The complexity of organic matter (OM) degradation
mechanisms represents a significant challenge for developing biogeochemical
models to quantify the role of aquatic sediments in the climate system. The
common representation of OM by carbohydrates formulated as CH2O in
models comes with the assumption that its degradation by fermentation
produces equimolar amounts of methane (CH4) and dissolved inorganic
carbon (DIC). To test the validity of this assumption, we modelled using
reaction-transport equation vertical profiles of the concentration and
isotopic composition (δ13C) of CH4 and DIC in the top 25 cm of the sediment column from two lake basins, one whose hypolimnion is
perennially oxygenated and one with seasonal anoxia. Furthermore, we modelled
solute porewater profiles reported in the literature for four other
seasonally anoxic lake basins. A total of 17 independent porewater
datasets are analyzed. CH4 and DIC production rates associated with
methanogenesis at the five seasonally anoxic sites collectively show that
the fermenting OM has a mean (± SD) carbon oxidation state (COS) value
of -1.4±0.3. This value is much lower than the value of zero
expected from carbohydrate fermentation. We conclude that carbohydrates do
not adequately represent the fermenting OM in hypolimnetic sediments and
propose to include the COS in the formulation of OM fermentation in models
applied to lake sediments to better quantify sediment CH4 outflux. This
study highlights the potential of mass balancing the products of OM
mineralization to characterize labile substrates undergoing fermentation in
sediments.
Introduction
Significant proportions of atmospheric methane (CH4) and carbon dioxide
(CO2), two powerful greenhouse gases, are thought to originate from
freshwater lake sediments
(Bastviken
et al., 2004; Turner et al., 2015; Wuebbles and Hayhoe, 2002), but large
uncertainties remain concerning their contribution to the global CO2
and CH4 budgets (Saunois et al., 2016). The
role of these waterbodies in the global carbon (C) budget has been
acknowledged for more than a decade (Cole et
al., 2007). Especially in the lake-rich boreal region, lakes are hotspots of
CO2 and CH4 release
(Hastie
et al., 2018; Wallin et al., 2018) and intensive sites of terrestrial C
processing (Holgerson and Raymond, 2016; Staehr et al.,
2012). Using high-resolution satellite imagery,
Verpoorter et al. (2014) estimated the number of lakes on earth larger than 0.01 km2 to about 27 million and reported
that the highest lake concentration and surface area are found in boreal
regions. Boreal lakes, which are typically small and shallow, are known to
store large amounts of organic C, to warm up quickly, and to develop anoxic
hypolimnia in the warm season
(Sabrekov et al.,
2017; Schindler et al., 1996). Owing to the great abundance of boreal lakes,
their sensitivity to climate change, and their foreseen important role in the
global C cycle, there is a need to further develop process-based models to
better quantify C processing reactions in these lakes and their alteration
under warming (Saunois et al., 2016).
In aquatic environments, CH4 is mainly produced (methanogenesis) in the sediment along with CO2 at depths where most electron acceptors (EAs) are depleted (Conrad, 1999; Corbett et al., 2013). During its upward migration to the atmosphere, CH4 is partly aerobically or anaerobically oxidized to CO2
(methanotrophy) in the upper strata of the sediments and in the water column
(Bastviken
et al., 2008; Beal et al., 2009; Egger et al., 2015; Ettwig et al., 2010;
Raghoebarsing et al., 2006). The oxidation of organic matter (OM) by EAs
such as O2, NO3-, Fe(III), Mn(IV), SO42-, and humic
substances as well as the partial fermentation of high-molecular-weight
organic matter (HMW OM) into lower-molecular-weight organic matter (LMW OM)
are also potential sources of CO2 in the sedimentary environment
(Corbett et
al., 2015). Predicting fluxes of CH4 and CO2 from the aquatic
sediments and water column to the atmosphere is challenging considering the
various transport processes and chemical and microbially mediated reactions
implicated and the complexity of natural OM, which serves as substrate
(Natchimuthu et al., 2017).
Process-based geochemical models taking into account both the numerous
biogeochemical reactions involving C and transport processes are powerful
tools that are able to interpret present-day sediment, porewater, and water-column
profiles of C species and offer great potential to forecast changes in
cycling of this element under variable environmental scenarios
(Arndt
et al., 2013; Paraska et al., 2014; Saunois et al., 2016; Wang and Van
Cappellen, 1996). Nonetheless, the performance of these models depends on
the correct formulation of the complete OM mineralization reactions, e.g.,
OM decomposition to DIC, phosphate, ammonium, and CH4 through oxidation
and fermentation reactions (Burdige, 1991), particularly in terms of the
metabolizable organic compounds involved. Up to now, carbohydrates,
represented as the simple chemical formula CH2O (or
C6H12O6), whose average carbon oxidation state (COS) is zero,
are commonly assumed to be representative of the bulk of metabolizable OM,
including the substrates involved in fermentation reactions (e.g.,
Arndt
et al., 2013; Arning et al., 2016; Paraska et al., 2014, and references
therein). The capacity of CH2O to represent adequately the ensemble of
labile organic compounds is, nevertheless, becoming increasingly questioned
in the literature given the variety and complexity of organic molecules
present in the environment
(Alperin
et al., 1994; Burdige and Komada, 2011; Clayer et al., 2016; Jørgensen
and Parkes, 2010). Based on the observation that methanogenesis produced
CH4 3 times faster than CO2 in the sediments of a boreal,
sporadically anoxic lake basin, Clayer et al. (2018) concluded that the fermenting OM had a markedly negative COS value of
-1.9. This COS value corresponds more closely to a mixture of fatty acids
and fatty alcohols than to carbohydrates (e.g., CH2O), which would have
yielded equivalent CH4 and CO2 production rates. The low COS
value of metabolizable OM in the sediment layer where methanogenesis
occurred in this lake has been attributed to the nearly complete consumption
of the most labile organic components (e.g., carbohydrates, proteins) during
its downward transport through the water column and the upper sediment
layers, thus leaving only material of lower lability such as fatty acids and
fatty alcohols available for methanogenesis. Such interpretation, however,
must be validated by investigating other lakes before revising the
formulation of the fermenting OM used in diagenetic models in order to
improve model predictions of C cycling, including greenhouse gas
production and emission from these environments.
In this study, the approach described in Clayer et al. (2018), combining
concentration and δ13C inverse modelling, is applied to the two
newly acquired datasets. These datasets include centimetre-scale vertical
porewater profiles of the concentrations and of the stable carbon isotope
ratios (δ13C) of CH4 and dissolved inorganic carbon (DIC)
as well as those of the concentrations of EAs from hypolimnetic sediments of
two boreal lake basins showing contrasted O2 dynamics: one whose
hypolimnion remains perennially oxygenated and the other whose hypolimnion
becomes anoxic for several months annually. This procedure enables us to
constrain the effective rates of OM mineralization reactions and calculate,
using a mass balance equation, the COS of the substrates fermenting in the
sediments in these two lake basins. In addition, we modelled solute
porewater profiles gathered from the scientific literature or from our data
repository for four other seasonally anoxic lake basins to estimate, using
the mass balance equation, the COS of the substrates fermenting in these
sediments. A total of 17 independent datasets are analyzed to provide
additional insight into the COS of the fermenting OM in boreal lakes and the
associated mineralization pathways.
Materials and methodsStudy sites
This study was carried out in two small, dimictic oligotrophic and
headwater lakes located within 50 km from Québec City, eastern Canada,
with fully forested and uninhabited watersheds (Fig. 1). Lake
Tantaré (47∘04′ N, 71∘32′ W) is part of the
Tantaré Ecological Reserve and has four basins connected by shallow
channels and a total surface area of 1.1 km2. Lake Bédard
(47∘16′ N, 71∘07′ W), lying in the protected Montmorency
Forest, comprises only one small (0.05 km2) basin. The samples for this
study were collected at the deepest sites of Lake Bédard (10 m) and of
the westernmost basin of Lake Tantaré (15 m), hereafter referred to as
Basin A of Lake Tantaré to remain consistent with our previous studies
(e.g., Clayer
et al., 2016; Couture et al., 2008). These two sampling sites were selected
based on their contrasting O2 regimes (Fig. 1): Lake Bédard
develops an anoxic hypolimnion early in the summer (D'Arcy, 1993),
whereas the hypolimnion of Lake Tantaré Basin A is perennially
oxygenated (Couture et al., 2008). The O2
diffusion depth in the sediments of Lake Tantaré Basin A, as measured
with a microelectrode, does not exceed
4 mm (Couture et al., 2016).
