Net Drawdown of Greenhouse Gases (CO2, CH4 and N2O) by a Temperate Australian Seagrass Meadow

Abstract

Seagrasses have some of the highest rates of carbon burial on the planet and have therefore been highlighted as ecosystems for nature-based climate change mitigation. However, information is still needed on the net radiative forcing benefit of seagrasses inclusive of their associated greenhouse gas (GHG) emissions. Here, we report simultaneous estimates of seagrass-associated carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) air–water emissions. Applying in situ sampling within a south-east Australian seagrass ecosystem, this study finds atmospheric GHG emissions from waters above seagrasses to range from − 480 ± 15.96 to − 16.2 ± 8.32 mg CO 2 -equivalents m ² d ⁻¹ (net uptake), with large temporal and spatial variability. Using a combination of gas specific mass balance equations, dissolved stable carbon isotope values ( δ ¹³ C) and in situ time-series data, CO 2 -e flux is estimated at − 21.74 mg m ² d ⁻¹ . We find that the net release of CH 4 (0.44 µmol m ² h ⁻¹ ) and net uptake of N 2 O (− 0.06 µmol m ² h ⁻¹ ) effectively negated each other at 16.12 and − 16.13 mg CO 2 -e m ² d ⁻¹ , respectively. The results of this study indicate that temperate Australian seagrasses may function as net sinks of atmospheric CO 2 -e. These results contribute towards filling key emission accounting gaps both in the Australian region, and through the simultaneous measurement of the three key greenhouse gas species.

Full text

Vol.:(0123456789)
1 3
Estuaries and Coasts
https://doi.org/10.1007/s12237-022-01068-8
Net Drawdown ofGreenhouse Gases (CO2, CH4 and N2O)
byaTemperate Australian Seagrass Meadow
QuinnR.Ollivier1 · DamienT.Maher2· ChrisPitfield3· PeterI.Macreadie1
Received: 10 May 2021 / Revised: 12 January 2022 / Accepted: 12 January 2022
© The Author(s) 2022
Abstract
Seagrasses have some of the highest rates of carbon burial on the planet and have therefore been highlighted as ecosys-
tems for nature-based climate change mitigation. However, information is still needed on the net radiative forcing benefit
of seagrasses inclusive of their associated greenhouse gas (GHG) emissions. Here, we report simultaneous estimates of
seagrass-associated carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) air–water emissions. Applying insitu
sampling within a south-east Australian seagrass ecosystem, this study finds atmospheric GHG emissions from waters above
seagrasses to range from − 480 ± 15.96 to − 16.2 ± 8.32mg CO2-equivalents m2 d−1 (net uptake), with large temporal and
spatial variability. Using a combination of gas specific mass balance equations, dissolved stable carbon isotope values (δ13C)
and insitu time-series data, CO2-e flux is estimated at − 21.74mg m2 d−1. We find that the net release of CH4 (0.44µmol
m2 h−1) and net uptake of N2O (− 0.06µmol m2 h−1) effectively negated each other at 16.12 and − 16.13mg CO2-e m2 d−1,
respectively. The results of this study indicate that temperate Australian seagrasses may function as net sinks of atmospheric
CO2-e. These results contribute towards filling key emission accounting gaps both in the Australian region, and through the
simultaneous measurement of the three key greenhouse gas species.
Keywords Emissions· Carbon dioxide· Methane· Nitrous oxide· Diurnal· Seagrass· Primary producer· Aquatic
Introduction
Atmospheric concentrations of carbon dioxide (CO2), meth-
ane (CH4) and nitrous oxide (N2O), have increased substan-
tially since pre-industrial periods and are the three dominant
greenhouse gases (GHGs) driving human-induced atmos-
pheric warming (Solomon etal. 2007). Quantifying the
global emissions of these gases is therefore of high priority
as we move towards strategies for climate change mitigation.
In the natural world, photosynthetic assimilation and storage
of atmospheric CO2 (i.e. biosequestration), and microbial
breakdown of organic material with the subsequent release
of GHGs through respiration (i.e. decay), are two of the most
fundamental processes influencing the carbon cycle. Coastal
vegetated ecosystems such as tidal marshes, mangroves and
seagrasses have potential for inclusion into future carbon
abatement programmes due to biosequestration rates up
to 35 times higher than tropical rainforests (Mcleod etal.
2011), and limited carbon decay due to saline-aquatic redox
conditions (Macreadie etal. 2019).
Despite covering only 0.1–0.2% of the ocean’s surface,
seagrasses are responsible for an estimated 10–18% of total
oceanic carbon burial (Duarte etal. 2005; Mcleod etal.
2011). Seagrasses are amongst the most productive coastal
ecosystems with average net primary production equating
to ~ 817g carbon (C) m2 yr−1 (Duarte and Cebrián 1996;
Mateo etal. 2006; Fourqurean etal. 2012), with seques-
tered carbon remaining in their anoxic sediments for mil-
lennia (Macreadie etal. 2015). However, a variety of human
activities that induced degradation of seagrasses, such as the
conversion of coastal areas for aquaculture, boat moorings
and eutrophication, can increase organic matter respiration
Communicated by Just Cebrian
* Quinn R. Ollivier
Quinn.Ollivier@gmail.com
1 Centre forIntegrative Ecology, School ofLife
andEnvironmental Sciences, Deakin University, Geelong,
Australia
2 School ofEnvironment, Science, andEngineering, Southern
Cross University, Lismore, NSW2480, Australia
3 Corangamite Catchment Management Authority, Colac,
VIC3250, Australia

Estuaries and Coasts
1 3
through the perturbation (and oxidation) of sediments and
increased nutrient supply, leading to decreased sedimen-
tary carbon and increased GHG release (Short and Wyllie-
Echeverria 1996; Waycott etal. 2009; Pendleton etal. 2012;
Macreadie etal. 2015).
