Materials and Methods
SI 5. Statistical analyses
The effect of location (littoral or center) and time of day (day or night) on greenhouse gas fluxes was modeled with linear mixed-effects models (LMM) using the lmer function of the ‘lme4’ package (Bates et al. 2015). Here, location and time were included as fixed effects (tested with and without interaction term). To deal with non-independence, stemming from repeated measurements, we used random intercepts for months. Furthermore, we expect the effect of time on gas fluxes to differ across months, for example due to higher irradiation during daytime in spring and summer and due to seasonal variance in day- vs nighttime water temperature. Similarly, the effect of location may differ across months, for example due to seasonal differences temperature profiles (e.g. higher sediment temperatures in shallow, littoral sediment in summer, while similar sediment temperature of shallow and deeper sediment in winter). Hence, we included by-month random slopes for the effect of time and location. To meet assumptions of normality and homoscedasticity, diffusive CO2 water-atmosphere flux data were square-root-transformed after adding 1037 to get rid of negative values. Diffusive CH4 water-atmosphere flux data were log-transformed to meet model assumptions. We used type-III ANOVAs to test the significance of fixed factors, with degrees of freedom and p-values calculated using the Kenward-Roger approximation (‘lmerTest’ and ‘pbkrtest’
packages; Kenward and Roger 1997, Halekoh and Højsgaard 2014, Kuznetsova et al. 2017). We modeled the effect of station and time (as well as their interaction) on CH4 ebullition using a generalized linear mixed-effects model (GLMM) with a Gamma distribution and log-link function using the glmer function of the
‘lme4’ package (Bates et al. 2015). To enable the use of this distribution and log-link function, ebullition data were added with a value of 1. GLMMs included the same random effects structure as described earlier for LMMs. GLMM parameter estimates were fit by maximum likelihood using Laplace approximation (Bolker et al. 2009). The significance of fixed effects was assessed using Wald t-tests (Bolker et al. 2009). Data of months without ebullition [ebullition generally stops below a sediment temperature of 10 °C (Aben et al. 2017) were excluded from the models as the many zeroes introduced by including these months violated model assumptions for all tested error distributions and link functions.
We also used a GLMM to model the effect of station, time and depth (shallow and deep incubations) for methane oxidation data. We used the random effects structure as described earlier with the addition of a by-month random slope for
the effect of depth. Methane oxidation data was added with 4882 to get rid of negative values, and subsequently rounded to whole numbers to enable the use of a Poisson distribution with square-root link function, as this error distribution proved best for meeting model assumptions. The significance of the fixed effects was assessed using Wald Z-tests (Bolker et al. 2009). For all models the normality assumption was violated by a single or few data points (1, 4, 2, and 2 data points for diffusive CO2, diffusive CH4, ebullition, and methane oxidation data, respectively). Hence, models were run with and without these data points. When omitted, these data points affected statistical significance, hence we reported the most conservative (least significant) result.
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CH4diffusion (g CO2-eq m-2d-1) central-night central-day
CH4ebullition (g CO2-eq m-2d-1) central
littoral
Fig. S1. Fluxes of CO2 diffusion, CH4 diffusion and CH4 ebullition (g CO2 eq m-2 d-1 ) ± SD (left to right: June 2013 till June 2014). Significant spatial and temporal differences in flux intensities are presented. At some dates, error bars (SD) are too small to show in the figure.
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
Measured ER (mg O2L-1h-1)
Modeled ER (mg O2L-1h-1) 1:1 line
Fig. S2. Sum of the modeled respiration of the major organism groups versus the ecosystem respiration (ER) calculated based on the dark period of the diel oxygen curves.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
July September November January March May
Contribution to measured ecosystem respiration (%)
Fish Picoplankton Sediment Methanotrophs Phytplankton Rest term Fig. S3. Modeled respiration of the major organism groups as a percentage of the ecosystem respiration (ER) calculated based on the dark period of the diel oxygen curves.
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Tables
Table S1. Empirical relationships between respiration of different organism groups (in mg O2 L-1 h-1) and water temperature (Tw; °C) as found in this study or derived from literature.
