Boreal upland forests are generally considered methane (CH4) sinks due
to the predominance of CH4 oxidizing bacteria over the methanogenic
archaea. However, boreal upland forests can temporarily act as CH4
sources during wet seasons or years. From a landscape perspective and in
annual terms, this source can be significant as weather conditions may cause
flooding, which can last a considerable proportion of the active season and
because often, the forest coverage within a typical boreal catchment is much
higher than that of wetlands. Processes and conditions which change mineral
soils from acting as a weak sink to a strong source are not well understood.
We measured soil CH4 fluxes from 20 different points from regularly
irrigated and control plots during two growing seasons. We also estimated
potential CH4 production and oxidation rates in different soil layers
and performed a laboratory experiment, where soil microcosms were subjected
to different moisture levels and glucose addition simulating the fresh
labile carbon (C) source from root exudates. The aim was to find the key
controlling factors and conditions for boreal upland soil CH4
production. Probably due to long dry periods in both summers, we did not
find occasions of CH4 production following the excess irrigation, with
one exception in July 2019 with emission of 18 200 µg CH4 m-2 h-1. Otherwise, the soil was always a CH4 sink (median
CH4 uptake rate of 260–290 and 150–170 µg CH4 m-2 h-1, in control and irrigated plots, respectively). The median soil
CH4 uptake rates at the irrigated plot were 88 % and 50 % lower
than at the control plot in 2018 and 2019, respectively. Potential CH4
production rates were highest in the organic layer (0.2–0.6 nmol CH4 g-1 d-1), but some production was also observed in the leaching
layer, whereas in other soil layers, the rates were negligible. Potential
CH4 oxidation rates varied mainly within 10–40 nmol CH4 g-1 d-1, except in deep soil and the organic layer in 2019, where potential
oxidation rates were almost zero. The laboratory experiment revealed that
high soil moisture alone does not turn upland forest soil into a CH4
source. However, a simple C source, e.g., substrates coming from root
exudates with high moisture, switched the soil into a CH4 source. Our
unique study provides new insights into the processes and controlling
factors on CH4 production and oxidation, and the resulting net efflux that
should be incorporated in process models describing global CH4 cycling.
Introduction
Methane (CH4) is a greenhouse gas with a significant impact on the
global climate. CH4 increases the global temperatures by absorbing
infrared radiation into its carbon–hydrogen bonds, resulting in a higher
amount of heat energy within the atmosphere (e.g., Chai et al., 2016;
Dlugokencky et al., 2011; Whalen, 2005). In soil, CH4 is predominantly
formed in biological anaerobic decomposition processes (Le Mer and Roger,
2001; Wuebbles and Hayhoe, 2002). Archaea called methanogens are responsible
for the biological production of CH4 in anoxic conditions, whereas
methanotrophs conduct aerobic CH4 oxidation (Hanson and Hanson, 1996;
Orata et al., 2018; Thauer et al., 2008). The dynamics behind soil CH4
sources and sinks depend on the ratio between CH4 production and
oxidation and its transport from the soil to the atmosphere, all of which
are affected by an extensive network of numerous biotic and abiotic
variables. The interannual fluctuations in global and regional CH4
emissions are influenced by so-far largely unknown variables, the
investigation of which is thus essential for understanding the changing
dynamics in the current and future CH4 budgets (Bousquet et al., 2006;
Crill and Thornton, 2017; Dlugokencky et al., 2011; Fischer et al., 2008;
Kirschke et al., 2013). The boreal zone in the Northern Hemisphere regularly
presents large CH4 emissions due to the abundance of anoxic wetlands,
but part is counterbalanced by high oxidation rates in boreal upland
forests. The CH4 emission estimates from the boreal zone lie between 25
and 100 Tg yr-1, which combined with subarctic tundra environments account
for approximately 3 %–10 % of the global CH4 emissions (Olefeldt et
al., 2013).
Boreal upland forests are broadly considered CH4 sinks due to strongly
oxic soils (Gulledge and Schimel, 2000; Megonigal and Guenther, 2008; Oertel
et al., 2016; Whalen et al., 1991; Yavitt et al., 1990, 1995). In upland
soils, high-affinity methanotrophs can consume CH4 at atmospheric
concentrations (Knief et al., 2003; Kolb, 2009). Despite the abundance of
oxygen in the boreal upland forest soil, there are some indications of
smaller-scale CH4-producing areas, such as wet depressions
(Christiansen et al., 2012; Megonigal and Guenther, 2008; Vainio et al.,
2021). In addition, some studies have found that upland forest soils may
become CH4 sources of varying significance after long periods of heavy
precipitation (Lohila et al., 2016; Savage and Moore, 1997). Methanogenic
population can stay constant in forest and other dry aerated soils and
becomes active under wet and anoxic conditions (Angel et al., 2012; Peter
Mayer and Conrad, 1990). With upland forests occupying a significant portion
of the boreal zone, a more thorough examination of the complex dynamics
behind the sink–source transitions of the forests is needed, especially in
the context of climate change which may alter global and regional
precipitation and temperature patterns (e.g., Beier et al., 2012; Lehtonen et
al., 2014; Lohila et al., 2016). Lohila et al. (2016) also suggested that
wet conditions can potentially affect the CH4 exchange patterns
differently in forests and wetlands by increasing and decreasing the
CH4 emissions in those ecosystems, respectively, amplifying the vital
role of upland forests in the regional CH4 balance in wet years.
Furthermore, as precipitation may increase during summer and autumn in
northern latitudes (Jylhä et al., 2009), this flooding-induced source of
CH4 may be activated more frequently in the future. This source is
accounted for in the models of global CH4 emissions, but there are
recent observation-based indications that its magnitude may be severely
underestimated. The global CH4 uptake by mineral soils is only 5 %
of global CH4 sink (625 Tg CH4 yr-1; Saunois et al., 2020) and
during wet years the CH4 sink of mineral soils is significantly
suppressed. It has already been suggested that the emissions from wet
mineral soils can be the primary driver for the interannual variability in
global CH4 emissions (Spahni et al., 2011).
Soil temperature and moisture manipulations in CH4 flux studies from
upland soils have been very few, but some existing manipulation studies
exist that focus on carbon dioxide (CO2) fluxes (Allison and Treseder,
2008; Billings et al., 2000; Niinistö et al., 2004; Wu et al., 2011).
Recommendations have been made to focus on precipitation manipulations
carried out either by wetting or drying and establishing those experiments
in mostly underrepresented forest ecosystems (Wu et al., 2011).
