Ocean alkalinity enhancement (OAE) is a proposed method to
counteract climate change by increasing the alkalinity of the surface ocean
and thus the chemical storage capacity of seawater for atmospheric CO2.
The impact of OAE on marine ecosystems, including phytoplankton communities
which make up the base of the marine food web, is largely unknown. To
investigate the influence of OAE on phytoplankton communities, we enclosed a
natural plankton community from coastal Tasmania for 22 d in nine microcosms during a spring bloom. Microcosms were split into three groups,
(1) the unperturbed control, (2) the unequilibrated treatment where
alkalinity was increased (+495 ± 5.2 µmol kg-1) but seawater
CO2 was not in equilibrium with atmospheric CO2, and (3) the
equilibrated treatment where alkalinity was increased (+500 ± 3.2 µmol kg-1) and seawater CO2 was in equilibrium with atmospheric
CO2. Both treatments have the capacity to increase the inorganic carbon
sink of seawater by 21 %. We found that simulated OAE had significant but
generally moderate effects on various groups in the phytoplankton community
and on heterotrophic bacteria. More pronounced effects were observed for the
diatom community where silicic acid drawdown and biogenic silica build-up were reduced at increased alkalinity. Observed changes in phytoplankton
communities affected the temporal trends of key biogeochemical parameters
such as the organic matter carbon-to-nitrogen ratio. Interestingly, the
unequilibrated treatment did not have a noticeably larger impact on the
phytoplankton (and heterotrophic bacteria) community than the equilibrated
treatment, even though the changes in carbonate chemistry conditions were
much more severe. This was particularly evident from the occurrence and peak
of the phytoplankton spring bloom during the experiment, which was not
noticeably different from the control. Altogether, the inadvertent effects
of increased alkalinity on the coastal phytoplankton communities appear to
be rather limited relative to the enormous climatic benefit of increasing
the inorganic carbon sink of seawater by 21 %. We note, however, that more
detailed and widespread investigations of plankton community responses to
OAE are required to confirm or dismiss this first impression.
Introduction
Keeping global warming below 2 ∘C requires drastic and rapid
emission reductions. In addition, a portfolio of carbon dioxide removal
(CDR) methods is required to extract several hundred gigatonnes of CO2
from the atmosphere and store it safely in other carbon reservoirs for
thousands of years (Rogelj et al., 2018). However, few CDR methods have been
proven to work at this scale, and all have potential side effects for the
Earth system (Fuss et al., 2018).
One potential method of CDR from the marine portfolio is ocean alkalinity
enhancement (OAE). The idea behind OAE is to increase the chemical storage
capacity of the ocean for atmospheric CO2 by adding proton-neutralizing
substances to the surface ocean (Kheshgi, 1995). This is measurable as an
enhancement of seawater alkalinity, the name-giving process behind OAE.
Enhanced alkalinity causes a shift in the inorganic carbon speciation in
seawater, from carbon dioxide (CO2) to bicarbonate (HCO3-)
and carbonate (CO32-), thereby making new space for additional
atmospheric CO2 to be absorbed (Hartmann et al., 2013). In addition to
generating CDR, the absorption of protons through OAE counteracts ocean
acidification (OA), which is considered an environmental threat for a range
of marine ecosystems (Doney et al., 2020).
OAE can be achieved through a variety of approaches (Renforth and Henderson,
2017). Most of these approaches are either directly or indirectly linked to
the chemical weathering of minerals, which neutralize protons when they
dissolve. The simplest approach is to extract suitable minerals via mining,
grind those minerals into a powder, and distribute them over land or ocean
surfaces where they can dissolve in aqueous media over days to decades (Feng
et al., 2017; Taylor et al., 2016). When applied on humid land surfaces,
this CDR method is usually referred to as “enhanced weathering” (Schuiling
and Krijgsman, 2006). Here, alkalinity and other mineral dissolution
products associated with the ground minerals such as dissolved silicate or
trace metals would primarily affect terrestrial ecosystems but ultimately
wash into the oceans via rivers (Köhler et al., 2010). When ground
minerals are added directly to the surface ocean (OAE), dissolution
products, such as trace metals, affect ocean biota immediately (Bach et al.,
2019). In both cases, the release of alkalinity and other dissolution
products is highly dependent on the applied source mineral (Renforth and
Henderson, 2017). Mineral weathering can be further accelerated when ground
minerals are dissolved in electrolysis cells for hydrogen production (Rau et
al., 2013). Here, hydrogen serves as a valuable co-product to CDR, with
alkalinity and other dissolution products still being formed and requiring
deposition in the environment where they potentially affect biota. Another
approach is the electrodialytic separation of water into acid and alkalinity
(de Lannoy et al., 2018). Here, alkalinity (in the form of hydroxide) is
maintained in the surface ocean, enabling CDR (de Lannoy et al., 2018). The
acid can be utilized commercially (e.g. as hydrochloric acid), stored in
geological reservoirs underground, or pumped into the deep ocean where it is
partially neutralized through the dissolution of carbonate sediments (Tyka
et al., 2022). The advantage of this approach is that it does not directly
depend on mineral weathering so that mineral supply chains become redundant
and no dissolution co-products (e.g. trace metals) are released into the
environment (Tyka et al., 2022).
It is currently not possible to predict which of the approaches described
above will be implemented in the future. Furthermore, it is unclear how
ocean ecosystems would be affected by OAE, as each method differs in the
quality and quantity of released dissolution products. However, what all
approaches have in common is the intentional change in carbonate chemistry
via the addition of alkalinity. It is therefore an important first step to
assess if increased seawater alkalinity constitutes a threat to the
environment or not (Bach et al., 2019).
This study investigates, for the first time, if and how the changes in
carbonate chemistry due to OAE influences coastal phytoplankton communities.
More explicitly, we compared the effects of two different alkalinity
addition scenarios. Scenario one assumes that the surface ocean is in
equilibrium with the overlying atmosphere so that the fugacity of CO2
(fCO2) in seawater is equal to that in the overlying atmosphere (the
equilibrated treatment). Scenario two assumes that alkalinity is added
but atmospheric CO2 has not yet been absorbed by the perturbed seawater
(the unequilibrated treatment). This second scenario is highly relevant
because CO2 equilibration can take months to years (Jones et al., 2014),
and carbonate chemistry changes are substantially more pronounced in this
unequilibrated transient state that occurs after the alkalinity addition
(Bach et al., 2019).
The treatments were tested with a natural plankton community from coastal
Tasmania and compared to an unperturbed control. The communities were
enclosed in nine identical microcosms in late winter with high nutrient
concentrations naturally available. Our goal was to study OAE effects during
the spring bloom, an ecologically and biogeochemically important event in
the seasonal cycle with the highest biomass accumulation rates during the
year.
MethodsMicrocosm setup and mixing methods
This experiment made use of Kegland® Fermzilla conical unitank fermenters as microcosms for the monitoring of coastal phytoplankton
communities (Fig. 1c). Each microcosm consisted of a ∼ 55 L PET conical tank and a butterfly dump valve connected to a 1 L
collection container (sediment collection cup) (Fig. 1c). Microcosms were
heated from the base of the conical tank using two 30 W heat belts to
induce convective mixing. This prevented the plankton community from sinking
out of the water column in a non-invasive way (i.e. without a stirrer; Fig. 1c). To test the efficiency of the convective mixing, we filled eight
microcosms with ∼ 50 L of seawater sourced from the Derwent
Estuary and placed them in a temperature-controlled room set to 8 ∘C. This temperature was selected so that, once heating was applied, the water
temperature in microcosms would be within the range observed in the Derwent
Estuary during late winter (12–14 ∘C). Once the enclosed seawater
had reached thermal equilibrium, the heating on four of the microcosms was
turned off. Thirty minutes later, 2.5 mL of blue dye (food colouring) was
added to all eight microcosms – four with no heating and four with heating
applied (Fig. 1e). The blue dye was added with a pipette to the uppermost
∼ 5 cm of seawater enclosed in microcosms. The rate of mixing
within microcosms was then assessed by regularly measuring the absorbance of
water samples taken from each microcosm in a spectrophotometer at 630 nm.
