A field campaign was conducted on Heron Island, southern Great Barrier Reef (23.44°S, 151.91°E), in the austral summer of 2016 (4^th to the 17^th of February) where dissolved CH₄ and CO₂ were measured alongside dissolved DMS (DMS_w) using a cavity-ring-down spectrometer (Picarro G2201-i) (Maher et al., 2013) and a Vapor Generation – Chemiluminescence (VG-CL) device (Nagahata et al., 2013), respectively. A Gas Chromatograph (Varian CP3800 GC) equipped with a pulsed flame photometric detector (GC-PFPD) was used to measure atmospheric DMS (DMS_a) (Swan et al., 2015; Swan et al., 2017).

A water pump and a 60 m suction-rated pipe were used to pump seawater from the Heron Island reef flat to the Heron Island Research Station (HIRS), where all instruments were operated. The seawater inlet was attached to a cinder block 50 cm above the sediment bed and about 50 cm below the low-tide mark. A non-return valve and a 1 mm mesh net were placed around the suction pipe inlet to prevent large pieces of sediment and seaweed getting through the suction line. The mesh was cleaned every 2-3 days to prevent biofouling. Part of the pumped seawater was diverted into a showerhead exchanger that was connected in line with the cavity-ring-down spectrometer for CH₄ and CO₂ measurements, while the remainder was diverted to the HIRS’ flow-through seawater recycling system. The recycling water outlet was used to manually sample seawater at a low flow rate using a 50 mL syringe for DMS_w analysis. A HYDROLAB HL4 sonde was placed within 1 m of the water intake to record seawater temperature, salinity, dissolved oxygen (DO), and depth, every 15 min. Tide predictions were sourced from the Bureau of Meteorology (BoM) with low tide times locally adjusted +1.25 h for the Heron Island reef flat according to Swan et al. (2017).

An air intake consisting of ~10 m Teflon™ tubing was fixed to the roof of the HIRS, within ~100 m in line of sight to the reef flat location where seawater was continuously pumped from. The air intake was shielded from rain. A wireless automated weather station (AWS, model XC0348, Electus Distribution) mounted within 1 m of the air intake provided data for wind speed (WS, ± 1 m s^-1 for WS < 10 m s^-1 and ± 10% for WS > 10 m s^-1), wind direction (WD), rainfall, air temperature (± 1°C), humidity (± 5%) and barometric pressure (± 3hPa), at 5-min intervals. Meteorological data at 5 min intervals was used to match to the 15 min interval chemical measurements. Solar irradiance at the proximity of the air intake was recorded using a HOBO (Onset Co., USA) light logger (upper limit ~6000 μE m^-2 s^-1). Light intensity HOBO Lux units were adjusted to a maximum light intensity of 2000 μEm⁻² s⁻¹. First and last daytime lights were at 5:14 and 18:57 respectively on average over the sampling period. An air quality monitor laser particle counter (Dylos DC1700, Dylos Corp, CA, USA) was used to record 1-min averaged particle number concentrations at ambient humidity in the two size range fractions 0.5-2.5 µm and > 2.5 µm every 15 min. The air quality monitor was placed in a shielded location near the air intake.

DMS_w concentrations were determined in triplicate every 15 min by placing 10 mL of seawater into a 50 mL sample tube that was manually shaken for 1 min, then pressurised with 30 mL of air and injected onto the chemiluminescence device (VG-CL) (Nagahata et al., 2013). When DMS_w mixes with ozone in the VG-CL, it generates an instant chemiluminescent emission where the light intensity is converted into a quantifiable electrical signal. The injection tube was rinsed with deionised water in between each injection to prevent analyte carry-over. A 6-point calibration was run at the beginning of the field campaign, and either a 4 nM or 10 nM DMSP standard was randomly run each day to monitor the reproducibility and stability of the system over time. Because these measurements are labour-intensive and could not be automated, sampling occurred at random times of the day and night over the 2-week field campaign.

DMS_a concentrations were determined on the GC-PFPD using an automated cryogenic trapping system that collected ~4L of air for analysis, providing a 0.1 nmol m^-3 (0.002 ppb) limit of detection. The expanded relative measurement uncertainty of the automated GC-PFPD was 13% (k = 2, for a 95% CI). A complete description of the configuration, operation, calibration, and uncertainty analysis of the automated GC-PFPD is described by Swan et al. (2015).

