The elemental stoichiometry of marine phytoplankton plays
a critical role in global biogeochemical cycles through its impact on
nutrient cycling, secondary production, and carbon export. Although
extensive laboratory experiments have been carried out over the years to
assess the influence of different environmental drivers on the elemental
composition of phytoplankton, a comprehensive quantitative assessment of the
processes is still lacking. Here, we synthesized the responses of P:C and
N:C ratios of marine phytoplankton to five major drivers (inorganic
phosphorus, inorganic nitrogen, inorganic iron, irradiance, and temperature)
by a meta-analysis of experimental data across 366 experiments from 104
journal articles. Our results show that the response of these ratios to
changes in macronutrients is consistent across all the studies, where the
increase in nutrient availability is positively related to changes in P:C
and N:C ratios. We found that eukaryotic phytoplankton are more sensitive to
the changes in macronutrients compared to prokaryotes, possibly due to their
larger cell size and their abilities to regulate their gene expression
patterns quickly. The effect of irradiance was significant and constant
across all studies, where an increase in irradiance decreased both P:C and
N:C. The P:C ratio decreased significantly with warming, but the response to
temperature changes was mixed depending on the culture growth mode and the
growth phase at the time of harvest. Along with other oceanographic
conditions of the subtropical gyres (e.g., low macronutrient availability),
the elevated temperature may explain why P:C is consistently low in
subtropical oceans. Iron addition did not systematically change either P:C
or N:C. Overall, our findings highlight the high stoichiometric plasticity
of eukaryotes and the importance of macronutrients in determining P:C and
N:C ratios, which both provide us insights on how to understand and model
plankton diversity and productivity.
Introduction
Elemental stoichiometry of biological production in the surface ocean plays
a crucial role in the cycling of elements in the global ocean. The elemental
ratio between carbon, nitrogen (N), and phosphorus (P) in exported organic
matter expressed in terms of the C:N:P ratio helps determine how much
atmospheric carbon is sequestered in the deep ocean with respect to the
availability of limiting nutrients. On geologic timescales, the N:P ratio
reflects the relative availability of nitrate with respect to phosphate,
both of which are externally supplied from the atmosphere via
nitrogen fixation and/or continents via river supply and lost by
denitrification and burial (Broecker,
1982; Lenton and Watson, 2000; Redfield, 1958; Tyrrell, 1999). On shorter
timescales, the average stoichiometry of exported bulk particulate organic
matter reflects the elemental stoichiometry of phytoplankton (Bonachela
et al., 2016; Garcia et al., 2018; Martiny et al., 2013b), with additional
influences from biological diversity and secondary processing of organic
matter by zooplankton and heterotrophic bacteria. In the face of global
change, understanding and quantifying the mechanisms that lead to
variability in C:N:P ratios are crucial in order to have an accurate
projection of future climate change.
A key unresolved question is what determines C:N:P of individual
phytoplankton. Phytoplankton grows in the upper light-lit layer of the
ocean, where the amount of inorganic nutrients, light, and temperature vary
spatially and temporally. Laboratory studies show that these fluctuations
trigger responses at the cellular level, whereby cells modify resource
allocation in order to adapt optimally to their ambient environment
(Geider and La Roche, 2002). For example,
phytoplankton may alter resource allocation between the P-rich biosynthetic
apparatus, N-rich light-harvesting apparatus, and C-rich energy storage
reserves (Moreno and Martiny, 2018). Under a
typical future warming scenario, the global ocean is expected to undergo
changes in nutrient availability, temperature, and irradiance
(Boyd et al., 2010). These changes are
likely to have profound effects on the physiology of phytoplankton
(Finkel et al., 2010; van de Waal et
al., 2010), and observations show that competitive phytoplankton species can
acclimate and adapt to changes in temperature, irradiance, and nutrients on
decadal timescales (Irwin et al., 2015). Numerous
laboratory and field experiments have been conducted thus far to study the
relationship between the C:N:P ratio of phytoplankton and environmental drivers.
It is, however, challenging to synthesize those studies and generalize the
response of phytoplankton C:N:P to changes in environmental drivers.
Individual studies employ different sets of statistical analyses to
characterize the effects of the environmental driver(s) on elemental ratios,
ranging from a simple t test to more complex mixed models, which makes
interstudy comparisons challenging. In addition, since environmentally
induced trait changes are driven by a combination of plasticity
(acclimation), adaptation, and life history (Collins et
al., 2020; Ward et al., 2019), stoichiometric responses of phytoplankton can
be variable even amongst closely related species.
Meta-analysis/systematic review is a powerful statistical framework for
synthesizing and integrating research results obtained from independent
studies and for uncovering general trends
(Gurevitch et al., 2018). The seminal
synthesis by Geider and La Roche (2002), as well
as the more recent work by Persson et al. (2010), has
shown that C:P and N:P could vary by up to a factor of 20 between
nutrient-replete and nutrient-limited cells. These studies have also shown
that the C:N ratio can be modestly plastic due to nutrient limitation. A
meta-analysis study by Hillebrand et
al. (2013) highlighted the importance of growth rate in determining
elemental stoichiometry and showed that both C:P and N:P ratios decrease
with the increasing growth rate. Yvon-Durocher et al. (2015)
investigated the role of temperature in modulating C:N:P. Although their
dataset was limited to studies conducted prior to 1996, they have shown a
statistically significant relationship between C:P and temperature increase. MacIntyre et al. (2002)
and Thrane et al. (2016) have shown that
irradiance plays an important role in controlling optimal cellular C:N and
N:P ratios. Most recently, Moreno and
Martiny (2018) provided a comprehensive summary of how environmental
conditions regulate cellular stoichiometry from a physiological perspective.
