Benoit Goussen, C├ęcilie Rendal, Antonio Franco, Oliver R. Price, Roman Ashauer
Salt Lake City 2015. Salt Lake City, UT, USA
Publication year: 2015

Presenting author: Oliver R. Price


Ecological relevance is increasingly recognised as an important challenge for environmental risk assessment (ERA). The integration of ecological scenarios with chemical effect models to achieve quantitative ERA promises to increase ecological relevance of ERA. We discuss some of the challenges and opportunities involved in bringing these new concepts into everyday risk assessment for down-the-drain chemicals. One of the key questions revolves around understanding the protection goal for anthropogenic stressors in specific ecological scenarios, and
indeed whether certain scenarios require specific modified protection goals. Once the specific protection goal has been established, a metric to suit both the specific ecological scenario and protection goal needs to be defined and agreed. The selection of this endpoint must be carefully considered as different options will lead to different interpretation. Population-level models linked with mechanistically based bioenergetics models are potential powerful tools for ecologically relevant ERA because they provide a quantitative link between chemical effects and ecological factors. However, the practical use and communication of ecological model predictions for decision making may not always be straight forward. Probabilistic environmental risk assessment (PERA) has been suggested as a way to make uncertainty more explicit and to account for biological, spatial and temporal variability. The outcome of a PERA is typically a measure of expected risk with an associated uncertainty interval. We present how probabilistic approaches and advancements in population modeling can be linked to incorporate more ecological relevance into risk assessments while keeping both uncertainty and variability explicit and transparent. To do so, we use an individual based model integrating a dynamic energy budget model to assess the potential impact of chemicals associated with local environmental characteristics. This mechanistic model must be applicable to multiple types of stress,
both anthropogenic (e.g., chemicals, wastewater plant effluents) and environmental (e.g., temperature, predation, starvation, competition). We also discuss how the magnitude of an adverse event will impact the level of risk we are willing to accept and argue that the relation between the level of acceptable risk and the severity of the effect should be made explicit to facilitate decision-making.