Assessing Pesticide Risk Using Mesocosm Studies

Our Challenge

Our client was seeking a more realistic way to model potential pesticide risks. Doing that involves numerous variables. We proposed using data from existing mesocosm (outdoor experimental systems under controlled conditions used to study ecosystems) studies and modeling software to create a model generic farm pond to then model against known toxicity data for the pesticide.

Our Approach

We established data quality objectives (DQOs) based on the model’s intended use and metrics for characterizing populations and the ecosystem. We evaluated how well the model fit potential ecosystem scenarios and checked these assumptions against mesocosm observations.

The parametrized model represented a generic farm pond food web in central Europe, including phytoplankton, zooplankton, periphyton, macrophytes, benthic and phytophilous macroinvertebrates, and fish. We calibrated the model through successive iterations, adjusting parameters until the team agreed it met project DQOs. During calibration, we applied Integral’s 3-Factor Approach for Model Testing. We then verified the model, conducted sensitivity analysis, and validated results.

Our Impact

Integral developed a mesocosm mimic model that was calibrated to consider the original studies’ seasons and durations. The calibrated mesocosm mimic model was then applied to evaluate alternate seasons, durations, food web compositions, toxicity thresholds, and exposure profiles beyond those included in the original mesocosm studies. We were able to establish model persistence under control conditions in different individual seasons, across multiple seasons, and across successive years. Using both experimental data and mathematical modeling, we assisted our client to evaluate potential effects of the pesticide beyond the temporal, spatial, and biological scenarios represented by the mesocosm studies.