Location map and bathymetry of lakes Tantaré and Bédard.
The bathymetric map of Lake Tantaré was reproduced from the map C-9287
of the Service des eaux de surface of the Québec Ministry of
Environment. The map of Lake Bédard was reproduced from D'Arcy (1993).
Dioxygen concentrations in the water column of Lake Tantaré Basins A and
B and of Lake Bédard are given for June (black lines) and October (red
lines).
The sediment accumulation rates are 4.0–7.3 and 2.4–46.8 mg cm-2 yr-1 at the deepest sites of Lake Tantaré Basin A and Lake
Bédard, respectively (Couture et al.,
2010). The relatively constant organic C (Corg) content (20±2 %; Fig. 2b), the elevated {Corg}:{N} molar ratio (17±2; Fig. 2b), and the
δ13C (-29 ‰; Joshani, 2015) and δ15N
(+0.5 ‰ to -2.5 ‰; Joshani, 2015) values reported for
the sediment OM over the top 30 cm in Lake Tantaré Basin A are typical
of terrestrial humic substances
(Botrel et
al., 2014; Francioso et al., 2005). The Corg content (21±2.7 %; Fig. 2a) and {Corg}:{N} molar ratio (14±1.9; Fig. 2a)
reported over the top 30 cm of Lake Bédard sediments show slightly more
variation with depth but are also typical of terrestrial OM. In addition,
the {Corg}:{S}
ratios of both lake basin sediments (50–200) are typical of those reported
for soil OM (∼125; Buffle, 1988).
Depth profiles of the organic C concentrations and of the
C:N molar ratio in sediment cores collected at the deepest sites of Lake Bédard (a) and Lake Tantaré Basin A (b).
Sample collection
Sediment porewater samples were acquired by in situ dialysis in October 2015 with
peepers (Carignan et al., 1985;
Hesslein, 1976) deployed by divers within a 25 m2 area at the
deepest site of each lake basin. Bottom water O2 concentrations were
∼2.5 and <0.1 mg L-1 in Lake Tantaré Basin A and in Lake Bédard, respectively. The acrylic peepers comprised two
columns of 4 mL cells, filled with ultrapure water and covered by a
0.2 µm Gelman HT-200 polysulfone membrane, which allowed porewater
sampling from about 23–25 cm below the sediment–water interface (SWI) to 5 cm above this interface (hereafter referred to as overlying water) at a
1 cm depth resolution. Oxygen was removed from the peepers prior to their
deployment, as described by Laforte
et al. (2005). Four peepers were left in the sediments of each lake basin
for at least 15 d, i.e., a longer time period than that required for solute
concentrations in the peeper cells to reach equilibrium with those in the
porewater (5–10 d; Carignan et al.,
1985; Hesslein, 1976). At least three independent porewater profiles of pH;
of the concentrations of CH4, DIC, acetate,
NO3-,
SO42-, Fe, and Mn; and of the δ13C of CH4 and DIC were generated for the two sampling sites. In
Lake Bédard, samples were also collected to determine three porewater
profiles of sulfide concentrations (ΣS(-II)). After peeper
retrieval, samples (0.9–1.9 mL) for CH4 and DIC concentrations and
δ13C measurements were collected within 5 min from the
peeper cells with He-purged polypropylene syringes. They were injected
through rubber septa into He-purged 3.85 mL Exetainers (Labco Limited)
after removal of a volume equivalent to that of the collected porewater. The
Exetainers were preacidified with 40–80 µL of HCl 1N to reach a final
pH ≤2. The protocols used to collect and preserve water samples for
the other solutes are given by Laforte et al. (2005).
Analyses
Concentrations and carbon isotopic composition of CH4 and DIC were
measured as described by Clayer et al. (2018). Briefly, the concentrations were analyzed within 24 h of peeper
retrieval by gas chromatography with a precision better than 4 % and
detection limits (DLs) of 2 and 10 µM for CH4 and DIC,
respectively. The 13C/12C abundance ratios of CH4 and
CO2 were determined by mass spectrometry with a precision of ±0.2 ‰ when 25 µmol of an equimolar mixture of
CH4 and CO2 was injected, and results are reported as
δ13C=100013Csolute12Csolutesample13C12Cstandard-1,
where the subscript solute stands for CH4 or DIC, and the reference
standard is Vienna Pee Dee Belemnite (VPDB). Acetate concentration was
determined by ion chromatography (DL of 1.4 µM) and those of Fe, Mn, NO3-, SO42-, and ΣS(-II), as
given by Laforte et al. (2005).
Inverse modelling of porewater solutes
The one-dimensional mass-conservation equation
(Boudreau, 1997)
∂∂xφDs∂[solute]∂x+φαIrrigation([solute]tube-[solute])+Rnetsolute=0
was used to separately model the porewater profile of each relevant solute,
i.e., CH4, DIC, O2, Fe, and
SO42-, considering steady-state
and negligible solute transport by bioturbation and advection. The validity
of these assumptions has been previously demonstrated for the study sites
(Couture et al., 2008, 2010; Clayer et al., 2016). In this
equation, [solute] and [solute]tube
denote a solute concentration in the porewater and in the animal tubes
(assumed to be identical to that in the overlying water), respectively;
x is depth (positive downward); φ is porosity;
Ds is the solute effective diffusion coefficient in
sediments; αIrrigation is the bioirrigation
coefficient; and Rnetsolute
(in moles per cubic centimetre of wet sediment per second) is the solute net production
rate (or consumption rate if Rnetsolute
is negative). Ds was assumed to be φ2Dw (Ullman and
Aller, 1982), where Dw is the solute tracer diffusion
coefficient in water. The values of Dw, corrected for
in situ temperature (Clayer et al., 2018), were 9.5 ×10-6,
6.01 ×10-6, 1.12 ×10-5, 5.81 ×10-6, 3.19 ×10-6, 1.17 ×10-5 cm2 s-1 for CH4, HCO3-,
CO2, SO42-, Fe, and O2,
respectively. The values of αIrrigation in Lake
Tantaré Basin A were calculated as in Clayer et al. (2016), considering
that αIrrigation varies linearly from
α0_Irrigation at the SWI
(calculated according to Boudreau, 1984, based on an inventory of
benthic animals; Hare et al., 1994) to 0
at 10 cm depth (the maximum depth at which chironomids are found in lake
sediments; Matisoff and Wang, 1998) and
were assumed to be 0 in Lake Bédard since its bottom water was anoxic
(Fig. 1).
The Rnetsolute values were determined
from the average (n=3 or 4) solute concentration profiles by numerically
solving Eq. (2) with the computer code PROFILE (Berg et
al., 1998). The boundary conditions were the solute concentrations at the
top and at the base of the porewater profiles. In situ porewater O2
profiles were not measured in Lake Tantaré Basin A. For modelling this
solute with PROFILE, we assumed that the [O2] in the overlying water
was identical to that measured in the lake bottom water and equal to 0 below
0.5 cm (based on O2 penetration depth; Couture et al., 2016). This
procedure provides a rough estimate of
RnetO2 at the same vertical
resolution as for the other solutes. The code PROFILE yields a discontinuous
profile of discrete Rnetsolute values
over depth intervals (zones), which are objectively selected by using the
least square criterion and statistical F testing (Berg et al., 1998). In
order to estimate the variability in
Rnetsolute related to heterogeneity
within the 25 m2 sampling area, additional
Rnetsolute values were obtained by
modelling the average profiles whose values were increased or decreased by
1 standard deviation. This variability generally ranges between 2 and
10 fmol cm-3 s-1.