Sea-to-air DMS fluxes were estimated based on the different parameterisations proposed by Liss and Merlivat (1986) (LM86), Nightingale et al. (2000) (N00) and Wanninkhof (2014) (W14) and by applying the approach of Lana et al. (2011) (L11) (see details in Supplementary Table 1 ). The sea-to-air DMS fluxes and uncertainties presented here correspond to the median and standard error of these combined fluxes.

Briefly, sea-to-air fluxes of DMS (F_DMS) (in µmol m^-2 d^-1) were estimated based on the following equation:

Where K_T is the gas transfer velocity constant (in m d^-1), α is the dimensionless Henry’s Law constant and C_w and C_g are DMS concentrations (in µmol m^-3) in the water and gas phase, respectively, with each parameterisation using a different approach to estimate K_T.

The dimensionless Henry’s Law Constant (α) for DMS solubility in seawater was calculated using the following equation:

Where H_K (atm L mol^-1) is the Henry’s Law Constant for DMS, R is the universal gas constant (0.082 L atm K⁻¹ mol⁻¹) and T is the seawater temperature in Kelvin. H_K was calculated based on the following equation by Dacey et al. (1984):

Sea-to-air CH₄, CO₂ and O₂ fluxes were calculated based on the parameterisation approaches proposed by Ho et al. (2006) and Wanninkhof (2014). Atmospheric concentrations were assumed to be constant (CH₄ 1.8 ppm, CO₂ 400 ppm and O₂ 21000 ppm). Solubility coefficients for CH₄ (Wiesenburg and Guinasso Jr, 1979), CO₂ (Weiss, 1974) and O₂ (Benson and Krause Jr, 1984) were calculated based on temperature and salinity.

Reef DMS, CH₄, CO₂ and O₂ production (RP, mol m^-2 h^-1) were calculated around each low tide using the following equation:

Where ΔC_W is the difference in dissolved concentrations between the highest point following low tide and the actual low tide, ΔT is the time difference between these 2 points, D is the average depth for that time period and MeanFlux is the average sea-to-air flux for that period.

The significance of the correlations between DMS, CH₄, CO₂ and O₂ were evaluated using the Pearson correlation method. Given that DMS and CH₄ showed the strongest correlation (R² = 0.7526, p <0.00001, n = 93), we specifically assessed what drives DMS and CH₄ fluxes by carrying out stepwise multiple linear regressions (MLRs) against seven potential predictors and their interactions (salinity, pH, depth, wind direction, dewpoint, windchill and solar irradiance). In order to avoid over-fitting and find a balance between model complexity and explanatory power, we followed a backward elimination process based on the Akaike Information Criterion (AIC, details in Supplementary Figure 1 ) starting with all seven potential measured predictors. Note that we opted not to include temperature and wind speed, which are covariates in flux calculations. Windchill and dewpoint are influenced by air temperature, which is not a covariate in flux calculations, and wind direction is not correlated to wind speed. Calculations were performed using the functions “boxplot”, “stepwisefit”, and “plotEffects” in MATLAB.

Sea surface temperature (SST) within the Heron Island reef lagoon fluctuated between 25.0 and 29.2°C over the course of the campaign with an average SST of 27.3 ± 0.94°C. Salinity and pH were on average 37.0 ± 0.25 ppt and 8.18 ± 0.19, respectively. Seawater depth ranged from 0.46 to 1.38 m, 10 m wind speed from 0 to 9.20 m s^-1, windchill from 21.3 to 29.9°C, dewpoint from 20.0 to 23.4°C and light intensity from 0 to 2000 µE m^-2 s^-1.

Sea-to-air DMS fluxes varied from not-detectable to 69.5 µmol m^-2 d^-1 with an average flux of 24.9 ± 1.81 µmol m^-2 d^-1 (mean± SE, n = 93; Figure 1A ). Sea-to-air CH₄ fluxes varied from -0.12 to 3.91 µmol m^-2 d^-1 with an average flux of 1.36 ± 0.11 µmol m^-2 d^-1 ( Figure 1B ). Atmospheric CO₂ and O₂ fluxes varied from -15.4 to 30.7 mmol m^-2 d^-1 and from -188 to 403 mmol m^-2 d^-1, with mean CO₂ and O₂ fluxes of 0.30 ± 0.71 and 70.5 ± 12.3 mmol m^-2 d^-1, respectively ( Figures 1C, D ).

