The effects of ocean acidification and warming on the concentrations of
dimethylsulfoniopropionate (DMSP) and dimethylsulfide (DMS) were investigated
during a mesocosm experiment in the Lower St. Lawrence Estuary (LSLE) in the
fall of 2014. Twelve mesocosms covering a range of pHT (pH on the
total hydrogen ion concentration scale) from 8.0 to 7.2, corresponding to a
range of CO2 partial pressures (pCO2) from 440
to 2900 µatm, at two temperatures (in situ and +5∘C; 10
and 15 ∘C) were monitored during 13 days. All mesocosms were
characterized by the rapid development of a diatom bloom dominated by
Skeletonema costatum, followed by its decline upon the exhaustion of
nitrate and silicic acid. Neither the acidification nor the warming resulted
in a significant impact on the abundance of bacteria over the experiment.
However, warming the water by 5 ∘C resulted in a significant
increase in the average bacterial production (BP) in all 15 ∘C
mesocosms as compared to 10 ∘C, with no detectable effect of
pCO2 on BP. Variations in total DMSP
(DMSPt= particulate + dissolved DMSP) concentrations
tracked the development of the bloom, although the rise in DMSPt
persisted for a few days after the peaks in chlorophyll a. Average
concentrations of DMSPt were not affected by acidification or
warming. Initially low concentrations of DMS (<1 nmol L-1)
increased to reach peak values ranging from 30 to 130 nmol L-1 towards
the end of the experiment. Increasing the pCO2 reduced the
averaged DMS concentrations by 66 % and 69 % at 10 and
15 ∘C, respectively, over the duration of the experiment. On the
other hand, a 5 ∘C warming increased DMS concentrations by an
average of 240 % as compared to in situ temperature, resulting in a
positive offset of the adverse pCO2 impact. Significant
positive correlations found between bacterial production and concentrations
of DMS throughout our experiment point towards temperature-associated
enhancement of bacterial DMSP metabolism as a likely driver of the mitigating
effect of warming on the negative impact of acidification on the net
production of DMS in the LSLE and potentially the global ocean.
Introduction
Dimethylsulfide (DMS) is ubiquitous in productive estuarine, coastal, and
oceanic surface waters (Barnard et al., 1982; Iverson et al., 1989; Kiene and
Service, 1991; Cantin et al., 1996; Kettle et al., 1999). With an estimated
average 28.1 Tg of sulfur (S) being transferred to the atmosphere annually
(Lana et al., 2011), DMS emissions constitute the largest natural source of
tropospheric S (Lovelock et al., 1972; Andreae, 1990; Bates et al., 1992). The
oxidation of atmospheric DMS yields hygroscopic sulfate (SO42-)
aerosols that directly scatter incoming solar radiation and act as nuclei
upon which cloud droplets can condense and grow, thereby potentially
impacting cloud albedo and the radiative properties of the atmosphere
(Charlson et al., 1987; Andreae and Crutzen, 1997; Liss and Lovelock, 2007;
Woodhouse et al., 2013). The scale of the impact of biogenic
SO42- particles on global climate, however, remains uncertain
(Carslaw et al., 2010; Quinn and Bates, 2011; Quinn et al., 2017). The
strength of DMS emissions depends on wind- and temperature-driven transfer
processes (Nightingale et al., 2000) but mostly on its net production in the
surface mixed layer of the ocean (Malin and Kirst, 1997). Net changes in the
aqueous DMS inventory are largely governed by microbial food webs (see
reviews by Simó, 2001; Stefels et al., 2007) whose productivity is
potentially sensitive to modifications in the habitats that sustain them.
Given the complexity of the biological cycling of DMS, understanding how
climate change related stressors could impact the production of this
climate-active gas is a worthy but formidable challenge.
DMS is produced, for the most part, from the enzymatic breakdown of
dimethylsulfoniopropionate (DMSP) (Cantoni and Anderson, 1956), a metabolite
produced by several groups of phytoplankton, with an extensive range in
intracellular quotas between taxa (Keller, 1989; Stefels et al., 2007).
Several species of the classes Haptophyceae and Dinophyceae are amongst the
most prolific DMSP producers, but certain members of Bacillariophyceae
(diatoms) and Chrysophyceae can also produce significant amounts of DMSP
(Stefels et al., 2007). The biosynthesis of DMSP is highly constrained by
abiotic factors and its up- or down-regulation may allow cells to cope with
environmental shifts in temperature, salinity, nutrients and light intensity
(Kirst et al., 1991; Karsten et al., 1996; Sunda et al., 2002), while its de
novo synthesis and exudation may also serve as a sink for excess carbon (C)
and sulfur (S) under unfavorable growth conditions (Stefels, 2000). Beyond
active exudation in healthy cells (Laroche et al., 1999), cellular or
particulate DMSP (DMSPp) can be transferred to the water column
as dissolved DMSP (DMSPd) through viral lysis (Hill et al., 1998;
Malin et al., 1998), autolysis (Nguyen et al., 1988; Stefels and Van Boeckel,
1993), and grazing by micro-, meso- and macro-zooplankton (Dacey and Wakeham,
1986; Wolfe and Steinke, 1996). The turnover rate of DMSPd in the
water column is generally very rapid (a few hours to days) as this compound
represents sources of C and reduced S for the growth of microbial organisms
(Kiene and Linn, 2000). Heterotrophic bacteria mediate most of the turnover
of S-DMSPd through pathways that constrain the overall production
of DMS: (1) enzymatic cleavage of DMSPd that yields DMS;
(2) demethylation/demethiolation of DMSPd that yields
methanethiol (MeSH); (3) production of dissolved non-volatile S compounds,
including SO42-, following oxidation of DMSPd;
(4) intracellular accumulation of DMSPd with no further
metabolization (Kiene et al., 1999, 2000; Kiene and Linn, 2000; Yoch, 2002).
A compilation of 35S-DMSPd tracer studies conducted with
natural microbial populations shows that microbial DMS yields rarely exceed
40 % of consumed DMSPd in surface coastal and oceanic waters
(see review table in Lizotte et al., 2017). Another potential fate of
DMSPd is its uptake by non-DMSP producing eukaryotic
phytoplankton such as certain diatoms (Vila-Costa et al., 2006b;
Ruiz-González et al., 2012) and cyanobacteria such as
Synechococcus and Prochloroccocus (Malmstrom et al., 2005;
Vila-Costa et al., 2006b), but the overall turnover of DMSPd
seems to be dominated by heterotrophic organisms.