Samples were carefully taken from the top of each microcosm using a pipette
at a depth of ∼ 5 cm below the water surface. After 3 h,
all microcosms were manually mixed with a plastic stirrer to ensure
homogeneity. After mixing, the absorbance was measured an additional three
times and used as a reference for a homogeneously mixed solution.
(a) Method and location of microcosm filling, (b) experimental
setup, (c) schematic diagram of the microcosms used in this study, (d) results
of the convective mixing test (microcosms with convective mixing are
indicated by red lines and no-convection microcosms indicated by blue
lines), (e) microcosm with dye addition for assessment of convective mixing,
and (f) formation of an aggregate within a microcosm.
Microcosms that had the convection system switched on were well mixed after
approximately 30 min (Fig. 1d). In contrast, the no-convection microcosms where the convection system was switched off remained relatively
un-mixed, expressed as variable dye concentrations measured with the
spectrophotometer (Fig. 1d). The variability in absorbance was consistent
with our observations, as filaments of high dye concentration were observed
inside the no-convection microcosms until they were manually mixed
(Video supplement 1). It is important to note that there was residual
convective mixing within the no-convection microcosms, as the convection
system was switched off only 30 min before the experiment, allowing
residual heat to enter the system (Video supplement 1). The rapid mixing
induced via convection as observed in the dye experiment was confirmed by
observations during the experiment, with large aggregates suspended in the
water column failing to sink into the sediment trap (Fig. 1f, Video supplement 2). Thus, the convection mechanism used here is an effective and
non-invasive method to keep plankton in suspension and prevent the
unrealistic sinking of particles.
Nevertheless, despite some potential advantages, we acknowledge and are
fully aware that our microcosm setup cannot reproduce the full physical (or
chemical/biological) complexity of nature (Carpenter, 1996). Enclosures of
any type will very likely induce so-called bottle effects (Bach and Taucher,
2019), which can alter the observed community succession and therefore
affect the transferability of the outcome to natural (non-enclosed)
communities (Carpenter, 1996). While this is a general limitation of these
kinds of experimental studies, we stress that bottle effects would occur in
all replicates so that the comparison between control and treatments (as
done in our study) is valid.
Enclosure of phytoplankton communities, treatment manipulation, and
initiation of the experiment
Nine microcosms were filled with seawater from the Derwent Estuary (August
2021) outside the University of Tasmania Institute for Marine and Antarctic
Studies building (42.53095∘ S, 147.20101∘ E; Fig. 1a). We refrained from
pumping the water into the microcosms as this may harm organisms and alter
the plankton community composition. Instead, microcosms were gently filled
from the base up (similar to a Niskin sampler) by lowering microcosms one at
a time into the water, approximately 5 m out from the edge of the
wharf (Fig. 1a). Water was filtered through a 2 mm mesh screen attached to
the top and base of microcosms prior to filling. The base of each microcosm
was submerged to a depth of ∼ 1 m below the surface and
the base closed using a rope attached to the valve handle. Sediment
collection cups were then attached to all microcosms with the valve closed.
The filling procedure lasted less than 30 min, ensuring enclosure of
similar water masses. All microcosms were weighed separately before and
after the filling procedure and contained volumes ranging between 55.2 and
55.9 L.
Microcosms were then transported to a temperature-controlled room set to
8 ∘C (± 2 ∘C) and heat belts attached as per the
methods outlined in Sect. 2.1 (Fig. 1b, c). To simulate natural light
conditions, 10 LED light strips were installed in the room, providing a cool
white light source with approximately 200 µmol photons m-2 s-1 inside each microcosm on a 12 : 12 light : dark cycle. Light intensity
was measured in the centre of each microcosm with a quantum light meter
(LI-COR Biosciences, Lincoln, USA). Due to slight variations in temperature
and irradiance throughout the room, microcosms were rotated around the room
once a day at ∼ 11:00 (Fig. 1b). The temperature of the
room was lowered from 8–6.5 ∘C over the course of
the experiment to ensure temperature stability within the microcosms at
12–14 ∘C (Fig. 2). This was necessary because the reduced volume
of water within microcosms due to sampling caused an increase in heat input
per volume via the heat belts so that the cooling from outside had to be
increased. Salinity of the seawater enclosed was 34.5 as measured with a 914
Metrohm salinometer.
Sampling schedule for given parameters and room temperature on a
given day (0–22) over the experimental period.
Microcosms were split into three groups: a control (M1, M4, M7), which
received no alkalinity manipulation; the unequilibrated group (M2, M5,
M8) enriched with 500 µL of NaOH (Merck, Titripur) per litre; and the
equilibrated group (M3, M6, M9) enriched with 423 µL of 1 M
NaHCO3 solution (prepared by dissolving 8.401 g of NaHCO3
(Sigma-Aldrich) in 100 mL of double-deionized water) per litre and 77 µL of NaOH (Merck, Titripur) per litre. The mixing ratio of
NaHCO3 and NaOH in the equilibrated group was determined with the
carbonate chemistry calculation software seacarb (Gattuso et al., 2021)
prior to the manipulation, assuming that the collected seawater had a total
alkalinity of 2280 µmol kg-1 and the fCO2 was in equilibrium with
the atmosphere (∼ 410 µatm). A more detailed
description of the calculation of carbonate chemistry conditions is provided
in Sect. 2.4. The whole procedure lasted 4 h, and we consider the end
of the manipulation as the beginning of the experiment.
Seawater sampling and particulate matter analyses
Samples were extracted from all microcosms between 07:00–09:00; however,
sampling intervals varied depending upon the parameter as indicated in Fig. 2. Prior to sampling, each microcosm was gently mixed in a circular motion
five times, using a 60 cm plastic stirrer to ensure no sedimentation bias
was introduced in the sampling (this was carried out as a precaution, even
though preliminary tests with flow cytometry illustrated that homogenization
was achieved with convective mixing alone; data not shown). Seawater was
sampled from the microcosms using either a silicon tube (particulate matter)
or a Tygon tube (nutrients, total alkalinity, flow cytometry) and pumped
directly into clean bottles (pre-rinsed with sample). Sampled volumes ranged
between 125–1250 mL, depending on the parameters assessed. Samples for
dissolved inorganic nutrients (nitrate + nitrite, phosphate, and silicate)
and total alkalinity were filtered through a syringe filter (0.2 µm,
Millipore) to minimize biological processes. Nutrient concentrations were
analysed within 5 h after sampling (Sect. 2.4). Total alkalinity samples
were stored at 6 ∘C in the dark for 0–14 d until analysis
(analyses described in Sect. 2.4).
Samples for chlorophyll a, biogenic silica (BSi), total particulate carbon
(TPC), and total particulate nitrogen (TPN) were taken by filtration of 150–240 mL at a mild
vacuum pressure of -200 mbar relative to the atmosphere. Blank filters
(placed onto the filtration rack without filtering particles onto them) were
prepared for all four parameters during each sampling day. TPC and TPN were
filtered on pre-combusted (6 h at 450 ∘C) quartz fibre (QMA,
Whatman) filters (nominal pore-size = 2.2 µm) and stored at
-4 ∘C in pre-combusted (6 h at 450 ∘C) glass petri
dishes for 3–25 d. Prior to analysis, filters were dried at 60 ∘C for 2 h, packaged into tin foil, and analysed using a Thermo Finnigan
EA 1112 Series Flash Elemental Analyser. Combustion of the pressed tin cups
was achieved in high-purity oxygen at 1000 ∘C using tungstic
oxide on alumina as an oxidizing agent followed by copper wires as a
reducing agent. The results were calibrated using a certified sulfanilamide
standard. Please note that we conducted flow-cytometric test measurements
where we filtered samples from the microcosms through the QMA filters to
test if pico-phytoplankton (0.2–2 µm) would be retained on the
filters. These measurements revealed that pico-phytoplankton did not pass
through the QMA filters; thus, the entire phytoplankton community was sampled
(Fig. A1).