In order to establish seagrasses’ net radiative forcing
benefit and therefore their potential use in natural climate
change mitigation, it is crucial to accurately incorporate their
baseline atmospheric GHG emissions. Common application
of benthic chamber sampling methodologies has facilitated
estimates of seagrass CO2 and CH4 flux rates between sedi-
ments and the water column (Maher and Eyre 2010; Barrón
etal. 2014; Macreadie etal. 2014). However, the propor-
tion of these seagrass sediment produced GHGs that pass
through the water column to be emitted to the atmosphere
is often unclear. For instance, aerobic microbially respired
CO2 can be recycled within photosynthetic cycles of the sea-
grass meadows, and anaerobically respired CH4 is subject to
methanotrophic oxidation in the water column. In addition,
air–water GHG flux is a product of specific gas solubility in
seawater (subject to temperature and salinity), air–water con-
centration gradients and gas exchange coefficients (Raymond
and Cole 2001; Middelburg etal. 2002; Borges etal. 2004).
As such, estimates of seagrass atmospheric GHG emissions,
based solely on benthic chamber methodologies, are sub-
ject to high variability and are unlikely to accurately capture
seagrass-associated atmospheric emissions. Additionally,
previous literature is largely constrained by region and sea-
grass species, whilst predominantly focusing on CO2 flux,
with fewer still incorporating CH4 flux (Tokoro etal. 2014;
Garcias-Bonet and Duarte 2017; Banerjee etal. 2018) and
N2O flux (Murray etal. 2015).
The application of insitu automated cavity ring-down
spectroscopy has been shown as an effective method for the
quantification of air–water gas flux in estuarine and mangrove
systems (Maher, etal. 2013a, b; Maher etal. 2015; Reading
etal. 2017). Seagrass meadows often span large areas and
experience regionalised variation in environmental condi-
tions that affect GHG dynamics, such as salinity (Bouillon,
etal. 2007a, b; Touchette 2007), nutrient inputs (Smith etal.
1999; Duarte 2002), water turbulence (Raymond and Cole
2001) and anthropogenic disturbance (Macreadie etal. 2015).
In addition, fluctuations in both light availability (and there-
fore photosynthesis) (Maher, etal. 2013a, b; Saderne etal.
2013) and tidal pumping of ground waters enriched in GHG
solutes, dissolved organic matter and nutrients can influence
seagrasses’ net autotrophic/heterotrophic balance over diel
and tidal cycles (Santos etal. 2008; Bauer and Bianchi 2011;
Gleeson etal. 2013). Automated fixed time-series measure-
ments with high sampling resolution (i.e. minutes) are an
effective way to incorporate temporal variation in emissions;
however, they do not account for spatial changes in envi-
ronmental conditions (Gazeau etal. 2005; Call etal. 2015;
Maher etal. 2015). Alternatively, and commonly, used are
continuous underway or discreet sampling spatial surveys
that both inherently incorporate spatial GHG variance due to
changes in sediment characteristics and seagrass cover etc.,
though do not integrate day-night fluctuations. Individually,
these methods may not adequately constrain GHG variability,
resulting in imprecise emission estimates (Gazeau etal. 2005;
Call etal. 2015; Maher etal. 2015).
Here, we measure dissolved GHG concentrations from
temperate seagrasses and model emission rates, with the
aim to quantify the net GHG balance. To achieve this aim,
we quantified CO2, CH4 and N2O dissolved concentrations
and diffusive fluxes in a temperate seagrass-dominated
estuarine embayment in south-east Australia. We used a
combination of fixed time-series measurements over a 44-h
period and a continuous underway spatial survey throughout
the embayment to assess temporal and spatial variability
in GHG fluxes and concentrations (Fig.1). Furthermore,
we measured salinity, temperature, wind speed, tidal depth,
dissolved oxygen and isotopic (δ13C) signatures of CO2-C
to help determine the major environmental conditions influ-
encing seagrass emissions. This study presents the first use
of insitu automated cavity ring-down spectroscopy within
an Australian seagrass to determine simultaneous CO2, CH4
and N2O atmospheric flux, including estimates of the cumu-
lative function of temperate seagrasses as a source or sink of
GHGs over the study period, providing data that can contrib-
ute to regional seagrass emission assessments.
Materials andMethods
To quantify the spatial and short-term temporal variabil-
ity in GHG fluxes, we employed a combination of survey
and time-series sampling techniques. A time series was
conducted for ~ 44h within a seagrass meadow in Swan
Bay, Victoria, Australia (− 38.2701N, 144.6349 E), and an
underway survey was undertaken within Swan Bay between
11:00 and 17:00 (Fig.1). The experiments were undertaken
over a 4-day period from the 4th to 7th of December during
the Austral summer. Swan Bay is located within Port Phil-
lip Bay, has an area of ~ 26 km2 and has extensive seagrass
beds composed of Zostera muelleri and Heterozostera tas-
manica species, which cover ~ 80% of the bay (Fig.1). Swan
Bay experiences a mean annual rainfall of 457mm, with a
mean maximum and mean minimum temperature of 20.4
and 9.4°C, respectively.
