Temporal variations in methane emissions from emergent aquatic macrophytes in two boreonemoral lakes.

Methane (CH4) emissions via emergent aquatic macrophytes can contribute substantially to the global CH4 balance. We addressed temporal variability in CH4 flux by using the static chamber approach to quantify fluxes from plots dominated by two species considered to differ in flux transport mechanisms (Phragmites australis, Carex rostrata). Temporal variability in daily mean emissions from early June to early October was substantial. The variable that best explained this variation was air temperature. Regular and consistent diel changes were absent and therefore less relevant to include when estimating or modelling CH4 emissions. Methane emissions per m2 from nearby plots were similar for Phragmites australis and Carex rostrata indicating that CH4 production in the system influenced emissions more than the species identity. This study indicates that previously observed diel patterns and species-effects on emissions require further evaluation to support improved local and regional CH4 flux assessments.

Introduction

Methane (CH4) impacts the global energy balance and climate, has substantially higher global warming potential than carbon dioxide (CO2) per kg in a 100-year perspective, and accounts for some 20 % of radiative forcing (Myhre ). Wetlands and inland waters are large contributors to overall CH4 emission (Laanbroek 2010; Bastviken ; Ciais ; Kirschke , where anaerobic degradation of organic matter produces CH4 through methanogenesis. Upon transport from the sediments, a large proportion of dissolved CH4 can be oxidized in surface sediments or in the water (Bastviken ; Conrad 2009; Laanbroek 2010; Duc ). Therefore, the fluxes of dissolved CH4 from surface waters are often smaller than flux pathways by which CH4 can ‘escape’ oxidation, such as ebullition (flux by bubbles from sediments) and flux through rooted emergent aquatic macrophytes (Joabsson ; Bastviken 2009). Such plants have well-developed arenchema in their stems and underground rhizomes that transport oxygen to roots (Laanbroek 2010). These gas transport systems can also transport CH4 molecules from the root systems, bypassing the oxidation zone in the sediments and release methane directly into the atmosphere via stomata (Yavitt and Knapp 1998).

Emergent aquatic macrophytes in the arctic wet tundra play an important role in the exchange of CH4 betweenpan> the anaerobic enpan>vironpan>menpan>t and the atmosphere (Joabssonpan> ; Dinpan>g ; Carmichael ). It is widely suggested that emergenpan>t aquatic macrophytes are key sources of CH4 emissions to the atmosphere through the aerenchyma (Ding ; Bhullar ). In addition, emergent aquatic macrophytes contribute carbon to the sediment during the growing season, by production of plant biomass and root leakage, further increasing CH4 production in wetlands (Ding ; Laanbroek 2010).

Molecular diffusion and convective flow are common processes that have been proposed to regulate transport of CH4 inpan> plant tissues (Kim ), the latter depenpan>dinpan>g onpan> the pressure gradienpan>t inpan> the plant (Tornpan>berg ). It has beenpan> suggested that observed differences between day and night in CH4 fluxes may be due to molecular diffusion during night and convective flow during the day (Kim ). The dominant type of gas transport can vary between species and previous work indicated a strong influence of convective gas flow in Phragmites australis (Armstrong and Armstrong 1991, 2014) whereas molecular diffusion through the aerenchyma has been suggested to dominate in Carex rostrata (Chanton and Whiting 1996). Therefore, it has been assumed that flux mediated by aquatic plants is highly species-dependent (Joabsson ).

While plant species and gas transport mechanisms have been seen as important for wetland CH4 emissionpan>s, fluxes can also be regulated by enpan>vironpan>menpan>tal variables, which can inpan>fluenpan>ce plant activity and ecosystem CH4 production. For example, air temperature, wind, pressure, humidity and light are factors suggested to control the convective flow (Kim ). Plant CH4 exchange may also be affected by plant growth and amount of active aboveground biomass, for which net ecosystem exchange (NEE; net CO2 exchange illustrating the overall balance between the photosynthesis and respiration) can be used as a proxy. The overall potential for CH4 emissions is regulated by CH4 production in sediments, which also depends on multiple environmental factors such as organic substrate supply and temperature (Yvon-Durocher ).

Knowing the spatial and temporal variability in methane emissionpan> from emergenpan>t aquatic macrophytes is essenpan>tial for assessinpan>g their importance, and for monpan>itorinpan>g and modellinpan>g such emissionpan>s. Spatial variability inpan> openpan> water fluxes has recently been shown to depend on distance to inlets and on water depth (e.g. Natchimuthu , 2017). For fluxes via emergent aquatic plants, spatial variability has frequently been associated with species distributions (see above), while considerable temporal variability has also been noted. Methane emissions from emergent aquatic macrophytes have been observed to vary over the growing season with a peak in the middle (Hyvönen ; Juutinen ; Kankaala ; Duan ; Kankaala ). In lakes in both boreal (Kankaala ) and arid environments (Duan ), maximum CH4 emission occurred in late June to mid-August, when temperature, growth and biomass peak. Other studies have also shown a positive relationship between CH4 emissions and plant biomass during the growing season (Hirota ; Kankaala ), but this pattern is not always prominent (Kankaala ).

There are also reports of considerable diel fluctuations in plant-mediated CH4 emissionpan>s inpan> the literature (Whitinpan>g and Chantonpan> 1996; Käki ; Juutinpan>enpan> ; Dinpan>g ; Duan ). Previous studies have observed that emissionpan>s from Typha latifolia peak in the morning (Whiting and Chanton 1996), whereas Peltandra virginica (Whiting and Chanton 1996) and Phragmites australis (Brix ) peak in the afternoon. Consequently, it has been suggested that diel fluctuations differ among species (Whiting and Chanton 1996). In addition, temperature, light, humidity and other environmental variables may influence diel patterns (Brix ; Bergström ; Duan ; Hirota ; Juutinen ; Kankaala ; Wang and Han 2005). The available studies on diel variations from specific stands of plants are limited as only a few diel cycles have been studied in each environment with relatively few measurements during the 24 h period (Hyvönen ; Käki ; Ding ; Juutinen ; Xing ).

