Introduction
Marine phytoplankton are responsible for approximately half of
global primary production (Field et al., 1998) with shelf sea communities
contributing an average of 15–30 % (Kuliński and Pempkowiak, 2011).
Since the industrial revolution, atmospheric carbon dioxide (CO2)
concentrations have increased by nearly 40 % due to anthropogenic
emissions, primarily caused by the burning of fossil fuels and deforestation
(Doney et al., 2009). Atmospheric CO2 dissolves in the oceans where
it forms carbonic acid that reduces seawater pH, which is a process commonly
termed ocean acidification (OA). Currently, along with warming sea surface
temperatures and changing light and nutrient conditions, marine ecosystems
face unprecedented decreases in ocean pH (Doney et al., 2009; Gruber, 2011).
Ocean acidification is considered one of the greatest current threats to
marine ecosystems (Turley and Boot, 2010) and has been shown to alter
phytoplankton primary production with the direction and magnitude of the
responses dependent on community composition (e.g. Hein and Sand-Jensen,
1997; Tortell et al., 2002; Leonardos and Geider, 2005; Engel et al., 2008;
Feng et al., 2009; Eberlein et al., 2017). Certain cyanobacteria, including
diazotrophs, demonstrate stimulated growth under conditions of elevated
CO2 (Qiu and Gao, 2002; Barcelos e Ramos et al., 2007; Hutchins, et
al., 2007; Dutkiewicz et al., 2015). However, no consistent trends have been
found for Synechococcus (Schulz et al., 2017 and references
therein). The responses of diatoms and coccolithophores also appear more
variable (Dutkiewicz et al., 2015 and references therein), although
coccolithophore calcification seems generally negatively impacted (Meyer and
Riebesell, 2015; Riebesell et al., 2017). OA has also been reported to
increase the abundances of small-sized photoautotrophic eukaryotes in
mesocosm experiments (Engel et al., 2008; Meakin and Wyman, 2011; Brussaard
et al., 2013; Schulz et al., 2017).
Recently, data regarding the effects of OA on taxa-specific phytoplankton
growth rates were incorporated into a global ecosystem model. The results
emphasized that elevated CO2 concentrations can cause changes in
community structure by altering the competitive fitness and thus the competition
between phytoplankton groups (Dutkiewicz et al., 2015). Moreover, OA was
found to have a greater impact on phytoplankton community size structure,
function and biomass than either warming or reduced nutrient supply
(Dutkiewicz et al., 2015). Many OA studies have been conducted using
single species under controlled laboratory conditions and therefore cannot
account for intrinsic community interactions that occur under natural
conditions. Alternatively, larger-volume mesocosm experiments allow for OA
manipulation of natural communities, and are more likely to capture
and quantify the overall response of the natural ecosystems. To date, the
majority of these experiments started under replete nutrient conditions or
received nutrient additions (Paul et al., 2015 and references therein). Thus,
limited data are available for oligotrophic conditions, which are present in
∼ 75% of the world's oceans (Corno et al., 2007).
Whilst environmental factors, such as temperature, light, nutrient and
CO2 concentrations, regulate gross primary production, loss factors
determine the fate of this photosynthetically fixed carbon. Grazing, sinking
and viral lysis affect the cycling of elements in different manners, i.e.
transferred to higher trophic levels through grazing, carbon sequestration in
deep waters and sediments, and cellular content release by viral lysis
(Wilhelm and Suttle, 1999; Brussaard et al., 2005). Released detrital and
dissolved organic matter (DOM) is quickly utilized by heterotrophic bacteria,
thereby stimulating activity within the microbial loop (Brussaard et
al., 2008; Lønborg et al., 2013; Sheik et al., 2014; Middelboe and Lyck,
2002). Consequently, bacteria may be affected indirectly by OA through
changes in the quality and/or quantity of DOM (Weinbauer et al., 2011). Viral
lysis has been found to be as important as microzooplankton grazing to the
mortality of natural bacterioplankton and phytoplankton (Weinbauer, 2004; Baudoux et
al., 2006; Evans and Brussaard, 2012; Mojica et al., 2016). Thus far, most
studies examining the effects of OA on microzooplankton abundance and/or
grazing have found little or no direct effect (Suffrian et al., 2008; Rose et
al., 2009; Aberle et al., 2013; Brussaard et al., 2013; Niehoff et
al., 2013). To our knowledge, no viral lysis rates have been reported for
natural phytoplankton communities under conditions of OA. A few studies have
inferred rates based on changes in viral abundances under enhanced
CO2, but the results are inconsistent (Larsen et al., 2008; Brussaard
et al., 2013). Therefore, the effect of OA on the relative share of these key
loss processes is still understudied for most ecosystems.
Here we report on the temporal dynamics of microbes (phytoplankton,
prokaryotes and viruses) under the influence of enhanced CO2
concentrations in the low-salinity (around 5.7) Baltic Sea. Using large
mesocosms with in situ light and temperature conditions, the pelagic
ecosystem was exposed to a range of increasing CO2 concentrations
from ambient to future and far future concentrations. The study was performed
during the summer in the Baltic Sea near Tvärminne when conditions were
oligotrophic. Our data show that over the 43-day experiment, enhanced
CO2 concentrations elicited distinct shifts in the microbial
community, most notably an increase in the net growth of small picoeukaryotic
phytoplankton.
The fCO2 concentrations (µatm) averaged over the
duration of the experiment (following CO2 addition) and subsequent
classification as low, intermediate or high. Mesocosms sampled for mortality
assays are denoted by an asterisk. The symbols and colours are used
throughout this paper and the corresponding articles in this issue.
Mesocosm
M1*
M5
M7
M6
M3*
M8
CO2 level
Low
Low
Intermediate
Intermediate
High
High
Mean fCO2 (µatm) days 1–43
365
368
497
821
1007
1231
Symbol
Materials and methods
Study site and experimental set-up
The present study was conducted in the Tvärminne Storfjärden
(59∘51.5′ N, 23∘15.5′ E) between 14 June and 7 August
2012. Nine mesocosms, each enclosing ∼ 55 m3 of water, were
moored in a square arrangement at a site with a water depth of approximately
30 m. The mesocosms consisted of open-ended polyurethane bags
2 m in diameter and 18.5 m in length mounted onto floating
frames covered at each end with a 3 mm mesh. Initially, the mesocosms were
kept open for 5 days to allow for rinsing and water exchange while excluding
large organisms from entering with the 3 mm mesh. During this time, the bags
were positioned such that the tops were submerged 0.5 m below the
water surface and the bottoms reached down to 17 m of depth in the water
column. Photosynthetically active radiation (PAR) transparent plastic hoods
(open on the side) prevented rain and bird droppings from entering the
mesocosms, which would affect salinity and nutrients, respectively. Five days
before the CO2 treatment was to begin, the water column of the
mesocosms was isolated from the influence of the surrounding water. To do so,
the 3 mm mesh was removed and sediment traps (2 m long) were attached to
close off the bottom of the mesocosms. The top ends of the bags were raised
and secured to the frame 1.5 m above the water surface to prevent
water from entering via wave action. The mesocosms were then bubbled with
compressed air for 3.5 min to remove salinity gradients and ensure
that the water body was fully homogeneous.
