Introduction
Combustion of fossil fuels and change in land use have caused increasing
atmospheric concentrations of carbon dioxide (CO2). Ca. 25 % of the
anthropogenic CO2 is absorbed by the oceans, thereby decreasing surface
water pH, a process termed ocean acidification (Le Quéré et al.,
2009). Ocean acidification and its alterations of aquatic ecosystems have
received considerable attention during the past decade, but there are many
open questions, in particular related to consequences for plankton-mediated carbon fluxes.
Some studies on ocean acidification have reported increased carbon fixation
(Egge et al., 2009; Engel et al., 2013), bacterial production (BP; Grossart et
al., 2006), and bacterial degradation of polysaccharides (Piontek et al.,
2010) at enhanced CO2 levels, with potential consequences for carbon
fluxes within pelagic ecosystems and export to the deep ocean, i.e., the
biological carbon pump. Increasing carbon fixation in a high-CO2
environment can translate into an enhanced sequestration of carbon
(Riebesell et al., 2007), but this depends on numerous environmental factors,
including phytoplankton community composition, aggregate formation, and
nutrient availability. For example, if the community shifts towards smaller
cell sizes and/or enhanced cycling of organic matter carbon, export from the
upper water layers may decrease (Czerny et al., 2013a).
The effect of ocean acidification has mostly been studied in marine
ecosystems under high phytoplankton biomass. Brackish water has lower
buffering capacity than ocean water, and the pH fluctuates more. The limited
number of studies of ocean acidification in brackish water and indications
that ocean acidification effects are greatest under nutrient limitation (De
Kluijver et al., 2010) motivated this mesocosm study in the Baltic Sea
during low-nutrient summer months.
The Baltic Sea is functionally much like a large estuary, with a salinity
gradient ranging from approximately 20 in the southwest to < 3 in
the northernmost Bothnian Bay. It is an almost-landlocked body of water with
a large population in its vicinity (∼ 80 million). Human
activities (e.g., agriculture, shipping, and fishing) cause a number of
environmental problems such as eutrophication and pollution. As a coastal
sea projected to change rapidly due to interaction of direct and indirect
anthropogenic pressures, the Baltic Sea can be seen as a model ecosystem for
studying global change scenarios (Niiranen et al., 2013).
Most primary data from this experiment are published in several papers of
this special issue (Riebesell et al., 2015). The aim of the present paper is
to provide an overarching synthesis of all information related to carbon
standing stocks and fluxes. This enabled us to calculate carbon budgets in
relation to different CO2 levels.
Materials and methods
Experimental setup
Six Kiel Off-Shore Mesocosms for Ocean Simulations (KOSMOS; with a
volume of ca. 55 m3) were moored at Storfjärden, on the south west
coast of Finland (59∘51.5′ N; 23∘15.5′ E) on 12 June
2012 (nine KOSMOS units were originally deployed, but three were lost due to
leaks). A more detailed description of the setup can be found in Paul et
al. (2015). The mesocosms extended from the surface down to 19 m depth and
had a conical bottom end, which enabled quantitative collection of the
settling material. Different CO2 levels in the bags were achieved by
adding filtered (50 µm), CO2-saturated seawater. The
CO2-enriched water was evenly distributed over the upper 17 m of the water
columns and added in four consecutive time steps (t0–t3).
Two controls and four treatments were used, and for the controls, filtered
seawater (without additional CO2 enrichment) was added. The CO2
fugacity gradient after all additions ranged from ambient (average
throughout the experiment: ∼ 370 µatm
fCO2) in the two control mesocosms (M1 and M5) up to
∼ 1200 µatm fCO2 in the highest treatment
(M8). We used the average fCO2 throughout this experiment
(t1–t43) to denote the different treatments: 365
(M1), 368 (M5), 497 (M7), 821 (M6), 1007 (M3), and 1231 (M8) µatm fCO2. On t15, additional CO2-saturated
seawater was added to the upper 7 m in the same manner as the initial
enrichment, to counteract outgassing of CO2.
We sampled the mesocosms every morning, but some variables were determined
only every second day. Depth-integrated water samples (0–17 m) were taken
by using integrating water samplers (IWS, HYDRO-BIOS, Kiel). The water was
collected into plastic carboys (10 L) and transferred to the laboratory for
sub-sampling and subsequent determination of carbon stocks.
Primary variables
For more detailed descriptions of the primary variables and the different
methods used during this CO2 mesocosm campaign, we refer to other papers
in this joint volume: i.e., total particulate carbon (TPC), dissolved organic
carbon (DOC), and dissolved inorganic carbon (DIC) are described by Paul et
al. (2015); micro- and nanophytoplankton enumeration by Bermúdez et
al. (2016); picophytoplankton, heterotrophic prokaryotes, and viruses by
Crawfurd et al. (2016); zooplankton community by Lischka et al. (2015);
primary production and respiration by Spilling et al. (2016a); BP
by Hornick et al. (2016); and sedimentation by Boxhammer et al. (2016) and Paul et al. (2015).
Briefly, samples for TPC (500 mL) were GF/F-filtered and determined using an
elemental analyzer (EuroAE). DOC was measured using the high-temperature
combustion method (Shimadzu TOC–VCPN) following Badr et al. (2003). DIC
was determined by infrared absorption (LI-COR LI-7000 on an AIRICA system).
