MAGMA: a Modeling Approach for Growth inhibition of MAcrophytes to predict effects of time variable exposure under laboratory conditions.

Abstract

In the European framework for assessing the ecological effects of plant protection products on aquatic organisms, standard tests usually rely on constant laboratory exposure. This Tier 1 approach may lead to overly conservative assessments for short-term exposures lasting only a few hours under realistic environmental conditions. To address this, the European Food Safety Authority proposed a Tier 2C assessment to incorporate more realistic dynamic exposure profiles through refined tests and toxicokinetic/toxicodynamic (TKTD) modeling. Currently, the only accepted TKTD model for macrophyte growth inhibition relates to the duckweed Lemna sp. We hypothesize that this model can be adapted for other macrophytes including sediment rooted macrophytes. We propose a Modeling Approach for Growth inhibition of MAcrophytes (MAGMA), to simulate both constant and dynamic exposure tests under laboratory conditions. The model was calibrated using experimental data from Myriophyllum spicatum exposed to two herbicides with different modes of action. Validation against single and two-pulse exposure experiments showed good agreement between model predictions and observed data. The modeling approach, MAGMA can therefore serve as a valuable Tier 2C tool for predicting macrophyte growth rates under dynamic exposure conditions.

Code and data availability

The paper's Myriophyllum spicatum phenotype (TSL) datasets, model input/output files, and code are not publicly available; they can only be requested via the Bayer CropScience transparency initiative. No public repository URL for the paper-specific data or code is provided.

No evidence-backed public reproduction asset is currently recorded.