The present paper includes results from only six of the original mesocosms
due to the unfortunate loss of three mesocosms, which were compromised by
leakage. The mean fugacity of CO2 (fCO2) during the
experiment, i.e. days 1–43, for the individual mesocosms were as follows:
M1, 365 µatm; M3, 1007 µatm; M5,
368 µatm; M6, 821 µatm; M7, 497 µatm; M8,
1231 µatm (Table 1). The gradient of non-replicated
fCO2 in the present study (as opposed to a smaller number of
replicated treatment levels) was selected as a balance between the necessary
but manageable number of mesocosms and to minimize the impact of the high
loss potential for the mesocosms to successfully address the underlying
questions of the study (Schulz and Riebesell, 2013). Moreover, it maximizes
the potential of identifying a threshold fCO2 level concentration
if present (by allowing for a larger number of treatment levels). Carbon
dioxide manipulation was carried out in four steps and took place between
days 0 and 4 until the target fCO2 was reached. The initial
fCO2 was 240 µatm. For fCO2 manipulations,
50 µm filtered natural seawater was saturated with CO2
and then injected evenly throughout the depth of the mesocosms as described
by Riebesell et al. (2013). Two mesocosms functioned as controls and were
treated in a similar manner using only filtered seawater. On day 15, a
supplementary fCO2 addition was made to the top 7 m of
mesocosms numbered 3, 6 and 8 to replace CO2 lost due to outgassing
(Paul et al., 2015; Spilling et al., 2016). Throughout this study we refer to
fCO2, which accounts for the nonideal behaviour of CO2 gas
and is considered the standard measurement required for gas exchange (Pfeil
et al., 2013).
Initial nutrient concentrations were 0.05, 0.15, 6.2 and
0.2 µmolL-1 for nitrate, phosphate, silicate and ammonium,
respectively. Nutrient concentrations remained low for the duration of the
experiment (Paul et al., 2015; this issue) and no nutrients were added.
Salinity was relatively constant around 5.7. Temperature was more variable;
on average temperature within the mesocosms (0–17 m) increased from
∼ 8 ∘C to a maximum on day 15 of ∼ 15 ∘C and
then decreased again to ∼ 8 ∘C by day 30. For further details
of the experimental set-up, carbonate chemistry dynamics and nutrient
concentrations throughout the experiment we refer to the general overview
paper by Paul et al. (2015).
Collective sampling was performed every morning using depth-integrated water
samplers (IWS; Hydro-Bios, Kiel). These sampling devices were gently lowered
through the water column collecting ∼ 5 L of water gradually
between 0 and 10 m (top) or 0 and 17 m (whole water column).
Water was collected from all mesocosms and the surrounding water. Subsamples
were obtained for the enumeration of phytoplankton, prokaryotes and viruses.
Samples for viral lysis and grazing experiments were taken from 5 m of depth
using a gentle vacuum-driven pump system. Samples were protected against
sunlight and warming by thick black plastic bags containing wet ice. Samples
were processed at in situ temperature (representative of 5 m of depth) under
dim light and handled using nitrile gloves. As viral lysis and grazing rates
were determined from samples taken from 5 m of depth, the samples for microbial
abundances reported here were taken from the top 10 m integrated samples.
The experimental period has been divided into four phases based on major
physical and biological changes (Paul et al., 2015): Phase 0 before
CO2 addition (days -5 to 0), Phase I (days 1–16), Phase II (days
17–30) and Phase III (days 31–43). Throughout this paper, the data are
presented using three colours (blue, grey and red), representing low
(mesocosms M1 and M5), intermediate (M6 and M7) and high (M3 and M8)
fCO2 levels (Table 1).
Microbial abundances
Microbes were enumerated using a Becton Dickinson FACSCalibur flow cytometer
(FCM) equipped with a 488 nm argon laser. The samples were stored on wet ice
and in the dark until counting. The photoautotrophic cells
(< 20 µm) were counted directly using fresh seawater and were
discriminated by their autofluorescent pigments (Marie et al., 1999). Six
phytoplankton clusters were differentiated based on the bivariant plots of
either chlorophyll (red autofluorescence) or phycoerythrin (orange
autofluorescence for Synechococcus and Pico-III) against side
scatter. The size of the different phytoplankton clusters was determined by
gentle filtration through 25 mm diameter polycarbonate filters (Whatman)
with a range of pore sizes (12, 10, 8, 5, 3, 2, 1 and 0.8 µm)
according to Veldhuis and Kraay (2004). Average cell sizes for the different
phytoplankton groups were 1, 1, 3, 2.9, 5.2 and 8.8 µm in diameter
for the prokaryotic cyanobacteria Synechococcus spp. (SYN),
picoeukaryotic phytoplankton I, II and III (Pico-I–III) and nanoeukaryotic
phytoplankton I and II (Nano-I, Nano-II), respectively. Pico-III was
discriminated from Pico-II (comparable average cell size) by a higher orange
autofluorescence signature, potentially representing small-sized cryptophytes
(Klaveness, 1989) or, alternatively, large single cells or microcolonies of
Synechococcus (Haverkamp et al., 2009). The cyanobacterial species
Prochlorococcus spp. were not observed during this experiment.
Counts were converted to cellular carbon by assuming a spherical shape
equivalent to the average cell diameters determined from size fractionations
and applying conversion factors of 237 fgCµm-3 (Worden et
al., 2004) and 196.5 fgCµm-3 (Garrison et al., 2000) for
pico- and nano-sized plankton, respectively. Microbial net growth and loss
rates were derived from exponential regressions of changes in the cell
abundances over time.
Abundances of prokaryotes and viruses were determined from 0.5 %
glutaraldehyde fixed, flash-frozen (-80 ∘C) samples according to
Marie et al. (1999) and Brussaard (2004). The prokaryotes
include heterotrophic bacteria, archaea and unicellular cyanobacteria, the
latter accounting for a maximal 10 % of the total abundance in our samples
as indicated by their autofluorescence. Thawed samples were diluted
with sterile autoclaved Tris-EDTA buffer (10 mM Tris-HCl and 1 mM EDTA;
pH 8.2; Mojica et al., 2014) and stained with the green fluorescent nucleic
acid-specific dye SYBR Green I (Invitrogen Inc.) to a final concentration of
the commercial stock of 1.0×10-4 (for prokaryotes) or 0.5×10-4 (for viruses). Virus samples were stained at 80 ∘C for
10 min and then allowed to cool for 5 min at room temperature
in the dark. Prokaryotes were stained for 15 min at room temperature
in the dark (Brussaard, 2004). Prokaryotes and viruses were discriminated in
bivariate scatter plots of green fluorescence versus side scatter. Final
counts were corrected for blanks prepared and analysed in a similar manner as
the samples. Two groups of prokaryotes were identified by their stained
nucleic acid fluorescence, referred here on as low (LNA) and high (HNA)
fluorescence prokaryotes.
Viral lysis and grazing
Microzooplankton grazing and viral lysis of phytoplankton was determined
using the modified dilution assay based on reducing grazing and viral lysis
mortality pressure in a serial manner allowing for increased phytoplankton
growth (over the incubation period) with dilution (Mojica et al., 2016).
Two dilution series were created in clear 1.2 L polycarbonate
bottles by gently mixing 200 µm sieved whole seawater with either
0.45 µm filtered seawater (i.e. microzooplankton grazers removed)
or 30 kDa filtered seawater (i.e. grazers and viruses removed) to final
dilutions of 20, 40, 70 and 100 %. The 0.45 µm filtrate was
produced by gravity filtration of 200 µm mesh sieved seawater
through a 0.45 µm Sartopore capsule filter. The 30 kDa
ultrafiltrate was produced by tangential flow filtration of
200 µm pre-sieved seawater using a 30 kDa Vivaflow 200 PES
membrane tangential flow cartridge (Vivascience). All treatments were
performed in triplicate. Bottles were suspended next to the mesocosms in
small cages at 5 m of depth for 24 h. Subsamples were taken at 0 and
24 h, and phytoplankton abundances of the grazing series
(0.45 µm diluent) were enumerated by flow cytometry. Due to time
constraints, the majority of the samples of the 30 kDa series were fixed with
1 % (final concentration) formaldehyde : hexamine solution (18 %
v/v : 10 % w/v) for 30 min at 4 ∘C, flash-frozen
in liquid nitrogen and stored at -80 ∘C until flow cytometry
analysis in the home laboratory. Fixation had no significant effect
(Student's t tests; p value > 0.05) as tested periodically against
fresh samples. The modified dilution assay was only run for mesocosms 1 (low
fCO2) and 3 (high fCO2) due to the logistics of handling
times. Experiments were performed until day 31. Grazing rates and the
combined rate of grazing and viral lysis were estimated from the slope of a
regression of phytoplankton apparent growth versus dilution of the
0.45 µm and 30 kDa series, respectively. A significant
difference between the two regression coefficients (as tested by analysis of
covariance) indicated a significant viral lysis rate. Phytoplankton gross
growth rate, in the absence of grazing and viral lysis, was derived from the
y-intercept of the 30 kDa series regression. Similarly, significant
differences between mesocosms M1 and M3 (low and high fCO2) were
determined through an analysis of covariance of the dilution series for the two
mesocosms. A significance threshold of 0.05 was used, and significance is
denoted throughout the paper by an asterisk (*). Occasionally, the
regression of apparent growth rate versus fraction of natural water resulted
in a positive slope (thus no reduction in mortality with dilution). In
addition, very low phytoplankton abundances can also prohibit the statistical
significance of results. Under such conditions dilution experiments were
deemed unsuccessful (for limitations of the modified dilution method, see
Baudoux et al., 2006; Kimmance and Brussaard, 2010; Stoecker et al., 2015).
Viral lysis of prokaryotes was determined according to the viral production
assay (Wilhelm et al., 2002; Winget et al., 2005). After reduction of the
natural virus concentration, new virus production by the natural bacterial
community is sampled and tracked over time (24 h). Free
viruses were reduced from a 300 mL sample of whole water by recirculation
over a 0.2 µm pore size polyether sulfone membrane (PES)
tangential flow filter (Vivaflow 50; Vivascience) at a filtrate expulsion
rate of 40 mLmin-1. The concentrated sample was then
reconstituted to the original volume using virus-free seawater. This process
was repeated a total of three times to gradually wash away viruses. After the
final reconstitution, 50 mL aliquots were distributed into six polycarbonate
tubes. Mitomycin C (Sigma-Aldrich; final concentration
1 µgmL-1; maintained at 4 ∘C), which induces
lysogenic bacteria (Weinbauer and Suttle, 1996) was added to a second series
of triplicate samples for each mesocosm. A third series of incubations with
0.2 µm filtered samples was used as a control for viral loss
(e.g. viruses adhering to the tube walls) and showed no significant loss of
free viruses during the incubations. At the start of the experiment, 1 mL
subsamples were immediately removed from each tube and fixed as previously
described for viral and bacterial abundance. The samples were dark incubated
at in situ temperature and 1 mL subsamples were taken at 3, 6, 9, 12 and
24 h. Virus production was determined from linear regression of viral
abundance over time. Viral production due to induction of lysogeny was
calculated as the difference between production in the unamended samples and
the production of samples to which mitomycin C was added. Although mortality
experiments were initially planned to be employed for mesocosms 1, 2 and 3
representing low, mid and high fCO2 conditions, mesocosm 2 was
compromised due to leakage. Additionally, due to logistical reasons assays
were only performed until day 21.
To determine grazing rates on prokaryotes, fluorescently labelled bacteria
(FLBs) were prepared from enriched natural bacterial assemblages (originating
from the North Sea) labelled with 5-([4,6-dichlorotriazin-2-yl]amino)fluorescein (DTAF 36565; Sigma-Aldrich; 40 µgmL-1) according
to Sherr et al. (1993). Frozen ampoules of FLB (1–5 % of total bacterial
abundance) were added to triplicate 1 L incubation bottles containing whole
water gently passed through 200 µm mesh. Samples of 20 mL were
taken immediately after addition (0 h) and the headspace was removed
by gently squeezing air from the bottle. The 1 L bottles were incubated on a
slow turning wheel (1 rpm) at in situ light and temperature
conditions (representative of 5 m of depth) for 24 h. Sampling was
repeated after 24 h. All samples were fixed to a 1 % final
concentration of gluteraldehyde (0.2 µm filtered; 25 %
EM-grade), stained (in the dark for 30 min at 4 ∘C) with
4',6-diamidino-2-phenylindole dihydrochloride (DAPI) solution
(0.2 µm filtered; Acrodisc®
25 mm syringe filters; Pall Life Sciences; 2 µgmL-1 final
concentration; Sherr et al., 1993) and filtered onto 25 mm,
0.2 µm black polycarbonate filters (GE Healthcare Life Sciences).
Filters were then mounted on microscopic slides and stored at
-20 ∘C until analysis. FLBs present on a ∼ 0.75 mm2
area were counted using a Zeiss Axioplan 2 microscope. Grazing
(µd-1) was measured according to NT24=NT0⋅e-μt, where NT24 and NT0
are the number of FLBs present at 24 and 0 h, respectively.
Statistics
Non-metric multidimensional scaling (NMDS) was used to follow microbial
community development in each mesocosm over the experimental period. NMDS is
an ordination technique which represents the dissimilarities obtained from an
abundance data matrix in a two-dimensional space (Legendre and Legendre, 1998).
In this case, the data matrix was comprised of abundance data for each
phytoplankton group in each mesocosm for every day of sampling. The treatment
effect was assessed by an analysis of similarity (ANOSIM; Clarke, 1993) and
inspection of the NMDS biplot. ANOSIM compares the mean of ranked
dissimilarities in mesocosms between fCO2 treatments (low: 1, 5, 7;
high: 6, 3, 8) to the mean of ranked dissimilarities within treatments per
phase. The NMDS plots allowed divergence periods in the development and
community composition between treatments to be visually assessed (period 1
from days 3–13 and period 2 from days 16–24). The net growth rates of each of
the different microbial groups were calculated for these identified
divergence periods. Relationships between net growth rates and peak cell
abundances with fCO2 were evaluated by linear regression against
the average fCO2 per mesocosm during each period or peak day. A
generalized linear model was used to test the relationship between prokaryote
abundance and carbon biomass with an ARMA correlation structure of order 3 to
account for temporal autocorrelation. The model fulfilled all assumptions,
such as homoscedasticity and avoiding autocorrelation of the residuals (Zuur
et al., 2007). A significance threshold of p≤0.05 was used, and
significance is denoted by an asterisk (*). All analyses were performed
using the statistical software program R with the packages nlme (Pinheiro et
al., 2017) and vegan (Oksanen et al., 2017; R core Team, 2017). Where
averages of low and high mesocosm abundance data are reported, the values
represent the average of mesocosms 1, 5 and 7 (mean fCO2
365–497 µatm) and 6, 3 and 8 (821–1231 µatm).
(a) Time series plot of depth-integrated
(0.3–10 m) total phytoplankton abundance (< 20 µm)
and (b) total eukaryotic phytoplankton abundance for each mesocosm
and the surrounding waters (Baltic). Dotted lines indicate the end of Phase I
and the end of Phase II. Colours and symbols represent the different
mesocosms and are consistent throughout the paper. Mean fCO2 during
the experiment (days 1–43): M1, 365 µatm; M3,
1007 µatm; M5, 368 µatm; M6, 821 µatm;
M7, 497 µatm; M8, 1231 µatm.