The DIC concentrations were converted from µmol kg-1 to µmol L-1 using the average seawater density of 1.0038 kg L-1
throughout the experiment. Settling particles were quantitatively collected
every other day from sediment traps at the bottom of the mesocosm units, and
the TPC was determined from the processed samples (Boxhammer et al., 2016) as
described above.
Mesozooplankton was collected by net hauls (100 µm mesh size), fixed
(ethanol), and counted in a stereomicroscope. Zooplankton carbon biomass (CB)
was calculated using the displacement volume (DV) and the equation of Wiebe (1988): (log DV + 1.429) / 0.82 = log CB. Micro- and nanoplankton (zoo- and
phytoplankton) CB was determined from microscopic counts of fixed (acidic
Lugol's iodine solution) samples, and the cellular bio-volumes were
determined according to Olenina et al. (2006) and converted to particulate organic carbon (POC) by the
equations provided by Menden-Deuer and Lessard (2000).
Picophytoplankton were counted using flow cytometry and converted to CB by
size fractionation (Veldhuis and Kraay, 2004) and cellular carbon conversion
factors (0.2 pg C µ m-3; Waterbury et al., 1986). Prokaryotes
and viruses were determined according to Marie et al. (1999) and Brussaard (2004), respectively. All heterotrophic prokaryotes, hereafter termed
bacteria, and viruses were converted to CB assuming 12.5 fg C cell-1
(Heinänen and Kuparinen, 1991) and 0.055 fg C virus-1 (Steward et
al., 2007), respectively.
The respiration rate was calculated from the difference between the O2
concentration (measured with a Fibox 3, PreSens) before and after a 48 h
incubation period in a dark climate-controlled room set to the average
temperature observed in the mesocosms.
BP was determined by 14C-leucine
(14C-Leu) incorporation (Simon and Azam, 1989) according to Grossart et
al. (2006). The amount of incorporated 14C-Leu was converted into BP
by using an intracellular isotope dilution factor of 2. A conversion factor
of 0.86 was used to convert the produced protein into carbon (Simon and
Azam, 1989).
Net primary production (NPP) was measured using radio-labeled
NaH14CO3 (Steeman-Nielsen, 1952). Samples were incubated for 24 h
in duplicate 8 mL vials moored on small incubation platforms at 2, 4, 6, 8,
and 10 m depth next to the mesocosms. The areal primary production was
calculated based on a simple linear model of the production measurements from
the different depths (Spilling et al., 2016a).
Gas exchange
In order to calculate the CO2 gas exchange with the atmosphere
(CO2flux), we used N2O as a tracer gas, added to
mesocosm M5 and M8 (control and high CO2 treatment) according to Czerny
et al. (2013b). The N2O concentration was determined every second day
using gas chromatography. Using the N2O measurements, the fluxes across
the water surface (FN2O) were calculated according to
FN2O=It1-It2/(A×Δt),
where It1 andIt2 are the bulk N2O concentration at time
t1 and t2, respectively; A is the surface area; and Δt is the time
difference between t1 and t2.
The flux velocity was then calculated by
KN2O=FN2O/CN2Ow-CN2Oaw,
where CN2Ow is the bulk N2O concentration in the water at a given
point in time and CN2Oaw is the equilibrium concentration for N2O
(Weiss and Price, 1980).
The flux velocity for CO2 was calculated from the flux velocity of
N2O according to
kCO2=kN2O/ScCO2/ScN2O0.5,
where ScCO2 and ScN2O are the Schmidt numbers for CO2 and
N2O, respectively. The CO2flux across the water surface was
calculated according to
FCO2=kCO2CCO2w-CCO2aw,
where CCO2w is the water concentration of CO2 and CCO2aw is
the equilibrium concentration of CO2. CO2 is preferentially taken
up by phytoplankton at the surface, where also the atmospheric exchange
takes place. For this reason, we used the calculated CO2 concentration
(based on the integrated CO2 concentration and pH in the surface) from
the upper 5 m as the input for Eq. (5).
In contrast to N2O, the CO2 flux can be chemically enhanced by
hydration reactions of CO2 with hydroxide ions and water molecules in
the boundary layer (Wanninkhof and Knox, 1996). Using the method outlined in
Czerny et al. (2013b), we found an enhancement of up to 12 % on warm
days, and this was included into our flux calculations.
Data treatment
The primary data generated in this study comprise carbon standing stock
measurements of TPC, DOC, and DIC, as well as carbon estimates of meso- and
microzooplankton, micro-, nano- and picophytoplankton, bacteria, and
viruses. Flux measurements of atmospheric CO2 exchange and sedimentation
of TPC as well as the biological rates of net primary production
(NPP14C),
BP, and total respiration (TR) enabled us to make carbon budget.
Based on the primary variables (chlorophyll a (Chl a) and temperature), the
experiment where divided into three distinct phases: phase I:
t0–t16; phase II: t17–t30; and phase III: t31–t43,
where, e.g., Chl a concentration was
relatively high during phase I, decreased during phase II, and remained low
during phase III (Paul et al., 2015). Measurements of pools and rates were
averaged for the two first sampling points of each experimental phase (n=2) and were normalized to square meters (m2) knowing the total depth (17 m, excluding
the sedimentation funnel) of the mesocosms. For phase III we used the
average of the last two measurements as the end point (n=2).