In addition, the computer program WHAM 6 (Tipping, 2002) was used,
as described by Clayer et al. (2016), to calculate the speciation of
porewater cations and anions. The solute activities thus obtained, together
with solubility products (Ks), were used to calculate saturation index
values (SI=logIAP/Ks, where IAP is the ion activity product).
Reaction network
The main reactions retained in this study to describe carbon cycling in the
sediments of the two lake basins are shown in Table 1.
Ri and αi denote,
respectively, the effective (or gross) reaction rate and the carbon isotopic
fractionation factor associated with each Reaction (Ri; Table 1). Once
oxidants are depleted, fermentation of metabolizable OM of general formula
CxHyOz can yield acetate, CO2, and H2 (Reaction 1). The
coefficient ν1 in Reaction (R1) constrains the relative contribution of
acetoclasty and hydrogenotrophy. The partial degradation of high-molecular-weight OM (HMW OM) into lower-molecular-weight OM (LMW OM) can also produce
CO2 (Reaction R2; Corbett et al., 2013, 2015). Acetoclasty (Reaction R3)
and hydrogenotrophy (Reaction R4) yield CH4. Moreover, CH4 (Reaction R5) and OM (Reaction R6)
can be oxidized to CO2 when electron acceptors such as O2, Fe(III),
and SO42- are present. Note that
the electron acceptors (EAs)
NO3- and Mn oxyhydroxides were
shown to be negligible in these two lake basins
(Clayer
et al., 2016; Feyte et al., 2012) as well as the precipitation of carbonates
whose saturation index values are negative (SI ≤-1.5), except for
siderite (Reaction R7) in Lake Bédard (SI =0.0 to 0.7 below 10 cm depth).
Reactions considered (R1–R7), their reaction rates
(R1–R7),
and carbon isotopic fractionation factors (α1–α7).
DescriptionReactionIDCO2 production due to complete fermentation of labile OM aCxHyOz+(x+ν1-z)H2O⟶R1α1x-ν12CH3COOH+ν1CO2+y2-z+2ν1H2(R1)CO2 production due to partial fermentation of HMW OMa,bν2HMWOM⟶R2α2ν3LMWOM+ν4CO2(R2)Methanogenesis via AcetoclastyCH3COOH⟶R3α3CH4+CO2(R3)HydrogenotrophyCO2+4H2⟶R4α4CH4+2H2O(R4)CO2 production due to MethanotrophyCH4+2Oxidants⟶R5α5CO2+2Reducers(R5)OM oxidationOM+Oxidant⟶R6α6CO2+Reducer(R6)Precipitation of siderite Fe2++CO2+H2O⟶R7α7FeCO3(s)+2H+(R7)
a Where ν1 can have any value between 0 and
x, and values for ν2–ν4 are unknown.
b HMW OM and LMW OM designate high- and lower-molecular-weight organic matter, respectively.
Determining realistic ranges for effective reaction rates
Considering the net reaction rates obtained by inverse modelling, a
realistic range of values can be given for each of the effective reaction
rates Ri in each depth interval using the general
equations described below. The detailed calculations for each
Ri at both study sites are described in Sect. S2.
From Table 1, the net rate of CH4 production,
RnetCH4, in the sediments
is
RnetCH4=R3+R4-R5,
where R3 and R4 are the rates
of acetoclastic (Reaction R3) and hydrogenotrophic (Reaction R4) production of CH4,
respectively, and R5 is the rate of DIC production
due to CH4 oxidation (Reaction R5). The net rate of DIC production,
RnetDIC, can be expressed as
RnetDIC=R1+R2+R3-R4+R5+R6-R7,
where R1, R2, and
R6 are the rates of DIC production due to complete
fermentation of labile OM (Reaction R1), partial fermentation of HMW OM (Reaction R2), and OM
oxidation (Reaction R6), respectively, and R7 is the rate of
DIC removal by siderite precipitation (Reaction R7). It can also be written that
RnetOx=-2R5-R6,
where RnetOx is the net reaction rate of
all relevant oxidant consumption, i.e., O2, Fe(III), and
SO42- only because
NO3- and Mn(IV) are negligible
(see above). For simplicity, RnetOx is
expressed in equivalent moles of O2 consumption rate, taking into
account that SO42- and Fe(III)
have twice and one quarter the oxidizing capacity of O2, respectively.
In practice, the value of RnetOx was
calculated by adding those of
RnetO2,
14RnetFe(III), and
2RnetSO42-,
where RnetO2,
RnetFe(III), and
RnetSO42-
were estimated with PROFILE. In this calculation, we assumed that all
dissolved Fe is in the form of Fe(II), and that the rate of Fe(II)
consumption through Reaction (R7) is negligible compared to those associated
with Reactions (R5) and (R6). Under these conditions,
RnetFe(III) equals -RnetFe.
It should be noted that using
RnetO2,
-RnetFe, and
RnetSO42-
to calculate RnetOx, we indirectly take
into account the reoxidation of reduced S and Fe(II), respectively, to
SO42- and Fe(III) by O2.
Indeed, with this procedure, we underestimate the terms
14RnetFe(III) and
2RnetSO42-
because reoxidation reactions are ignored, but we overestimate by the same
amount the term RnetO2. In
other words, omission of these reoxidation reactions affects only the
relative consumption rates of individual oxidants and not the value of
RnetOx, which is of interest here.
Constraining effective reaction rates with δ13C modelling
Once the range of values has been determined for each of the effective
rates Ri (see Table S2), they can be used in another
reaction-transport equation to model the δ13C profiles of
CH4 and DIC. Only sets of Ri values that yield
acceptable modelled δ13C profiles, i.e., which fall within 1
standard deviation of the measured δ13C profiles (grey area
fills in Fig. 4), were kept for COS calculation below (Sect. 2.8). The
δ13C modelling procedure is summarized below and described in
detail in Section S2. This procedure takes into account the effect of
diffusion and bioirrigation (in Lake Tantaré Basin A) and the isotopic
fractionation effect of each Reaction (Ri).
Briefly, the δ13C profiles of CH4 (δ13C–CH4) and DIC (δ13C–DIC) were simulated with a
modified version of Eq. (1) (Clayer et al., 2018):
δ13C=100013CCsample13C12Cstandard-1,
where C is the total CH4 or DIC concentration
([12C] can be replaced by [C] since ∼99 % of C is
12C), and 13C is the
isotopically heavy CH4 or DIC concentration. Equation (6) allows
for calculating a δ13C profile once the depth distributions of
13C and C are known. This information is obtained by solving the
mass-conservation equations of C and 13C for CH4 and DIC. The
one-dimensional mass conservation of [C] is given by Eq. (2), where [solute] is
replaced by [C], whereas that for [13C] is the following modified
version of Eq. (2) (Clayer et al., 2018):
∂∂xφDsf∂13C∂x+φαIrrigation13Ctube-13C+∑i=15Riαiδ13Cireactant1000+113C12Cstandard=0,
where f, the molecular diffusivity ratio, is the diffusion coefficient of
the regular solute divided by that of the isotopically heavy solute;
αi is the isotope fractionation factor in
Reaction (Ri); and δ13Cireactant is the δ13C of the reactant leading to the formation of the solute (CH4
or DIC) in Reaction (Ri). Input and boundary conditions used to
numerically solve Eqs. (2) and (7) for C and 13C, respectively, via the bvp5c
function of MATLAB® are described in Sects. 3.4
and S2 of the Supplement.
The goodness of fit of the model was assessed with the norm of residuals
(Nres):
Nres=∑x=0.522.5δ13Cm-δ13Cs2,
where δ13Cm and
δ13Cs are the measured and
simulated δ13C values, respectively. The norm of residuals
(Nres) varies between 0 and infinity, with smaller
numbers indicating better fits.
