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Coupling different mechanistic effect models for capturing individual- and population-level effects of chemicals: Lessons from a case where standard risk assessment failed

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  • Gabsi, Faten
  • Hammers-Wirtz, Monika
  • Grimm, Volker
  • Schäffer, Andreas
  • Preuss, Thomas G.

Abstract

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.

Suggested Citation

  • Gabsi, Faten & Hammers-Wirtz, Monika & Grimm, Volker & Schäffer, Andreas & Preuss, Thomas G., 2014. "Coupling different mechanistic effect models for capturing individual- and population-level effects of chemicals: Lessons from a case where standard risk assessment failed," Ecological Modelling, Elsevier, vol. 280(C), pages 18-29.
  • Handle: RePEc:eee:ecomod:v:280:y:2014:i:c:p:18-29
    DOI: 10.1016/j.ecolmodel.2013.06.018
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    References listed on IDEAS

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    1. Preuss, Thomas Günter & Hammers-Wirtz, Monika & Hommen, Udo & Rubach, Mascha Nadine & Ratte, Hans Toni, 2009. "Development and validation of an individual based Daphnia magna population model: The influence of crowding on population dynamics," Ecological Modelling, Elsevier, vol. 220(3), pages 310-329.
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    2. Dur, Gaël & Won, Eun-Ji & Han, Jeonghoon & Lee, Jae-Seong & Souissi, Sami, 2021. "An individual-based model for evaluating post-exposure effects of UV-B radiation on zooplankton reproduction," Ecological Modelling, Elsevier, vol. 441(C).
    3. Richard Ottermanns & Kerstin Szonn & Thomas G Preuß & Martina Roß-Nickoll, 2014. "Non-Linear Analysis Indicates Chaotic Dynamics and Reduced Resilience in Model-Based Daphnia Populations Exposed to Environmental Stress," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-13, May.

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