Results
Total phytoplankton dynamics in response to CO2 enrichment
During Phase 0, low variability in phytoplankton abundances in the different
mesocosms (1.5±0.05×105 mL-1) indicated good
replicability of initial conditions prior to CO2 manipulation
(Fig. 1). This was further supported by the high similarity between microbial
communities in the different mesocosms as indicated by the tight clustering
of points in the NMDS plot during this period (Fig. 2). During Phase 0, the
phytoplankton community (< 20 µm) was dominated by pico-sized
autotrophs, with the prokaryotic cyanobacteria Synechococcus (SYN)
and Pico-I accounting for 69 and 27 % of total phytoplankton abundance,
respectively. After CO2 addition, there were two primary peaks in
phytoplankton, which occurred on day 4 in Phase I and day 24 in Phase II
(Fig. 1a). The phytoplankton community became significantly different over
time in the different treatments (ANOSIM, p=0.01; Fig. 2). Two periods
were identified based on their divergence (Fig. 2). The first (NMDS-based
period 1) followed the initial peak in abundance (days 3–13) with the highest
abundances occurring in the elevated CO2 mesocosms (Fig. 1a). During
the second period (NMDS-based period 2; days 16–24), abundances were higher
in the low fCO2 mesocosms (Fig. 1a). In general the NMDS plot shows
that throughout the experiment, mesocosm M1 followed the same basic
trajectory as mesocosms M5 and M7, whilst mesocosm M3 followed M6 and M8
(Fig. 2). Thus, the two mesocosms (representing high and low fCO2
treatments) deviated from each other during Phase I and were clearly
separated during Phases II and III (Fig. 2).
Non-metric multidimensional scaling (NMDS) ordination plot of
microbial community development in each mesocosm and the surrounding waters
(Baltic) over the experimental period. Phases are indicated by different open
symbols. Days of experiment (DoE) when communities separate (3, 13, 16 and
24) are indicated by different closed symbols. Phytoplankton groups are
denoted as SYN (Syn), Pico-I (P-I), Pico-II (P-II), Pico-III (P-III), Nano-I
(N-I), Nano-II (N-II), low NA prokaryotes (LNA) and high NA prokaryotes
(HNA).
Phytoplankton abundances in the surrounding water started to differ from the
mesocosms during Phase 0 (on average 44 % lower), which was primarily due
to lower abundances of SYN. This effect was seen from day -1 prior to
CO2 addition but following bubbling with compressed air (day -5).
On day 15, a deep mixing event occurred as a result of storm conditions (with
consequent alterations in temperature and salinity). As a result
phytoplankton abundances in the surrounding open water diverged more strongly
from the mesocosms but remained similar in their dynamics (Fig. 3). Microbial
abundances in the 0–17 m samples were slightly lower but showed very
similar dynamics to those in the 0–10 m samples (Fig. S1 in the Supplement).
Synechococcus
The prokaryotic cyanobacteria Synechococcus (SYN) accounted for the
majority of total abundance, i.e. 74 % averaged across all mesocosms over
the experimental period. Abundances of SYN showed distinct variability
between the different CO2 treatments,starting on day 7, with the low
CO2 mesocosms exhibiting nearly 20 % lower abundances between
days 11 and 15 compared to high fCO2 mesocosms (Fig. 3a). SYN net
growth rates during days 3–13 (NMDS-based period 1) were positively
correlated with CO2 (p=0.10, R2=0.53; Table 2, Fig. S2a).
One explanation for higher net growth rates at elevated CO2 could be
the significantly (p<0.05) higher grazing rate in the low fCO2
mesocosm M1 (0.56 d-1) compared to the high fCO2 M3
(0.27 d-1) as measured on day 10 (Fig. 4a). After day 16, SYN
abundances increased in all mesocosms, and during this period (days 16–24)
net growth rates had a significant negative correlation with fCO2 (p=0.05, R2=0.63; Figs. 3a and S3a, Table 2). Consequently, the net
increase in SYN abundances during this period was on average 20 % higher
at low fCO2 compared to high fCO2. This corresponded to
higher total loss rates in high fCO2 treatments measured on day 17
(0.33 vs. 0.17 d-1 for M3 and M1, respectively; Fig. 4a). The
higher net growth most likely led to the peak in SYN abundance observed on
day 24 (maximum 4.7×105 mL-1), which was negatively
correlated with fCO2 (p=0.01, R2=0.80; Table 3, Fig. S4a).
After this period (days 24–28), SYN abundances declined at comparable rates
in the different mesocosms irrespective of fCO2 (Fig. 3a).
Abundances in the low fCO2 mesocosms remained higher into Phase III
(Fig. 3a). SYN abundances in the surrounding water were generally lower than
in the mesocosms, with the exception of days 17–21.
Time series plot of depth-integrated (0.3–10 m) abundances
of (a) Synechococcus (SYN), (b) picoeukaryotes I
(Pico-I), (c) picoeukaryotes II (Pico-II),
(d) picoeukaryotes III (Pico-III), (e) nanoeukaryotes I
(Nano-I) and (f) nanoeukaryotes II (Nano-II) distinguished by flow
cytometric analysis of the microbial community in each mesocosm and the
surrounding waters (Baltic). Dotted lines indicate the end of Phase I and the end
of Phase II; grey areas indicate NMDS-based periods 1 and 2 during which net growth
rates were analysed.
Total mortality rates (i.e. grazing and lysis; solid bars) and
gross growth rates (striped bars) d-1 of the different
phytoplankton groups in mesocosms M1 (blue) and M3 (red) on the day
indicated: (a) Synechococcus (SYN),
(b) picoeukaryotes I (Pico-I), (c) picoeukaryotes II
(Pico-II), (d) picoeukaryotes III (Pico-III),
(e) nanoeukaryotes I (Nano-I) and (f) nanoeukaryotes II
(Nano-II). Significant (p≤0.05) differences between mesocosms are
indicated by an asterisk above the relevant bar (either total loss or gross
growth). A coloured zero indicates that a rate of zero was measured in the
mesocosm of the corresponding colour; the absence of a bar or zero
indicates a failed experiment. Dotted lines indicate the end of Phase I and
the end of Phase II.
The fit (R2) and significance (p value) of linear regressions
applied to assess the relationship between net growth rate and temporally
averaged fCO2 for the different microbial groups distinguished by
flow cytometry. The results presented are for two periods distinguished from
NMDS analysis: NMDS-based period 1 (days 3–13) and 2 (days 16–24). A
significance level of p≤0.05 was taken and significant results are shown
in bold.
Phytoplankton
NMDS period 1
NMDS period 2
group
(days 3–13)
(days 16–24)
p
R2
p
R2
SYN
0.10
0.53
0.05
0.63
Pico-I
0.01
0.80
0.05
0.64
Pico-II
0.52
0.11
0.10
0.52
Pico-III
0.04
0.67
<0.01
0.91
Nano-I
0.01
0.79
0.26
0.30
Nano-II
0.20
0.36
0.06
0.61
HNA
0.05
0.64
0.89
0.00
LNA
<0.01
0.95
0.02
0.76
The fit (R2) and significance (p value) of linear regressions
used to relate peak abundances and net growth rate with temporally averaged
fCO2 for the different microbial groups distinguished by flow
cytometry during specific periods of interest. A significance level of p≤0.05 was taken and significant results are shown in the table
below.