Ecosystem
component Relations describing component-specific respiration rates R2 p n F Phytoplankton Ra (mg O2 µg chl-α-1 L-1 h-1) = 7.3 *10-5 * Tw – 2.4 * 10-4 0.94 0.001 6 60.61 Heterotrophic
picoplankton Rp (mg O2 L-1 h-1) = 9 *10-3 * Tw + 1.97 * 10-2 0.82 0.01 6 18.31 Sediment
organisms Rs (mg O2 L-1 h-1) = 1.2 *10-3 * Tw + 1.43 * 10-2 0.85 0.03 6 17.20
Fish Rf = exp ( * ln Q10 + ln 0.03) Q10 = 1.83 (Clarke and Johnston 1999)
Table S2. Average monthly quantified CO2 and CH4 fluxes (± SD) and the ratio between the CO2 and CH4 flux (including diffusion and ebullition) in CO2 equivalents. A factor of 34 was used to convert CH4 to CO2 eq (IPCC 2013).
Month Diffusive CO2 flux
(g CO2 eq m-2 d-1 ) Diffusive CH4 flux
(g CO2 eq m-2 d-1 ) Ebullitive CH4 flux
(g CO2 eq m-2 d-1 ) Ratio CO2/CH4 in CO2 eq
July 5.0 ± 1.3 3.5 ± 4.0 17.2 ± 7.1 0.2
August 7.1 ± 3.3 3.1 ± 4.9 13.6 ± 5.9 0.4
September 1.8 ± 0.8 1.3 ± 2.7 9.4 ± 20.1 0.2
October 1.5 ± 0.5 0.1 ± 0.0 0 15.4
November 6.0 ± 2.4 0.3 ± 0.2 0 21.5
December 4.6 ± 0.8 0.3 ± 0.1 0 13.8
January 4.9 ± 1.5 0.4 ± 0.2 0 13.9
February 1.3 ± 0.5 0.3 ± 0.5 0.04 ± 0.05 4.3
March 0.5 ± 0.5 0.5 ± 0.2 0.03 ± 0.16 1.0
April 2.3 ± 0.7 0.4 ± 0.3 2.5 ± 1.1 0.8
May 2.7 ± 0.9 0.8 ± 11.7 2.8 ± 1.6 0.7
Table S3. Literature overview of observed CH4 fluxes of waters in temperate regions, based on a search of different combinations of keywords: “ebullition AND temperate”, “methane emissions AND temperate”,
“methane AND lake” (search engine: Web of Science).
Type of system Number of
lakes sampled Average methane
flux (mg CH4 m-2 d-1) Reference
Hydropower reservoir 1 342 DelSontro et al. (2016) D, E
Shallow, artificially aerated lake 1 309 Martinez and Anderson (2013) D, E
Small productive lake 1 192 Casper et al. (2000) D, E
Small urban pond 1 168 This study D, E
Small hydroelectric reservoir 1 145 Sobek et al. (2012) D, E
Lakes inside peatlands 5 120 Schrier-Uijl et al. (2011) D
Lakes surrounded by forest 2 70 Striegl and Michmerhuizen
(1998) D Small lakes surrounded by forest 3 19 Bastviken et al. (2008) D, E
Small headwater lakes 121 6 Whitfield et al. (2011) D
D Average methane flux includes water-atmosphere diffusion
E Average methane flux includes water-atmosphere ebullition
Acknowledgments
We thank K. F. Ettwig for her advice and discussions. We thank Paul van de Ven, Germa Verheggen, Stefan Weideveld, Haijun Wang, Maarten Schimmel, Carlos Henrique Duque Estrada and Felipe Rust for their assistance during the experiment and for helping with analyses. We also thank Jan Schaart (HSV Heumen) for providing an estimation of fish biomass and species in the pond and for logistic support on site. Additionally, we thank Roel van Veen from water authority Rivierenland for providing us groundwater seepage estimates and Simone Cardoso for advice on the statistical analyses. This study was conducted under the auspices of the CAPES (Brazil)/NUFFIC (The Netherlands), project 004/2008. N.B. was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) process number 3934‐13‐6 and CAPES (Brazil)/NUFFIC (The Netherlands) project 045/2012; S.K. was supported by NWO‐VENI (grant 86312012); and V.H. was partially supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil).