Introduction
Land use activities have greatly enhanced inputs of carbon (C) and nitrogen
(N) from terrestrial or atmospheric sources to the aquatic environment,
reducing the terrestrial C sink function and aggravating global climate
change (Dawson and Smith, 2007; Regnier et al., 2013; Vitousek et al., 1997).
The terrestrial C sink is largely determined by forest ecosystems which
contribute to a net uptake of greenhouse gases (GHG) from the atmosphere
(Goodale et al., 2002; Myneni et al., 2001). This net uptake can be further
increased by well-informed forest harvesting strategies (Kaipainen et al.,
2004; Liski et al., 2001). Hence, forest management is a widely used
instrument to fulfill GHG budget commitments under the Kyoto Protocol (IGBP
Terrestrial Carbon Working Group, 1998). Yet, mitigation measures neglect to
consider that a significant part of terrestrial C and N taken up by forests is
exported to aquatic systems (Battin et al., 2009; Öquist et al., 2014;
Sponseller et al., 2016). These exports are sensitive to logging activity
(Nieminen, 2004; Schelker et al., 2012; Lamontagne et al., 2000), and a large
proportion is processed in inland waters and emitted back to the atmosphere
as GHGs such as carbon dioxide (CO2), methane (CH4) and
nitrous oxide (N2O) (Cole et al., 2007; Seitzinger and Kroeze, 1998).
Revealing potential changes in the GHG budget of the aquatic environment
downstream forest clear-cuts is therefore crucial to evaluate the overall
potential of forestry to mitigate climate warming.
Forestry effects on aquatic GHG emissions are largely unknown and difficult
to predict due to multiple processes involved. In boreal headwaters, stream
and lake CO2 and CH4 originate largely from soils (Bogard and
del Giorgio, 2016; Hotchkiss et al., 2015; Rasilo et al., 2017). These
soil-derived inputs typically increase after forest clear-cutting because of
increased soil respiration (Bond-Lamberty et al., 2004; Kowalski et al.,
2003) and discharge (Andréassian, 2004; Martin et al., 2000). Forest
clear-cutting often also increases dissolved organic carbon (DOC) export to
streams and lakes (Schelker et al., 2012; Nieminen, 2004; France et al.,
2000), where it stimulates respiration and reduces light penetration, lake
primary production, and net CO2 uptake (Ask et al., 2012; Lapierre et
al., 2013). Therefore, any elevated terrestrial C inputs due to forest
clear-cutting may further increase net heterotrophy and CO2 emissions
(Ouellet et al., 2012) or stimulate methanogenic bacterial activity in lakes
(Huttunen et al., 2003). Forest clear-cuts also often enhance nutrient
exports, with less pronounced changes for phosphorous but large increases
for N, especially for nitrate (Nieminen, 2004; Palviainen et al., 2014;
Schelker et al., 2016). Nitrate leakage affect GHG cycling in boreal inland
waters, yet predictions on the direction of net effects are difficult.
Nitrate inputs may suppress (Liikanen et al., 2003) or stimulate (Bogard et
al., 2014) CH4 production, enhance CH4 oxidation (Deutzmann
et al., 2014), and promote denitrification and N2O emissions
(McCrackin and Elser, 2010; Seitzinger and Nixon, 1985). Nitrate inputs to N-limited boreal aquatic systems stimulate phytoplankton production and thereby
enhance CO2 uptake and oxygen (O2) production
(Bergström and Jansson, 2006). Increases in DOC would, however, consume
O2 (Houser et al., 2003) and changes in O2 concentrations
have been identified to influence the balance between methanogenesis and
methanotrophy (Bastviken et al., 2008) as well as nitrification and
denitrification (Mengis et al., 1997). Removal of riparian vegetation may
increase littoral light availability and water temperature (Steedman et al.,
2001; Moore, 2005), with
potential effects on net CO2 and CH4
production (Wik et al., 2014; Yvon-Durocher et al., 2012, 2014). Forest
clear-cuts could also increase wind exposure (Tanentzap et al., 2008;
Xenopoulos and Schindler, 2001) and thus result in increased gas transfer
velocities as indicated by the wind based relationships found in lakes (Cole
and Caraco, 1998). Likewise, enhanced discharge may affect turbulence and gas
transfer velocities in streams (Raymond et al., 2012). Clear-cut effects on
hydrology and biogeochemistry can be further amplified by site preparation,
i.e., the trenching of soils before replanting (Schelker et al., 2012; Palviainen et
al., 2014).
