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Simulating Plankton- Getting It Right in the Era of Digital Twins in the Ocean; Core Features of Plankton Digital Twins

Authors: Flynn, Kevin John; Atkinson, Angus; Beardall, John; Berges, John; Boersma, Maarten; Brunet, Christophe; Calbet, Albert; +26 Authors

Simulating Plankton- Getting It Right in the Era of Digital Twins in the Ocean; Core Features of Plankton Digital Twins

Abstract

This work describes the outcomes from a subcomponent of a project funded by the NERC (UK) during 2023, with the overarching aim of facilitating the construction of the next generation of plankton simulation models by engaging with experts in real plankton physiology and ecology. Over 30 experts, covering plankton from viruses to krill, contributed to various facets of the project. They were selected specifically for their empirical interests; modellers per se were not included. This component had 32 contributors. Contributors were asked to configuring aspirational plankton digital twins (PDT). To protect the intellectual property of the contributors, their PDT concepts are not presented. The PDT concepts were analysed with respect to the required abiotic and biotic components and input/output needs. Most PDT concepts were for short duration (<1mo) laboratory or mesocosm scenarios, some with chemostat-like settings. Field scenarios were 1D (with depth). Most concepts required multiple plankton function types, with host-virus, prey-predator and competition (allelopath) interactions. There was also interest in single organism concepts for photophysiology studies. Concepts included scope for multiple stressor/resource descriptions, most commonly including temperature, light (irradiance, light:dark cycle, with some interests in spectra), and pH/pCO2. While dissolved inorganics were the most requested nutrient form, over a third expressed interest in detailed dissolved organics (i.e., multiple DOM forms and DOC). Two-way interactions were of interest not only for inorganics (with nutrient regeneration), but also for pH, CO2/O2, dissolved organics and the fate of debris (including mortality). Details of the plankton themselves were invariably expected to include biomass with variable stoichiometry (some including fatty acids), and also numeric abundance (often with explicit allometry). Interests in photopigments were dominated by chlorophyll, with some interest in other pigments such as carotenoids. About a third expressed a requirement for inclusion of allelopaths or toxins. Almost all expressed a requirement for detailed physiological and/or behavioural descriptions for the plankton, including variable prey or host selectivity. Explicit descriptions of ontogeny and of vertical migration or other depth-linked aspects were also of interest. A few required explicit inclusion of omics-linkages within the PDT. Input and outputs expectations included an interest in a rewind function during the simulation to facilitate ‘what-if?’ testing, and multiple time and x,y scatter plot options. In short, the general expectation for a PDT, or at least for first-generation PDTs, describes an in silico laboratory in which a range of plankton types would be described to high levels of detail with respect to physiology and behaviour, in a chemically detailed but dimensionally simple (0D or 1D) setting for short-period incubations.

Country
United States
Keywords

Data, 570, Data Science, Modeling, Plankton, Systems biology, Oceanography, Digital twins

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green