Modeled temperature dependencies of macromolecular allocation and elemental stoichiometry in phytoplankton

Abstract

Graphical abstract

Saturating relationship between phytoplankton growth rate and nutrient concentration explained by macromolecular allocation

“…Despite such a simplification, our model may well capture the elemental stoichiometry across taxa ( Inomura et al., 2020 ), which may suggest that the factors that we simplified have only secondary effects. We note that despite these simplifications, our model resolves more detailed macromolecular allocations than widely used models (e.g., Droop types), and thus these simplifications are done at more detailed levels ( Armin and Inomura, 2021 ; Inomura et al., 2020 ) than widely used models, including Monod kinetics ( Monod, 1949 ). Further experiments must be performed for the incorporation of further details.…”

“…A mechanistic model (i.e., Cell Flux Model of Phytoplankton, CFM-Phyto) was recently developed which outputs the relationship between growth rate, elemental stoichiometry, and macromolecular allocations (e.g., proteins, DNA, RNA, carbohydrates, and chlorophyll) in phytoplankton given different environmental conditions. Initial environmental parameters which can be used include varying nutrient regimes, temperature, and light intensity ( Armin and Inomura, 2021 ; Inomura et al., 2020 ). CFM-Phyto has been shown to well capture the observed trends of elemental stoichiometry of various phytoplankton ( Chalup and Laws, 1990 ; Healey, 1985 ; Inomura et al., 2020 ; Sakshaug and Andersen, 1989 ), supporting its structural robustness.…”

“…CFM-Phyto has been shown to well capture the observed trends of elemental stoichiometry of various phytoplankton ( Chalup and Laws, 1990 ; Healey, 1985 ; Inomura et al., 2020 ; Sakshaug and Andersen, 1989 ), supporting its structural robustness. The model has been used for various purposes, such as predicting C:P ratios in the ocean based on the satellite remote sensing data ( Tanioka et al., 2020 ) and light and temperature dependencies of C:N:P ratios ( Armin and Inomura, 2021 ; Inomura et al., 2020 ). Accordingly, CFM-Phyto demonstrated again that phytoplankton are critical components in the ocean and have a significant impact on global primary production, biogeochemical cycles, and marine ecosystems.…”

“…Accordingly, CFM-Phyto demonstrated again that phytoplankton are critical components in the ocean and have a significant impact on global primary production, biogeochemical cycles, and marine ecosystems. Not only this, but CFM-Phyto also provided key insights to cellular physiology under varying nutrient conditions (e.g., nitrogen limitation and phosphorus limitation), temperature ( Armin and Inomura, 2021 ), and light intensity ( Inomura et al., 2020 ). Furthermore, CFM-Phyto continually demonstrates that the model is capable of accurately capturing realistic trends consistent with observations.…”

Modeling the elemental stoichiometry and silicon accumulation in diatoms

“…The linear relationship of synthetic protein to the growth rate is consistent with prior observations of linearly increasing investment to ribosomal proteins and protein-based N with growth rate ( Rhee, 1978 ; Jahn et al., 2018 ; Zavřel et al., 2019 ). We set the value for to be 0.1 mol N mol C −1 d, which is in a reasonable range consistent with previous studies ( Inomura et al., 2020 ; Armin and Inomura, 2021 ). N storage only occurs when N is in excess, therefore the N-limited simulation does not include N storage.…”