Introduction
Anthropogenic emissions have increased the fugacity of atmospheric carbon
dioxide (pCO2) from the pre-industrial value of 280 µatm to the
present-day value of over 400 µatm, and these values will further
increase to 800–1000 µatm by the end of this century (Gattuso et
al., 2015). The dissolution of this excess CO2 into the surface of the
ocean directly affects the carbonate system and has lowered the pH by 0.1 units,
from 8.21 to 8.10 over the last 250 years. Further decreases of
0.3–0.4 pH units are predicted by the end of this century (Doney et al.,
2009; Orr et al., 2005; Gattuso et al., 2015), which is commonly referred to
as ocean acidification. The physiological and ecological aspects of the
phytoplankton response to this changing environment can potentially alter
marine phytoplankton community composition, community biomass, and feedback
to biogeochemical cycles (Boyd and Doney, 2002). These changes
simultaneously have an impact on some volatile organic compounds produced by
marine phytoplankton (Liss et al., 2014; Liu et al., 2017), including the
climatically important trace gas dimethylsulfide (DMS) and a number of
volatile halocarbon compounds.
DMS is the most important volatile sulfur compound produced from
dimethylsulfoniopropionate (DMSP), which is ubiquitous in marine
environments, mainly synthesized by marine microalgae (Stefels et al., 2007),
a few angiosperms, some corals (Raina et al., 2016), and several
heterotrophic bacteria (Curson et al., 2017) through complex biological
interactions in marine ecosystems. Although it remains controversial, DMS and
its by-products, such as methanesulfonic acid and non-sea-salt sulfate, are
suspected of having a prominent part in climate feedback (Charlson et al.,
1987; Quinn and Bates, 2011). The conversion of DMSP to DMS is facilitated by
several enzymes, including DMSP lyase and acyl CoA transferase (Kirkwood et
al., 2010; Todd et al., 2007); these enzymes are mainly found in
phytoplankton, macroalgae, symbiodinium, bacteria and fungi (de Souza and
Yoch, 1995; Stefels and Dijkhuizen, 1996; Steinke and Kirst, 1996; Chl a
and Yoch, 1998; Yost and Mitchelmore, 2009). Several studies have shown a
negative impact of decreasing pH on DMS-production capability (Hopkins et
al., 2010; Avgoustidi et al., 2012; Archer et al., 2013; Webb et al., 2016),
while others have found either no effect or a positive effect (Vogt et al.,
2008; Hopkins and Archer, 2014). Several assumptions have been presented to
explain these contrasting results and attributed the pH-induced variation in
DMS-production capability to altered physiology of the algae cells or of
bacterial DMSP degradation (Vogt et al., 2008; Hopkins et al., 2010,
Avgoustidi et al., 2012; Archer et al., 2013; Hopkins and Archer, 2014; Webb
et al., 2015).
Halocarbons also play a significant role in the global climate because they
are linked to tropospheric and stratospheric ozone depletion and a
synergistic effect of chlorine and bromine species has been reported,
accounting for approximately 20 % of the polar stratospheric ozone
depletion (Roy et al., 2011). In addition, iodocarbons can release atomic
iodine quickly through photolysis in the atmospheric boundary layer and
iodine atoms are very efficient in the catalytic removal of O3, which
governs the lifetime of many climate-relevant gases, including methane and
DMS (Jenkins et al., 1991). Compared with DMS, limited attention was received
about the effect of ocean acidification on halocarbon concentrations. Hopkins
et al. (2010) and Webb et al. (2015) measured lower concentrations of several
iodocarbons, while bromocarbons were unaffected by elevated pCO2 in
two acidification experiments. In addition, another mesocosm study did not
elicit significant differences from any halocarbon compounds at up to
1400 µatm pCO2 (Hopkins et al., 2013).
Taken together, the data indicate that the response of DMS and halocarbon
release to elevated pCO2 is complex and controversial. DMS and
halocarbons play a significant role in the global climate and will perhaps
act to a greater extent in the future. An intermediate step between
laboratory and natural community field experiments was designed in this
study to understand the response of the release of DMS and halocarbon to
ocean acidification in Chinese coastal seas using isolates of non-axenic
phytoplankton added to filtered natural water. We hypothesized that the
response of DMS and halocarbon release to elevated pCO2 in natural
seawater can be better presented after minimizing the shifting composition
of the natural phytoplankton and microbial communities.