Forest–stream–lake continuum before (a, d) and
after (b, c, e, f) clear-cutting in the ice-covered lake
Lillsjölidtjärnen (a–c) and the inlet of
Struptjärn (d–f). The dashed line shows the contours of
Lillsjölidtjärnen. Note the soil trenches (snow-free patches) after
site preparation (c) and the storm damage of the riparian buffer
vegetation (f).
Even though spatial surveys indicate that changes in vegetation (Maberly et
al., 2013; Urabe et al., 2011), forest fires (Marchand et al., 2009), and
forestry activities (Ouellet et al., 2012) affect the GHG balance of inland
waters, mechanistic evidence from whole-catchment forest manipulation
experiments is lacking. Here, the impact of forest clear-cuts and site
preparation on the summer season's (June–September) means of air–water
CO2, CH4, and N2O fluxes was experimentally assessed
for streams and lakes in four boreal headwater catchments. A whole-catchment
manipulation experiment was performed using a before-after control-impact
(BACI) design. Two “impact” catchments received a forest clear-cut and site
preparation following 1 year of pretreatment sampling. Two “control”
catchments were left untreated throughout the whole study period of 4 years. We hypothesized an increase in aquatic CO2, CH4, and
N2O emissions in response to forest clear-cuts and site preparation.
Methods
Study sites
Sampling was carried out during June–September 2012–2015 in four headwater
lakes and three lake inlet streams (one lake lacks an inlet stream) in the
catchments (220–400 m a.s.l.) of Övre Björntjärn,
Stortjärn, Struptjärn and Lillsjölidtjärnen, northern Sweden
(Table 1, Fig. 1). During the experimental period, mean annual temperature in
the region was 1–3 ∘C higher than the long-term average
(1960–1990) of 1.0 ∘C, while annual precipitation was close to the
long-term average of 500–600 mm in all years except for 2012, which had 800 mm
(http://www.smhi.se/klimatdata/meteorologi, last access: 14 September 2018). In the study catchments, mean
summer air temperatures and precipitation sums (June–September) varied
between 11.1 ∘C and 342 mm in 2012 and 12.8 ∘C and 245 mm
in 2014, respectively (Table S1 in the Supplement). Catchment soils were
typically well drained and characterized by podzol with a 10–15 cm thick
organic layer developed on locally derived glacial till and granitic bedrock.
The catchments were mainly (>85 %) covered by managed coniferous
forest (Picea abies, Pinus sylvestris) with scattered birch
trees (Betula sp.) and minerogenic oligotrophic mires (<15 %). Site quality class was rather low with timber productivities of
2–3 m3 ha-1 yr-1 (SLU, 2005). The catchments were drained
by a hand dug ditch network established in the early 20th century to improve
the forest's productivity. The riparian zone was about 2–6 m wide and
characterized by organic rich peat soils. The regional hydrology is
characterized by pronounced snowmelt episodes in April and May, summer and
winter low-flow periods, and autumn storms with enhanced precipitation.
Drainage channels all culminate in single lake inlets with average specific
discharges of 1.0, 1.4, and 1.5 mm d-1 in Struptjärn,
Lillsjölidtjärnen, and Övre Björntjärn in June–September 2012.
The study lakes were shallow, small, humic, and dimictic with a seasonal
mixed layer depth of 0.5–2 m during summer stratification lasting from late
May to mid-September. Lake ice was present from late October to mid-May
during the study period.
Morphological and physicochemical characteristics of the lakes and
streams of the experimental catchments. Temporally variable parameters are
given as mean values of the pretreatment period (June–September 2012).
Abbreviations: a420 is the spectral absorbance at 420 nm,
DOC is dissolved organic carbon concentration, TP is total phosphorous
concentration, and TN is total nitrogen concentration.