During the time-series measurements, water tempera-
ture, depth, DO, pH and salinity were measured using a
calibrated water quality sonde (Eureka Manta II) deployed
within the seagrass meadow. Using a bilge pump, water
was pumped from the location of the water quality sonde to
an air–water equilibrator on the shore. The pump was fixed

Estuaries and Coasts
1 3
at a depth of 15cm above the sediment surface. The head-
space from the equilibrator was pumped in a closed loop to
two cavity ring-down spectrometers (Picarro G2201-i and
Picarro G2308) after passing through a desiccant (Drierite)
to measure the partial pressure of CO2 (± 200ppb), CH4
(± 80ppb), N2O (± 10ppb) and δ13C-CO2 as described by
Maher etal. (2013a, b). A 5-min rolling average was applied
to the data, and to account for equilibration times of the
various gases data was shifted by 10min for CO2 and N2O,
and 30min for CH4 (Webb etal. 2016). The same instru-
ment setup was deployed for the underway surveys. The
instruments were installed on a small (4m) shallow draft
aluminium boat. The pump and water quality sonde were
fixed at a depth of ~ 30cm, and a GPS (Garmin Map72)
was used to record the track. Due to the time lag in gas
equilibration times, the process results in a deconvolution of
spatial positioning. However, due to the slow transit speed
(~ 3km/h) the effect is small.
The dry molar fractions of each gas were converted to
partial pressures using the procedures outlined in Pierrot
etal. (2009). Briefly, water vapour pressure and the virial
coefficients of N2O were calculated according to Weiss
and Price (1980), and the virial coefficients for CO2 were
calculated as described by Weiss (1974). Partial pres-
sures were converted to concentrations and percentage
saturations using the temperature and salinity-dependent
solubility coefficients for each gas (CH4, Wiesenburg and
Guinasso (1979); CO2, Weiss (1974); N2O, Weiss and
Price (1980)), and atmospheric concentrations of 1.8ppm,
405ppm and 328ppb for CH4, CO2 and N2O respectively.
Air-sea fluxes of each gas were calculated according to
where k is the gas transfer velocity, α is the solubility coef-
ficient and Δpgas is the difference in partial pressure of the
gas between the water and atmosphere. As we do not have
data on current speeds (although qualitative observations
suggest this was low), we used the wind-based gas transfer
parametrization of Wanninkhof (2014):
where u is the windspeed measured at a height of 10m (m
s−1) measured on site using a sonic anemometer (Airmar
PB200 weather station), and Sc is the Schmidt number of
the gas of interest. The Schmidt number was calculated as
a function of temperature and salinity according to
Wanninkhof (2014), assuming a linear dependence upon salinity.
A mass balance of each individual gas was constructed
for a 24-h period of the time series, which accounted for
sources and sinks, to determine the production or con-
sumption of each seagrass associated gas:
where Fseagrass is the water column flux from samples
within the meadow (mmol m−2 d−1), Fatm is the air water
flux (mmol m2 h−1), Δvol is the change in volume (m3 h−1)
calculated per m2 by the change in depth and conc is the
average concentration of the gas over the time interval of
interest. Lateral exchange is accounted for by the
Δvol.conc
term; however, due to a lack of detailed carbonate chemistry
data, there is uncertainty in our approach associated with
DIC speciation.
We used the Keeling method to determine the time
series δ13C signature of dissolved CO2 source material
from within the meadow (Keeling 1958). Keeling method-
ology is based on the conservation of mass mixing model
(1)
F=k
𝛼
(Δpgas)
(2)
k=
0.251u2
(Sc∕
660
)−0.5
(3)
F
seagrass =∫
24
0
Fatm − (Δvol.conc
)
Fig. 1 Distribution of seagrass (predominantly Zostera spp.) within
temperate Australian Swan Bay. Each yellow circle indicates an indi-
vidual sample across the spatial survey. The blue circle with point
indicates the location of a 44-h time-series sampling setup, which was
visually confirmed to be the site of a fringing seagrass meadow. The
white square in panel (a) indicates the extent of panel (b). Figure cre-
ated with ArcMap (V10.2.2), data from Department of Environment,
Land, Water & Planning 2017; Department of Economic Develop-
ment, Jobs, Transport and Resources 2018; Department of Environ-
ment, Land, Water & Planning 2019

Estuaries and Coasts
1 3
between the background δ13C (i.e. oceanic DIC which we
assume is constant over the short timeframe of the experi-
ment) and source δ13C values, where the source δ13C sig-
nature (+ 95% CI) can be determined as the y-intercept
of a type II ordinary least squares regression (“lmodel2”
function within “lmodel2” package, permutations = 1000)
between 1/concentration of measured CO2 and the meas-
ured δ13C-CO2 (Pataki etal. 2003; Maher etal. 2017). We
assumed that the background concentration and δ13C-CO2
of atmospheric CO2 did not change during the 44-h period.
All statistics were performed using R-statistics (V.3.5.3).