For fluxes and biological rates we used the average for the whole periods
normalized to days (day-1). The same was done for rates of change
(ΔTPC, ΔDOC, and ΔDIC), which accounted for the
difference between the start and end of each phase for all carbon pools
(TPCpool, DOCpool, DICpool). All error estimates were
calculated as standard error (SE), and this was calculated using all
measurements within each phase (e.g., calculating the ΔTPC SE using
the difference between each TPC measurement). The three different phases of
the experiments were of different length, and each variable had a slightly
different sampling regime (every 1–3 days, with some measurements missing due
to technical problems). The exact sample number (n) for each SE is presented
in the Table legends 1–3. The SE for estimated rates was calculated from
the square root of the sum of variance for all the variables (Eq. 5–10
below). The primary papers mentioned above (Sect. 2.2.) present detailed
statistical analyses, and we only refer to those here.
NPP was measured directly, and we additionally estimated the net community
production (NCP). This was done in two different ways, from the organic
(NCPo) and the inorganic (NCPi)
fractions of carbon. NCPo was calculated from changes in the
organic fraction plus the exported TPC (EXPTPC) according to
NCPo=EXPTPC+ΔTPC+ΔDOC.
Direct measurements using 14C isotope incubations should in principal
provide a higher value than summing up the difference in overall carbon
balance (our NCPo), as the latter would incorporate total respiration
and not only autotrophic respiration. NCPi was calculated through
changes in the dissolved inorganic carbon pool, corrected for CO2 gas
exchange with the atmosphere (CO2flux) according to
NCPi=CO2flux-ΔDIC.
In order to close the budget, we estimated GPP and
DOC production (DOCprod). GPP is defined as the photosynthetically
fixed carbon without any loss processes (i.e., NPP + autotrophic
respiration). GPP can be estimated based on changes in organic (GPPo)
or inorganic (GPPi) carbon pools, and we used these two different
approaches providing a GPP range:
GPPo=NCPo+TR,GPPi=TR+CO2flux-ΔDIC.
During phase III, TR was not measured, and we estimated TR based on the
ratios between NCPo and BP to TR during phase II. The minimum
production of DOC (DOCminp) in the system was calculated assuming
bacterial carbon uptake was taken from the DOC pool according to
DOCminp=ΔDOC+BP.
However, this could underestimate DOCprod as a fraction of bacterial
DOC uptake is respired. Without direct measurement of (heterotrophic
prokaryote) bacterial respiration (BR), we estimated BR from TR. The share
of active bacteria contributing to bacterial production is typically in the
range of 10–30 % of the total bacterial community (Lignell et al., 2013).
We used the fraction of bacterial biomass (BB) of total biomass (TB) as the
maximum limit of BR (BR ≤ BB / TB) and hence calculated max DOC
production (DOCmaxp) according to
DOCmaxp=ΔDOC+BP+(BB×TR/TB).
We assumed that carbon synthesized by bacteria was added to the TPC pool.
There are a number of uncertainties in these calculations, but this
budgeting exercise provides an order-of-magnitude estimate of the flow of
carbon within the system and enables comparison between the treatments. The
average of the two controls (M1 and M5) and the two highest CO2 treatments
(M3 and M8) were used to illustrate CO2 effects.
The different fractions of carbon in the control mesocosms (M1 and
M5) at the start of phases I (t0), II (t17), and III
(t31), in mmol C m-2 ± SE (n=2). The differences
between the controls and elevated CO2 concentration are discussed in the
text. The size of the boxes indicates the relative size of the carbon
standing stocks.
Results and discussion
Change in plankton community, from large to small forms over
time
The overall size structure of the plankton community decreased over the
course of the experiment. Figure 1 illustrates the carbon content in
different plankton groups in the control mesocosms. During phase I, the
phytoplankton abundances increased at first in all treatments before starting
to decrease at the end of phase I (Paul et al., 2015). At the start of
phase II (t17), the phytoplankton biomass
was higher than at the start of the experiment
(∼ 130 mmol C m-2 in the controls) but decreased throughout
phases II and III. The fraction of picophytoplankton increased in all
treatments, but some groups of picophytoplankton increased more in the high
CO2 treatments (Crawfurd et al., 2016).
Nitrogen was the limiting nutrient throughout the entire experiment (Paul et
al., 2015), and primary producers are generally N-limited in the main
sub-basins of the Baltic Sea (Tamminen and Andersen, 2007). The
surface-to-volume ratio increases with decreasing cell size, and consequently small
cells have higher nutrient affinity and are better competitors for scarce
nutrient sources than large cells (Reynolds, 2006). The prevailing
N limitation was likely the reason for the decreasing size structure of the
phytoplankton community.
Micro- and mesozooplankton standing stock was approximately half of the
phytoplankton biomass initially but decreased rapidly in the control
treatments during phase I (Fig. 1). In the CO2-enriched
treatments, the zooplankton biomass
also decreased but not to the same extent as in the control treatments
(Spilling et al., 2016a). Overall, smaller species benefitted from the extra
CO2 addition, but there was no significant negative effect of high
CO2 on the mesozooplankton community (Lischka et al., 2015).
Bacterial biomass was the main fraction of the plankton carbon throughout
the experiment. The bacterial numbers largely followed the phytoplankton
biomass with an initial increase then decrease during phase I, increase
during phase II, and slight decrease during phase III (Crawfurd et al.,
2016). The bacterial community was controlled by mineral nutrient
limitation, bacterial grazing, and viral lysis (Crawfurd et al., 2016), and
bacterial growth is typically limited by N or a combination of N and C in
the study area (Lignell et al., 2008, 2013).