System
Catchment
Catchment
Lake area
Mean
Lake water
Stream
a420
pH
DOC
TP
TN
Catchment/
Buffer
Proportion of
Proportion
area [ha]
[ha]/
depth
residence
discharge
[m-1]
[mg L-1]
[µg L-1]
[µg L-1]
stream
strip
catchment cover
of forest
stream
[m]
time [d]
[L s-1]
slope [%]
width
Forest
Mire
clear-cut
length [km]
[m]
[%]
[%]
[%]
Lake
Stortjärn
82
3.9
2.7
96
–
13
4.5
20
13
403
6.2
–
88
12
–
Övre Björntjärn
284
4.8
4.0
64
–
12
4.0
22
22
398
8.3
–
84
16
–
Struptjärn*
79
3.1
3.8
387
–
10
4.9
19
24
367
7.4
50
96
4
18
Lillsjölidtjärnen*
25
0.8
3.8
115
–
7
5.6
15
19
345
13.1
20
98
2
44
Stream
Övre Björntjärn
233
3.0
0.3
–
40.9
12
3.9
28
26
503
1.9
–
90
10
–
Struptjärn*
46
1.4
0.2
–
5.5
10
4.2
36
24
762
1.9
6
94
6
17
Lillsjölidtjärnen*
19
0.6
0.1
–
3.0
5
4.9
21
15
829
4.0
35
100
0
51
* Clear-cut.
Forest clear-cutting and site preparation procedure
Forest clear-cutting was carried out on snow-covered (∼60 cm) frozen
soil in February 2013 in the catchments of Struptjärn and
Lillsjölidtjärnen by national or private forest companies according
to common practice methods of whole-tree harvesting where about 30 %
of tops, twigs, and needles were left on-site (Fig. 1). The forests cut were
coniferous forests with an age of about 90–120 years. In early November 2014,
clear-cuts were site-prepared by disc trenching, a common soil scarification
method to improve planting conditions (Fig. 1c). Clear-cut areas were defined
by the forest companies, 14 and 11 ha in size, and corresponded to 18 %
and 44 % of total lake watershed areas, respectively (Table 1).
Clear-cuts covered 40 % and 60 % of the stream reaches of the inlets
of Struptjärn and Lillsjölidtjärnen. Along the inlet stream of
Lillsjölidtjärnen, 10–70 m wide stream buffer strips were left and
remained intact throughout the study period. Buffer strips along the inlet
stream of Struptjärn were <10 m (Fig. 1e) and damaged by a wind throw
event in winter 2014/15, where 70 % of trees within the buffer strip fell
along half of the clear-cut affected reach, causing a bank collapse and soil
erosion (Fig. 1f). Lake buffer strips were 15–60 m wide in both catchments
and stayed largely intact throughout the study period. Hereafter, treated
catchments and sites are referred to as “impact” and untreated ones as
“control”.
Maps of the experimental lakes and streams (a–d), their
catchments (a.i–d.i) and their location in Sweden (e–f).
Detailed maps show the lake bathymetry; the main channel of the inlet
stream; the location of gas concentration sampling sites in lakes, streams, and hillslope groundwater; floating CH4 chambers; and weather stations.
Additional physicochemical sampling was done at the master stream site. White
frames and dots in smaller-scale maps illustrate the extent or location of
corresponding larger-scale maps, respectively. Panel labeling is consistent
across all map scales and is as follows: (a) Stortjärn,
(b) Övre Björntjärn, (c) Struptjärn, and
(d) Lillsjölidtjärnen.
Water sampling and physicochemical analysis
Surface water was sampled biweekly for dissolved CO2, CH4, and
N2O concentrations at stream sites located 40 to 180 m from the
lake inlets (hereafter referred to as master stream sites) and the deepest
point of the lakes (Fig. 2) as described in Text S1 in the Supplement. Control
and impact catchments were typically sampled within 2 or 3 days from each other (but
never more than 7 days). To account for temporal
variability, surface water CO2 concentrations were also monitored at
the deepest point of the lakes and the master stream sites at 2 h intervals
using non-dispersive infrared CO2 sensors (see Text S2 for details).