All 24h-integrated sea-to-air fluxes showed a diurnal trend with DMS, CO₂ and CH₄ fluxes being generally greater at night than during the day while O₂ fluxes showed the opposite trend ( Figure 1 ). Sea-to-air CO₂ fluxes showed the most variability during the first hours of sunlight while DMS and O₂ fluxes showed the most variability between dusk and midnight. CH₄ fluxes showed the most variability both at night and during the first hours of sunlight.

Sea-to-air O₂ fluxes negatively correlated with DMS (R² = 0.0569, n = 93, p = 0.02), CH₄ (R² = 0.1476, n = 93, p < 0.001) and CO₂ (R^{2 =} 0.346, n = 93, p <0.00001), although the negative relationship between O₂ and CO₂ was clearly the strongest ( Figure 2 ). Sea-to-air CO₂ and CH₄ fluxes were weakly positively correlated (R^{2 =} 0.1709, n = 93, p <0.001) while CO₂ and DMS fluxes were not significantly correlated (R^{2 =} 0.0242, n = 93, p > 0.05). Sea-to-air DMS and CH₄ fluxes showed the strongest positive correlation (R² = 0.7526, n = 93; p < 0.00001).

Dissolved DMS, CH₄, CO₂ and O₂ concentrations are presented in the supplementary material ( Supplementary Figure 2 ). Because fluxes are calculated based on concentrations and transfer velocity, and the transfer velocity is shared across fluxes, the relationships between dissolved DMS and dissolved CH₄, CO₂ and O₂ concentrations were also plotted. Dissolved DMS concentrations positively correlated with dissolved CH₄ (R² = 0.7706, n = 93) and CO₂ (R² = 0.2273, n = 93) concentrations and negatively correlated with dissolved O₂ concentrations (R² = 0.3847, n = 93) ( Supplementary Figure 3 , p < 0.00001).

The backward elimination process used in the stepwise Multiple Linear Regressions (MLRs) revealed that salinity, pH, depth, wind direction, dewpoint and wind chill were the six main drivers of sea-to-air DMS fluxes and that pH, depth, wind direction, dewpoint, wind chill and light were the six main drivers of CH₄ fluxes ( Table 1 ). The linear fit between measured and predicted DMS and CH₄ fluxes exhibited an R² of 0.717 and 0.631 (n = 93, p <0.00001), respectively ( Figures 3A, B ). The most negative drivers of DMS emissions were wind chill and pH whereas the most positive driver was wind direction ( Figure 3C ). Similarly, the main negative and positive drivers of CH₄ emissions were pH and wind direction, respectively ( Figure 3D ). When plotting wind direction (in azimuth degrees) against DMS and CH₄ fluxes ( Supplementary Figure 4 ), it appeared that the wind direction leading to the greatest DMS and CH₄ emissions was predominantly at 180°, which corresponds to the direction of the dominant southerly trade winds at Heron Island.

The DMS reef production exhibited positive values at all times, with a few sporadic spikes around peak hours of sunlight (~ 11:00) and in the middle of the night (~23:00) ( Figure 4A ). Although an order of magnitude lower, the CH₄ reef production was also positive at all times but with greater values recorded at night ( Figure 4B ). The CO₂ reef production was positive at night and for the first half of the day but negative from about 12:00 to 20:00 ( Figure 4C ). As expected, the O₂ reef production was essentially positive during the day until dusk, and negative at night ( Figure 4D ).

Over the course of the study, intermediate (< 0.5-2.5 µm) and large (> 2.5 µm) aerosol particle number concentrations averaged 5.51 x 10⁶ ± 1.73 x 10⁵ m^-3 and 1.15 x 10⁶ ± 4.63 x 10⁴ m^-3, respectively ( Figures 5A, B ). Particle numbers in the 0.5µm-2.5 µm portion size fraction were highest in the early morning (7:00 and 11:00) and again at the end of the day around and following sunset (from 18:30 to 20:00). Particle numbers in the > 2.5 µm size fraction occurred slightly later in the morning (~11:00), early afternoon (11:00 to 15:00) and in the evening between 18:30 and 20:00.