Here, we present results from a systematic literature review and subsequent
meta-analysis to quantify how five key environmental drivers affect C:P and
C:N ratios of marine phytoplankton. Unlike previous meta-analyses on the
elemental stoichiometry of phytoplankton that strictly synthesized the
effect of a single environmental driver, our study assessed the effects of
five drivers, specifically for marine phytoplankton species. Importantly, we
use a unique newly defined measure of effect size, a stoichiometry sensitivity factor (Tanioka and Matsumoto, 2017), which is a
dimensionless parameter that relates a fractional change in P:C or N:C to a
fractional change in a particular environmental driver. We compute the
effect size for each driver–stoichiometry pair from independent studies and
subsequently determine the weighted mean effect size for P:C and N:C ratios.
Further, we compute the mean effect size within different subgroups of
moderators such as plankton types and growth conditions to detect any
systematic heterogeneity between those subgroups.
Materials and methodsBibliographic search and screening
We systematically screened peer-reviewed publications on monoculture
laboratory experiment studies that assessed the effects of dissolved
inorganic phosphorus, dissolved inorganic nitrogen, dissolved iron,
irradiance, and temperature on P:C and N:C ratios of marine phytoplankton.
These five environmental drivers are considered to be the top drivers of the
open-ocean phytoplankton group in studies (Boyd et al., 2010, 2015).
Although CO2 is another potentially important driver, we did not
consider the effects of CO2 on elemental ratios. The previous
meta-analysis studies showed that no generalization could be made concerning
the direction of trends in P:C or N:C ratios as a function of CO2 concentration both in the laboratory-based experiments
(Liu et al., 2010) and mesocosm/field-based
experiments (Kim et al., 2018).
Firstly, we conducted a literature search using Web of Science (last
accessed in February 2019) with the sequence of key terms (Table 1). This
search yielded 4899 hits. We also closely inspected all the primary studies
mentioned in the eight recent review papers on the elemental stoichiometry
of phytoplankton in aquatic environments (Flynn
et al., 2010; Geider and La Roche, 2002; Hillebrand et al., 2013; Moreno and
Martiny, 2018; Persson et al., 2010; Thrane et al., 2016; Villar-Argaiz et
al., 2018; Yvon-Durocher et al., 2015). The list is also augmented with six
additional studies that did not appear in the literature search or the
review papers but were cited elsewhere. Papers were further screened and
selected to meet the following criteria. First, experiments must be carried
out in controlled laboratory environments, where all the environmental
factors, including temperature, photon flux density, salinity, and any other
relevant conditions, are controlled. Second, all outdoor experiments, such
as mesocosm or pond experiments, are excluded. Third, experiments must be
conducted under unialgal/monoculture settings. However, we note that not all
the experiments are carried out under strictly axenic conditions (i.e., not
completely devoid of bacteria and viruses). Lastly, experiments must be
conducted with replicates and must report either standard deviations or
standard errors. Subsequent selection processes based on abstracts, graphs,
tables, full text, and removal of duplicates led to a total of 104 journal
articles (Fig. 1).
Keyword search terms used for literature search (Web of Science,
February 2019). In the search field, “TS” refers to a field tag for
“topic” and “*” is a wildcard search operator.
Key search terms(TS = (phytoplankton OR algae OR microalgae OR diatom OR coccolithophore* OR cyanobacteri* OR diazotroph*) AND TS = (stoichiometr* OR “chemical composition” OR “element* composition” OR “nutritional quality” OR “nutrient composition” OR “nutrient content” OR “nutrient ratio*” OR C:N OR C:P OR N:P OR P:C OR N:C OR “cellular stoichiometr*” OR C:N:P OR “element* ratio*” OR “food qualit*” OR “nutrient concentration” OR “carbon budget”) AND TS = (phosph* OR “phosph* limit*” OR nitr* OR “nitr* limit*” OR iron OR “iron limit*” OR nutrient OR “nutrient limit*” OR “nutrient supply” OR “nutrient availabilit*” OR “supply ratio*” OR eutrophication OR fertili* OR enrichment OR temperature OR warming OR light OR irradiance OR “light limit*”) AND TS = (marine or sea or ocean OR seawater OR aquatic)).
Flow chart showing (1) the preliminary selection criteria and (2) the refined selection criteria used for determining s factors. Numbers (k values) correspond to the number of journal articles. See Supplement (Sect. S1) for a full list of studies included in the
meta-analysis.
Data extraction
Data with means and standard deviations of P:C and N:C under varying
environmental values provided by the original studies are used directly.
GraphClick (Arizona Software Inc., 2010) was used to read off values from graphs
when necessary. In cases where N:P and only one of either P:C or N:C is
provided, the remaining ratio is determined by either multiplying or
dividing by N:P. Similarly, elemental ratios are computed from the
measurements of phytoplankton particulate organic carbon (POC), particulate organic nitrogen (PON), and particulate organic phosphorus (POP) when the ratios are not
explicitly given in the original studies.
For nutrient (P, N, or Fe) manipulation studies, we selected two end-members
(nutrient limited and nutrient replete) based on the definition given in the
original studies. For batch and semicontinuous batch experiments, we
compared the fractional change in initial concentrations between the
nutrient-replete and nutrient-limited conditions when calculating the stoichiometry
sensitivity factor (see Sect. 2.3.2). For continuous (chemostat or
turbidostat) nutrient experiments, we used the difference in the inflow
concentrations of the nutrient-replete and nutrient-limited cultures to determine the
stoichiometry sensitivity factor. When multiple levels of concentrations are
used, we selected two end-member points, one with the lowest growth rate and
the other with the highest growth rate. When the growth rate was not
provided in the original study, we selected two end-member values based on
the highest and lowest nutrient uptake rate, chlorophyll concentration, or
total concentration level with the underlying assumption that phytoplankton
growth is nutrient limited within the range of nutrient levels considered.