The bacterial carbon pool was higher than the measured TPC. Part of the
bacteria must have passed the GF/F filters
(0.7 µm), and assuming pico- to mesoplankton was part of the TPC,
> 50 % of the bacterial carbon was not contributing to the
measured TPC. The conversion factor from cells to carbon is positively
correlated to cell size, and there is consequently uncertainty related to the
absolute carbon content of the bacterial pool (we used a constant conversion
factor). However, bacteria are known to be the dominating carbon share in the
Baltic Sea during the N-limited summer months (Lignell et al., 2013), and
their relative dominance is in line with this.
The standing stock of total particulate carbon (TPCpool),
dissolved organic carbon (DOCpool), and dissolved inorganic carbon
(DICpool) at the start of phase I in mmol C m-2 ± SE
(n=2). The DOCpool was missing some initial measurements and is
the average for all mesocosms assuming that the DOC concentration was similar
at the onset of the experiment. The net changes in TPC (ΔTPC), DOC
(ΔDOC), and DIC (ΔDIC) are average changes in the standing
stocks during phase I in mmol C m-2 day-1± SE (n=8).
Flux measurements of atmospheric gas exchange (CO2flux) and
exported carbon (EXPTPC) plus biological rates – total respiration
(TR), bacterial production (BP), and net primary production (NPP14C) – and net
community production estimated based on organic carbon pools'
(NCPo) net primary production are all averages for the whole
of phase I in mmol C m-2 day-1± SE (n=13, 9, 16, 7, and 11
for CO2flux, EXPTCP, TR, BP, and NPP14C,
respectively). SE for NCPo was calculated from the square root of the sum of
variance of the three variables used in Eq. (6). The NCPo was
calculated from the net change in carbon pools plus carbon export, whereas
NPP14C was measured carbon fixation using radio-labeled 14C
over a 24 h incubation period in situ. TR was measured as O2
consumption, and for comparison with carbon fixation we used a respiratory
quotient (RQ) of 1. CO2flux was only calculated for the period
after full addition of CO2 (t4–t16). A total budget
of carbon fluxes for ambient and high CO2 treatments is presented in
Fig. 5.
Phase I (t0–t16)
CO2 treatment (µatm fCO2)
365
368
497
821
1007
1231
Mesocosm number
M1
M5
M7
M6
M3
M8
TPCpool
417 ± 38
425 ± 39
472 ± 48
458 ± 38
431 ± 48
446 ± 57
DOCpool
7172 ± 87
7172 ± 87
7172 ± 87
7172 ± 87
7172 ± 87
7172 ± 87
DICpool
25 158 ± 9
25 182 ± 10
25 628 ± 8
26 295 ± 22
26 637 ± 36
26 953 ± 48
ΔTPC
-4.6 ± 15
-5.2 ± 13
-8.3 ± 13
-8.2 ± 17
-7.0 ± 13
-6.3 ± 20
ΔDOC
15.5 ± 58
18.3 ± 30
18.5 ± 33
25.0 ± 36
18.5 ± 73
18.1 ± 63
ΔDIC
5.5 ± 5.2
6.9 ± 9.2
-6.1 ± 11
-24 ± 14
-32 ± 20
-49 ± 42
CO2flux
4.4 ± 0.2
4.8 ± 0.3
-0.8 ± 0.5
-11 ± 1.0
-17 ± 1.4
-23 ± 2.0
EXPTPC
6.6 ± 0.10
5.6 ± 0.04
5.4 ± 0.07
6.0 ± 0.07
5.6 ± 0.06
6.0 ± 0.05
TR
107 ± 9
82 ± 7
81 ± 6
80 ± 8
75 ± 8
74 ± 8
BP
27 ± 8
41 ± 6
43 ± 8
41 ± 4
36 ± 5
46 ± 9
NPP14C
4.8 ± 0.8
11.4 ± 2.1
14.9 ± 3.6
12.3 ± 2.3
11.3 ± 2.4
14.5 ± 2.7
NCPo
17.4 ± 33
18.7 ± 20
15.6 ± 30
22.8 ± 28
17.1 ± 25
17.8 ± 28
The standing stock of total particulate carbon (TPCpool),
dissolved organic carbon (DOCpool), and dissolved inorganic carbon
(DICpool) at the start of phase II in mmol C m-2± SE (n=2). The net changes in TPC (ΔTPC), DOC (ΔDOC), and DIC
(ΔDIC) are average changes in the standing stocks during phase II in
mmol C m-2 day-1± SE (n=7). Flux measurements of
atmospheric gas exchange (CO2flux) and exported carbon
(EXPTPC) plus biological rates – TR, BP, and measured (NPP14C) – and net community production
estimated based on organic carbon pools (NCPo) are all averages
for phase II in mmol C m-2 day-1± SE (n=8, 7, 14, 5, and
14 for CO2flux, EXPTCP, TR, BP, and
NPP14C, respectively). See Table 1 legend for further details.