Wind speed positively correlated with the abundance of intermediate (R² = 0.3337, n = 93, p < 0.0001) and large (R² = 0.1424, n = 93, p = 0.0002) aerosol particles ( Figures 5C, D ). Sea-to-air DMS fluxes also positively correlated with the abundance of intermediate (R² = 0.1669, n = 93, p = 0.0001) and large (R² = 0.0869, n = 93, p = 0.004) aerosol particles ( Figures 5E, F ).

DMS fluxes reported in this study (Min = not-detectable, Max = 69.5 µmol m^-2 d^-1, Mean = 24.9 ± 1.81 µmol m^-2 d^-1, n = 93) fell within the range of previously reported sea-to-air DMS fluxes for 5 coral reef systems across the Great Barrier Reef, which varied from not-detectable to 153 µmol m^-2 d^-1 in the austral summer wet season (Jones et al., 2018). However, seasonal DMS fluxes recorded in the current study were about five and four times greater on average than the 2012 summer wet season study conducted on Heron Island (5.0 µmol m^-2 d^-1 , n = 651) (Swan et al., 2017) and the average DMS fluxes reported by Jones et al. (2018) (6.4 µmol m^-2 d^-1, n = 237), respectively. Since DMS_a (mean ± SD) at Heron Island in the 2012 and 2016 summers were similar (i.e. 3.9 ± 1.5, n = 651, and 3.7 ± 0.8, n = 761 nmol m^-3, respectively, data not shown), the high fluxes in this study most likely result from temporally elevated DMS production in the Heron Island reef lagoon in the year 2016 or on the section of the Heron Island reef flat where seawater samples were collected. The significant differences in the average DMS fluxes for the summers of 2012 and 2016 at Heron Island might also reflect differences in the employed flux calculation (photochemical ambient mass balance approach used by Swan et al. (2017) as opposed to the gradient method flux calculation used in this study). Similarly, Jones et al. (2018) used the LM86 gradient method flux parameterisation, which was shown to give much lower flux estimates than the other gradient method flux parameterisations used in the current study (see Supplementary Table 1 ), hence indicating that the approach employed can lead to significantly different values. The estimated flux difference might also reflect the limitations of the non-automated VG-CL instrument used in this study, which ultimately led to preferential DMS_w sampling during the day and evenings, with a lack of night-time measurements to match with the continuous DMS_a measurements.

Sea-to-air fluxes for CH₄ (Min = −0.12 µmol m^-2 d^-1, Max = 3.91 µmol m^-2 d^-1, mean = 1.36 ± 1.03 mmol m^-2 d^-1) and CO₂ (Min = −15.4, Max = 30.7 mmol m^-2 d^-1, mean = 0.30 mmol m^-2 d^-1) were consistent with previously reported water-air fluxes for the Great Barrier Reef (3.4 ± 0.1 µmol m^-2 d^-1 (O’Reilly et al., 2015) and 2.2 ± 0.5 µmol m^-2 d^-1 (Reading et al., 2021) for CH₄; −5.4 ± 0.8 mmol m^-2 d^-1 (O’Reilly et al., 2015), 1.44 ± 0.15 mmol m⁻² d⁻¹ (Lønborg et al., 2019) and 1.9 ± 0.4 mmol m^-2 d^-1 (Reading et al., 2021) for CO₂).

Sea-to-air O₂ fluxes in this study (Min = −188 mmol m^-2 d^-1, Max = 403 mmol m^-2 d^-1, mean = 70 ± 12 mmol m^-2 d^-1) were similar to water-air fluxes reported for a Puerto Rican coral reef, with rates varying between −285 and 329 mmol m^-2 d^-1 (McGillis et al., 2011). However, they were rather low compared to O₂ fluxes reported for another coral reef system in the Florida Keys, (Min = −450 mmol m^-2 d^-1, Max = 4500 mmol m^-2 d^-1) (Long et al., 2013), which most likely reflects differences between the phototrophic communities of different reef systems (e.g. algal versus coral cover, phytoplankton composition).

The strong positive correlation between water-air DMS and CH₄ fluxes (R² = 0.7526) suggests that DMS and CH₄ emissions from the Heron Island reef lagoon are closely linked. Further to this observation, the MLR analysis revealed that sea-to-air DMS and CH₄ fluxes were both driven by pH, depth, wind direction, dewpoint and wind chill. This indicates that DMS and CH₄ fluxes are driven by very similar environmental factors, thus potentially explaining part of the correlation between DMS and CH₄ emissions at the Heron Island reef lagoon. Dissolved concentrations of DMS and CH₄ were also strongly correlated (Supplementary Figure 3), which indicates that the production of DMS and CH₄ in this reef system is also intimately linked. In contrast there was no clear correlation between the RPs of DMS and CH₄, which suggests that DMS and CH₄ were subject to different accumulation rates and thus to different production and/or degradation processes.

