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Published November 15, 2019 | public
Journal Article

A general model for carbon isotopes in red-lineage phytoplankton: Interplay between unidirectional processes and fractionation by RubisCO

Abstract

The carbon isotopic composition of organic matter preserved in marine sediments provides a window into the global carbon cycle through geologic time, including variations in atmospheric CO_2 levels. Traditional models for interpreting isotope records of marine phytoplankton assume that these archives primarily reflect kinetic isotope discrimination by the carbon-fixing enzyme RubisCO. However, some in vivo and in vitro measurements appear to contradict this assumption, indicating that significant questions remain about the mechanistic underpinning of algal isotopic signatures, including the role of carbon concentrating mechanisms (CCMs). Here, we present a general model to explain photosynthetic carbon isotope fractionation (ε_P) in marine red-lineage phytoplankton groups; the model reproduces existing chemostat and batch culture datasets with a normalized root mean squared error (nRMSE) of 6.8%. Our framework proposes that a nutrient- and light-dependent step upstream of RubisCO is a kinetic barrier to carbon acquisition and therefore represents a significant source of isotopic fractionation. We suggest this step represents a carbon concentrating strategy that becomes favorable to cells under conditions of excess photon flux. The primary implications are that RubisCO is predicted to exert minimal isotopic control in photon-rich, nutrient-limited regimes but becomes influential as growth becomes light-limited. This framework enables both environment-specific and taxon-specific isotopic predictions. By refining the mechanistic understanding of marine photosynthetic carbon isotope fractionation, we may begin to reconcile existing datasets and reexamine Phanerozoic isotope records—including the resulting CO_2 reconstructions—by emphasizing the influence of different types of resource limitation on photosynthetic carbon acquisition.

Additional Information

© 2019 Elsevier Ltd. Received 10 April 2019, Revised 29 August 2019, Accepted 31 August 2019, Available online 10 September 2019. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE1144152 to E.B.W. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Funding from the Agouron Institute (to E.B.W.) also supported this work. A.P. acknowledges funding from the Gordon and Betty Moore Foundation and NASA-NAI CAN6 (PI Roger Summons, MIT). We thank Pratigya Polissar, David Johnston, Boswell Wing, Itay Halevy, Alex Worden, and Rich Pancost for helpful discussions, John Kondziolka for assistance with Matlab script development, Sarah Feakins for editorial handling, and three anonymous reviewers for their valuable comments.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023