One of the great challenges in biogeochemical research over the
past half a century has been to quantify and understand the mechanisms
underlying stable carbon isotope fractionation (εp) in
phytoplankton in response to changing CO2 concentrations. This interest is partly grounded in the use of fossil photosynthetic organism
remains as a proxy for past atmospheric CO2 levels. Phytoplankton
organic carbon is depleted in 13C compared to its source because of
kinetic fractionation by the enzyme RubisCO during photosynthetic carbon
fixation, as well as through physiological pathways upstream of RubisCO.
Moreover, other factors such as nutrient limitation, variations in light
regime as well as phytoplankton culturing systems and inorganic carbon
manipulation approaches may confound the influence of aquatic CO2
concentrations [CO2] on εp. Here, based on
experimental data compiled from the literature, we assess which underlying
physiological processes cause the observed differences in εp for various phytoplankton groups in response to C-demand/C-supply, i.e., particulate organic carbon (POC) production / [CO2]) and test potential confounding factors.
Culturing approaches and methods of carbonate chemistry manipulation were
found to best explain the differences in εp between
studies, although day length was an important predictor for εp in haptophytes. Extrapolating results from culturing experiments to
natural environments and for proxy applications therefore require caution,
and it should be carefully considered whether culture methods and
experimental conditions are representative of natural environments.
Introduction
Understanding of past climates, in particular variations in atmospheric
CO2 concentrations and concomitant temperatures, may help to improve
climate models and constrain the global temperature response to the projected
CO2 rise (Rohling
et al., 2012; Zhu et al., 2020; Tierney et al., 2020). Reconstructions of
past CO2 concentrations [CO2] beyond the reach of ice cores rely
on proxy estimates. These are based on biogeochemical relations between
[CO2] in the atmosphere and the chemical or morphological properties of
biogenic carbonates, other minerals, fossil leaves or various types of
organic matter that can be found in sediments (Foster
et al., 2017; Macdonald, 2020). All these proxies are based on assumptions
and exhibit large uncertainties that are ideally constrained iteratively.
One line of proxies uses the CO2-dependence of 13C fractionation
during photosynthetic carbon fixation in phytoplankton
(O'Leary, 1984; Sharkey and Berry, 1985;
Farquhar et al., 1989). Several components found in sediments have been
proposed for this, including bulk organic matter (Hayes et
al., 1999) and algae-derived molecules, such as porphyrins (Freeman and Hayes, 1992) and phytane (Bice et
al., 2006) produced by all photosynthetic organisms. Also, the potential of
more specific proxies has been tested, including alkenones originating from
coccolithophores (Jasper and Hayes, 1990) and resting cysts
produced by dinoflagellates (Hoins et al., 2015; Sluijs
et al., 2018). The δ13C signal of various algal remains is
thought to follow the δ13C signal of dissolved inorganic carbon
(DIC), in particular the δ13C signal of CO2, modulated by
CO2-dependent fractionation during photosynthetic carbon fixation.
Therefore, ultimately, the δ13C signal in algal fossil remains
may be used for estimating atmospheric CO2 levels through geological
time. Accurate use of this proxy relies on the mechanistic understanding of
carbon isotope fractionation (εp) in phytoplankton, which
is obtained by different culturing approaches and assays targeting relevant
physiological pathways.
Fractionation occurs during fixation of CO2 by
ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) (Raven
and Johnston, 1991), and is further dependent on the C-supply to this enzyme
as well as the C-demand of the cells
(Rau et al., 1996; Bidigare et al.,
1997; Hoins et al., 2016a). RubisCO discriminates against the 13C
isotope resulting in biomass being 13C-depleted relative to its source
CO2. In higher plants, the intrinsic fractionation value of RubisCO
(εf) is estimated to be 26 ‰–30 ‰
(Roeske and O'Leary, 1984; McNevin et al., 2007).