DMS production mainly relies on the enzymatic cleavage of DMSP in the water column (Simó, 2001) while methanogenesis mainly depends on the conversion of CO₂ and acetate into CH₄ under anaerobic conditions (Liu and Whitman, 2008), which most likely occurs in marine sediments in coral reef ecosystems (Deschaseaux et al., 2019; Reading et al., 2021). The main sinks of dissolved DMS are expected to be biological and photochemical oxidation (del Valle et al., 2009) as well as emission fluxes from the water column to the atmosphere (Lana et al., 2011). Similarly, oceanic emissions (Weber et al., 2019) and microbial oxidation to CO₂ through both aerobic and anaerobic pathways (Pain et al., 2019) are considered the main sinks of dissolved CH₄ in marine systems. However, the rate and magnitude of DMS and CH₄ sinks rely on different degradation processes (e.g. microbial communities, vertical distribution in the water column). For instance, the magnitude of the aerobic and anaerobic CH₄ oxidation sinks is dictated by oxygen gradients and groundwater residence time, respectively (Pain et al., 2019). The different sources and sinks of DMS and CH₄ in coral reef systems thus most likely explain the divergence in correlation between DMS and CH₄ sea-to-air emissions on one hand and the RPs of DMS and CH₄ on the other hand.

At the Heron Island reef lagoon, negative correlations were found between dark DMS and CH₄ fluxes from permeable carbonate coral reef sediments to the water column (Deschaseaux et al., 2019), suggesting that part of the DMS produced in coral reef sediments was degraded to CH₄ under dark anoxic conditions, most likely by anaerobic methanogens (Kiene and Visscher, 1987). However, CH₄ production can also occur under aerobic conditions and across all living organisms, including marine phytoplankton (Klintzsch et al., 2019; Ernst et al., 2022). As such, DMS-to-CH₄ hydrolysis may not only occurs in permeable carbonate coral reef sediments, but also in reef waters through phytoplankton activity and possibly within the coral tissue, where DMS plays a major role in structuring coral-associated bacterial communities (Raina et al., 2010).

Growing evidence shows that organic compounds containing sulfur- bonded methyl groups such as DMS are a source of CH₄ under both anaerobic and aerobic conditions (Liu and Whitman, 2008; Ernst et al., 2022), with CH₄ production by certain bacteria being enhanced in the presence of reactive oxygen species under oxidative stress (Ernst et al., 2022). DMS concentrations in marine algae and corals also increase under oxidative stress due to DMS being used as an antioxidant (Sunda et al., 2002; Deschaseaux et al., 2014). This suggests that the increase in CH₄ production might be due to more sulfur-methylated compounds made readily available to methylotrophs in various types of organisms.

It was estimated that DMS contributes up to 28% of CH₄ production in various marine systems (Kiene, 1988; Florez-Leiva et al., 2013), which could well be the case for coral reef ecosystems. At the Heron Island reef lagoon, it was estimated that a small portion (~6.5%) of the CH₄ produced in coral reef sediments under dark anoxic conditions might originate from DMS (Deschaseaux et al., 2019). Here, the slope of the regression between DMS and CH₄ indicates that 0.05 mole of CH₄ is emitted for each mole of DMS being emitted to the atmosphere. This shows that sea-to-air CH₄ emissions from the Heron Island reef lagoon represents 5% of that of DMS, which is consistent with the average sea-to-air DMS (24.9 ± 1.81 µmol m^-2 d^-1) and CH₄ (1.36 ± 0.11 µmol m^-2 d^-1) fluxes reported in this study. However, without using isotopic tracers, we cannot draw conclusions on the actual portion of CH₄ emissions from coral reef systems that actually originate from DMS hydrolysis.