Experimental method
Experimental setup
To investigate the response of DMS and halocarbon release to ocean
acidification, a mesocosm experiment was carried out on a floating platform
(set in seawater, about 150 m from the shore) at the Facility for Ocean
Acidification Impacts Study of Xiamen University (FOANIC-XMU;
24.52∘ N, 117.18∘ E) (for full technical details of the
mesocosms, see Liu et al., 2017). Six cylindrical transparent thermoplastic
polyurethane bags with domes were deployed along the southern side of the
platform. The width and depth of each mesocosm bag were 1.5 and 3 m,
respectively. Filtered (0.01 µm ultrafiltration water purifier,
MU801-4T, Midea, Guangdong, China) in situ seawater was pumped into the six
bags simultaneously within 24 h. A known amount of NaCl solution was added
to each bag to calculate the exact volume of seawater in the bags, according
to a comparison of the salinity before and after adding salt (Czerny et al.,
2013). The initial in situ pCO2 was about 650 µatm. To
set the low (400 µatm) and high pCO2
(1000 µatm) levels, we added Na2CO3 solution and
CO2 saturated seawater to the mesocosm bags to alter total alkalinity
and dissolved inorganic carbon (Gattuso et al., 2010; Riebesell et al.,
2013). Subsequently, during the whole experimental process, air at the
ambient (400 µatm) and elevated pCO2
(1000 µatm) concentrations was continuously bubbled into the
mesocosm bags using a CO2 Enricher (CE-100B, Wuhan Ruihua Instrument
& Equipment Ltd., Wuhan, China). Seawater taken from the coastal
environment was first filtered to remove algae and their attached bacteria
before usage in mesocosm bags. Bacterial abundance in the pre-filtered water
was less than 103 cell mL-1, which was 3 magnitudes lower than
the bacterial abundance in the natural water and close to the detection limit
of the flow cytometer. The trace gases, including DMS, bromodichloromethane
(CHBrCl2), methyl bromide (CH3Br), dibromomethane
(CH2Br2), and iodomethane (CH3I) produced in the
environment, did not affect the mesocosm trace gas concentrations after the
bags were sealed.
Algal strains
Before being introduced into the mesocosms, the three phytoplankton species
Phaeodactylum tricornutum (P. tricornutum), Thalassiosira weissflogii
(T. weissflogii) and Emiliania huxleyi (E. huxleyi) were cultured in autoclaved, pre-filtered seawater from
Wuyuan Bay at 16 ∘C (similar to the in situ temperature of Wuyuan
Bay) without any addition of nutrients. Cultures were continuously aerated
with filtered ambient air containing 400 µatm of CO2 within
plant chambers (HP1000G-D, Wuhan Ruihua Instrument & Equipment, China) at
a constant bubbling rate of 300 mL min-1. The culture medium was
renewed every 24 h to maintain the cells of each phytoplankton species in
exponential growth. When the experiment began, these three phytoplankton
species were inoculated into the mesocosm bags, with an initial
diatom/coccolithophorid cell ratio of 1:1. The initial concentrations of P. tricornuntum,
T. weissflogii, and E. huxleyi inoculated into the mesocosm were 10, 10, and 20 cells mL-1,
respectively. P. tricornuntum and T. weissflogii were obtained from the Center for Collections of Marine
Bacteria and Phytoplankton of the State Key Laboratory of Marine
Environmental Science (Xiamen University). P. tricornuntum was originally isolated from the
South China Sea in 2004 and T. weissflogii was isolated from Daya Bay in the coastal South
China Sea. E. huxleyi was originally isolated in 1992 from the field station of the
University of Bergen (Raunefjorden; 60∘18′ N, 05∘15′ E).