Whereas the role of bacteria in the production of DMS via DMSPd
is well recognized, an increasing number of studies have shown that the
phytoplankton-mediated enzymatic conversion of total DMSP (DMSPt)
into DMS can also be significant when communities are dominated by DMSP-lyase
producing phytoplankton groups such as Dinophyceae and Haptophyceae (Niki et
al., 2000; Steinke et al., 2002; Stefels et al., 2007; Lizotte et al., 2012),
particularly under high doses of solar radiation (Toole and Siegel, 2004;
Toole et al., 2006, 2008; Vallina et al., 2008). Removal processes of DMS
from surface waters include photo-oxidation, bacterial degradation, and
efflux across the air–sea interface which individually depends on several
factors such as light intensity, wind velocity, the depth of the surface
mixed layer, and the gross production of DMS (Brimblecombe and Shooter, 1986;
Simó and Pedros-Alió, 1999; Nightingale et al., 2000; Hatton et al.,
2004; Simó, 2004). Additionally, the biological and photochemical
oxidation of dimethylsulfoxide (DMSO) is an important sink for DMS, while
DMSO reduction represents a DMS source (Stefels et al. 2007; Spiese et al.,
2009; Asher et al., 2011). Overall, production and turnover of DMS and its
precursor DMSP are unequivocally linked with microbial activity, both
autotrophic and heterotrophic. The associated biological processes and
interactions amongst these microorganisms have been shown to be sensitive to
fluctuations in abiotic factors and may thus be further modulated by multiple
drivers of climate change.
Since the pre-industrial era, atmospheric CO2 concentrations have
risen from 280 ppm, and, according to the results of the global ocean
circulation models under the condition of the business-as-usual scenario RCP
8.5, are expected to reach 850–1370 ppm by 2100 (IPCC, 2013). The oceans
have already absorbed about 28 % of the anthropogenic CO2
emitted into the atmosphere (Le Quéré et al., 2015), leading to a pH
decrease of 0.11 units in surface waters (Gattuso et al., 2015), a phenomenon
called ocean acidification (OA). An additional decrease in pH by
0.3–0.4 units is expected by the end of this century, and could reach
0.8 units by 2300 (Caldeira and Wickett, 2005; Doney et al., 2009; Feely et
al., 2009). In addition to the oceanic sink, a similar fraction of
anthropogenic CO2 emissions has been captured by terrestrial
vegetation, while the anthropogenic CO2 remaining (45 % of
total emissions) in the atmosphere (Le Quéré et al., 2013) has led to
an estimated increased greenhouse effect of 0.3–0.6 W m-2 globally
over the past 135 years (Roemmich et al., 2015). Ninety percent of this
excess heat has been absorbed by the ocean, increasing sea surface
temperatures (SST) by ∼0.1∘C per decade since 1951, and could
increase SST by 3–5 ∘C before 2100 (IPCC, 2013). Leading experts in
the field of global change have called upon the scientific community to
address critical knowledge gaps, among which a top priority remains the
assessment of the impact of multiple environmental stressors on marine
microorganisms (Riebesell and Gattuso, 2015).
The sensitivity of natural planktonic assemblages to OA, along with their
production of DMSP and DMS, has been investigated in several experimental
studies (see review table in Hussherr et al., 2017). The majority of these
experiments have shown a decrease in both DMSP and DMS concentrations with
increasing pCO2 (Hopkins et al., 2010; Avgoustidi et al.,
2012; Park et al., 2014; Webb et al., 2015). The decrease in DMSP production
has largely been attributed to the deleterious impact of decreasing pH on the
coccolithophore Emiliania huxleyi, the dominant DMSP producer in
several of these studies. Nevertheless, OA does not always result in a
concomitant decrease in DMSP and DMS production. For example, the
pCO2-induced decrease in DMS reported by Archer et
al. (2013) in Arctic waters was accompanied by an increase in DMSP
concentrations, indicating that DMS production is at least partly dependent
on the turnover of DMSP, rather than on the DMSP pool. A modeling study
showed that the specific implementation of the negative effect of OA on DMS
net production in a coupled ocean-atmosphere model reduces global DMS
production by 18±3 %, resulting in an additional warming of
0.23–0.48 K by 2100 under the A1B scenario (Six et al., 2013). Schwinger et
al. (2017) further showed that the OA-induced decreases in oceanic DMS
emissions could result in a transient global warming of 0.30 K, mostly
resulting from a reduction of cloud albedo. These first attempts to model the
potential effect of OA on climate through its impact on DMS oceanic
production show that OA may significantly affect climate by reducing marine
emissions of DMS but also highlight the importance of carefully assessing the
robustness of the DMS-OA negative relationship. This is particularly relevant
considering that some experiments reveal a neutral or positive effect of
increasing pCO2 on DMS net production (Vogt et al., 2008;
Kim et al., 2010; Hopkins and Archer, 2014). Regional or seasonal differences
in phytoplankton taxonomy, microzooplankton grazing, and bacterial activity
have been proposed as key drivers of the discrepancies between these
experimental results.
Whereas studies of the impact of OA on DMS cycling have gained momentum, the
importance of assessing how combined drivers of change may impact the
structure and the functioning of ocean ecosystems, using multifactorial
approaches, is now increasingly recognized (Boyd et al., 2015, 2018;
Riebesell and Gattuso, 2015; Gunderson et al., 2016). Thus far, only two
mesocosm studies assessed the combined effect of OA and warming on DMS
dynamics by natural plankton assemblages. The two studies, both conducted
with coastal waters, led to contrasting results. The first study showed an
80 % increase in DMS concentrations under high pCO2
conditions (900 ppm vs. 400 ppm), and a reduction by 20 % of this
stimulating effect when the increase in pCO2 was
accompanied by a 3 ∘C warming (Kim et al., 2010). However, the
absence of a specific stand-alone warming treatment did not allow the authors
to assess the sole impact of temperature on DMS net production. The second
study showed decreasing DMS concentrations under both acidification and
greenhouse conditions, with the lowest DMS concentrations measured under
combined acidification and warming treatments (Park et al., 2014). The
authors attributed these contrasting responses to differences in the
phytoplankton assemblages, DMSP-related algal physiological characteristics,
and microzooplankton grazing. Nevertheless, questions remain as to the
combined effect of pCO2 and warming on DMS net production
since the temperature treatments were not conducted over the full range of
pCO2 tested (Kim et al., 2010; Park et al., 2014).
The combined influence of acidification and warming on the dynamics of the
St. Lawrence Estuary phytoplankton fall bloom was investigated during a full
factorial mesocosm experiment (Bénard et al., 2018a). During this
experiment, a bloom of Skeletonema costatum developed in all
mesocosms, independently of the pCO2 gradient (from 440 to
2900 µatm) and temperatures tested (10 and 15 ∘C). The
increase in pCO2 had no influence on the bloom, but
warming accelerated the growth rate of the diatoms and hastened the decline
of the bloom (Bénard et al., 2018a). Here, we report on the impacts of
acidification and warming on DMSP and DMS concentrations with a focus on the
dynamics of heterotrophic bacteria, a component of the marine food web known
to affect the turnover of DMSP and DMS.