Methanotrophs are known to be more sensitive to soil drying than methanogens
(Ebrahimi and Or, 2018; Megonigal and Guenther, 2008). Since the processes
and conditions that change mineral soils from CH4 sink to a source are
not sufficiently well understood, direct laboratory measurements of CH4
formation in different soil layers under controlled temperature and moisture
conditions are needed to explain the processes in mineral soil in greater
detail.
In this study, changes in forest floor CH4 fluxes were assessed with an
irrigation experiment during the growing period in a boreal upland forest in
Kenttärova in northern Finland over 2 years. Kenttärova was chosen
as the study site due to significant soil CH4 emissions detected after
a long period of abundant precipitation in 2011 by Lohila et al. (2016). In
addition, CH4 production and oxidation potentials were determined in
different soil layers at flux measurement points. Finally, a laboratory
microcosm experiment was used to investigate the conditions (temperature,
moisture) needed to initialize CH4 production from the upland soil. The
aims of this study were: (1) to find if the irrigation has any impact on the
soil CH4 flux and oxidation and production potentials; (2) to find which
soil layers are most significant for CH4 production and oxidation; and
(3) to find the optimal conditions and key controlling factors for upland
soil CH4 production and oxidation. We hypothesized that: (1) wet
conditions prevailing for one or two summers could be seen in the response
of microbial populations so that at the irrigated plot, the potential
CH4 oxidation would be smaller and at least short production episodes
could be detected in the latter part of the summer either after both summers
or at least after the second wet summer; (2) highest CH4 oxidation
potential are found in the surface soils while the maximum production
potentials are found in the deeper layers; and (3) both wet conditions and fresh
organic carbon are needed to create conditions suitable for CH4
production in podzolic forest soil.
Materials and methodsStudy site
The study was carried out at the Kenttärova forest (67∘59.237′ N, 24∘14.579′ E) in the Kittilä municipality in Finland
at the transition zone of the northern-boreal and subarctic zones (Fig. 1).
The site is located on a hilltop plateau with an approximate elevation of
347 m above sea level and 60 m above the surrounding plains (Aurela et al.,
2015). The study site has climatic and vegetational characteristics typical
for a northern-boreal environment. The long-term (1981–2010) annual
temperature and precipitation within the area are -1.0∘C and
521 mm, respectively, with long-term averages in January and July being -14 and 14 ∘C (Pirinen et al., 2012). The maximum snow
depth (average peak: 73 cm) is typically observed in late March; the median
end date of snowmelt is 14 May and snow cover start date 24 October,
respectively (Lohila et al., 2015). The soil type is podzol with glacial
till as soil parent material (Aurela et al., 2015). Typical of the region
and soil type, the site represents Hylocomium-Myrtillus type (HMT; Cajander, 1926;
Ylläsjärvi and Kuuluvainen, 2009), Picea abies being the dominant tree species
mixed with a variety of some deciduous trees such as Betula pubescens, Populus tremula, and Salix caprea. The forest
floor vegetation at Kenttärova consists primarily of forest shrubs, such
as Vaccinium myrtillus, Empetrum nigrum, and Vaccinium vitis-idaea, and a continuous and vigorous feather moss cover of Pleurozium schreberi,
Hylocomium splendens, and Dicranum polysetum with sporadic occurrences of lichens (Aurela et al., 2015). The
dominant height of the uneven-aged (1–250 years) tree stand reached
approximately 15 m while the heights of individual spruce trees varied
greatly. Some of the birches at Kenttärova were logged for firewood in
the 1960s, but since then the forest has grown without human disturbances
(Aurela et al., 2015).
Experimental setup and location of the study site. Aerial image by
Bastian Steinhoff-Knopp (Leibniz University Hannover, September 2018).
Experimental setup
For examining causal relationships between CH4 flux and soil moisture
and temperature, the field study included two plots: irrigation (Si)
and control (Sc) without irrigation treatment (Fig. 1). The surface
areas of Sc and Si were approximately 280 and 120 m2,
respectively. Both plots included 10 measurement points. Measurement points
were assigned somewhat randomly in both plots, with the aim to represent as
similar vegetational, topographical, and sun aspect characteristics as
possible. Both the Sc and Si and measurement points were connected
with wooden boardwalks to minimize soil and vegetation disturbance from
trampling.
Soil moisture was manipulated by irrigating part of the experimental area
with two water sprinklers. The irrigation periods were 28 May to 7 September 2018 and 6 June to 29 August 2019. The sprinklers were set in
the plot so that the irrigated water would evenly reach each measurement
point. The irrigated area in practice reached approximately 118 m2 with
3–5.5 m width and 10–21 m length, depending on the wind conditions. Tap
water was transported to the experimental site with a 1000 L IBC water tank.
For ensuring a relatively even distribution of irrigated water in the plot,
the spatial distribution of irrigation was checked with rain gauges (unit:
mm) and plastic buckets. The amount of water in each bucket was later
proportioned to the rain gauges (in mm) based on their dimensions. The
precipitated water was measured after each irrigation from the end of May to mid-June 2018, after which the precipitated water was measured only
when the weather was notably windy and/or natural rainfall occurred during
the irrigation. It was estimated that 1000 L irrigation resulted on average
to 11 mm and 2000 L to 21 mm of precipitation. The amount of rainfall added
with irrigation was 11 mm on 2 d per week during 28 May to 1 June 2018,
after which the amount was increased to 11 mm on 3 d per week during
7–18 June 2018 and eventually to 21 mm on 5 d per week during 20 June to 7 September 2018. In 2019, the plot was irrigated with 11 mm three
times per week throughout the summer.
CH4 flux measurements and calculation
Chamber measurements started on 30 May 2017 on eight measurement points, of
which four were located on Si and Sc, respectively. The flux
measurements in 2017 were made to check possible differences between the
experimental plots before starting the irrigation experiment in 2018. Six
additional points were added to both Si and Sc, and the
measurements from these points started on 29 May 2018. The measurements were
made mainly between June and September every 2 weeks in 2017 and weekly in
2018 and 2019. The measurements ended on 19 September 2019.
CH4 fluxes were measured with 5 min closure time on the forest floor by
the closed-chamber system with an opaque rectangular chamber (60 × 60 × 20 cm, length × width × height). The chamber included a fan to mix the air
inside the chamber and a vent tube to prevent pressure differences between
the chamber headspace and the atmosphere. Also, chamber headspace
temperature was recorded with HOBO Pendant Temperature Data Logger (Onset
Computer Corporation, MA, USA). The bottom of the chamber edges had
a foam layer to prevent leakage between the collar and the chamber. All the
measurement points had metal collars (58 × 58 × 30 cm, length × width × height) installed about 2 cm deep into the soil. CH4 and water vapor
(H2O) mixing ratios were measured with G2301
and G1301-m (both from Picarro Inc., CA, USA) before and after 28 June 2018,
respectively. The gas analyzer was located inside a cabin about 20 m away
from the measurement point. The gas sample from inside the chamber was
transported to the analyzer by 20 m long tubing (inner diameter 3.1 mm,
Bevaline IV) with a 1 L min-1 flow rate where the mixing ratio was
sampled every 3–4 s. The sampled gas was not returned to the chamber,
which causes underpressure inside the chamber and underestimating the flux
estimation. Because the chambers had a vent tube, we corrected the leakage
with an assumption that the underpressure consisted of ambient air.
