Embracing multimodal optimization to enhance Dynamic Energy Budget parameterization

Abstract Parameterization is one of the most challenging steps in the construction of individual-based models, and it is particularly relevant for the case of Dynamic Energy Budget (DEB) theory given that DEB parameters are mapped to a multimodal fitness landscape. This multimodal fitness landscape could correspond to parameterizations that provide the right outcome for the wrong reasons. Given the lack of available data to directly parameterize some aspects of DEB models, mathematical tools are becoming the state-of-the-art approach to estimate or refine unknown parameters. The aim of this study is to explore the use of a novel mathematical algorithm that recognizes the multimodal nature of the fitness landscape as a way to provide alternative equally good parameterizations for DEB models. The Multimodal Optimization for Model CAlibration (MOMCA) framework was used to calibrate a DEB model for the blue mussel Mytilus edulis using datasets that included environmental information, growth, and physiological rates. The inclusion of physiological rates, an uncommon approach in DEB parameterization, allowed for constraining the range of solutions, and reducing parameter uncertainty. The application of the MOMCA framework allowed for the identification of the energy acquisition sub-model as one of the top priorities for improving the mechanistic understanding of mussel bioenergetics, and consequently for enhancing model performance. The MOMCA framework could complement the standard procedures to estimate DEB parameters.