Data Analyses
To analyse conditions related to GHG dynamics within
seagrass beds of a temperate Australian bay, carbon diox-
ide (CO2), methane (CH4) and nitrous oxide (N2O) percent
saturation (%sat) levels were separately run through a series
of linear models fit with generalized least squares ( “gls()”
function within the “nlme” package) (Pinheiro etal. 2017).
During spatial surveys, data was removed in small sections
where the water sampling unit was exposed to the air (e.g.
due to shallow depth). To achieve data normality and meet
the assumption of homogeneity of residual variance, CH4
was natural log transformed, and N2O was square root trans-
formed. To test the assumption of model non-collinearity,
all pair-wise combinations of environmental variables were
assessed using linear models (“lm()” function within “nlme”
package): pH, dissolved oxygen (DO), salinity (PSS), tidal
depth (m). Both tidal depth and pH were removed from the
model due to a relationship with DO of 0.8 and 0.61 adjusted
R-squared, respectively. Weak collinearity between salinity
and DO was also detected, though a variance inflation factor
of 1 indicated that the interaction between these independent
variables was negligible to the model (Miles 2014) (“vif()”
function within the “car” package). To constrain both salin-
ity and DO covariate residual non-normality, a power vari-
ance function was introduced to the model (“varCom()”and
“varPower()” function from “nlme” package). Akaike’s
information criterion (AIC) model selection was used to
confirm the best model fit based on weighted structures,
resulting in weights being applied to both DO and salin-
ity. Final model structure was as follows: (Sqrt.GSi ~ Salin-
ity + DO, weighted.variance = Salinity + DO), where GSi
represents each Gas Species, CO2, CH4 and N2O. Model
validation was conducted using a plot of residual distribu-
tion, residuals over predicted values and a quantile–quantile
plot (Q-Q plot, “qqnorm()” function in the “stats()” pack-
age). Lastly, to obtain an F-statistic, an analysis-of-variance
(“anova()” function, type II “marginal”, in “stats” package)
was performed on the resulting model objects.
Results
Time Series
Cumulative mass balance of GHGs over a 44 h period,
incorporating changes in dissolved gas concentrations,
atmospheric flux and water volume, showed a distinct diel
pattern in CO2 with net uptake of atmospheric CO2 dur-
ing the day and net release of CO2 to the atmosphere dur-
ing the night, with a net negative 24 h flux of − 20.57μmol
m2 h−1 (Fig.2). Net CO2 forcing was qualitatively associated
with high windspeeds during the day and low windspeeds
at night (Fig.3). CH4 maintained a constant net release
Fig. 2 Cumulative integrated
mass balance of CO2, CH4
and N2O over a 44-h sampling
period. Grey shading indicates
night time periods, whilst
red dotted lines indicate the
24 h period used to calculate
seagrass daily greenhouse gas
production

Estuaries and Coasts
1 3
to the atmosphere, with a 24 h release rate of 0.44µmol
m2 h−1, whilst seagrasses were a net sink of N2O over 24h,
at − 0.06µmol m2 h−1 (Fig.2). The estimated δ13C signa-
ture of the predominant CO2-C source material, established
through Keeling plots, was − 13.71 (− 13.75 to 13.67, 95%
confidence interval).
Time-series sampling established distinct diel fluctua-
tions in GHGs above seagrass beds, with mean CO2 and
CH4 percent saturation (%sat) being 32 and 28% higher
during the night, whilst N2O was 11% higher during the
day (Fig.4, Table1). Day time periods were 83% higher
in tidal depth, 80% higher in DO and maintained 66%
higher wind speeds (Table1). Collinearity between these
environmental variables, as assessed through linear mod-
els, showed that DO%sat was weakly positively corre-
lated with salinity concentrations (P < 0.001, Coef = 37.2,
adjusted R-squared = 0.11), whilst pH levels (P < 0.001,
Coef = 210.82, adjusted R-squared = 0.61) and tidal depth
(m) (P < 0.001, Coef = 0.39, adjusted R-squared = 0.8)
showed stronger autocorrelation (Table1). Tidal depth was
only mildly correlated with salinity (P < 0.001, Coef = 41.55,
adjusted R-squared = 0.03).
Spatial Survey
Across the survey area, N2O percent saturation (%sat)
showed the least variation with a range of 119.36–85.6%,
whilst CO2 ranged from 96.4 to 20.1%, and CH4 had the
largest variation with a range of 428.5–87.35%. CO2%sat
levels were highest at the opening of Swan Bay (Fig.5a),
whilst CH4%sat was highest in areas only sparsely covered
by seagrass towards the south-west Swan Bay (Figs.5b and
1). Across the bay, salinity averaged 33.56 ± 0.07 with a
range of 36.04–29.8. Akaike’s information criterion (AIC)
determined that spatial survey CO2, CH4 and N2O%sat were
best modelled with both salinity and dissolved oxygen (DO).
The variance inflation factor for all survey models was low,
ranging from 1.004 to 1.006, indicating that the variance
around the model was only minutely affected by collinear-
ity between salinity and DO. Water column DO%sat was
inversely related to CO2%sat (f(1, 298) = 264.81, P < 0.001,
Coef =—0.25) (Fig. 6a), positively related to N2O%sat
(f(1, 298) = 17.47, P < 0.001, Coef = 0.001) (Fig.6e) and did
not correlate with CH4 (Fig.6c). Salinity was not signifi-
cantly related to CO2 or CH4%sat; however, it was nega-
tively correlated with N2O%sat (f(1,298) = 32.96, P < 0.001,
Coef =—0.06) (Fig.6f). To note, the linear relationships
between N2O%sat and both DO%sat and salinity (PSS)
retained moderate variance and were heavily influenced by
data groupings (Fig.6e–f), as such inference from these rela-
tionships should be treated carefully.