Sampling for DMS(P) and halocarbons
DMS(P) and halocarbon samples were taken from the above-mentioned mesocosm
bags at 09:00; then all collected samples were transported into a dark cool
box back to the laboratory onshore for analysis within 1 h. For the DMS
analysis, a 2 mL sample was gently filtered through a 25 mm GF/F (glass
fiber) filter and transferred to a purge and trap system linked to a Shimadzu
GC-2014 gas chromatograph (Tokyo, Japan) equipped with a glass column packed
with 10 % DEGS on Chromosorb W-AW-DMCS (3 m × 3 mm) and a
flame photometric detector (Zhang et al., 2014). For total DMSP analysis, a
10 mL water sample was fixed using 50 µL of 50 %
H2SO4 and sealed (Kiene and Slezak, 2006). After >1-day
preservation, DMSP samples were hydrolyzed for 24 h with a pellet of KOH
(final pH > 13) to fully convert DMSP to DMS. Then, 2 mL of the
hydrolyzed sample was carefully transferred to the purge and trap system
mentioned above for extraction of DMS. For halocarbons, a 100 mL sample was
purged at 40 ∘C with pure nitrogen at a flow rate of
100 mL min-1 for 12 min using another purge and trap system coupled
to an Agilent 6890 gas chromatograph (Agilent Technologies, Palo Alto, CA,
USA) equipped with an electron capture detector (ECD) as well as a 60 m
DB-624 capillary column (0.53 mm ID; film thickness, 3 µm) (Yang
et al., 2010). The analytical precision for duplicate measurements of DMS(P)
and halocarbons was >10 %.
Measurements of chlorophyll a
Chlorophyll a (Chl a) was measured in water samples (200–1000 mL)
collected every 2 days at 09:00 by filtering onto Whatman GF/F filters
(25 mm). The filters were placed in 5 mL 100 % methanol overnight at
4 ∘C and centrifuged at 5000 g for 10 min. The absorbance of the supernatant (2.5 mL) was measured
from 250 to 800 nm using a scanning spectrophotometer (DU 800, Beckman
Coulter Inc., Brea, CA, USA). Chl a concentration was calculated according
to the equation reported by Porra (2002).
Enumeration of DMSP-consuming bacteria
The number of DMSP-consuming bacteria in the mesocosms was estimated using
the most probable number methodology. The medium consisted of a mixture
(1:1 v/v) of sterile artificial seawater and a mineral medium (Visscher
et al., 1991), 3 mL of which was dispensed into 6 mL test tubes, which were
closed by an over-sized cap, allowing gas exchange. Triplicate dilution
series were set up. All test tubes contained 1 mmol L-1 DMSP as the
sole organic carbon source and were kept at 30 ∘C in the dark. After
2 weeks, the presence/absence of bacteria in the tubes was verified by DAPI
staining (Porter and Feig, 1980). Three tubes containing 3 mL ASW without
substrate were used as controls.
Statistical analysis
One-way analysis of variance (ANOVA), Tukey's test, and the two-sample
t test were carried out to demonstrate the differences between treatments. A
p value < 0.05 was considered significant. Relationships between
DMS(P), halocarbons and a range of other parameters were detected using
Pearson's correlation analysis via SPSS 22.0 for Windows (SPSS Inc.,
Chicago, IL, USA).
Dissolved inorganic carbon (DIC), pH, pCO2 and nutrient
concentrations in the mesocosm experiments. “–” means that the values were
below the detection limit.
pH
DIC
pCO2
NO3-+NO2-
NH4+
PO43-
SiO32-
(µmol kg-1)
(µatm)
(µmol L-1)
(µmol L-1)
(µmol L-1)
(µmol L-1)
Day 0
Low pCO2
8.0 ± 0.1
2181 ± 29
1170–1284
52–56
19–23
2.6 ± 0.2
38–40
High pCO2
7.5 ± 0.1
2333 ± 34
340–413
51–55
19–23
2.5 ± 0.2
38–39
Phase I
Low pCO2
7.9–8.4
1825–2178
373–888
15–52
1.6–20
0.5–2.6
31–38
High pCO2
7.4–8.2
2029–2338
1295–1396
47–54
0.2–21
0.7–2.5
34–39
Phase II
Low pCO2
8.4–8.5
1706–1745
46–749
-15.9
–
0.1–0.5
10–24
High pCO2
8.4–8.6
1740–1891
59–1164
1.1–25
–
-0.1
29–30
Phase III
Low pCO2
8.5–8.8
1673–1706
30–43
–
–
–
10–16
High pCO2
8.6–8.7
1616–1740
34–110
–
–
-0.3
24–25
Temporal development of pH in the high pCO2
(1000 µatm, solid squares) and low pCO2
(400 µatm, white squares) mesocosms. Data are mean ± SD, n=3 (triplicate independent mesocosm bags) (Origin 8.0).