To account for spatial variability in CO2 and CH4
concentrations within streams, 300 m long stream transects were sampled at
five sites chosen to represent the variability in riparian vegetation and
turbulence patterns of the catchment stream. Spatial variability within lakes
was accounted for by biweekly sampling of CO2 concentrations at
an additional four nearshore locations (Fig. 2). Average nearshore
concentrations did not differ from concentrations at the deepest point
(linear regression with insignificant intercept and slope = 0.97±0.01, p<0.001, R2=0.99, n=130). Therefore, only data from the
deepest point were used for the remainder of this work. Within-lake
variability in CH4 concentrations was accounted for by floating-chamber deployments as described further below. In impact catchments,
groundwater was sampled biweekly for dissolved inorganic carbon (DIC) and
CH4 concentrations from wells at depth-integrated locations
(5–105 cm) and depth-specific locations (37.5–42.5 cm). These depths were
chosen to separate responses in the whole-soil profile and in shallow
groundwater that is tightly connected to streams in our study region (Leith
et al., 2015). Wells were located at two forested hillslope sites: one
affected by forest clear-cutting, and one serving as an untreated control
(Fig. 2, Text S1).
Profiles of dissolved O2 concentrations and photosynthetic active
radiation (PAR) were measured biweekly at the deepest point in each lake
using handheld probes (ProODO, YSI Inc., Yellow Springs, OH, USA; LI-193
spherical quantum sensor, LI-COR Biosciences, Lincoln, NE, USA). At the
deepest point of each lake, at the stream master site, and at the groundwater
wells, additional water samples were collected biweekly in acid-washed plastic
bottles for physicochemical analysis. At the master stream sites, samples
were taken daily by an automatic water sampler (ISCO 6712 full-size portable
sampler, Teledyne Inc., Lincoln, NE, USA). At each field visit, a subset of
these samples were randomly chosen based on the recorded hydrograph during
the past 2-week period: two samples in the absence of flood events and up
to four samples before, during, and after a flood event.
We also monitored lake water temperature profiles, stream temperature and
discharge, and weather conditions including wind speed, air temperature,
precipitation, air pressure, relative humidity, and light intensity at
5–60 min intervals using logger systems described in detail in Text S2.
Between 3 % and 12 % of logger data were missing and filled using
multiple imputation, linear regression, or linear interpolation methods (see
Text S3 and Table S2).
To characterize water color, spectral absorbance at a wavelengths of 420 nm
(a420) was measured on filtered lake and stream water (acid-washed
Whatman GF/F 0.7 µm) using a spectrophotometer (V-560 UV-VIS, Jasco
Inc., Easton, MD, USA). Filtered water from lake, stream, and
depth-integrated groundwater sampling was acidified with 500 µL of
1.2 M HCl per 50 mL of sample prior to analysis for DOC and total N (TN)
analysis on a total organic analyzer (IL 500 TOC-TN analyzer, Hach,
Loveland, CO, USA). For dissolved inorganic nitrogen
(DIN = NO2-+NO3-+NH4+)
analysis, water from lake sampling, stream sampling, and depth-integrated
groundwater sampling was filtered through 0.45 µm cellulose acetate
filters prior to freezing and analyzed using an automated flow injection
analyzer (FIAstar 5000, FOSS, Hillerød, Denmark). All chemical analyses
were performed at the Department of Ecology and Environmental Science (EMG),
Umeå University. All data analysis described in the following was done
using the statistical program R 3.2.2 (R Development Core Team, 2015), if not
declared otherwise.
Lake physics calculations
Lake thermal characteristics were calculated based on 5 min temperature
profile data using functions provided by the R-package rLakeAnalyzer
(Read et al., 2011). This included epilimnion, hypolimnion and whole-lake
mean temperatures; Schmidt stability; the depths of the actively mixing layer
(zmix); and the upper and lower boundary of the metalimnion
(zupr and zlwr, respectively). For zmix,
we chose a density gradient threshold value of 0.1 kg m-3 m-1.
Mean O2 concentrations for the epilimnion (water surface to
zupr), hypolimnion (zlwr to lake bottom), and the
whole lake were then calculated by weighting O2 concentrations by
the areal proportion of the depth stratum they represent and integrating
these numbers over all depths, following the whole-lake depth-integrated
approach by Sadro et al. (2011). Stratum-specific areas were derived from
hypsographic curves, established from bathymetric data. Bathymetry data were
collected using an echo sounder with an internal GPS antenna (Lowrance HDS-5
Gen2) and interpolated by ordinary kriging (root mean square error
(RMSE) = 0.3 m) using the geostatistical analysis package in ArcMap 10.1
(ESRI, Redlands, CA, USA). Light extinction coefficients (kd) were calculated as the
slope of the linear regression between natural logarithm of
photosynthetically active radiation and depth.