COS calculation
Considering the complete fermentation of metabolizable OM of general formula
CxHyOz and making two assumptions, described below for
clarity, the COS of the fermenting molecule is given by combining Eqs. S8
and S15 (see Sect. S2 for details):
COS=-4RnetCH4-RnetDIC-RnetOx+R2RnetCH4+RnetDIC+1-χMRnetOx-R2,
where χM is the fraction of oxidants consumed by
methanotrophy. Equation (9) is only valid if (i) Reaction (R1) is the only source of
substrates for hydrogenotrophy and acetoclasty (this assumption is discussed
in Sect. 4.2 below) and if (ii) siderite precipitation (Reaction R7) is
negligible (saturation index for siderite is negative except below 10 cm
depth in the sediment of Lake Bédard; this case is considered in Sect. S2.1.2.2). With values of
RnetCH4 and
RnetOx obtained from PROFILE (Sect. 2.4) and values of R2 and χM
constrained by δ13C modelling (Sect. 2.7), Eq. (9) can be used
to calculate the COS of the fermenting molecule.
Data treatment of other datasets
To better assess the COS of the fermenting OM in lakes, relevant sets of
porewater concentration profiles (CH4, DIC, EAs, Ca) available from the
literature or from our data repository have been modelled with the code
PROFILE, as described in Sect. 2.3, to extract their
RnetCH4,
RnetDIC, and
RnetOx profiles. These porewater
datasets, described in Sect. S3 of the Supplement, had been generated by sampling
porewater in the hypolimnetic sediments of (i) Lake Bédard and Basin A
of Lake Tantaré on other dates than for this study (Clayer et al.,
2016); (ii) Basin B of Lake Tantaré (adjacent to Basin A; Fig. 1) on four
occasions (Clayer et al., 2016; 2018); (iii) Williams Bay of Jacks Lake
(44∘41′ N, 78∘02′ W), located in Ontario, Canada, on
the edge of the Canadian Shield
(Carignan and Lean, 1991); and (iv) the southern basin of the subalpine Lake Lugano (46∘00′ N, 3∘30′ E),
located in Switzerland, on two occasions
(Lazzaretti-Ulmer and Hanselmann, 1999). All lake
basins except Basin A of Lake Tantaré develop an anoxic hypolimnion.
ResultsSolute concentration profiles
Differences among the replicate profiles of CH4, DIC,
SO42-, ΣS(-II), and Fe
(Fig. 3) at the two sampling sites are generally small (except perhaps those
of SO42- in Lake Bédard) and
should be mainly ascribed to spatial variability within the 25 m2
sampling area. Indeed, the main vertical variations in the profiles are
defined by several data points without the sharp discontinuities expected
from sampling and handling artifacts. Note that the acetate concentrations,
which were consistently low (<2µM), are not shown.
Replicate porewater profiles of CH4(a, i), δ13C–CH4(b, j), DIC (c, k), δ13C–DIC (d, l), SO42-(e, m), Fe, and ΣS(-II) (f, n), and comparison of the modelled
(blue lines) and average (n=3) measured (symbols) concentration profiles
of CH4(g, o) and DIC (h, p) in Lakes Tantaré Basin A (a–h) and Bédard (i–p). Different symbols indicate data from different peepers and empty symbols are for concentrations below detection limit. The horizontal dotted line indicates the sediment–water interface. The thick and thin blue lines represent the net solute reaction rate (Rnetsolute)
and the modelled concentration profiles, respectively. The red area fills
correspond to the sediment zones Z2.
The low Fe (<5µM; Fig. 3f) and CH4 (<2µM; Fig. 3a) concentrations as well as the relatively high
SO42- concentrations (36 ± 2.1 µM; Fig. 3e) in the sediment-overlying water of Lake Tantaré
Basin A are all consistent with the [O2] (∼2.5 mg L-1) measured in the bottom water and are indicative of oxic
conditions at the sediment surface. The sharp Fe gradients near the SWI
indicate an intense recycling of Fe oxyhydroxides (Fig. 3f; Clayer et al.,
2016), and the concave-down curvatures in the
SO42- profiles (Fig. 3e) reveal
SO42- reduction near the SWI. In
contrast to Lake Tantaré Basin A, high Fe (>200µM),
measurable CH4 (>200µM), low
SO42- (2.7 ± 1.4 µM), and detectable ΣS(-II) concentrations in the overlying waters
of Lake Bédard (Fig. 3i, m and n) are consistent with anoxic conditions
at the sediment surface. The absence of a sharp Fe gradient at the SWI in
Lake Bédard suggests that Fe oxyhydroxides were not recycled in these
sediments when porewater sampling occurred.
In the two lake basins, SO42-
concentrations reach a minimum between the SWI and 5 cm depth (Fig. 3e and
m) and increase below these depths. While increasing
SO42- concentrations with sediment
depth are unusual in lake sediments, the mineralization of sulfur-containing
OM in the sediment can be a source of
SO42- in low-SO42- environments
(Fakhraee et al., 2017). Although unravelling
the sediment sulfur cycling is beyond the scope of this study, we note that
SO42- generation through OM
mineralization would be consistent with the fact that the dominant sulfur
pool in Lake Tantaré Basin A is bound to sediment OM (Couture et al.,
2016).
The concentrations of CH4 (<1.5 mM; Fig. 3a and i) are well
below saturation at 4 ∘C and in situ pressure (4.4–5.5 mM; Duan and Mao,
2006), implying that CH4 ebullition is a negligible CH4 transport
process. The CH4 values increase from <2µM in the
overlying water to 0.18–0.20 mM at the base of the Lake Tantaré Basin A
profiles (Fig. 3a) and from 0.2–0.5 to 1.0–1.4 mM in those of Lake
Bédard (Fig. 3i). The three CH4 profiles from Lake Tantaré
Basin A (Fig. 3a) show a modest concave-up curvature in their upper part,
close to the SWI, indicative of a net CH4 consumption, and a convex-up
curvature in their lower part, typical of a net CH4 production. Such
trends, however, are not observed in Lake Bédard sediments. The CH4
profiles from this lake exhibit a convex-up curvature over the whole
sediment column, although it is more pronounced in its upper part (Fig. 3i).
The DIC concentrations consistently increase from 0.27–0.32 and
1.2–1.5 mM in the sediment-overlying water to 0.76–0.83 and 3.5–4.3 mM
at the bottom of the profiles in Lake Tantaré Basin A and Lake
Bédard, respectively (Fig. 3c and k). All DIC profiles show a similar
shape with a slight concave-up curvature in their lower segment and a
convex-up curvature in their upper portions.
Modelled CH4 and DIC concentration profiles
The modelled [CH4] and DIC profiles accurately fit the average (n=3
or 4) data points (r2>0.996 and r2>0.998 for CH4 and DIC, respectively; Fig. 3g, h, o, and p). The
RnetCH4 profiles reveal
three zones in each lake basin, numbered Z1, Z2, and Z3, from
the sediment surface whose boundaries match those defined by the
RnetDIC profiles. For Lake Tantaré
Basin A, Z1 corresponds to a net CH4 consumption and Z2 and
Z3 to net CH4 production, with the highest rate in Z2 (Fig. 3g). In contrast, the three zones in Lake Bédard show net CH4
production, with the highest rate in Z1 and the lowest in Z3 (Fig. 3o). The RnetDIC profiles in both lake
basins show a zone of net DIC consumption below two zones of net DIC
production, with the highest rate values in the Z1 and Z2 for Lake
Tantaré Basin A and Lake Bédard, respectively.
