Benoit Goussen, Cecilie Rendal, Emma Butler, Roman Ashauer, Oliver R. Price
SETAC Brussels 2017. Brussels, Belgium
Publication year: 2017


The lack of ecological realism in current prospective environmental risk assessment is widely recognised as a limitation in this field. As organisms are living in a multistressed environment, involving both chemical and environmental stressors, it is worth understanding how these combined stressors will affect the organisms and subsequently the populations. A way forward to include more ecological relevance in ERAs is the use of environmental scenarios that will represent key differences in environmental factors such as the food availability, the temperature variability, the predation, etc. and in exposure factors.

All these factors will influence the capability of an organism to grow and reproduce as well as its resilience to additional stressors. As growth and reproduction are driven by an organisms’ energy balance, Dynamic Energy Budget models are particularly well suited to integrate toxicant and environmental stressors. Indeed, the DEB theory analyses the fluxes of energy within an organism, how stressors can impact these fluxes, and how this will affect the organism’s life history traits.

In this project, we assessed how DEB modelling can predict effects of various chemicals in variable environmental conditions. To do so, we produced two set of data for each chemical. In the first one, Ceriodaphnia dubia individuals fed ad-libitum and were maintained at reference temperature (25°C). In the second set of data, C. dubia individuals were maintained at a different temperature and feeding regime. For the purpose of this exercise, this second set of data was not disclosed to the team in charge of the DEB modelling.

We used the first set of data only to calibrate the DEB model for C. dubia. Then knowing only the experimental conditions of the second set, we performed a blinded prediction of the growth, reproduction, and survival of C. dubia under environmental conditions that have not been used for the calibration. Blinded predictions and datasets were then compared and the ability of the DEB model to handle and accurately predict non-tested environmental conditions (temperature and feeding level) was analysed and discussed.


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