Gas transfer velocity estimates
For both lakes and streams, gas transfer velocities (k) – the water column
depth that equilibrates with the atmosphere per unit time – were expressed
as k600, representing CO2 transfer at 20 ∘C water
temperature. For lakes, three published k600 models were used to account
for prediction uncertainties, including two wind speed based models
calibrated for small sheltered lakes (Cole and Caraco, 1998) and boreal lakes
of various sizes (Vachon and Prairie, 2013), and a surface renewal model
calibrated for small boreal lakes (Heiskanen et al., 2014). Calculations were
based on scripts provided by the R-package LakeMetabolizer (Winslow et al.,
2016). Measured input variables included wind speed, wind mast height,
latitude, lake area, air pressure, air temperature, relative humidity, and
surface water temperature. Modeled input variables included kd, zmix, incoming
shortwave radiation (sw = lux/244.2, following Kalff, 2002), and net
longwave radiation (calculated from measured input variables using the
“calc.lw.net.base” function in the R-package “rLakeAnalyzer”; Read et
al., 2011). To match temporal resolutions, biweekly kd values were
interpolated linearly to 10 min resolution. Wind speed was corrected from
mast height to 10 m above ground, assuming a logarithmic wind profile
following Crusius and Wanninkhof (2003). To account for uncertainties in
k600 estimates for lakes due to gap filling of input variables, we
applied a bootstrapping approach (Text S6). For streams, k600 was
estimated separately for the four sub-reaches that are bound by the five
stream sampling sites. Estimations were based on a total of 23 propane
injection experiments and 282 triplicate gas flux chamber measurements
carried out at three representative sites per sub-reach (Fig. S1 in the Supplement). Propane
injection experiments and flux chamber measurements were repeated 5–10 times
per sub-reach during autumn 2013–spring 2015 to cover a wide range of flow conditions
(0.01–0.95th, 0.10–0.99th, and 0.25–0.99th percentile of discharge
measurements during 1 June–30 September in 2012–2015 in Övre
Björntjärn, Struptjärn, and Lillsjölidtjärnen). Details
on gas transfer measurements in streams are given in Text S4.
Flux chamber measurements and propane injection experiments were used to
establish predictive models of k600 based on stream discharge. Stream
discharge was used instead of the more mechanistically relevant variable of
flow velocity because both variables were highly correlated (marginal R2=0.71), but only discharge was available at hourly intervals. Hence, hourly
k600 was computed for each sub-reach in four steps. First, the
arithmetic mean k600 of site-specific flux chamber measurements was
calculated for each sub-reach. These sub-reach specific k600 values agreed
relatively well with k600 values from propane-injection experiments (R2=0.58, Fig. S2). However, flux chamber measurements were restricted to
relatively smooth water surfaces, excluding waterfalls and rapids, and
therefore underestimated reach-scale k600 by a factor of 0.61. Second,
flux chamber derived k600 was corrected using linear relationships
(median R2=0.90) with propane derived k600 whenever they were
statistically significant or R2 was >90 %. Third, corrected
flux chamber derived k600 was combined with propane derived k600
values to establish sub-reach specific linear regression models that predict
k600 based on local discharge (R2=0.56–0.94, Table S3). Whenever
the best linear model had a negative intercept, the model was refitted,
constraining the intercept to zero to avoid a negatively predicted k600.
Fourth, the k600 discharge models were used to predict k600 based
on hourly time series of discharge measured at the master stream site and
scaled to the respective sub-reach using the mean discharge ratio measured at
both sites during propane injection experiments. Throughout these
experiments, discharge ratios varied by 5±2 %. To account for
uncertainties in k600 estimates for streams, we propagated errors from
flux chamber and propane injection experiments and discharge rating curves
(Text S6).
Gas flux estimates
The diffusive gas flux (F) across the lake or stream water interface was
calculated using Fick's law:
F=acwat-ceqk,
where cwat is the measured gas concentration of the surface water,
ceq is the gas concentration of surface water if it was in equilibrium with
ambient air calculated from measured air concentration and water temperature
using Henry's constant, and a is the chemical enhancement factor (for
CO2 transfer only) set to 1, as enhancement is negligible if
pH<8 (Wanninkhof and Knox, 1996). Atmospheric CO2 and
N2O concentrations were 425 ppm and 350 ppb (median of biweekly
in situ measurements using gas chromatography, Text S1), respectively, and
atmospheric CH4 concentrations were below the detection limit of our
gas chromatographer (∼3 ppm) and assumed to be 1.893 ppm
(http://cdiac.ornl.gov/pns/current_ghg.html, last access: 14 September 2018). The coefficient k was
calculated from k600 following Jähne et al. (1987), with the Schmidt
coefficient set to -0.5 and gas-specific parameterizations of Schmidt
numbers used for in situ water temperature according to Wanninkhof (1992). Errors
in F were propagated from standard errors in cwat and k
(Fig. S3).