The RnetCH4 and
RnetDIC profiles displayed in Fig. 3
are, among all the possible solutions, the ones that give the simplest rate
profile while providing a satisfying explanation of the averaged solute
concentration profile as determined by statistical F testing implemented in
the code PROFILE (P value ≤0.001 except for the
RnetDIC profile in Lake Bédard, whose
P value is ≤0.005). As an additional check of the robustness of the
depth distribution of
RnetCH4 and
RnetDIC provided by PROFILE, we used
another inverse model, i.e., Rate Estimation from Concentrations (REC;
Lettmann et al., 2012), to model the average CH4 and DIC profiles. Note
that the statistical method, implemented in REC to objectively select the
depth distribution of the net reaction rates, i.e., the Tikhonov
regularization technique, differs from that of PROFILE. Figure S1 (Supplement) shows
that the two codes predicted mutually consistent
RnetCH4 and
RnetDIC profiles, with rate values of
similar magnitude. PROFILE was also used to estimate
RnetSO42-,
RnetFe, and
RnetO2 in order to calculate the value of
RnetOx in each zone at both sampling
sites (see Sect. 2.3 for details). The modelled
[SO42-] and [Fe] profiles
accurately fit the data points (r2>0.983; Fig. S3). As
expected from the contrasting O2 regimes of the two lake basins,
RnetOx values for Lake Tantaré Basin A were 1 to 2 orders of magnitude higher than those for Lake Bédard.
The values of RnetCH4,
RnetDIC, and
RnetOx estimated in each zone of each
lake basin are reported in Table 2.
Net production rates (Rnetsolute)
of CH4, DIC, and oxidants obtained with the code PROFILE in the three CH4 consumption and production zones (Z1, Z2, and Z3) for both sampling sites.
The δ13C–DIC values increase from -28.2±0.4 ‰ and -17.2±0.7 ‰ in
the overlying water to -5.1±1.0 ‰ and
3.6 ± 1.7 ‰ at the base of the profiles in Lake
Tantaré Basin A and Lake Bédard, respectively (Figs. 3d and l).
Similarly, the δ13C–CH4 values in Lake Bédard increase
steadily from -82.5±3.3 ‰ in the overlying
water to -74.0±1.5 ‰ at 24.5 cm depth (Fig. 3j). Regarding Lake Tantaré Basin A, the CH4 concentrations above
1.5 cm depth were too low for their 13C/12C ratio to be
determined. Starting at 1.5 cm depth, the δ13C–CH4 values
first decrease from -91.1±11.1 ‰ to
-107.0±6.8 ‰ at 2.5 cm depth and then
increase progressively to -83.5±1.6 ‰ at the
base of the profiles (Fig. 3b). Note that a shift toward more positive
δ13C–CH4 values upward, generally attributed to the
oxidation of CH4
(Chanton
et al., 1997; Norði et al., 2013), is only observed in the profiles of
Lake Tantaré Basin A (Fig. 3b).
As shown in Fig. S2 (Supplement), the isotopic signatures of nearly all samples from
the two lake basins fall within the ranges reported for hydrogenotrophic
methanogenesis, i.e., CO2 reduction, in a δ13C–CO2
vs. δ13C–CH4 graph similar to that proposed by
Whiticar (1999). Indeed, the
values of δ13C–CH4, all of which are lower than
-70 ‰ over the whole profiles in the two lake basins,
and the large difference (67 ‰ to 92 ‰) between the
δ13C of gaseous CO2 (δ13C–CO2) and
δ13C–CH4 strongly contrast with the typical δ13C–CH4 values (-68 ‰ to -50 ‰) and with
the difference between δ13C–CO2 and δ13C–CH4 (39 ‰ to 58 ‰) reported for
acetoclasty (Whiticar, 1999). The δ13C results reported
previously for another basin of Lake Tantaré (Basin B; Clayer et al.,
2018) also show in the hydrogenotrophy domain in Fig. S2.
Modelled δ13C profiles
In order to model the δ13C profiles with Eq. (6), accurate
profiles of C and 13C first need to be determined by numerically solving Eqs. (2) and (7), respectively. The modelled profiles of [CH4] and DIC obtained
with Eq. (2) perfectly replicated the measured profiles of these two solutes.
Getting a truthful profile of [13C] with Eq. (7) requires accurate values
of δ13Cireactant, αi, and Ri for each of the reactions given in Table 1 and of f
for both CH4 (f-CH4) and DIC (f-DIC). The multistep procedure to
obtain the best [13C] profiles for CH4 and DIC is described in
Sect. S2 (Supplement) and allowed us to constrain the f, χMαi, and Ri values.
Molecular diffusivity ratio of CH4 (f-CH4) as well as the
isotopic fractionation factors (α1, α2, α4–α7), the fraction of oxidant used by methanotrophy
(χM), and rates (R1,
R2, R4–R7; fmol cm-3 s-1) of each reaction
involved in OM mineralization in each zone and for the whole sediment column
(ΣRi; fmol cm-2 s-1) corresponding to the lowest
values of Nres. At both study sites, R3 was shown to be
negligible. See Sect. S2 of the Supplement for details.
Study siteZonesf-CH4α1α2α4α5α6α7R1R2R4R5R6R7χMTantaréZ11.0031.000–1.0941.0241.000–132–11912684–0.75Basin AZ21.0031.000–1.0871.0051.000–126-783926–0.75Z31.003––1.085–––––1––––ΣRi931–721592394––BédardZ11.0031.000–1.074–––165–100––––Z21.003–0.984a1.074–––72b145b50––––Z31.003––1.074––0.995––5––8–ΣRi853522612––114–
a The optimal value of α2, given here for a COS value of -1.5, varies slightly with the COS value (see Sect. S2.2.2.3 of the Supplement).
b The values of R1 and R2, given herefor a COS value of -1.5, vary with the COS value (see Sect. S2.2.2.3 of the Supplement).
The best fits between the simulated and measured δ13C profiles
of CH4 and DIC for Lake Tantaré Basin A and Lake Bédard (red
lines in Fig. 4) were obtained with the f, αi, and Ri
values displayed in Table 3. The optimal αi and f values were
within the ranges reported in the literature for both lake basins and
similar to those reported in our previous study on Lake Tantaré Basin B
(Clayer et al., 2018), except for the lower-than-expected value of α2 (0.984) in the Z2 of Lake Bédard. Modelled δ13C
profiles were considered acceptable only when they fell within 1 standard
deviation of the measured δ13C profiles (grey area fills in
Fig. 4). Acceptable modelled δ13C profiles were obtained only
when methanogenesis was 100 % hydrogenotrophic, i.e., when
R3=0 (see Sect. S2.2.2.1).
Comparison of the simulated (lines) and measured average (n=3) δ13C profiles of CH4 (circles) and DIC (squares) in the porewater of Lake Tantaré Basin A (a) and Lake Bédard (b). The dotted horizontal line indicates the sediment–water interface. The variability in δ13C values (±1 standard deviation – σ) related to the spatial heterogeneity within the sampling area is shown by the grey area fills. The zone Z2 is delimited by the blue area fill. In panel (b), the blue lines are the profiles simulated with the default rate values and optimal αi
and f values as described in Sect. S2.2.1. The red lines in panel (b) are the profiles simulated with α2 values of 0.980–0.984 (see Sect. 4.1 for details).
The sharp upward depletion in 13C–CH4 leading to a minimum δ13C–CH4 value at 2.5 cm depth in Lake Tantaré Basin A
sediments (Fig. 4a) was unanticipated since it occurs in the methanotrophic
zone, i.e., where the remaining CH4 is expected to be 13C-enriched
as a result of CH4 oxidation. Marked 13C–CH4 depletions at
the base of the sulfate–methane transition zone, where CH4 is consumed
via SO42- reduction, have often
been observed in marine sediments
(Burdige et al., 2017, and
references therein). Such features are generally attributed to the
production of CH4 by hydrogenotrophy from the 13C-depleted DIC
resulting from the anaerobic CH4 oxidation, a process referred to as
intertwined methanotrophy and hydrogenotrophy (e.g., Borowski
et al., 1997; Burdige et al., 2017; Pohlman et al., 2008). Here the modelled
δ13C–CH4 profile captured the minimum in δ13C–CH4 in the Z1 by simply assuming concomitant
hydrogenotrophy and methanotrophy in this zone and an upward-increasing
α4 value from 1.085 in the Z3 to 1.094 in the Z1 (Sect. S2.2.1 of the Supplement). A small variation with sediment depth in the
fractionation factor α4 is arguably possible since its value
depends on the types of microorganisms producing CH4
(Conrad, 2005).