Methodical Comparison
Across both time-series and spatial sampling, CO2 per-
cent saturation (%sat) ranged from 20 to 266, CH4 ranged
from 87 to 712% and N2O from 56 to 119%. The mean
percent saturation of GHGs were markedly different
between survey and time-series methodology, with CO2
and CH4 being 192% and 112% higher in time-series
Fig. 3 Wind speeds from local
weather station used in water-
atmosphere gas calculations.
The mean ± SE and range of
peaks in the day and lows in the
night can be viewed in Table1

Estuaries and Coasts
1 3
measurements, respectively, whilst N2O was 22% higher
in spatial surveys (Fig.7a). The mean time-series CO2
flux was − 0.46 ± 0.18mmol m2 d−1, 96% higher than
that of spatial sampling, at − 11.48 ± 0.28mmol m2 d−1
(Fig.7b). Time-series CH4 flux estimates were also 125%
higher than spatial surveys, at 10.29 ± 0.17μmol m2 d−1
and 4.56 ± 0.19μmol m2 d−1, respectively (Fig.7b), whilst
N2O mean flux rates were 772% lower in time-series meas-
urements compared to those of surveys, at − 1.07 ± 0.02
and − 0.12 ± 0.02μmol m2 d−1, respectively (Fig.7b).
Converting these GHG fluxes to CO2-equivalents (CO2-e)
using the 20-year sustained global warming potentials of
Neubauer and Megonigal (2015) equates to an average time
series and spatial survey CO2-e flux of − 16.2 ± 8.32mg m2
d−1 and − 480.94 ± 15.96mg m2 d−1, respectively.
Discussion
Through the capture of particulate organic matter and pho-
tosynthetic fixation of dissolved carbon dioxide (CO2),
seagrass ecosystems play a major role in oceanic carbon
storage (Macreadie etal. 2019; Serrano etal. 2019). Recent
literature has highlighted seagrasses’ potential in natural
carbon-offset strategies; however, their net carbon balance
inclusive of atmospheric greenhouse gas (GHG) emissions
is yet to be fully established (Macreadie etal. 2019; Serrano
etal. 2019). Using a combination of spatial survey and time-
series sampling methodologies across a temperate Australian
seagrass meadow, we present evidence that during our study,
the seagrasses represented net sinks of CO2-equivalent gases
inclusive of CO2, methane (CH4) and nitrous oxide (N2O).
Fig. 4 Tidally induced changes in water depth, greenhouse gas saturation and greenhouse gas concentration over a 44 h period. Grey shading
indicates night time periods, whilst 100% atmospheric saturation is indicated in orange (panels a–c)

Estuaries and Coasts
1 3
In addition, we highlight the need for the incorporation of
both spatial and temporal focused sampling designs when
measuring seagrass emissions and discuss GHG environ-
mental drivers. It is important to note, however, that due to
the experiments being undertaken over a short period during
the Austral summer period, our results may not be indicative
of annual fluxes.
Through mass balance of insitu time-series sam-
pling, seagrasses were shown to be a net sink of GHGs.
The seagrass-associated fluxes of GHGs were estimated
at − 493.68μmol CO2 m2 d−1, 10.47μmol CH4 m2 d−1
and − 1.46μmol N2O m2 d−1, which at the 20-year sus-
tained global warming potential of CO2 equates to a net
flux of − 21.74mg CO2-e m2 d−1. In addition, we found
that the fluxes of CH4 and N2O negated each other at 16.12
and − 16.13mg CO2-e m2 d−1, respectively. Previous stud-
ies on CO2 dynamics have shown that through photosyn-
thetic production and the burial of organic carbon into
their sediments, seagrasses act as net autotrophic coastal
ecosystems (Gattuso etal. 1998; Gazeau etal. 2005).
However, quantification of seagrass sediment gas flux has
indicated that a portion of this sequestration may be off-
set by the release of radiatively potent CH4 (Al‐Haj and
Fulweiler 2020; Alongi etal. 2008; Bahlmann etal. 2015;
Barber and Carlson 1993; Deborde etal. 2010; Garcias-
Bonet and Duarte 2017; Oremland 1975). Our data sug-
gest that although temperate seagrasses are indeed a net
source of CH4, these emissions may be counterbalanced by
the uptake of N2O; however, as this result is from a single
meadow in temperate Australia the finding should be used
as a reference for further investigations.
The seagrass-associated CO2 metabolism of − 0.49mmol
CO2 m2 d−1 (uptake) within this study is two orders of mag-
nitude lower than previous global estimates of seagrass
net metabolism by Duarte etal. (2010), based on 155 sites
at − 99.45 ± 22mmol CO2 m2 d−1, and more recent estimates
of an Australian Zostera meadow at − 99.9mmol CO2 m2 d−1
by Maher etal. (2011). Whilst the net ecosystem metabolism
(NEM = gross primary productivity − ecosystem respiration)
of seagrasses varies as a result of a range of biotic and abi-
otic conditions including, faunal assemblage (Spivak etal.