Fluxes of CH4 were measured in 2012 and 2014 using floating chambers
according to Bastviken et al. (2010) with the following modifications: 26–32
chambers were placed in each lake to cover five depth zones (water depth
0–1, 1–2, 2–3, 3–4, and >4 m), with one chamber placed at the deepest
point and the remainder arranged along depth transects of 3–4 chambers
(Fig. 2). Depth transects were chosen to represent the typical shoreline
characteristics (inlets, mires, forests). A volume of 50 mL of gas was
sampled weekly from June to August from each floating chamber before and
after an accumulation period of 24 h using polyethylene syringes. A volume
of 30 mL of sampled gas was injected to glass vials (22 mL; PerkinElmer
Inc., Waltham, MA, USA) sealed with natural pink rubber stoppers (Wheaton 224100-171) and
filled with saturated NaCl solution. During gas transfer, the vials were held
upside down to let the excess solution escape through an open syringe needle
until around 2 mL solution was left in the vial. To minimize leakage, vials
were stored upside down until analysis with a gas chromatograph (7890A,
Agilent Technologies, Santa Clara, CA, USA) with a Supelco Porapak Q 80/100 column, a flame
ionization detector (FID) and a thermal conductivity detector (TCD) at the
Department of Thematic Studies – Environmental Change, Linköping
University, Sweden. In addition to chamber sampling, surface water was
sampled at the beginning and the end of each 24 h accumulation period in the
middle of each transect and analyzed for dissolved CH4 as
described above. Chamber-specific total CH4 fluxes were calculated
and separated into diffusion and ebullition components using the statistical
approach described in Bastviken et al. (2004). Whole-lake fluxes were
calculated as the area-weighted mean of depth-zone specific fluxes, which in
turn were the arithmetic mean flux of all chambers located at the respective
depth.
Statistical analysis
Clear-cut and site preparation effects were assessed following the paired
BACI approach of Stewart-Oaten et al. (1986). To test for “clear-cut”
effects, i.e., the general response in the first 3 years after clear-cutting,
the “after” period was set to the years 2013–2015. To evaluate whether
effects after site preparation differed from general responses, we also
tested for “site preparation” effects by setting the after period to the
year 2015. The “before” period was always the year 2012. Treatment effects were analyzed in terms of
effect sizes (ESs), which are defined as the arithmetic mean change
(after - before) in the sampling specific differences between
groundwater, stream, or lake pairs (impact-control). The significance of the
ES was tested using a linear mixed-effects model (LME) with “paired
difference” as the dependent variable and “time” (before-after) as a fixed
effect. The factor “pair” was included as a random effect on both slopes
and intercepts to account for potential natural variability in responses
across the two impact catchments. Reported ESs, slopes, and intercepts are
arithmetic means over the pairs. Each impact lake was paired with the
arithmetic mean of the control lakes, each impact stream with the control
stream, and each impact groundwater sampling site with the respective control
site in the impact catchments. All LMEs were analyzed by means of the lme
function in the R-package nlme (Pinheiro et al., 2015) using the restricted
maximum likelihood approach after BACI model assumptions were evaluated
(Text S5). Whenever temporal autocorrelation was significant (Text S5), a
first-order autocorrelation term (corAR1, for time series of biweekly
observations) or an autoregressive moving-average correlation structure
(corARMA, for time series of daily means derived from hourly discharge or
2-hourly CO2 flux data) was included.