DiscussionOrganic matter mineralization pathways at the sampling sites
The porewater data as well as the combined modelling of carbon isotopes and
concentration profiles allow us to highlight key OM mineralization mechanisms
and to quantify the relative contribution of methanogenesis and fermentation
to OM degradation at both sampling sites. The 13C isotopic signatures,
i.e., highly negative values of δ13C–CH4 and large
differences between δ13C–CO2 and δ13C–CH4 (Sect. 3.3 and Fig. S2 in the Supplement), as well as the
modelling of the δ13C–CO2 and δ13C–CH4
profiles (Sect. S2.2.2.1 and Fig. S4a and b in the Supplement) all point to
hydrogenotrophy as being the only pathway for methanogenesis in the two lake
basins. The dominance of hydrogenotrophy is also consistent with the finding
that acetate concentrations were close to or below DL in the porewater
samples. Under the condition that acetoclasty is negligible (i.e.,
x=ν1), Reaction (R1) from Table 1 becomes
CxHyOz+2x-zH2O⟶R1xCO2+2x+y2-zH2.
Methanogenesis was also reported to be essentially hydrogenotrophic in the
sediments of Basin B of Lake Tantaré (Clayer et al., 2018). The absence of
acetoclasty in the sediments of the oligotrophic lakes Bédard and
Tantaré is consistent with the consensus that hydrogenotrophy becomes an
increasingly important CH4 production pathway (i) when labile OM is depleted (Chasar
et al., 2000; Hornibrook et al., 2000; Whiticar et al., 1986), (ii) with
increasing sediment or soil depth
(Conrad et al., 2009;
Hornibrook et al., 1997), or (iii) with decreasing rates of primary
production in aquatic environments
(Galand et
al., 2010; Wand et al., 2006).
The modelling of concentrations and δ13C profiles revealed that
oxidative processes occurred essentially in the upper 7 cm of the sediments
of the perennially oxygenated Lake Tantaré Basin A, i.e., mainly in the
Z1 and, to a lesser extent, in the Z2 (Table 3 and Sects. S2.1.2.1 and S2.1.2.2 of the Supplement). Moreover, it showed that methanotrophy was
the dominant oxidative reaction in these sediment layers since 75 % of the
oxidants were consumed through Reaction (R5) (Sect. S2.2.2.2 of the Supplement). This outcome
is consistent with several studies showing that methanotrophy occurs at
higher rates than OM oxidation at low EA concentrations
(Kankaala
et al., 2013; Pohlman et al., 2013; Sivan et al., 2007; Thottathil et al.,
2019). Methanotrophy is also evidenced in the Z1 of this lake basin by
the negative RnetCH4 value
and by a shift of the δ13C–CH4 profiles to more positive
values in their upper part (Fig. 3b and g). Use of Eq. (2) to model the EA
profiles with the code PROFILE predicts that O2 was by far the main EA
involved either directly or indirectly via the coupling with the Fe or S
cycles in the oxidative processes. Indeed, comparing the values of
RnetO2 and
RnetOx (see Sect. 3.2 and Table 2)
shows that O2 accounts for 87 % and 70 % of the oxidants consumed
in the Z1 and Z2 of Lake Tantaré Basin A, respectively. Since
O2 penetration in the sediment by molecular diffusion is limited to
∼4 mm, a significant amount of O2 is predicted by Eq. (2) to be
transported deeper in the sediment through bioirrigation. The predominance
of O2 among the EAs consumed in the sediments is consistent with our
previous study in this basin of Lake Tantaré (Clayer et al., 2016).
Given that methanotrophy is the dominant oxidative process and that O2
is the main oxidant consumed, it is probable that aerobic oxidation of
methane prevails over its anaerobic counterpart in this lake basin. This is
in line with the common thinking that CH4 oxidation in freshwater lake
sediments is carried out by methanotrophs essentially in the uppermost oxic
sediment layer (Bastviken et al., 2008, and references therein).
In the Z2 of Lake Bédard, the net rate of DIC production (i.e., 167 fmol cm-3 s-1) was more than 3 times that of CH4 production
(50 fmol cm-3 s-1; Table 2). Given that the
RnetOx was negligible in this zone (i.e.,
R5=R6=0), we obtain from Eqs. (3) and (4) and Table 2 RnetCH4=R4=50 fmol cm-3 s-1 and
RnetDIC=R1+R2-R4=167 fmol cm-3 s-1 (see Sect. S2.1.2.2 of the Supplement). Should we assume that DIC production by Reaction (R2) is
negligible, i.e., R2=0, an R1/R4 ratio of 4.3 would be
obtained. This high ratio indicates that DIC was not produced by
fermentation (Reaction R1) alone in the Z2 of this lake. Indeed, methanogenesis
through the coupling of Reactions (R1) and (R4) yields an R1/R4 ratio of 2 if the
fermenting substrate is carbohydrates (COS of 0) and lower than 2 if the
fermenting substrate has a negative COS value. We thus attributed the
production of the additional DIC to the partial fermentation of HMW OM, an
assumed nonfractionating process reported to occur in wetlands (Corbett et
al., 2015). The better fitting of the δ13C–DIC profile when
α2 is set to 0.980–0.984 rather than to 1.000 in the Z2
(compare the blue and red lines in Fig. 4b) suggests that C fractionates
during this partial fermentation process.
Table 3 displays the depth-integrated reaction rates (ΣRi) over
the top 21 cm of the sediment column, which are given by
ΣRi=∑j=13ΔxjRi,
where Δxj (cm) is the thickness of the
zone Zj. In this calculation, we assume that other zones of CH4 or
DIC production are absent below 21 cm. Values of ΣRi clearly
show that anaerobic carbon mineralization reactions (fermentation and
methanogenesis) are important contributors to the overall OM mineralization
in the two studied lake basins. Indeed, the sum of the rates of CH4
production (ΣR4) and DIC production due to fermentation associated
with CH4 formation (ΣR1-ΣR4) and HMW OM
partial fermentation (ΣR2) represents 54 % and 100 % of the
total OM degradation rate (ΣR1+ΣR2+ΣR6) in the sediment of lakes Tantaré (Basin A) and Bédard,
respectively. Considering the sediment accumulation rate and sediment
Corg content given in Sect. 2.1, we calculate an average accumulation
rate of Corg of 4.7×10-11 to 1.0×10-10
and 2.9×10-11 to 7.6×10-10 mol C cm-2 s
-1 for lakes Tantaré (Basin A) and Bédard, respectively. Hence,
the total sediment OM degradation rate (ΣR1+ΣR2+ΣR6) of 1.3×10-12 and 1.4×10-12 reported in this study for lakes Tantaré Basin A and
Bédard, respectively, would involve only 1.2 %–2.8 % and 0.2 %–4.8 %
of the total Corg deposited. Given that the remaining 95.2 %–99.8 %
of the deposited Corg is preserved in the sediment, it is not
surprising that the sediment Corg concentration is constant with depth
(Fig. 2).
The contribution of anaerobic mineralization for Lake Tantaré Basin A is
about 1.8 times higher than the average of 30 % reported for this lake
basin in a previous study (Clayer et al., 2016). This significant
discrepancy arises because these authors, in the absence of isotopic data to
adequately constrain the Ri values, assumed that R4=0 in the
net methanotrophic zone Z1. Should we make the same assumption in the
present study, we would also estimate that fermentation and methanogenesis
represent only 30 % of the total rate of OM degradation in the oxygenated
Lake Tantaré Basin A, and we would thus underestimate the importance of
methanogenesis. The inclusion of δ13C data in the present
modelling study thus allowed us to better constrain the effective rates of
CH4 production (R4). Indeed, a value of
R4=119 fmol cm-3 s-1 was required in Eq. (7) to
produce an acceptable δ13C–CH4 profile (Table 3 and Fig. S3).
