Although mechanistic effect models are widely recognized as potentially valuable tools, they are still not regularly used or accepted for regulatory Ecological Risk Assessments (ERA). There are several possible reasons for this, including the lack of trust and transparency in the modeling process, but more importantly the mismatch between the endpoints they yield and those that have been traditionally applied and the lack of “bright lines”.

This presentation aims to open a thorough discussion among all the parties involved in the risk assessment process to understand how we could enhance the use of mechanistic effect models in different regulatory contexts. To this end, we present a brief history of mechanistic effect models for ERA and give an overview of their goals within the ERA context. We show some examples of effect models developed within different stakeholders (US EPA, academia, business) and highlight their similarities and differences. 

In this context, we suggest focusing on a few important points:

• Define standardized outputs of interest, such as population abundance, population decline, recovery, or extinction probability and how these outputs can be applied in decision making.
• Determine which and how environmental scenarios  should be applied across models.
• Find agreement on those model features deemed essential to represent populations such that risks can be adequately assessed.
• Underline the importance of identifying model use and objectives before model development and ensure transparency and consistency in the overall process. 

We also tackle some common issues linked to model acceptance and conceptual misunderstandings. For example, models are currently often newly developed or adapted for the context of a specific risk assessment and thus, include different processes relevant to the system and objectives. This reduces consistency across models and increases the effort involved in reviewing them.  Moreover, there are different points of view regarding how to best use data obtained from surrogate species, how to deal with data gaps, and how to address model uncertainty. In conclusion, we hope to foster dialog among stakeholders to ensure the use of the best available science in a standardized way to support ecological risk assessments of chemicals.

SETAC North America 2022.

Accolla, C., Schmolke, A. (Waterborne), Forbes, V. E. (Florida Atlantic University),  Galic, N., Vaugeois (Syngenta Crop Protection LLC), Raimondo, S. (US Environmental Protection Agency). Mechanistic effect models: A brief history to highlight benefits and obstacles in using them for chemical risk assessment.