primary focus, from which sub-and supraorganismic levels are considered.…”

“…The end result is that the DEB approach offers unifying metabolic theory that can be applied to any species with a small number of parameters (Kearney et al 2015). In addition, the theory can be used to model effects of chemicals on individual organisms (e.g., Jager et al 2006Muller et al 2010) and effects of other environmental drivers and stressors in a single integrative modeling framework (e.g., Muller and Nisbet 2014;Pieters et al 2006) Moreover, the approach offers the possibility to extrapolate population-level and higher dynamics from an individual-level energy budget by individual-based modeling (Martin et al 2013a(Martin et al , 2014Gergs et al 2014Gergs et al , 2016. This suggests that there is potential to provide a connection from an AOP to ecologically important levels of organization, but only if we have some quantitative approach for relating qAOP and DEB models.…”