Phase II (t17–t30)
CO2 treatment (µatm fCO2)
365
368
497
821
1007
1231
Mesocosm number
M1
M5
M7
M6
M3
M8
TPCpool
339 ± 14
337 ± 20
331 ± 22
318 ± 9
312 ± 12
339 ± 23
DOCpool
7435 ± 38
7483 ± 37
7487 ± 43
7597 ± 37
7487 ± 61
7479 ± 37
DICpool
25 247 ± 34
25 269 ± 34
25 639 ± 8
26 177 ± 25
26 413 ± 28
26 757 ± 45
ΔTPC
-2.4 ± 5
-2.3 ± 8
-1.6 ± 14
0.3 ± 6
2.8 ± 4
3.2 ± 8
ΔDOC
-0.6 ± 39
2.4 ± 30
3.6 ± 40
8.4 ± 31
11.3 ± 58
9.1 ± 36
ΔDIC
22.4 ± 12
17.6 ± 8.1
-0.4 ± 4.5
-10.5 ± 16
-14.2 ± 10
-23.1 ± 13
CO2flux
1.7 ± 0.3
1.2 ± 0.3
-2.6 ± 0.3
-10 ± 0.5
-14 ± 0.6
-19 ± 1.0
EXPTPC
3.3 ± 0.08
2.6 ± 0.06
2.5 ± 0.08
2.6 ± 0.06
2.8 ± 0.07
2.9 ± 0.06
TR
140 ± 7
127 ± 5
103 ± 3
103 ± 4
101 ± 5
86 ± 4
BP
66 ± 17
57 ± 8
61 ± 7
57 ± 7
43 ± 6
47 ± 6
NPP14C
3.8 ± 0.6
11.2 ± 1.9
10.8 ± 2.0
14.3 ± 2.8
10.4 ± 2.1
12.0 ± 2.5
NCPo
0.3 ± 20
2.7 ± 15
4.5 ± 22
11.4 ± 16
16.9 ± 19
15.2 ± 16
Although there is some uncertainty in the carbon estimate (Jover et al.,
2014), viruses make up (due to their numerical dominance) a significant
fraction of the pelagic carbon pool. Of the different plankton fractions the
virioplankton have been the least studied, but their role in the pelagic
ecosystem is ecologically important (Suttle, 2007; Brussaard et al., 2008;
Mojica et al., 2016). Viral lysis rates were equivalent to the grazing rates
for phytoplankton and for bacteria in the current study (Crawfurd et al.,
2016). As mortality agents, viruses are key
drivers of the regenerative microbial food web (Suttle, 2007; Brussaard et
al., 2008). Overall, the structure of the plankton community reflected the
nutrient status of the system: the increasing N limitation favored
development of smaller cells and increased dependence of the primary
producers on regenerated nutrients.
The standing stock of total particulate carbon (TPCpool),
dissolved organic carbon (DOCpool), and dissolved inorganic carbon
(DICpool) at the start of phase III in mmol C m-2± SE
(n=2). The net change in TPC (ΔTPC), DOC (ΔDOC), and DIC
(ΔDIC) are average changes in the standing stocks during phase III in
mmol C m-2 day-1± SE (n=6), using the average of the
last two sampling days as the end point. Flux measurements of atmospheric gas
exchange (CO2flux) and exported carbon (EXPTPC) plus
biological rates – BP and net community production
estimated based on organic carbon pools (NCPo) – are all
averages for phase III in mmol C m-2 day-1± SE (n=7, 6, and 7 for
CO2flux, EXPTCP, and BP, respectively). See Table 1
legend for further details. During phase III we did not have direct
measurements of net primary production (NPP14C) or TR.
Phase III (t31–t43)
CO2 treatment (µatm fCO2)
365
368
497
821
1007
1231
Mesocosm number
M1
M5
M7
M6
M3
M8
TPCpool
306 ± 12
304 ± 20
309 ± 20
323 ± 2
351 ± 13
384 ± 16
DOCpool
7426 ± 16
7469 ± 20
7485 ± 92
7553 ± 20
7593 ± 30
7562 ± 38
DICpool
25 557 ± 9
25 545 ± 10
25 648 ± 13
26 030 ± 19
26 197 ± 31
26 371 ± 32
ΔTPC
-3.8 ± 10
0.3 ± 7
3.3 ± 14
3.3 ± 10
-1.4 ± 8
-4.8 ± 8
ΔDOC
9.8 ± 5
8.8 ± 7
8.9 ± 43
9.2 ± 10
5.7 ± 17
16.3 ± 20
ΔDIC
4.3 ± 3.9
5.5 ± 8.7
6.2 ± 11
-12.3 ± 7.2
-16.3 ± 14
-20.1 ± 14
CO2flux
-0.3 ± 0.7
-0.8 ± 0.6
-3.0 ± 0.5
-7.3 ± 0.5
-9.4 ± 0.6
-13 ± 0.6
EXPTPC
1.5 ± 0.07
1.4 ± 0.05
0.4 ± 0.07
1.9 ± 0.05
1.6 ± 0.04
1.7 ± 0.05
BP
31 ± 6.8
37 ± 1.4
38 ± 1.4
27 ± 2.1
17 ± 3.8
28 ± 2.3
NCPo
7.6 ± 16
10.5 ± 13
12.7 ± 20
14.3 ± 13
6.0 ± 10
13.2 ± 14
The DIC pool and atmospheric exchange of CO2
The DIC pool was the largest carbon pool: three–four-fold higher than the DOC pool
and roughly 60-fold higher than the TPC pool (Tables 1–3). After the
addition of CO2, the DIC pool was ∼ 7 % higher in the
highest CO2 treatment than in the control mesocosms (Table 1). The
gas exchange with the atmosphere was the most apparent flux affected by
CO2 addition (Tables 1–3). Seawater in the mesocosms with added
CO2 was supersaturated; hence CO2 outgassed throughout the
experiment. The control mesocosms were initially undersaturated; hence
ingassing occurred during phases I and II (Fig. 2). In the first part of
phase III, the control mesocosms reached equilibrium with the atmospheric
fCO2 (Fig. 2). The gas exchange had direct effects on the DIC
concentration in the mesocosms (Fig. 3). From the measured gas exchange and
change in DIC it is possible to calculate the biologically mediated carbon
flux. In the mesocosms with ambient CO2 concentration, the flux
measurements indicated net heterotrophy throughout the experiment. The
opposite pattern, net autotrophy, was indicated in the two mesocosms with
the highest CO2 addition (Fig. 3; see also Sect. 3.7.).