Results and discussion
Temporal changes in pH, Chl a, P. tricornuntum, T. weissflogii, and E. huxleyi during the experiment
During the experiment, the seawater in each mesocosm was well mixed, and the
temperature and salinity remained stable, with means of 16 ∘C and
29, respectively, in all mesocosm bags. We observed significant differences
in pH levels between the two CO2 treatments on days 0–11, but the
differences disappeared with subsequent phytoplankton growth (Fig. 1). The
phytoplankton growth process was divided into three phases in terms of
variations in Chl a concentrations in the mesocosm experiments as described
in Liu et al. (2017): (i) the logarithmic growth phase (phase I, days 0–13),
(ii) a plateau phase (phase II, days 13–23, bloom period), and (iii) a
secondary plateau phase (phase III, days 23–33) attained after a decline in
biomass from a maximum in phase II. The initial chemical parameters of the
mesocosm experiment are shown in Table 1. The initial mean dissolved nitrate
(including NO3- and NO2-), NH4+,
PO43- and silicate (SiO32-) concentrations were 54,
20, 2.6 and 41 µmol L-1, respectively, for the low
pCO2 treatment and 52, 21, 2.4 and 38 µmol L-1,
respectively, for the high pCO2 treatment. The nutrient
concentrations (NO3-, NO2-, NH4+ and
phosphate) during phase I were consumed rapidly and their concentrations were
below or close to the detection limit during phase II (Table 1).
SiO32- was detectable during the entire experimental period, and
was unlikely to be a limiting factor for phytoplankton growth during the
experiment. In addition, although dissolved inorganic nitrogen
(NH4+, NO3-, and NO2-) and phosphate
were depleted, Chl a concentration in both treatments (biomass dominated by
P. tricornuntum) remained constant over days 12–22, and then
declined over subsequent days. T. weissflogii was found throughout
the entire period in each bag, but the maximum concentration was
8120 cells mL-1, which was far less than the concentration of
P. tricornutum with a maximum density of about
1.5 million cells mL-1 (Liu et al., 2017). It is possible that
P. tricornutum outcompeted T. weissflogii because of its
higher surface-to-volume ratio and/or species-specific physiology, which
would enhance the efficiency of nutrient uptake and related metabolism
(Alessandrade et al., 2007). E. huxleyi was only found in phase I
and its maximal concentration reached 310 cells mL-1 according to the
results of Liu et al. (2017). Previous studies have reported that the maximum
specific growth rate of T. weissflogii and P. tricornutum
is about 1.2 d-1 (Li et al., 2014; Sugie and Yoshimura, 2016), while
that of E. huxleyi is about 0.8 d-1 (Xing et al., 2015). This
might be the main reason why diatoms overwhelmingly outcompeted the
coccolithophores during this experiment.
Correlation between dimethylsulfide (DMS),
dimethylsulfoniopropionate (DMSP), chlorophyll a (Chl a),
bromodichloromethane (CHBrCl2), methyl bromide (CH3Br),
dibromomethane (CH2Br2), iodomethane (CH3I), DMSP-consuming
bacteria, Thalassiosira weissflogii (T. weissflogii) and
Phaeodactylum tricornutum (P. tricornutum) concentrations in
the low pCO2 treatments.
DMS
DMSP
Chl a
CHBrCl2
CH3Br
CH2Br2
CH3I
DMSP-consuming
T. weissflogii
P. tricornutum
bacteria
DMS
1
DMSP
0.701∗∗
1
Chl a
0.597∗∗
0.792∗∗
1
CHBrCl2
0.526
0.280
0.559
1
CH3Br
-0.413
-0.230
0.196
0.313
1
CH2Br2
0.310
0.180
0.001
-0.136
-0.308
1
CH3I
0.694∗∗
0.654∗∗
0.717∗∗
0.596∗
-0.151
0.129
1
DMSP-consuming bacteria
0.643∗∗
0.520∗
0.522∗
0.394
-0.268
-0.038
0.762∗∗
1
T. weissflogii
0.410
0.617∗∗
0.899∗∗
0.301
0.322
0.028
0.680∗∗
0.399
1
P. tricornutum
0.560∗
0.961∗∗
0.821∗∗
0.528
-0.032
0.162
0.588∗∗
0.334
0.685∗∗
1
∗ Correlation is significant at the 0.05 level
(two-tailed). ∗∗ Correlation is significant at the 0.01 level
(two-tailed).