BSi was filtered on 3 µm nitrocellulose membrane filters which were
then stored in plastic petri dishes for 51–73 d at -4 ∘C until
samples were analysed. For the analysis, BSi first needed to be converted
into silicic acid. For this, filters were put into 60 mL polypropylene vials
filled with 0.1 M NaOH solution; the vials were then firmly closed and heated for 135 min at 80 ∘C in a temperature-controlled bath.
Afterward, the vials were allowed to cool down to room temperature, and the
silicic acid concentration was measured photometrically following Hansen and
Koroleff (1999).
Chlorophyll a samples were filtered through glass fibre filters (GF/F,
nominal pore size = 0.7 µm). After filtration, filters were
carefully folded, placed in 15 mL polypropylene tubes wrapped in aluminium
foil, and immediately frozen and stored at -80 ∘C. After
extraction with 10 mL of methanol (100 %) for 14 h, samples were
analysed fluorometrically on a Turner fluorometer following the
acidification method outlined by Evans et al. (1987).
Samples for scanning electron microscopy (SEM) were taken by filtration of
30 mL at a mild vacuum pressure of -200 mbar relative to the atmosphere
through 0.2 µm polycarbonate filters and dried for 2 h at
60 ∘C in a desiccator. Prior to analysis, samples were glued onto
aluminium stubs and sputtered with gold–palladium. Samples were analysed in
a Hitachi SU-70 analytical field emission scanning electron microscope.
Nutrient and carbonate chemistry analyses
Dissolved nutrient concentrations were determined via spectrophotometric
methods developed by Hansen and Koroleff (1999). Nitrate + nitrite
(NOx-) was determined by first briefly running samples through a
peristaltic pump, mixing samples with an ammonium-chloride buffer before
being passed through a cadmium reductor to reduce nitrate to nitrite. The
reduced sample was mixed with sulfanilamide and
N-1-naphthyl-ethylenediamine-dihydrochloride, and absorption was measured in a
spectrophotometer at 542 nm. Dissolved inorganic phosphate was determined by
mixing samples with ascorbic acid and a mixed reagent containing 4.5 M
H2SO4, ammonium-molybdate solution, and potassium antimony
tartrate solution, forming blue heteropoly acid. The absorption of the
solution was measured at 882 nm. Dissolved silicate was determined by mixing
a mixed reagent containing equal amounts of molybdate solution and 3.6 M
H2SO4 with the sample, followed by ascorbic acid and oxalic
acid. Sample absorbance was then measured at 810 nm. Nutrient concentrations
were calibrated with standards of known nitrate, phosphate, and silicate
concentrations. The performance of the cadmium reductor and methods used for
nutrient analysis were monitored by analysing the same calibration series
for each sample day and recording the absorbance and slopes of the
calibration series over time. Each sample was measured in duplicate to
assess technical variability between measurements. Differences were on
average 0.061, 0.001, and 0.122 µmol L-1 for NOx-, phosphate,
and silicate concentrations, respectively
The carbonate chemistry conditions were determined based on potentiometric
pH and total alkalinity measurements. pH was measured daily at
∼ 07:00 inside each microcosm with a Metrohm 914 pH meter and a Metrohm Aquatrode Plus coupled glass and reference electrode, which
also includes a PT1000 temperature sensor. We recorded voltage for
subsequent pH calculations (see below) and temperature after observed
readings had stopped drifting. This was achieved by carefully stirring the
electrode for ∼ 2–5 min in the upper 10 cm of the water
column. pH was calibrated to the total scale (pHT) using the certified Tris
buffer provided by Andrew Dickson's laboratory at Scripps Institution
of Oceanography as described in SOP6a by Dickson et al. (2007). The
calibration was conducted by cooling the Tris buffer to ∼ 4 ∘C and measuring voltage in the buffer while it was gradually
warmed to 25 ∘C. That way, we generated a temperature vs. voltage
correlation (26 steps along the temperature gradient), and we used the
fitted equation (R2=0.999) to obtain a reference voltage
(required for the pHT calculation with Eq. 3 in SOP6a of Dickson
et al., 2007) for every possible temperature in the microcosms. We omitted
the step described by Dickson et al. (2007) that involves the use of AMP
buffer to test for ideal Nernst behaviour of the electrode, but we note that
we used a new, high-quality electrode for our measurements. Repeat
measurements in buffers on different days during the experiment were within
± 0.005 pH units, suggesting limited drift and comparatively high
precision.
Total alkalinity (TA) was determined every fourth day with an open-cell
titration following SOP3b in Dickson et al. (2007) using a Metrohm 862
Compact Titrosampler coupled with an Aquatrode Plus with PT1000 temperature
sensor. Between 52–61 g of sample was added to plastic beakers (weighed
using a Mettler Toledo balance with a precision of ± 0.02 mg) and
acclimated to room temperature. The samples were titrated in a two-step
procedure: an initial increment of 2.5 mL of ∼ 0.05 M HCl
(dissolved in double deionized water enriched with 0.6 mol kg-1 NaCl) was
added to the beaker, followed by a 300 s waiting period with constant
stirring. Afterward, the titration continued with additions of 0.1 mL per
time step (30–60 s between additions depending on drift). The
titration curves were evaluated following Dickson et al. (2007) using the
“calkulate” script within PyCO2sys by Humphreys et al. (2022). Certified
reference material (CRM, batch 192) provided by Dickson were included in some analytical runs for accuracy control. In the runs where no CRMs were
included, we included internal seawater standards (0.02 % HgCl2 poisoned),
which were thoroughly referenced against Dickson's CRMs. Although such a procedure is clearly not recommended, this was unavoidable due to the
Coronavirus pandemic and CRM supply shortage. We note, however, that in
analytical runs where both CRMs and internal standards were included, we
calculated almost identical TAs, regardless of whether we used CRMs or
internal standards for accuracy control. The deviation between duplicate
measurements was usually below ± 3 µmol kg-1 and rarely above
± 5 µmol kg-1, suggesting reasonable precision of the
measurement.
Carbonate chemistry conditions were calculated from measured pHT, TA,
phosphate, silicate, salinity, and temperature, with equilibrium constants
recommended by Orr et al (2015) (e.g. K1 and K2 from Lueker et al., 2000),
using the “SIR_full” function in the carbonate chemistry
software “seacarb” for R (Gattuso et al., 2021).
Flow cytometry sampling and analyses
Flow cytometry samples for phytoplankton (3.5 mL) and bacteria (1 mL) were
collected with pipettes from the bottles used for particulate matter sample
collection (see Sect. 2.3). Care was taken to gently mix the bottles before
sub-sampling to avoid sedimentation bias. During the main phytoplankton
bloom (days 4–10), we collected additional samples in between regular
sampling days to achieve daily resolution. These samples were collected
directly from the microcosms using pipettes (∼ 5 cm below
surface) after carefully stirring the microcosms as described in Sect. 2.3.
Samples were immediately fixed with 100 µL of a formalin/hexamine
mixture for phytoplankton and 20 µL glutaraldehyde for bacteria,
stored at 4 ∘C for 25 min, and then flash-frozen in liquid
nitrogen and stored at -80 ∘C until analysis 1–5 weeks later. For
the measurements, samples were thawed at 37 ∘C, and then 500 µL
for phytoplankton and 30 µL for bacteria were immediately analysed
with the CYTEK Aurora flow cytometer. Phytoplankton populations were
distinguished by encircling phytoplankton populations on the cytogram plots
(also known as “gating”) based on the signal strength of the forward light scatter
(FSC) and several fluorescence colours (Fig. A2). Bacterial DNA was stained
with SYBR Green I (diluted in dimethylsulfoxide) and added to samples in a
final ratio of 1:10000 (SYBR Green I : sample) prior to analysis. This allowed
us to distinguish them from other particles in the size range of bacteria
(Fig. A2). Small phytoplankton were distinguished from bacteria by excluding
all particles with chlorophyll autofluorescence from the bacteria gate.