Peak abundance
Net growth rate
p
R2
p
R2
SYN day 24
0.01
0.80
–
–
Pico-I day 5
0.01
0.81
–
–
Pico-I day 13
< 0.01
0.94
–
–
Pico-I day 21
0.01
0.84
–
–
Pico-II day 17
< 0.01
0.93
–
–
Pico-III day 24
< 0.01
0.91
–
–
Nano-I day 17
0.04
0.67
–
–
Pico-I days 1–5
–
–
< 0.01
0.90
Pico-I days 5–9
–
–
< 0.01
0.89
Pico-II days 12–17
–
–
0.01
0.82
Picoeukaryotes
In contrast to the prokaryotic photoautotrophs, the eukaryotic phytoplankton
community showed a strong positive response to elevated fCO2
(Fig. 1b). Pico-I was the numerically dominant group of eukaryotic
phytoplankton, accounting for an average 21–26 % of total phytoplankton
abundances. Net growth rates leading up to the first peak in abundance (from
days 1 to 5) had a strong positive correlation with fCO2 (p<0.01,
R2=0.90; Figs. 3b and S5a, Table 3). Accordingly, the peak on day 5
(maximum 1.1×105 mL-1; Fig. 3b) was also correlated
positively with fCO2 (p=0.01, R2=0.81; Table 3, Fig. S4b).
During Phase I from days 3 to 13 (i.e. NMDS-based period 1), net growth rates
of Pico-I remained positively correlated with CO2 concentration (p=0.01, R2=0.80; Table 2, Fig. S2b). However, during this period there
was also a decline in abundance (days 5–9; p<0.01, R2=0.89;
Table 3, Fig. S5b) with 23 % more cells lost in the low fCO2
mesocosms. Accordingly, following this period, gross growth rate was
significantly higher in the high fCO2 mesocosm M3 compared to
the low fCO2 mesocosm M1 (day 10, p<0.05; Fig. 4b). Pico-I
abundances in the surrounding open water started to deviate from the
mesocosms after day 10 and were on average around half that of the low
fCO2 mesocosms (Fig. 3b). Following a brief increase (occurring
between days 11 and 13) correlated with fCO2 (p<0.01, R2=0.94;
Table 3, Fig. S4c), abundances declined sharply between days 13 and 16
(Fig. 3b), coinciding with a significantly higher total mortality rate in the
high fCO2 mesocosm M3 (day 13; Fig. 4b). Viral lysis was a
substantial loss factor relative to grazing for this group, comprising an
average 45 and 70 % of total losses in M1 and M3, respectively
(Table S1). During NMDS-based period 2, net growth rates of Pico-I were
significantly higher at high fCO2 (p=0.05, R2=0.64;
Table 2, Fig. S3b). By day 21, abundances in the high fCO2
mesocosms were (on average) ∼ 2-fold higher than at low fCO2
(maximum abundances 8.7×104 and 5.9×104 mL-1 for
high and low fCO2 mesocosms; p=0.01, R2=0.84; Table 3,
Fig. S4d). Standing stock of Pico-I remained high in the elevated
fCO2 mesocosms for the remainder of the experiment (7.9×104 vs. 4.3×104 mL-1 on average for high and low
fCO2 mesocosms, respectively; Fig. 3b). Additionally, gross growth
rates during this final period were relatively low (0.14 and
0.16 d-1 in M1 and M3, respectively) and comparable to total loss
rates (averaging 0.13 and 0.10 d-1 over days 25–31 for M1 and
M3, respectively; Fig. 4b).
Another picoeukaryote group, Pico-II, slowly increased in abundance until
day 13, when it increased more rapidly (Fig. 3c). Gross growth rates measured
during Phase I were high (0.69 and 0.72 d-1 on average in the low
and high fCO2 mesocosms M1 and M3, respectively; Fig. 4c) and
comparable to loss processes (0.46 and 0.58 d-1), indicative of a
relatively high turnover rate of production. Overall net growth rates during
days 3–13 (NMDS-based period 1) did not correlate with CO2 (p=0.52, R2=0.11; Table 2, Fig. S2c). However, during periods of rapid
increases in net growth, abundances were positively correlated with CO2
concentration (days 12–17; p=0.01, R2=0.82; Table 3, Fig. S5c).
Accordingly, the peak in abundances of Pico-II on day 17 displayed a distinct
positive correlation with fCO2 (p<0.01, R2=0.93; Table 3,
Fig. S4e) with maximum abundances of 4.6×103 and 3.4×103 mL-1 for the high and low fCO2 mesocosms,
respectively (Fig. 3c). In M8 (the highest fCO2 mesocosm),
abundances increased for an extra day with the peak occurring on day 18,
resulting in an average of 23 % higher abundances. During the decline in the
Pico-II peak (days 16–24), net growth rates were negatively correlated with
fCO2 (p=0.10, R2=0.52; Table 2, Fig. S3c). Moreover, the
rate of decline was faster for the high fCO2 mesocosms during days
18–21 (p<0.01, R2=0.85). The Pico-II abundances in the surrounding
water were comparable to the mesocosms during Phases 0 and I, lower during
Phase II and higher during Phase III (Fig. 3c).
Pico-III exhibited a short initial increase in abundances in the low
fCO2 treatments, resulting in nearly 2-fold higher abundances at
low fCO2 by day 3 compared to the high fCO2 treatment
(Fig. 3d). After this initial period, net growth rates of this group had a
significant positive correlation with fCO2 (days 3–13; p=0.04,
R2=0.67; Table 2, Fig. S2d). In general, during Phase I gross growth (p<0.01; days 1, 3, 10; Fig. 4d) and total mortality (p<0.05; days 1, 6,
10; Fig. 4d) were significantly higher in the low fCO2 mesocosm M1
compared to the high fCO2 mesocosm M3, resulting in low net growth
rates. During Phase II (days 16–24; NMDS-based period 2) the opposite
occurred; i.e. net growth rates were negatively correlated with
fCO2 (p<0.01, R2=0.86; Table 2, Fig. S3d). Maximum
Pico-III abundances (day 24: 4.2×103 and 8.3×103 mL-1 for high and low fCO2) had a strong negative
correlation with fCO2 (p<0.01, R2=0.91; Table 3,
Fig. S4f). Pico-III abundances remained noticeably higher in the low
fCO2 mesocosms during Phases II and III (on average 80 %;
Fig. 3d). Unfortunately, almost half of the mortality assays in this second
half of the experiment failed (see Sect. 2), but the successful assays
suggest that losses were minor (< 0.15 d-1; Fig. 4d) and
primarily due to grazing, as no significant viral lysis was detected
(Table S1).
Nanoeukaryotes
Nano-I showed maximum abundances (4.3±0.4×102 mL-1) on
day 6 (except M1, which peaked on day 5) independent of fCO2 (p=0.23, R2=0.33; Fig. 3e). There was, however, a negative correlation of
net growth rate with fCO2 during days 3–13 (NMDS-based period 1;
p=0.01, R2=0.79; Table 2, Fig. S2e). A second major peak in
abundance of Nano-I occurred on day 17, with markedly higher numbers in the
low fCO2 mesocosms (4.1×102 mL-1 compared to
2.4×102 mL-1 in high fCO2 mesocosms; p=0.04, R2=0.67; Figs. 3e and S4g, Table 3). Total loss rates in the high
fCO2 mesocosm M3 on days 6 and 10 were 2.3-fold higher compared to
the low fCO2 mesocosm M1 (Fig. 4e), which may help to explain this
discrepancy in total abundance between low and high fCO2 mesocosms.
