Methane (CH4) emissions from reservoirs are
responsible for most of the atmospheric climatic forcing of these aquatic
ecosystems, comparable to emissions from paddies or biomass burning.
Primarily, CH4 is produced during the anaerobic mineralization of
organic carbon in anoxic sediments by methanogenic archaea. However, the
origin of the recurrent and ubiquitous CH4 supersaturation in oxic
waters (i.e., the methane paradox) is still controversial. Here, we
determined the dissolved CH4 concentration in the water column of
12 reservoirs during summer stratification and winter mixing to explore
CH4 sources in oxic waters. Reservoir sizes ranged from 1.18 to 26.13 km2. We found that dissolved CH4 in the water column varied
by up to 4 orders of magnitude (0.02–213.64 µmol L-1), and all oxic depths
were consistently supersaturated in both periods. Phytoplanktonic sources
appear to determine the concentration of CH4 in these reservoirs
primarily. In anoxic waters, the depth-cumulative chlorophyll a
concentration, a proxy for the phytoplanktonic biomass exported to
sediments, was correlated to CH4 concentration. In oxic waters, the
photosynthetic picoeukaryotes' abundance was significantly correlated to the
dissolved CH4 concentration during both the stratification and the
mixing. The mean depth of the reservoirs, as a surrogate of the vertical
CH4 transport from sediment to the oxic waters, also contributed
notably to the CH4 concentration in oxic waters. Our findings suggest
that photosynthetic picoeukaryotes can play a significant role in
determining CH4 concentration in oxic waters, although their role as
CH4 sources to explain the methane paradox has been poorly explored.
Introduction
Lakes and reservoirs are significant sources of methane (CH4),
affecting the atmospheric climatic forcing
(Deemer et al., 2016). The estimated
contribution of lakes to the global emission budget is ca. 71.6 Tg CH4 yr-1 (Bastviken et al., 2011), and
the specific contribution of reservoirs ranges between 4 and 70 Tg CH4 yr-1, representing up to 10 % of total CH4 emissions
(Deemer et
al., 2016). Although freshwater only covers about 5 %–8 % of the
Earth's surface (Mitsch et al.,
2012), it emits more CH4 than the ocean surface
(Saunois
et al., 2016). Traditionally, the net CH4 production is determined by
archaeal methanogenesis, which produces methane as an end product of organic
matter degradation in anoxic conditions, and to methanotrophs, which consume
it in oxic conditions (Schubert and
Wehrli, 2018). In freshwater ecosystems, the anoxic sediments are a primary
source of CH4 (Segers, 1998), where methanogens are
very sensitive to temperature and quantity and quality of the organic matter
used as substrate
(Marotta
et al., 2014; Rasilo et al., 2015; Sepulveda-Jauregui et al., 2018;
Thanh-Duc et al., 2010; West et al., 2012; Yvon-Durocher et al., 2014). They
are also affected by the extent of anoxia in the sediments insomuch as they
are obligate anaerobes and will not survive and produce CH4 under
aerobic conditions
(Chistoserdova et al.,
1998; Schubert and Wehrli, 2018). However, many observations from
freshwater and marine water have detected CH4 supersaturation in the
oxic layers, a widespread phenomenon described as the “methane paradox”
(Bogard
et al., 2014; Damm et al., 2010; Donis et al., 2017; Grossart et al., 2011;
Kiene, 1991; Murase et al., 2003; Owens et al., 1991; Schmidt and Conrad,
1993; Schulz et al., 2001; Tang et al., 2014, 2016).
This persistent CH4 supersaturation in oxic layers of marine and
freshwater ecosystems requires extra inputs to compensate for the CH4
losses by methanotrophy and the emissions toward the atmosphere. CH4
inputs may come from anoxic sediments or from in situ sources in the oxic layers.
The transport of CH4 from the bottom and littoral sediments in
shallow zones has been proposed to explain the supersaturation in the
surface waters of some lakes
(Bastviken
et al., 2004; Encinas Fernández et al., 2016; Michmerhuizen et al.,
1996; Murase et al., 2003; Peeters et al., 2019; Rudd and Hamilton, 1978).
The vertical transport may be relevant in small lakes, but in deep and
thermally stratified systems, the vertical diffusion rates of dissolved
gases across the thermocline are too low, and there is no apparent CH4 upward movement from the hypolimnion
(Peeters
et al., 1996; Rudd and Hamilton, 1978). In fact,
Thalasso et al. (2020) determined that there
was no exchange between the hypolimnion and the epilimnion in a Siberian
lake. The CH4 produced in the sediments and the hypolimnion was
assimilated there. Consequently, the CH4 in the epilimnion came from
lateral transport and in situ production. Lateral CH4 transport from shallow
sediments of the littoral zones may be a significant source in the open
surface of some lakes and reservoirs.
DelSontro et al. (2018) found that
CH4 transport from littoral zones was relevant for the dissolved
CH4 in the epilimnion of small lakes. However, lateral transport does
not fully explain CH4 supersaturation in the open ocean, and large
freshwater ecosystems, and, hence, other in situ CH4 sources, likely occur
(Damm
et al., 2010; DelSontro et al., 2018; Grossart et al., 2011; Khatun et al.,
2020; Owens et al., 1991; Schmidt and Conrad, 1993; Schulz et al., 2001;
Scranton and Brewer, 1977; Tang et al., 2014; Tilbrook and Karl, 1995).
Previous works demonstrated the in situ CH4 production in oxic waters
using stable isotope techniques in experiments, mesocosms, and field samples
(Bižić
et al., 2020; Bogard et al., 2014; DelSontro et al., 2018; Hartmann et al.,
2020; Tang et al., 2016) and using molecular approaches
(Grossart
et al., 2011; Khatun et al., 2020; Yao et al., 2016a). In the literature,
there are different alternatives proposed as CH4 sources. On the one
hand, there is the occurrence of methanogenesis in micro-anoxic niches in the guts of
zooplankton and within sinking particles
(de Angelis and Lee, 1994; Karl and
Tilbrook, 1994). In both micro-anoxic niches, the CH4 production appeared to
be too low to sustain the total CH4 supersaturation of the oxic waters
(Schmale et al., 2018;
Tang et al., 2014). On the other hand, there is a consistent link between
dissolved CH4 concentration and autotrophic organisms, primary
production, and chlorophyll a concentration
(Bogard
et al., 2014; Grossart et al., 2011; Owens et al., 1991; Schmidt and Conrad,
1993; Tang et al., 2014). Grossart et al. (2011) detected potential methanogenic Archaea attached to photoautotrophs as
Chlorophyta (Eukarya) and cyanobacteria (Bacteria) in the epilimnion of an oligotrophic lake and confirmed the
production of CH4 in the presence of oxygen in laboratory incubations.
If occurring, that symbiosis would require that the methanogenic
microorganisms tolerate the oxygen exposure, as has been observed by
several authors
(Angel
et al., 2011, Angle et al., 2017; Jarrell, 1985), in contrast to general
belief. New findings suggest that the link between phytoplankton and
dissolved CH4 may rely on diverse metabolic pathways in Bacteria and Eukarya. These
metabolic pathways contribute to the dissolved CH4 in oxic waters due
to the degradation of methylated compounds. In the open ocean, archaea and
bacteria appear to metabolize the algal osmolyte dimethylsulfoniopropionate,
producing methane as a by-product (Damm
et al., 2008, 2010, 2015; Zindler et al., 2013). Common methyl-containing
substances like methionine produce methane in algae, saprotrophic fungi, and
plants
(Lenhart
et al., 2012, 2015, 2016). Another reported pathway is the degradation of
methylphosphonates (MPn's) as an alternative source of phosphorus (P) in
phosphate-starved bacterioplankton. The hydrolysis of these compounds, using
the enzyme C–P lyase, also releases methane as a by-product. This pathway
appears in chronically P-starved ecosystems as the ocean gyres, oligotrophic
lakes, and microbial mats
(Beversdorf
et al., 2010; Carini et al., 2014; Gomez-Garcia et al., 2011; Karl et al.,
2008; Repeta et al., 2016; Teikari et al., 2018; del Valle and Karl, 2014;
Wang et al., 2017; Yao et al., 2016a). Recent studies using phytoplankton
cultures and stable isotope techniques propose that the production of
CH4 may rely directly on the photoautotrophic carbon fixation of algae
and cyanobacteria
(Bižić
et al., 2020; Hartmann et al., 2020; Klintzsch et al., 2019; Lenhart et al.,
2016). These sources of CH4 in oxic waters, however, still have not
been tested simultaneously in reservoirs, despite the known high
contribution of these freshwater ecosystems to global CH4 emissions.