CH4 fluxes were calculated as:
F=dC(t)dtt=0MPVRTA,
where dC(t)dtt=0 is the concentration change
over time from an exponential model (e.g., Korkiakoski et al., 2017) at the
beginning of the closure, M is the molecular mass of CH4 or N2O
(16.04 and 44.01 g mol-1, respectively), P is air pressure, R is the
universal gas constant (8.314 J mol-1 K-1), T is the mean chamber
headspace temperature during the closure, and V is the air volume of the
chamber and the collar, and A is the base area of the chamber or collar. The
snow depth and the height of mosses and other vegetation in the chamber
headspace volume were taken into account, ignoring the pore space in the
soil and snow. The height of the vegetation was measured once a summer. The
vegetation height was assumed to remain constant for that year.
When calculating the CH4 balances, measured CH4 fluxes were
assumed to be daily mean fluxes. The gaps in the data were filled by linear
interpolation. To avoid a biased 2019 balance estimate for point I1, the
CH4 emission peak observed on 27 June 2019 was ignored when calculating
the balance.
The micrometeorological sign convention is used throughout the paper: a
positive flux indicates a flux from the ecosystem to the atmosphere (net
emission), and a negative flux indicates a flux from the atmosphere into the
ecosystem (net uptake).
CH4 production and oxidation potential measurements
Samples for the potential CH4 production and oxidation were taken on 23 August 2018 and 26 August 2019. Six composite samples were collected from
both Si and Sc next to the chamber collars. Composite soil samples
were combined from three to five core samples taken by soil auger separating four soil
horizons: the organic layer without vegetation (O) and the three mineral
soil layers below (zone of eluviation, i.e., leaching layer, E; zone of
illuviation, i.e., enrichment layer, I; and C-horizon representing the bottom
layer, C). Samples were kept at 4 ∘C during the shipment into the
lab and before analyses. The mean depths of the soil layers were 5.7,
10.5, 18.9, and 32.3 cm, while the mean thicknesses were 5.7, 4.8, 8.4, and 13.4 cm for the O, E, I, and C layers, respectively. The layer
depths and thicknesses were determined from six spots inside the experimental
area.
Soil moisture and organic matter contents of the samples were determined
with a TGA analyzer (LECO TGA-701, Leco Corp., MI, USA) with the standard
method (ISO11465), which measures weight loss as a function of temperature
in a controlled environment. Soil pH was determined from methane oxidation
bottles after measurement by increasing the ratio of 1 : 3 of deionized
H2O and measuring them after 24 h. Average soil pH, soil moisture,
and organic matter contents for the 2018 and 2019 samples are presented in
Table S1 in the Supplement. Total nutrients and C and N contents were determined from soil
samples taken in 2018 with standard methods (ISO11466, 10694, and 13878).
Samples for the total nutrients were digested by the closed wet HNO3-HCl
digestion method in a microwave (CEM MDS 2000), and the extract was analyzed
by iCAP 6500 DUO ICP-emission spectrometer (Thermo Fisher Scientific, MA, USA). Total C
and N were measured from sieved and air-dried samples on a CN analyzer
(Leco-TruMac, Leco Corp., MI, USA). Total nutrient, C, and N contents
for the year 2018 samples are shown in Table S2 in the Supplement.
Fresh sieved soil (with 2 mm mesh size) was placed into 120 mL sterile
incubation bottles with a standardized volume-based measuring scoop (20 mL), and
10 ppm of CH4 were added as a substrate into the bottles for
determining potential CH4 oxidation rates. Oxidation was measured by
gas chromatograph (GC) for 24 h. Two volumes of deionized H2O were
added into production potential bottles and incubated two times with pure
N2 gas to remove oxygen and create anoxic conditions. Production
bottles were measured by GC first twice, and then once, a week for 42 d to
detect productions. Potential rates were calculated from the linear part of
the curve showing the decrease or increase in CH4 concentrations in
time. The final potential rates are presented as nmol CH4 g-1 (dry
mass of soil) d-1.
Microcosm experiment
A microcosm experiment was designed to determine the conditions (temperature
and moisture) that are needed to initialize the CH4 production from the
soil. For the experiment, soil profile samples were taken from the pit next
to the Si. Artificial soil profiles were constructed into the plastic
jars (volume of 1.6 L), including the vegetation and organic layer and two
mineral soils layers (leaching and enrichment layers). Half of the jar
volume was left empty for headspace measurements. Jars were placed into two
different growth chambers (Binder KBW, Tuttlingen, Germany) with two different
temperatures at 15 and 25 ∘C. Both temperature
conditions had three replicate jars, including controls without moisture
increase (C) and two different levels of moisture increase, lower (M1) and
higher (M2) moisture. The experiment also included separate triplicate jars
with glucose added into controls (Cglu) and moisture increase (M1glu, M2glu)
treatments. Glucose was added at the beginning of the measurements to
simulate the effect of fresh, simple C source for microbes such as exist
in root exudates. We added 14 mL of 1 M glucose solution into each jar so
that they had two times more C that is approximated to be bound into
microbial biomass in forest soils to see the possible effect. Two moisture
conditions for the jars were adjusted to be different enough to detect
changes between the treatments. Average final moisture conditions were
adjusted so that in the jars, the lower moisture content (M1) was about 50 % and the higher content (M2) 80 % and the control jars (C)
represented the average moisture content in the soil, which was about 30 %–35 % (Table S1).
Light conditions in the growth chambers were adjusted to mimic the natural
light conditions at the end of August in northern Finland (about 15 h
light and 9 h dark). Every week, the jars were switched from one growth
chamber to another to avoid the differences due to features in the chamber
itself. Moisture conditions were kept constant by weighing the jars twice a
week and adding the water to minimize the effect of evaporation. CH4
fluxes were measured from the headspace of the jars once a week with LI-7810
(LI-COR Biosciences, NE, USA). The fluxes for the 5-week measurement
period were calculated from the exponential model the same way as described in
Sect. 2.3.
