DEB-tox and Data Gaps: Consequences for individual-level outputs

Abstract

In this paper, we analyze the impact of data gaps in the context of ecotoxicology when parameterizing a species using a widely recognized theory, the Dynamic Energy Budget (DEB) theory. DEB-based models are used in many ecological domains, and have been recognized as particularly useful in ecotoxicology. However, available datasets are often insufficient to accurately estimate all model parameters.We utilized a data-rich, parameterized species that is widely used in laboratory tests, Danio rerio (zebrafish). We compared two versions (“old” and “new”) of DEB parametrization methods when (i) removing datasets one-by-one, to understand if one dataset was particularly relevant for parameter estimation; and (ii) removing datasets cumulatively, to test how many datasets were necessary to achieve a meaningful parametrization. Using the results of the newer version of the parametrization routine, we checked how differences in parameter estimations could affect the modeled length and egg production of zebrafish. Finally, we assessed the relevance of these differences in an ecotoxicological context. For this purpose, we applied five hypothetical stressors, with different Physiological Modes of Action, to understand the impact of data gaps on estimating stressor effects on individual fish length and egg production.Our work shows that the new parametrization method is robust and efficient even when used with the minimum amount of data suggested by DEB theory. The parameters that are affected the most by data gaps are the maturity threshold parameters. At the individual level, data gaps affect mostly egg production. However, the link between data gaps and individual egg production is not always straightforward. Stressor effects can amplify or decrease differences between data-rich and data-poor scenarios, but usually their effects are consistent within each scenario, confirming DEB models as a powerful tool in ecological and ecotoxicological studies. We suggest careful consideration of the potential effects of data gaps when implementing DEB-based population models.