Altogether, there are a number of common assumptions about how species versus environmental factors influence fluxes, and about diel flux variability, that are based on limited data, and it is not clear to what extent the prevailing views are valid across ecosystems and over time. Hence, it is important to study diel variability for species with differenpan>t evolutionpan>ary history as well as differences in morphological and anatomical characteristics over whole growth seasons to resolve the influence of environmental variables versus plant species differences and to allow the development of generally valid models.

In the present study, both diel and seasonal variability in CH4 emissions from areas dominpan>ated by the emergenpan>t aquatic macrophytes Phragmites australis (Cav.) Steud. and Carex rostrata Stokes in two hemiboreal lakes in the southwest of Sweden were measured. The aims were:

Evaluate diel and seasonal patterns during the same time periods in plant-mediated CH4 emissionpan>s from emergenpan>t aquatic macrophytes species reported to have different dominating flux transport mechanisms (convective or diffusive);

To evaluate links between CH4 fluxes and enpan>vironmenpan>tal variables that are potenpan>tially related to temporal patterns inpan> plant mediated CH4 emissions, such as air temperature, light, air pressure, humidity, NEE of CO2, wind, species and biomass.

Methods

Study site

The field work was carried out from June to October 2014 in two lakes located in the Skogaryd Research Catchment (www.fieldsites.se (13 July 2017); near Vänersborg, Sweden): Lake Erssjön and Lake Följesjön in the drainage area of Bäveån in Västra Götaland County. Both lakes are located in the hemiboreal zone in the southwest of Sweden. Erssjön (N 58° 22.2786', E 12° 9.7175') is a small open-water lake (6.2 ha) with a mean depth of 1.7 m and a maximum depth of 4.5 m. The vegetationpan> alonpan>g the lake marginpan> is dominpan>ated by Phragmites australis, and different species of Carex, including C. rostrata in the shallower parts, with smaller contributions of Equisetum fluviatile and Typha latifolia, while Nymphaea alba is present at deeper water down to ∼2 m depth. The surrounding area of Erssjön consists of coniferous forest with a small proportion of deciduous trees, as well as agricultural land adjacent to the northern shore of the lake. Följesjön (N 58° 22.5250', E 12° 9.2237') on the other hand, is smaller (3.8 ha), shallower (mean and max depth of 0.5 and 1.5 m, respectively) and almost the entire surface of the lake is dominated by emergent and floating-leaved wetland species. Only small patches of open water remain and even if named as a lake (‘sjön’  =  ‘the lake’ in Swedish), large parts of Följesjön share most characteristics with a plant-covered fen-type wetland. In the middle part of Följesjön, vegetation is dominated by Phragmites australis, Carex spp. with smaller contributions of Typha latifolia while Sphagnum spp. and Myrica gale are abundant along the shore. The vegetation surrounding Följesjön is similar to the vegetation around Erssjön, except that there is no agricultural land adjacent to Följesjön. The lakes are hydrologically connected, as one of the main streams providing water to Följesjön comes from Erssjön. The distance between the lakes is ∼1 km.

The concentration of P, N and Fe was higher inpan> Följesjön than inpan> Erssjön, whereas mean dissolved O2 concentration was lower in Följesjön than in Erssjön (Table 1). Weather variables did not differ considerably between the two lakes.

Table 1

Lake characteristics in Lake Erssjön and Lake Följesjön. Wind, temperature and air pressure records from May to October 2014.

Lake	Erssjön		Följesjön
Area (ha)	6.2		3.8
Mean depth (m)	1.7		0.5
	Mean	SD	Mean	SD
tot-P (µg L−1)	33.73	10.56	50.77	13.73
tot-N (mg L−1)	1.03	0.16	1.43	0.25
tot-Fe (µg L−1)	1378	671	1592	500
tot-Mn (µg L−1)	135	97	108	47
O2 (mg L−1)	7.37	1.39	4.72	1.70
Wind (m s−1)	1.68	1.01	1.24	0.35
Air temperature (°C)	14.3	5.99	15.18	5.89
Incoming rad (short wave) (W m−2)	250	151	246	123
Air pressure (atm)	0.99	0.00	0.99	0.00

Sampling sites and duration

Five sampling sites (three in Erssjön and two in Följesjön) were chosen considering both accessibility and the abundance of the target plant species (Fig. 1). Within each lake, the sites were all located close to each other to minimize the differenpan>ce betweenpan> the sites inpan> other aspects than the dominpan>atinpan>g plant species. The water depth in each sampling site was ∼10–20 cm. Each site was dominated by one species; P. australis or C. rostrata. The sampling sites were located in the northern part of Erssjön and in the central part of Följesjön. Initially there were four sampling sites, three of them possible to reach from boardwalks and the fourth site directly reachable from the shore of Erssjön. Due to the high water level in Erssjön in the end of July, the original shore sampling site could not be reached without disturbing the sediment (mixing the sediment can affect the release of CH4). This sampling site was therefore moved permanently to a similar nearby area (the fifth site), easily reached from a boardwalk, for the remaining four measurement occasions (Fig. 1).

Figure 1.

Map of the study area and the sampling sites. Squares represent Carex rostrata; circles, Phragmites australis (in Erssjön, where the sampling point was moved, the initial one is the left of the two circles).

Map of the study area and the sampling sites. Squares represent n class="Species">Carex rostrata; circles, n class="Species">Phragmites australis (in Erssjön, where the sampling point was moved, the initial one is the left of the two circles).

Measurements in Erssjön were conducted once each hour during 24 h on the two first measurement occasions (9–10 June and 29–30 June 2014) and once every second hour during 24 h on five later occasions (20–21 July, 8–9 August, 29–30 August, 21–22 September and 12–13 October). Samplings in Följesjön took place each hour during 24 h on 1–2 July and every second hour during 24 h on five later measurement occasions (21–22 July, 9–10 August, 30–31 August, 23–24 September and 13–14 October).