Soil temperature and moisture measurements
Multiple soil temperature (ST) and moisture (SM) sensors were used to record
said variables next to the CH4 flux measurement points. ST was measured
with 10 HOBO Pendant data loggers (Onset Computer Corporation, MA, USA) and
SM with 9 EC5 Soil Moisture Smart Sensor (Onset Computer Corporation, MA,
USA) with HOBO U30 USB Weather Station Data Logger (Onset Computer
Corporation, MA, USA). In addition, 7 Soil Scout online sensors (Soil Scout
Ltd, Helsinki, Finland) were used to measure both ST and SM. The time
intervals for ST logging were 20 and 30 min for Soil Scouts and HOBO
sensors, respectively. All the sensors were installed during 23 May to 6 June 2018 5 cm below the soil surface in the mineral soil layer next to the
collar and covered carefully with soil. The measurements continued until the
experiment ended, except the SM measurements made with EC5 sensors, which
broke down at the beginning of June 2019. The locations of the installed
sensors are listed in Table S3 in the Supplement.
SM was also measured from two different locations about 10 m distance from
the Sc and Si. In both locations, SM was measured at 5 and 20 cm
depths with ThetaProbe soil moisture sensor (type ML2, Delta-T Devices Ltd,
Cambridge, UK). In addition, ST was measured next to one of the soil
moisture sensors at 5 cm depth (PT100, PT4T, Nokeval Oy, Nokia, Finland).
Statistical methods
Fluxes between the different moisture levels and glucose addition in the
microcosm experiment were compared by using the one-way analysis of variance
(ANOVA) by using aov command in R programming language (R Core Team, 2021,
v4.0.5). The same method was used for comparing the CH4 production and
oxidation potentials between the plots and years. In the microcosm
experiment, the glucose addition was compared only with the sample without
added glucose on the same moisture level and temperature. The effect of
three different moisture levels was compared separately for added glucose
and without added glucose groups by using Tukey's honestly significant
difference (HSD) test by “multcomp” package in R (v1.4-14; Hothorn et al., 2008).
Linear mixed-effect model with Tukey's HSD post hoc test was used for
testing the statistical significance of differences in CH4 fluxes
between the Si and Sc. The linear mixed-effect model was carried
out with the R programming language using “lme4” package (Bates et al.,
2015). The chamber points were treated as a random effect. The normality of
the model residuals was visually checked using the quantile–quantile plot
(Q–Q plot) method.
The linear mixed-effect model was also used for finding the most significant
variables affecting CH4 fluxes (FCH4). The variables used in the
modeling were: 5 cm soil temperature (ST) and moisture (SM), CH4
oxidation potential (OPCH4,x, where x is one of the O, E, I soil layers
or the mean of all layers), CH4 production potential (PPCH4,x,
where x is one of the O, E, I soil layers or the mean), and carbon and nitrogen
content (CCx or NCx, where x is one of the O, E, I soil layers or the
mean). The model runs were divided into three parts: using mean values of all
soil layers, using only values of a specific soil layer, and combining values of
multiple different soil layers. Even though SM and temperature were only
measured at 5 cm depth, they were included in all the model runs.
Measurement points were always treated as a random effect (u). The best
model was selected by using stepwise selection. We started with a full model
and reduced the number of variables one by one using the Akaike information
criterion (AIC), which was conducted using the drop1
function in R. The initial model in all but the combination model run was:
FCH4=β0+β1ST+β2SM+β3PPCH4,x+β4OPCH4,x+β5CCx+β6NCx+β7(u+e),
where e is the model error, β0 is the model's intercept, and
parameters from β1 to β7 are the regression
coefficients of the explaining variables. We used a 95 % confidence
interval (p<0.05) to determine whether the results were
statistically significant.
Pearson correlation matrix including potential CH4 production and
oxidation rates and soil data (SM, organic matter, pH, nutrient elements)
were created using commands rcor and corrplot in R. Significance level for
correlation coefficients between variables was p=0.01. In addition,
simple linear regressions at 95 % confidence level with Pearson's
correlation coefficient and smoothed marginal histograms were used for
primary correlation analyses between CH4 flux and SM and CH4 flux
and ST using “ggpubr” (v0.4.0; Kassambara, 2020) and “cowplot” (v1.1.1;
Wilke, 2020) packages in R.
Meteorological conditions
The mean air temperatures in the May–September period were 8.0, 11.0, and
8.9 ∘C for 2017–2019. Compared with the long-term (1981–2010;
Pirinen et al., 2012) mean temperature of the same period (9.3 ∘C), 2017 was cooler and 2018 warmer than the average, respectively. In 2019,
the monthly temperatures during the measurement period were close to
long-term averages (Table S4 in the Supplement). The year 2017 was the coolest year of the measurement
period, primarily attributed to a much cooler May and a slightly cooler August
than other years (Table S4). On the other hand, 2018 was the warmest year,
primarily due to a much warmer May and July. July 2018 was exceptionally warm
(18.8 ∘C) compared with other years (2017: 13.0 ∘C;
2019: 12.6 ∘C) and long-term mean (13.9 ∘C).
The precipitation sums in the May to September period were higher than the
long-term average (296 mm) in 2017 (335 mm) and 2019 (357 mm), but about the
same in 2018 (293 mm). However, there were notable differences when
inspecting monthly precipitation sums. In 2017, May, June, and September were
drier than in 2018 and 2019 (Table S4). On the other hand, in July 2017, the
amount of precipitation (129 mm) was about 100 mm higher than in 2018 (28 mm) and 2019 (33 mm). Therefore, 2017 was markedly wetter compared with the
long-term average in July (75 mm). On the other hand, 2018 and 2019 were
markedly drier than on average. In 2019, excluding July, the monthly
precipitation sums were very similar and higher than the long-term mean. The
snow cover melted on 9 June 2017, 21 May 2018, and 26 May 2019. In 2017 and
2019, the first measurement day was made when snow was still on the ground
(Fig. 2).
The meteorological data reported in this section was observed by an official
weather station (Kittilä Kenttärova; ID: 101987) maintained by the
Finnish Meteorological Institute and it was located about 80 m northeast
from the experimental site.
Daily mean air temperature (a), daily mean 5 cm soil temperature (b), daily precipitation sum (c), and daily snow depth (d) measured at
Kenttärova weather station in May to September 2017–2019.
ResultsImpact of irrigation on soil moisture and temperature
The growing seasons of the study years (2018 and 2019) were generally dry,
based on the soil moisture data collected in long-term pits near the
experimental area (Fig. 3). While in 2019, the whole growing season was dry,
in 2018, the driest month was July. On the other hand, August was relatively
wet in terms of precipitation (Table S4), but after a severe drought, the
high precipitation was not enough to increase the soil moisture to the same
range observed in 2017. There were large differences in SM profiles (located
outside the experimental area) between the years (Fig. 3). In 2017, 5 cm
soil moisture (SM5cm) mainly remained between 25 vol % and 35 vol %. Also,
SM5cm in 2019 was relatively stable, varying within 15 vol %–20 vol %, but
it was markedly lower than in the other years, except in July 2018.
SM5cm in 2018 had much temporal variation. In May 2018, SM5cm rose
to 50 vol % but fell quickly to 25 vol % after the snow had melted. In July
2018, the SM5cm fell quickly below 15 vol % and kept decreasing down to
12 vol % until the beginning of May 2019, after which it started recovering
up to 25 vol % until the measurement period ended in September 2019. In terms
of absolute values, 20 cm soil moisture (SM20cm) did not differ between
years compared with SM5cm. In June, August, and September, the SM20cm
did not usually differ more than 3 vol % between the years. In May, the
rapid increases and decreases in SM20cm associated with snowmelt
occurred at different strengths and times. In July, SM20cm in 2017 was
about 5 vol % higher than in the other years, but the first half of August 2019 was drier than the other years.
The daily mean (a) 5 cm and (b) 20 cm soil moisture time series
measured from two different locations outside the experimental area from May
to October in 2017 (solid gray line), 2018 (solid black line), and 2019 (dashed black line).
At the experimental area, SM5cm was on average 6.5 vol % lower at the
Sc than at the Si in June to September 2018. SM5cm in 2018
varied typically within 14 vol %–23 vol % and 6 vol %–14 vol % at the Si and
Sc, respectively. However, one SM sensor measured about 10 vol % higher
values than the other sensors at the Sc (Fig. 4). Also, at the
beginning of August, SM5cm increased at one of the measurement points
by 5 vol %. SM5cm remained on that higher level until the end of the
measurement period in 2018. In 2019, SM5cm at the Si remained
within 15 vol %–18 vol %, except in August and September.
Hourly mean soil moisture time series measured at control (gray)
and irrigation (black) plots in August 2018. Vertical blue lines show the
times when the irrigation plot was irrigated.
In 2018, irrigation was performed on weekdays, and during irrigation
SM5cm rose by 10 vol %–15 vol % (Fig. 4). However, the SM5cm decreased
fast and usually returned to the pre-irrigation level before the next
irrigation 24 h later (Fig. 4). In 2019, the rise of 5 cm SM5cm due
to irrigation was usually between 2 vol % and 5 vol %.
The 5 cm daily mean soil temperatures (ST5cm) were on average 0.7 ∘C higher at the Si compared with the Sc in
June to September 2018 (Fig. 5). Also, spatial variation was higher at the
Sc (Fig. 5). The biggest difference in daily mean ST5cm between
the plots was observed around mid-July 2018 when the ST5cm at the
Si was on average 2.0 ∘C higher than at the Sc.
However, in 2019, the difference in daily mean ST5cm between the plots
was small, and the Si was only about 0.2 ∘C warmer on
average than the Sc. Also, the maximum difference between the plots was
about 0.6 ∘C, which occurred at the end of July and August.
Daily mean CH4 flux measured at the irrigated (black) and
control (gray) plots in 2018 (a) and 2019 (b). The error bars show the
standard error of the mean. Blue (irrigated) and red (control) lines
represent daily mean 5 cm soil temperature (ST) and shading shows the
minimum and maximum daily values measured by different sensors (irrigation:
n=7; control: n=9).
The effect of irrigation on CH4 uptake
Before the irrigation experiment started, all the measured CH4 fluxes
were negative, indicating CH4 uptake, and did not differ significantly
between Sc and Si. In 2017, the fluxes were measured from four
points at each plot and median fluxes (Sc: -220µg CH4 m-2 h-1; Si: -230µg CH4 m-2 h-1) and
mean June to September CH4 balances were similar between the plots (Fig. 6). However, there was notable spatial variation between the points as the
June to September CH4 balances varied between -950 and -470 mg CH4 m-2).
In 2018 and 2019, when irrigation started, the fluxes measured at the
Si and Sc differed significantly from each other in terms of
points where measurements had started already in 2017 (I2, I3, I4, I9, C1, C4,
C6, C8; 2018: p<0.001; 2019: p=0.01). The mean summer
CH4 uptake rates of these points in 2018 were 37 % larger and 15 % smaller than in 2017 at Sc and Si, respectively (Fig. 6;
Table S5 in the Supplement). In 2019, the mean June to September balances (Sc: -940±120 mg CH4 m-2; Si: -660±70 mg CH4 m-2;
Fig. 6; Table S5) remained at about the same level as in 2018 and the fluxes
did not differ significantly from fluxes measured in 2018.
CH4 fluxes measured by long-term (C1, C4, C6, C8, I2, I3, I4,
I9) (a) and all (b) chamber points in May to September in different years.
Positive flux values indicate net emission and negative values indicate net
uptake. For comparison, the flux of the points located on the irrigated plot
in 2018 and 2019 have been calculated already for 2017, even though the
irrigation setup was established only in 2018. The boxes show the quartiles
of the dataset and the horizontal line inside the boxes is the median flux.
Whiskers show the range of the data, except for the points that are
determined to be outliers, which are shown with black diamonds.
The median measured CH4 uptake rate (Fig. 6; Table S5) across all the
measurement points at the Si (150 µg CH4 m-2 h-1)
was 48 % lower than at the Sc (290 µg CH4 m-2 h-1) in 2018 and 35 % lower in 2019 (Si: 170 µg CH4 m-2 h-1; Sc: 260 µg CH4 m-2 h-1). The
fluxes differed significantly between the plots in both years (p<0.001). All but one of the measured fluxes were negative, indicating
CH4 uptake. One large CH4 emission pulse (18 200 µg CH4 m-2 h-1) was observed in point I1 on 27 June 2019. Similar
differences were also observed in mean 4-month (June to September)
CH4 balances (Table S5). There was lots of variation in fluxes between
the measurement points. CH4 balances varied from -1280 to -480 mg CH4 m-2 at the Sc and from -740 to -180 mg CH4 m-2 at the Si in 2018. Some
of the measurement points at the Si had higher CH4 uptake rates
than some points located at the Sc, but on average CH4 uptake
rates were noticeably larger at the Sc (-850±80 mg CH4 m-2) than at the Si (-450±60 mg CH4 m-2). In
2019, CH4 uptake rates increased in most of the points at the Si,
averaging at -570±60 mg CH4 m-2, but the balances remained
mostly the same at the Sc (mean: -830±70 mg CH4 m-2).