In this study, we measured the dissolved CH4 concentration in the water
column of 12 reservoirs that cover a broad spectrum of sizes, ages,
morphometries, and trophic states during the summer stratification and
winter mixing (León-Palmero et al., 2020).
Our objective was to assess the relative contribution of different sources
of CH4 in the oxic waters and to shed light on the methane paradox
depending on reservoir properties. We explored the following CH4
sources in oxic waters: (1) vertical and lateral transport of CH4 from
hypolimnetic and littoral waters, (2) in situ production by methanogenic Archaea tolerant
to oxygen, (3) in situ production by methylphosphonate degradation, and (4) in situ production by
photosynthetic microorganisms. We used the concentration chlorophyll a, the
primary production, and the abundance of photosynthetic picoeukaryotes and
cyanobacteria as variables for the photosynthetic signatures. The
photosynthetic picoeukaryotes are a relevant part of the freshwater
phytoplankton, but their role in the methane paradox has been particularly
little studied.
MethodsStudied reservoirs, morphometry, and vertical profiles
We sampled 12 reservoirs located in southern Spain (Fig. 1)
between July 2016 and August 2017 once during the summer stratification and
once during winter mixing. In Table 1, we show the geographical coordinates,
age, and the morphometric description of the studied reservoirs. The reservoirs
were built between 1932 and 2003, for water supply and agriculture
irrigation, and they are located in watersheds with different lithologies and
land uses (more details can be found in
León-Palmero et al., 2019, 2020).
These reservoirs differ in morphometric, chemical, and trophic
characteristics, covering a wide range of concentrations of dissolved organic
carbon (DOC), total nitrogen (TN), total phosphorus (TP), and chlorophyll a
(Table 2). All raw data for the water column were deposited in the PANGAEA
database (https://doi.org/10.1594/PANGAEA.912535, last access: 14 May 2020.).
Geographical location and morphometric
description of the studied reservoirs.
Sampling date; mean values of
the DOC, TN, and TP concentrations; DIN : TP ratio; and chlorophyll a
concentration in the water column of the studied reservoirs during the
stratification and the mixing period.
ReservoirPeriodSamplingDOCTN TP DIN : TPChl adate(µmol C L-1)(µmol N L-1)(µmol P L-1)(µmol N : µmol P)(µg L-1)CubillasStratification15 Jul 2016172.160.41.842317.8Mixing6 Feb 2017240.5115.40.781118.4ColomeraStratification22 Jul 201699.4181.40.782362.1Mixing7 Mar 2017123.3112.50.442910.5NegratínStratification27 Jun 2016109.721.20.80231.2Mixing16 Feb 2017148.919.70.24657.7La BoleraStratification28 Jun 2016123.717.30.61122.0Mixing8 Apr 2017107.434.40.151760.8Los BermejalesStratification7 Sep 201694.230.40.42521.8Mixing17 Mar 2017101.530.60.318813.1IznájarStratification9 Sep 2016116.8278.50.396755.1Mixing15 Mar 2017147.5298.71.163921.1Francisco AbellánStratification28 Sep 201690.627.80.28791.9Mixing21 Mar 2017118.029.20.47631.1BéznarStratification7 Oct 201674.374.20.681036.0Mixing23 Feb 2017121.6113.00.951043.7San ClementeStratification17 Jul 2017104.132.00.39393.5Mixing28 Mar 017119.435.90.211451.1El PortilloStratification18 Jul 201778.022.80.171032.4Mixing30 Mar 201776.434.40.261081.7JándulaStratification24 Jul 2017359.937.20.78432.3Mixing5 Apr 2017399.446.20.371031.2RulesStratification10 Jul 201781.223.20.21823.7Mixing7 Apr 201768.538.00.431433.3
We obtained the reservoir surface area, perimeter, and volume using the
following open databases: Infraestructura de Datos Espaciales de
Andalucía (IDEAndalucia;
http://www.ideandalucia.es/portal/web/ideandalucia/, last access: 4 February 2018).) and the Ministerio
para la Transición Ecológica (https://www.embalses.net/, last access: 15 September 2019).
The mean depth was calculated as follows (Eq. 1):
Meandepth(m)=Volumem3Surfaceaream2.
The shoreline development ratio (DL) (Aronow, 1982) is a
comparative index relating the shoreline length (i.e., the perimeter of the
reservoir) to the circumference of a circle that has the same area. The
closer this ratio is to 1, the more circular the lake. A large ratio
(≫1) indicates that the shoreline is more scalloped than
a low ratio. The equation is as follows (Eq. 2):
DL=Lengthoftheshoreline(m)2πAream2.
The shallowness index (m-1) was obtained by dividing the shoreline
development index (DL) by the mean depth (m), as in Eq. (3):
Shallownessindexm-1=DLMeandepth(m).
We sampled the water column near the dam, in the open water of the
reservoir. During the stratification and the mixing period, we selected the
same location. First, we performed a vertical profile of the reservoir using
a Sea-Bird 19plus CTD profiler, coupled to a Spherical Underwater Quantum
Sensor (LI-193R), and a fluorimeter Turner® SCUFA (model
CYCLOPS-7) for continuous measurements of temperature (∘C), dissolved oxygen (µmol L-1), conductivity (µS cm-1), turbidity
(FTU – formazin turbidity unit), density (kg m-3), photosynthetic active radiation, chlorophyll a
fluorescence (µg L-1), specific conductance (µS cm-1), and
salinity (psu – practical salinity units). Then, based on the temperature and oxygen profiles, we
selected six to nine depths, representative of the oxic and anoxic layers and
the transition between them in the different reservoirs. We took the water
samples using a UWITEC sampling bottle of 5 L with a self-closing
mechanism. We collected samples for the dissolved CH4 analysis in 125
or 250 mL airtight Winkler bottles in duplicate (250 mL) or in triplicate (125 mL). We filled up the bottles very carefully from the bottom to avoid the
formation of bubbles and minimize the loss of CH4 during field
sampling. We preserved the samples with a solution of HgCl2 (final
concentration 1 mmol L-1) to inhibit biological activity and sealed the bottles
with Apiezon® grease to prevent gas exchanges. We also took
samples from each depth from the chemical and biological analysis explained
below. We also measured barometric pressure using a multi-parameter probe
(Hanna HI 9828) for the gas saturation calculations. We calculated the
saturation values (%) for dissolved oxygen as the ratio of the dissolved
gas measured and the gas concentration expected in equilibrium. We
calculated the gas concentration in equilibrium, taking into account the
differences in temperature, salinity, and barometric pressure
(Mortimer, 1956).
Dissolved CH4 in the water column
We stored the Winkler bottles in the dark at room temperature until analysis
in the laboratory. We measured dissolved CH4 using headspace
equilibration in a 50 mL airtight glass syringe (Agilent P/N 5190–1547)
(Sierra et al., 2017). We
obtained two replicates for each 150 mL Winkler bottle and three replicates
for each 250 mL Winkler bottle. We took a quantity of 25 g of water (±0.01 g) using the airtight syringe and added a quantity of 25 mL of a
standard gas mixture that had a methane concentration similar to atmospheric
values (1.8 ppmv) to complete the volume of the syringe. The syringes were
shaken for 5 min (Vibromatic, Selecta) to ensure mixing, and we waited 5 min
to reach complete equilibrium. Then, the gas in the syringe was injected
manually into the gas chromatograph (GC; Bruker® GC-450)
equipped with a hydrogen flame ionization detector (FID). We calibrated
the detectors daily using three standard gas mixtures with CH4 mixing ratios
of 1952, 10 064, and 103 829 ppbv, made and certified by Air Liquide (France). We
calculated the gas concentration in the water samples from the concentration
measured in the headspace using the Bunsen functions for CH4 (Yamamoto et al., 1976; Wiesenburg and Guinasso, 1979). The
precision in the quantification of the gas mixture of CH4 used in the
headspace equilibrium (1.8 ppmv) expressed as the coefficient of variation
was 3.7 % (n=123). The precision of the measurement of the dissolved
CH4 concentration, which included the analytical processing of the
samples and the equilibration step, was 3.6 % for four to six replicates
of each sample. We calculated the saturation values (%) as the ratio
of the concentration of the dissolved gas measured to the gas
concentration expected in equilibrium considering the temperature, salinity,
and barometric pressure of each reservoir. We used the atmospheric gas
concentrations provided by the Global Greenhouse Gas Reference Network website
(https://www.esrl.noaa.gov/gmd/ccgg/index.html, last access: 20 September 2019), which is part of the
National Oceanic and Atmospheric Administration (NOAA) Earth System Research
Laboratory in Boulder, Colorado. We calculated the 2016 global mean
atmospheric concentrations for CH4 (Dlugokencky, 2019)
from the 2016 global monthly mean. The differences among these values and
the local atmospheric concentrations are assumed to be small compared with
the high dissolved concentrations obtained in the studied reservoirs.