2009; Kristensen etal. 2012), temperature (Staehr and Borum
2011), seagrass species (Duarte etal. 2010) and sediment
quality (Udy and Dennison 1997; Terrados etal. 1999), such
a large difference between previous literature and this study is
notable. We suggest three reasons for this relationship:
Table 1 Greenhouse gas concentrations, atmospheric flux and asso-
ciated environmental conditions in a temperate Australian seagrass
meadow. TimeS represents single point time series, whilst Spatial
survey represents continuous underway spatial sampling. Negative
flux indicates uptake into the water column
TimeS day (6am–6pm) TimeS night (6pm–6am) TimeS overall Spatial survey
Mean Range Mean Range Mean Range Mean Range
CO2 flux
(mmol m2
d−1)
− 0.66 ± 0.37 14.38
– − 21.14 1.06 ± 0.16 12.99
– − 18.08 − 0.46 ± 0.18 26.52
– − 24.31 − 11.49 ± 0.28 − 0.66
– − 20.37
CO2 (%sat) 121.79 ± 1.52 189.62–61.38 160.56 ± 1.46 266.23–58.70 140.93 ± 1.17 266.23–41.63 48.2 ± 1.04 96.4–20.1
CH4 flux
(μmol m2
d−1)
13.26 ± 0.26 22.93–0.67 7.95 ± 0.24 44.87–0.01 10.29 ± 0.17 44.87–0.001 4.56 ± 0.19 22.08–0.11
CH4 (%sat) 312.59 ± 2.3 434.31–202.2 401.03 ± 3.05 712.78–
203.15 357.73 ± 2.12 712.87–202.2 173.57 ± 3.13 428.5–87.35
N2O flux
(μmol m2
d−1)
− 1.71 ± 0.03 − 0.23
– − 2.79 − 0.9 ± 0.02 < − 0.01
– − 2.65 − 1.07 ± 0.02 1.24 – − 2.8 − 0.12 ± 0.03 1.88 – − 1.56
N2O (%sat) 84.99 ± 0.5 98.78–60.14 76.58 ± 0.24 97.01–56.02 81.29 ± 0.25 106.09–56.02 99.01 ± 0.28 119.36–85.6
δ13C-CO2 − 9.64 ± 0.08 − 6.2
– − 12.71 − 10.85 ± 0.05 − 6.09
– − 13.41 − 10.22 ± 0.04 − 4.93
– − 13.45 − 6.02 ± 0.09 − 1 – − 10.57
Wind (m s−1)4.95 ± 0.08 7.78–1.11 2.98 ± 0.05 6.67–0 3.86 ± 0.04 7.78–0 4.68 ± 0.03 5.56–3.61
DO (%sat) 121.9 ± 2.46 196.3–20.8 67.57 ± 1.21 184.5–16.9 94.4 ± 1.27 203.5–16.9 170.28 ± 2.5 249–104.3
Air temp
(°C) 17.09 ± 0.11 20.2–9.3 13.72 ± 0.07 18–8.7 14.98 ± 0.06 20.20–8.70 21.26 ± 0.17 29.86–18.42
Salinity
(PSS) 35.55 ± 0.03 36.42–33.62 35.92 ± 0.01 36.47–34.09 35.7 ± 0.01 36.47–33.62 32.56 ± 0.7 36.04–29.8
Tidal depth
(m) 0.51 ± 0.01 0.68–0.25 0.28 ± 0.00 0.62–0.15 0.37 ± 0.00 0.68–0.15 - -
pH 8.43 ± 0.01 8.8–7.08 8.36 ± 0.00 8.79–8.09 8.41 ± 0.00 8.84–7.08 8.97 ± 0.02 9.67–8.62

Estuaries and Coasts
1 3
1. Previous estimates of seagrass metabolism are predomi-
nantly based on the use of benthic chamber methodology
(Duarte etal. 2010), where changes in the concentration
of O2 or CO2 are directly linked to the sediment–water
interface, whereas this study used an insitu open water
sampling technique that inherently homogenizes with
other dissolved gas sources, such as tidal pumping and
microbial processes, that may affect the net balance of
CO2 flux estimates. Microbial breakdown and respira-
tion of dissolved organic carbon associated with sea-
grass meadows have been shown as a significant term in
net seagrass carbon budgets (Barrón etal. 2014; Maher
and Eyre 2010) and may have offset the reductions in
water column CO2 from photosynthesis (Linto etal.
2014; Call etal. 2015).
2. The average solar irradiance during this sampling
period was relatively low due to dense cloud cover at
372.98 ± 9.97J m2 s−1, whilst the comparable monthly
average for this region is ~ 14.4% higher at 426.62J
m2 s−1 (Australian Bureau of Meteorology). Variation
in light conditions directly affects photosynthetic pro-
duction in seagrass beds, with lower irradiance often
resulting in lower rates of dissolved CO2 uptake (Gacia
etal. 2005; Touchette 2007).
3. A qualitatively large amount of seagrass wrack could be
seen along the banks of the seagrass meadow (roughly 10m
from submerged sampling station), suggesting large con-
centrations of dead biomass in the water column. Micro-
bial decay of leaf biomass is a major contributor to net
ecosystem respiration within seagrass systems (Harrison
and Mann 1975; Blum and Mills 1991; Mateo and Romero
1997; Liu etal. 2019), indicating a potential for large alter-
native CO2 inputs and therefore potential offsets of mass-
based estimates of net photosynthetic uptake.