CH4 production and oxidation potentials
Oxidation potential rates were quite similar in all soil layers, except for
the C layer where the rates were lower. Between the years, however, the rates
differed as those in 2019 were generally higher and more variable than in
2018 (Fig. 7a, b). The most notable increase was detected in the organic
layer, where the oxidation potential rates were mainly non-existent in 2018,
but about 15 nmol CH4 g-1 d-1 in 2019 at both Si and
Sc. However, the change was significant only at the Si (p=0.03). Oxidation rates were significantly (p=0.03) higher in 2019
(median: 22 nmol CH4 g-1 d-1) than in 2018 (median: 15 nmol CH4 g-1 d-1) also in the I layer at the Sc, but there was no
significant difference in the same layer at the Si. There were no
statistically significant differences between the years in any other soil
layers at either plot. Comparing the soil layers between the plots revealed
that the oxidation rates were significantly higher (p=0.01) in the C
layer at the Sc than in Si in 2018. The rates were significantly
higher (p<0.01) at the Sc in the I layer in 2019, but there were
no other significant differences between the plots in other soil layers.
CH4 oxidation (a, b) and production (c, d) potentials in
different podzolic soil layers (organic layer, O, mean depth: 5.7 cm;
leaching layer, E, mean depth: 10.5 cm; enrichment layer, I, mean depth:
18.9 cm; bottom layer, C, mean depth: 32.3 cm) in 2018 (a, c) and 2019 (b, d) (n=9). The boxes show the quartiles of the dataset and the vertical
line inside the boxes is the median flux. Whiskers show the range of the
data, except for the data that are determined to be outliers, which are
shown with black diamonds.
The highest CH4 production potential rates occurred in the O layer and some
production potential was observed in the E layer, while in the lowest soil
layers, the production rates were negligible (Fig. 7c, d). At the Si, the
production potential rates were significantly lower in 2019 than in 2018 in the
O (p=0.04) and I (p=0.001) layers. At the Sc, the production
rates differed significantly (p<0.02) only in the C layer, but the
rates were negligible in both years. Comparing the production rates between
the plots revealed that the rates were significantly (p<0.04)
higher in the O layer at the Sc in 2019. A significant difference (p<0.01) between the plots was also found in the C layer in 2018.
Potential CH4 production rates in 2018 had strong positive Pearson
correlation coefficients (ρ) with organic matter content (ρ=0.94), moisture content (ρ=0.90), and total N and C amounts (ρ=0.95 and 0.94, respectively) determined from the soil samples (Fig. S1a in the Supplement). Potential CH4 production rates in 2019 had a similar stronger
positive correlation with organic matter (ρ=0.96) and moisture
content (ρ=0.93; Fig. S1b in the Supplement).
Factors controlling field CH4 fluxes
Correlations between field CH4 flux and SM were nearly negligible
(ρ<0.2) in both Si and Sc in both years (Fig. 8),
with the exception of Si in 2019 with a ρ value of -0.5 (p<0.001). In both 2018 and 2019, correlation trends were weakly
negative between CH4 flux and SM, except for Sc in 2018 with a
weak positive correlation (ρ=0.17, p=0.07). In contrast,
CH4 flux and ST had generally stronger correlations in both Si and
Sc, the latter having the highest ρ values in both years (2018:
ρ=-0.57; 2019: ρ=-0.49), only 2018, however, being
statistically significant (p<0.001). Si showed differing
correlation trends between years, 2018 having relatively weak positive
(ρ=0.4, p=0.01) and 2019 almost negligible negative
(ρ=-0.19, p=0.16) correlations.
Correlations (Pearson's coefficient, ρ) between soil
moisture and CH4 flux (a, c) and soil temperature and CH4 flux (b, d) with smoothed frequency histograms in 2018 and 2019. The emission case of
27 June 2019 was removed from the data in the correlation analyses for more
clear presentation.
Several mixed-effect model runs were made to investigate the environmental
drivers behind CH4 fluxes. SM5cm and ST5cm were among the significant
variables explaining CH4 fluxes in all the model runs. The rest of the
significant drivers varied depending on the soil layer. In the organic
layer, the most significant model, in addition to SM5cm and ST5cm,
included oxidation potential and nitrogen content. The model explained 51 % of the variation in CH4 fluxes (Table 1). The significant drivers
were otherwise similar to the O layer in the E layer, except nitrogen content was
replaced by carbon. However, the model had weaker explanative power
(rfix2=0.44) than the O layer model (rfix2=0.51). It
should be noted that carbon and nitrogen contents had strong
cross-correlation, and using either of them in the model would have given
almost the same result. The I layer model had the weakest model explaining
CH4 fluxes (rfix2=0.42), and the significant drivers
included only SM5cm and ST5cm and the carbon content in the I
layer. Using the drivers' mean values over all soil layers also resulted in
a relatively weak model (rfix2=0.44), and it included only
oxidation potential and SM5cm and ST5cm. Finally, a model
combining drivers from multiple depths was made, and it explained the
CH4 flux the best (rfix2=0.65). In that model, CH4
flux was most influenced by SM5cm and ST5cm, oxidation potential
in the organic layer, production potentials in the organic and E layer, and
carbon content in the E layer (Table 1).
Linear mixed-effect models fitted against CH4 fluxes
(FCH4) and experimental factors. The fixed effects in the model were:
SM – 5 cm soil moisture; ST – 5 cm soil temperature; OPCH4,x –
CH4 oxidation potential at soil layer x (O, E, I soil layers or the
mean of all layers); PPCH4,x – CH4 production potential at soil
layer x; CCx – carbon content at soil layer x; and NCx – nitrogen
content at soil layer x. The table shows the r2 of the fixed effects
(rfix2) and the whole model (fixed effects + random effects,
rmod2), p value of the model (p), AIC of the model and the degrees
of freedom (df). The models in bold are the best-fitted models.
Mixed-effect model equationsRfix2Rmod2pAICdfMean of layersModel 1FCH4∼ SM + ST + OPCH4,mean+ PPCH4,mean+Nmean0.380.78< 0.0011662.98Model 2FCH4∼ SM + ST + OPCH4,mean+ PPCH4,mean0.410.76< 0.0011660.97Model 3FCH4∼SM+ST+OPCH4,mean0.440.74<0.0011658.96Organic layerModel 1FCH4∼ SM + ST + OPCH4,O+ PPCH4,O+ NCO0.500.77< 0.0011659.68Model 2FCH4∼SM+ST+OPCH4,O+NCO0.510.76<0.0011658.17E layerModel 1FCH4∼ SM + ST + OPCH4,E+ PPCH4,E+ CCE0.420.75< 0.0011660.38Model 2FCH4∼SM+ST+OPCH4,E+CCE0.440.75<0.0011659.47I layerModel 1FCH4∼ SM + ST + OPCH4,I+ PPCH4,I+ CCI0.370.78< 0.0011662.88Model 2FCH4∼ SM + ST + OPCH4,I+ CCI0.400.76<0.0011660.87Model 3FCH4∼SM+ST+CCI0.420.74<0.0011659.36CombinationModel 1FCH4∼SM+ST+OPCH4,O+PPCH4,O+PPCH4,E+CCE0.650.74<0.0011650.29Microcosm experiment
Adding glucose to the sample and keeping the moisture level similar did not
cause significant changes in the potential CH4 uptake rate, but on
average, CH4 uptake rate was lower or the CH4 emission was higher
with added glucose on the same moisture level (Fig. 9). There was an
exception to this case at high 25 ∘C temperature and moderate
moisture (M1), where the added glucose samples had a higher CH4 uptake
rate, but as said above, these were not statistically significant
differences.