Viral lysis accounted for up to 98 % of total losses in the high fCO2
mesocosm M3 during this period, whilst in M1 viral lysis was only detected on
day 13 (Table S1 in the Supplement). Peak abundances (around 5.0×102 mL-1) were much lower compared to those in the surrounding
waters (max ∼ 2.4×103 mL-1; Figs. 3e and S6a).
During Phase II, Nano-I abundances in the surrounding waters displayed rather
erratic dynamics compared to those of the mesocosms but converged during
certain periods (e.g. days 19–22). No significant relationship was found
between net loss rates and fCO2 for the second NMDS-based period
(p=0.26, R2=0.30; Table 2, Fig. S3e). At the end of Phase II,
abundances were similar in all mesocosms but diverged again during Phase III
(days 31–39) due primarily to a negative effect of CO2 on Nano-I
abundances, as depicted in the average 36 % reduction in Nano-I.
The temporal dynamics of Nano-II, the least abundant phytoplankton group
analysed in our study, displayed the largest variability (Fig. 3f), perhaps
due to the spread of this cluster in flow cytographs (which may indicate that
this group represents several different phytoplankton species). No
significant relationship was found between net growth rate and fCO2
for this group for the two NMDS-based periods (Table 2, Figs. S2f and S3f)
nor with the peak in abundances on day 17 (p=0.13, R2=0.46;
Fig. S4h). Moreover, no consistent trend was detected in mortality rates
(Fig. 4f). Similar to Nano-I, abundances in the surrounding water were often
higher than in the mesocosms (maximum 3.5×102 mL-1 vs.
1.1×104 mL-1, respectively; Figs. 3f and S6b).
Time series plot of the mean phytoplankton carbon biomass in high
fCO2 (M3, M6, M8; red) and low fCO2 (M1, M5, M7; blue)
mesocosms of (a) Pico-I and Pico-II combined and (b) SYN,
Pico III, Nano-I and Nano-II combined. Error bars represent 1 standard deviation
from the mean. Carbon biomass is calculated assuming a spherical diameter
equivalent to the mean average cell diameters for each group and conversion
factors of 237 fgCµm-3 (Worden et al., 2004) and
196.5 fgCµm-3 (Garrison et al., 2000) for pico- and
nano-sized plankton, respectively. Dotted lines indicate the end of Phase I
and the end of Phase II.
Time series plot of depth-integrated (0.3–10 m) abundances
of (a) total prokaryotes, (b) high fluorescent nucleic acid
prokaryote population (HNA), (c) low fluorescent nucleic acid
prokaryote population (LNA) and (d) total virus. Dotted lines
indicate the end of Phase I and the end of Phase II; grey areas indicate
NMDS-based periods during which net growth rates were analysed.
Prokaryote mortality rates: (a) total grazing
(d-1) and (b) viral lysis rates as % of prokaryote
standing stock in mesocosms M1 (low fCO2; blue) and M3 (high
fCO2; red). Grazing rates were determined from fluorescently
labelled prey, and viral lysis rates from viral production assays. Error bars
represent 1 standard deviation of triplicate assays. Significant (p≤0.05) differences between mesocosms are indicated by an asterisk. Dotted
lines indicate the end of Phase I.
Correlation between total carbon biomass (µmolL-1)
and total prokaryote abundance in low fCO2 mesocosms (M1, M5,
M7; blue) and high fCO2 mesocosms (M3, M6, M8; red) throughout the
experiment (days -2 to 39).
Algal carbon biomass
The mean combined biomass of Pico-I and Pico-II showed a strong positive
correlation with fCO2 throughout the experiment (p<0.05, R2=0.95; Fig. 5a), an effect already noticeable by day 2. Their biomass in the
high fCO2 mesocosms was, on average 11 % higher than in the low
fCO2 mesocosms between days 10 and 20 and 20 % higher between days
20 and 39. Conversely, the remaining algal groups showed an average 10 %
reduction in carbon biomass at enhanced fCO2 (days 3–39, the sum
of SYN, Pico-III, Nano-I and II; p<0.01; Fig. 5b). The most notable
response was found for the biomass of Pico-III, which showed an immediate
negative response to CO2 addition (Fig. S7a) and remained on
average 29 % lower throughout the study period (days 2–39). For Nano-I
and Nano-II the lower carbon biomass only became apparent during the end of
Phase I and the beginning of Phase II (days 14–20; Fig. S7b). Due to its small
cell size, the numerically dominant SYN accounted for an average of 40 %
of total carbon biomass.
Prokaryote and virus population dynamics
Prokaryote abundance in the mesocosms was positively related to total algal
biomass independent of treatment (p<0.05, R2=0.33; Fig. 8) and
generally followed total algal biomass (Fig. S7c). The initial increase in
total prokaryote abundances occurred during the first few days following the
closure of the mesocosms (Fig. 6a). This was primarily due to increases in
the HNA prokaryote group (Fig. 6b), which displayed higher net growth rates
(0.22 d-1) compared to the LNA prokaryotes (0.14 d-1 on
days -3 to 3; Fig. 6c). A similar, albeit somewhat lower, increase was also
recorded in the surrounding waters (Fig. 6a). The decline in the first peak
in prokaryote abundances coincided with the decay in phytoplankton
abundance and biomass (Figs. 1a and S7c). Concurrently the share of viral lysis
increased, representing 37–39 % of total mortality on day 11 (Fig. 7b).
No measurable rates of lysogeny were found for the prokaryotic community
during the experimental period (all phases). From days 10 to 15 prokaryote
dynamics (total, HNA and LNA) became noticeably affected by CO2
concentration with a significant positive correlation between net growth and
fCO2 during Phase I (days 3–13; NMDS-based period 1; Table 2,
Fig. S2g and h). In the higher fCO2 mesocosms, the decline in
prokaryote abundance occurring between days 13 and 16 (Fig. 6a) was largely
(70 %) due to decreasing HNA prokaryote numbers (Fig. 6b). The grazing
was 1.6-fold higher in the high fCO2 mesocosm M3 compared to M1
(0.36±0.13 and 0.14±0.08 d-1 on day 14; Fig. 7a). At the
same time, viral abundance increased in the high fCO2 mesocosms
(Fig. 6d).
During Phase II, prokaryote abundances increased steadily until day 24 (for
both HNA and LNA), corresponding to increased algal biomass (Figs. 6 and S7c)
and lowered grazing rates (Fig. 7a). Specifically, during days 16–24
(NMDS-based period 2), the HNA prokaryotes showed an average 10 % higher
abundances in the low compared to the high fCO2 mesocosms
(Fig. 6b). However, a significant negative correlation of net growth rates
and fCO2 was only found for LNA (Table 2, Fig. S3g and h). No
significant differences in loss rates between M1 and M3 were found during
Phase II (p=0.22 and p=0.46 on days 18
and 21, respectively; Fig. 7). Halfway through Phase II (day 24), the
prokaryote abundance in the surrounding water levelled off (Fig. 6a).
Prokaryote abundance ultimately declined during days 28–35 (Fig. 6a), and
the net growth of LNA was again negatively correlated with enhanced
CO2 (p=0.02, R2=0.76; Table 2, Fig. S3g). Unfortunately, no
experimental data on grazing and lysis of prokaryotes are present after
day 25. However, viral abundances increased steadily at 2.2×106 d-1 concomitant with a decline in prokaryote abundance
(Fig. 6a and d). There was no significant correlation between viral
abundances and fCO2 during Phases II and III (p=0.36, R2=0.21).