We used the FSC signal strength to estimate how much each phytoplankton
group contributed to the total phytoplankton community during each day. For
this calculation, we multiplied the abundance of each group within a given
gate by the mean “FSC-area” signal strength measured for that group. Please note that “area” in FSC area refers to the integrated area below the
FSC emission peak of each particle. We assume “area” to be the better metric
for biomass estimates than the height of the FSC peak because elongated
particles (e.g. diatom chains) will have a more-stretched-out FSC peak with
a lower peak height.
Sediment traps
The butterfly valves at the bottom of the microcosms were initially closed
so that no material could sink into the sediment collection cups. On day 4
we opened the butterfly valves, allowing water from the microcosms to enter
the collection cups. This was done to enable the sedimentation of the large
aggregates which had begun to flocculate within the microcosms (Fig. 1f,
Video supplement 2). Due to the high effectiveness of our convection
mixing mechanism, which kept large aggregates in suspension, we assisted the
sedimentation process by turning off the heating and setting the room
temperature to 12 ∘C for 24 h. On day 5 the butterfly valves
were closed and the sediment collection cups were removed to take samples for
flow cytometry and filtrations. Fifty millilitres of water containing
sedimented material was collected with a 50 mL pipette from the base of each
cup. These samples were collected in small plastic beakers and carefully
homogenized before filtering TPC/TPN and BSi samples. Filtrations followed
the same procedure as described above, with reduced volumes ranging between
0.5–1 mL due to the increased concentrations of organic matter in the
sediment slurry. After sampling, the cups were reattached to the
corresponding microcosm, butterfly valves were opened, heating belts were turned on,
and the room temperature returned to 7 ∘C. The same process was
repeated on days 6–7 and 8–9, with the exception that the traps were emptied
entirely and cleaned on day 9 before being reattached with the valves
closed. Finally on day 12, the traps were reopened and any remaining
aggregates allowed to drop out of suspension before sampling and removal of
the traps from the microcosms for the remainder of the experiment. (Please
note that the cleaning of collection cups during the last two samplings was
conducted because the major sedimentation of organic material from the bloom
was complete by day 9, and we wanted to avoid the leakage of nutrients from
the collection cups back into the water column.)
Statistical analysis
We used generalized additive mixed models (GAMMs) to assess statistically
significant differences in phytoplankton growth (abundance and biomass) as
well as nutrient and particulate matter concentrations over the experimental
period. GAMMs were fitted using R v. 1.4.1717 (RStudio Team, 2022), and the
package “mgcv” (Wood, 2015). Prior to fitting the GAMMs, nutrient
and particulate matter concentrations were log10(x) transformed and
phytoplankton count data square root transformed. Four different models were
fitted to explore the potential changes in temporal trends and absolute
values of each parameter as a result of alkalinity treatments (Fig. 3). All
models allowed temporal trends to occur with either no difference between
treatments (model 1), differences in temporal trends between treatments but
no difference in absolute values (model 2), differences in absolute values
between treatments but not in temporal trends (model 3), or differences in
both temporal trends and absolute values as a result of the treatments
(model 4). Individual microcosms were fitted as a random intercept in each
model to account for any unknown differences between the individual
microcosms. In addition, heteroscedasticity and temporal autocorrelation of
the residuals within models was visually assessed to ensure model
assumptions were satisfied. Models were then compared by means of the Akaike
information criterion (AIC), with lower AIC values indicating preferred
models with an improved ratio between the explained variance and number of
variables. Predictor variables included in the preferred models were
considered to have a statistically significant influence on the assessed
parameter. Plots with fitted smoothers and corresponding confidence
intervals were produced using the models with the lowest AIC value. The
occurrence of significant differences between the treatments and the control
could then be visually assessed by the absence of overlapping smooths and
their confidence intervals between the treatments.
GAMM results (AIC and R2) from the preferred model for each
parameter with descriptive plots indicating the hypothesized smoothers for
each model (phytoplankton biomass indicated with a B for each group).
All smoothers had a p value < 0.05, indicating smoothers were
significantly different from a straight line. For a given dependent variable,
the model with the lowest AIC was considered to best represent the temporal
trends during the experiment and is present in the figure above.
ResultsCarbonate chemistry and dissolved inorganic nutrients
The addition of NaOH (for the unequilibrated treatment) and NaOH and
NaHCO3 (for the equilibrated treatment) resulted in an increase in
total alkalinity (TA) from 2164.6 ± 3.1 µmol kg-1 in the controls
to 2660.1 ± 8.4 µmol kg-1 in the unequilibrated and 2665.2 ± 2.2 µmol kg-1 in the equilibrated microcosms (Fig. 4a). TA remained relatively constant at these levels, apart from minor increases
within the first 8 d of ∼ 5 µmol kg-1 likely due to
the uptake of NOx- during the phytoplankton bloom. The addition of
NaHCO3 in the equilibrated treatment increased dissolved inorganic carbon (DIC) to 2406.1 ± 2.1 µmol kg-1, approximately 400 µmol kg-1 more than the control
(2019.1 ± 4.1) and the unequilibrated treatment (2007.9 ± 9.4)
(Fig. 4c). DIC decreased during the bloom with the most pronounced decline
in the control, consistent with the highest build-up of TPC (Figs. 4c, 5b). DIC gradually increased in all microcosms after bloom collapse, due to
biomass respiration. CO2 uptake from the atmosphere could have only had
a small influence on DIC as the microcosms were tightly sealed and only
opened for ∼ 20 min per sampling day through a 2 cm
opening. The different scenarios of alkalinity enrichment increased pHT
to 8.128 ± 0.009 (equilibrated) and 8.662 ± 0.005
(unequilibrated) relative to 7.945 ± 0.007 in the control (Fig. 4b).
Changes in pHT reflect the phytoplankton bloom with increasing pHT
until the peak of the bloom and gradually decreasing pHT thereafter.
The amplitude of the pHT change during the bloom was mitigated by
increased TA (Fig. 4b). However, the mitigation of the amplitude is obscured
by the logarithmic nature of the pH scale, as it is important to
consider absolute changes in the free proton ([H+]F) concentration
as this reflects what organisms experience (Fassbender et al., 2021), Fig. 4d). fCO2 was initially 489.2 ± 9.5 (control), 373.1 ± 8.4
(equilibrated), and 76.6 ± 0.9 µatm (unequilibrated) (Fig. 4e).
The temporal trends were driven by the phytoplankton bloom and largely
resembled those of [H+]F. Finally, the saturation state of the
calcium carbonate (CaCO3) mineral calcite (Ωcalcite) was
greatly elevated in the unequilibrated treatment with an initial value of
11.06 ± 0.03 in comparison to 2.59 ± 0.02 in the control and
4.61 ± 0.03 in the equilibrated treatment (Fig. 4f). Ωcalcite increased further during the bloom but gradually declined
thereafter. Inorganic precipitation of CaCO3 was not observed.
Temporal variation in measured (a) total alkalinity, (b) pHT,
and calculated (c) dissolved inorganic carbon, (d) proton concentration on the
free scale ([H+]F), (e)fCO2 with overlaid boxplot
illustrating the range of fCO2 observed in the Derwent/Storm Bay area,
Tasmania (42.84–43.10∘ S, 147.46–147.31∘ E), based on 10 857 measurements between 1993–2019 (Bakker et al., 2016), (f)Ωcalcite, as well as
dissolved inorganic (g) nitrate + nitrite concentrations, (h) phosphate
concentration, and (i) silicate concentration within the treatment groups.
Coloured shading around the respective means represents standard deviation
within a treatment group.