Sampling method and measurement duration

A closed-chamber method was used to measure CH4 flux from the areas with emergenpan>t aquatic macrophytes. This means that we measured the flux through macrophytes combinpan>ed with flux through water (the latter being approximately one order of magnitude smaller than the former in the present study). The chamber (d 43 cm, w 43 cm, h 137.5 cm: volume 197.4 L) was made of plastic pipes, built in a rectanglar block shape (Fig. 2) and covered with transparent airtight plastic. The plastic material was made of four layers: 40 pm nylon, 6.5 pm methylene-vinyl-alcohol, 40 pm nylon and 100 pm polyethylene (Otto Nielsen Emballage, 2800 Lyngby, Denmark). The chamber was then put over a stand of emergent aquatic macrophytes in order to collect the gas, floating on Styrofoam rods along the lower edges of the plastic pipe frame and with the plastic entering 5 cm into the water ensuring a good seal. The chamber was equipped with two computer CPU fans, run with 12 V batteries for air circulation and a thermometer to measure the temperature inside the chamber. Gas was sampled from the inside of the chamber through two transparent PVC tubes (outer diameter 5 mm and inner diameter 3 mm) being ∼5 m long. One end was attached inside the chamber and the other end to an LGR-UGGA (Los Gatos Research Ultraportable Greenhouse Gas Analyser), one tube for air entering the LGR-UGGA and the other for outgoing air back to the chamber creating a closed loop between the chamber and the LGR-UGGA. Concentrations of CH4 and CO2 were logged with the LGR-UGGA every 20 s during 5 min on each plant site. Between measurements, the chamber was removed from the site and vented until the gas levels decreased to the same level as the surrounding air.

Figure 2.

Model of the chamber used for measuring plant-mediated CH4 emissions. A is a fan run with a 12 V battery (placed at 100 as well as 60 cm height), B is two transparent PVC tubes transporting the air between the chamber and the LGR (Los Gatos Research (LGR) Ultraportable Greenhouse Gas Analyser, UGGA). The chamber is covered with airtight plastic.

Model of the chamber used for measuring plant-mediated n class="Chemical">CH4 emissions. A is a fan runpan> with a 12 V battery (placed at 100 as well as 60 cm height), B is two transparenpan>t PVC tubes transportinpan>g the air betweenpan> the chamber and the LGR (Los Gatos Research (LGR) Ultraportable Greenpan>house Gas Anpan>alyser, UGGA). The chamber is covered with airtight plastic.

Environmental variables

Air temperature (°C), relative humidity (%) and air pressure (mb) were measured outside the chamber, with a pocket weather station (Anemometer Silva ADC) during each CH4 flux measuremenpan>t. Winpan>d speed (m s−1) was obtainpan>ed from a weather stationpan> near each lake (measured winpan>d speed at 30 minpan> inpan>tervals at 4.7 m height inpan> Erssjön and at 2.3 m height inpan> Följesjön (data provided by Eric Sahlée and Anpan>na Rutgerssonpan>, Departmenpan>t of Earth Scienpan>ces, Uppsala University and Leif Klemedtssonpan>, Departmenpan>t of Earth Scienpan>ces, University of Gothenpan>burg). Missinpan>g values for winpan>d speed were replaced by values from another nearby weather stationpan> located onpan> a mire 500 m east of Erssjön (measured at 2.25 m; provided by University of Gothenpan>burg). Light (inpan>cominpan>g solar radiationpan>) was measured next to the chamber, with a PAR senpan>sor (HOBO Photosynpan>thetic Active Light Smart Senpan>sor) conpan>nected to a HOBO© H21-002 Micro Stationpan> Data Logger, which logged inpan>cominpan>g light at 10 s inpan>tervals. The O2 concentration in the water was measured every 15 min at a depth of 0.5 m below the water surface with a HOBO oxygen logger probe (HOBO U26 Dissolved Oxygen Logger). Samples for total phosphorous P, total N, and total Fe and total Mn were collected in 0.5 L polyethylene bottles at 0.5 m depth (one sample per location collected every other week as a part of the regular monitoring program in the area). Total N and P were analysed spectrophotometrically after chemical reduction to ammonia (N) or oxidation to phosphate (P), respectively, and addition of reagent compounds, according to analytical standard ISO 15681-1:2003 and SS-EN ISO 11905-1:1997, respectively. Fe and Mn were analysed using a Nexion 300D ICP-MS (Perkin Elmer).

Biomass estimation

The aboveground biomass in each chamber was estimated after every sampling day, in total seven times per site. For P. australis, plant height was recorded and the number of shoots counpan>ted. Biomass inpan> each chamber was estimated from regressionpan> equationpan>s that were developed by measurinpan>g the height of plants and counpan>tinpan>g shoots inpan> nearby plots, followed by harvestinpan>g shoots at the water surface to determine dry weight biomass. For Carex rostrata, the shoot density and average height was measured in the chambers on each measurement day. Then, after the measurements, a nearby area (i.e. outside the chambers) with similar shoot density and average height were harvested for biomass determination. The harvested shoots for both species were dried for 48 h at 60 °C and plant biomass dry weight (g DW m−2) was determined.

Calculations

The plant CH4 flux was calculated from the linpan>ear change inpan> the chambers gas conpan>cenpan>trationpan> over time accordinpan>g to Bäckstrand . The method was modified to fit a larger chamber and a different type of gas measurement. The first minute of each 5 min measurement period was omitted, due to heterogeneous mixing-effects before the gas in the tubing and the LGR measurement cell reflected average chamber gas content. Multiple slopes of the gas concentration versus time were calculated for 2-min periods, each period offset by 20 s. In total seven such 2-min slopes were calculated for each 5-min period. From the seven slopes, the one with the highest r2-value was chosen for flux estimation.