Materials and methodsMesocosm setup
The mesocosm experimental setup is described in detail in Bénard et
al. (2018a). Briefly, mesocosm experiments were conducted at the ISMER marine
research station of Rimouski (Québec, Canada) in the fall of 2014. The
twelve 2600 L cylindrical (2.67 m × 1.4 m), conical bottom,
mesocosms were housed in two temperature-controlled, full-size shipping
containers each containing six mesocosms (Aquabiotech Inc., Québec,
Canada). Each mesocosm is mixed by a propeller secured near the top of the
enclosure to ensure homogeneity of the water column. The mesocosms are sealed
by a Plexiglas cover transmitting 50 %–85 % of solar UVB
(280–315 nm), 85 %–90 % of UVA (315–400 nm), and 90 % of
photosynthetically active radiation (PAR; 400–700 nm) of the natural
incident light. Independent temperature probes (AQBT-Temperature sensor,
accuracy ±0.2∘C) were installed in each mesocosm, recording
temperature every 15 min and either triggering a resistance heater (Process
Technology TTA1.8215) or a glycol refrigeration system activated by an
automated pump. The pH of the mesocosms was measured every 15 min by
Hach® PD1P1 probes (±0.02 pH units)
linked to Hach® SC200 controllers. To
maintain pH, two reservoirs of artificial seawater were equilibrated with
pure CO2 before the start of the experiment and positive deviations
from the target pH values in each mesocosm activated peristaltic pumps that
injected the CO2 supersaturated seawater into the mesocosm water.
This control system was able to maintain the pH in the mesocosms within ±0.02 pH units of the targeted values during the initial bloom development by
lowering the pH, but it could not increase the pH during the declining phase
of the bloom.
Experimental approach
Prior to the onset of the experiment, all the mesocosms were meticulously
washed with diluted Virkon™, an anti-viral
and anti-bacterial solution, according to the manufacturer's instructions
(Antec International Limited), and thoroughly rinsed. The experimental
approach is also detailed in Bénard et al. (2018a). To fill the
mesocosms, water from ∼5 m depth was collected near the Rimouski
harbour (48∘28′39.9′′ N, 68∘31′03.0′′ W) on the
27 September 2014 (day -5). Initial conditions were practical
salinity = 26.52, temperature = 10 ∘C, nitrate
(NO3-) =12.8±0.6µmol L-1, silicic
acid (Si(OH)4) =16±2µmol L-1, and
soluble reactive phosphate (SRP) =1.4±0.3µmol L-1. Following its collection, the water was
screened through a 250 µm mesh while the mesocosms were
simultaneously gravity-filled by a custom made “octopus” tubing system. The
initial in situ temperature of 10 ∘C was maintained in all mesocosms
for the first 24 h (day -4). On day -3, the six mesocosms in one of the
containers were gradually heated to 15 ∘C while the mesocosms in the
other container were maintained at 10 ∘C. No manipulations were
performed on day -2 to avoid excessive stress, and acidification was
carried out on day -1. The mesocosms were initially set to cover a gradient
of pHT (total proton concentration scale) of ∼8.0 to 7.2
corresponding to a range of pCO2 from 440 to
2900 µatm. Two mesocosms, one in each container (at each
temperature), were not pH-controlled to assess the effect of freely
fluctuating pH condition. These two mesocosms were called drifters since the
in situ pH was allowed to drift over time throughout the bloom development.
To achieve the initially targeted pHT, CO2-saturated
artificial seawater was added to mesocosms M1, M3, M5, M7, M8, M10
(pHT 7.2–7.6) while mesocosms M2, M4, M6, M9, M11, M12
(pHT 7.8–8.0 and the drifters) were openly mixed to allow
CO2 degassing. Then, the automatic system controlling the
occasional addition of CO2-saturated artificial seawater maintained
the pH equal or below the targeted pH, except for the drifters.
Seawater analysis
Daily sampling of the mesocosms was carried out between 05:00 and 08:00 every
day (EDT) as described in Bénard et al. (2018a). Samples for carbonate
chemistry, nutrients, DMSP, and DMS were collected directly from the
mesocosms prior to filling of 20 L carboys from which seawater for the
determination of chlorophyll a (Chl a), bacterial abundance, and
bacterial production (BP) was subsampled. Samples were collected directly
from the mesocosms and the artificial seawater tank on days -3, 3, and 13
for practical salinity determinations. The samples were collected in 250 mL
plastic bottles and stored in the dark until analysis was carried out on a
Guildline Autosal 8400B salinometer in the months following the experiment.
Carbonate chemistry and nutrients
Analytical methods used to determine the carbonate parameters are described
in detail in Bénard et al. (2018a). Briefly, pH was determined every day
by transferring samples from the mesocosms to 125 mL plastic bottles without
headspace. The samples were analyzed within hours of collection on a
Hewlett-Packard UV-Visible diode array spectrophotometer (HP-8453A) and a
5 cm quartz cell using phenol red (PR; Robert-Baldo et al., 1985) and
m–cresol purple (mCP; Clayton and Byrne, 1993) as indicators after
equilibration to 25.0±0.1∘C in a thermostated bath. The pH on
the total proton scale (pHT) was calculated according to
Byrne (1987), with the salinity of the sample and the HSO4-
association constants given by Dickson (1990). The reproducibility of pH
measurements, based on replicate measurements of the same samples and values
derived from both indicators, was on the order of 0.003. Samples for total
alkalinity (TA) were collected every 3–4 days in 250 mL glass bottles to
which a few crystals of HgCl2 were added before sealing with ground
glass stoppers and Apiezon® Type-M
high-vacuum grease. The TA determinations were carried out within 1 day of
sampling by open-cell automated potentiometric titration (Titrilab 865,
Radiometer®) with a pH combination
electrode (pHC2001, Red Rod®) and a dilute
(0.025 M) HCl titrant solution calibrated against Certified Reference
Materials (CRM Batch#94, provided by A. G. Dickson, Scripps Institute of
Oceanography, La Jolla, USA). The average relative error, calculated from the
average relative standard deviation on replicate standards and sample
analyses, was <0.15 %. The computed pHT at 25 ∘C,
measured TA, silicic acid, and SRP concentrations were used to calculate the
in situ pHT, pCO2, and saturation state of the
water in each mesocosm using CO2SYS (Pierrot et al., 2006) and the
carbonic acid dissociation constants of Cai and Wang (1998).
The samples for the determination of NO3-, Si(OH)4,
and SRP were filtered through Whatman GF/F filters, collected in acid-washed
polyethylene tubes, and stored at -20∘C. Analysis was carried out
using a Bran and Luebbe Autoanalyzer III using the colorimetric methods of
Hansen and Koroleff (2007). The analytical detection limit was
0.03 µmol L-1 for NO3- plus nitrite
(NO2-), 0.02 µmol L-1 for NO2-,
0.1 µmol L-1 for Si(OH)4, and
0.05 µmol L-1 for SRP.
Biological variables
Chl a determination methods are presented in Bénard et al. (2018a).
Succinctly, duplicate 100 mL samples were filtered onto Whatman GF/F
filters. The filters were soaked in a 90 % acetone solution at
4 ∘C in the dark for 24 h; the solution was then analyzed by a
10-AU Turner Designs fluorometer (acidification method: Parsons et al.,
1984). The analytical detection limit for Chl a was
0.05 µg L-1.
Samples for the determination of free-living heterotrophic bacteria were kept
in sterile cryogenic polypropylene vials and fixed with glutaraldehyde
Grade I (final concentration = 0.5 %, Sigma Aldrich; Marie et al.,
2005). Duplicate samples were placed at 4 ∘C in the dark for
30 min, then frozen at -80∘C until analysis by a FACS Calibur
flow cytometer (Becton Dickinson) equipped with a 488 nm argon laser. Before
enumeration, the samples were stained with SYBR Green I (0.1 % final
concentration, Invitrogen Inc.) to which 600 µL of a Tris-EDTA
10 × buffer of pH 8 were added (Laboratoire MAT; Belzile et al.,
2008). Fluoresbrite beads (diameter 1 µm, Polysciences) were also
added to the sample as an internal standard. The green fluorescence of SYBR
Green I was measured at 525±5 nm. Bacterial abundance was determined
as the sum of low and high nucleic (LNA and HNA) counts (Annane et al.,
2015).