Correlation between dimethylsulfide (DMS),
dimethylsulfoniopropionate (DMSP), chlorophyll a (Chl a),
bromodichloromethane (CHBrCl2), methyl bromide (CH3Br),
dibromomethane (CH2Br2), iodomethane (CH3I), DMSP-consuming
bacteria, Thalassiosira weissflogii (T. weissflogii) and
Phaeodactylum tricornutum (P. tricornutum) concentrations in
the high pCO2 treatments.
DMS
DMSP
Chl a
CHBrCl2
CH3Br
CH2Br2
CH3I
DMSP-consuming
T. weissflogii
P. tricornutum
bacteria
DMS
1
DMSP
0.752∗∗
1
Chl a
0.318∗
0.738∗∗
1
CHBrCl2
0.324
0.094
0.326
1
CH3Br
-0.410
-0.349
0.065
0.076
1
CH2Br2
0.540∗
0.352
0.142
0.233
-0.377
1
CH3I
0.694∗∗
0.816∗∗
0.741∗∗
0.690∗
-0.407
0.316
1
DMSP-consuming bacteria
0.544∗
0.522
0.549∗
0.532
-0.311
0.368
0.851∗
1
T. weissflogii
0.355
0.743∗∗
0.930∗∗
0.304
0.076
0.233
0.690∗∗
0.567
1
P. tricornutum
0.635∗∗
0.954∗∗
0.803∗∗
0.143
-0.257
0.267
0.834∗∗
0.559
0.820∗∗
1
∗ Correlation is significant at the 0.05 level
(two-tailed). ∗∗ Correlation is significant at the 0.01 level
(two-tailed).
Impact of elevated pCO2 on DMS and DMSP production
DMSP concentrations in the high pCO2 and low pCO2
treatments increased significantly along with the increase in Chl a
concentrations and algal cells, and remained relatively constant over the
following days. A significant positive relationship was observed between DMSP
and phytoplankton in the experiment (r=0.961, p<0.01 for P. tricornuntum, r=0.617, p<0.01 for T. weissflogii in the low
pCO2 treatment, Table 2; r=0.954, p<0.01 for P. tricornuntum, r=0.743, p<0.01 for T. weissflogii in the
high pCO2 treatment, Table 3). DMS was maintained at a low level
during phase I (mean of 1.03 nmol L-1 in the low pCO2 and
0.74 nmol L-1 in the high pCO2 treatments, respectively)
compared with DMSP. DMS concentrations began to increase rapidly on day 15,
peaked on day 25 in the low pCO2 treatment (112.1 nmol L-1)
and on day 29 in the high pCO2 treatment (101.9 nmol L-1),
respectively, and then decreased in the following days. A moderate positive
relationship was observed between DMS and P. tricornuntum (r=0.560, p<0.05 in the low pCO2 treatment; r=0.635, p<0.01 in the high pCO2 treatment), while no relationship was
observed between DMS and T. weissflogii (Tables 2 and 3) during the
experiment. Similar to DMS, DMSP-consuming bacteria also maintained a low
level during phase I (mean of 0.57 × 106 and
0.40 × 106 cells mL-1 in the low pCO2 and high
pCO2 treatments, respectively). DMSP-consuming bacterial
concentrations peaked on days 19
(11.65 × 106 cells mL-1) and 21
(10.70 × 106 cells mL-1) in the low pCO2 and
high pCO2 treatments, respectively.
Temporal development in dimethylsulfide (DMS),
dimethylsulfoniopropionate (DMSP) and DMSP-consuming bacteria concentrations
in the high pCO2 (1000 µatm, black squares) and low
pCO2 (400 µatm, white squares) mesocosms. Data are
mean ± SD, n=3 (triplicate independent mesocosm bags).
