TY - JOUR A1 - Gabsi, Faten A1 - Hammers-Wirtz, Monika A1 - Grimm, Volker A1 - Schaeffer, Andreas A1 - Preuss, Thomas G. T1 - Coupling different mechanistic effect models for capturing individual- and population-level effects of chemicals: Lessons from a case where standard risk assessment failed JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Current environmental risk assessment (ERA) of chemicals for aquatic invertebrates relies on standardized laboratory tests in which toxicity effects on individual survival, growth and reproduction are measured. Such tests determine the threshold concentration of a chemical below which no population-level effects are expected. How well this procedure captures effects on individuals and populations, however, remains an open question. Here we used mechanistic effect models, combining individual-level reproduction and survival models with an individual-based population model (IBM), to understand the individuals' responses and extrapolate them to the population level. We used a toxicant (Dispersogen A) for which adverse effects on laboratory populations were detected at the determined threshold concentration and thus challenged the conservatism of the current risk assessment method. Multiple toxicity effects on reproduction and survival were reported, in addition to effects on the F1 generation. We extrapolated commonly tested individual toxicity endpoints, reproduction and survival, to the population level using the IBM. Effects on reproduction were described via regression models. To select the most appropriate survival model, the IBM was run assuming either stochastic death (SD) or individual tolerance (IT). Simulations were run for different scenarios regarding the toxicant's effects: survival toxicity, reproductive toxicity, or survival and reproductive toxicity. As population-level endpoints, we used population size and structure and extinction risk. We found that survival represented as SD explained population dynamics better than IT. Integrating toxicity effects on both reproduction and survival yielded more accurate predictions of population effects than considering isolated effects. To fully capture population effects observed at high toxicant concentrations, toxicity effects transmitted to the F1 generation had to be integrated. Predicted extinction risk was highly sensitive to the assumptions about individual-level effects. Our results demonstrate that the endpoints used in current standard tests may not be sufficient for assessing the risk of adverse effects on populations. A combination of laboratory population experiments with mechanistic effect models is a powerful tool to better understand and predict effects on both individuals and populations. Mechanistic effect modelling thus holds great potential to improve the accuracy of ERA of chemicals in the future. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved. KW - Individual-based modelling KW - TK/TD modelling KW - Daphnia magna KW - Risk assessment Y1 - 2014 U6 - https://doi.org/10.1016/j.ecolmodel.2013.06.018 SN - 0304-3800 SN - 1872-7026 VL - 280 SP - 18 EP - 29 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Grimm, Volker A1 - Augusiak, Jacqueline A1 - Focks, Andreas A1 - Frank, Beatrice M. A1 - Gabsi, Faten A1 - Johnston, Alice S. A. A1 - Liu, Chun A1 - Martin, Benjamin T. A1 - Meli, Mattia A1 - Radchuk, Viktoriia A1 - Thorbek, Pernille A1 - Railsback, Steven Floyd T1 - Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - The potential of ecological models for supporting environmental decision making is increasingly acknowledged. However, it often remains unclear whether a model is realistic and reliable enough. Good practice for developing and testing ecological models has not yet been established. Therefore, TRACE, a general framework for documenting a model's rationale, design, and testing was recently suggested. Originally TRACE was aimed at documenting good modelling practice. However, the word 'documentation' does not convey TRACE's urgency. Therefore, we re-define TRACE as a tool for planning, performing, and documenting good modelling practice. TRACE documents should provide convincing evidence that a model was thoughtfully designed, correctly implemented, thoroughly tested, well understood, and appropriately used for its intended purpose. TRACE documents link the science underlying a model to its application, thereby also linking modellers and model users, for example stakeholders, decision makers, and developers of policies. We report on first experiences in producing TRACE documents. We found that the original idea underlying TRACE was valid, but to make its use more coherent and efficient, an update of its structure and more specific guidance for its use are needed. The updated TRACE format follows the recently developed framework of model 'evaludation': the entire process of establishing model quality and credibility throughout all stages of model development, analysis, and application. TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use. We introduce the new structure and revised terminology of TRACE and provide examples. (C) 2014 Elsevier B.V. All rights reserved. KW - Standardization KW - Good modelling practice KW - Risk assessment KW - Decision support Y1 - 2014 U6 - https://doi.org/10.1016/j.ecolmodel.2014.01.018 SN - 0304-3800 SN - 1872-7026 VL - 280 SP - 129 EP - 139 PB - Elsevier CY - Amsterdam ER -