Organic substrates for methanogenesis at the sampling sites
Table 3 indicates that hydrogenotrophy (Reaction R4) coupled to the complete
fermentation of OM (Reaction R1) produces CH4 at higher rates (R4) than DIC
(R1-R4) in the Z1 and Z2 of both lake basins. This
outcome is inconsistent with the equimolar production of CH4 and DIC
expected from the fermentation of glucose (C6H12O6), the
model molecule used to represent labile OM in diagenetic models (Paraska et
al., 2014), thus suggesting that the fermentation of this compound is not
the exclusive source of the H2 required for hydrogenotrophy. Had OM
been represented by C6H12O6 in Reaction (R1), the rate of H2
production by this reaction would have been twice that of CO2, i.e.,
2R1. For its part, the rate of H2 consumption
through hydrogenotrophy is 4 times that of the CH4 production, i.e.,
4R4. Hence, an additional H2 production
at rates of up to 212 and 70 fmol cm-3 s-1, i.e.,
4R4-2R1, is
needed to balance the H2 production rate expected from the fermentation
of C6H12O6 and the H2 consumption rate by
hydrogenotrophy observed in the sediments of Lake Tantaré Basin A and
Lake Bédard, respectively. As discussed by Clayer et al. (2018), this
additional production rate of H2 is likely provided by the fermentation
of organic substrates that are more reduced than glucose.
Introducing the values of
RnetCH4,
RnetDIC,
RnetOx, χM, and
R2 (Tables 2 and 3) into Eq. (9), we
calculate COS values of -3.2 and -0.9 for the Z1 and Z2 of
Lake Tantaré Basin A, respectively, and of -1.0 to -1.1 for the
Z1 of Lake Bédard, respectively. Note that we were unable to
constrain with Eq. (9) the COS for the Z2 of Lake Bédard since we had
to assume a COS value to estimate the Ri, and the COS has no influence
on the modelled δ13C profiles (Sect. S2.2.2.3 of the Supplement).
Negative COS values between -0.9 and -1.1 suggest that fermenting OM in
the sediments of the two lake basins would be better represented by a
mixture of fatty acids and fatty alcohols than by carbohydrates, as
suggested by Clayer et al. (2018) for the sporadically anoxic Lake
Tantaré Basin B. For its part, the highly negative COS value of -3.2
calculated for the Z1 of Lake Tantaré Basin A is unreasonable, and
the inaccuracy of the COS determination in this lake basin is discussed in
Sect. 4.3.
Reduced organic compounds as methanogenic substrates in lake sediments
In order to better appraise the COS of the fermenting OM in lakes, relevant
datasets of porewater solute concentration profiles were gathered from our
data repository and from a thorough literature search. To be able to obtain
by reactive-transport modelling the
Rnetsolute required to calculate the COS
with Eq. (9), the datasets had to (i) comprise porewater concentration
profiles of CH4 and DIC and, ideally, those of the EAs; (ii) reveal a
net methanogenesis zone; and (iii) enable the estimation of the carbonate
precipitation and dissolution contribution to
RnetDIC. Detailed information on the
origin and processing of the 17 selected datasets, acquired in six different
lake basins from one subalpine and three boreal lakes sampled at various
dates and/or depths, is given in Sect. S3 of the Supplement. The CH4 and DIC
porewater profiles determined at hypolimnetic sites of these lake basins and
their modelling with the code PROFILE are shown in Fig. 5, whereas the
RnetCH4,
RnetDIC, and
RnetOx values determined from this
modelling are regrouped in Table 4. The COS values displayed in Table 4 for
all lake basins and dates were calculated by substituting the appropriate
RnetCH4,
RnetDIC,
RnetOx, and R2 values in Eq. 9 and
varying χM between 0 and 1, except for Lake
Tantaré Basin A, for which χM equals 0.75 (Table 3). When the
value for R2 was not available, we assumed R2=0. Equation (9) indicates that R2>0 would yield lower COS values than
those reported in Table 4.
Comparison of the modelled (blue lines) and average (n=3)
measured concentration profiles of CH4 (squares) and DIC (circles) in Lake Tantaré Basin A (a–d) and Basin B (e–h), Lake Bédard (i), Jacks Lake (j–k), and Lake Lugano (l–o) on various sampling dates. The thick red lines represent the net solute reaction rate (Rnetsolute).
Net reaction rates
(Rnetsolute;
fmol cm-3 s-1) of CH4, DIC, and oxidants in the zone with the highest production rate of CH4 as well as the O2 concentration in the bottom water ([O2] in mg L-1), the R2 rates (fmol cm-3 s-1), and the average carbon oxidation state (COS) of the fermenting OM at the origin of CH4 calculated with Eq. (9) at both study sites, Lake Tantaré Basin B (Fig. 1), Jacks Lake (Carignan and Lean, 1991), and Lake Lugano (Lazzaretti-Ulmer and Hanselmann, 1999) for various
sampling dates.
∗ Minimum and maximum COS values were obtained by setting χM to 0 and 1 in Eq. (9), except for Tantaré Basin A in October 2015, for which χM is known to be 0.75.
References: (1) Clayer et al. (2016), (2) Clayer et al. (2018), (3) see
Supplement, (4) Carignan and Lean (1991), (5) Lazzaretti-Ulmer
and Hanselmann (1999). N/A: not available.
According to Table 4 the COS values are systematically negative at all dates
for Lake Tantaré Basin B, Lake Bédard, Jacks Lake, and the two sites
of Lake Lugano, and they vary generally between -0.9 and -1.9, with the
exception of a value of -2.5 obtained for Lake Tantaré Basin B in July
2007. This latter value is likely too low to be representative of fermenting
material and should be rejected. The mean (± SD) COS values are -1.7±0.4 for Lake Tantaré Basin B, -1.4±0.4 for Lake
Bédard, -1.4±0.2 for Jacks Lake, and -1.4±0.3 for
Lake Lugano. These COS values, representative of a mixture of fatty acids
(COS of -1.0 for C4 fatty acids to about -1.87 for C32 fatty acids) and
of fatty alcohols (COS =-2.00), strongly support the idea that
methanogenesis in oligotrophic boreal lake sediments and possibly other
lake types is fuelled by more reduced organic compounds than glucose. Lipids
such as fatty acids and fatty alcohols with similar COS are naturally
abundant in sediments to sustain the estimated rates of CH4 and DIC
production during fermentation
(Burdige,
2007; Cranwell, 1981; Hedges and Oades, 1997; Matsumoto, 1989). As discussed
by Clayer et al. (2018) the most labile organic compounds (i.e., proteins
and carbohydrates) can be rapidly degraded during their transport through
the water column and in the uppermost sediment layer, leaving mainly lipids
as metabolizable substrates at depths where fermentation and methanogenesis
occur. This interpretation is consistent with thermodynamic and kinetic
evidence that proteins and carbohydrates are more labile and are degraded
faster than lipids (LaRowe and Van Cappellen, 2011).
The COS values determined for the perennially oxygenated Basin A of Lake
Tantaré (mean of -0.6±1.1; range of -3.2 to 2.1; Table 4)
are much more variable than for the five other seasonally anoxic lake basins,
including unrealistic values for October 2015 in the Z1 (-3.2),
September 2016 (0.4–0.6), and October 2005 (1.8–2.1). Indeed, the very
negative value of -3.2 does not correspond to any degradable compound
under anoxic conditions, whereas the positive values of 0.4–0.6 and 1.8–2.1
would involve either amino acids and nucleotides, which are very labile
(LaRowe and Van Cappellen, 2011) and tend to be degraded in the water column
(Burdige, 2007), or oxidized compounds, such as ketones, aldehydes, and
esters, known to be quickly reduced to alcohols. Possible sources of
uncertainty in the COS estimation include misquantification of
bioirrigation and DIC production through HMW OM fermentation (Reaction R2;
Corbett et al., 2013). Clayer et al. (2016) provided evidence that sediment
irrigation by benthic animals is effective in Lake Tantaré Basin A and
that reaction rates are sensitive to the bioirrigation coefficient.