For temperature and irradiance manipulations studies, we selected the lowest
value and the optimal or saturating value that led to the maximum growth
rate for phytoplankton. When the growth rate was not explicitly mentioned,
we selected the lowest and the highest treatment values with the assumption
that the phytoplankton is temperature or light limited within the range of
values considered.
When more than two factors were manipulated in the same study, multiple
experimental units are extracted if and only if each environmental driver
was manipulated separately (i.e., conducted in a factorial manner). For
example, we obtained a total of four experimental units from a two-by-two factorial
study on temperature and nutrient: (1) comparing nutrient-limited vs.
nutrient-replete treatment at low temperature; (2) same as in (1) at high
temperature; (3) comparing low- vs. high-temperature response at nutrient-limited conditions; and (4) as in (3) at nutrient-replete conditions. An
experimental unit refers to a controlled experiment of the same
phytoplankton species between control and treatment groups, while all the
other environmental factors are kept constant. If an experiment reported
multiple measurements over time, only the final value was extracted.
We also extracted information on phytoplankton functional type (i.e.,
diatoms, coccolithophores, dinoflagellates, other eukaryotes,
nondiazotrophic cyanobacteria, and diazotrophs; eukaryotes vs. prokaryotes;
cold-water vs. temperate species), growth mode (i.e., batch vs.
semicontinuous vs. continuous), growth phase at harvest (i.e., lag,
exponential, stationary, decline), N form [NO3-, NH4+,
NO3-+NH4+, N2], and light regime (i.e.,
continuous vs. periodic light). Cold-water species is operationally defined
if the control temperature (for P, N, Fe, or I manipulated experiments) or
the maximum treatment temperature (for T manipulated experiments) was less
than the threshold temperature of 10 ∘C. Attempted but ultimately
discarded moderators for subsequent analysis mainly due to the lack of
sample size include salinity, the axenic nature of the culture, and the number
of generations required for acclimation before the start of the experiment.
Our final dataset consists of 241 experimental units of P:C and 366
experimental units of N:C from 104 journal articles encompassing seven
taxonomic phyla (Bacillariophyta, Chlorophyta, Cryptophyta, Cyanobacteria,
Haptophyta, Miozoa, and Ochrophyta) and six plankton functional types
(diatoms, coccolithophores, dinoflagellates, other eukaryotes,
nondiazotrophic cyanobacteria, and diazotrophs), and they are available in the
Zenodo data repository (10.5281/zenodo.3723121; Tanioka and Matsumoto, 2020).
Statistical analysis
We used two different measures of effect size for this study. One is a
commonly used natural-logarithm-transformed response ratio, ln(RR)
(Hedges et al., 1999), and the other is the stoichiometry
sensitivity factor (Tanioka and Matsumoto, 2017).
By using two separate measures, we can give a more robust prediction on how
elemental stoichiometry varies with a change in a given environmental driver.
All statistical analyses were performed with R v3.5.2 (R Core
Team, 2018).
Response ratio
The natural-logarithm-transformed response ratio ln(RR) of the individual
experimental unit and its variance (v) were calculated following
Lajeunesse (2015):
1lnRR=lnYtYc+12St2Nt⋅Yt2-Sc2Nc⋅Yc2,2v=St2Nt⋅Yt2+Sc2Nc⋅Yc2+12St4Nt2⋅Yt4+Sc2Nc2⋅Yc4.Y denotes mean P:C or N:C, S is the standard deviation of that mean, and N is
the sample size for the treatment (subscript t) and the control (subscript c) groups. We removed any experimental unit with a studentized residual value
of ln(RR) exceeding the absolute value of 3 as an outlier
(Viechtbauer and Cheung, 2010).
Stoichiometry sensitivity factor
The second effect size is the newly defined stoichiometry sensitivity factor
sXY (Tanioka and Matsumoto, 2017), which
relates a fractional change in an elemental stoichiometry (response variable
Y) to a fractional change in an environmental driver (variable X):
sXY=(Yt-Yc)/Yc(Xt-Xc)/Xc.
We estimated the variance of sXY from the simple error propagation
of Eq. (3) by assuming that the uncertainties associated with the
environmental driver X are negligible compared to the errors associated with Y:
vXY=(Yt-Yc)/Yc(Xt-Xc)/Xc2St2/Nt+Sc2/NcYt-Yc2+Sc2Nc⋅Yc2.
In essence, the magnitude of the s factor is a measure of how sensitive Y (P:C or
N:C) is to a change in stressor level X, and the sign indicates whether Y
changes in the same direction as X (positive sign) or in the opposite
direction to X (negative sign). The s factor allows for different kinds of
response: a linear response of Y with respect to X (sXY=1), a near
hyperbolic response that saturates at high X (0<sXY<1), a
logarithmic growth (1<sXY), a decay (0>sXY), and the
null response (sXY=0). This s-factor metric is conceptually similar
to the homeostasis coefficient H (Persson et al., 2010), which
relates the fractional change in resource nutrient stoichiometry to the
fractional change in the organism's nutrient stoichiometry.
Importantly, the advantage of using sXY as effect size is that its
magnitude is a direct, quantitative measure of the strength of environmental
drivers over the range of values examined. In contrast, ln(RR) only compares
the effect of stressors without taking changes in the value of stressors into
an account. Further, we can directly compare the strength of
sXY across different pairs of X and Y as
it is nondimensional. For convenience, we use the term “s factor” in the
rest of this paper when describing sXY in a generic sense.