The following equation was used to calculate the regression slope for each 2-min period, in parts per million per day, where ppmi and ti are the starting concentration (in ppm) and starting time for each 2-min period and and are the mean of ppm and time for each 2-min period.

The CH4 flux (F) for the selected 2-minpan> period was calculated accordinpan>g to where ppm/day is the selected regressionpan> slope (Equationpan> 1) from CH4 concentration measurements, P is the measured air pressure (atm), V is chamber volume (L), R is the universal gas constant (0.082056 L atm K−1 mol−1), T is temperature (K) and A is the chamber’s base area (m2). The value 1000 is used for unit conversion from mol m−2 d−1 to mmol m−2 d−1. CH4 emissions from the emergent aquatic macrophytes were also calculated per g DW biomass in each measurement site (mmol g DW−1 d−1). When calculating net flux in this manner, all three types of CH4 release from the area covered by the chamber are included, i.e. emission via plants, diffusion across the water surface and minor events of ebullition (larger events were excluded, see below).

Ebullition

Ebullition releases can be suspected when there is a rapid sudden increase in CH4 conpan>cenpan>trationpan> (also resultinpan>g inpan> a reduced R2 over the whole 2-minpan> period). Large ebullitionpan> evenpan>ts are therefore easily detected inpan> the regressionpan> plot of CH4 against time. Such an ebullition event occurred one time only, during the last measurement in Följesjön, and this value was excluded. Ebullition events being too small to detect in this way can be considered insignificant compared with the plant-mediated fluxes.

Diffusive CH4 flux

CH4 concenpan>tration inpan> the water was measured twice in each lake, once in July and once in October, to estimate the diffusive CH4 flux. Water samples were taken with a 10 mL plastic syringe and ∼5 mL water was collected to rinse the syringe prior to sampling. During this rinsing, care was taken to also remove air bubbles from the syringe. When the rinsing water had been discarded, 5 mL water were sampled 3 cm below the surface, and, after visually confirming absence of bubbles, the sample was injected into a pre-capped vial (20 mL) filled with 100 µL H3PO4 and N2 at atmospheric pressure. CH4 concentration in the water was analysed in the laboratory by a gas chromatograph (Agilent 6890 with Haysep N column and flame ionization detector calibrated with certified gas standards).

The diffusive CH4 flux can be expressed with the equationpan>: where F is diffusive CH4 flux in mmol m−2 d−1, k is the gas transfer velocity (m d−1), C is the measured CH4 concentration in the water (mmol m−3), and C is the CH4 concentration in the water if it was in equilibrium with the air concentration (estimated from air concentrations and Henry’s Law). The k-value was derived from independent flux and concentration measurements on the nearby open water (see, e.g. Bastviken and Natchimuthu for method details). This can be considered to yield overestimated diffusive flux as previous work has indicated that k-values are much lower in stands of macrophytes where the water surface is wind-sheltered than for open water (Kosten ; Attermeyer ).

Statistical analyses

Methane emissions were analysed inpan> three ways, with focus (i) on the possible existenpan>ce of diel patterns; (ii) simple relationships betweenpan> mean diel CH4 flux per m2 and different environmental variables; and (iii) on modelling all data using all explanatory variables.

To evaluate whether there were consistent diel patterns in n class="Chemical">CH4 emissions among the 25 daily time series (one inpan>complete series excluded), we used inpan>tra-class correlation (Rusak ). We first divided the day inpan>to 10 time classes, calculatinpan>g an average per class (there were 1–4 values per time class). These calculations were conducted with SPSS 24, usinpan>g the two-way mixed model, with absolute agreemenpan>t.

The relationships between mean diel CH4 flux and temperature, winpan>d, light and net CO2 exchange, respectively, were evaluated using linear regression. Relationships for which the slopes had P-values < 0.05 were considered significant.

In approach (iii), all 5 min flux measurements (N  =  288) were analysed using a generalized linear model (GLZ, McCullagh and Nelder 1989) with a log-link function and Gamma distribution using the software R (R Core Team 2013). Explanatory variables were lake, plant species, air temperature, temperature inside the chamber, light, air pressure, air humidity, wind speed, plant biomass, CO2 flux, date and time, where date and time were treated as separate variables inpan> the model. A quadratic term of time and date was inpan>cluded, as we expected a nonpan>linpan>ear responpan>se of CH4 flux during the day and over the growing season (based on previous studies, a peak in plant CH4 emission was expected during the day and highest emissions were expected in the summer during the peak of the growing season; Van der Nat ; Kankaala ). An AIC-based model averaging and selection approach was used (Symonds and Moussalli 2011).

In model selection and averaging, candidate models are the set of all possible models that are nested within the full model and the AIC values (Akaike Information Criterion; Akaike 1974) for candidate models are used as a measure of model fit. The difn class="Chemical">ferenpan>ce inpan> AIC betweenpan> models is used to rank models, to determinpan>e the importance of explanatory variables and to estimate regression coefficienpan>ts. Model selection was implemenpan>ted usinpan>g the MuMIn-package (Barton 2016), and continpan>uous explanatory variables were standardized to have mean = 0 and SD = 0.5 prior to analysis. Correlations betweenpan> explanatory variables were calculated to assess the degree of collinpan>earity. Humidity and temperature inpan>side the chamber were excluded from the model due to their high correlation with air temperature (r > 0.7). The fitted full model was tested for collinpan>earity (Zuur ) with the vif funpan>ction (car-package; Fox and Weisberg 2011); detectinpan>g only minpan>imal collinpan>earity (vif < 3). Furthermore, residuals from the fitted model did not show heteroscedasticity.

Linear regression is sensitive to un-equal sampling intensity along the explanatory gradients. The greater number of measurements in the beginning of the growing season might bias the results and thus lead to erroneous conclusions. Therefore, every second value, from a total of 24 measurements in 9–10 June, 29–30 June and 1–2 July were omitted, in order to have a consistent dataset with twelve measurements per day. Autocorrelation between consecutive measurement during a day was detected with the ar-function (in the stats-package in R), for days with 24 measurements. However, there was less autocorrelation when every second value was deleted, so no further process for handling autocorrelation in the model was needed.