Chemical analysis in the water column
From the discrete sampling, we selected thee or four representative depths of the
epilimnion, metalimnion (oxycline), and hypolimnion and bottom
layers for
nutrient analysis during the stratification period. We also selected three or four
equivalent depths during the mixing period. In total, we analyzed 77 samples: 41 samples from the stratification period and 36 samples from the
mixing period. We determined total nutrients using unfiltered water, while
we filtered the samples through pre-combusted 0.7 µm pore-size Whatman
GF/F glass-fiber filters for the dissolved nutrients. We acidified the
samples for dissolved organic carbon (DOC), total dissolved nitrogen (TDN),
and total nitrogen (TN) with phosphoric acid (final pH <2).
We measured DOC, TN, and TDN by high-temperature catalytic oxidation using
a Shimadzu total organic carbon (TOC) analyzer (Model TOC-VCSH) coupled to
a nitrogen analyzer (TNM-1). We calibrated the instrument using a four-point
standard curve of dried potassium hydrogen phthalate for DOC and dried
potassium nitrate for TN and TDN (Álvarez-Salgado
and Miller, 1998). We analyzed two replicates and three to five injections
per replicate for each sample. We purged the DOC samples with phosphoric
acid for 20 min to eliminate all the dissolved inorganic carbon. The
precision of the DOC measurements expressed as the mean coefficient of
variation was 3.0 %. The mean precision for the TN and TDN was 8.2 % and
2.9 %, respectively.
We measured the NO3- concentration in duplicate with the
ultraviolet spectrophotometric method, using a Perkin Elmer UV Lambda 40 spectrophotometer at wavelengths of 220 nm and correcting for DOC absorbance
at 275 nm (APHA, 1992). The mean coefficient of
variation was 0.5 %. We measured NO2-
concentrations by inductively coupled plasma optical emission spectrometry
(ICP-OES). Dissolved inorganic nitrogen (DIN) was calculated as the addition
of the NO3- and NO2- concentrations.
The detection limits for the NO2-
concentration was 1.4 µmol L-1. We
measured total phosphorus (TP) concentration in triplicate using the
molybdenum blue method (Murphy and Riley, 1962)
after digestion with a mixture of potassium persulfate and boric acid at
120 ∘C for 30 min (APHA, 1992). The
precision in the quantification of the TP concentration was 11.1 %.
Chlorophyll a, phytoplankton, and primary production in the water column
We determined the chlorophyll a concentration and the abundances of
cyanobacteria and photosynthetic picoeukaryotes in all the depths sampled
during the discrete samplings (n=178). We determined the chlorophyll a
concentration by filtering the particulate material of 500 to 2000 mL of
water through pre-combusted Whatman GF/F glass-fiber filters. Then, we
extracted the pigments from the filters with 95 % methanol in the dark at
4 ∘C for 24 h (APHA, 1992). We measured
chlorophyll a (Chl a) absorption using a Perkin Elmer UV Lambda 40 spectrophotometer at the wavelength of 665 nm and for scattering correction
at 750 nm. The detection limit was 0.1 µg L-1.
To obtain the cumulative chlorophyll a in the whole water column (mg Chl a m-2), from the discrete depths, we summed the concentration of Chl a
from each stratum using the trapezoidal rule
(León-Palmero et al., 2019), as indicated in Eq. (4):
CumulativeChla=∑k=1nXik⋅Zk+1-Zk-12,
where Z stands for the depth considered, and n is the number of depths
sampled. Zk stands for the n sampled depth; Xij is the
Chla concentration (µg L-1) at the depth Zk.
We determined in triplicate the abundances of cyanobacteria and
photosynthetic picoeukaryotes using flow cytometry using unfiltered water.
We collected and fixed the samples with a mixture of 1 % paraformaldehyde
and 0.05 % glutaraldehyde for 30 min in the dark at 4 ∘C. Then,
we froze the samples in liquid nitrogen and stored them at -80 ∘C until analysis. We analyzed the samples in the FACSCalibur flow cytometer
equipped with the BD CellQuest Pro software for data analysis. We used
yellow–green 0.92 µm latex beads (Polysciences) as an internal standard
to control the cytometer performance every day. We used different signals
for groups determination: the side scatter (SSC), chlorophyll a (red
fluorescence – FL3), phycoerythrin (orange fluorescence – FL2), and
phycocyanin (blue fluorescence – FL4), following the protocols and
indications for data analysis of previous works
(Cellamare
et al., 2010; Collier, 2000; Corzo et al., 1999; Gasol and Giorgio, 2000;
Liu et al., 2014). In Fig. S13 in the Supplement, we show a cytogram of the populations of
cyanobacteria and photosynthetic picoeukaryotes. The mean coefficient of
variation for the abundances of cyanobacteria and photosynthetic
picoeukaryotes was 8.8 % and 11.4 %, respectively.
We estimated gross primary production (GPP), net ecosystem production (NEP),
and ecosystem respiration (R) by measuring temporal changes in dissolved
oxygen concentration and temperature using a miniDOT (PME) submersible waterlogger during the stratification period. We recorded measurements every 10 min for 24–48 h during the same sampling days. Briefly, the equation
for estimating free-water metabolism from measurements of dissolved oxygen
was established by Odum (1956) (Eq. 5):
ΔO2/Δt=GPP-R-F-A,
where ΔO2/Δt is the change
in dissolved oxygen concentration through time, F is the exchange of
O2 with the atmosphere, and A is a term that
combines all other processes that may cause changes in the dissolved oxygen
concentration as horizontal or vertical advection, and it is often assumed
to be negligible. The calculations were performed as in
Staehr et al. (2010). The physical
gas flux was modeled as follows (Eq. 6):
FgO2m-2h-1=kO2meas-O2sat,
where F is the physical gas flux, and k (m h-1) is the piston velocity estimated
following the equation of Jähne et al. (1987)
and the indications of Staehr et
al. (2010). O2meas is the actual oxygen
concentration (mg mL-1), and O2sat is the
oxygen concentration in water in equilibrium with the atmosphere at ambient
temperature and salinity.
We calculated the hourly net ecosystem production
(NEPhr) and the daytime net ecosystem production
(NEPdaytime) following Eqs. (7)
(Cole et al., 2000) and (8):
7NEPhrgO2m-3h-1=ΔO2gm-3h-1-F/Zmix,8NEPdaytimegO2m-3daylightperiod-1=meanNEPhrduringdaylightgO2m-3h-1×Lighthours(h).NEPhr is directly derived from the changes in
dissolved oxygen (ΔO2), after
accounting for physical gas flux with the atmosphere (F).
Zmix is the depth of the mixed layer (m), which
was inferred from the temperature profile as the upper mixed zone where the
temperature remains constant. NEPdaytime is the
portion of NEP between sunrise and sunset, when the photosynthesis takes
place. We obtained the exact light hours from an online solar calculator
(https://es.calcuworld.com/calendarios/calcular-salida-y-puesta-del-sol/, last access: 24 May 2018).
We established the start and the end time for photosynthesis as 30 min
before sunrise and 30 min after dawn (Schlesinger and
Bernhardt, 2013). We obtained hourly R (Rhr), R
during the daytime (Rdaytime), and R throughout the
whole day (Rday), following Eqs. (9), (10), and (11),
respectively:
9RhrgO2m-3h-1=meanNEPhrduringdarknessgO2m-3h-1,10RdaytimegO2m-3daylightperiod-1=RhrgO2m-3h-1×Lighthours(h),11RdaygO2m-3d-1=RhrgO2m-3h-1×24(h).
We calculated the respiration rate during the night (the period between 60 min after dawn and 60 min before sunrise)
(Staehr et al., 2010), and we
assumed that the respiration rate overnight was similar to the respiration
rate throughout the day. Finally, we obtained the GPP and NEP for the day
(Eqs. 12 and 13):
12GPPgO2m-3d-1=NEPdaytime+Rdaytime,13NEPgO2m-3d-1=GPP-Rday.
DNA analysis
We selected three or four representative depths for determining the abundance of
the functional genes of the epilimnion, metalimnion (oxycline), and
hypolimnion and bottom layers during the stratification period. We also selected
three or four equivalent depths during the mixing period. In total, we analyzed 41 samples from the stratification period and 36 samples for the mixing period.