Physicochemical characteristics of lake water, stream water, and groundwater
at control and impact sites before and after forest clear-cutting. Also given are the estimated effect sizes (linear mixed-effects model slope), their
standard errors (SEs), degrees of freedom (df), t and p values, and Cohen'
D, summarized as arithmetic means over ten bootstrap runs that take
uncertainty from gap filling into account (see Fig. S3). This uncertainty is
expressed as bootstrap standard errors (bse) of p values. Statistically
significant p values (<0.05) are highlighted bold. Water levels [cm] are
relative to the soil surface. Groundwater data refer to depth-integrated
locations (5–105 cm). Abbreviations: Epi is Epilimnion,
Hypo is hypolimnion, DOC is Dissolved organic carbon concentration
[mg L-1], TN is total nitrogen concentration
[µg L-1], DIN is dissolved inorganic nitrogen concentration
[µg L-1], a420 is the spectral absorbance at 420 nm
[m-1].
Variable
System
Unit
Before
After
Cohen's D
Control
Impact
Control
Impact
Effect size (slope)
Mean
SE
Mean
SE
n
Mean
SE
Mean
SE
n
Mean
SE
df
t
p
bse
Wind speed
Open mirea
m s-1
1.8
0.1
1.0
0.1
244
2.0
0.0
0.9
0.0
732
0.0
0.1
973
-0.8
0.42
0.03
-0.12
Discharge
Streama
L s-1
40.9
3.4
4.2
0.4
244
27.0
1.8
3.3
0.2
732
0.2
0.2
973
1.0
0.32
0.01
0.13
Water level
Groundwater
cm
34.5
1.8
34.6
1.8
17
42.1
1.9
40.4
1.8
55
-25.1
51.9
69
-0.5
0.63
–
-0.22
Light intensity
lake
lux
31 199
1096
22 408
898
244
34 371
598
23 230
554
732
-2379
2308
973
-1.0
0.30
0.01
-0.11
Streama
5907
248
3402
169
244
6408
104
9969
357
732
0.2
0.2
973
1.1
0.27
0.01
0.84
Temperature
Stream
∘C
8.6
0.1
8.1
0.1
244
9.0
0.1
8.4
0.1
732
-0.1
0.4
973
-0.2
0.84
0.00
-0.02
Lake Epi
14.4
0.2
14.8
0.2
245
15.9
0.1
16.2
0.1
732
-0.1
0.2
974
-0.9
0.39
0.02
-0.08
Lake Hypo
7.0
0.0
6.0
0.1
227
7.5
0.1
6.5
0.0
716
-0.1
0.2
940
-0.6
0.58
0.01
-0.05
Whole lakeb
11.1
0.1
10.7
0.1
245
12.3
0.1
11.5
0.1
732
-0.4
0.1
974
-2.8
0.01
0.00
-0.20
Mixing depth
Lakea
m
1.8
0.1
1.7
0.1
227
1.8
0.0
1.5
0.0
716
-0.2
0.1
940
-2.4
0.02
0.00
-0.15
Schmidt Stability
Lake
J m-2
13.3
0.6
12.8
0.5
245
16.5
0.4
15.4
0.3
731
-0.7
0.5
973
-1.4
0.17
0.00
-0.09
Oxygen
Lake Epi
mg L-1
8.2
0.1
8.1
0.2
20
8.2
0.1
8.1
0.1
58
-0.1
0.2
75
-0.4
0.66
–
-0.06
Lake Hypob
2.2
0.5
0.8
0.4
17
2.4
0.3
0.7
0.2
53
0.2
1.0
67
0.2
0.85
–
0.05
Whole Lake
6.4
0.3
5.1
0.3
20
6.2
0.2
5.1
0.2
58
0.1
0.3
75
0.6
0.56
–
0.07
DOC
Lake Epi
mg L-1
21
0.7
18
0.9
20
21
0.3
19
0.6
58
0.6
1.9
75
0.3
0.76
–
0.11
Stream
29
0.9
28
1.4
59
29
0.8
25
0.9
234
-2.9
1.9
290
-1.5
0.13
–
-0.15
Groundwater
67
3.0
77
2.4
14
63
2.6
75
2.4
45
2.7
9.0
56
0.3
0.77
–
0.08
TN
Lake Epi
µg L-1
409
15.7
367
14.3
20
446
7.3
432
11.7
58
28.5
27.8
75
1.0
0.31
–
0.27
Streamc
498
13.5
595
35.3
58
531
10.6
505
14.2
234
-120.0
58.1
289
-2.1
0.04
–
-0.24
Groundwater
1572
180.4
1798
83.8
14
1664
83.8
1958
127.5
45
72.7
348.9
56
0.2
0.84
–
0.06
DIN
Lake Epi
µg L-1
20
1.6
19
2.0
20
16
1.1
14
1.6
58
-2.1
3.3
75
-0.6
0.54
–
-0.12
Stream
21
2.0
23
2.2
57
23
1.2
32
2.2
224
6.1
5.2
278
1.2
0.24
–
0.15
Groundwaterc
467
98.9
523
42.4
13
526
61.6
538
43.2
38
-37.5
104.9
48
-0.4
0.72
–
-0.05
pHd
Lake Epic,a
4.2
0.1
5.1
0.1
20
5.0
0.0
5.4
0.1
58
3×10-5
2×10-5
75
2.3
0.03
–
0.30
Streama
3.9
0.1
4.4
0.1
20
4.8
0.0
4.6
0.1
58
8×10-5
2×10-5
75
4.8
0.00
–
0.37
a420
Lake Epi
m-1
12.4
0.4
9.3
0.6
20
12.7
0.2
9.9
0.4
58
0.001
0.008
75
0.2
0.88
–
0.03
Stream
15.1
0.4
13.6
0.7
53
13.8