Bacterial production was estimated in each mesocosm except the drifters on
days 0, 2, 4, 6, 8, 10, 11, and 13 by measuring incorporation rates of
tritiated thymidine (3H-TdR), using an incubation and filtration
protocol based on Fuhrman and Azam (1980, 1982). Twenty mL water subsamples
were transferred from glass Erlenmeyers to five sterile glass vials, three as
“measured” values and two as blanks. In all blank vials, 0.2 mL of
formaldehyde 37 % was added immediately after the sampling to stop all
biological activities. Then, 1 mL of 3H-TdR solution
(4 µmol L-1), prepared from commercial solution
(63 Curie mmol-1; 1 mCurie mL-1,
10 µmol L-13H-TdR, MP Biomedicals), was added in
all vials. Samples were incubated for 2.5 h at experimental temperatures (10
or 15 ∘C), and then 0.2 mL of formaldehyde 37 % was immediately
added in the three “measure” vials. Bacteria were then collected by
filtration (diameter 25 mm; 0.2 µm porosity) and filters were
treated according to Fuhrman and Azam (1980, 1982). 3H-TdR
incorporation was measured using a scintillation counter (Beckman LS5801) and
results were expressed in dpm. Blank values were subtracted from “measured”
values to remove background radioactivity. 3H-TdR incorporation
rates were converted into mole of 3H-TdR incorporated per unit of
volume and time, before converting to rate of carbon production using the
carbon conversion factor of Bell (1993).
DMSP and DMS concentrations
For the quantification of DMSPt, duplicate 3.5 mL samples of
seawater were collected into 5 mL polyethylene tubes. Samples were preserved
by adding 50 µL of a 50 % sulfuric acid solution
(H2SO4) to the tubes before storage at 4 ∘C in the dark
until analysis in the following months. Samples for the quantification of
DMSPd were taken daily, but a technical problem during storage
and transport of the samples led to a loss of all samples. To quantify
DMSPt, 1 mL of NaOH (5 M) was injected into a purge and trap
(PnT) system prior to the 3.5 mL sample to hydrolyze DMSP into DMS following
a mole-to-mole conversion. Ultrapure helium was used to bubble the heated
chamber (70 ∘C; 50±5 mL min-1; 4 min) trapping the gas
sample in a loop immersed in liquid nitrogen. The loop was then heated in a
water bath to release the trapped sample and analyzed using a Varian 3800 Gas
Chromatograph equipped with a pulsed flame photometric detector (PFPD, Varian
3800) and a detection limit of 0.9 nmol L-1 (Scarratt et al., 2000;
Lizotte et al., 2012). DMSP concentrations were determined against a
calibration curve using standardized DMSP samples prepared by diluting known
concentrations of DMSP standard (Research Plus Inc.) into deionized water and
analyzed following the same methodology.
Samples for the quantification of DMS were directly collected from the
mesocosms into 20 mL glass vials with a butyl septa and aluminum crimp. The
samples were kept in the dark at 4 ∘C until analysis was carried out
within hours of collection by injecting the 20 mL sample in the PnT system
described above, without the prior addition of NaOH. DMS concentrations were
calculated against microliter injections of DMS diluted with ultrapure helium
using a permeation tube (Certified Calibration by Kin-Tek Laboratories Inc.;
Lizotte et al., 2012).
Statistical analyses
The statistical analyses were performed using the nlme package in R (R Core
Team, 2016). The data were analyzed using a general least squares (gls)
approach to test the linear effects of the two treatments (temperature,
pCO2) and their interaction on the variables (Paul et al.,
2016; Hussherr et al., 2017; Bénard et al., 2018a). The analyses were
conducted on the averages of the measured parameters over the whole duration
of the experiment, and separate regressions for pCO2 were
performed for each temperature when the latter had a significant effect. The
residuals were checked for normality using a Shapiro–Wilk test (p>0.05), and data were transformed (square root or natural
logarithm) if necessary. In addition, squared Pearson's correlation
coefficients (r2) with a significance level of 0.05 were used to
evaluate correlations between key variables.
ResultsPhysical and chemical conditions during the experiments
The practical salinity was 26.52±0.03 on day -4 in all mesocosms and
remained constant throughout the experiment, averaging 26.54±0.02 on
day 13 (Bénard et al., 2018a). The temperature of the mesocosms in each
container remained within ±0.1∘C of the target temperature
throughout the experiment and averaged 10.04±0.02∘C for
mesocosms M1 through M6, and 15.0±0.1∘C for mesocosms M7
through M12 (Fig. 1a). The pHT remained relatively stable
throughout the experiment in the pH-controlled treatments, but decreased
slightly as the experiment progressed, deviating by an average of -0.14±0.07 units relative to the target pHT on the last day (Fig. 1b).
The pH variations corresponded to changes in pCO2 from an
average of 1340±150µatm on day -3, and ranged from 564 to
2902 µatm at 10 ∘C and from 363 to 2884 µatm at
15 ∘C on day 0 following the acidification (Fig. 1c). The in situ
pHT in the drifters (M6 and M11) increased from 7.896 and 7.862
on day 0, at 10 and 15 ∘C, respectively, to 8.307 and 8.554 on
day 13, reflecting the balance between CO2 uptake and metabolic
CO2 production over the duration of the experiment. On the last
day, pCO2 in all mesocosms ranged from 186 to
3695 µatm at 10 ∘C and from 90 to 3480 µatm at
15 ∘C.
Temporal variations over the course of the experiment for
(a) temperature, (b) pHT, and
(c)pCO2. For symbol attribution to treatments,
see the legend. Adapted from Bénard et al. (2018a).
Temporal variations and averages over the course of the experiment
(day 0 to day 13) for (a–b) chlorophyll a (adapted from
Bénard et al., 2018a), (c–d) free-living bacterial abundance,
and (e–f) bacterial production. For symbol attribution to
treatments, see the legend.
Nitrate (NO3-) and silicic acid (Si(OH)4)
concentrations averaged 9.1±0.5 and 13.4±0.3µmol L-1 on day 0, respectively (Bénard et al.,
2018a). The two nutrients displayed a similar temporal depletion pattern
following the development of the phytoplankton bloom. NO3-
concentrations reached undetectable levels (<0.03µmol L-1)
in all mesocosms by day 5. Likewise, Si(OH)4 fell below the
detection limit (<0.1µmol L-1) between days 1 and 5 in
all mesocosms except for those whose pHT was set at 7.2 and 7.6
at 10 ∘C (M5 and M3) and in which Si(OH)4 depletion
occurred on day 9.
