Upregulation of phytoplankton carbon concentrating mechanisms during low CO2 glacial periods and implications for the phytoplankton pCO2 proxy

Abstract

Published alkenone  p records spanning known glacial pCO 2 cycles show considerably less variability than predicted by the diffusive model for cellular carbon acquisition and isotope fractionation. We suggest this pattern is consistent with a systematic cellular enhancement of the carbon supply to photosynthesis via carbon concentrating mechanisms under the case of carbon limitation during low pCO 2 glacial time periods, an effect also manifest under carbon limitation in experimental cultures of coccolithophores as well as diatoms. While the low-amplitude  p signal over glacial pCO 2 cycles has led some to question the reliability of  p for reconstructing long-term pCO 2 , the [CO 2 ] aq in the tropical oceans during glacial pCO 2 minima represents the most extreme low CO 2 conditions likely experienced by phytoplankton in the Cenozoic, and the strongest upregulation of carbon concentrating mechanisms. Using a statistical multilinear regression model, we quantitatively parse out the factors (namely light, growth rate, and [CO 2 ] aq ), that contribute to variation in  p in alkenone-producing algae, which confirms a much smaller dependence of  p on [CO 2 ] aq in the low [CO 2 ] aq range, than inferred from the hyperbolic form of the diffusive model. Application of the new statistical model to two published tropical  p records spanning the late Neogene produces much more dynamic pCO 2 estimates than the conventional diffusive model and reveals a significant pCO 2 decline over the last 15 Ma, which is broadly consistent with recent results from boron isotopes of foraminifera. The stable isotopic fractionation between coccolith calcite and seawater dissolved inorganic carbon (here ∆ coccolith-DIC ) also shows systematic variations over glacial-interglacial cycles which may, following future experimental constraints, help estimate the degree of upregulation of parts of the algal carbon concentrating mechanism over glacial cycles.This simplified formulation clarifies the dependence of b on variation in the cellular C content and surface area, which scale with cell size; as well as variation in the growth rate and the effective permeability to CO 2 . When the effects of these factors are considered in aggregate, e.g. by empirical derivations of b from photic zone or culture samples, it must be remembered that the covariation and relative weight of each of these factors spatially in the modern ocean, or in culture experiments, may differ from past temporal significance and covariation of these factors. In practice, however, most previous work has interpreted variation in b to reflect either changes only in the growth rate parameter (Bidigare et al., 1997;Seki et al., 2010), or over long timescales also changes in the cell size and consequently in /S (Henderiks and Pagani, 2008;Seki et al., 2010). Potential variations in P have not been evaluated for glacial samples or the full range of published experiments with  p determinations in experimental culture, although some previous studies have acknowledged that the b...

Validation of carbon isotope fractionation in algal lipids as a <i>p</i>CO<sub>2</sub> proxy using a natural CO<sub>2</sub> seep (Shikine Island, Japan)

“…There are several possible explanations as to why there is an underestimation. As discussed above, carbonate concentration mechanisms may be operating in a large number of phytoplankton, such that they become relatively enriched in 13 C and thus lead to lower reconstructed pCO 2 values (Stoll et al, 2019;Badger et al, 2019). There is also a large uncertainty in the b value applied, which may be much lower than the value assumed here.…”

“…The b value is difficult to estimate as it is a catchall for factors other than pCO 2 that affect fractionation and is particularly difficult to estimate for general algal biomarkers because they are derived from a multitude of species. Previous studies using phytol's diagenetic product phytane as a pCO 2 proxy (Bice et al, 2006;Sinninghe Damsté et al, 2008;van Bentum et al, 2012) have used a mean value of 170 ‰ kg µM −1 , similar to the mean of alkenone producers. This is supported by a compilation of the δ 13 C values of modern surface sediment organic matter mean average of 168 ± 43 ‰ kg µM −1 (Witkowski et al, 2018) and a single study on phytol in the equatorial Pacific Ocean (Bidigare et al, 1997).…”

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

“…While molecular and physiological evidence indicates that phytoplankton deploy a wide range of CCMs to ameliorate the kinetic limitations of RubisCO, the details of these mechanisms, their isotopic expression under natural environmental conditions, and their relevance to geochemical signatures of global carbon cycling are still debated (Reinfelder, 2011, Bolton and Stoll, 2013, Pagani 2014, Hopkinson et al, 2016, Stoll et al, 2019). Here we assume that the dominant processes influencing organic carbon signatures in marine red-lineage phytoplankton can be captured by a single, simplified carbon isotope flux-balance model, because in chemostat culture studies, representatives from three redlineage clades with different RubisCOs and life strategies (including presence and absence of biomineralization) display similar isotopic responses to µ/[CO 2(aq) ] (Figure 1).…”

Shifts in Phytoplankton Composition and Stepwise Climate Change During the Middle Miocene

“…(b) High-resolution benthic foraminiferal δ 18 O records from the eastern equatorial Pacific (ODP Site 1237) and South China Sea (ODP Site 1146) that served to define the durations of the MCO and MMCT (indicated by shadings; Holbourn et al, 2005Holbourn et al, , 2007, overlain by benthic foraminiferal δ 18 O measured at Site 608 (Diester-Haass et al, 2009;Wright et al, 1992). (c) Compilation of alkenone-based paleo-pCO 2 estimates at Site 608 (Super et al, 2018; blue shadings are 68% and 95% CI), ODP Site 925 (Zhang et al, 2013; squares: diffusive model calibration; crosses: recalculated, statistical model estimates by Stoll et al, 2019), and boron isotope-based estimates at ODP Sites 926 and 761 (Foster et al, 2012). See original publications for error estimates.…”