The water enclosed within microcosms was rich in dissolved inorganic
nutrients due to winter mixing. This allowed a phytoplankton spring bloom to
occur without further additions of nutrients. Initial nutrient
concentrations were 6.39 ± 0.19 µmol L-1 for NOx-, 0.78
± 0.01 µmol L-1 for PO43-, and 9.65 ± 0.39 µmol L-1 for Si(OH)4. Nutrient drawdown occurred from the onset of the experiment, with the most rapid drawdown occurring between days 4–7 (Fig. 4g, i). Statistical analysis of dissolved inorganic nutrient concentrations
revealed the drawdown of PO43- and Si(OH)4 varied significantly
between the control and treatments, whereas NOx- did not (Fig. 3g, h, i). Visual inspection of the PO43- trends indicates that drawdown occurred slightly later in the unequilibrated and equilibrated treatments when compared to the control, although differences were small
(Fig. 4h). The equilibrated treatment displayed elevated PO43-
values between days 10 and 14, although again the difference was small. The
drawdown of Si(OH)4 was slightly delayed and considerably slower in the
unequilibrated treatment and even more so in the equilibrated treatment (Fig. 4i). In
the controls, Si(OH)4 was fully depleted on day 8 while depletion
continued gradually in the equilibrated and unequilibrated treatments after
the bloom but did not show complete depletion until the end of the
experiment (Fig. 4i).
Particulate matter and chlorophyll a dynamics
The drawdown of inorganic nutrients early in the experiment coincided with
increasing Chl a, TPC, TPN, and BSi concentrations (Figs. 5a–d). After the
peak of the phytoplankton bloom on day 6, Chl a, TPC, TPN, and BSi declined
relatively quickly until day 8–10 and continued to decline at a slower rate
until the end of the experiment. The alkalinity treatments had a significant
influence on the temporal trends and absolute values of TPC and TPN while
they only influenced the absolute values of Chl a and BSi (Figs. 3, 5a–d).
Visual inspection of the data revealed similar trends in TPC and TPN, with
control microcosms displaying greater concentrations after the bloom phase
for both parameters (Fig. 5b, c). Differences between the treatments were
less apparent for Chl a, with visual inspection of the trends revealing
minimal differences (Fig. 5a). In contrast, BSi trends supported the
significant difference observed in the model selection process as well as
the silicate trend, with control microcosms displaying elevated levels of
BSi across most of the experimental period (Fig. 5d).
Temporal trends of (a) chlorophyll a, (b) total particulate carbon,
(c) total particulate nitrogen, and (d) biogenic silica concentrations, as
well as molar ratios of (e) TPC to TPN and (f) BSi to TPN within microcosms
and molar ratios of (g) TPC to TPN and (h) BSi to TPN within sediment
collection cups, denoted by “C : Ncup” or “SI : Ncup”. Coloured
shading around the respective means represents the standard deviation.
Stoichiometric ratios
The molar ratio of TPC to TPN (C : N) varied both temporally and in absolute
values as a result of the alkalinity treatments. C : N declined from the
initiation of the experiment until the bloom phase, with the ratio of C : N
then rising rapidly in the control when compared to the alkalinity
treatments, which displayed a delayed increase and lower absolute C : N value
(Fig. 5e). After the bloom phase, the C : N ratio was more variable between
microcosms, with the control and unequilibrated treatment having a higher
C : N in comparison to the equilibrated treatment. Differences in the drawdown
of inorganic nutrients, particularly PO43 and Si(OH)4 (Fig. 4), may have enabled or amplified differences in organic matter stoichiometry, which developed in the post-bloom period. However, it is important to keep in mind that such developments (when significant) were
ultimately caused by the treatments, even if they are indirectly induced by
direct effects on nutrient drawdown that occurred earlier in the experiment.
Similar trends in the ratio of C : N between the treatments were also visible
in the sediment collection cups, with discernibly greater values in the
control and unequilibrated treatment, compared to the equilibrated treatment
(Fig. 5g). The ratio of BSi to TPN (Si : N) declined rapidly from the onset of
the experiment, with two small increases on day 8 and 15 (Fig. 5f).
Statistical analysis of the trend revealed the control to have a marginally
higher Si : N despite the unequilibrated treatment being the greatest at the
two peaks. There was no discernible difference between treatments for Si : N
ratios of organic matter from the sediment collection cups (Fig. 5h).
Changes in the phytoplankton community determined via flow cytometry
The GAMM analyses of flow cytometry count data revealed microphytoplankton
to be unaffected by alkalinity enrichment, while nanophytoplankton and
bacteria showed a shift in temporal trends and Synechococcus, cryptophytes, and
picoeukaryotes exhibited a shift in both temporal and absolute counts (Fig. 3). In contrast, relative biomass contributions by cryptophytes were
unaffected by alkalinity treatments, whereas contributions by
Synechococcus displayed shifts in temporal trends, and those by picoeukaryotes,
nanophytoplankton, and microphytoplankton displayed shifts in absolute biomass (Fig. 3). Synechococcus was initially abundant, but due to their small size their contribution to
total biomass was only ∼ 4 % (Fig. 6a, b). Synechococcus abundance
declined from the start of the experiment in both alkalinity treatments,
while the decline occurred 2 d later in the control (Fig. 6a). There
were also significant temporal differences between treatments in
Synechococcus biomass, with an earlier decline in the equilibrated treatment followed by
the control and then unequilibrated treatment (Figs. 3, 6b). After day 8,
Synechococcus abundance remained close to the detection limit and provided minimal
contribution to the plankton community biomass thereafter (Fig. 6a, b).
Picoeukaryote abundance and biomass showed little variation between the
control and equilibrated treatment throughout the experiment but was
significantly smaller and slightly delayed in the unequilibrated treatment
during the bloom (Fig. 6c). This trend was reflected in the biomass
contribution of picoeukaryotes, which was notably lower in the
unequilibrated treatment during the bloom (Fig. 6d). Cryptophytes
contributed up to 20 % to the total plankton biomass with no temporal or
absolute difference between treatments (Fig. 3). Cryptophyte abundance was
significantly elevated and peaked earlier in the control compared to the two
alkalinity treatments. After the bloom, cryptophyte abundance declined close
to or below the detection limit in all treatments and did not contribute
significantly to total phytoplankton biomass thereafter (Fig. 6e, f).
Nanophytoplankton abundance increased during the bloom phase of the
experiment, but there was no significant difference observed between the
treatments. However, during the post-bloom phase, abundances were
significantly elevated in the unequilibrated treatment (Fig. 6g). The
nanophytoplankton group initially contributed ∼ 60 % to
phytoplankton biomass, with marginally greater biomass in the unequilibrated
treatment in comparison to the equilibrated treatment over the extent of the
experimental period (Fig. 6h). Microphytoplankton abundances increased
during the bloom and peaked on day 6, but as analysis revealed model 1 to be
the preferred model, we conclude that there were no statistically
significant differences between the treatments (Figs. 3, 6i). However, there
was a significant trend in microphytoplankton contribution to total biomass,
with a peak of ∼ 35 % during the bloom phase before dropping
to ∼ 1 %–25 % for the remainder of the experiment (Fig. 5j).
Microphytoplankton contributed marginally but significantly more biomass in
the control microcosms during the last 6 d of the study (Fig. 5j).
Finally, bacteria showed variations in temporal trends as a result of the
treatments with a greater abundance in high-alkalinity treatments during the
phytoplankton bloom and more constant abundances throughout the experiment,
whereas abundances in the control were low during the bloom but increased
rapidly thereafter (Fig. 5k).
Temporal trends of phytoplankton group abundance (left column) and
percent biomass contribution (right column) determined by flow cytometry.
Group names provided in the top right of each plot. Coloured shading around
the respective means represents standard deviation within a treatment group.
Discussion
Alkalinity had a noticeable influence on the characteristics of the
phytoplankton bloom and associated succession of the phytoplankton
community. However, finding unequivocal explanations for how alkalinity
altered succession patterns is very difficult in this form of community
experiment due to the numerous degrees of freedom in complex food webs.
Therefore, we use the discussion henceforth to present potential
explanations, which we believe to be particularly plausible while
emphasizing that none of these can be exclusively proven or excluded. This
leads to many speculations with regards to data interpretation as the reader
will likely notice in the text below. However, our observations are still
highly valuable as they reveal important patterns and any strong effects of
alkalinity on components of the phytoplankton community that can then be
investigated in more targeted future studies.