The calculated exchange of CO2 between the mesocosms and the
atmosphere. Positive values indicate net influx (ingassing), and negative
values net outflux (outgassing) from the mesocosms. The flux was based on
measurements of N2O as a tracer gas and calculated using Eqs. (2)–(5).
The DOC pool, DOC production, and remineralization
The DOC pool increased throughout the experiment in all mesocosm bags, albeit
more in the treatments with elevated CO2 concentration. The initial DOC
standing stock in all treatments was approximately 7200 mmol C m-2.
At the end of the experiment, the DOC pool was ∼ 2 % higher in the
two highest CO2 treatments than in the controls (Fig. 4), and there
is statistical support for this difference between CO2 treatments
(phase III, p=0.05) (Paul et al., 2015). Interestingly, the data do not
point to a substantially higher release of DOC at high CO2 (Figs. 4 and
5). The bacterial production was notably lower during phase II in
the high CO2 treatments (Hornick et al., 2016) and of similar magnitude
to the rate of change in DOC pool (Tables 2 and 3), indicating reduced
bacterial uptake and remineralization of DOC. The combined results suggest
that the increase in the DOC pool at high CO2 was related to reduced DOC
loss (uptake by bacteria), rather than increased release of DOC by the
plankton community, at elevated CO2 concentration.
Change in DIC pool and the atmospheric
CO2 exchange (Fig. 2). All values are average
mmol C m-2 day-1 ± SE for the three different phases (n=13, 8, and 7 for phases I, II, and III, respectively) in the control mesocosms
(M1 + M5) and high-CO2 mesocosms (M3 + M8). Solid black arrows
indicate measured fluxes. Dashed grey arrows are estimated by closing the
budget and indicate the net community production based on inorganic carbon
budget (NCPi), which equals biological uptake or release of
CO2.
Standing stocks of total particulate carbon (TPC) and dissolved
organic carbon (DOC) at the last day of the experiment (t43), plus the sum
of exported TPC throughout the experiment; all values are in
mmol C m-2 ± SE (n=2). The values are averages of the two
controls (M1 and M5) and the two highest CO2 treatments (M3 and M8). Red
circles indicate statistically significant higher standing stocks in the high
CO2 treatments (further details in text). The size of the boxes
indicates the relative size of the carbon standing stocks and export.
The Baltic Sea is affected by large inflow of freshwater containing high
concentrations of refractory DOC, such as humic substances, and the
concentration in the Gulf of Finland is typically 400–500 µmol C L-1
(Hoikkala et al., 2015). The large pool of DOC and turnover times of
∼ 200 days (Tables 1–3) are most likely a reflection of the
relatively low fraction of labile DOC, but bacterial limitation of mineral
nutrients can also increase turnover times (Thingstad et al., 1997).
The DOC pool has been demonstrated to aggregate into transparent
exopolymeric particles (TEPs) under certain circumstances, which can increase
sedimentation at high CO2 levels (Riebesell et al., 2007). We did not
have any direct measurements of TEP, but any CO2 effect on its
formation is highly dependent on the plankton community and its
physiological status (MacGilchrist et al., 2014). No observed effect of
CO2 treatment on carbon export suggests that we did not have a
community where the TEP production was any different between the treatments
used.
The TPC pool and export of carbon
There was a positive effect of elevated CO2 on TPC relative to the
controls. At the start of the experiment, the measured TPC concentration in
the enclosed water columns was 400–500 mmol C m-2 (Table 1). The TPC
pool decreased over time, albeit less in the high CO2 treatment, and at the
end of the experiment the standing stock of TPC was ∼ 6 %
higher (phase III, p=0.01; Paul et al., 2015) in the high CO2
treatment (Fig. 4).
The export of TPC was not dependent on the CO2 concentration but varied
temporally. The largest flux of TPC out of the mesocosms occurred during
phase I with ∼ 6 mmol C m-2 day-1. It decreased to
∼ 3 mmol C m-2 day-1 during phase II and was
∼ 2 mmol C m-2 day-1 during phase III (Tables 1–3). The
exported carbon as the percent of average TPC standing stock similarly decreased
from ∼ 1.3 % during phase I to 0.3–0.5 % during phase III.
The initial increase in the autotrophic biomass was likely the reason for
relatively more of the carbon settling in the mesocosms in the beginning of
the experiment, whereas the decreasing carbon export was most likely caused
by the shift towards a plankton community depending on recycled nitrogen.
The relatively high initial sedimentation reduced the overall suspended TPC and also the average plankton size in
the community.
Average carbon standing stocks and flow in the control mesocosms
(M1 + M5) and high-CO2 mesocosms (M3 + M8) during the three
phases of the experiment. All carbon stocks (squares) – dissolved inorganic
carbon (DIC), total particulate carbon (TPC), and dissolved organic carbon
(DOC) – are averages from the start of the period in
mmol C m-2 ± SE (n=2). Fluxes (arrows) and net changes
(Δ) are averages for the whole phase in
mmol C m-2 day-1 ± SE (n presented in Table legends
1–3). Solid black arrows indicate measured fluxes (Tables 1–3): TR, BP, and exported TPC (EXPTPC).
