End-to-end models for the analysis of marine ecosystems: challenges, issues and next steps
There is growing interest in models of marine ecosystems that deal with the effects of climate change through the higher trophic levels. Such end-to-end models combine physicochemical oceanographic descriptors and organisms ranging from microbes to higher-trophic-level (HTL) organisms, including humans, in a single modeling framework. The demand for such approaches arises from the need for quantitative tools for ecosystem-based management, particularly models that can deal with bottom-up and top-down controls that operate simultaneously and vary in time and space and that are capable of handling the multiple impacts expected under climate change. End-to-end models are now feasible because of improvements in the component submodels and the availability of sufficient computing power. We discuss nine issues related to the development of end-to-end models. These issues relate to formulation of the zooplankton submodel, melding of multiple temporal and spatial scales, acclimation and adaptation, behavioral movement, software and technology, model coupling, skill assessment, and interdisciplinary challenges. We urge restraint in using end-to-end models in a true forecasting mode until we know more about their performance. End-to-end models will challenge the available data and our ability to analyze and interpret complicated models that generate complex behavior. End-to-end modeling is in its early developmental stages and thus presents an opportunity to establish an open-access, community-based approach supported by a suite of true interdisciplinary efforts.
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Authors: Rose, Kenneth A., Allen, J. Icarus, Artioli, Yuri, Barange, Manuel, Blackford, Jerry, Carlotti, François, Cropp, Roger, Daewel, Ute, Edwards, Karen, Flynn, Kevin, Hill, Simeon L. ORCID record for Simeon L. Hill, HilleRisLambers, Reinier, Huse, Geir, Mackinson, Steven, Megrey, Bernard, Moll, Andreas, Rivkin, Richard, Salihoglu, Baris, Schrum, Corinna, Shannon, Lynne, Shin, Yunne-Jai, Smith, S. Lan, Smith, Chris, Solidoro, Cosimo, St. John, Michael, Zhou, Meng