Results of the generalized least squares models (gls) tests for the
effects of temperature, pCO2, and their interaction over
the duration of the experiment (day 0 to day 13). Separate analyses with
pCO2 as a continuous factor were performed when
temperature had a significant effect. Averages of bacterial abundance and
production, DMSPt, DMS, Chl a-normalized DMSPt and
DMS concentrations, and DMS : DMSPt ratios are
presented with corresponding degrees of freedom (df). Natural logarithm
transformation is indicated when necessary. Significant results are in bold.
Response variableFactordft-valuep-valueFree-living bacterial abundanceTemperature80.6350.543(×109 cells L-1)pCO28-0.0830.936pCO2× temperature80.2210.830Bacterial productionTemperature62.4540.050(µg C L-1 d-1)pCO2 (10 ∘C)3-0.2720.803pCO2 (15 ∘C)30.7460.510DMSPtTemperature80.5090.625(nmol L-1)pCO28-0.7670.465pCO2× temperature80.1340.897DMSTemperature86.822<0.001(nmol L-1)pCO2 (10 ∘C)4-4.4830.011pCO2 (15 ∘C)4-3.7990.019DMSPt: Chl a ratioTemperature82.6270.030(nmol (µg Chl a)-1)pCO280.1230.908pCO2× temperature80.6210.568DMS : Chl a ratioTemperature85.225<0.001(nmol (µg Chl a)-1)pCO2 (10 ∘C)4-1.3730.242pCO2 (15 ∘C)4-2.2270.090Log(DMS : DMSPt)Temperature85.131<0.001pCO2 (10 ∘C)4-1.8440.139pCO2 (15 ∘C)4-3.1380.035Phytoplankton, bacterial abundance, and production
Chl a concentrations were below 1 µg L-1 following the
filling of the mesocosms (day -4), and had already increased to an average
of 5.9±0.6µg L-1 on day 0 (Fig. 2a). At
10 ∘C, Chl a quickly increased to reach maximum concentrations
around 27±2µg L-1 on day 3±2, and decreased
progressively until the end of the experiment. Increasing the temperature by
5 ∘C resulted in a more rapid development of the bloom and a
speedier decrease in Chl a concentrations during the declining phase of the
bloom. The maximum Chl a concentration reached at the peak of the bloom
was, however, not significantly affected by the difference in temperature. We
found no significant effect of the pCO2 gradient on the
mean Chl a concentrations measured over days 0–13, nor during the
development phase and the declining phase of the bloom as described in
Bénard et al. (2018a) (Fig. 2a–b; Table 1).
Temporal variations and averages over the course of the experiment
(day 0 to day 13) for (a–b) DMSPt, (c–d) DMS,
and (e–f) the natural logarithm of the DMS : DMSPt
ratio. For symbol attribution to treatments, see the legend.
Maximum concentrations reached over the course of the experiment for
(a) DMSPt and (b) DMS. For symbol attribution
to treatments, see the legend.
The free-living bacterial abundance was ∼1.2×109 cells L-1 on day -4, and increased rapidly to reach 3.1±0.6×109 cells L-1 on day 0 (Fig. 2c). This initial increase
in abundance probably resulted from the release of dissolved organic matter
(DOM) during pumping of the seawater and filling of the mesocosms. The
subsequent decrease in bacterial abundance during the development phase of
the bloom suggests that the initial pool of DOM was fully utilized and that
freshly released DOM was scarce. As expected, bacterial abundance increased
during the declining phase of the bloom at 10 ∘C. Under warmer
conditions, bacterial abundance decreased earlier during the initial bloom
development than was observed at 10 ∘C, but was also marked by an
earlier peak during the decline of the bloom, followed by a second, more
variable peak in abundance. These variations in abundances probably reflect
changes in the balance between bacterial growth and loss by grazing. When
averaged over the experiment, we observed no effect of the treatments on the
mean bacterial abundance (Fig. 2c–d; Table 1). At 10 ∘C, bacterial
production was low at the beginning of the experiment and increased gradually
during the development and declining phases of the bloom to reach peaks
values of 9.3±0.9µg C L-1 d-1 (Fig. 2e).
Bacterial production increased faster at 15 ∘C and reached maximal
production rates of 19±1µg C L-1 d-1 on day 11.
Results of the gls model show no effect of the pCO2
gradient on bacterial production, but a positive effect of warming was
observable throughout the experiment (Fig. 2f; Table 1).
DMSPt and DMS
At in situ temperature, DMSPt concentrations averaged 9±2 nmol L-1 on day 0 and increased regularly in all mesocosms up to
day 10 before they plateaued or slightly decreased over the last 2–3 days
(Fig. 3a). These results reveal that DMSP accumulation persisted for several
days after the bloom peaks, to reach a maximum value between days 8 and 13 of
366±22 nmol L-1. At 15 ∘C, DMSPt
concentrations similarly increased after the maximum Chl a concentrations
were reached, but increased faster than at in situ temperature. The maximum
DMSPt concentrations were 396±19 nmol L-1 at
15 ∘C, a value that is not statistically different from the peak
values measured at 10 ∘C (Fig. 4a; Table 2). A greater loss of DMSP
took place in the last days of the experiment at 15 ∘C. By day 13,
79±3 % of the peak DMSPt concentration was lost in the
15 ∘C mesocosms, while 19±4 % of the peak
DMSPt concentration was lost at 10 ∘C. When averaged
over the duration of the experiment, the mean DMSPt
concentrations were not significantly affected by the pCO2
gradient, the temperatures, or the interaction between these two factors
(Fig. 3b; Table 1).
Results of the generalized least squares models (gls) tests for the
effects of temperature, pCO2, and their interaction on the
maximum values of the parameters measured during the experiment. Separate
analyses with pCO2 as a continuous factor were performed
when temperature had a significant effect. Maxima of DMSPt and
DMS concentrations are presented with corresponding degrees of freedom (df).
Significant results are in bold.
Over the 13 days, the DMSPt: Chl a ratio averaged 11.4±0.4 nmol (µg Chl a)-1 at 10 ∘C and was not
affected by increasing pCO2 (Fig. 5; Table 1). Due to the
aforementioned mismatch between the peaks in Chl a and DMSPt,
the average DMSPt: Chl a ratios were significantly higher
at 15 ∘C, averaging 19±1 nmol (µg Chl a)-1
over the experiment (Fig. 5; Table 1). However, we found no significant
relationship between DMSPt: Chl a and the
pCO2 gradient.
Averages of the DMSPt: Chl a ratio over the
course of the experiment (day 0 to day 13). For symbol attribution to
treatments, see the legend.
Initial DMS concentrations were below the detection limit on day 0 (<0.9 nmol L-1) and slowly increased during the first 7 days, while
most of the build-up took place after day 8 in all treatments (Fig. 3b). The
net accumulation of DMS was faster at 15 ∘C than at 10 ∘C,
with higher daily DMS concentrations at 15 ∘C compared to
10 ∘C from day 3 until day 13. At the end of the experiment, DMS
concentrations averaged 21±4 nmol L-1 at 10 ∘C and 74±14 nmol L-1 at 15 ∘C. Over the full duration of the
experiment, we found significant negative effects of increasing
pCO2 on mean DMS concentrations at the two temperatures
tested (Fig. 3d; Table 1). At 10 ∘C, we measured a ∼67 %
reduction of mean DMS concentrations from the drifter relative to the most
acidified treatment (∼345 ppm vs. ∼3200 ppm), with values
decreasing from 10±2 to 3.2±0.8 nmol L-1. At 15 ∘C,
the mean DMS concentrations decreased by roughly the same percentage (∼69 %) as pCO2 increased from the drifter to the most
acidified treatment (∼130 ppm vs. ∼3130 ppm). Nevertheless, the
mean DMS concentrations were higher at 15 ∘C, ranging from 34±13 to 11±3 nmol L-1, an average increase of ∼240 %
compared to the DMS concentrations at 10 ∘C (Fig. 3c; Table 1).
Similarly, the peak DMS concentrations decreased linearly with increasing
pCO2 at both temperatures, and concentrations were always
higher at 15 than at 10 ∘C for any given pCO2
(Fig. 4b; Table 2).
The DMS : DMSPt ratio exhibited the same general pattern as
the DMS, i.e. low and stable values during the first 8 days, and increasing
values between days 8 and 13 (Fig. 3e). The natural logarithm of the
DMS : DMSPt ratio was not affected by the
pCO2 gradient at 10 ∘C when averaged over the
13-day experiment, but a significant decrease in the
DMS : DMSPt ratios was observed with increasing
pCO2 at 15 compared to 10 ∘C (Fig. 3f; Table 1).
Moreover, there was a significant positive correlation between bacterial
production and DMS concentrations, as 64 % of the variability of DMS
concentrations is explained by variations in bacterial production (r2=0.64, p<0.001, n=70; Fig. 6).
Linear regression between DMS concentrations and bacterial
production during the experiment.
DiscussionGeneral characteristics
As far as we know, this study is the first full factorial mesocosm experiment
where all pCO2 treatments (pHT from 8.0 to
7.2) were replicated at two different temperatures (in situ and
+5∘C), to assess the impact of ocean acidification and warming on
the dynamics of DMSP and DMS concentrations during a phytoplankton bloom. A
diatom bloom dominated by Skeletonema costatum developed in all
mesocosms, regardless of the treatments. This chain-forming centric diatom is
a cosmopolitan species in coastal and estuarine systems and a frequent
bloomer in the Lower St. Lawrence Estuary (LSLE) (Kim et al., 2004; Starr et
al., 2004; Annane et al., 2015). The 13 days where treatments were applied
allowed us to capture the development and declining phases of the bloom. The
impacts of the treatments on the dynamics of the bloom during these two
phases are described in greater detail in Bénard et al. (2018a). Briefly,
the acidification had no detectable effect on the development rate of the
diatom bloom and on the maximum Chl a concentrations reached. However,
increasing the water temperature by 5 ∘C increased the growth rate
of the diatoms, shortening the development phase of the bloom, from 4–7 days
at 10 ∘C to 1–4 days at 15 ∘C. However, these changes in
the bloom timing did not alter the overall primary production throughout the
experiment. Hereafter, we discuss how increasing pCO2
(lowering the pH) affected DMSP and DMS concentrations and how a
5 ∘C increase in temperature altered the impacts of the
pCO2 gradient during the experiment.
DMSP dynamics
The buildup of the phytoplankton biomass during the bloom development was
coupled with a rapid increase in DMSPt concentrations (Fig. 3a).
Assuming that S. costatum was responsible for most of the DMSP
production, our results indicate a low sensitivity of the DMSP synthesis
pathway to acidification in this species. The net accumulation of
DMSPt persisted several days after the peaks in Chl a,
indicating a decoupling between DMSP synthesis, algal growth and nitrogen
metabolism (Bénard et al., 2018a).
Effects of acidification on DMSP
At in situ temperature, the averaged DMSPt concentrations were
not affected by the increase in pCO2 (Fig. 3b; Table 1).
The lack of significant changes in the DMSPt: Chl a ratio
as a function of the pCO2 gradient also supports this
conclusion (Fig. 5; Table 1). This result is consistent with those of
previous studies that showed a relatively weak effect of an increase in
pCO2 on DMSP concentrations (Vogt et al., 2008; Lee et
al., 2009; Avgoustidi et al., 2012; Archer et al., 2013; Webb et al., 2015).
Furthermore, much like the patterns observed at 10 ∘C, there was no
relationship between the concentrations of DMSPt and the
pCO2 gradient observable at 15 ∘C (Table 1).
Effects of warming on DMSP
In contrast to the absence of effects of acidification on DMSP, warming has
been previously shown to affect DMSP concentrations in nature. Results from
shipboard incubation experiments conducted in the North Atlantic have
revealed an increase in particulate DMSP (DMSPp) concentrations
due to a 4 ∘C warming (Lee et al., 2009). During this last study,
the higher DMSPp concentrations were attributed to a
temperature-induced shift in community structure toward species with higher
cellular DMSP content. During our study, the pCO2 and
temperature treatments did not alter the structure of the community
(Bénard et al., 2018a). Most of the DMSP synthesis was likely linked to
the numerically dominant diatoms, as all other algal groups identified
contributed to less than 10 % of the total algal abundance (see Fig. 6 in
Bénard et al., 2018a). Our results thus suggest that DMSP synthesis by
S. costatum during the nitrate-replete growth phase was not
significantly affected by warming. Rather, it is the accelerated growth rate
of S. costatum that promoted the concurrent accumulation of biomass
and DMSPt, while the higher DMSPt: Chl a ratio
observable at 15 ∘C may be explained by the faster degradation of
cells under warming. Several empty frustules were found during the last days
of the experiment at 15 ∘C, suggesting a loss of integrity of the
cells and potential increase in the release of intracellular dissolved
organic matter, including DMSP. However, the absence of dissolved DMSP
measurements prevents the verification of this suggestion. The increase in
the abundance of bacteria and in bacterial production (Fig. 2c, e) during
that period also suggest that more dissolved organic matter was produced
during the decline of the bloom, as previously reported (Engel et al., 2004a,
b). During our experiment, transparent exopolymer particles (TEP)
concentrations increased during this period (Gaaloul, 2017), adding to the
evidence for heightened DOM production by the decaying bloom, with a
potential increase in DMSP metabolization by heterotrophic bacteria under
warming.
DMS dynamics
DMS concentrations remained very low during the development phase of the
bloom (day 8) and increased in the latter days of the experiment. Most of the
DMS accumulation in the mesocosms took place between days 8 and 13 and likely
originated from DMSP that may have been released during cell lysis (Kwint and
Kramer, 1995) or upon zooplankton grazing (Cantin et al., 1996). Unbalanced
growth and photosynthesis of algal cells under nitrogen deficiency during
that period may also be responsible for a greater production and active
exudation of DMSP (Stefels, 2000; Kettles et al., 2014).
Effects of acidification on DMS
At in situ temperature, we observed a significant linear decrease in DMS
concentrations (both averaged over the full duration of the experiment and
peak concentrations) with increasing pCO2 (Figs. 3c, 4b;
Tables 1 and 2). A few studies have shown a neutral or positive effect of increasing pCO2 on DMS
concentrations, stemming from altered phytoplankton taxonomy,
microzooplankton grazing, or diverging bacterial activity promoting DMS
production (Vogt et al., 2008; Kim et al., 2010; Hopkins and Archer, 2014).