In this study, no difference in mean DMSP concentrations was observed between
the two treatments, indicating that elevated pCO2 had no
significant influence on DMSP production in P. tricornuntum and
T. weissflogii. However, significant reductions in mean DMS
concentration (28 %) and DMSP-consuming bacteria (29 %) were detected
during phase I in the high pCO2 treatment compared with those in
the low pCO2 treatment, indicating that elevated pCO2
inhibited DMSP-consuming bacteria and DMS production during the logarithmic
growth phase. In addition, the peak DMS concentration in the high
pCO2 treatment was delayed 4 days relative to that in the low
pCO2 treatment during phase II (Fig. 2a). This result has been
observed in previous mesocosm experiments and it was attributed to
small-scale shifts in community composition and succession (Vogt et al.,
2008; Webb et al., 2016). However, this phenomenon during the present study
can be explained in another straightforward way. Previous studies have shown
that marine bacteria play a key role in DMS production, and that the
efficiency of bacteria converting DMSP to DMS may vary from 2 to 100 %
depending on the nutrient status of the bacteria and the quantity of
dissolved organic matter (Simó et al., 2002, 2009; Kiene et al., 1999,
2000). In addition, a significant positive relationship was observed between
DMS and DMSP-consuming bacteria (r=0.643, p<0.01 in the low
pCO2 treatment; r=0.544, p<0.01 in the high pCO2
treatment) during this experiment. All of these observations point to the
importance of bacteria in DMS and DMSP dynamics. During the present mesocosm
experiment, DMSP concentrations in the low pCO2 treatment decreased
slightly on day 23, while the slight decrease appeared on day 29 in the high
pCO2 treatment (Fig. 2b). In addition, the time that the DMSP
concentration began to decrease was very close to the time when the highest
DMS concentration occurred in both treatments. Similar to DMS, DMSP-consuming
bacteria were also delayed in the high pCO2 mesocosm compared to
those in the low pCO2 mesocosm (Fig. 2c). Taken together, we
inferred that the elevated pCO2 first delayed growth of
DMSP-consuming bacteria; then the delayed DMSP-consuming bacteria postponed
the DMSP degradation process, and eventually delayed the DMS concentration in
the high pCO2 treatment. In addition, considering that algae and
bacteria in natural seawater were removed through a filtering process before
the experiment (Huang et al., 2018), we further concluded that the elevated
pCO2 controlled DMS concentrations mainly by affecting
DMSP-consuming bacteria attached to T. weissflogii and P. tricornuntum.
Temporal development in bromodichloromethane (CHBrCl2),
methyl bromide (CH3Br), dibromomethane (CH2Br2), and
iodomethane (CH3I) concentrations in the high pCO2
(1000 µatm, black squares) and low pCO2
(400 µatm, white squares) mesocosms. Data are mean ± SD,
n=3 (triplicate independent mesocosm bags).
Impact of elevated pCO2 on halocarbon compounds
The temporal development in CHBrCl2, CH3Br, and
CH2Br2 concentrations is shown in Fig. 3a, b, and c,
respectively. The temporal changes in their concentrations were substantially
different from those in DMS, DMSP, P. tricornuntum and T. weissflogii. The mean concentrations of CHBrCl2, CH3Br,
and CH2Br2 for the entire experiment were 8.58, 7.85, and
5.13 pmol L-1 in the low pCO2 treatment and 8.81, 9.73, and
6.27 pmol L-1 in the high pCO2 treatment. The concentrations
of CHBrCl2, CH3Br, and CH2Br2 did not
increase with the Chl a concentration compared with those of DMS and DMSP,
and no major peaks were detected in the mesocosms. In addition, no effect of
elevated pCO2 was identified for any of the three bromocarbons,
which compared well with previous mesocosm findings (Hopkins et al., 2010,
2013; Webb et al., 2016). No clear correlation was observed between the three
bromocarbons and any of the measured algal groups (Tables 2 and 3),
indicating that P. tricornuntum and T. weissflogii did not
primarily release these three bromocarbons during the mesocosm experiment.
Previous studies reported that large-sized cyanobacteria, such as
Aphanizomenon flos-aquae, could produce bromocarbons (Karlsson et
al., 2008). Significant correlations between the abundance of cyanobacteria
and several bromocarbons have been reported in the Arabian Sea (Roy et al.,
2011). However, the filtration procedure led to the loss of cyanobacteria in
the mesocosms and finally resulted in low bromocarbon concentrations during
the experiment, although P. tricornuntum and T. weissflogii
abundances were high.
The temporal dynamics of CH3I in the high pCO2 and low
pCO2 treatments are shown in Fig. 3d. The CH3I
concentrations in the low pCO2 treatment varied from 0.38 to
12.61 pmol L-1, with a mean of 4.76 pmol L-1. The
CH3I concentrations in the high pCO2 treatment ranged
between 0.44 and 8.78 pmol L-1, with a mean of 2.88 pmol L-1.