We used the same set of experimental units used in calculating ln(RR) to
calculate s factors (i.e., any outliers are carried over). However, we did
not calculate s factors for iron because the fractional change in dissolved
iron concentration, often spanning multiple orders of magnitude, is
substantially greater compared to the fractional change in P:C or N:C
ratios, leading to an extremely low s factor. For temperature-manipulated
experiments, we converted degrees Celsius into absolute temperature scale
Kelvin. We used photon-flux density (PFD) measured in micromoles of photons per square meter per second
(µmol photons m-2 s-1) for irradiance and micromolar (µM) for inorganic phosphorus and
nitrogen experiments.
Meta-analysis and weighted mean responses
We calculated the weighted mean ln(RR) (ln(RR)‾) and s factor
(sXY‾) using the mixed-effects model with the R package
metafor (Viechtbauer, 2010). The weighted mean
(M) and its variance (V) were calculated as
5M=∑j=1kWjMj∑j=1kWj,6V=1∑j=1kWj,
where k is the total number of experimental units, Mj is effect size
(ln(RR) or sXY) in experimental unit j, and Wj is the weighting
factor, which is the inverse of the variance (Hedges et al.,
1999). The 95 % confidence interval (CI) for the weighted mean was
computed as
CI=M±1.96×V.
In the subsequent sections of this paper, the values of ln(RR)‾ are
back-transformed and represented as percent change:
eln(RR)‾-1×100%,
and they are considered statistically significant if the 95 % CIs do not overlap with
zero.
Testing the effect of moderators
We determined the effects of moderators by the rma function of the metafor package, which is an
omnibus test of between-moderator heterogeneity based on χ2
distribution (Liang et al.,
2020). The moderators we tested are plankton functional type (PFT), N form, growth mode, growth phase at
extraction, and light regime (continuous vs. periodic). The effect of a
moderator is considered significant when P value is less than 0.05. We use
the weighted mean s factors in determining the effects of moderators except
for iron experiments, where we used ln(RR) instead.
Results
Phosphate addition increases both the mean P:C (235 % [95 % CI: 169 %,
322 %]) and N:C (23 % [13 %, 34 %]) significantly (Fig. 2b). The
mean stoichiometric sensitivity factor of P:C
(sPP:C) for change in phosphate is 0.21
[0.12, 0.29] (Table 2), which means that on average the P:C ratio of
phytoplankton changes by 0.21 % for every 1 % increase in PO4 concentration. The effect of phosphate on N:C is an order of magnitude
smaller but also statistically significant and positively correlated
(sPN:C=0.023 [0.004, 0.042]).
Eukaryotic phytoplankton have significantly larger
sPP:C than prokaryotes (P<0.05,
Fig. 3a), and the diatoms and coccolithophores especially have noticeably
large sPP:C (Fig. S1a, Table S1 in the Supplement). In
addition, phytoplankton grown under chemostat experiments have significantly
larger stoichiometric sensitivity compared to those grown under batch or
chemostat conditions (Fig. 3b, P<0.001). There was no
between-moderator heterogeneity in sPN:C
(Table S1).
Summary of the meta-analysis using the stoichiometry sensitivity
factor and natural-logarithm-transformed response ratio. n, number of
experimental units (numbers in bracket represent the number of outlier studies);
sXY‾, weighted mean stoichiometry sensitivity factor with environmental driver X and response variable Y; ln(RR)‾, weighted mean
value of the natural-logarithm-transformed response ratio; ci.lb, lower
boundary of 95 % CI; ci.ub, upper boundary of 95 % CI; sig.,
significance of the mean weighted effect size; ns, P>0.05; *P<0.05; **P<0.01; ***P<0.001. Any experiment with a studentized residual
value of ln(RR) exceeding 3 was removed as an outlier. Bold texts
highlight statistically significant environmental drivers.
Summary plot showing weighted mean responses of P:C and N:C using the
(a) stoichiometry sensitivity factor and (b) % changes between control
and treatment. Numbers next to the plots in (b) correspond to the number of
experimental units, and the numbers are identical in (a). Numbers in the
outside column are the weighted means. *P<0.05; **P<0.01; ***P<0.001; ns, not significant. Note that the x axis is different for temperature
experiments in (a).
The response of N:C to changes in inorganic nitrogen is similar to the
response of P:C to PO4 changes where an increase in inorganic nitrogen
raises N:C on average by 70 % [49 %, 93 %] (Fig. 2b), with the positive
overall mean s factor sNN:C of 0.14
[0.08, 0.20] (Table 2). Again, eukaryotic phytoplankton have higher
stoichiometric sensitivity than prokaryotes (Fig. 3a, P<0.05).
Nitrogen addition does not affect the weighted mean P:C (Fig. 2).
Surprisingly, however, phytoplankton grown with the culture made up of
nitrate and ammonia have significantly larger
sNP:C compared to those grown with
nitrate only, with ammonia only, or under semidiazotrophic conditions
(Fig. S2, Table S1). The small sample size, however, precludes us from
making any firm conclusions.
Summary plot showing statistically significant effects of
moderators. (a) Eukaryotes vs. prokaryotes, (b) growth mode, (c) growth phase
at harvest, and (d) light regime. *P<0.05; **P<0.01; ***P<0.001; ns, not significant.
An increase in iron availability does not lead to significant changes in
both P:C and N:C (Fig. 2b). In addition, the effects of any moderators are
not statistically significant (Table S1). Although diazotrophs that utilize
N2 as their nitrogen source display a large response compared to other PFTs
(-20 % [-36 %, 1 %]) (Table S1), their stoichiometric response is not
quite statistically significant.
