The lack of ecological realism in current prospective environmental risk assessment (ERA) is widely recognised as a limitation in this field. Since organisms live in a multistressed environment, involving both chemical and environmental stressors, it is important to understand how these combined stressors can affect individuals and subsequently their populations. One way of including more ecological relevance in ERAs is the use of environmental scenarios that represent variation in environmental factors such as food, temperature, predation, competition and in factors that influence exposure.
All these factors will influence the capability of an organism to grow and reproduce as well as its resilience to additional stressors. Since 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 individual, how stressors can impact these fluxes, and how this will affect the organism’s life history traits. This mechanistic description of an individual can then be used as a building block for a population model.
In this project, we performed a meta-analysis of the literature in order to analyse the effects of combinations of ecological and chemical stressors on individuals. We then used this analysis to derive quantitative relationships that describe the effects on their bioenergetic fluxes. We analysed how these stressor combinations affected the life-history traits of the organisms and their ability to cope.
We assessed the implementation of these patterns using a DEB based model for Ceriodaphnia dubia. Using the derived quantitative relationships, we predicted the effects of mixtures of environmental and/or chemical stressors on the growth, the reproduction and the survival of individuals. This mechanistic description of the organisms can then be used as a building block in order to assess the effects of these mixtures on a higher level of organisation, through a population model for instance. The accuracy of our predictions was evaluated by comparison with experimental data.
The resulting models will have the potential to account for the effect of chemicals on individuals and populations in different environmental scenarios
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