Bay wide surveys showed large variations in CO2 and
CH4 saturation, whilst N2O saturation remained relatively
homogenous (Fig.5a–c and Table1). Across medium to
large spatial scales, variation in environmental conditions
such as, topography, slope/depth, species composition and
freshwater inputs (both from riverine and groundwater ori-
gins) can directly affect the production and chemical fate
of GHGs in coastal systems (Gazeau etal. 2005; Allen
etal. 2007; Maher etal. 2015). In accordance with our
hypothesis, we found that both dissolved CO2 and CH4
were elevated in localised “hot spots” throughout the bay,
whilst CO2 and N2O showed strongly inverse, and weak
positive, correlations with dissolved oxygen, respectively
(Fig.6a, c). The photosynthetic productivity of seagrass
meadows is reliant on a range of factors that include light
availability, nutrient concentrations and substrate suit-
ability, amongst others. In areas less suitable to seagrass
establishment, a lack of photosynthetic productivity will
lead to lower uptake of dissolved CO2 and less oxygenated
water. We found here that CO2 concentrations were highest
in the east of the bay, which is more influenced by mixing
with oceanic waters (Fig.5a).
Fig. 5 Thematic map of the dissolved CO2, CH4 and N2O gas con-
centrations in the surface waters of a temperate Australian seagrass
meadow. Panels (a) and (b) show high spatial variation

Estuaries and Coasts
1 3
Fig. 6 Generalized linear mod-
els of greenhouse gas relation-
ships with dissolved oxygen and
salinity across Swan Bay. Data
was sampled through spatial
surveys (one measurement
per minute). %sat represents
percent saturation, whilst PSS
represents the practical salinity
scale and * represents a sig-
nificant correlation (P < 0.05).
Data points are representative
of CO2; non-transformed, CH4;
natural log and N2O; square
root. Solid lines indicate the
predicted model fit, and shaded
areas represent 95% confidence
interval

Estuaries and Coasts
1 3
High concentrations of CH4 in the south-eastern area of
the bay (Fig.5b) were less easily explained, whereby no
significant relationship with either oxygen or salinity was
established, and both wind speed and pH remained relatively
homogenous. Previous evaluation of coastal system sediment
CH4 flux has correlated higher CH4 emissions with greater
seagrass biomass (Bahlmann etal. 2015; Barber and Carlson
1993); however, in contrast to the literature we found the
highest CH4 concentrations above sediments with low sea-
grass colonization (Figs.5b and 1). The observed pattern in
CH4 may instead be due to a combination of organic matter
supply from tidal inundation of the large adjacent tidal marsh
(Fig.1) and relatively anoxic sediments in the absence of
seagrass O2 production, facilitating a shift towards metha-
nogenic microbial processes (Sansone and Martens 1981;
Ollivier etal. 1994).
The concentrations of seagrass-associated dissolved
GHGs were unsurprisingly shown to vary markedly over diel
cycles. Both CO2 and CH4 percent saturation (%sat) in the
water column were found to be 32 and 28% higher at night,
whilst N2O was 11% higher during the day (Fig.4). The
greater concentrations of dissolved CO2 during the night are
consistent with previous literature and our hypotheses that
water column CO2 is largely controlled by photosynthesis/
respiration dynamics driven by light (Maher, etal. 2013a,
b; Saderne etal. 2013). In addition, high oxygen concentra-
tions facilitate nitrification in aquatic systems, where N2O
is produced during the metabolism of ammonium (NH4+)
into nitrate (NO3−) (Elkins etal. 1978; Nishio etal. 1983).
Higher N2O concentrations during the day, in combination
with the positive relationship between N2O and dissolved
oxygen saturation (DO%) from spatial surveys, indicate that
oxygen-dependent aerobic nitrification is the predominant
N2O production pathway in temperate seagrasses (Xia etal.
2013; Johansson etal. 2011), although important to note is
that throughout the entire diel cycle, N2O was rarely above
100% atmospheric saturation (Fig.4c). This suggests that
similar to mangroves (Maher etal. 2016), seagrass systems
may act as a sink of N2O due to low nutrient concentrations
and high rates of complete denitrification (Welsh etal. 2000;
Eyre and Ferguson 2002; Eyre etal. 2016). Diel oscillations
in autotrophic and heterotrophic respiration cycles within
seagrass sediments may also lead to fluctuations in anaerobic
methanogenesis and aerobic oxidation of dissolved meth-
ane (King etal. 1990; Maher, etal. 2013a, b). However,
we did not find a significant relationship between DO and
CH4 as would be expected from respiration cycle-driven
CH4 production. Alternatively, CH4 concentrations may be
more tightly linked to fluctuations in tidal height that alter
the exchange of sediment pore water rich in organic matter,
with the water column, a process known as tidal pumping
(Borges etal. 2003; Bouillon, etal. 2007a, b; Atkins etal.
2013; Maher, etal. 2013a, b; Macklin etal. 2014). Qualita-
tive analyses of diel trends from this study show an inverse
relationship between tidal height and CH4%sat (Fig.4b), a
relationship that matches those of previous studies of tidal
pumping in mangrove systems (Linto etal. 2014; Call etal.
2015); We therefore suggest that tidal processes were likely
the predominant driver of CH4 concentrations.