Treatment effects on chlorophyll a, carbon, nitrogen, and silicon
dynamicsBuild-up of chlorophyll a during the phytoplankton bloom
A significant difference in chlorophyll a of ∼ 3 µg L-1
was observed between the control and equilibrated treatments during the peak
of the phytoplankton bloom, while no significant differences were observed
between the control and unequilibrated treatment. The lower peak chlorophyll a in the equilibrated treatment was unexpected as CO2 and H+, two
carbonate chemistry parameters believed to drive phytoplankton growth (Paul
and Bach, 2020), were relatively similar to the control and within natural
ranges (Fig. 4d, e). We suspect the low peak in chlorophyll a concentration
may be due to differences in the predominant species driving chlorophyll a
build-up. This is supported by careful inspection of the raw flow cytometry
data where we noticed that different types of phytoplankton occurred within
the flow cytometry gate denoted as nanophytoplankton (Fig. A3). The majority
of the population was closer to the upper edge of the nanophytoplankton gate
in the equilibrated treatment in comparison to the control on day 6.
Although speculative, lower concentrations of chlorophyll a could also be due
to increased grazing in the equilibrated treatment. However, the influence
of grazing was not assessed in this study.
In contrast, and even more unexpected, there was no significant difference
in peak chlorophyll a between the control/equilibrated and the unequilibrated
treatment. The fCO2 was as low as ∼ 70 µatm in the
unequilibrated treatment, which is substantially lower than what is
encountered by phytoplankton in coastal Tasmania over the course of a season
(Figs. 4e, A4; see also Pardo et al., 2019). Previous studies have
revealed that growth rates of phytoplankton are relatively unaffected by low
CO2, as long as CO2 concentrations are only mildly reduced
(Riebesell et al., 1993, Wolf-Gladrow et al. 1999). However, rapid declines
in growth were frequently observed once CO2 concentrations fell below
species-specific thresholds, with such thresholds usually being well above
70 µatm (Riebesell et al., 1993; Chen et al., 1994; Hinga, 2002;
Berge et al., 2010; Paul and Bach, 2020). Based on these studies, we
expected a delay in the peak of the phytoplankton bloom and/or reduced bloom
intensity. The fact that neither of these occurred suggests (1) that the
phytoplankton species growing during the bloom were unaffected by
(i.e. well adapted to) low CO2 or (2) that certain species within the
community were adapted to low CO2 and could compensate for less
well-adapted species. While our data do not provide a definitive answer to
this, there are two arguments that favour the second explanation. First, BSi
build-up and corresponding Si(OH)4 drawdown strongly suggest that the
alkalinity treatments affected the diatom community during the bloom.
Second, there were significant differences in picoeukaryote and cryptophyte
abundances during the bloom, with lower abundance and contribution to
biomass in the unequilibrated treatment (see Sect. 4.3 for further
discussion on picoeukaryote responses). Together, these observations suggest
that the addition of alkalinity without immediate CO2 equilibration
with the atmosphere may have less of an impact on phytoplankton bloom
dynamics than previously thought. However, phytoplankton species composition
may still be affected, with implications for energy transfer to higher
trophic levels and biogeochemical fluxes, both of which are strongly
dependent on phytoplankton species composition (Mallin and Paerl, 1994;
Wassmànn, 1997).
Carbon and nitrogen dynamics
TPC, TPN, and the C : N ratio were all significantly greater in the control
compared to the high-alkalinity treatments during the phytoplankton bloom
(days 4–8). In contrast, minor differences were observed between the two
alkalinity treatments during this period. Previous experiments have shown
that carbonate chemistry conditions can affect the build-up and
stoichiometric relationship of organic carbon and nitrogen, but the effect
is highly variable and dependent on the composition of the plankton
community (Taucher et al., 2020). The key outcome reported by Taucher et al. (2020) was that heterotrophic processes seem to have an important influence
on C : N stoichiometries. Consistent with their observation, we observed
significant increases in TPC and C : N in the control during the bloom, while
bacterial abundances remained relatively low (compare Figs. 5e, 6k). In
contrast, bacterial abundances were significantly higher in the alkalinity
treatments, indicative of higher respiratory activity, which may have
limited the build-up of TPC (Figs. 5e, 6k). Furthermore, differences in
diatom growth and/or community composition between the control and the
alkalinity treatments (discussed in Sect. 4.1.3) can also offer a direct
explanation for the differences in TPC build-up and C : N ratios observed
during the bloom. Diatoms often dominate phytoplankton blooms where they
exude DOC, which partially aggregates to form “transparent exopolymer
particles” (TEPs) (Passow, 2002). TEPs have high C : N ratios which commonly
exceed the Redfield ratio (Engel and Passow, 2001) and would be part of the
TPC pool measured in our study. The production of TEPs has been found to vary
significantly between diatom species, with a laboratory study revealing four
species to produce significantly different concentrations of TEPs per cell
volume (Fukao et al., 2010; Passow, 2002). As such it is plausible that
alkalinity treatments altered the abundance and/or composition of the diatom
community (see Sect. 4.1.3.), leading to fewer TEPs, measurable as higher TPC
build-up and C : N.
Diatoms are between a few micrometres to a few millimetres in size
(Armbrust, 2009). The largest diatom cells in our experiment were roughly 50 µm, and we did not find any diatoms smaller than 3 µm
(determined from SEM). Thus, all diatom cells are most likely found in the
nano- and microphytoplankton groups in flow cytometry data. Although not
statistically significant, visual inspection of microphytoplankton abundance
during the peak of the bloom (day 6) revealed greater abundances in the
unequilibrated treatment followed by the control and then equilibrated
treatment. This indicates differences in the phytoplankton communities
between the treatments and the control with potential influence on TPC
build-up and C : N ratios. In addition, significant differences in the
build-up of BSi and drawdown of Si(OH)4 between the control and
treatments strongly suggests that the alkalinity treatments influenced the
diatom communities.
In summary, the evidence provided herein suggests that the altered carbonate
chemistry conditions due to elevated alkalinity caused changes in the
autotrophic and heterotrophic communities which collectively altered TPC
build-up and C : N ratios. Accordingly, anthropogenic increase in ocean
alkalinity may have the capacity to influence ecological processes, with
implications for biogeochemical processes. Crucial next steps are to confirm
such impacts in community studies, other environments, and to reveal the
underlying mechanism(s) responsible for triggering the observed community
changes in response to alkalinity additions.
Biogenic silica and dissolved inorganic silicate drawdown
Scanning electron microscopy investigations of samples taken before, during,
and after the phytoplankton bloom revealed that diatoms were the only
silicifiers detected in the plankton community. Therefore, the drawdown of
Si(OH)4 and build-up of BSi within microcosms can be attributed to the
diatom community. BSi increased during the peak bloom before declining and
remaining rather constant from day ∼ 12 onwards, with
significantly higher concentrations in the control than in the alkalinity
treatments (Fig. 5d). The greater concentration of BSi in the control is
consistent with a more complete drawdown in Si(OH)4 (Figs. 4i, 5d).
There was no significant difference observed in the build-up of BSi between
the two alkalinity treatments even though the drawdown of Si(OH)4 was
significantly greater in the unequilibrated treatment. There are two Si
pools that were not quantified in our study where the additional Si consumed
in the unequilibrated treatment could have gone. These are (i) the walls of
the microcosms where benthic diatoms may have grown and consumed Si or (ii) the sediment traps where relatively more BSi from sinking diatoms may have
been collected (please note that we quantified elemental ratios of sinking
organic matter collected in the sediment traps but not total mass flux as
this requires sampling of all collected material for which we did not have
the capacity).