Dashed grey arrows are estimated by closing the budget: gross primary production
(GPP) using Eqs. (7) and (8), and DOC production (DOCprod)
using Eqs. (9) and (10). Bacterial respiration was calculated using Eq. (10)
and is a share of TR (indicated by the parenthesis). Aggregation was
assumed to equal BP. Red circles indicate statistically significant higher values (p < 0.05, tests
presented in the primary papers described in Sect. 2.2.). The size of the
boxes indicates the relative size of the carbon standing stocks.
Biological rates: respiration
TR was always lower in the CO2-enriched treatments
(Tables 1–2). The average TR was
83 mmol C m-2 day-1 during phase I, and initially without any
detectable treatment effect. The respiration rate started to be lower in the
high CO2 treatments than in the controls in the beginning of phase II.
At the end of phase II there was a significant difference (p=0.02;
Spilling et al., 2016a) between the treatments (Table 2) and 40 % lower
respiration rate in the highest CO2 treatment than in the controls
(Spilling et al., 2016a).
Cytosol pH is close to neutral in most organisms, and reduced energetic cost
for internal pH regulation (e.g., transport of H+) and at lower external
pH levels could be one factor reducing respiration (Smith and Raven, 1979).
Hopkinson et al. (2010) found indirect evidence of decreased respiration
and also proposed that increased CO2 concentration (i.e., decreased pH)
reduced metabolic cost of remaining intracellular homeostasis. Mitochondrial
respiration in plant foliage decreases in high-CO2 environments,
possibly affected by respiratory enzymes or other metabolic processes
(Amthor, 1991; Puhe and Ulrich, 2012). Most inorganic carbon in water is in
the form of bicarbonate (HCO3-) at relevant pH, and many aquatic
autotrophs have developed carbon-concentrating mechanisms (CCMs) (e.g., Singh
et al., 2014) that could reduce the cost of growth (Raven, 1991). There are
some studies that have pointed to savings of metabolic energy due to
downregulation of carbon-concentrating mechanisms (Hopkinson et al., 2010)
or overall photosynthetic apparatus (Sobrino et al., 2014) in phytoplankton
at high CO2 concentrations. Yet other studies of the total plankton
community have pointed to no effect or increased respiration at elevated
CO2 concentration (Li and Gao, 2012; Tanaka et al., 2013), and the
metabolic changes behind reduced respiration remain an open question.
Membrane transport of H+ is sensitive to changes in external pH, but the
physiological impacts of increasing H+ need further study to better
address effects of ocean acidification (Taylor et al., 2012). An important
aspect is also to consider the microenvironment surrounding plankton;
exchange of nutrients and gases takes place through the boundary layer,
which might have very different pH properties than bulk water measurements
(Flynn et al., 2012).
Biological rates: bacterial production
BP became lower in the high CO2 treatment in the
latter part of the experiment. During phase I, BP ranged from 27 to 46 mmol C m-2 day-1 (Table 1). The difference in BP between treatments
became apparent in phases II and III of the experiment. The average BP was
18 and 24 % higher in the controls than in the highest CO2
treatments during phases II and III, respectively (Tables 2 and 3).
The lower bacterial production accounted for ∼ 40 % of the
reduced respiration during phase II, and the reduced respiration described
above could at least partly be explained by the lower bacterial activity.
This raises an interesting question: what was the mechanism behind the
reduced bacterial production/respiration in the high CO2 treatment?
There are examples of decreased bacterial production (Motegi et al., 2013) and
respiration (Teira et al., 2012) at elevated CO2 concentration.
However, most previous studies have reported no change (Allgaier et al.,
2008) or a higher bacterial production at elevated CO2 concentration
(Grossart et al., 2006; Piontek et al., 2010; Endres et al., 2014). The
latter was also supported by the recent study of Bunse et al. (2016),
describing upregulation of bacterial genes related to respiration, membrane
transport, and protein metabolism at elevated CO2 concentration; however,
this effect was not evident when inorganic nutrients had been added (high
Chl a treatment).
In this study, the lower bacterial activity in the high
CO2 treatments could either be due to limitation and/or inhibition of
bacterial growth or driven by difference in loss processes. Bacterial
grazing and viral lysis were higher in the high CO2 treatments during
periods of the experiment (Crawfurd et al., 2016) and would at least partly
be the reason for the reduced bacterial production at high CO2
concentration.
N limitation increased during the experiment (Paul et al., 2015), and mineral
nutrient limitation of bacteria can lead to accumulation of DOC, i.e.,
reduced bacterial uptake (Thingstad et al., 1997), similar to our results.
Bacterial N limitation is common in the area during summer (Lignell et al.,
2013), however, this N limitation was not apparently different in the
controls (Paul et al., 2015), and CO2 did not affect N fixation (Paul et
al., 2016a). In a scenario where the competition for N is fierce, the balance
between bacteria and similar sized picophytoplankton could be tilted in favor
of phytoplankton if they gain an advantage by having easier access to carbon,
i.e., CO2 (Hornick et al., 2016). We have not found evidence in the
literature that bacterial production will be suppressed in the observed pH
range inside the mesocosms, varying from approximately pH 8.1 in the control
to pH 7.6 in the highest fCO2 treatment (Paul et al., 2015), although
enzyme activity seems to be affected even by moderate pH changes. For
example, some studies report on an increase in protein-degrading enzyme
leucine aminopeptidase activities at reduced pH (Grossart et al., 2006;
Piontek et al., 2010; Endres et al., 2014), whereas others indicate a reduced
activity of this enzyme (Yamada and Suzumura, 2010). A range of other factors
affect this enzyme, for example the nitrogen source and salinity
(Stepanauskas et al., 1999), and any potential interaction effects with
decreasing pH are not yet resolved. Any pH-induced changes in bacterial
enzymatic activity could potentially affect bacterial production.