Discussion
In most experimental mesocosm studies, nutrients have been added to stimulate
phytoplankton growth (Schulz et al., 2017); therefore limited data exists for
oligotrophic phytoplankton communities. In this study, we describe the impact
of increased fCO2 on the brackish Baltic Sea microbial community
during summer (nutrient depleted; Paul et al., 2015). Small-sized
phytoplankton numerically dominated the autotrophic community, in particular
SYN and Pico-I (both about 1 µm in cell diameter). Our results
demonstrate variable effects of fCO2 manipulation on temporal
phytoplankton dynamics, dependent on phytoplankton group. In particular,
Pico-I and Pico-II showed significant positive responses, whilst the
abundances of Pico-III, SYN and Nano-I were negatively influenced by elevated
fCO2. The impact of OA on the different groups was, at times, a
direct consequence of alterations in gross growth rate, whilst overall
phytoplankton population dynamics could be explained by the combination of
growth and losses. OA effects on community composition in these systems may
have consequences on both the food web and biogeochemical cycling.
Comparison with surrounding waters
During Phase 0, the microbial assemblage showed good replicability among all
mesocosms; however, they had already began to deviate from the community in
the surrounding waters. This was most likely a consequence of water movement
altering the physical conditions and biological composition of the
surrounding water body. The dynamic nature of water movement in this region
has been shown to alter the entire phytoplankton community several times over
within a few months due to fluctuations in nutrient supply, advection,
replacement or mixing of water masses and water temperature (Lips and Lips,
2010). Alternatively, the effects of enclosure and the techniques (bubbling)
used to ensure a homogenous water column may have stimulated SYN within the
mesocosms, which has been found to occur in several mesocosm experiments
(Paulino et al., 2008; Gazeau et al., 2017). By Phases II and III, the
microbial abundances within the mesocosms were distinctly different from the
surrounding waters, with generally fewer SYN and Pico-I and more Nano-I and
Nano-II. Our statistical analysis shows that during this time, there was
little similarity between the surrounding waters and mesocosms regardless of
the CO2 treatment level. Thus, the deviations during this time were
most likely due to an upwelling event in the archipelago (days 17–30; Paul
et al., 2015). Cold, nutrient-rich deep water has been shown to upwell during
summer with a profound positive influence on ecosystem productivity
(Nômmann et al., 1991; Lehmann and Myrberg, 2008). A relaxation from
nutrient limitation in vertically stratified waters disproportionately
favours larger-sized phytoplankton due to their higher nutrient requirements
and lower capacity to compete at low concentrations dictated by their lower
surface to volume ratio (Raven, 1998; Veldhuis et al., 2005). Inside the
mesocosms, which were isolated from upwelled nutrients, picoeukaryotes
dominated similar to a stratified water column. Following this upwelling
event, the pH of the surrounding waters dropped from 8.3 to 7.8, a level
comparable to the highest CO2 treatment (M8) on day 32 (Paul et
al., 2015). This suggests that other factors contributed to the observed
differences between mesocosms and the surrounding water than can be accounted
for by CO2 concentration alone, e.g. nutrients. Alternatively, the
magnitude and source of mortality occurring in the surrounding water may have
been altered compared to within the mesocosms after such an upwelling event.
Although the grazer community in the surrounding waters was not studied
during this campaign, it is likely that the grazing community was completely
restructured during the upwelling event (Uitto et al., 1997). It is
nonetheless noteworthy that the phytoplankton groups with distinct responses
to CO2 enrichment (either positive or negative) in the low (ambient)
fCO2 mesocosms diverged from those in the surrounding water before
the upwelling event occurred.
Phytoplankton dynamics
Synechococcus showed significantly lower net growth rates and peak
abundances at higher fCO2. Both in laboratory and mesocosm
experiments, Synechococcus has been reported to have diverse
responses to CO2 with approximately equal accounts of positive (Lu et
al., 2006; Schulz et al., 2017), negative (Paulino et al., 2008; Hopkins et
al., 2010; Traving et al., 2014,) and insignificant changes (Fu et al., 2007;
Lu et al., 2006) in net growth rate with fCO2. This variable
response is probably due, at least in part, to the broad physiological and
genetic diversity of this species. In the Gulf of Finland alone, 46 different
strains of Synechococcus were isolated in July 2004 (Haverkamp et
al., 2009). Direct effects on physiology have been implied from laboratory
studies. One isolate, a phycoerythrin-rich strain of Synechococcus
WH7803 (Traving et al., 2014), elicited a negative physiological effect on
the growth rate from increased CO2. This was most likely a
consequence of higher sensitivity to the lower pH (Traving et al., 2014) and
the cellular cost of maintaining pH homeostasis or, conversely, a direct
effect on protein export. Additionally, Lu et al. (2006) reported increased
growth rates in a cultured phycocyanin-rich but not a phycoerythrin-rich
strain of Synechococcus, suggesting that pigments may play some part
in defining the direct physiological response within Synechococcus.
In addition, within natural communities (Paulino et al., 2008; Hopkins et
al., 2010; Schulz et al., 2017) variability can also arise from indirect
effects, such altering competition with other picoplankton (Paulino et
al., 2008). The delay and dampened effect of fCO2 on SYN abundances
within our study was more likely due to indirect effects arising from
alterations in food web dynamics than to direct impacts on the physiology of
this species. Specifically, significant differences in grazing rates of SYN
between M1 and M3 (days 10 and 17, no significant lysis detected) could be
responsible for the differing dynamics between the mesocosms at the end of
Phase I and the beginning of Phase II.
The gross growth rates of Pico-I were significantly higher (p<0.05) at
high fCO2 compared to the low CO2 concentrations during the
first 10 days of Phase I. Moreover, no differences were detected in the
measured loss rates, demonstrating that increases in Pico-I were the due to
increases in growth alone. The stimulation of Pico-I by elevated
fCO2 may be due to a stronger reliance on diffusive CO2
entry compared to larger cells. Model simulations reveal that whilst
near-cell CO2 and pH conditions are close to those of the bulk water
for cells < 5 µm in diameter, they diverge as cell diameters
increase (Flynn et al., 2012). This is due to the size-dependent thickness of
the diffusive boundary layer, which determines the diffusional transport
across the boundary layer and to the cell surface (Wolf-Gladrow and
Riebesell, 1997; Flynn et al., 2012). It is suggested that larger cells may
be more able to cope with fCO2 variability as their carbon
acquisition is more geared towards handling low CO2 concentrations in
their diffusive boundary layer, e.g. by means of active carbon acquisition
and bicarbonate utilization (Wolf-Gladrow and Riebesell, 1997; Flynn et
al., 2012). Moreover, as the Baltic Sea experiences particularly large
seasonal fluctuations in pH and fCO2 (Jansson et al., 2013) due to
the low buffering capacity of the waters, phytoplankton here are expected to
have a higher degree of physiological plasticity. Our results agree with
previous mesocosm studies, which reported enhanced abundances of
picoeukaryotic phytoplankton (Brussaard et al., 2013; Davidson et al, 2016;
Schulz et al., 2017), particularly the prasinophyte Micromonas pusilla at higher fCO2 (Engel et al., 2008; Meakin and Wyman,
2011). Furthermore, Schaum et al. (2012) found that 16 ecotypes of
Ostreococcus tauri (another prasinophyte similar in size to Pico-I)
increased in growth rate by 1.4–1.7-fold at 1000 compared to
400 µatm fCO2. All ecotypes increased their
photosynthetic rates, and those with the most plasticity (those most able to
vary their photosynthetic rate in response to changes in fCO2) were
more likely to increase in frequency within the community. It is possible
that Pico-I cells are adapted to a highly variable carbonate system regime
and are able to increase their photosynthetic rate when additional
CO2 is available. This ability would allow them to out-compete other
phytoplankton (e.g. nanoeukaryotes in this study) in an environment when
nutrients are scarce.