We pre-filtered the water through 3.0 µm pore-size filters and
extracted DNA following the procedure developed by
Boström et al. (2004) for
environmental samples. During the DNA extraction protocol, we combined a
cell recovery step by centrifugation of 12–20 mL of the pre-filtered
water, a cell lysis step with enzyme treatment (lysozyme and proteinase K),
and, finally, the DNA recovery step with a co-precipitant (yeast tRNA) to
improve the precipitation of low-concentration DNA. DNA was quantified using
a DNA quantitation kit (Sigma-Aldrich) based on the fluorescent dye
bisbenzimide (Hoechst 33258). Extracted DNA served as the template for PCR
and quantitative PCR (qPCR) analysis to test the presence and abundance of
the mcrA gene and the phnJ gene. For PCR analysis, we used the recombinant Taq DNA
Polymerase (Thermo Fisher Scientific) using the Mastercycler X50 thermal
cycler (Eppendorf). We ran the qPCR plates using SYBR Green as the reporter
dye (PowerUp™ SYBR™ Green Master Mix, Thermo
Fisher Scientific) in the Applied Biosystems 7500 Real-Time PCR System and
the 7500 Software. In both cases, PCR and qPCR, we designed the standard
reaction mix recipes and the thermocycling conditions using the provider
specifications and primer requirements. We chose specific primers from
studies performed in natural samples of freshwater. We used pure cultures
as positive controls (more details below).
We targeted the alpha subunit of methyl-coenzyme reductase (mcrA) as a genetic
marker to determine the existence and abundance of methanogenic Archaea in our
samples. This gene appears to be an excellent marker, since all known
methanogens have the methyl-coenzyme M reductase, which is the enzyme responsible for the conversion of
a methyl group to CH4 (Grabarse et al.,
2001). We used specific primers from West
et al. (2012), adapting their procedure. The forward primer was mcrAqF
(5′-AYGGTATGGARCAGTACGA-3′), the reverse primer was mcrAqF (5′-TGVAGRTCGTABCCGWAGAA-3′), and the annealing temperature was 54 ∘C. The expected size of the PCR product was ∼200 bp (bp – base pair).
We used a culture of Methanosarcina acetivorans (ATCC 35395) as a positive control. We tested all the
samples (n=77). We also tested the presence of the phnJ gene, which encodes a
subunit of the C–P lyase complex
(Seweryn et al., 2015; White and
Metcalf, 2007). This enzyme cleaves C–P bonds in phosphonate compounds,
releasing methane, and changes in response to the phosphate availability
(Yao et al., 2016a). We ran the amplification
with a pair of primers previously used by
Fox et al. (2014) and Yao
et al. (2016a). The forward primer was PhnJoc1 (5′-AARGTRATMGAYCARGG-3'),
and the reverse was PhnJoc2 (5′-CATYTTYGGATTRTCRAA-3′), adapting the PCR
procedure from Yao et al. (2016a). The
annealing temperature was 52.5 ∘C, and the positive
controls were run using a pure culture of Rhodopseudomonas palustris (ATCC 33872). The expected size
of the PCR product was ∼400 bp. We checked the result of the
amplification by running 1.5 % (w/v) agarose gel electrophoresis. If we
did not detect amplification in the PCR or qPCR samples, we changed the
standard procedure by increasing the DNA amount and the primers'
concentration to corroborate the negative results. We tested all the samples
(n=77).
Statistical tests
We conducted all the statistical analysis in R (R Core Team, 2014),
using the packages “car” (Fox and Weisberg, 2011), “nortest”
(Gross and Ligges, 2015), and “mgcv”
(Wood, 2011). We performed the
Shapiro–Wilk test of normality analysis and Levene's test for homogeneity of
variance across groups. We performed a one-way analysis-of-variance test
(ANOVA) when the data were normally distributed. In case the data did not
meet the assumptions of normality, we used the paired Kruskal–Wallis
rank-sum (K–W) or Wilcoxon (V) tests. We analyzed the potential sources of
dissolved CH4 using simple regression analysis and generalized additive
models (GAMs) (Wood, 2006). A GAM is a generalized model with a
linear predictor involving a sum of smooth functions of covariates
(Hastie and Tibshirani, 1986,
1990). The model structure is shown in Eq. (4):
yi=f1x1i+f2x2i+…+fnxni+∈i,
where fj is the smooth functions, and ∈i is
independent identically distributed N (0,σ2) random
variables. We fit smoothing functions by penalized cubic regression splines.
The cross-validation method (generalized cross-validation – GCV – criterion)
estimates the smoothness of the functions. We fitted the models to minimize
the Akaike information criterion (AIC) and the GCV values. We calculated the
percentage of variance explained by the model (adjusted R2) and the quality
of the fit (deviance explained). We also fixed the effect of each predictor
to assess the contribution of the other predictor on the total deviance
explained. Then, the sum of the deviance explained by two predictors can be
different from the deviance explained by the model due to interactive
effects.
Results and discussionProfile description
We found pronounced differences in the concentration of dissolved CH4
of the studied reservoirs among depths and seasonal periods (Figs. 2–4 and
S1–9). The concentration of dissolved CH4 ranged up to 4 orders of
magnitude, from 0.06 to 213.64 µmol L-1, during the summer stratification (n=96), and it was less variable during the winter mixing (n=84),
ranging only from 0.02 to 0.69 µmol L-1. All depths were consistently
supersaturated in CH4, during both the stratification and mixing period
(Table S1 in the Supplement). The dissolved CH4 concentration and the percentage of saturation
values were significantly higher during the stratification period than
during the mixing period (V=78, p value <0.001, and V=78,
p value <0.001, respectively). These differences in the
concentration of dissolved CH4 are coherent with the differences found
in the CH4 emissions from these reservoirs in the stratification and
mixing periods (León-Palmero et al., 2020).
The wide range in CH4 concentrations found in this study covers
values reported in temperate lakes
(Donis
et al., 2017; Grossart et al., 2011; Tang et al., 2014; West et al., 2016),
to those found in tropical lakes and reservoirs
(Murase
et al., 2003; Naqvi et al., 2018; Okuku et al., 2019; Roland et al., 2017).
In the surface mixing layer during the stratification period (i.e.,
epilimnion), we found values from 0.06 to 8.18 µmol L-1 (Table S1), which is
about 80 times the maximum values found in the surface waters of Lake
Kivu (Africa) by Roland
et al. (2017) and similar to the concentrations reported in subtropical and
tropical reservoirs
(Musenze et al., 2014,
and references therein).
Vertical profiles of physicochemical and
biological variables in Béznar reservoir. Dissolved methane
concentration (CH4, µM, mean ± standard error), temperature
(∘C), dissolved oxygen (DO) concentration (µM),
chlorophyll a (Chl a) concentration (µg L-1), abundance of
photosynthetic picoeukaryotes (×103 cells mL-1, mean ± standard deviation), and abundance of cyanobacteria (×103 cells mL-1, mean ± standard deviation) during the stratification period
(a) and the mixing period (b). The grey area represents
the anoxic zone (DO <7.5µM). Note the logarithmic scales in
the x axis of the dissolved CH4 profiles. The sampling for the
stratification period was on 7 October 2016 and 23 February 2017 for the
mixing period.
Vertical profiles of physicochemical and
biological variables in Negratín reservoir. Dissolved methane
concentration (CH4, µM, mean ± standard error), temperature
(∘C), dissolved oxygen (DO) concentration (µM),
chlorophyll a (Chl a) concentration (µg L-1), abundance of
photosynthetic picoeukaryotes (×103 cells mL-1, mean ± standard deviation), and abundance of cyanobacteria (×103 cells mL-1, mean ± standard deviation) during the stratification period
(a) and the mixing period (b). The sampling for the
stratification period was on 27 July 2016 and 16 February 2017 for the
mixing period.
The dissolved CH4 profiles showed considerable differences among depths
during the summer stratification (Figs. 2a–4a and S1a–9a) but were very
homogeneous during the winter mixing in all the reservoirs (Figs. 2b–4b and
S1b–9b) (Table S1). Based on the differences found during the
stratification period in the dissolved CH4 profiles, we sorted the
reservoirs into three types. The first type of CH4 profile included six
reservoirs that were characterized by an increase in the dissolved CH4
from the oxycline to the anoxic bottom, just above the sediments, where
CH4 concentration reached its maximum. In these reservoirs, the
oxycline may be spatially coupled to the thermocline or not. When the
oxycline and the thermocline were spatially coupled, the dissolved CH4
concentration increased exponentially from the thermocline along the anoxic
hypolimnion to the sediments. The reservoirs Béznar, San Clemente, and
Iznájar showed this type of profile (Figs. 2a, S1a, and S2a). The
existence of a sizeable almost-anoxic hypolimnion led to a massive
accumulation of CH4 in this layer. The differences in the CH4 concentration between the surface and bottom waters were up to 3
orders of magnitude, as we found in Béznar (from the 0.25 to 56.17 µmol L-1; Fig. 2a), San Clemente (from the 0.23 to 45.15 µmol L-1; Fig. 1a), and
Iznájar (from the 0.82 to 213.64 µmol L-1; Fig. S2a). When the
oxycline and the thermocline were not spatially coupled, the dissolved
CH4 concentration increased just above the sediments, where the
anoxic–oxic interface was near to the bottom. The reservoirs Cubillas, La
Bolera, and Francisco Abellán showed this profile type (Figs. S3a, S4a,
and S5a). This accumulation of CH4 in the hypolimnion and above
sediments might be related to the high rates of methanogenesis in the
sediments and its subsequent diffusion to the water column. Dissolved
CH4 concentration declines at the oxycline level, where the highest
rates of CH4 oxidation usually occur
(Oswald et al.,
2015, 2016). The CH4 profiles in this group were similar to the ones
found in tropical eutrophic and temperate reservoirs
(Naqvi et al., 2018; West et
al., 2016). The second profile type presents a small peak of metalimnetic
CH4, concomitant with peaks of dissolved oxygen, chlorophyll a,
photosynthetic picoeukaryotes, and cyanobacteria (Fig. 3a). In the
Negratín reservoir, we found the maximum concentration of CH4 in
the oxic hypolimnion. Unlike several previous works in lakes
(Blees
et al., 2015; Grossart et al., 2011; Khatun et al., 2019; Murase et al.,
2003), we did not find a metalimnetic CH4 maximum.
Khatun et al. (2019) described the existence of a
metalimnetic CH4 maximum in 10 out of 14 lakes. The metalimnetic
CH4 maximum may represent a physically driven CH4 accumulation due
to solubility differences with the temperature at the thermocline, the
epilimnetic CH4 losses by emission, and the lateral inputs from the
littoral zone
(Donis et
al., 2017; Encinas Fernández et al., 2016; Hofmann et al., 2010). The
metalimnetic CH4 maximum can also be determined by biological factors,
including the light inhibition of the methane oxidation
(Murase and
Sugimoto, 2005; Tang et al., 2014) or the distinctive methane production by
phytoplankton due to availability of nutrients, light, or precursors at this
layer (Khatun et al., 2019). The third profile type
included five reservoirs, in which the dissolved CH4 profile presented
a CH4 accumulation more significant in the epilimnion than in the
hypolimnion. The reservoirs Jándula, Bermejales, Rules, El Portillo, and
Colomera showed this profile type (Figs. 4a and S6a–9a). These reservoirs
had a mean CH4 concentration in the water column significantly lower
than the reservoirs from the first type. Similar profiles have been reported
in temperate (Tang et al., 2014) and tropical
lakes (Murase et al., 2003).
Vertical profiles of physicochemical and
biological variables in Jándula reservoir. Dissolved methane
concentration (CH4, µM, mean ± standard error), temperature
(∘C), dissolved oxygen (DO) concentration (µM),
chlorophyll a (Chl a) concentration (µg L-1), abundance of
photosynthetic picoeukaryotes (×103 cells mL-1, mean ± standard deviation), and abundance of cyanobacteria (×103 cells mL-1, mean ± standard deviation) during the stratification period
(a) and the mixing period (b). The grey area represents
the anoxic zone (DO <7.5µM). The sampling for the
stratification period was on 24 July and 5 April 2017 for the mixing
period.
CH4 sources in the water column
We found two well-differentiated groups of CH4 data sorted by the
dissolved oxygen (DO) concentration (Fig. S10), as in previous studies (Tang
et al., 2014). The first dataset included the samples with a DO lower than
7.5 µmol L-1 (n=18, hereafter anoxic samples). These samples belong to
the hypolimnion of the studied reservoirs during the stratification period.
The second dataset included the samples with DO higher than 7.5 µmol L-1 (n=160, hereafter oxic samples). All the samples from the mixing period (n=82) and most of the samples from the stratification period (n=78)
belong to this second dataset. We found significant differences (W=2632,
p value <0.001) between the concentration of CH4 in the
anoxic samples (median = 15.79, min = 0.35, max = 213.64 µmol L-1) and in the oxic samples (median = 0.15, min = 0.02, max = 8.17 µmol L-1). Since these two groups of samples are
different, we determined their sources and drivers separately (Table S2).
CH4 sources in anoxic waters
Archaeal methanogens are obligate anaerobes that decompose the organic
matter and produce CH4 in anoxic environments, as freshwater
sediments. We analyzed the presence of the methanogenic Archaea in the anoxic
samples of the water column by targeting the gene mcrA. From the 77 samples
selected for genetic analysis, 12 of them were anoxic. We did not detect
the amplification of the mcrA gene in the PCR or the qPCR analysis in these
12 samples. Therefore, we assumed that the methanogenic Archaea were not
present, as free-living microorganisms, in the water column of the anoxic
samples. However, they may still be present in micro-anoxic zones in the
water column (i.e., in the guts of zooplankton or within exopolymeric
particles). Methanogenesis is a microbial process particularly sensitive to
temperature
(Marotta
et al., 2014; Sepulveda-Jauregui et al., 2018; Yvon-Durocher et al., 2014).
However, we did not find a significant relationship between the water
temperature and the dissolved CH4 concentration in the anoxic samples
(n=17, p value =0.66). The lack of a detection of the mcrA gene in the
hypolimnetic waters and the absence of a relationship between the dissolved
CH4 and water temperature suggest that CH4 production is not
happening in the water column of the studied reservoirs. We think that most
methanogenic archaea must be present in the sediments, where they produce
CH4 that diffuses up to the water column, producing vast accumulations
of CH4 in the hypolimnion.
Methanogenesis in the sediments may be affected by organic matter quantity
and quality (West et al., 2012). Organic
matter quantity is measured as the dissolved organic carbon concentration,
whereas the organic matter quality usually is related to their
phytoplanktonic versus terrestrial origin. In the studied reservoirs, the
dissolved organic carbon concentration did not show a significant
relationship with the dissolved CH4 concentration (n=12, p value =0.10; Table S2). We examined the importance of the autochthonous organic
matter produced by primary producers using the total cumulative
Chl a (mg m-2). The cumulative Chl a is considered to be a
surrogate for the vertical export of the phytoplankton biomass in the whole
water column. We found that the CH4 concentrations in anoxic samples
were correlated to the cumulative Chl a following a power function (CH4=3.0×10-4 cumulative Chl a2.28, n=17, adjusted R2=0.40,
p value <0.01; Table S2) (Fig. 5). The autochthonous organic matter
appeared to be a better predictor for the concentration of CH4 in
anoxic waters than the dissolved organic matter concentration. In the studied reservoirs, the dissolved organic carbon concentration was significantly
related to the age of the reservoirs and the forestry coverage in their
watersheds (León-Palmero et al., 2019). Therefore, in
terms of quality, the total pool of dissolved organic carbon may be more
representative of the carbon fraction that is allochthonous, recalcitrant, and more resistant to
microbial degradation. In contrast, the autochthonous
organic matter may represent a more labile and biodegradable fraction.
Previous experimental studies have demonstrated that the addition of algal
biomass on sediment cores increases the CH4 production more than the
addition of terrestrial organic matter
(Schwarz
et al., 2008; West et al., 2012, 2015). The stimulation of the
methanogenesis rates appears to be related to the lipid content in
phytoplankton biomass (West et al., 2015).
West et al. (2016) found a significant
relationship between the chlorophyll a concentration in the epilimnion and the
potential methanogenesis rates from sediment incubations. In this study, we
corroborate the importance of the autochthonous-derived organic matter
determining the CH4 concentrations in anoxic waters. Since we did not
detect the existence of the mcrA gene in the water column, we considered that
the production of methane by methanogenic Archaea occurred primarily in the
sediments and was affected by the sedimentation of organic matter derived
from phytoplankton.
Power relationship between the depth-cumulative
chlorophyll a concentration and the concentration of dissolved CH4 in
the anoxic waters during the stratification period (CH4=3.0×10-4 cumulative Chl a2.28, n=17, adjusted R2=0.40).
Note that both axes are at logarithmic scale. More statistical details can be found in
Table S2.
CH4 sources in oxic waters
In this study, the concentration of dissolved CH4 ranged from 0.02 to 8.18 µmol L-1, and all the samples of the oxic waters were
supersaturated, with values always above 800 % and ranging more than 2
orders of magnitude (Table S1). To determine the origin of this CH4 supersaturation, we examined the following potential sources: (1) the
vertical and lateral CH4 transport from deep layers and littoral
zones, (2) the in situ CH4 production by methanogenic Archaea potentially
tolerant to oxygen or by the methylphosphonate degradation under severe
P limitation, and (3) the in situ CH4 production by processes
associated to the phytoplanktonic community.
Vertical and lateral CH4 transport from anoxic sediments to oxic waters
Several previous works have pointed out that CH4 supersaturation in
oxic waters can be explained by the vertical transport from the bottom
sediments and the lateral inputs from the littoral zones that are in
contact with shallow sediments where methanogenesis occurs
(Bastviken et al.,
2004; Encinas Fernández et al., 2016; Michmerhuizen et al., 1996). To
test the importance of the lateral and vertical transport explaining the
concentration of CH4 in the oxic waters of the studied reservoirs, we
used two morphometric parameters: the mean depth (m) as a proxy for the
vertical transport and the shallowness index as a proxy for the lateral
transport. The dissolved CH4 concentration was an exponential decay
function of the reservoir mean depth (Fig. 6a) both during the
stratification period (CH4=4.0×10-2e(50.0/meandepth), adjusted R2=0.95) and during the mixing period (CH4=3.7×10-2e(22.9/meandepth), adjusted R2=0.54) (Fig. 6a). We observed that in reservoirs with a mean depth shallower than 16 m, the dissolved CH4 concentration increased exponentially (Fig. 6a). Several studies have proposed that the vertical transport of CH4
from bottom sediments explains the supersaturation in surface waters
(Rudd
and Hamilton, 1978; Michmerhuizen et al., 1996; Murase et al., 2003; Bastviken
et al., 2004). However, the vertical diffusion rates of dissolved gases
across the thermocline are too low in deep and thermally stratified systems,
and no movements of methane upwards from the hypolimnion have been detected
(Rudd and Hamilton, 1978). However, in
shallow reservoirs, the hydrostatic pressure might be reduced, promoting
CH4 diffusion from the anoxic layers.
Reservoir morphometry and the dissolved
CH4 concentration in the oxic zone. (a) Exponential decay relationships of the dissolved CH4 concentration and
the mean depth (m) during the stratification period (CH4=4.0×10-2e(50.0/meandepth), n=78, adjusted R2=0.95)
and the mixing period (CH4=3.7×10-2e(22.9/meandepth), n=82, adjusted R2=0.54). (b) Scatterplot of dissolved
CH4 concentration and the reservoir shallowness index during the
stratification period (p value = 0.134) and the mixing period (n=0.114). More statistical details can be found in Table S2.
The shallowness index increases in elongated and dendritic reservoirs, with
a greater impact of the littoral zone, and decreases in near-circular reservoirs,
with low shoreline length per surface. However, we did not find a
significant relationship between the shallowness index and the dissolved
CH4 concentration (Fig. 6b). One explanation for the absence of this
relationship could be the relatively large size of the reservoirs. Although
the reservoir size covered more than 1 order of magnitude (Table 1), all
reservoirs have a size larger than 1 km2. Previous studies have shown
that CH4 lateral diffusion may be an important process in areas near to
the littoral zone and small lakes. Hofmann et al. (2010) found higher
concentrations in the shallow littoral zones than in the open waters.
DelSontro et al. (2018) predicted that
lateral inputs from littoral zones to pelagic waters are more critical in
small and round lakes than in large and elongated lakes. Nevertheless, the
differences between the observations and predictions from the model
suggested that these lateral inputs may not be enough to explain CH4
concentration in open waters, where in situ production may prevail over
lateral transport (DelSontro et al., 2018).
In situ CH4 production by methanogenic Archaea or methylphosphonate
degradation
The ubiquitous CH4 supersaturation found in oxic waters appears not to
be fully explained by the vertical and lateral transport, underlining that
there is an in situ production of CH4, as proposed by
Bogard
et al. (2014), DelSontro et al. (2018), and Grossart et al. (2011). We
studied the presence of the methanogenic Archaea in the oxic samples by targeting
the gene mcrA, but we were unable to detect this gene (Fig. S11). This result
indicates that methanogenic Archaea were not present, at least as free-living
microorganisms, in a significant number in the water column of the oxic
samples. The classical methanogens (i.e., Archaea with the mcrA gene) are obligate
anaerobes without the capacity to survive and produce CH4 under aerobic
conditions (Chistoserdova et al., 1998). Previous
studies by Angel et al. (2011) and Angle et al. (2017) showed that
methanogens might tolerate oxygen exposure in soils, and
Grossart et al. (2011) detected potential
methanogenic Archaea attached to photoautotrophs in oxic lake waters.
Unfortunately, we did not test their occurrence in large particles,
phytoplankton, or zooplankton guts, although some authors have detected them
in these microsites' particles
(de Angelis and Lee, 1994; Karl and
Tilbrook, 1994).
We also considered the possibility of methylphosphonate degradation as an
in situ CH4 source. This metabolic pathway appears in the
bacterioplankton under chronic starvation for phosphorus
(Karl et al., 2008). Several pieces of evidence
have shown that marine bacterioplankton can degrade the MPn's and produce
CH4 through the C–P lyase activity in typically phosphorus-starved
environments, like the ocean gyres
(Beversdorf
et al., 2010; Carini et al., 2014; Repeta et al., 2016; Teikari et al.,
2018; del Valle and Karl, 2014). Freshwater bacteria can also degrade the
MPn's and produce CH4, as has been demonstrated in Lake Matano
(Yao et al., 2016a,
b). Lake Matano is an ultra-oligotrophic lake with a severe P deficiency
(below 0.050 µmol P L-1) due to the permanent stratification, iron
content, and extremely low nutrient inputs
(Crowe et al., 2008; Sabo et al.,
2008). The ratio of dissolved inorganic nitrogen (DIN) to total phosphorus
(TP) (µmol N : µmol P) is widely used to evaluate P limitation
(Morris and Lewis, 1988). DIN : TP
ratios greater than 4 are indicative of phosphorus limitation
(Axler et al., 1994). In the studied reservoirs, the
TP concentration ranged from 0.13 to 1.85 µmol P L-1 during the
stratification period and from 0.10 to 2.17 µmol P L-1 during the
mixing period. The mean DIN : TP ratio ranged from 12 to 675 during the
stratification period and from 63 to 392 during the mixing period. The more
severe the P limitation conditions are, the higher the CH4 production by
methylphosphonates degradation is. However, we did not find a significant
relationship between the DIN : TP ratio and the CH4 concentration (Fig. 7). We also analyzed the presence and abundance of the gene phnJ, which encodes
the enzyme complex C–P lyase that hydrolyzes the MPn's and changes in
response to phosphate availability. We did not detect the phnJ gene in the PCR
or the qPCR analysis in any of the study samples (Fig. S12). These results
indicate that the MPn degradation was not a quantitatively relevant source
of CH4 in the oxic waters of the studied reservoirs. Our results are in
concordance with Grossart et al. (2011), who did
not detect CH4 production by adding inorganic phosphate or
methylphosphonates to lake samples in laboratory experiments. Although we
used different methodologies, both studies may indicate that MPn degradation
is only an important source of CH4 in ultra-oligotrophic systems, as in
Lake Matano or ocean gyres.
Phosphorus limitation and the dissolved
CH4 concentration in the oxic waters. Scatterplot of
dissolved CH4 concentration and the ratio between dissolved inorganic
nitrogen (DIN) and the total phosphorus (TP) (µmol N : µmol P).
Note the logarithmic scale in both axes.
In situ CH4 production coupled to photosynthetic organisms
In the studied reservoirs, we analyzed the relationship between photosynthetic
organisms and the dissolved CH4 concentration using the GPP (g O2 m-3 d-1), NEP
(g O2 m-3 d-1), the concentration of Chl a (µg L-1), and the abundance of photosynthetic
picoeukaryotes (PPEs; cells mL-1) and cyanobacteria (CYA; cells mL-1). We determined GPP and NEP just once per reservoir during the
stratification period (i.e., n=12).
The PPEs are essential components of the marine and freshwater
phytoplankton, and they are eukaryotes with a size of 3.0 µm or less.
In the freshwater, the PPEs include species from different phyla, like
unicellular Chlorophyta (green algae) and Haptophyta. Using optical microscopy, we determined
the main groups of photosynthetic picoeukaryotes in the studied reservoirs.