In addition to pH, depth, wind direction, dewpoint and wind chill, DMS emissions were also positively driven by salinity. These findings could be counter intuitive given that the most intense plume of DMS measured over the Heron Island reef occurred in the winter of 2013 at low tide when rainfall on the aerially exposed reef apparently caused a combined hypo-salinity osmotic and hypo-thermic temperature shock to the coral (Swan et al., 2017). However, salinity in this study only ranged from 36.4 to 37.4 ppt, and thus DMS emissions under rainfall at low tide was not captured. Nevertheless, it could also be that it was not only low salinity that led to high DMS emissions reported by Swan et al. (2017), but also temperature stress and/or other rain-induced environmental conditions. For instance, rain increases transfer velocity (Ho et al., 1997), which could explain the higher emissions in Swan et al. (2017) under rainfall at low tide.

Similarly, in addition to pH, depth, wind direction, dewpoint and wind chill, CH₄ emissions were also negatively affected by light whereas previous studies show that CH₄ production by phytoplankton was accentuated under increased light conditions (Klintzsch et al., 2020). Given that light mainly influences photosynthesis and O₂ production in biological systems, this data suggests that light-driven O₂ production might negatively impact CH₄ production or lead to enhanced CH₄ oxidation in coral reefs. This is consistent with sea-to-air O₂ fluxes being negatively correlated with both CH₄ and CO₂ in this study, with CO₂ being a precursor of CH₄ production (Liu and Whitman, 2008). Although CH₄ can originate from various biogenic sources in the presence of oxygen (Ernst et al., 2022), it could be that the main source of CH₄ in coral reef systems are methanogen-rich permeable carbonate sediments (Deschaseaux et al., 2019), which are productive under dark anoxic conditions.

Maximum dissolved DMS and DMSP concentrations in coral reef systems coincide with low pH, especially over areas dominated by seagrass and macroalgae (Burdett et al., 2013), which is consistent with pH negatively affecting DMS emissions in the current study. Coral-reef DMS emissions are thus expected to increase under low pH conditions, possibly due to macroalgae using DMSP to maintain metabolic functions during periods of low carbonate saturation state (Burdett et al., 2013). Sea-to-air CH₄ fluxes at Heron Island were also negatively correlated with pH, which suggests that ocean acidification may increase CH₄ emissions in coral reef systems, possibly due to low pH enhancing organic matter degradation in coral reefs like it is the case in seagrass sediments (Ravaglioli et al., 2020).

Spikes in DMS emissions at the Heron Island reef lagoon were previously detected at low tide generally under low wind speeds < 2 m s^-1 (Swan et al., 2017), which agrees with depth being a negative driver of DMS emissions in this study. On the other hand, it is unknown whether dewpoint and windchill have ever been considered as environmental factors driving DMS and CH₄ emissions in marine systems, although it seems that low dewpoint and air temperatures coincided with high DMS and CH₄ emissions at the Heron Island reef system. This suggests that air temperature also plays a role in the gas transfer velocity of DMS and CH₄, most likely as it influences water temperature and thus the temperature-dependent gas solubility of these two compounds.

DMS and CH₄ emissions were highest under southerly winds, which travelled over the Heron Island’s reef and the adjacent large Wistari reef, directly south of our sampling site. This indicates that Wistari reef is possibly the major source of coral-reef-derived DMS_a measured at Heron Island and that coral reefs are a more predominant source of DMS and CH₄ than the surrounding ocean.

Although there is insufficient information to attribute the particle counts in this study to new particle formation, because the measured size ranges are far above that of new nanoparticles (Clarke et al., 2006), it was interesting to see that sea-to-air DMS fluxes positively correlated with intermediate (0.5-2.5 µm) and large (> 2.5 µm) particles, and that a stronger correlation occurred between DMS emissions and the abundance of particles in the intermediate size range. This likely indicates that DMS oxidation products possibly contributed to the growth of existing particles within the 0.5-2.5 µm measured size range, which are suspected to be predominantly sea spray aerosols (SSA) (Quinn et al., 2015). Given that number concentrations of intermediate size particles were correlated with wind speed (R² = 0.3337, Figure 5C ) and that wind speed is a covariate in DMS flux, the observed correlation is expected to be linked to wind speed, which exchanges more DMS and SSA from the ocean. Given that coral-reef derived DMS and CH₄ emissions could be linked, with a small portion of DMS being hydrolysed to CH₄ in the water column, and that CH₄ is a GHG with a stronger greenhouse potential than that of CO₂ (Shine et al., 2005), it is important to consider both dissolved DMS and CH₄ when predicting BVOC and GHG emissions from marine systems.