Population-level effects in Amphiascus tenuiremis: Contrasting matrix- and individual-based population models

• Population models have the potential to add ecological relevance to risk assessment.
• Different model approaches with different advantages are available.
• Life-cycle effect data of lindane in the copepod Amphiascus tenuiremis was used.
• A matrix model and an individual-based model were contrasted with the data.
• It was shown that the matrix model was unable to capture the effects on development.

Environmental risk assessment (ERA) is generally based on individual-level endpoints, even though protection goals in ERA intend higher biological levels. Population models have the potential to translate individual-level endpoints to population-level responses and range from simple demographic equations to highly complex individual based models (IBMs). The aims of the current study were to develop a matrix model (MM) with the structure and parameterization proposed in the draft OECD guideline “Harpacticoid copepod development and reproduction test with Amphiascus tenuiremis”, and an IBM with the same data requirements. Experimental data from lindane exposure from validation studies of the OECD guideline was projected to the population level. Lindane does not only cause effects on survival and reproduction, but also on the time it takes to develop from larvae to adults. The two model approaches were contrasted in terms of their ability to properly project these effects on development. The MM projected smaller effects of the lindane treatments on population growth rate compared to the IBM since in its proposed structure, it did not include the delay in development explicitly. Population-level EC10 for population growth rate in the IBM was at the same level as the most sensitive individual-level endpoint, whereas the EC10 from the MM was not as sensitive. Based on these findings, our conclusion is that the IBM (or an improved MM) should be used for datasets including shifts in development, whereas the simpler MM is sufficient for datasets where only mortality and reproduction are affected, or as a screening tool in lower-tier population-level ERA.