The maximum CH3I concentrations in the high pCO2and low
pCO2 treatments were both observed on day 23. The range of
CH3I concentrations during this experiment was similar to that
measured in the mesocosm experiment (<1–10 pmol L-1) in
Kongsfjorden conducted by Hopkins et al. (2013). In addition, the mean
CH3I concentration in the low pCO2 treatment was similar
to that measured in the East China Sea, with an average of
5.34 pmol L-1 in winter and 5.74 pmol L-1 in summer (Yuan et
al., 2016). Meanwhile, a positive relationship was detected between
CH3I and Chl a, P. tricornuntum and T. weissflogii (r=0.588, p<0.01 in the low pCO2 treatment; r=0.834, p<0.01 in the low pCO2 treatment for P. tricornuntum; r=0.680, p<0.01 in the low pCO2 treatment;
r=0.690, p<0.01 in the high pCO2 treatment for
Thalassiosira weissflogii; r=0.717, p<0.01 in the low
pCO2 treatment; r=0.741, p<0.01 in the high pCO2
treatment for Chl a). This result agrees with previous mesocosm (Hopkins et
al., 2013) and laboratory experiments (Hughes et al., 2013; Manley and De La
Cuesta, 1997) identifying diatoms as significant producers of CH3I.
Moreover, similar to DMS, the maximum CH3I concentration also
occurred after the maxima of P. tricornuntum and T. weissflogii, at about 4 days (Fig. 3d). This result was similar to the
conclusions reported by Hopkins et al. (2010) and Wingenter et al. (2007)
during two mesocosm experiments conducted in Norway. Their results confirmed
that iodocarbon gases generally occur after the Chl a maxima. Furthermore,
the mean CH3I concentration measured in the high pCO2
treatment was significantly lower (40 %) than that measured in the low
pCO2 treatment during the mesocosm experiment. This result is in
accordance with Hopkins et al. (2010) and Webb et al. (2015), who also
reported that elevated pCO2 leads to a reduction in iodocarbon
concentrations, but is in contrast to the findings of Hopkins et al. (2013)
and Webb et al. (2016), who showed that elevated pCO2 does not
significantly affect the iodocarbon concentrations in the mesocosms.
Considering that the phytoplankton species did not show significant
differences in the high pCO2 and low pCO2 treatments
during the experiment, this reduction in the high pCO2 treatment
was likely not caused by phytoplankton. Apart from direct biological
production via methyl transferase enzyme activity by both phytoplankton and
bacteria (Amachi et al., 2001), CH3I is produced from the breakdown
of higher molecular weight iodine-containing organic matter (Fenical, 1982)
through photochemical reactions between organic matter and light (Richter and
Wallace, 2004). Both bacterial methyl transferase enzyme activity and
photochemical reaction could be responsible for the reduction of
CH3I concentrations in the high pCO2 treatment, but
further experiments are needed to verify this result.
Conclusions
In this study, the effects of increased levels of pCO2 on marine
DMS(P) and halocarbon release were studied in a controlled mesocosm facility.
During the logarithmic growth phase, the elevated pCO2 led to a
reduction in mean DMSP-consuming bacteria (29 %) and DMS concentration
(28 %) compared with those in the low pCO2 treatment. In
addition, a 4-day delay in DMS concentration was observed in the high
pCO2 treatment due to the effect of elevated pCO2, and we
attribute this delay in DMS concentration to the DMSP-consuming bacteria
attached to P. tricornuntum and T. weissflogii. Due to the
loss of main bromocarbon-producing species affected by the filtration
procedure, three bromocarbon compounds measured in this study were not
correlated with P. tricornuntum and T. weissflogii, and
Chl a. In addition, elevated pCO2 had no effect on any of the
three bromocarbons. The temporal dynamics of CH3I, combined with
strong correlations with P. tricornuntum and T. weissflogii, and Chl a, indicate that P. tricornuntum and
T. weissflogii play a critical role in controlling CH3I
concentrations. In addition, the production of CH3I was sensitive
to pCO2, with a significant increase in CH3I
concentration at higher pCO2. However, without additional
empirical measurements, it is unclear whether this decrease was caused by
bacterial methyl transferase enzyme activity or by photochemical degradation
at higher pCO2.