However, the majority of studies have shown a decreasing trend of DMS
concentrations with increasing pCO2 similar to our results
(Hopkins et al., 2010; Archer et al., 2013; Park et al., 2014; Webb et al.,
2015, 2016). In these studies, the pCO2-induced decreases
in DMS were generally attributed to changes in the microbial community
speciation and structure, or to microzooplankton grazing, although decreases
in bacterial DMSP-to-DMS conversion or increases in DMS consumption have also
been suggested (Archer et al., 2013; Hussherr et al., 2017). During our
study, the decrease in DMS concentrations with increasing
pCO2 cannot be directly attributed to a decrease in
DMSPt since this pool was not affected by the
pCO2 gradient (Figs. 3b, 4a; Tables 1 and 2). In Park et
al. (2014), the increase in pCO2 led to the reduction in
the abundance of Alexandrium spp., an active DMSP and DMSP-lyase
producer, and a concomitant reduction of the associated microzooplankton
grazing. As Alexandrium spp. was less numerous, the associated
attenuation of microzooplankton grazing resulted in a reduction of the mixing
of DMSP and DMSP-lyase, leading to less DMSP-to-DMS conversion. Given the
strong contribution of S. costatum to the bloom, a species with no
reported DMSP-lyase, it can be assumed that most, if not all, of the DMS
produced was driven by bacterial processes following DMSP release by the
diatoms. Thus, the decrease in DMS concentrations in our study could have
been the result of altered bacterial mediation, through either reduced
bacterial production of DMS or heightened bacterial consumption of DMS.
Whereas a reduction in bacterial uptake of DMSP is unlikely, given that the
bacterial abundance and production were unaffected by the
pCO2 gradient (Table 1), the observed decrease in DMS
concentrations could imply that at higher pCO2 the
bacterial yields of DMS are abated. The relative proportion of DMSP consumed
by bacteria and further cleaved into DMS is closely tied to bacterial demand
in carbon and sulfur as well as to the availability of DMSP relative to other
sources of reduced sulfur in the environment (Levasseur et al., 1996; Kiene
et al., 2000; Pinhassi et al., 2005). The absence of a significant
pCO2 effect on the concentrations of DMSP during this
study may be interpreted as a pCO2-related alteration of
the microbially mediated fate of consumed DMSP. Unfortunately, in the absence
of detailed 35S-DMSPd bioassays, it is impossible to
confirm the outcome of the DMSP metabolic pathways including the DMSP-to-DMS
conversion efficiency in relation to the pCO2 gradient. A
few studies (Grossart et al., 2006; Engel et al., 2014; Webb et al., 2015)
have reported enhanced bacterial abundance and production at high
pCO2, especially for attached bacteria as opposed to
free-living bacteria (Grossart et al., 2006). However, regardless of the
temperature treatment, neither the abundance nor the activity of bacteria
seemed to be significantly impacted by pCO2 in this study.
A pCO2-induced increase in bacterial DMS turnover could
also explain the decrease in DMS concentrations, but several studies suggest
that bacterial DMS consumption in natural systems is often tightly coupled to
DMS production itself (Simó, 2001, 2004). Furthermore, while one
laboratory study reported that non-limiting supplies of DMS may be used as a
substrate by several members of Bacteroidetes (Green et al., 2011), another
study showed that only a subset of the natural microbial population may turn
over naturally occurring levels of DMS (Vila-Costa et al., 2006b).
Nevertheless, the sensitivity of these DMS-consuming bacteria to decreasing
pH remains unknown. Likewise, whereas we cannot exclude a potential impact of
pCO2 on DMS turnover via bacterioplankton, it is plausible
that the pCO2 gradient may have affected a widespread
physiological pathway among bacteria, specifically, the metabolic breakdown
of DMSP.
Effects of warming on DMS
A warming by 5 ∘C increased DMS concentrations at all
pCO2 tested, resulting in an offset of the negative
pCO2 impact when compared to the in situ temperature. This
result differs from the observation of Kim et al. (2010) and Park et
al. (2014) in two ways. First, our results show an increase in DMS
concentrations in the warmer treatment, while the two previous studies
reported a decrease. Second, our results confirm that a temperature effect
may be measured over a large range of pCO2. It is
noteworthy that the increase in DMS concentrations at the two temperatures
tested varied from 110 % at pH 8.0 up to 370 % at pH 7.4. This
highlights the scaling of the temperature effect over an extensive range of
pCO2 and the importance of simultaneously studying the
impact of these two factors on DMS production. As observed at 10 ∘C,
both the average and the peak DMS concentrations decreased linearly as
pCO2 increased in the warm treatment (Figs. 3d, 4b;
Tables 1 and 2). Nevertheless, the pCO2-induced decrease
in DMS concentrations at 15 ∘C cannot be directly attributed to a
decrease in DMSPt concentrations given that an increase in
pCO2 had no discernable effect on DMSPt
concentrations. In contrast to our observations at the in situ temperature,
where DMSPt continued to increase until day 12, DMSPt
concentrations at 15 ∘C typically decreased from day 8 onward
(Fig. 3a). This loss in DMSPt suggests that microbial consumption
of DMSP exceeded DMSP algal synthesis. In light of the dominance of
S. costatum, a phytoplankton taxon not known to exhibit DMSP-lyase,
the bulk of microbial DMSP mediation was likely associated with heterotrophic
bacteria. In support of this hypothesis, the bacterial production was ∼2 times higher at 15 than at 10 ∘C between days 8 and 13 (19±1µg C L-1 d-1 vs. 9.3±0.9µg C L-1 d-1) (Fig. 2), and we observed a
significant correlation between the quantity of DMSPt lost
between the day of the maximum DMSPt concentrations and day 13,
and the quantity of DMS produced during the same period (coefficient of
determination, r2= 0.60, p<0.01, n=11). Assuming that all
the DMSPt lost was transformed into DMS by bacteria, we
calculated that DMS yields could have varied by 0.5 % to 32 % across
the pCO2 gradient (mean =13±11 %). These
very rough estimates of DMS yields are likely at the lower end since measured
DMS concentrations also reflect losses of DMS through photo-oxidation and
bacterial consumption. Nevertheless, we cannot exclude the possibility of
some passive uptake of DMSP by the picocyanobacterial population in the
mesocosms, although this pathway is not considered to be significant in
natural systems (Malmstrom et al., 2005; Vila-Costa et al., 2006a) and does
not lead to the production of DMS. Moreover, our estimates do not account for
the possible DMSP assimilation by grazers, reducing the DMSPd
available for bacteria, and would lead to an increase in DMS yields. Our
“minimum community” DMS yield estimates agree with an expected range of
microbial DMS yields in natural environments, from 2 % to 45 % (see
review table in Lizotte et al., 2017). These gross but realistic estimates of
heterotrophic bacterial DMSP-to-DMS conversions could explain the bulk of the
DMS present in our study, a hypothesis also supported by the strong positive
correlation (r2=0.64, p<0.001, n=70; Fig. 6) between overall
DMS concentrations and bacterial production. Combined, these findings
reinforce the idea that bacterial metabolism, rather than bacterial stocks,
may significantly affect the fate of DMSP (Malmstrom et al., 2004a, b, 2005;
Vila et al., 2004; Vila-Costa et al., 2007; Royer et al., 2010; Lizotte et
al., 2017). Consequently, drivers of environmental change, such as
temperature and pH, could alter bacterial activity and strongly impact the
concentrations of DMS by controlling the rates of production and loss of DMS
by bacteria. Specific measurements of bacterial DMSP uptake and DMS yields
using 35S-DMSPd should be conducted to assess the
impacts of pCO2 and temperature on the microbial fate of
DMSP.