However, εf can differ between phytoplankton taxa and
species (e.g., Maberly et al., 1992;
McNevin et al., 2007), and is indeed an important source of variation in
13C fractionation among phytoplankton groups. Several catalytically and
phylogenetically distinct forms of RubisCO in phytoplankton exist, including
forms IA, IB, ID and II (Whitney et al., 2011;
Tabita et al., 2008). Direct in vitro measurements of εf yielded
values of ∼ 11 ‰ for the haptophyte
Emiliania huxleyi (Boller et al., 2011) and ∼ 18.5 ‰ for the diatom Skeletonema costatum (Boller et
al., 2015). Much higher fractionation values have been estimated from in vivo
experiments under nitrate-limited conditions, with values as high as
∼ 25 ‰ for the diatoms Phaeodactylum tricornutum and Porosira glacialis, and for E. huxleyi (Popp
et al., 1998), and ∼ 27 ‰ for the
dinoflagellate Alexandrium tamarense (Wilkes et al., 2017).
These large differences between phytoplankton groups and across treatments
point towards physiological processes that can affect fractionation, notably
those involved in so-called carbon concentrating mechanisms (CCMs). The CCMs
have evolved over time as a response to declining atmospheric CO2
concentrations to ensure effective carboxylation in the vicinity of RubisCO
in oxygenated waters (Giordano et al., 2005). Phytoplankton
CCMs comprise a variety of physiological adaptations, and include active
uptake of CO2 and HCO3-, the use of carbonic anhydrase (CA)
to accelerate the interconversion between CO2 and HCO3-, and
ways to minimize the CO2 efflux from the cell
(Badger et al., 1998; Reinfelder, 2011; Rokitta
et al., 2022). These processes can strongly influence 13C fractionation
patterns of phytoplankton (Sharkey and Berry, 1985). For
instance, HCO3- is enriched in 13C relative to CO2 (by
∼ 10 ‰), and a high uptake and
assimilation of HCO3-can therefore lower apparent 13C
fractionation values. In addition, alterations in the CO2 efflux over
total carbon uptake (i.e., leakage) also affect 13C fractionation, as
faster replenishment of the intracellular CO2 pool prevents a build-up
of 13CO2 and thus allows RubisCO to fully express its intrinsic
fractionation. Different modes of CCMs are employed by different
phytoplankton species, likely attributing to species-specific or
group-specific differences in 13C fractionation
(Badger et al., 1998; Van de Waal et al., 2019;
Tortell, 2000).
The observed differences in εp between nutrient-limited
and nutrient-replete cultures have been attributed to differences in the
regulation of carbon uptake relative to carbon fixation
(Laws et al., 2001). This variation may, at least partly,
be caused by culturing methods, as chemostat cultures that were limited by
nutrients or light showed similar responses of εp to
changes in [CO2], while responses in light-controlled dilute batch
cultures were markedly different (Laws et al., 2001).
Likewise, discrepancies in measured δ13C values in different
species of coccolithophores have been ascribed to varying culture methods,
in particular to methods of CO2 manipulation
(Liu et al., 2018; Hermoso et al., 2016).
A recent study furthermore attributed differences in apparent fractionation
to a regulatory CCM pathway upstream of RubisCO (Wilkes and
Pearson, 2019). This pathway was suggested to alleviate excess photon flux
when cells are nutrient-limited by shunting energy towards carbon uptake and
hydroxylation reactions that increase εp.
Here, we aim to elucidate which underlying physiological processes cause the
observed differences in 13C fractionation in phytoplankton under
different [CO2], and how this is influenced by experimental settings.
To this end, we collected data from all available culture studies, including
a range of phytoplankton species from different groups, and evaluated
systematic trends and offsets in 13C fractionation as a function of
environmental, physiological, and experimental factors. This analysis
compares the drivers behind phytoplankton 13C fractionation, assesses
relations between 13C fractionation and culturing settings, and
discusses implications for proxy development.
Material and methodsLiterature review
We compiled data on 13C fractionation (εp) in
phytoplankton species under a range of [CO2] and experimental
conditions. A literature search was performed in Web of Science (https://www.webofknowledge.com/, last access: 25 February 2020) using the query “phytoplankton” OR
“algae” OR “microalgae” OR “picoplankton”) AND “climate change” OR
“ocean acidification” OR “CO2” OR “carbon dioxide” OR “global
change” OR “pCO2” OR “carbonate chemistry” AND “13C fractionation”
OR “εp” OR “carbon isotope” OR “isotope fractionation”. Data on 13C fractionation, growth rates (μ), and
particulate organic carbon (POC) content under different experimental
conditions and [CO2] were extracted using Engauge software when needed
(Mitchell et al., 1991). To get an estimate of the carbon
demands of the cells, we calculated POC production by multiplying the POC
content with the instantaneous growth rate (μi). Using μi, we yield POC production that corresponds to the carbon fixation
during the photoperiod (Riebesell et al.,
2000a,b; Rost et al., 2002; Burkhardt et al., 1999a), and therefore
corrects for difference in day length between studies. In addition,
information was extracted on experimental settings (i.e., irradiance,
light-dark cycle, salinity, temperature, nutrients), culturing approach
(i.e., batch, chemostat, dilute batch, dilute chemostat), and type of
carbonate chemistry manipulation resulting in different concentrations of
dissolved inorganic carbon (DIC; i.e., aeration of culture with CO2,
pre-aeration of culture medium with CO2) or total alkalinity (TA; i.e.,
acid or base addition). Under non-limiting growth conditions, δ13C
of phytoplankton cells was measured during the exponential growth phase. The
database includes only marine and estuarine phytoplankton species, with data
acquired through single species culture experiments.
Statistical analyses
All analyses were performed in R version 4.0.3 (R Core Team,
2020). Significant differences in εp between different
experimental conditions and culturing methods were calculated by means of a
linear model followed by pairwise comparisons (Tukey method). To assess the
relationship between εp and POC production / [CO2], a
linear model was fitted to the data for each of the distinct phytoplankton
groups, and for each of the distinct species and study combinations. Data on
POC production / [CO2] was first log transformed, as this improved
normality. To assess which of the influential conditions (i.e., nutrient
conditions, carbonate chemistry manipulation method, culture approach,
irradiance or light-dark cycle) could best explain the variation in
εp, along with POC production/[CO2], we compared
different models using the lmer function in R from the package “lme4”
(Bates et al., 2015). In these models, POC
production / [CO2] and one of the influential conditions were fitted as
fixed effects, including interaction terms, while species was fitted as a
random intercept for each of the distinct phytoplankton groups (excluding
cyanobacteria due to lack of data). Models were subsequently compared based
on their Akaike Information Criterion (AIC) and Bayesian Information
Criterion (BIC). For the different phytoplankton groups, we also tested how
much explanatory power we could generate for the εp and
POC production / [CO2] relationship by including different environmental
variables and using a multiple regression approach.
ResultsDataset on 13C fractionation
The literature search yielded a total of 509 results, first titles and
subsequently abstracts were reviewed, which led to a selection of 77
publications for screening. After careful screening for suitability, a total
of 25 publications, containing 58 unique datasets, were included in our
database. It contains data on four of the major marine phytoplankton groups,
namely dinoflagellates (15 datasets), diatoms (24 datasets), haptophytes (17
datasets) and cyanobacteria (2 datasets).
Across all phytoplankton groups, there is a negative log-linear relationship
between 13C fractionation (εp) and POC production
over [CO2] (Fig. 1). This relationship is also apparent in each
phytoplankton group (Fig. 2), although the slope of this curve varies
between groups, and also strongly between species and studies (see also Fig. 3). As not all studies reported POC contents per cell, we also tested
μi/ [CO2] to assess more species (especially for diatoms),
finding similar pattern as POC production / [CO2] (Fig. S1 in the Supplement). Across all
phytoplankton groups, however, the explanatory power of the negative
log-linear relationship between εp and μi/[CO2] was considerably smaller compared to the εp and POC production / [CO2] relationship (Fig. S2). Within each
phytoplankton group, the explanatory power of εp and
μi/ [CO2] did hold up for diatoms and dinoflagellates but
became insignificant for haptophytes (Fig. S3).