Results

Plant mediated and diffusive CH4 emissions

Our flux estimates ranged from 1 to 87 mmol m−2 d−1 and 1 to 73 mmol m−2 d−1 for P. australis and C. rostrata, respectively (Table 2; equivalent to 0.7–58 and 0.7–49 mg CH4 m−2 h−1). When calculating CH4 emissions from the emergent aquatic macrophytes per m2, the diffusive flux of CH4 and ebullition were included in the total emission. The measured diffusive emissions ranged from 0.11 to 2.46 mmol m−2 d−1, which represented 1–22 % of the total mean emission from the emergent aquatic macrophytes on the day that measurements were made (Table 2). In an extensive, previous 2-year study of the two lakes, median diffusive fluxes ranged from 0.1 to 1.0 mmol m−2 d−1 (Natchimuthu ) confirming that diffusive flux, where plants were present, was small relative to plant-mediated fluxes. Further, these numbers are most likely overestimates of the diffusive flux at the measurement plots because gas exchange rates are lower among plants than for open water (see Methods section).

Table 2

Maximum, minimum and mean of CH4 emission (mmol m−2 d−1) for P. australis and C. rostrata in Lake Erssjön and Lake Följesjön, throughout the study period in 2014 (n = 7).

CH4 emission (mmol m−2 d−1)			Measurement occasion
			9–10 June	29 June to 2 July	20–22 July	8–10 Aug.	29–31 Aug.	21–24 Sep.	12–14 Oct.
Erssjön	P. australis	Maximum	86.7	15.4	37.6	38.0	26.8	11.7	5.46
		Minimum	6.60	2.39	1.87	6.07	6.78	0.94	1.60
		Mean	21.6	4.78	11.1	15.4	12.7	4.11	3.42
	C. rostrata	Maximum	20.7	10.8	–	17.8	14.5	4.21	5.10
		Minimum	7.20	5.20	–	9.09	8.2	2.01	1.05
		Mean	14.7	6.75	–	13.0	10.0	2.88	2.14
Följesjön	P. australis	Maximum	–	44.1	55.4	77.6	40.1	59.2	35.40
		Minimum	–	2.69	19.3	11.7	8.57	1.98	5.74
		Mean	–	16.9	32.6	49.9	19.8	15.6	13.7
	C. rostrata	Maximum	–	31.5	73.0	54.1	29.1	28.4	34.2
		Minimum	–	9.90	24.6	20.6	8.29	4.12	4.05
		Mean	–	18.6	37.0	32.5	16.0	10.6	15.1

Maximum, minimum and mean of CH4 emissionpan> (mmol m−2 d−1) for P. australis and C. rostrata in Lake Erssjön and Lake Följesjön, throughout the study period in 2014 (n = 7).

Variability between lakes, plant species and measurement days

The 24-h mean CH4 emissionpan>s per m2 differed between lakes with higher fluxes in Följesjön (Fig. 3). There were no differences in these CH4 emissions (per m2) between P. australis and C. rostrata within the same lake (Fig. 3). The highest 24-h mean fluxes occurred in the summer (June–August) and the lowest fluxes were recorded during September and October in both lakes (Table 2 and Fig. 3). There was a 5-fold difference between measurement days with highest and lowest flux for both plants species. The variability between measurement days were consistent for both P. australis and C. rostrata, i.e. low and high fluxes occurred during the same days for both species.

Figure 3.

Fluxes of CH4 (diel average with CI95%, n=7) from sites dominated by Phragmites australis and Carex rostrata in Erssjön and Följesjön during June to October 2014. Top graphs show fluxes calculated per m2 and bottom graphs fluxes calculated per g DW.

Fluxes of CH4 (diel average with CI95%, n=7) from sites dominpan>ated by Phragmites australis and Carex rostrata in Erssjön and Följesjön during June to October 2014. Top graphs show fluxes calculated per m2 and bottom graphs fluxes calculated per g DW.

When expressing 24-h mean fluxes per g DW (instead of per m2) the temporal pattern among measurement days remains, but there were differenpan>ces betweenpan> plots with different plants, with lower fluxes per g DW from Carex rostrata stands some of the days (Fig. 3).

Diel variation in CH4 emission

There was variability in CH4 emissions withinpan> days (Fig. 4). However, recurrenpan>t peaks inpan> CH4 emissions at the same time on the different measurement days (i.e. a consistent diel pattern) were not found for the two species (intra-class correlation coefficient based on both species: 0.055, P = 0.316; coefficients for P. australis and C. rostrata were 0.014 and 0.017, respectively). Variation during the days was higher for P. australis than for C. rostrata in Erssjön, where P. australis had both higher and lower emissions registered each measurement day compared to C. rostrata (Table 2).

Figure 4.

Diel variation in CH4 fluxes from sites dominated by Phragmites australis and Carex rostrata in Erssjön and Följesjön, for each measurement occasion during the growing season (June to October, n=7). Measured in Swedish summertime (GMT+2).

Diel variation in CH4 fluxes from sites dominpan>ated by Phragmites australis and Carex rostrata in Erssjön and Följesjön, for each measurement occasion during the growing season (June to October, n=7). Measured in Swedish summertime (GMT+2).

Mean daily CH4 flux and single environmental variables

There was a significant relationship between mean daily CH4 emissions per m2 and mean diel temperature (Fig. 5). There were no significant relationships betweenpan> diel means of winpan>d speed, light levels or net CO2 exchange and CH4 emissions (Fig. 5).

Figure 5.