Increase in light availability significantly decreases both P:C (-21 %
[-38 %, -0.4 %]) and N:C (-18 % [-23 %, -12 %]), with overall
negative s factors (sIP:C=-0.034
[-0.062, -0.007], sIN:C=-0.024
[-0.034, -0.013]). Although the magnitudes of both the response ratios and
s factors are small compared to those of macronutrients, the responses
across PFTs are consistent (Fig. S1c, f, Table S1). Phytoplankton grown
under chemostat or batch conditions have significantly more negative
sIN:C compared to those grown under
semicontinuous environments (Fig. 3b, P<0.01). Also, plankton
grown under periodic light cycles have significantly lower
sIN:C compared to those grown under
continuous light (Fig. 3d, P<0.05).
The response of P:C to warming is significant, where on average P:C
decreases by 15 % [-24 %, -5 %] with negative mean s factor of
sTP:C=-3.6 [-6.8, -0.4] (Fig. 2a, b).
The large magnitude of the s factor compared to that of other drivers reflects
the fact that the fractional change in temperature (measured in kelvins) is
considerably smaller than the fractional change in P:C. There is a
significant variability due to growth mode where batch culture and chemostat
culture experiments respectively have more negative s factors for P:C and
N:C (Fig. 3b, P<0.05). Further, phytoplankton extracted during the
exponential phase have noticeably more negative s factors than those
extracted during the stationary growth phase (Fig. 3c) for both P:C (P<0.001) and N:C (P<0.05). The difference in mean response
s-factor ratio amongst PFTs and between cold vs. temperate species is not
statistically significant (Fig. S1e, Table S1). The responses of N:C are
mixed, and the weighted mean effect sizes are therefore not statistically
significant.
DiscussionBasic framework
One of the fundamental tenets of chemical oceanography is the Redfield
ratio, which implies that phytoplankton cells achieve a constant cellular
C:N:P ratio at the well-known molar ratio of 106 : 16 : 1
(Redfield et al., 1963). Constant C:N:P is achieved
for algal cells growing under steady-state conditions, where the balance is
achieved between uptake of elements and assimilation into a cellular
functional pool (Berman-Frank and
Dubinsky, 1999; Klausmeier et al., 2004). Under such conditions, the growth
rate of all cellular constituents averaged over one generation is the same,
whether it is the carbon-specific, nitrogen-specific, or phosphorus-specific
growth rates (Falkowski and Raven, 2007). In the real ocean,
however, balanced growth is not always achieved due to short-term and
long-term changes in the physical conditions of the ocean (Moore
et al., 2013; Moore and Doney, 2007). For example, the deficiency of
essential nutrients limits the formation of building blocks of new cells
(e.g., N for proteins, P for nucleic acids and ATP), light limitation slows
carbon assimilation (i.e., making of carbohydrates and reductants), and low
temperature slows down the essential cellular transport and enzymatic
reactions for growth (Madigan et al., 2006). A good example of
unbalanced growth is phytoplankton blooms in the spring, where the transient
changes in surface temperature, irradiance, and nutrient supply rate alter
the growth rate and the elemental stoichiometry of phytoplankton (Polimene
et al., 2015; Talarmin et al., 2016). In addition, future environmental
variabilities caused by climate change are expected to cause temporal shifts
in phytoplankton C:N:P on longer timescales (Kwiatkowski
et al., 2018, 2019; Tanioka and Matsumoto, 2017).
The degrees to which phytoplankton C:N:P ratios are affected by
environmental stresses depend both on the cellular stress response
mechanisms and the magnitude of the environmental change as well as temporal
variability of environmental drivers. Most types of stress responses can be
divided into a stress-specific, primary response and a general secondary
response (Brembu et al., 2017). The
stress-specific responses are strong, robust, and consistently observed
across photosynthetic organisms, while secondary responses are variable
amongst different microorganisms. Primary and secondary responses are
closely related to acclimation (plasticity response) and adaptation
(evolutionary response), respectively. In essence, acclimation refers to the environmentally induced trait change of an organism in the absence of any
genetic modification, while adaptation involves genetic changes driven by
natural selection (Collins et al., 2020). Since
primary responses do not involve genetic adjustment or natural selection,
the responses are fast and often commonly shared amongst different marine
phytoplankton. For example, changing the nutrient uptake affinity of a
lineage within a generation in response to changing nutrient supply is a
widely seen trait across all phytoplankton groups.
On the other hand, the secondary response depends both on the environmental
condition and genotype (Brembu et al., 2017). The
secondary responses take more time (usually up to a few hundred
generations), and there is typically no single, unique response even when
referring to a single species or functional group and a specific
environmental driver (Collins et al., 2020). In the
subsections below, we discuss any possible underlying cellular mechanisms
responsible for producing changes in C:N:P ratios (see Fig. 4 for schematic
illustration).
Illustration of how the five environmental drivers under a typical
future climate scenario affect the cellular allocation of volume between
P-rich (red), N-rich (blue), and C-rich (orange) pools. The values for
projected changes in C:P and C:N between 1981–2000 and 2081–2100 are given
in Table 3.
Macronutrients (phosphate and nitrate)
Overall, we observe a consistent trend across all studies where P:C and N:C
increases with an increase in the supply of dissolved inorganic phosphorus
and nitrogen, respectively (Fig. 2). Since the changes in X:C and the supply
of element X are positively related,
sPP:C and
sNN:C are both positive. Observations of
phosphate (nitrate) against particulate organic matter P:C (N:C) across the
global ocean indeed broadly follow this general trend
(Galbraith and Martiny, 2015;
Tanioka and Matsumoto, 2017).