εp against POC production / [CO2]
(C-demand/C-supply; log-transformed) across all phytoplankton groups. Colors
indicate the light-dark cycle; marker shapes indicate the culturing
approach. Black line illustrates the log-linear relationship (R2 and
p-value indicated in the panel).
εp against POC production / CO2
(C-demand/C-supply; log-transformed) for the different phytoplankton groups,
where the colored points indicate the respective light-dark cycle, and the
shape of the points indicates the culturing approach. Black line illustrates
the linear relationship (R2 and p-values indicated in the panels).
Slopes of εp in response to POC
production / [CO2] for the different species and studies using a linear
fit. Numbers between brackets refer to the different studies (Table S1).
Blue dots represent diatoms, orange triangles dinoflagellates, and red
squares haptophytes. Significance is indicated by the asterisks (***P< 0.001, **P< 0.01, *P< 0.05).
Differences in εp between (a) type of carbonate
chemistry manipulation (P-A is pre-aeration, and C-A is continuous
aeration), (b) light-dark cycle, where colors indicate the different
phytoplankton groups and shapes indicate type of limitation (N is nitrogen,
P is phosphorus, T is temperature, and No is non-limited), (c) culturing
approach, where colors indicate type of carbonate chemistry manipulations
and shapes type of limitation again, and (d) type of limitation, where colors
indicate culturing approach and shapes light hours per day. Significant
differences between experimental conditions are indicated by the letters in the respective panels.
The explanatory power of the relationship between εp and
POC production / [CO2] within each phytoplankton group further
increased when we included the amount of light hours per day, and the
information whether there was nutrient limitation, yielding an R2 of
0.63 for diatoms (p< 0.001), 0.41 for dinoflagellates (p< 0.001), and 0.77 for haptophytes (p< 0.001).
Experimental settings and 13C fractionation
Some of the variation in εp can be explained by the
different experimental settings between the studies (Fig. 4). For instance,
phytoplankton grown under nitrogen limitation and lower temperatures show
higher εp than those grown under light-controlled or
non-limiting growth conditions (Fig. 4d). However, 13C fractionation
also varied across the different types of carbonate chemistry manipulations
and culturing approaches (Fig. 4a, c). Closed systems (i.e., pre-aeration
with CO2 and acid or base addition) had lower εp values
than open systems (continuous aeration with CO2), and cultures that
were grown in chemostats with high biomass had higher overall εp values than those grown in dilute cultures. Moreover, light-dark
cycle also strongly influences 13C fractionation, with cultures that
experience continuous irradiation having higher εp values
than cultures that are exposed to a dark-cycle (Fig. 4b). This was
especially apparent for haptophytes and dinoflagellates, where cultures with
continuous light grown in nitrogen-limited chemostats had higher
εp values than those with a dark-cycle grown under replete
dilute batch conditions (Fig. 2).
Some confounding experimental conditions across studies appear in our
database. Notably, nutrient limitation experiments are almost always
performed in chemostats with continuous aeration and without a light-dark
cycle. In addition, non-limiting or light-controlled culture studies, with a
light-dark cycle, are almost entirely performed using a dilute batch, with
pre-aeration or acid or base addition (Fig. 4). To tease apart which of these
confounding factors (i.e., nutrient conditions, type of carbonate chemistry
manipulation, culturing approach, and light-dark cycle) can best explain the
differences in εp, besides C-demand/C-supply, we compared
different models including POC production / [CO2] and one of the
variables for each of the distinct phytoplankton groups (Tables S2–S4). For
haptophytes, inclusion of light-dark cycle could best explain the data (AIC
711 and BIC 738), while the culturing approach yielded the best results for
dinoflagellates (AIC 490 and BIC 520), and for diatoms this was either the
culturing approach (AIC 600) or the method of carbonate chemistry manipulation
(BIC 623).