Biological rates: primary production
There was an effect of CO2 concentration on the net community
production based on the organic carbon fraction (NCPo). NCPo was
higher during phase I than during the rest of the experiments and during
this initial phase without any apparent CO2 effect. There was no
consistent difference between CO2 treatments for NPP14C
(p > 0.1), but NCPo increased with increasing CO2
enrichment during phase II (phase II; linear regression p=0.003; R2=0.91). This was caused by the different development in the TPC and DOC
pools. The pattern of GPP was similar to
NCPo during phases I and II. During phase III there was no respiration
or NPP14C measurements, and the estimated GPP is more uncertain. The
NCPo and GPP indicated a smaller difference between treatments during
phase III than phase II.
The measures of NPP14C and NCPo were of a similar
magnitude (Tables 1–3). During phase I,
NPP14C < NCPo (Table 1); this relationship
reversed for most treatments during phase II, with the exception of the
highest CO2 levels (Table 2). The difference between NPP14C
and NCPo suggests that observed reduction in respiration at
elevated CO2 could be mainly heterotrophic respiration. However, in
terms of the NPP14C < NCPo, the
uncertainty seems to be higher than the potential signal of heterotrophic
respiration. This would also indicate that the NPP14C during
phase I have been underestimated, in particular for the control mesocosm M1.
During phase II, the NPP14C was higher than NCPo,
except for the two highest CO2 treatments, more in line with our
assumption of NPP14C > NCPo. The
systematic offset in NPP14C during phase I could be due to
changed parameterization during incubation in small volumes (8 mL; Spilling
et al., 2016a), for example increased loss due to grazing.
The results of the DIC pool and atmospheric exchange of CO2 provide
another way of estimating the net community production based on inorganic
carbon (NCPi). There was some discrepancy between the NCPo and
NCPi as the latter suggested net heterotrophy in the ambient
CO2 treatments, whereas the high CO2 treatments were net autotrophic during all three
phases of the experiment (Fig. 3). For the NCPo there was no indication of
net heterotrophy at ambient CO2 concentration. In terms of the absolute
numbers, the NCPi estimate is probably more uncertain than NCPo.
Calculating the CO2 atmospheric exchange from the measurements of a
tracer gas involves several calculation steps (Eq. 1–4), each adding
uncertainty to the calculation. However, both estimations (NCPi and NCPo)
indicate that increased CO2 concentrations lead to higher overall
community production, supporting our overall conclusion.
Budget
A carbon budget for the two control mesocosms and two highest CO2
additions is presented in Fig. 5. During phase I the estimated GPP was ∼ 100 mmol C fixed m-2 day-1,
from which 75–95 % was respired, ∼ 1 % ended up in the
TPC (including export), and 5–25 % added to the DOC pool. The main
difference between CO2 treatments became apparent during phase II when
the NCPo was higher in the elevated CO2 treatments. The
respiration loss increased to ∼ 100 % of GPP at the ambient
CO2 concentration, whereas respiration was lower (85–95 % of GPP) in
the highest CO2 treatment. Bacterial production was ∼ 30 % lower, on average, at the
highest CO2 concentration than in the controls during phase II. The share of NCPo of GPP ranged from
2 to 20 %, and the minimum flux to the DOC pool was 11 to 18 % of
TPC.
The overall budget was calculated by using the direct measurements of
changes in standing stocks and fluxes of export, respiration, and bacterial
production rates. The most robust data are the direct measurements of carbon
standing stocks and their development (e.g., ΔTPC). These are based
on well-established analytical methods with relatively low SE
of the carbon pools. However, the dynamic nature of these pools made
the relative SE for the rate of change much higher, reflecting that the rate
of change varied considerably within the different phases.
The rate variables, calculated based on conversion factors, have greater
uncertainty, although their SEs were relatively low, caused by uncertainty
in the conversion steps. For example, the RQ was set
to 1, which is a good estimate for carbohydrate oxidation. For lipids and
proteins the RQ is close to 0.7, but in a natural environment RQ is often
> 1 (Berggren et al., 2012) and is affected by physiological
state, e.g., nutrient limitation (Romero-Kutzner et al., 2015). Any temporal
variability in the conversion factors would directly change the overall
budget calculations, e.g., RQ affecting total respiration and gross primary
production estimates. However, the budget provides an order-of-magnitude
estimate of the carbon flow within the system. Some of the variables such as
GPP were estimated using different approaches, providing a more robust
comparison of the different treatments.
The primary effect of increasing CO2 concentration was the higher
standing stocks of TPC and DOC compared with ambient CO2 concentration.
The increasing DOC pool and relatively higher TPC pool were driven by
reduced respiration and bacterial production at elevated CO2
concentration. Decreasing respiration rate reduced the recycling of organic
carbon back to the DIC pool. The lower respiration and bacterial production
also indicate reduced remineralization of DOC. These two effects caused the
higher TPC and DOC pools in the elevated CO2 treatments. The results
highlight the importance of looking beyond net changes in carbon standing
stocks to understand how carbon fluxes are affected under increasing ocean
acidification.