Large differences in mean atmospheric GHG flux were
observed between spatial and temporal sampling methodolo-
gies, though both methods estimated flux to be of net uptake
into the water column. In coastal ecosystems, variation of
environmental conditions, such as DO, salinity, temperature
and turbulence, often occurs across both spatial and tem-
poral scales (Smith etal. 1999; Raymond and Cole 2001;
Duarte 2002; Bouillon, etal. 2007a, b; Touchette 2007).
As photosynthetic production, microbial respiration and gas
transfer dynamics are highly linked to these environmental
conditions, accounting for both spatial and temporal vari-
ation in gas concentrations is important for accurate GHG
emission estimates (Gazeau etal. 2005; Call etal. 2015;
Maher etal. 2015). Here, net atmospheric GHG uptake
Fig. 7 Bar graph displaying the mean ± standard error of greenhouse
gas concentration and atmospheric flux between two sampling meth-
odologies. TimeS represents single point time series, whilst Survey
represents continuous underway spatial sampling

Estuaries and Coasts
1 3
rates from bay-wide surveys were ~ 30 times greater than
those from the time-series experiment, at − 480 ± 15.96
and − 16.2 ± 8.32mg CO2-e m2 d−1 respectively, a disparity
primarily attributed to much lower CO2 uptake rates during
the time-series sampling (Table1). Variation in GHG flux
between spatial and temporal sampling methodologies is in
accordance with previous literature from mangrove and estu-
arine systems (Maher, etal. 2013a, b; Ho etal. 2014; Maher
etal. 2015) and further highlights the need for the inclusion
of both of these processes when estimating seagrass atmos-
pheric emissions. Important to note, is that whilst the survey
methodology from this study maintained a high sampling
frequency (i.e. one sample per minute), it was both limited
to the outer regions of the bay (Fig.1) and did not incor-
porate diel or seasonal variations. The survey was carried
out during daylight hours, amplifying the role of primary
production on our estimates. Similarly, time-series meth-
odology is constrained to a single point in the bay, whilst
diel variation in GHG emissions is likely to vary across the
bay. Therefore, the observed variations in flux between the
methodologies can only be assessed qualitatively due to the
lack of direct overlap, and a robust correlative equation for
day-night GHG variations could not be established, though
it is of high importance for more accurate future estimates of
seagrass emissions based on spatial surveys alone.
The carbon isotopic signature (δ13C) of water column dis-
solved CO2 indicated that allochthonous terrestrial carbon
was not a major contributor to microbial community respira-
tion within Swan Bay. Seagrasses are effective ecosystems
for the biosequestration of atmospheric carbon; however,
they are also able to trap and bury allochthonous carbon
from terrestrial run-off and other coastal ecosystems higher
in elevation such as mangroves and tidal marsh (Agawin
and Duarte 2002; Duarte etal. 2005; Kennedy etal. 2010;
Deegan etal. 2012). In addition, a range of periphytic organ-
isms establish themselves on seagrass blades and may con-
tribute to sedimentary carbon concentrations (Walker and
Woelkerling 1988; Gacia etal. 2002). Understanding the
carbon source of microbial respiration within seagrasses
allows for more targeted management of emissions (Geraldi
etal. 2019), for example, terrestrial carbon run-off may be
reduced through better up-stream effluent management or
increased fencing along water ways. Here, we find that the
predominant CO2-C source as established through Keeling
plots (Keeling 1958; Pataki etal. 2003; Maher etal. 2017)
was − 13.75 ‰, an isotopic value that closely matches that
of Zostera spp. and their associated periphyton (− 12.2
– − 13 δ13C) (Thayer etal. 1978). These results suggest that
terrestrial C3 carbon inputs do not play a significant role in
aquatic metabolism in the bay, and the GHG fluxes measured
are likely indicative of insitu processes.
In conclusion, this study provides the first simultane-
ous insitu assessment of CO2, CH4 and N2O atmospheric
emissions from temperate Australian seagrasses. We find
that the seagrasses studied were a net CO2-e sink, due to
CO2 and N2O uptake outweighing CH4 release. We also note
that seagrass atmospheric emissions were heavily linked to
diel fluctuations in light availability and tidal pumping, and
that omission of these variables when estimating seagrass
emissions is likely to lead to inadequate estimates. Finally,
we demonstrate large variation in CO2 and CH4 emissions
at the habitat scale, highlighting the need for the inclusion
of spatial parameters when upscaling seagrass emission esti-
mates. As seagrasses maintain some of the highest rates of
carbon sequestration and storage on the planet, they repre-
sent potential ecosystems for nature-based carbon offsetting.
Further research into seagrasses overall carbon sink potential
incorporating their associated GHG emissions is extremely
important for future offset investments.
Acknowledgements Thank you to Alice Gavoille for helping during
the sample collection. This research was in‐part funded by a Holsworth
Wildlife Research Endowment, and we thank the Holsworth foundation
for its ongoing support of critical ecological research. We thank the
Corangamite Catchment Management Authority for their funding and
collaborative support.
Funding Open Access funding enabled and organized by CAUL and
its Member Institutions. We thank Deakin University’s Centre for Inte-
grative Ecology and Department of Life and Environmental Sciences
for ongoing funding support provided to DTM via the Researcher in
Residence programme. We also thank the Australian Research Council
for their funding support, LP160100242 and DE150100581.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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