PPEs were non-colonial green algae from the order Chlorococcales (class Chlorophyceae, phylum Chlorophyta) and the
genus Chrysochromulina spp. (class Coccolithophyceae, phylum Haptophyta). The cyanobacteria detected were mainly
phycoerythrin-rich picocyanobacteria, although we also detected
phycocyanin-rich picocyanobacteria in one reservoir (Béznar). We show
the vertical profiles of the Chl a concentration and the abundance of PPEs
and CYA profiles of each reservoir in Figs. 2–4 and S1–S9. We also
report the minimum, the quartiles, and the maximum values for the Chl a
concentration and the abundance of PPEs and CYA during the stratification
and the mixing periods in Table S2. The abundance of cyanobacteria ranged
from 1.51×103 to 2.04×105 cells mL-1 and was more than
1 order of magnitude higher than the abundance of PPEs that ranged from 32
to 7.45×103 cells mL-1.
Equations for the relationships between the phytoplanktonic
variables and the dissolved CH4 concentration in the oxic waters. n.m. means not measured.
We found that the relationship between the gross primary production and the
dissolved CH4 concentration was only marginally significant (p value = 0.077, n=12) and not significant with the net ecosystem production
(Table 3). The Chl a concentration showed a significant relationship with the
GPP (p value <0.01, n=12, adjusted R2=0.55), but the
abundance of cyanobacteria or the abundance of the photosynthetic
picoeukaryotes did not show a significant relationship with the GPP (p value = 0.911, n=12, and p value = 0.203, n=12, respectively). We found
significant power relationships between the Chl a concentration, the
abundance of photosynthetic picoeukaryotes, and the abundance of
cyanobacteria with the concentration of dissolved CH4 during the
stratification period (Fig. 8a, b, and c, respectively, and Table 3).
During the mixing period, the only significant predictor of the dissolved
CH4 concentration was the abundance of photosynthetic picoeukaryotes
(Fig. 8b). The slope of the relationship (i.e.,
the exponent in the power relationship) between the dissolved CH4 and
the abundance of photosynthetic picoeukaryotes was higher during the
stratification than during the mixing (Table 3). By comparing the
stratification slopes, the effect per cell of PPEs on CH4 concentration
was slightly higher than the impact of cyanobacteria (Table 3). These
results agree with previous studies that showed a closed link between the
CH4 concentration and the photosynthetic organisms, primary production,
or chlorophyll a concentration
(Bogard
et al., 2014; Grossart et al., 2011; Schmidt and Conrad, 1993; Tang et al.,
2014). In this study, we show that the PPE abundance was a better predictor
of the CH4 concentration than the abundance of cyanobacteria. In the
studied reservoirs, the PPE group included members from green algae and
Haptophyta, which are regular components of the marine plankton. Therefore, these
results may also be relevant for marine waters. Cyanobacteria have received
more attention as potential producers of CH4 in oxic conditions than
photosynthetic picoeukaryotes
(Berg
et al., 2014; Bižić et al., 2020; Teikari et al., 2018).
Klintzsch et al. (2019)
demonstrated that widespread marine and freshwater haptophytes like Emiliania huxleyi,
Phaeocystis globosa, and Chrysochromulina sp. produce CH4 under oxic conditions. They also observed that the cell
abundances were significantly related to the amount of CH4 produced.
Interestingly, Chrysochromulina was one of the genera of PPEs that we detected in the studied
reservoirs. Grossart et al. (2011) also found
CH4 production in laboratory cultures of cyanobacteria and green algae.
Phytoplanktonic variable coupled with the
dissolved CH4 concentration in the oxic waters. (a) The dissolved CH4 concentration was significantly related to the
chlorophyll a concentration during the stratification period (p value <0.001), but they were not related during the mixing period
(p value = 0.469). The relationship during the stratification period was a
power function (CH4= 0.14 Chl a0.97, n=78, adjusted
R2=0.40). (b) Relationships between dissolved CH4
concentration and the abundance of photosynthetic picoeukaryotes (PPEs)
during the stratification period (CH4= 0.0072 PPEs0.65, n=78, adjusted R2=0.55, p value <0.001) and the mixing period (CH4= 0.032 PPEs0.16, n=82, adjusted R2=0.12, p value <0.001). (c) Relationship between dissolved CH4 concentration
and the cyanobacteria abundance (CYA; cells mL-1). A power function
described the relationship between the dissolved CH4 and the CYA during
the stratification period (CH4= 0.0017 CYA0.53, n=78, adjusted R2=0.17, p value <0.001). The
relationship was not significant during the mixing period (p value = 0.666).
Overall, these results indicate a clear association between the CH4
production and the photosynthetic organisms from both Eukarya (picoeukaryotes) and
Bacteria (cyanobacteria) domains. The pathways involved in the CH4 production
may be related to the central photosynthetic metabolism or the release of
methylated by-products, different from methylphosphonates during the
photosynthesis. Previous studies demonstrated the CH4 production in
laboratory cultures using 13C-labeled bicarbonate in haptophytes
(Klintzsch
et al., 2019; Lenhart et al., 2016); in marine, freshwater, and terrestrial
cyanobacteria (Bižić et al.,
2020); and in major groups of phytoplankton
(Hartmann et al., 2020). In these studies, the
photosynthetic organisms uptake bicarbonate in the reductive pentose
phosphate cycle (Calvin–Benson cycle)
(Berg, 2011; Burns
and Beardall, 1987). Therefore, CH4 production may be a common pathway
in the central metabolism of photosynthesis of all the cyanobacteria and
algae in freshwater and marine environments.
On the other hand, the production of CH4 can also be related to the
production of methylated compounds during photosynthesis.
Lenhart et al. (2016) and
Klintzsch et al. (2019)
also detected the CH4 production in cultures from the sulfur-bound
methyl group of the methionine and methyl thioethers. Common substances like
methionine can act as a methyl-group donor during the CH4 production in
plants and fungi
(Lenhart
et al., 2012, 2015). Besides, algae use part of the methionine for the
synthesis of dimethylsulfoniopropionate (DMSP), an abundant osmolyte, the
precursor of dimethyl sulfide (DMS), and dimethyl sulfoxide (DMSO). These
methylated substances produce methane during their degradation
(Damm
et al., 2008, 2010, 2015; Zindler et al., 2013).
Bižić-Ionescu et al. (2018) also
suggested that CH4 could be produced from methylated amines under oxic
conditions. These substances, together with other organosulfur compounds,
can also produce CH4 abiotically
(Althoff et al., 2014;
Bižić-Ionescu et al., 2018). The production of DMSP, DMS, and other
methylated substances like isoprene has been extensively studied in marine
phytoplankton, showing that taxa as photosynthetic picoeukaryotes and the
cyanobacteria are relevant sources
(Shaw et al., 2003; Yoch,
2002). Recent studies have also reported that freshwater algae and
cyanobacteria also produced DMS and isoprene
(Steinke et al., 2018). Further studies are
needed to quantify the potential role of all these methylated by-products as
potential CH4 sources quantitatively relevant in freshwater.
Modeling the CH4 production in oxic waters
The explanation of the CH4 supersaturation in oxic waters in relatively
large systems relies on the interaction of several processes as the transport
from anoxic environments and the biological activity
(DelSontro et al., 2018). In this study, we
found that vertical transport (mean depth as surrogate), water temperature,
and the abundance of photosynthetic picoeukaryotes and cyanobacteria had a
significant effect on the dissolved CH4 concentration. We combined
these explanatory variables with significant effects using GAMs. The GAM for the stratification period (n=78)
had a fit deviance of 82.7 % and an explained variance (adjusted R2) of
81.4 % (Table S3). The explanatory variables, in decreasing order, were as follows:
the photosynthetic picoeukaryotes' abundance (log10 PPEs), the
reservoir mean depth, the cyanobacteria abundance (log10 CYA), and
the water temperature (Fig. 9a). The function obtained was as follows: log10CH4=-4.05+0.34 log10 PPEs +e(6.7/meandepth)+0.17 log10 CYA + 0.027 Temperature.
The abundance of PPEs was the variable explaining most of the variance of
dissolved CH4 concentration (log10CH4) during the
stratification period, with an effect higher than the cyanobacteria
abundance. Figure 9b–e shows the partial responses of each
explanatory variable.
Results of the generalized additive model (GAM)
fitted for the concentration of dissolved CH4 in the
oxic waters during the stratification period. (a) Bar plot showing the
significance of the smooth terms from the fitted GAM (F values).