Mean diel CH4 emissions in relation to air temperature (tair), wind, light and CO2 flux (i.e. net ecosystem CO2 exchange) for measurement plots with Phragmites australis and Carex rostrata in Lake Erssjön and Lake Följesjön, respectively. Lines show significant linear regressions. The CH4 flux (FCH4; mmol m−2 d−1) were similar among species (see text) and the dashed and thin solid lines represent the regressions for both species combined in Följesjön (FCH4 = 1.529·tair – 0.068; P = 0.009; adj. R2 = 0.46) and Ersjön (FCH4 = 0.811·tair – 2.487; P < 0.001; adj. R2 = 0.63), respectively. The thick solid line denotes the overall regression for all data (FCH4 = 1.136·tair – 0.984; P = 0.003; adj. R2 = 0.30). No significant relationships were found for wind, light or CO2 flux.

Mean diel CH4 emissions inpan> relation to air temperature (tair), winpan>d, light and CO2 flux (i.e. net ecosystem CO2 exchange) for measurement plots with Phragmites australis and Carex rostrata in Lake Erssjön and Lake Följesjön, respectively. Lines show significant linear regressions. The CH4 flux (FCH4; mmol m−2 d−1) were similar among species (see text) and the dashed and thin solid lines represent the regressions for both species combined in Följesjön (FCH4 = 1.529·tair – 0.068; P = 0.009; adj. R2 = 0.46) and Ersjön (FCH4 = 0.811·tair – 2.487; P < 0.001; adj. R2 = 0.63), respectively. The thick solid line denotes the overall regression for all data (FCH4 = 1.136·tair – 0.984; P = 0.003; adj. R2 = 0.30). No significant relationships were found for wind, light or CO2 flux.

Modelling CH4 flux

The results from the regressions of daily means on CH4 emissionpan> per m2 were conpan>firmed by the AIC-based GLZ model selectionpan> and averaginpan>g procedure usinpan>g N = 288 data poinpan>ts. The analysis founpan>d that models that conpan>tainpan>ed lakes and air temperature were better at explainpan>inpan>g methane flux (Table 3). These variables were included in the all best fitting candidate models (i.e. relative importance = 1). The model average parameter estimate indicated that methane flux increased with temperature and was higher in Följesjön than in Erssjön (Table 3). An increase in light and wind had a (significant) negative effect on CH4 emissions, while plant species, biomass dry weight, NEE of CO2, and time of day were much less important (Table 3).

Table 3

The effect of categorical and continuous variables on emergent aquatic macrophytes CH4 emissions per m2. Data from the full generalized linear model (GLZ; estimate) and from a model selection and averaging of nested candidate GLZ-models with standardized continuous variables (model average estimate, standard error (SE), z-value, 95 % confidence interval (CI) and relative importance for variables). Values marked with bold text are statistically significant.

Methane flux (mmol m−2 d−1)	Estimate	Standardized values
		MAE*	SE	z-value	CI interval		RI*
					Lower	Upper
Intercept	2.502e + 07	2.637	0.069	37.962	2.501	2.773
Lake Erssjön (compared with Lake Följesjön)	−9.995e − 01	−0.991	0.097	10.202	−1.181	−0.800	1.0
Air temperature (C°)	5.826e − 02	0.896	0.172	5.182	0.557	1.235	1.0
Light (µmol m−2 s−1)	−2.929e − 04	−0.242	0.111	2.178	−0.459	−0.024	0.9
Wind (m/s)	−8.890e − 02	−0.218	0.095	2.291	−0.404	−0.031	0.93
Date 1 (Measurement day)	1.894e − 05	−0.279	0.135	2.057	−0.545	−0.013	0.9
Date 2 (Measurement day)**	−6.740e − 15	−0.342	0.187	1.821	−0.710	0.026	0.9
P. australis (compared to C. rostrata)	1.488e − 01	0.172	0.090	1.913	−0.004	0.348	0.77
Air pressure (atm)	6.357e − 01	0.028	0.102	0.270	−0.172	0.227	0.27
Biomass (g DW)	−1.011e − 03	−0.145	0.115	1.259	−0.370	0.081	0.50
NEE (net ecosystem exchange of CO2)	9.841e − 05	0.092	0.085	1.077	−0.075	0.259	0.48
Time 1 (Time of the day)	−3.516e − 02	−0.013	0.089	0.148	−0.189	0.163	0.12
Time 2 (Time of the day)**	1.234e − 11	0.115	0.238	0.481	−0.353	0.582	0.12

MAE (Model Average Estimate) and RI (Relative Importance).

Quadratic term.

The effect of categorical and conpan>tinpan>uous variables onpan> emergenpan>t aquatic macrophytes CH4 emissions per m2. Data from the full generalized linear model (GLZ; estimate) and from a model selection and averaging of nested candidate GLZ-models with standardized continuous variables (model average estimate, standard error (SE), z-value, 95 % confidence interval (CI) and relative importance for variables). Values marked with bold text are statistically significant.

Discussion

No clear diel flux patterns

While our flux values correspond with previously recorded ranges for CH4 flux via P. australis and various Carex species (mean fluxes reported to 0–80 and 0–33 mg CH4 m−2 h−1; examples of ours and previously reported fluxes provided in Table 4), our results challenge common views on diel variability. We found that peaks in plant-mediated CH4 emissions occurred during all hours of the day but were not very strong, nor did they dominate the total daily flux (Fig. 4). Furthermore, the diel patterns for P. australis did not coincide with patterns for C. rostrata.

Table 4

Examples of studies addressing diel variability in CH4 flux from areas with Phragmites australis or Carex species.