Discussion
In our analyses of the current literature on εp responses,
we observed a high dependence on C-demand/C-supply (i.e., POC
production / [CO2]) across and within different phytoplankton groups
(Figs. 1, 2), where the inclusion of light regime and nutrient limitation
further increased the explanatory power of this relationship. The correction
step for C-demand (as compared to growth rate) is essential, as already
identified in previous work, because different growth rates and cellular carbon contents reflect the different C requirements of phytoplankton cells
(Rau et al., 1996; Bidigare et al.,
1997; Hoins et al., 2016b). Moreover, estimating C requirements based on
instantaneous growth rates alone is not sufficient to reflect C-demand,
especially for haptophytes (Figs. S2, S3). Variation in the εp relationship with POC production / [CO2] was, however, observed
between the different species and studies (Fig. 3). Next to species-specific
differences, this may be attributed to the contrasting experimental settings
and culture methods. In the following, we discuss the variation in
fractionation patterns between the phytoplankton species and groups,
highlighting the potential role of CCMs, how different experimental settings
may result in isotopic disequilibrium conditions, and the implications for
CO2 proxies based on carbon isotope fractionation.
Fractionation patterns and underlying processes
Across and within phytoplankton groups, the relationship between
εp and POC production / [CO2] follows a decay function
(i.e., see untransformed data Fig. S4), which highlights the active role of
CCMs in C uptake in all groups. If species relied on diffusive CO2
uptake alone, a more linear relationship can be expected. The presence of
CCMs is further supported by some of the low εp signals
(Fig. 1), indicating a higher contribution of HCO3- to C fixation
and/or decreased leakage. Intrinsic RubisCO fractionation values
(εf) of 18 ‰ and
11 ‰ were measured in diatoms and haptophytes,
respectively (Boller et al., 2011, 2015). The
εp values exceeding εf for both of these
groups, and possibly also for dinoflagellates, therefore remain puzzling and
indicate fractionation steps occurring upstream of RubisCO
(Wilkes and Pearson, 2019).
In cyanobacteria, CO2 fixation by RubisCO takes place in the
carboxysome, which is a distinct cellular compartment. The membrane of this
compartment prevents diffusion of CO2, while it is permeable for
HCO3- which is converted to CO2 via carboxysomal CA, thereby
accumulating CO2 in the vicinity of RubisCO
(Espie and Kimber, 2011; Dou et al., 2008;
Price et al., 2008). To prevent CO2 efflux out of the cell, and
likewise facilitate diffusive CO2 uptake, cytosolic CO2 is
actively converted to HCO3- by the NAD(P)H dehydrogenase (NDH)
complex in the cytoplasm (Price et al., 2002; Maeda
et al., 2002). It was proposed that these specific processes modify and in
fact raise εp values in cyanobacteria. A strong
disequilibrium in the cytosol may, for instance, favor a unidirectional
conversion of CO2 to HCO3- that would result in an additional
fractionation step of at least ∼ 13 ‰
(O'Leary et al., 1992), and potentially up to
20 ‰–33 ‰ (Zeebe and Wolf-Gladrow,
2001; Zeebe, 2014; Siegenthaler and Münnich, 1981; Clark and Lauriol,
1992). If this conversion step is furthermore mediated by NDH, this enzyme
will likely discriminate against 13C resulting in additional
fractionation (Eichner et al., 2015). Overall, this “internal
C-cycling” around NDH would yield higher εp values than
otherwise expected based on the CO2 and HCO3- fluxes over the
plasma membrane assuming equilibrium (Eichner et al.,
2015; Sharkey and Berry, 1985).
Similar strategies may be present in the other algal groups that increase
εp. Effective CO2 fixation in diatoms relies on
biophysical CCMs that facilitate or actively transport CO2 and
HCO3 through a 4-layered chloroplast membrane system
(Keeling, 2013), which principally makes the uptake more
challenging but also confers additional control on the DIC fluxes
(Matsuda et al., 2017; Nakajima et al., 2013). Numerous
subcellular localized CAs are present in diatoms, which accelerate the
interconversion of CO2 and HCO3-, also within the pyrenoid,
where RubisCO is localized (Tachibana et al.,
2011; Kikutani et al., 2016; Samukawa et al., 2014). Chemical disequilibrium
environments between compartments, unidirectional conversion of CAs, or
13C discrimination associated with HCO3- transporters (solute
carrier type transporters; SLC) may represent additional, but likely small
sources of 13C fractionation for diatoms.