Phytoplankton can temporally store excess nutrient intracellularly until the
rate of carbon assimilation catches up to achieve steady-state balanced
growth. Excess phosphorus, for example, can be stored mainly as
polyphosphate (Dyhrman, 2016), and excess nitrate can be
stored primarily as protein and free amino acids
(Liefer et al., 2019; Sterner and
Elser, 2002). Phytoplankton can consume these internal stores of nutrients
(e.g., polyphosphates under P limitation) while maintaining the same level
of carbon fixation, when the uptake of the nutrients does not meet their demand for growth (Cembella et al.,
1984). Also, phytoplankton can reduce their number of ribosomes and RNA
content under P limitation as RNA typically accounts for 50 % of
nonstorage phosphorus (Hessen et al.,
2017; Lin et al., 2016). Similarly, cells can reduce the synthesis of N-rich
protein content under N limitation, resulting in a lower N:C ratio
(Grosse et al.,
2017; Liefer et al., 2019). These transient processes controlling the
intracellular content of P or N (but not C content as much) likely result in
a positive correlation between P:C and N:C with macronutrient
concentrations.
Although sPP:C and
sNN:C are consistently positive across
all the studies, they are noticeably higher for eukaryotic phytoplankton
than for prokaryotes (Fig. 3a). There are several hypotheses for explaining
this trend. One of the most plausible explanations is related to the cell
size and storage capacity difference amongst phytoplankton groups
(Edwards et al., 2012;
Lomas et al., 2014). Since eukaryotes are generally larger and possess more
storage capacity, they are capable of greater luxury uptake and accumulation
of internal P and N reserves when the nutrient is in excess
(Talmy et al., 2014; Tozzi
et al., 2004). When nutrients are scarce, the large cell size of eukaryotes
allows them to increase their carbon content considerably by accumulating
excess carbon as polysaccharides and lipids (Liefer et
al., 2019; Lin et al., 2016). Another plausible hypothesis concerns
variability in the acclimation/adaptation strategy at the genetic level
(Dyhrman, 2016). Recent studies suggest that different
phytoplankton groups exhibit different levels of transcriptional
responsiveness and have different strategies for using nitrate (Lampe et
al., 2019) and phosphate (Martiny et al., 2019). For
example, diatoms have superior abilities to uptake and store nutrients by
being able to quickly regulate their gene expression patterns required for
nutrient uptake compared to other phytoplankton groups
(Cáceres et al., 2019;
Lampe et al., 2018, 2019). These hypotheses provide plausible explanations
for why eukaryotes have elevated stoichiometry sensitivity to macronutrients
compared to prokaryotes.
Iron
Iron is used in key biochemical processes such as electron transport,
respiration, protein synthesis, and N fixation
(Marchetti and Maldonado, 2016; Twining and Baines,
2013). Many of the iron-dependent processes are required for harvesting
energy and for synthesizing biochemical intermediates
(Price, 2005). As energy
acquisition is equivalent to light acquisition in phototrophs, it makes
sense that percent changes in stoichiometry for iron are similar in sign and
magnitude to that of light (Fig. 2b). Although the effect of increasing iron on
N:C is similar in sign and magnitude to that of light, increasing iron
availability does not lead to a significant change in mean N:C (Fig. 2b).
This suggests smaller-than-expected changes in the carbon or the nitrogen
content (e.g., compounds such as porphyrin and phycobiliprotein that are
essential for light harvesting) under Fe limitation
(Falkowski and Raven, 2007; Twining and Baines,
2013). Alternatively, Fe availability may be affecting cellular C, N, and P
more or less proportionally for all phytoplankton, leading to constant P:C
and N:C (Greene
et al., 1991; van Oijen et al., 2004; La Roche et al., 1993; Takeda, 1998).
We also did not find noticeable heterogeneities in P:C and N:C amongst
different moderators. Yet a number of laboratory studies, particularly those
of picocyanobacteria (Prochlorococcus and Synechococcus), display significant effects of iron on C:N:P (e.g., Cunningham and John,
2017). Despite their ecological importance (Biller et al.,
2015; Flombaum et al., 2013), these taxa are understudied compared to
diazotrophic cyanobacteria and diatoms. Future studies could focus on these
picocyanobacteria and combine cellular C:N:P information with other measures
of phytoplankton physiology (e.g., chlorophyll fluorescence, Fv/Fm ratio) to
provide a more coherent, mechanistic picture of how changes in iron
availability affect their physiology.
Irradiance
Light availability affects the photoacclimation strategy of phytoplankton
and, subsequently, the cellular allocation of volume between the N-rich
light-harvesting apparatus, P-rich biosynthetic apparatus, and C-rich energy
storage reserves (Falkowski and
LaRoche, 1991; Moreno and Martiny, 2018). At a fixed growth rate, high
irradiance should downregulate the production of N-rich light-harvesting
proteins and pigments to minimize the risk of photooxidative stress. Excess
carbon fixed under high-irradiance conditions is stored as C-rich storage
compounds such as lipids and polysaccharides
(Berman-Frank and Dubinsky, 1999). As a result,
N:C is expected to decrease under high light. In contrast, under low-light
conditions, the macromolecular composition should favor the N-rich light-harvesting apparatus over C-rich storage reserves, thus elevating N:C. This
line of reasoning would predict a negative relationship for the effect of
irradiance increase on N:C, which is borne out in our meta-analysis (Fig. 2). Similarly, the P quota should be affected by a change in irradiance (Moreno
and Martiny, 2018). P:C is expected to decrease at the increased light level
because the total supply of inorganic phosphorus will not be able to keep up
with the increase in photosynthetic carbon fixation, leading to a decoupled
uptake of C and P (Hessen
et al., 2002, 2008). Conversely, P:C is expected to increase at lower
irradiance because carbon fixation decreases while phosphorus uptake remains
constant (Urabe and Sterner, 1996).