Type of envrionment and location	Dominating plants	Time period	Mean flux range (mg CH4 m−2 d−1)		Diel flux pattern	No of diel cycles reported	Method used for flux measurements	Source
			Low	High
Lakeshore sites, central Finland	P. australis	July to October	0	32	24h mean = 0.58 to 0.91 times the mean for daytime flux. Most shallow sites showed less clear diel variability	many (unclear)	Automatic flux chamber; ca 5 measurements per diel cycle	Juutinen et al. (2004)
Constructed wetland, The Netherlands	P. australis	Unclear	20	80	Flux positively related with PAR. Up to 2-fold difference between day and night.	1 (unclear)	Chambers connceted to analyzer. 15-50 min enclosure.	Van der Nat et al. (1998)
Eutrophic lake shore, sourthern Finland	P. australis	May to September	1	18	Highest flux at noon in August and September. Irregular patterns other times.	7	manual sampling over 9 min every 6th h.	Käki et al. (2001)
Temperate fen, Germany	P. australis	9 days, August	3.5	11	incrasing mean fluxes from 06-14 (2-fold increase)	1-6 (unclear)	Transparent chambers; 40 min incubation; syringe sampling. Time period of 06-14 studied.	Günther et al. (2014)
Two fens in Belarus; focus on shallow water plots	P. australis	2 days, June, August	2	20	Fluxes in both transparent and dark chambers increased with PAR. >2-fold higher flux during daytime.	2	Chamber; discrete samples taken over 8-12 min	Minke et al. (2014)
March, Nebraska, USA	P. australis	65 days, July–September	8	25	Fluxes highest midmorning-noon. 2-fold higher flux during daytime.	4; 2-day periods	Eddy covariance	Kim et al. (1998)
Hemiboreal lake-wetland, southwest Sweden	P. australis	June to October	11	33	No consistent diel pattern	6 x 2	Chambers connected to analyzer. 5 min enclosure; fluxes measured every 2h.	This study
Hemiboreal lake shore, southwest Sweden	P. australis	June to October	3	58	No consistent diel pattern	7 x 2	Chambers connected to analyzer. 5 min enclosure; fluxes measured every 2h.	This study
Lakeshore sites, central Finland	Carex spp.	August	0	32	No strong diel pattern	12	Automatic flux chamber; ca 5 measurements per diel cycle	Juutinen et al. (2004)
Temperate fen, Germany	C. acutiformis	9 days, August	8	11	No clear diel patterns	1–6 (unclear)	Transparent chambers; 40 min incubation; syringe sampling; time period of 06-14 studied.	Günther et al. (2014)
Eutrophic marsh, China	C. lasiocarpa	August	20	33	Increasing flux from 03 to 09 (up to 1.5-fold increase)	3	Flux chambers sampled manually; 30 min enclosure time; 3 h enclosure frequency	Ding et al. (2004)
Hemiboreal lake-wetland, southwest Sweden	C. rostrata	June to October	3	16	No consistent diel pattern	6	Chambers connceted to analyzer. 5 min enclosure; fluxes measured every 2 h.	This study
Hemiboreal lake shore, southwest Sweden	C. rostrata	June to October	3	14	No consistent diel pattern	6	Chambers connceted to analyzer. 5 min enclosure; fluxes measured every 2 h.	This study

Examples of studies addressing diel variability in n class="Chemical">CH4 flux from areas with n class="Species">Phragmites australis or Carex species.

Previous studies of diel variability often found clear flux peaks and it has been suggested that predominating convective flux mechanisms actively regulated by the plants lead to a stronger diel cycle than when diffuse and more passive flux through the plant dominates (Whiting and Chanton 1996; Juutinen ; Duan ). The timing of the observed flux peaks has been shown to vary between species (Brix ; Whiting and Chanton 1996). However, it is sometimes unclear how stable the diel patterns were over time when patterns from only a single day are reported (e.g. Whiting and Chanton 1996; Van der Nat ; Ding ). The present study reports diel measurements during seven measurement days, and clearly shows that the diel patterns vary considerable among them.

Gas exchange by plants is thought to be controlled by stomatal opening allowing gas diffusionpan> inpan>to or out from the aerenpan>chyma, inpan> some species facilitated by pressure-drivenpan> conpan>vectionpan>. The conpan>vectionpan> is conpan>trolled by inpan>ternpan>al/externpan>al differences in relative humidity (RH) or temperature, or by wind (reviewed by Sorrell and Brix 2013 and Armstrong and Armstrong 2014). Aquatic plants do not need to save water and can keep stomata open during night (Käki ). Accordingly, diffusion-driven gas exchange is often found to lack clear diel patterns for emergent aquatic plants. The absence of strong and consistent diel patterns was therefore expected for C. rostrata, but was surprising for P. australis, which commonly is found to have gas exchange that is positively related to insolation (Table 4). Light has been suggested to influence convection-driven diel variability in fluxes, presumably by affecting RH and temperature gradients. The detailed AIC-based GLZ model selection and averaging showed that light and wind were of intermediate importance and had a weak negative correlation with CH4 flux (Table 3). The GLZ includes all time points and combines both diel and seasonal data so the wind and light correlations may not primarily be associated with diel variability. However, it is possible that the long days and short (if any) dark periods during summertime at northern latitudes may make the diel gas exchange patterns less predictable than at more southern latitudes having more pronounced day-night differences in light. For example, diel patterns related to the light cycle were observed from measurements in August and September only (when nights get darker) at a lakeshore site in Finland, while patterns were unclear during May to July (brighter nights) (Käki ). Altogether, our result indicated that CH4 fluxes from P. australis may not always show distinct diel patterns, and that further work addressing diel variability at different locations and under different conditions is needed.

What controls plant-mediated CH4 emissions?

Methane emissionpan>s from both species were onpan> average three times higher inpan> Följesjön than inpan> Erssjön (Table 1). This is inpan> linpan>e with the study by Natchimuthu that conpan>cludes that the amounpan>t of released CH4 from the open water areas in Följesjön exceeded open water fluxes from Erssjön. Possible explanations include that Följesjön was very shallow, had thick organic-rich sediments, and a higher standing crop of emergent vegetation leading to a high production of organic matter substrates for methanogenesis per unit area. This is a speculation based on the overall abundance of macrophytes and bulk organic matter, as measuring the in-situ production of the specific original plant material fuelling microbial communities including methanogens in sediments is difficult and beyond the scope of this study. However, variables associated with high primary production or high levels of organic matter are known to stimulate CH4 production and fluxes (e.g. Segers 1998; Bastviken ; Bridgham ), giving some support for this potential explanation of the higher overall fluxes from Följesjön.