Nevertheless, additional simulations show that changing the bioirrigation
coefficient by a factor of 2 (increased and decreased) did not result in
significant changes in COS values (<0.2). Bioirrigation might also
be misrepresented. Indeed, the term used in Eq. (2) to calculate this
contribution, i.e., φαirrigation([solute]tube-[solute]), is indeed an approximation of intricate 3-D processes variable in
space and time (Meile et al., 2005; Boudreau
and Marinelli, 1994; Forster and Graf, 1995; Gallon et al., 2008;
Riisgård and Larsen, 2005). On the other hand, DIC production through
HMW OM fermentation (Reaction R2; Corbett et al., 2013) was constrained by
default in Lake Tantaré Basin A (Table 4). Indeed, fitting with Eq. (7)
the experimental δ13C data do not allow partitioning of the
production of DIC between Reactions (R1) and (R2) given that both processes share the same
fractionation factor (α1=α2=1.000).
Equation 9 indicates that, to obtain negative COS values for Lake Tantaré
Basin A in September 2006 and October 2005, R2 should be >11 and >110 fmol cm-2 s-1,
respectively. These R2 values correspond to transferring >9 % and >44 % of the rate of DIC production from R1 to
R2 for September 2006 and October 2005, respectively. Hence, owing to
the imperfection in the COS estimations for Lake Tantaré Basin A, COS
values estimated for this site should be treated with caution. Note that the
sediment surface was also oxic at the sites Melide and Figino of Lake Lugano
in March 1989 (Table 4), as revealed by detectable bottom water [O2]
(Table 4) and by low [Fe], undetectable ΣS(-II) and [CH4],
and relatively high [SO42-] in overlying water
(Lazzaretti et al., 1992;
Lazzaretti-Ulmer and Hanselmann, 1999). Despite this, the COS values
determined for the two sites of Lake Lugano appear realistic and consistent
with those calculated for Lake Tantaré Basin B, Lake Bédard, and Jacks Lake.
This disparity between Lake Tantaré Basin A and Lake Lugano could be
explained by the presence of benthic organisms in the former (Hare et al.,
1994), but their absence in the latter, as shown by the presence of varves
(Lazzaretti et al., 1992) and the absence of
benthos, remains in the recent sediments of Lake Lugano
(Niessen
et al., 1992).
Conclusions
Our results show that fermentation and methanogenesis represent about 50 %
and 100 % of OM mineralization in the top 25 cm of the sediments at the
hypolimnetic sites in Lake Tantaré Basin A and Lake Bédard, respectively;
that methane is produced only by hydrogenotrophy; and that fermentation substrates
have a negative COS at these two sites. The association of hydrogenotrophy
with the fermentation of reduced OM (COS <-0.9; implying that
labile compounds are depleted) in the studied lake sediments is consistent
with the fact that hydrogenotrophy becomes increasingly important when
labile OM is depleted (Chasar
et al., 2000; Hornibrook et al., 2000; Whiticar et al., 1986).
Reactive-transport modelling of 12 datasets of porewater profiles from
three boreal lakes – i.e., Lake Bédard, Lake Tantaré (Basin B), and Jacks Lake – as
well as of the subalpine Lake Lugano (Melide and Figino sites) consistently
showed that the main substrates for sediment methanogenesis at deep
seasonally anoxic hypolimnetic sites have a mean COS value of -1.4±0.3. The OM in the sediment of the three boreal lakes, as well as their
O2 seasonal dynamics, is typical of boreal forest lakes. While Lake
Bédard experiences prolonged episodes of extended hypolimnetic anoxia,
Lake Tantaré Basin B and Jacks Lake show more moderate seasonal anoxia,
where some years the hypolimnion of Lake Tantaré Basin B is only hypoxic
(Clayer et al., 2016; Carignan et al., 1991). Hence, the selective
mineralization of OM described by Clayer et al. (2018), involving the mineralization of the
most labile compounds during OM downward migration in the
water column and at the sediment surface, leaving mainly reduced organic
compounds to fuel methanogenesis in the sediments, likely applies to a large
portion of boreal lakes.
Hence, the current representation of the fermenting OM, i.e., CH2O, in
process-based biogeochemical models entails a significant risk of
underestimating sedimentary CH4 production and release to the bottom
water and, to a certain extent, of its evasion to the atmosphere under
transient environmental scenarios. To better constrain CH4 and CO2
production within sediments, we suggest taking specifically into account the
COS of the fermenting OM in formulating the reactions of methanogenesis
associated with fermentation in these models. For example, the rates of
CH4 (RCH4) and DIC
(RDIC) production during fermentation coupled to
hydrogenotrophy can be expressed as
12RCH4=R4=4-COS8R113RDIC=R1-R4=R11-4-COS8.
Given these rate expressions, the stoichiometric formulation of a typical
fermentation reaction producing CH4 becomes
CHaOb→4-COS8CH4+4+COS8CO2,
where a equals 2-COS2 and b equals 1+COS4. Introducing the average COS values
reported in this study (-1.4±0.3) into Eq. (14), the coefficients
a and b would take values of 2.7±0.15 and 0.65±0.125,
respectively, and the CH4 and CO2 stoichiometric coefficients
would be 0.68±0.04 and 0.32±0.04, respectively. Note that the
same stoichiometric formulation would be obtained with any possible
combination of acetoclasty and hydrogenotrophy. Under these conditions,
fermentation (Reaction R1) coupled to methanogenesis (Reaction R4) yields 2.2±0.4 times
more CH4 than DIC for the studied lake sediments. Ignoring the
implications of the present study regarding the COS of the fermenting OM
could lead to the underestimation of CH4 sediment outflux or of the
rate of oxidant consumption required to mitigate this efflux by a factor of
up to 2.6.
The approach used to estimate the COS of the fermenting OM, although
successful for the seasonally anoxic basins, failed to produce reliable COS
values when applied to the perennially oxygenated Basin A of Lake
Tantaré. We attribute this peculiarity to a misestimation and/or
misrepresentation of the benthic irrigation and to the impossibility of
partitioning the DIC production between Reactions (R1) and (R2), which share the
same fractionation factor value. Similar problems would likely also be
encountered in other lake ecosystems such as epilimnetic sediments and
wetlands where solute transport processes remain ill-known. Indeed, these
shallow aquatic environments are subject to enhanced benthic activity
(Hare, 1995), to plant-mediated transport of CH4
and O2
(Chanton et
al., 1989; Wand et al., 2006), and to turbulence
(Poindexter et al., 2016), which complicates the
estimation of CH4 and CO2 production and consumption rates. Hence,
the remaining challenge resides in the robust estimations of the COS of the
fermenting OM in epilimnetic sediments and shallow freshwater environments
(e.g., ponds, wetlands) since these environments were shown to be the main
contributors to freshwater CH4 release to the atmosphere
(Bastviken et al., 2008;
DelSontro et al., 2016). One potential solution is to investigate trends in
the oxygen isotope signatures in the sedimentary DIC in addition to δ13C values since it is also influenced by the source of the OM
undergoing degradation (e.g., Sauer et al.,
2001).
Data availability
Data generated during this study are available at 10.4211/hs.38e069761d7b4cf4abe3cbcaaac06016 (Clayer et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-4571-2020-supplement.
Author contributions
FC, AT, and CG designed the study. FC and YG performed laboratory analyses. FC developed the model and performed the data analysis. CG, AT, and YG supervised and financed the study. FC prepared the manuscript with contributions from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Paul Boissinot, Lise Rancourt, Philippe Girard, Jean-François Dutil, Sébastien Duval, Alexandre Royer-Lavallée, Anthony Laberge, and Andrew Barber for research and field work
assistance. We are also thankful to Jean-François Hélie, from the Laboratoire de
géochimie des isotopes stables légers (UQÀM), who graciously
calibrated our δ13C internal standard. Permission from the Québec Ministère du
Développement durable, de l'Environnement et de la Lutte contre les
changements climatiques to work in the Tantaré Ecological Reserve is
gratefully acknowledged.
Financial support
This research has been supported by the Natural Sciences and Engineering Research Council of Canada and the Fonds de Recherche Québécois – Nature et Technologies.
Review statement
This paper was edited by Perran Cook and reviewed by three anonymous referees.
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