Generally, increasing soil moisture with no added glucose decreased the mean
potential CH4 uptake rate, but even with the high SM (M2) group, the
soil did not turn into a CH4 source (Fig. 9). Also, the differences
between the different moisture groups were generally not statistically
significant. Significant differences were only observed between the M2 and
the control group in the first 2 weeks of measurements. The weekly mean
CH4 uptake rates also decreased further in time in all groups, except
in the M2 group, where the changes in time were negligible.
In samples with added glucose, increasing SM significantly (p<0.05)
decreased potential CH4 uptake rate in both M1 and M2 groups compared
with the control group. On the other hand, M1 and M2 groups did not differ
significantly, except in week two at 25 ∘C temperature. In that
case, relatively high CH4 emission was measured in the M2 group with
added glucose, but emission dropped rapidly already in the third week,
although it remained a small CH4 source (Fig. 8). At 15 ∘C
temperature, there was no such CH4 emission peak in the M2 group.
Weekly mean CH4 flux measured at 15 ∘C (a) and 25 ∘C (b) without (solid lines) and with (dashed lines) added
glucose on different moisture levels (black: control; green: low added
moisture; blue: high added moisture). The error bars show the standard error
of the mean (n=3). Positive flux values indicate net emission and negative
values indicate net uptake.
Discussion
In this study, our initial aim was to mimic a wet growing season in a boreal
upland forest with podzol soil in northern Finland by irrigating the area
regularly and studying the conditions needed to switch the forest floor to a
CH4 source. Earlier, we discovered that the soil of the same site
turned into a CH4 source in August after long-lasting rains during the
growing season of 2011 (Lohila et al., 2016). Therefore, we assumed that we
could reach the conditions needed to initiate the CH4 production in the
podzolic soil by at least tripling the long-term mean precipitation.
However, the two study summers of 2018 and 2019 turned out to be the driest
summers of the decade, with a long warm and dry period in June to July 2018 and
generally dry summer in 2019. Unfortunately, due to the remote location of
the experimental site and a distance of several kilometers to the closest
water tap, we were not able to counter the effect of the droughts. As a
result, our control plot could be considered a drought experiment, while the
irrigated plot followed the moisture and CH4 flux patterns of a
“normal” summer. Therefore, it is recommended that with similar studies in the
future, the experiment be set in a location where the irrigation system is
able to distribute higher amounts of water and with higher frequency than
what was practically possible in this study. In the Lohila et al. (2016)
study, we also speculated that the reason for the CH4 emission
occurring in August and not in spring after the snowmelt could be that fresh
carbon substrates consumed by soil microbes are needed to make the soil
anoxic, i.e., the wet soil alone is not enough to initiate CH4
production. To confirm this hypothesis, we conducted a
laboratory mesocosm experiment in which the temperature and moisture
responses were studied, and glucose was added to some of the samples to
mimic the root exudation providing fresh carbon substrates to the soil
microbes.
We found that the field CH4 uptake at the control plot was higher
during the study years than a more typical summer of 2017. This comparison
was possible since some of our study plots had been established already a year
before the experiment. On the other hand, the irrigated plot showed similar
uptake rates during the previous summer, which was close to normal in terms
of temperature and precipitation. The same pattern was observed for the soil
moisture: the soil was as moist in the irrigated plot in 2018 and 2019 as it
was without irrigation in 2017. One single occasion when clear CH4
emission was detected took place in the irrigated plot at the end of June
2019, but the emission was only observed in one of the irrigated plots. The
mean emission rate during that day from the irrigated plots was 1670 µg CH4 m-2 h-1 (data not shown, the point removed from Fig. 5b). Although encouraging, the observation unfortunately did not provide
means to systematically study the conditions needed to switch the soil into
a CH4 source, since the soil moisture or any other variable at the same
measurement point did not differ from the other points.
The laboratory experiments for studying the possible differences in the
CH4 production and oxidation potentials indicated no significant
differences between the control and irrigated soils. Initially, we
hypothesized that the wet conditions prevailing for one or two summers could
be seen in the response of microbial populations so that at the irrigated
plot the oxidation would be smaller and the production higher either after
both summers or at least after the second wet summer. Unfortunately, the dry
summers turned the whole setup around so that we ended up examining the
effect of dry growing seasons on the response of microbial populations.
Hence, our results suggest that the period of one or two dry summers did not
impact soil production or oxidation potentials, although we found
differences in the actual CH4 uptake between the irrigated and control
plots. Therefore, it seems likely that the differences in observed field
fluxes were due to the impact of soil moisture on the gas diffusion rate:
the drier the soil, the higher the air-filled porosity and the quicker the
diffusion of oxygen and CH4 into the soils, and the higher the CH4
uptake rates (Dörr et al., 1993; Van Den Pol-van Dasselaar et al., 1998;
Striegl, 1993).
We also hypothesized that the oxidation potentials would be highest in the
topsoil, which is closest to the main source of the substrate for oxidation,
namely atmospheric CH4 (Bradford et al., 2001), while the production
potentials would be higher deeper in the soil, where the oxygen is more
likely to be depleted periodically after wet conditions when diffusion rates
are suppressed. The oxidation potentials indeed peaked in the topsoil, but
interestingly, so did the production potentials, showing clearly the highest
rates in the organic/humus layers (Fig. 7).