No internal membrane systems with localized CAs associated to C fixation as
present in diatoms have been recognized in haptophytes and dinoflagellates
(Rokitta et al., 2022). In fact, some dinoflagellate species
even lack the pyrenoid compartment, where RubisCO is located in most
eukaryotic algae (Ratti et al., 2007). The contribution of
HCO3- to photosynthesis is high in both groups
(Rost et al., 2006; Rokitta and
Rost, 2012; Bach et al., 2013; McClelland et al., 2017), and fractionation
due to chemical disequilibria within the cell can therefore occur to some
degree, e.g., by favoring unidirectional conversion of CO2 to
HCO3- and vice versa. However, stronger internal C-cycling to
maintain high CO2 accumulation in the proximity of RubisCO by decreasing
CO2 leakage from the cell (Cassar et al., 2006;
Schulz et al., 2007; Eichner et al., 2015; Hoins et al., 2016a) and higher
contribution of HCO3- to net C fixation generally lead to higher
build-up of 13C within the cell (i.e., stronger internal Rayleigh
fractionation) and consequently lower εp values. Thus,
while described modes of CCMs for the different groups are mostly in line
with observed fractionation patterns (Schulz et al., 2007;
Eichner et al., 2015; Hoins et al., 2016a; McClelland et al., 2017),
εp values exceeding the intrinsic fractionation of RubisCO
remain puzzling.
Wilkes and Pearson (2019) recently proposed that certain
components of the CCM are differently regulated in nutrient-limited,
light-replete cultures compared to light-controlled cultures, which could
explain the often observed differences in εp patterns
between chemostat and dilute batch cultures (Fig. 4), and likewise reconcile
why εp values can exceed εf estimates
under some conditions. More specifically, the authors suggested that when
cells are nutrient-limited they can experience excess photon flux, which may
be alleviated through fueling photocatalytic dehydration reactions of
HCO3- by internal CAs localized in the thylakoid lumen. The acidic
environment in the thylakoid favors the unidirectional conversion of
HCO3- to CO2, while the alkaline environment in the
chloroplast favors the unidirectional conversion of CO2 to
HCO3-. Light-induced stimulation of these processes may increase
fractionation due to unidirectional hydration of CO2 and dehydration of
HCO3- (up to ∼ 25 ‰ and
∼ 34 ‰, respectively; Wilkes
and Pearson, 2019). However, the proposed εp difference
between light-limited and nutrient-limited cultures was not consistently
found (Fig. 4; Laws et al., 2001; Hoins et
al., 2016a). Moreover, our results suggest this “light-driven CCM”
activity stems from the absence of a light-dark cycle during culture growth
rather than from nutrient or light limitation (Figs. 1, 4b). This was
especially the case for haptophytes and dinoflagellates, where εp values were consistently elevated under continuous irradiance (Fig. 2). In addition to differences in light-dark cycle, other culturing
variables also differed between the studies reviewed by Wilkes
and Pearson (2019).
Experimental settings and resulting isotopic disequilibria
Studies yielding exceptionally high εp values (apparently
higher than εf) were, next to nutrient limitation and
continuous irradiance, also performed in high-biomass chemostats under
continuous aeration with CO2 (Fig. 4). In haptophytes, the light-dark cycle
could, next to POC production / [CO2], best explain differences in
εp (Table S2). The important role of light on
fractionation was already discussed by Rost et al. (2002),
and more recently highlighted by Phelps et al. (2021). They found that in coccolithophores, εp depended
more strongly on light intensity and light-dark cycle than on CO2
concentrations (higher εp values with more light
exposure), even when corrected for C-demands (Phelps et al., 2021; Rost et al., 2002). While
continuous light led to higher εp also in one
dinoflagellate and several diatoms (Burkhardt et al.,
1999b; Wilkes et al., 2017), the light-dependency of εp
seems strongest in haptophytes and may thus relate to changes in C flow
between photosynthetic C fixation and calcification under changing light
conditions (Krumhardt et al., 2017; Bolton
and Stoll, 2013; Phelps et al., 2021). For instance, day length had a
significant influence on εp of E. huxleyi (up to
8 ‰), as the preferred carbon source shifted from
CO2 under continuous light to HCO3- uptake under light-dark
cycles (Rost et al., 2002, 2006). However,
not all phytoplankton species' fractionation responds similarly to changes
in day length. For two diatoms and one dinoflagellate species, for instance,
εp values were similar for cultures grown under continuous
light or light-dark cycles (Burkhardt et al., 1999b).
In diatoms and dinoflagellates, the culturing approach or method of
carbonate chemistry manipulation was, next to POC production / [CO2], the
best predictor of changes in εp (Tables S3, S4). Rost et al. (2008) pointed out that, next to aspects of
the CCM itself, different carbonate chemistry manipulations and culturing
methods can lead to different CO2-dependencies between studies and
different experimental set-ups.
Importantly, in the calculations for the δ13C of CO2 and
thus also εp, chemical and isotopic equilibrium is
assumed. In “open” carbonate chemistry systems with a continuous supply of
CO2, however, an equilibrium situation may not yet be reached before
phytoplankton assimilate carbon (Zeebe et al., 1999;
Rost et al., 2008), which is even more so in the case of high-biomass cultures.
Recent work from Zhang et al. (2022) showed that it takes
much longer (several hours to days) for an isotopic equilibrium to be
reached in empty algal culturing vessels than a chemical equilibrium and
that this should be considered in εp calculations. This
discrepancy could lead to an overestimation of εp in
“open” carbonate chemistry systems compared to “closed” carbonate
chemistry systems, which has been set up by pre-aeration with a certain
[CO2] or by acid or base additions, such as observed in Fig. 4c. This
may especially be true for cultures with high CO2 treatments (high
carbon supply) and high overall carbon demands (high biomass), as both favor
disequilibrium situations (see Fig. S5 for example of the dinoflagellate
Alexandrium tamarense). In chemostats that were run with low cell densities, on the other hand,
isotopic disequilibria may not play a role and therefore yield similar
εp values comparable to dilute batch studies (Fig. 4c).
Hence, biases in εp values introduced by isotopic
disequilibria can be misinterpreted as treatment effects, e.g., as an effect
of nitrate-limited vs. light-limited growth. Moreover, chemostat systems that are
maintained with high biomass, even though they are meant to mimic
oligotrophic systems, are not representative for natural environments as
these systems support only low biomass concentrations (Van
de Waal et al., 2014).
Implications for proxies
The extent to which experiments reflect natural conditions is important
regarding proxy development, as they feed the mechanistic model of
CO2-dependent carbon isotope fractionation and confounding factors. A more standardized approach
in performing these types of experiments, e.g. representative of natural light settings, using only one type of carbonate
chemistry manipulation, and maintaining cultures at low biomass, would
already substantially reduce variation in the CO2-dependent
εp responses between studies (Fig. 3). Both
species-specific differences and the effects of drivers (nutrient
limitation, temperature conditions, etc.) would then be more straightforward
to distinguish, as the study design would not interfere as a concomitant source
of εp variation (Fig. 4). We note, however, that even when
studies use comparable methods, findings can still vary. For example, not
all phytoplankton strains tested showed a negative relationship between
εp and POC production / [CO2] (Fig. 3), meaning that
other environmental factors can mask the CO2 dependence, which urges
for caution when using 13C fractionation during photosynthetic C
fixation as a CO2 proxy. Quantitative constraints on these confounding
factors are crucial to guarantee that reconstructed signals exceed the
related uncertainties.
Quantitative constraints on physiological variables, implied to be growth
rate and cell geometry, but also membrane permeability to CO2, and the
boundary layer thickness dependent on temperature, pH, and salinity, are in
place in CO2 proxy work with a catch-all term called b
(Popp et al., 1998; Rau et al.,
1996; Jasper and Hayes, 1990; Bolton et al., 2016; Stoll et al., 2019). The
b value is often linearly correlated with modern dissolved reactive phosphate
concentration in surface seawater (Bice et al.,
2006; Bidigare et al., 1997; Pagani et al., 2005), as phosphate is a major
nutrient that often co-limits with other important micronutrients such as
iron, zinc, and cobalt, which affect phytoplankton growth rate and cell size
(Bidigare et al., 1997). However, this is quite a simplistic
view and b can vary substantially over time and between locations
(Zhang et al., 2019).
Better constraints on b may further advance CO2 proxy development based
on specific algal biomarkers, although this remains a challenge due to the
sparsity of useful parameters on confounding factors in the
paleo-environment. This is especially true for biomarker proxies, such as
phytane and alkenones, as these biomarkers are produced by multiple species,
certainly through geological time (Witkowski et al., 2018).
These species may have had different modes of carbon acquisition, growth
rates and cellular carbon contents, and discrepancies between alkenone proxy
and ice core records were also attributed to CCM activity
(Badger et al., 2019; Badger, 2021). The preference
for more specific proxy work is also needed as the analyzed phytoplankton
groups show different slopes for the εp versus POC
production / [CO2] relationship (Fig. 2). However, a better selection of
study sites (i.e., located in more productive ocean regions with possibly
similarly responsive species as well) can reproduce CO2 estimates that
are in agreement with the ice core records even with constant b values
(Zhang et al., 2019). Moreover, including estimations of
light regime and nutrient status may further improve CO2 estimates
based on algal proxies.
Another phytoplankton group and even species-specific line of CO2
proxies under development is the 13C fractionation of dinoflagellate
resting cysts (Hoins et al., 2015; Sluijs et al., 2018).
Single cysts can be analyzed with a recent analytical set-up
(van Roij et al., 2017), which provides the advantage that
specific species can be selected so that an estimate of cell size can be
made. Vegetative cell sizes of dinoflagellates generally correspond to sizes
of their resting cysts (Finkel et al., 2007).
Cyst size may be used to infer cellular carbon contents, and together with
phosphate concentrations for growth rate, can give a better estimate for
carbon demands and therefore improve the constraints of b in this line of
proxies.
Conclusions
Our results illustrate that the POC production / [CO2]-dependency of
εp can vary significantly between different phytoplankton
species and groups, but also as a result of different culturing methods and
differences in day length, especially for haptophytes. Extrapolating results
to natural environments and for proxy applications therefore requires
caution, and it should be carefully considered if culture methods and
experimental conditions are representative of natural environments. Better
approximations for carbon demands (described by μi and POC
contents) in εp-based CO2 proxies could also greatly
improve their estimates. This will be challenging in the paleo-environment,
especially with proxies that rely on biomarkers. Alternatively, careful
selection of sites with more similar environments and phytoplankton species
could also further improve proxy estimates.
Data availability
All data presented in this study are available in the open data repository
Dryad (10.5061/dryad.hmgqnk9k8, Brandenburg, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-3305-2022-supplement.
Author contributions
All authors contributed to the study design.
Preliminary data for the study were collected by MH. KMB collected
additional data, performed statistical analyses and wrote the
first draft. All authors discussed the results and AS, MH, DBvdW, and BR provided feedback on the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors thank Gert-Jan Reichart for constructive discussions that helped
shape the presented ideas and are grateful for the constructive comments
received from two reviewers. Appy Sluijs thanks the European Research Council for
Consolidator Grant 771497.
Financial support
This research has been supported by the H2020 European Research Council (SPANC, grant no. 771497).
Review statement
This paper was edited by Tina Treude and reviewed by two anonymous referees.
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