The significant and pronounced differences in Si(OH)4 drawdown and BSi build-up between the control and the alkalinity treatments are arguably one
of the most striking observations in this experiment. It suggests that
alkalinity enhancements and associated changes in carbonate chemistry can
have considerable effects on diatom communities. Carbonate chemistry changes
invoked by simulated ocean acidification have been shown to have a
significant influence on BSi content, silicate metabolism, growth, and
diatom silicification (Milligan et al., 2004; Hervé et al., 2012; Petrou
et al., 2019) albeit the sign and magnitude of diatom responses were
species-specific and dependent on the communities investigated (Pedersen and
Hansen, 2003; Bach and Taucher, 2019; Petrou et al., 2019). To the best of
our knowledge, there is currently no established mechanistic framework that
can explain the variable responses of diatoms to carbonate chemistry,
although useful concepts exist that link the carbonate chemistry sensitivity
to diatom size (Flynn et al., 2012; Wolf-Gladrow and Riebesell, 1997; Wu et
al., 2014). The observation is also remarkable because the differences in
BSi occur between the control and both alkalinity treatments even though
differences in CO2 and [H+]F are much larger between the
equilibrated and unequilibrated alkalinity treatments (Fig. 4d, e). This
suggests (i) that an unexpected factor in the carbonate chemistry drove the
diatom response or (ii) that the carbonate chemistry effect on diatoms was
indirect, e.g. transmitted through altered grazing pressure. The second
scenario could for example be caused by the additions of acid and base in
the treatments, which may have harmed the grazers and affected the grazing
pressure. Either of these (or other) physiological and/or ecological explanations for the treatment effects on Si(OH)4 drawdown and BSi build-up should be visible as a change in the diatom abundance and/or community composition.
For example, there could be a shift in the diatom community towards smaller,
less heavily silicified species and/or a higher fraction of non-silicifying
phytoplankton. To explore this possibility, we analysed the diatom community
at peak bloom (day 6) via scanning electron microscopy. However, there were
no clear differences in composition or biovolume of the diatom community
between the control and alkalinity treatments on day 6 (Fig. A5).
Furthermore, ratios of carbon to silica did not differ between treatments
across the experimental period supporting SEM count data (Fig. A6). Thus,
although we suspect that shifts within the diatom community were responsible
for the observed differences in silicon dynamics, we are currently unable to
provide a definitive mechanism for these observations.
Treatment effects on the phytoplankton community determined via flow
cytometry
The aim of this experiment was to assess the influence of alkalinity
enhancement on the various stages of a spring bloom. This included periods
at which nutrients were in excess, declining, and depleted. The effect of
nutrient depletion on the phytoplankton community in the absence of enhanced
alkalinity was observable in the control treatment. However, it is possible
that OAE treatments affected nutrient drawdown during the bloom so that
differential nutrient concentrations in the post-bloom phase amplified the
emerging differences between the control and OAE treatments. Alkalinity
treatments were found to significantly influence the abundance and biomass
of five out of the six phytoplankton groups assessed via flow cytometry and
analysed using GAMMs (Fig. 3). The majority of the detected differences were
in absolute values during the peak bloom and small temporal shifts between
treatments.
Comparatively pronounced differences between treatments and the control were
identified within the groups Synechococcus, cryptophytes, and picoeukaryotes, where
alkalinity treatments negatively influenced abundance during the bloom phase
and/or delayed the peak bloom. The unequilibrated treatment had the greatest
influence on these groups, suggesting that the significantly lower
concentration of CO2 and/or increased pH negatively affected these
groups. Previous micro- and mesocosm research on ocean acidification has
found variable responses of Synechococcus and cryptophytes, indicating that their
responses to carbonate chemistry may be (i) population-specific, thus
varying between experiments, or (ii) transmitted indirectly through food web
interactions, which also vary across experimental communities (Sala et al.,
2016; Schulz et al., 2017; Bach et al., 2017).
The response of picoeukaryotes to ocean acidification (i.e. increasing CO2,
declining pH) has been remarkably consistent through experiments in various
climatic and experimental settings (Thomson et al., 2016; Maugendre et al.,
2015; Sala et al., 2016; Schulz et al., 2017; Davidson et al., 2016; Hoppe
et al., 2018; Newbold et al., 2012; Schaum et al., 2012; White et al.,
2020). Our results are consistent with these findings as we reveal the
opposite trend occurred when carbonate chemistry changes were reversed; i.e.
when we decrease CO2 and increase pH, we observe a reduction in
picoeukaryote abundance. This is illustrated by the equilibrated treatment
where relatively small differences in CO2 and pH result in little to no
differences in picoeukaryote abundance, whereas large differences between
the control and unequilibrated treatment had a pronounced effect on
picoeukaryote abundance (Fig. 6c). It has been speculated that the influence
of CO2 on picoeukaryotes is due to their increased reliance on
diffusive CO2 entry in comparison to other functional groups which rely
more heavily on carbon concentrating mechanisms (CCMs) and the substantially
larger HCO3- pool (Crawfurd et al., 2016; Meakin and Wyman, 2011;
Engel et al., 2008). The operation of CCMs is energetically costly; however,
larger cells have been revealed to be more efficient at transporting carbon
using CCMs with a reduction in CO2 leakage as a function of size (Engel
et al., 2008; Malerba et al., 2021). Within this framework, smaller cells
such as picoeukaryotes would be at a disadvantage at lower CO2
concentrations in comparison to larger cells (Malerba et al., 2021; Meakin
and Wyman, 2011). Our results support this as picoeukaryotes were apparently
more sensitive to low CO2 or high pH than the larger phytoplankton
groups such as microphytoplankton (discussed below).
Differences between the treatments were less apparent for the
nanophytoplankton group, with no differences during the bloom phase and
slightly greater abundance during the post-bloom phase for the
unequilibrated treatment. The nanophytoplankton group contributed the
largest proportion to total biomass of all the assessed groups, increasing
from 55 %–65 % at the initiation of the experiment up to 95 % at the end.
The nanophytoplankton cluster in the flow cytometer is usually variable
across or within treatments as there are many species in this approximate
size range that could be captured. It is therefore possible, if not likely,
that there was a succession towards different nanophytoplankton species
between the control and treatments, which may explain different succession
patterns. Treatment-specific differences in nanophytoplankton abundances are
usually hard to interpret as it is mostly unclear what species are
contributing to the cluster and what physiological/ecological responses to
perturbation we can expect.
The microphytoplankton group did not display statistically significant
differences in absolute abundances or temporal shifts for cell counts.
However, as discussed in Sect. 4.1.2, we argue that there may have been
higher microphytoplankton abundances in the unequilibrated treatment during
the peak of the phytoplankton bloom (Fig. 6i), but this was too short to be
detected as a significant difference in the statistical analysis. The
absence of a negative effect of low CO2 and high pH in the unequilibrated treatment was surprising as theory predicts more pronounced constraints on diffusive CO2 uptake of larger phytoplankton species (Wolf-Gladrow and Riebesell, 1997; Flynn et al., 2012). Our experimental approach does not
reveal how this absence of an effect could be explained. As argued in
Sect. 4.1.2 and 4.1.3, we speculate that the most likely explanation is a
shift in the species composition where species that are more capable at
low-CO2 and high-pH conditions may have compensated for those with reduced
capacity. This important observation warrants further investigation.
Implications of the environmental assessment of ocean alkalinity
enhancement
The amount of alkalinity added in our experiment increases the capacity of
seawater to store atmospheric CO2 by 21 %. It is crucial to
understand that this is a massive enhancement of the inorganic carbon sink
of seawater. For example, 21 % of all DIC in the ocean equals
∼ 8000 GtC, > 10 times more than all carbon
emissions since 1750 (Friedlingstein et al., 2019). The inadvertent effect
of a 21 % sink enhancement on the phytoplankton community seems
justifiable in our experiments in relation to the substantial benefits such
permanent (>> 1000 years) CO2 storage would have
for the climate. Other marine CO2 removal methods such as ocean iron
fertilization are likely associated with at least equally pronounced
perturbations of the phytoplankton community (Quéguiner, 2013), for the
benefit of an approximately 1 % non-permanent (< 100 years)
enhancement of the marine carbon sink observed during mesoscale iron
fertilization experiments in the Southern Ocean (Bakker et al., 2005).