Limitations
During our study, the pCO2 changes were applied abruptly,
over a day, from in situ values to pCO2 levels exceeding
the most pessimistic pCO2 scenarios for the end of the
century. Compared to our manipulation, ocean acidification will proceed at a
much slower rate, potentially allowing species to adapt and evolve to these
changing conditions (Stillman and Paganini, 2015; Schlüter et al., 2016).
However, in the LSLE, the upwelling of low oxygenated waters can rapidly
reduce the pHT to ∼7.62, or even lower with contributions
of low pHT (7.12) freshwaters from the Saguenay River during the
spring freshet (Mucci et al., 2017). Thus, the swift and extensive
pCO2 range deployed in our experiment may seem improbable
for the open ocean on the short term, but may not be inconceivable for this
coastal region. However, the warming of 5 ∘C used in this mesocosm
study possibly exceeds the upper limit of temperature increase for the end of
the century in this region. In the adjacent Gulf of St. Lawrence (GSL),
surface water temperature (SST) correlates strongly with air temperature,
allowing the estimation of past SST. This relationship showed that SST has
increased in the GSL by 0.9 ∘C per century since 1873 (Galbraith et
al., 2012), although additional positive anomalies of 0.25–0.75 ∘C
per decade have been shown between 1985 and 2013 (Galbraith et al., 2016). In
the LSLE, the highest temperatures occur at the end of summer/early fall, and
gradually dissipate by heating the subjacent cold intermediate layer through
vertical mixing (Cyr et al., 2011). The extent of the projected warming in
the LSLE is recondite, but will likely result from the multifaceted
interactions between heat transfer from the air and physical factors
controlling the water masses.
The results from our study could also be influenced by the absence of
macrograzers in the mesocosms. An additional grazing pressure could limit the
growth of the blooming species, reducing the amount of DMSP produced, or
could increase the release of DMSPd through sloppy feeding after
the initial bloom (Lee et al., 2003). It is unclear how an increase in
grazing pressure would have impacted the concentrations of DMS in our
experiment. On the one hand, increased predation could have limited the net
accumulation of DMSPp, with a possible reduction in DMS
production. On the other hand, increased grazing could have favored the
release of DMSPp as DMSPd, thus increasing the availability of
this substrate for microbial uptake, mediation, and possible conversion into
DMS. Despite the absence of reported changes in community composition in our
study, many OA mesocosm experiments have described changes in DMS
concentrations associated with shifts in community structure in the past
(Vogt et al., 2008; Hopkins et al., 2010; Kim et al., 2010; Park et al.,
2014; Webb et al., 2015). Nonetheless, our results align with those of other
OA studies (Archer et al., 2013; Hussherr et al., 2017), suggesting that the
mediation of heterotrophic bacteria plays a major role in DMS cycling in the
absence of reported phytoplanktonic DMSP-lyase, such as in a diatom-dominated
bloom in the LSLE.
Conclusions
The objective of this study was to quantify the combined impact of increases
in pCO2 and temperature on the dynamics of DMS during a
fall diatom bloom in the St. Lawrence Estuary. Our mesocosm experiment
allowed us to capture the development and declining phases of a bloom
strongly dominated by the diatom Skeletonema costatum and the
related changes in bacterial abundance and production. As expected, warming
accelerated the development of the bloom, but also its decline. Both
DMSPt and DMS concentrations increased during the development
phase of the bloom, but their peak concentrations were reached as the bloom
was declining. Increasing pCO2 had no discernable effect
on the total amount of DMSPt produced at both temperatures
tested. In contrast, increasing the pCO2 to the value
forecasted for the end of this century resulted in a linear decrease in DMS
concentrations by 33 % and by as much as 69 % over the full
pCO2 gradient tested. These results are consistent with
previous reports that acidification has a greater impact on the processes
that control the conversion of DMSP to DMS than on the production of DMSP
itself. The pCO2-induced decrease in DMS concentrations
observed in this study adds to the bulk of previous studies reporting a
similar trend. In diatom-dominated systems, such as the one under study in
this experiment, heterotrophic processes underlying DMS production seem to be
most sensitive to modifications in pCO2. Whereas predatory
grazing and its associated impacts on DMS production cannot be ruled out
entirely, the decreases in DMS concentrations in response to heightened
pCO2 are likely related to reductions in
bacterial-mediated DMS production, a hypothesis partly supported by the
significant positive correlations found between DMS concentrations and
bacterial production. Whereas the DMS concentrations decreased significantly
with increasing pCO2 at both 10 and 15 ∘C,
warming the mesocosms by 5 ∘C translated into a positive offset in
concentrations of DMS over the whole range of pCO2 tested.
Higher DMSP release and increased bacterial productivity in the warm
treatment partially explain the stimulating effect of temperature on DMS net
production. Overall, results from this full factorial mesocosm experiment
suggest that warming could mitigate the expected reduction in DMS production
due to ocean acidification, even increasing the net DMS production with the
potential to curtail radiative forcing. Further studies should focus on the
relationship between bacterial conversion of DMSP to DMS and
pCO2, to mechanistically verify the suggested cause of the
DMS reduction observed in this experiment. Moreover, an extended range of
temperature should also be considered for future multiple stressors
experiment as warming had, more often than not, a stronger effect on the
community than acidification.
Data availability
The data are freely accessible via
https://doi.org/10.1594/PANGAEA.886887 (Bénard et al., 2018b) or
can be obtained by contacting the author (robin.benard.1@ulaval.ca).
Author contributions
RB was responsible for the experimental design elaboration, data
sampling and processing, and writing of this article. Several co-authors
supplied specific data included in this article, and all the co-authors
contributed to this final version of the article.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors wish to thank Station Aquicole-ISMER, particularly Nathalie Morin
and her staff, for their support during the mesocosm experiment. We also wish
to acknowledge Gilles Desmeules, Bruno Cayouette, Sylvain Blondeau,
Claire Lix, Rachel Hussherr, Liliane St-Amand, Marjolaine Blais,
Armelle Galine Simo Matchim, and Marie-Amélie Blais for their invaluable
help over the duration of the experiment. This study was funded by a team
grant from the Fonds de recherche du Québec – Nature et technologies
(FRQNT-Équipe-165335), the Canada Foundation for Innovation, the Canada
Research Chair on Ocean Biogeochemistry and Climate, Fisheries and Oceans
Canada, and the Major International Joint Research Project of the National
Natural Science Foundation of China (grant no. 41320104008). This is a
contribution to the research program of Québec-Océan. Edited by: Koji Suzuki Reviewed by: three
anonymous referees
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