Our findings are parallel with the previous ones from forest soils since the
highest CH4 oxidation has been detected both in the uppermost mineral
soil below the organic layer (Saari et al., 1998) and, on the other hand, in
the organic layer (Wang and Ineson, 2003). Thus, the distribution of
CH4 consuming organisms in the upland soil horizon seems to vary
somewhat depending on the year and prevailing physical and chemical
conditions. High potential CH4 productions in the surface layers in
2018 and 2019 (Fig. 7) are most likely linked to higher soil organic matter
and moisture content of soils, which is also supported by a strong positive
correlation with the soil organic matter and moisture content. Potential
CH4 oxidation did not show a strong correlation with these. Similar
results obtained from upland soils and especially from forest soils are hard
to find. However, high organic C content has simulated CH4 production
under hypoxia in agricultural soil (Brzezińska et al., 2012), and water
content was observed as a major influencing factor regarding CH4
production potential in subalpine upland soil (Praeg et al., 2014). Thus,
over 2 times higher moisture content and about 10 times higher organic
matter content in the organic layer compared with mineral layers below most
likely explain partly the higher CH4 production potentials observed in
this study.
The mesocosm experiment provided interesting insights into the CH4
dynamics of the podzolic soil. First of all, this experiment confirmed the
result of field fluxes by showing that the CH4 uptake decreased along
with higher soil moisture. Also, CH4 uptake was totally ceased in the
high soil moisture treatment (M2) due to suppression of diffusion rates in
waterlogged conditions. The higher temperature increased the net uptake,
most likely by increasing the oxidation, but this was true only for the
mesocosms with “field conditions” (no water added). In other words,
CH4 uptake was higher in warmer soils but smaller in wetter soils (as
expected), so it seemed that increasing either the temperature or the soil
moisture, or both, affect the CH4 oxidation straightforwardly but is
not able to induce CH4 production in the soil. However, only if the
soils were made wet enough and glucose was added, significant CH4
production was initiated, which was further increased by higher
temperatures. Thus, the results obtained here supported our hypothesis that
both excess moisture and easily decomposable carbon are needed to initiate
CH4 production in podzolic soil. Indeed, the root exudate analogues
containing simple sugars accelerated CH4 production in tropical peat
soil (Girkin et al., 2018). However, in a study conducted in Japanese upland
soil, added glucose was rapidly decomposed within 7 d of the
incubation, and part of the glucose-derived C flow ended up to methanogens
even under unflooded conditions (Watanabe et al., 2011). Even though it is
largely known that methanogens can survive and tolerate dry and oxic
conditions for some periods, they become active only when the conditions
turn favorable for CH4 production (i.e., wet and anoxic). Since
methanogenic archaea cannot use glucose directly as C source, methanogens
probably utilized acetate or CO2 produced by the glucose-decomposing
bacteria. Thus, the obtained results from the microcosm experiment may
reflect the situation that in wet conditions, glucose has increased the
activity of microbial communities that supply methanogenic substrates
(hydrogen-producing bacteria or acetyl-producing bacteria), promoting the
activity of methanogens production as was detected in a forested wetland
(Koh et al., 2009). Simultaneously decreased activity of CH4 oxidizers
may have been followed by the competition of other aerobic microorganisms,
which have metabolized glucose rapidly, creating more anaerobic conditions
favoring CH4 production. However, the comparison of the obtained
results with earlier findings is rather obscure since similar experiments
conducted in boreal forest soil do not exist. Thus, our results are one of
the first attempts to understand the complex conditions which initiate
CH4 production. In addition, our study is unique since we are
presenting both CH4 fluxes and laboratory CH4 potentials from the
soil taken from the same field points.
Conclusions
Based on our field and laboratory experiments, the main conclusion is that
CH4 production from boreal upland forest soil cannot occur solely by
prolonged wet conditions, but there also has to be enough fresh carbon in
the soil. Therefore, we expect the possible CH4 production episodes to
occur in late summer and autumn rather than in spring, even though the soil
can be very wet after snowmelt. These findings can be applied in CH4
process models to improve estimations of regional and global upland forest
CH4 balances.
We did not observe any changes in CH4 production and oxidation
potentials due to irrigation over two summers, meaning microbial communities
were not very sensitive to environmental variables. This suggests that the
measured field fluxes are rather controlled by the physical soil conditions
by limiting gas diffusion rates and not by the changes due to microbial
function. One conclusion from our results is that CH4 production and
oxidation are controlled by different driving variables and processes: the
oxidation is boosted when the conditions for higher gaseous diffusion are
optimal (dry soil), while the production is boosted only if anoxic
conditions are created (wet soil reducing diffusion + microbial activity
consuming oxygen) and there are fresh organic substrates available for
CH4 production. In our field experiment, CH4 production episodes
were not detected (with one exception), and the changes in net field
CH4 flux were solely caused by the changes in CH4 oxidation. The
net CH4 flux (here total oxidation) was primarily controlled by soil
temperature and soil moisture. Increasing soil temperature enhanced
oxidation and gas diffusion, and increasing soil moisture limited oxidation
by making conditions for methanotrophs unfavorable and diminished
diffusion. We also found that upland forest soils have the potential to
produce CH4, but contrary to wetlands, the potential is highest near
the soil surface and decreases rapidly as a function of soil depth. This
could happen because the conditions for methanogenic archaea are more
favorable in the topsoil layer due to the higher amount of organic matter.
Our study confirms that soil moisture is a critical variable in explaining
the soil CH4 uptake rate and suggests that the diffusion rate of both
CH4 and oxygen into the soil is the primary constraint of oxidation.
For the onset of CH4 production in podzolic soil, not only high soil
moisture but also the addition of sugar, mimicking root exudates from trees,
was needed. Glucose impacts CH4 production mainly by boosting the
consumption of oxygen in the soil and providing substrates for CH4
production. We also found that the highest potential production and
oxidation rates were found in the same topsoil layers, suggesting that the
surface soil plays the main role in the soil–atmosphere exchange of CH4
in boreal upland forest
Data availability
The flux, meteorological, and soil data are available at Zenodo
(10.5281/zenodo.5153347, Korkiakoski et al., 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-2025-2022-supplement.
Author contributions
AL, TP, and KP designed the study. MK, AL, TP, and TM constructed the
experimental site. KP took the soil samples and calculated methane
production and oxidation potentials and did the laboratory work for the
microcosm experiment. TM made the flux measurements and took part in the
data analysis. MK calculated the fluxes for the field and microcosm
experiments and did the statistical analysis. MK prepared the paper
with contributions from all co-authors.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Valtteri Hyöky, Päivi Pietikäinen, Stephanie Gerin, and Petri Salovaara are acknowledged for assisting in the fieldwork. We also
thank Bastian Steinhoff-Knopp for letting us use his aerial photo of the
experimental site, and the Finnish Forest Administration
(Metsähallitus) for their generous cooperation during the fieldwork in
2018–2019.
Financial support
This research has been supported by the Academy of Finland (grant no. 308511).Open-access funding was provided by the Helsinki University Library.
Review statement
This paper was edited by Lutz Merbold and reviewed by two anonymous referees.
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