One particularly interesting observation was that the unequilibrated
alkalinity treatment was not noticeably more affected by the perturbation
than the equilibrated treatment (Figs. 5, 6), despite substantially larger
differences in carbonate chemistry relative to the control (Fig. 4). This is
of significant importance as equilibrated alkalinity additions will likely
be associated with additional costs, due to engineering efforts and
energetic requirements of equilibrating systems (e.g. CO2 bubbling and
associated pumping). However, the release of alkalinity into the marine
environment without a controlled influx of atmospheric CO2 leads to
verification challenges as it remains unclear where and when the CO2
influx will occur (Orr and Sarmiento, 1992; Gnanadesikan and Marinov, 2008;
Bach et al., 2021). Verification is important to refinance and incentivize
CO2 removal efforts (Hepburn et al., 2019; Rickels et al., 2021). Thus,
if not for environmental reasons, an engineered and controlled influx of
atmospheric CO2 after alkalinity additions as tested in the
equilibrated treatment may still be important for economic reasons.
One limitation of our experimental microcosm setup was the consistently high
alkalinity (+498 ± 5.2 µmol kg-1) in the treatments for the
entire 22 d experiment. In real-world OAE applications,
alkalinity-enriched seawater from point sources (e.g. electrochemical
facilities, de Lannoy et al., 2018) or mineral-powder-enriched surface
ocean areas (Renforth and Henderson, 2017) will be diluted over time with
surrounding seawater of lower alkalinity. The degree of dilution with
unperturbed water is site-specific and depends on the type of application
(e.g. more dilution for a small point source in a system with high mixing
rates). It can be expected that the dilution of alkalinity-enriched seawater
would weaken the impact of alkalinity on the plankton community because of
decreasing changes in carbonate chemistry relative to the non-perturbed
state. Thus, our experimental setup simulated a relatively high intensity of
perturbation as any impact mitigation through dilution is excluded.
OAE can be achieved through a variety of approaches, ranging from
distributing pulverized minerals onto the sea surface to splitting water
into acid and base using electrochemistry (Renforth and Henderson, 2017).
All methods seek to increase surface ocean alkalinity, but the by-products
generated in the various processes are highly variable. In this study, we
utilized laboratory grade NaOH to increase the alkalinity of microcosms, a
perturbation scenario representative of OAE via the electrochemical
splitting of water (de Lannoy et al., 2018). Here, no other chemicals than
strong acid (HCl) and base (NaOH) are generated, and only the base is
released into the surface ocean (de Lannoy et al., 2018; Tyka et al., 2022).
OAE approaches associated with the release of other bioactive components
such as trace metals could have more substantial effects on the plankton
community. We emphasize this aspect to stress that our observations of
relatively moderate impacts of equilibrated and unequilibrated
alkalinity perturbations cannot be generalized for all OAE approaches. From
this perspective, our simulated perturbation arguably tested a mild
version of OAE. The environmental assessment of OAE needs to remain in close
contact with geochemical research in order to anticipate which OAE
approaches have the greatest chance for upscaling. This will allow for a
targeted assessment of the perturbations associated with the OAE approaches
most likely to be implemented in the future.
Conclusion and outlook
This study is the first study to report on the effects of OAE on a coastal
plankton community. Our key findings are the following.
Two different scenarios of alkalinity enhancement (CO2 equilibrated
with the atmosphere and unequilibrated) had a significant influence on the
succession of the phytoplankton community and heterotrophic bacteria.
There were pronounced effects of alkalinity enhancement on diatoms even
though dissolved Si concentrations were not manipulated in this study.
Consistent with previous research on ocean acidification, we found that
low-CO2/high-pH conditions are detrimental for picoeukaryote
phytoplankton.
Surprisingly, the unequilibrated alkalinity treatment did not have a
noticeably greater effect on the phytoplankton community than the
equilibrated treatment, despite much larger changes in physiologically
important carbonate chemistry parameters.
Altogether our findings suggest that sudden increases in alkalinity leave a
noticeable imprint on the succession of the phytoplankton community.
However, as highlighted in the concluding sentence of the abstract, the
environmental effects investigated here appeared to be moderate when
compared to the enormous climatic benefit of increasing the inorganic carbon
sink of seawater by 21 %.
It is generally problematic to quantify changes in plankton communities as
positive or negative as this depends on the perspective. More than two
decades of ocean acidification research have shown that there will be
winners and losers in plankton communities when carbonate chemistry is
perturbed (Schulz et al., 2017; Alvarez-Fernandez et al., 2018; Taucher et
al., 2020). These shifts were often perceived as negative (Falkenberg et
al., 2020; le Quesne et al., 2012; Doney et al., 2020) but occasionally also
as positive (Sswat et al., 2018; le Quesne et al., 2012). Mixed (or
perspective-dependent) outcomes can also be expected for the assessment of
OAE. From a human perspective, plankton community shifts affecting trophic
transfer and ultimately fish production are comparatively easy to quantify
as positive or negative. Our dataset did not provide insights on this aspect
as we focussed only on the lowest trophic level. It is possible that the
seemingly moderate effects of alkalinity observed at the lowest trophic
level could have been amplified in higher trophic levels. Future studies
should aim for a comprehensive assessment of higher trophic levels to better
understand how lower trophic level change affects upper trophic levels and
also to reveal potential top-down effects of OAE. Furthermore, other pelagic
and benthic ecosystems, from arctic to tropical, need to be investigated to
gather a reliable and comprehensive assessment of OAE effects on marine
ecosystems. This study can therefore only be seen as a small first step.
Cytograms used to determine the size of particles filtered by QMA
filters used in TPC and TPN analysis. Plot (a) depicts a water sample
filtered through a QMA filter (2.2 µm) and plot (b) an unfiltered
sample. Both plots were produced using the same sample from microcosm M4 on
day 6.
Gating strategy when analysing data via flow cytometry. Plot (a)
illustrates the intensity of fluorescence for each channel in the total
sample. Plots (b)–(d) show gates for picoeukaryotes, nanophytoplankton,
microphytoplankton, Synechococcus, and cryptophytes in microcosm M3 on day 5. Plot (e) shows the gate for bacteria in microcosm M4 on day 12.
Cytograms depicting differences within gates, between treatments.
Plots are labelled according to corresponding microcosms so that M1, M4, and M7 represent
the unperturbed control; M2, M5, and M8 represent the unequilibrated treatment;
and M3, M6, and M9 represent the equilibrated treatment. All plots are from
samples taken during the peak bloom on day 6.
Boxplot depicting seasonal values of fCO2 recorded between
1993–2019 at Storm Bay (43.1–42.8442∘ S, 147.307–147.46∘ E), Tasmania
(Bakker et al., 2016).
Average diatom (a) biovolume and (b) abundance, during the peak
bloom (day 6) within treatments determined via SEM. Data are presented as
mean values ± SD.
Temporal variation in the molar ratios of TPC to BSi within
microcosms. Coloured shading around the respective means represents the
standard deviation.
Data availability
Data are available from the Institute for Marine and Antarctic Studies (IMAS)
data catalogue, University of Tasmania (UTAS) (10.25959/8PEA-SW88, Federer, 2021a).
Video supplement
Video supplement 1 contains a time lapse of the convective mixing test described in Sect. 2.1, taken on 3 August 2021. The video can be accessed online at 10.5446/55861 (Federer, 2021b).
Video supplement 2 contains a time lapse depicting aggregate formation and
suspension within a microcosm as a result of convective mixing, taken on 15
August 2021. The video can be accessed online at 10.5446/55860 (Federer, 2021c).
Author contributions
LTB designed the experiment. AF was responsible for the investigation with
the help of LTB and FK. AF was also responsible for the data curation,
formal analysis, and writing. LTB and FK supervised data collection. AF
wrote the manuscript with contributions from LTB, FK, ZC, and KGS.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank Sandrin Feig, Thomas Rodemann, and Terry Pinfold for
support on scanning electron microscopy, particulate organic matter, and
flow cytometry measurements. This research was funded by a Future Fellowship
(FT200100846) by the Australian Research Council awarded to LTB. This
research was also conducted while Aaron Ferderer was in receipt of an Australian
Government Research Training Program (RTP) scholarship.
Financial support
This research has been supported by the Australian Research Council (grant no. FT200100846).
Review statement
This paper was edited by Carol Robinson and reviewed by Alex Poulton and two anonymous referees.
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