The magnitudes of the weighted mean s factors for both P:C and N:C, however,
are small, and the heterogeneity amongst PFTs is not discernible. This
result agrees with a previous study that compiled experimental data prior to
1997 (MacIntyre et al.,
2002). It is possible, however, that s factors obtained in our meta-analysis
are underestimated as several factors may mute the effect of irradiance on
the N:C ratio of phytoplankton. For example, an increase in nitrogen
requirement for Rubisco (Li et al.,
2015) and nutrient uptake machinery
(Ågren, 2004) at high irradiance could
partly offset the reduction in N content resulting from the downregulation
of the light-harvesting apparatus. In addition, multiple studies have noted an
increase in the protein demand (e.g., D1 protein) for repairing the damaged light-harvesting apparatus at high irradiance (Demmig-Adams
and Adams, 1992; Li et al., 2015; Talmy et al., 2013), which also works in
favor of stabilizing N content. Furthermore, we may have underestimated our
s factor if the high end-member irradiance were above the optimal light
level. This last reason is a fundamental limitation of s-factor
determination as most studies we selected do not measure the actual
optimal irradiance but simply report an arbitrary value that is either
“high” or “light replete”.
Interestingly, we observed larger stoichiometric shifts in nutrient-replete
batch and chemostat culture experiments compared to those conducted under
semicontinuous settings (Fig. 3b). In addition, we found that experiments
conducted under periodic daily light cycles have larger negative s factors
compared to those experiments carried out under continuous light (Fig. 3d).
These results are consistent with the global observation
(Martiny et al., 2013a) and
model studies (Arteaga
et al., 2014; Talmy et al., 2014, 2016) which have shown that both the
magnitude and temporal variability of N:C is higher in the nutrient-rich,
light-limited polar regions than in the light-replete subtropics.
Temperature
We found that the P:C ratio decreases as temperature increases, while N:C
remains relatively unchanged. Our result is consistent with a previous
meta-analysis (Yvon-Durocher et al.,
2015) that showed a decrease in phytoplankton P:C with temperature increase
under laboratory and field settings. Moreover, our study and the study by
Yvon-Durocher et al. (2015) support the idea that P:C is more flexible than N:C
with respect to change in temperature, which suggests that intracellular P
content is more sensitive to change in temperature than intracellular N
content. Although the underlying mechanism for explaining lower P:C at
higher temperature is not fully understood, there are currently three main
hypotheses (Paul et al., 2015): (1) increase in metabolic stimulation of
inorganic carbon uptake over phosphorus uptake; (2) increase in nutrient use
efficiency which enables greater carbon fixation for given nutrient
availability; and (3) translation compensation theory, which predicts
that less P-rich ribosomes are required for protein synthesis and growth as
the translation process becomes kinetically more efficient (McKew
et al., 2015; Toseland et al., 2013; Woods et al., 2003; Xu et al., 2014;
Zhu et al., 2017).
Differences in s factors amongst PFTs were not statistically significant,
and none of the PFT displayed a statistically significant response in
isolation. In other words, we did not see any PFT-specific
adaptive/evolutionary response to warming (Schaum
et al., 2018; Taucher et al., 2015). However, we observed noticeable
variability due to the difference in culture growth mode (Fig. 3b) and the
growth phase at extraction (Fig. 3c). The latter factor is particularly
noticeable for P:C, where phytoplankton extracted during the nutrient-replete
exponential growth phase have significantly more negative stoichiometric
flexibility with a larger magnitude compared to those extracted during
the nutrient-deplete stationary phase. This is consistent with multiple recent
studies which suggest that the effect of temperature on growth and metabolic
rates is greater when plankton are not nutrient or light limited (Aranguren-Gassis
et al., 2019; Marañón et al., 2018; Roleda et al., 2013). This leads
us to hypothesize that change in the P:C ratio due to ongoing warming will
be more noticeable in the nutrient-rich polar regions, especially given the
fact that temperature is already increasing at a startling rate due to polar
amplification (Post et al., 2019).
Limitations and caveats
In the real ocean, none of the environmental changes discussed will likely
occur in isolation because changes in irradiance, temperature, and nutrient
availability are often linked. For example, an increase in sea surface
temperature enhances the vertical stratification of the water column, which
leads to greater levels of irradiance and nutrient limitation for
phytoplankton trapped in a more shallow mixed layer
(Boyd et al., 2015; Hutchins and Fu, 2017).
Indeed, a meta-analysis on the pair-wise effects of environmental drivers on
the elemental stoichiometry of phytoplankton has shown that the interactions
of two environmental stressors can impose predominantly nonadditive effects
to C:N:P of phytoplankton so that the overall effect of multiple stressors
is more than simply the sum of its parts (Villar-Argaiz et al.,
2018). In addition to the individual phytoplankton stoichiometry, the bulk
organic matter stoichiometry also reflects the phytoplankton community
composition (Bonachela
et al., 2016; Weber and Deutsch, 2010) as well as the stoichiometry of
detrital material. Processes such as decomposition (Karl
and Dobbs, 1998; Verity et al., 2000; Zakem and Levine, 2019), viral shunt
(Jover et al., 2014), and preferential
remineralization of phytoplankton macromolecules (Frigstad
et al., 2011; Grabowski et al., 2019; Kreus et al., 2015) can also decouple
phytoplankton C:N:P from the bulk organic matter C:N:P.
Projected changes in C:P (molar) and C:N (molar) between 1981–2000
and 2081–2100 given model-based projected changes in environmental drivers
from Boyd et al. (2015). Changes in C:N and C:P are calculated separately
for each driver, with s factors from Table 2 combined with reference C:N:P of
146 : 20 : 1, a global biomass-weighted mean ratio of particulate organic matter (Martiny et al., 2013b). Ranges are derived from
propagating uncertainties for the weighted mean s factors in Table 2. We
used Eq. (9) in the main text for estimating the combined effect of
multiple drivers.
Change in environmental drivers P (-28.0 %)N (-18.7 %)I (+0.7 %)T (+0.9 %)Fe (+6.5 %)CombinedΔ (C:P) (molar)+10.4 (5.9–14.6)/+0.03 (0.01–0.06)+3.7 (0.4–7.1)/+16 (6–25)Δ (C:N) (molar)+0.06 (0.01–0.10)+0.22 (0.12–0.31)<+0.01//+0.3 (0.1–0.4)Implications for global ocean biogeochemistry
Recent global biogeochemical models are starting to incorporate a more
realistic representation of plankton physiology, which includes flexible
phytoplankton C:N:P (e.g.,
Buchanan et al., 2018). Modeling studies with flexible phytoplankton
stoichiometry have demonstrated that proliferation of C-rich phytoplankton
under future climate scenarios has the potential to buffer expected future
decline in carbon export and net primary productivity caused by increased
stratification (Kwiatkowski
et al., 2018; Moreno et al., 2018; Tanioka and Matsumoto, 2017). This
buffering effect cannot be simulated by biogeochemical models with fixed
phytoplankton C:N:P.
One way to model the dependencies of multiple environmental drivers (e.g.,
P, N, irradiance, and temperature) on C:N:P of marine phytoplankton is the
power-law formulation by Tanioka and Matsumoto (2017):
X:C=X:C0[PO4][PO4]0sPO4X:C[NO3][NO3]0sNO3X:C⋅II0sIX:CTT0sTX:C(X=P or N),
where
subscript “0” indicates reference values. The s factors obtained from this
meta-analysis are the exponents of Eq. (9) for different environmental
drivers. Within the context of the power-law formulation, our results would
indicate, for example, that eukaryotic phytoplankton would have the largest
plasticity in P:C and N:C compared to prokaryotes with respect to the change
in nutrient availability. Under future warming, high s factors of eukaryotes
may thus play an important role in buffering the expected future decline in
carbon export and net primary productivity
(Kemp and Villareal, 2013).
We can give a first-order estimate of how much the elemental stoichiometry
of marine phytoplankton may change in the future using Eq. (9) given a
typical projection of the change in the key environmental drivers (Table 3;
Fig. 4). Global climate models generally predict a decline in macronutrients
and an increase in temperature and irradiance as a result of surface warming,
increased vertical stratification, and reduced mixed layer depth (Bopp
et al., 2013; Boyd et al., 2015). With large projected declines in
macronutrients (-28.0 % for phosphate, -18.7 % for nitrate) we can
predict an increase in C:P and C:N by ∼10 units (molar ratio)
and ∼0.2 units, respectively, assuming the mean
biomass-weighted particulate organic matter C:N:P of 146:20:1 as the
present-day value (Martiny et al., 2013b). Further
increase in C:P is expected due to the temperature increase of around 1 %
(∼3 K). The total C:P change ranges from +6 to +25, considering all the uncertainties associated with the s factors. For
C:N, we estimate an overall increase by 0.1–0.4 units largely
driven by a decrease in nitrogen availability. The effect of change in
irradiance is noticeably smaller (Table 3). In summary, this simple
calculation highlights a potentially large shift for C:N:P, whose change is
predominantly driven by a reduction in macronutrients and temperature
increase.
Conclusions
Our meta-analysis represents an important bottom-up approach in predicting
how elemental stoichiometry of phytoplankton may evolve with climate change.
We conclude that macronutrient availability is the most significant and
shared environmental driver of C:N:P. Changes in C:N:P by macronutrients are
driven by primary/plasticity responses commonly shared across phytoplankton.
Our analysis shows that eukaryotic phytoplankton have higher stoichiometric
plasticity compared to prokaryotes. Eukaryotes' large stoichiometric
flexibility and high intrinsic growth rate can explain their unexpectedly
high diversity (Malviya et al., 2016) and large
contribution to carbon export globally, even in oligotrophic regions
(Agusti et al.,
2015; Nelson and Brzezinski, 1997). The effects of temperature on C:P are
also significant, suggesting that a future ocean with elevated temperature
and increased stratification will favor the production of carbon-rich
organic matter. Future laboratory-based studies exploring how the multiple
environmental drivers interactively alter the elemental composition of
phytoplankton would be needed for a complete understanding. In addition, a
further investigation on how a change in environmental drivers affects the
stoichiometry of heterotrophs and zooplankton will be useful in filling the
gaps to gain more mechanistic views on how these drivers affect the whole
marine ecosystem.
Data availability
All the data and codes used in the meta-analysis are available in the
Zenodo data repository (10.5281/zenodo.3723121, Tanioka and Matsumoto, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-2939-2020-supplement.
Author contributions
TT and KM designed the study. TT carried out the literature review, data
selection, and analysis and created the figures. Both TT and KM wrote the
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Tatsuro Tanioka acknowledges support from the University of
Minnesota Doctoral Dissertation Fellowship. Katsumi Matsumoto acknowledges sabbatical
support by the Leverhulme Trust Visiting Professorship and the University of
Oxford. We thank Carolyn Bishoff, Julia Kelly, and Amy Riegelman from the
University of Minnesota Library for helping out the literature search and
data selection. We also thank James Cotner for providing us feedback on the
manuscript.
Financial support
This research has been supported by the National Science Foundation, Division of Ocean Sciences (grant no. 1827948).
Review statement
This paper was edited by Carol Robinson and reviewed by Alex Poulton and one anonymous referee.
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