The net growth rates and peak abundances of Pico-II were also positively
affected by fCO2. Gross growth rates were significantly higher at
high fCO2 on only two occasions (days 10 and 20) and were
accompanied by high total mortality rates. Pigment analysis suggests that
both Pico-I and Pico-II are chlorophytes (Paul et al., 2015) and as such may
share a common evolutionary history (Schulz et al., 2017); thus Pico-II may
be stimulated by fCO2 in a similar manner to Pico I. Chlorophytes
are found in high numbers at this site throughout the year (Kuosa, 1991),
suggesting the ecological relevance of Pico-I and Pico-II in this ecosystem.
In addition, Pico-II bloomed exactly when Pico-I declined, which may suggest
potential competitive exclusion.
Pico-III showed the most distinct and immediate response to CO2
addition. The significant reduction in gross growth rates observed during
Phase I suggests a direct negative effect of CO2 on the physiology of
these cells. For this group, the lower gross growth rates were matched by
lower total mortality rates with increased fCO2. Although the mean
cell size of Pico-III and Pico-II were comparable (2.9 and
2.5 µm, respectively), they showed opposing responses to
fCO2 enrichment (lower Pico-III abundances at high fCO2).
These differences may arise from taxonomic differences between the two
groups. Pico-III displayed relatively high phycoerythrin orange
autofluorescence, likely representing small-sized cryptophytes (Klaveness,
1989), although rod-shaped Synechococcus up to 2.9 µm in
length (isolated from this region; Haverkamp et al., 2009) or
Synechococcus microcolonies (often only two cells in the Baltic;
Motwani and Gorokhove, 2013) cannot be excluded. In agreement with Pico-III
response to CO2 enrichment, Hopkins et al. (2010) reported reduced
abundances of small cryptophytes under increased CO2 in a mesocosm
study in a Norwegian fjord near Bergen.
Lastly, the two nanoeukaryotic phytoplankton groups also displayed a negative
response to fCO2 enrichment; Nano-II was the least defined, most
likely due to a high taxonomic diversity in this group. Nano-I started to
display lower abundances at high fCO2 during Phase I (after
day 10), which was likely the result of greater differences between gross
growth and total mortality (compared to low fCO2). Alternatively,
enhanced nutrient competition due to increased abundances of SYN and Pico-I
(and later also Pico-II) at elevated fCO2 may also have contributed
to the dampened response of Nano-I in the high fCO2 mesocosms. The
overall decline in Nano-I during Phase II and the sustained low abundances
during Phase III may well have been the result of grazing by the increased
mesozooplankton abundances during Phase II (Lischka et al., 2017).
Microbial loop
The strong association of prokaryote abundance with algal biomass, which was
present throughout the experiment, suggests that the effect of CO2
was an indirect consequence of alterations in the availability of
phytoplankton carbon. Others have reported a tight coupling of autotrophic
and heterotrophic communities at this location, with an estimated 35 % of
the total net primary production being utilized directly by bacteria or
heterotrophic flagellates (Kuosa and Kivi, 1989), suggesting a highly
efficient microbial loop in this ecosystem. In addition to phytoplankton
exudation, viral lysis may also contribute to the dissolved organic carbon
pool (Wilhelm and Suttle, 1999; Brussaard et al., 2005; Lønborg et
al., 2013). We calculated that viral lysis of phytoplankton between days 9
and 13 resulted in the release of 1.3 and 13.1 ngCmL-1 for M1
and M3, respectively. Assuming a bacterial growth efficiency of 30 % and
cellular carbon conversion of 7 fgCcell-1 (Hornick et
al., 2017), we estimate that the organic carbon required to support bacterial
dynamics during this period (taking into account the net growth and loss
rates) was 2.9 and 11.5 ngCmL-1 in the low and high
fCO2 mesocosms M1 and M3, respectively. These results suggest that
viral lysis of phytoplankton was an important source of organic carbon for
the bacterial community. Our results are consistent with
bacterial–phytoplankton coupling during this eastern Baltic Sea mesocosm
study (Hornick et al., 2017) and agree with earlier work on summer carbon
flow in the northern Baltic Sea showing that prokaryotic growth was largely
supported by recycled carbon (Uitto et al., 1997). The average net growth
rates of the prokaryotes during the first period of increase in Phases 0
and I (0.2 d-1) were comparable to rates reported for this region
(Kuosa, 1991). In order to sustain the concomitant daily mortality (between
0.3 and 0.5 d-1) measured during our study, prokaryotic gross
growth rates must have been close to one doubling a day
(0.5–0.7 d-1). During Phase I, grazing was the dominant loss
factor of the prokaryotic community, although there was also evidence that
viral lysis was occurring. Bermúdez et al. (2016) reported the highest
biomass of protozoans around day 15. This was predominantly the heterotrophic
choanoflagellate Calliacantha natans, which selectively feeds on
particles < 1 µm in diameter (Marchant and Scott, 1993;
Hornick et al., 2017). Indeed, an earlier study in this area showed that
heterotrophic nanoflagellates were the dominant grazers of bacteria
responsible for the ingestion of approximately 53 % of bacterial
production compared to only 11 % being grazing by ciliates (Uitto et
al., 1997). During the first half of Phase II, grazing was reduced and likely
contributed to the steady increase in prokaryote abundances. Specifically, a
negative relationship between the abundances of HNA prokaryotes and
fCO2 was detected and corresponded to reduced bacterial production
and respiration at higher fCO2 (Hornick et al., 2017; Spilling et
al., 2016). Although CO2 enrichment may not directly affect bacterial
growth, a co-occurring global rise in temperature can increase enzyme
activities, affecting bacterial production and respiration rates (Piontek et
al., 2009; Wohlers et al., 2009; Wohlers-Zöllner et al., 2011). The
enhanced bacterial remineralization of organic matter may stimulate
autotrophic production by the small-sized phytoplankton (Riebesell et
al., 2009; Riebesell and Tortell, 2011; Engel et al., 2013), intensifying the
selection of small cell sizes.
Mean viral abundances were higher under CO2 enrichment towards the
end of Phase I and into Phase II, which is expected under conditions of
increased phytoplankton and prokaryote biomass. The estimated average viral
burst size obtained from this increase in total viral abundance and
concurrent decline in bacterial abundances was about 30, which is comparable
to published values (Parada et al., 2006; Wommack and Colwell, 2000). Viral
lysis rates of prokaryotes were measured until day 25 and indicated that
during days 18–25 an average 10–15 % of the total prokaryote population
was lysed per day. Moreover, the concurrent steady increase in viral
abundances during Phase III indicates that viral lysis of the prokaryotes
remained important. Thus, the combined impact of increased viral mortality
together with reduced production (Hornick et al., 2017) ultimately led to the
decline in prokaryote abundance (this study). Lysogeny did not appear to be
an important life strategy of viruses during our campaign. Direct effects of
higher fCO2 on viruses are not expected, as marine virus isolates
are quite stable (both in terms of particle decay and loss of infectivity)
over the range of pH in the present study (Danovaro et al., 2011; Mojica and
Brussaard, 2014). The few studies which have inferred viral lysis rates based
on changes in viral abundances show reduced abundances of algal viruses (e.g.
Emiliania huxleyi) under enhanced CO2 (Larsen et al., 2008),
while mesocosm results by Brussaard et al. (2013) indicated a stronger impact
of viruses on bacterial abundance dynamics with CO2 enrichment.