(b–e) Partial response plots from the fitted GAM, showing the
additive effects of the covariates on the dissolved CH4 concentration:
the photosynthetic picoeukaryotes' abundance (log10 PPEs)
(b), the mean depth (c), the cyanobacteria abundance
(log10 CYA) (d), and water temperature (e). In
partial response plots, the lines are the smoothing functions, and the shaded
areas represent 95 % pointwise confidence intervals. Rugs on x axis
indicate the distribution of the data. More details are provided in Table S3.
The GAM for the mixing period (n=82) only included two explanatory
variables: the reservoir mean depth and the abundance of the photosynthetic
picoeukaryotes. The reservoir mean depth was the variable explaining most of
the variance of the dissolved CH4 concentration (log10CH4)
during the mixing period, closely followed by the abundance of PPEs (Fig. 10a). We observed that the function of the effect of the mean depth on the
CH4 concentration changed between the two periods (Figs. 9c and
10b). The function was more linear during the mixing period than during the
stratification period, likely because the mixed water column enabled the more
uniform distribution of the CH4 produced in the sediment, while the
thermocline acted as a barrier to the diffusion during the stratification
period. The model function for the mixing period was log10CH4=-2.07+1.5e(-0.04meandepth)+0.18
log10 PPEs, with a fit deviance of 53.9 % and an explained variance
(adjusted R2) of 52.1 % (Table S3). In Fig. 10b and c, we show the
partial response plots for these two variables. The results show that the
abundance of photosynthetic picoeukaryotes can be key for explaining the
dissolved CH4 concentration in oxic waters, even though they have
received less attention than cyanobacteria in previous studies
(Berg
et al., 2014; Bižić et al., 2020; Teikari et al., 2018). Finally, we
have also included a simple model to explain the dissolved CH4
concentration (log10CH4) using the data of both periods
(n=160) and including widely used variables like the water temperature
(∘C), mean depth (m), and Chl a concentration
(µg L-1) for future comparisons. The function of this model
is log10CH4=-2.02+0.05Temperature+e(7.73/meandepth)-e(-0.05log10(Chla)). This GAM
had a fit deviance of 69.3 % and an explained variance (adjusted R2) of
68 % (Table S3).
Results of the generalized additive model (GAM)
fitted for the concentrations of CH4 in the oxic
waters during the mixing period. (a) Bar plot showing the significance of
the smooth terms from the fitted GAM (F values). Panels (b) and (c) show partial response plots from the fitted GAM, showing
the additive effects of the covariates on the dissolved CH4
concentration: the mean depth (b) and the abundance of
photosynthetic picoeukaryotes (log10 PPEs) (c). In partial
response plots, the lines are the smoothing functions, and the shaded areas
represent 95 % pointwise confidence intervals. Rugs on x axis indicate
the distribution of the data. More details are provided in Table S3.
Overall, during the stratification period, the in situ CH4 production
was coupled to the abundance of photosynthetic picoeukaryotes in oxic waters
(Fig. 9a) and mean depths. This CH4 source, due to photosynthetic
picoeukaryotes, can be crucial in large, deep lakes and reservoirs and the
open ocean, since the impact of the CH4 transport from sediments (i.e.,
mean depth) decreases with increasing depths. In deeper reservoirs, the
thermal stratification during the summer that produced the vertical
diffusion rates of CH4 from sediments is limited.
Rudd and Hamilton (1978) did not detect
any movement of CH4 upwards from the hypolimnion during the
stratification. Previous studies have suggested that the CH4 produced
in the oxic water column is the primary source of CH4 in large and deep
lakes
(Bogard
et al., 2014; DelSontro et al., 2018; Donis et al., 2017; Günthel et
al., 2019). Günthel et al. (2019) showed that large
lakes have a lower sediment area in comparison to the volume of the surface
mixed layer than small lakes and that this fact determines the higher
contribution of the oxic methane production to surface emission in large
(>1 km2) lakes than in small ones. The photosynthetic
picoeukaryotes identified in the studied reservoirs are considered indicators
of eutrophic conditions, and they are bloom-forming genera
(i.e., Chlorococcales and Chrysochromulina spp.)
(Edvardsen and
Paasche, 1998; Reynolds, 1984; Willén, 1987). Global future estimations
suggest a rise in eutrophication and algal bloom over the next century due
to climate change and the growing human population
(Beaulieu et al., 2019). In that situation,
photosynthetic picoeukaryotes
like Chlorococcales and Chrysochromulina spp., and cyanobacteria, would lead to an increment in CH4
production and emissions. Further studies are needed to understand
the role of the photosynthetic picoeukaryotes in the production of CH4
in oxic waters better and to quantify their influence in the methane
supersaturation and CH4 fluxes from inland and oceanic waters.
Conclusions
The dissolved CH4 concentration in the studied reservoirs showed a
considerable variability (i.e., up to 4 orders of magnitude) and presented
a clear seasonality. Surface waters were always supersaturated in CH4.
The concentration of CH4 was closely linked to the photosynthetic
organisms. In the anoxic waters, the depth-cumulative chlorophyll a
concentration, a proxy for the phytoplanktonic biomass exported to
sediments, determined the CH4 concentration. In the oxic waters, we
considered different potential CH4 sources, including the vertical
and lateral transport of CH4 from anoxic zones and in situ production. The
mean depth of the reservoirs, as a surrogate of the CH4 transport from
sediment to the oxic waters, contributed in shallow systems. We did not
detect methanogenic Archaea or methylphosphonates degradation target genes (i.e.,
mcrA and phnJ genes, respectively), which suggests that these pathways are not
responsible for the in situ production of CH4 in the oxic waters of the
studied reservoirs. We found that dissolved CH4 was coupled to the
abundance of photosynthetic picoeukaryotes (PPEs) during both periods and to
chlorophyll a concentration and the abundance of and cyanobacteria during
the stratification period. These PPEs were non-colonial green algae from the
order Chlorococcales (class Chlorophyceae, phylum Chlorophyta) and the genus Chrysochromulina spp. (class Coccolithophyceae, phylum Haptophyta). Finally, we
combined all the explanatory variables with significant effects and
determined their relative contribution to the CH4 concentration using
generalized additive models (GAMs). The abundance of PPEs was the variable
explaining most of the variance of dissolved CH4 concentration during
the stratification period, with an effect higher than the cyanobacteria
abundance. During the mixing period, the reservoir mean depth and the
abundance of the PPEs were the only drivers for CH4 concentration. Our
findings show that the abundance of PPEs can be relevant for explaining the
dissolved CH4 concentration in oxic waters of large lakes and
reservoirs.
Data availability
Additional figures and tables can be found in the Supplement.
The dataset associated with this paper will be available from PANGAEA (León-Palmero et al., 2020):
dissolved concentrations of CH4, nutrients, and biological parameters in the
water column of 12 Mediterranean reservoirs in Southern Spain
(https://doi.org/10.1594/PANGAEA.912535, last access: 14 May 2020, and primary production of 12
Mediterranean reservoirs in southern Spain
(https://doi.org/10.1594/PANGAEA.912555, last access: 14 May 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-3223-2020-supplement.
Author contributions
ELP, RMB, and IR contributed equally to this work. RMB and IR
designed the study and obtained the funds. ELP, RMB, and IR
contributed to data acquisition during the reservoir samplings. ELP
processed most of the chemical and biological samples. ACR performed the
flow cytometry and part of the molecular analysis, and AS collaborated
with the dissolved CH4 analysis using gas chromatography. ELP,
RMB, and IR analyzed the data and discussed the results. ELP wrote
the first draft of the paper, which was complemented by significant
contributions from RMB and IR.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We
especially thank Eulogio Corral for helping in the field, Jesús Forja and Teodora Ortega for helping with gas chromatography analysis
at the University of Cádiz, and David Fernández Moreno from the
Department of Botany at the University of Granada for the taxonomical
identification of the phytoplankton community. We thank the Hydrological
Confederations of Guadalquivir and the Agencia Andaluza del Medio Ambiente y Agua (AMAYA) for facilitating the reservoir sampling.
Financial support
This research has been supported by the Spanish Ministry of Economy and Competitiveness (grant no. CGL2014-52362-R); the University of Granada – Unidades de Excelencia (grant no. UCE.PP2017.03); the Consejería de Economía, Conocimiento, Empresas, y Universidad from Andalucia; and the European Regional Development Fund (ERDF; grant no. SOMM17/6109/UGR). Elizabeth León-Palmero and Ana Sierra were supported by PhD fellowships from the Ministry
of Education, Culture and Sport (grant nos. FPU014/02917 and FPU2014-04048, respectively). Alba Contreras-Ruiz was supported by the Youth Employment Initiative (YEI) from the Junta de Andalucía and financed by the European Commission (grant no. 6017).
Review statement
This paper was edited by Carolin Löscher and reviewed by three anonymous referees.
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