The temporal variability of CH4 emissionpan>s withinpan> each system was related to temperature (Table 3 and Fig. 5). Although we used air temperature, the temperature of the sedimenpan>t was probably more important (Johanssonpan> ; Duan ), which provides an additionpan>al reasonpan> for the lake differences; Följesjön was the shallowest lake, hence likely to have a higher sediment temperature during the growing season. The sediment in a shallower lake with a smaller water volume is also likely to respond faster to changes in air temperatures. Several studies have demonstrated a relationship between CH4 emissions and sediment temperature, in accordance with the high temperature-sensitivity of methanogenesis (Segers 1998; Wang and Han 2005; Duc ; Yvon-Durocher ; Turetsky ). In contrast, oxidation of CH4 seems unaffected by temperature (most often limited by substrate supply; Nykänen ; Duc ).

It should be noted that temperature can have a direct influence on process rates, as discussed above, but also is correlated with seasonal fluctuations and production of organic substrates for methanogenesis (Laanbroek 2010; Carmichael ). Several studies reported maximum biomass during the growing season as the key explanatory factor for CH4 emissionpan>s (Hirota ; Juutinpan>enpan> ; Kankaala & Bergström 2004). However, inpan> the presenpan>t study and inpan> that by Kankaala , biomass could not explainpan> the temporal variability inpan> CH4 emissions per m2 (Table 3), suggesting that direct temperature effects or organic matter from other sources (e.g. the catchment) could also influence fluxes.

Wind speed and light were shown to weakly affect CH4 emissions from P. australis and C. rostrata negatively in both lakes (Table 3). In contrast, many previous studies showed that light had a positive effect on the CH4 emission from P. australis in relation to diel patterns (discussed above; Table 4). According to Duan light can affect CH4 emissions also on a larger scale (e.g. seasonal patterns) during the period when the emergent aquatic macrophytes transport gas with convective flow (light-dependent plant activity suggested to regulate gas exchange). In the study by Juutinen , light could explain 39–73 % of the variation in plant-mediated CH4 emissions during the middle of the growing season. However, light often co-varies with temperature, other weather variables, and primary production or net CO2 exchange, and it is often unclear if effects from light, temperature and other variables can be separated. Our AIC-based GLZ model selection and averaging procedure is one way to approach this challenge and it clearly showed that temperature was more strongly related to the flux than light and wind, with no clear link between CH4 flux and net CO2 exchange.

Did plant species identity influence emissions?

Plant species are expected to differ inpan> the amounpan>t of CH4 they can emit due to, e.g. the extent of their root system, the amount of biomass they have, and differences in flux modes (diffusive or convective). Plant-mediated CH4 emissions are usually reported per m2, a unit useful for scaling up estimates of emission. On the other hand, CH4 emissions per plant biomass unit may be more appropriate if focusing on emission mechanisms and plant architecture. In the present study we therefore reported both. We expected that the convective flux mode (P. australis) should cause higher emissions than the passive one (C. rostrata). However, sites with P. australis and C. rostrata always showed similar fluxes per m2 in the same systems on nearby sites (Fig. 3). Further, if comparing results from the lakes in Fig. 3, it is clear that the same flux rates could be sustained by widely different plant densities (maximum biomass values for P. australis were 25 and 60 g m−2 and for C. rostrata 204 and 139 g m−2 for Ersjön and Följesjön, respectively). The differences found in the fluxes per g DW plant biomass therefore largely reflected differences in standing crop biomass of the two species, and did not provide any clear information about fluxes per se or about flux regulation. Hence, most of the variability in plant-mediated flux per m2 appeared related to temperature and between-lake differences in environmental conditions as discussed above, while plant species/flux mode had, at most, a minor influence. We suggest additional studies in multiple systems to investigate under what conditions plant species communities affect fluxes, while trying to separate effects of plant species and other variables regulating CH4 production on the specific site.

Conclusions

In summary, and in contrast to many previous studies on n class="Chemical">CH4 emissions via aquatic macrophytes, we founpan>d the followinpan>g:

Diel variability in CH4 fluxes from P. australis and C. rostrata, representing plants with convective and diffusive flux modes, respectively, were irregular in magnitude and timing and unpredictable based on data from seven days distributed from June to October.

The 24-h mean fluxes per m2 on nearby sites were similar between species and highly temperature-dependent. Differenpan>ces betweenpan> lakes were conpan>sistenpan>t with factors inpan>fluenpan>cinpan>g system CH4 fluxes such as macrophyte standing biomass and water depth. No clear influence of other studied potential predictors such as light, wind, pressure, and NEE of CO2 were found.

All available data from this study indicate similar total CH4 flux per m2 from plots with P. australis and C. rostrata. Hence, fluxes were not controlled by the dominating plant species but more likely by the overall CH4 production in the systems.

Given these results, the presence and importance of diel variability in plant fluxes, as well as the importance of macrophyte species composition under various conditions needs to be re-evaluated and assessed systematically over time across a range of environments and species.

Sources of Funding

Grants from the Swedish Research Council and the European Research Council to DB (VR 2012-48, VR 2011-3575, ERC no. 725546), and from Stiftelsen Oscar och Lili Lamms Minne to LT (EX2014-0010) supported this study. The Skogaryd Research Catchment is part of the SITES infrastructure, which received funding from two Swedish research council: VR and Formas.

Contributions by the Authors

D.B. and P.M. conceived the study; all authors contributed to the design; L.T. collected the data; L.T. and L.W. analysed the data; all authors contributed to evaluation and interpretation of the results; L.T. drafted the text (as part of a MSc thesis) with all authors contributing substantially to the current version.

Conflict of Interest Statement

None declared.