0.2
11.9
0.4
237
-0.001
0.006
287
-0.2
0.85
–
-0.01
a LME estimates based on log-transformed data.
b Assumption on constancy of paired differences in before period
not met. c Assumption on nonadditivity of paired differences in
before period not met. d Mean and LME estimates
based on H+ concentrations, SE based on pH
value.
To guarantee homoscedasticity and normality of model residuals, the dependent
variables were log + n-transformed if necessary prior to model fitting,
where n is the smallest value that leads to normal data when added. To
assess the statistical and biogeochemical significance of clear-cutting
effects, we used the p value and slope of the LMEs (as an estimate of ES)
and Cohen's D, defined as D=ES/2s, where s is the standard
deviation of paired differences in the before period (Osenberg and Schmitt,
1996). Cohen's D were “small” if D<0.2, “medium”
if 0.2≤D<0.8, and “large” if 0.8≤D. Uncertainties in
BACI statistics for gas fluxes and gap-filled logger data were accounted for
by combining standard methods of error propagation and bootstrapping (see
Text S6 and Fig. S3).
Clear-cut effects on CO2 and CH4 fluxes were also
investigated for potential differences along the stream reaches (Fig. 2)
depending on the site-specific percentage of the drainage area affected by
forest clear-cutting. First, stream-site-specific drainage areas were
delineated from a 2 m digital elevation model (DEM) derived from airborne
laser scanning (Swedish National Land Survey, 2015) using Hydrology tools in
ArcGIS 10.1 (ESRI, Redlands, CA, USA). Modeled flow direction in some ditches
were not well represented by the model compared to field observations. In
this case, the DEMs were manually corrected (elevation ±20 cm) to
emphasize observed ditch flow directions. Next, BACI analyses were performed
as described above, where each impact stream site was paired with the
respective site in the control stream with respect to the order regarding
their distance from the lake inlet. In addition, tests were carried out on
linear relationships between the effect size (weighted by SE) of each stream
site and the respective percentage of forest clear-cut using an LME with
“stream” as random effects on both slopes and intercepts. Here, the
dependence of sites within streams was accounted for by setting the
alpha level of the statistical analysis to 0.01. Alpha levels of all other
statistical analyses were set to 0.05.
Results
Hydrological and physicochemical response
Forest clear-cuts did not affect riparian groundwater levels or stream
discharge (Table 2). Instead, these hydrological characteristics were more
regulated by interannual and intra-annual variability in precipitation and snow meltwater inputs. Groundwater levels decreased from 34 to 35 cm depth in the
relatively wet before period to 40–42 cm depth in the relatively drier
after period (here and hereafter, we refer to arithmetic mean values over
each time period; see Table S1 for precipitation data). At the same time,
stream discharge decreased from 41 to 27 L s-1 in the control
catchment and from 4 to 3 L s-1 in the impact catchments,
corresponding to a decrease in specific discharge from 1.5 to 1.0 and 1.1 to
0.9 mm d-1, respectively. Other physical parameters such as wind speed, light
intensity, epilimnion and hypolimnion temperature, and Schmidt stability also
remained largely unaffected. Light intensities tripled in impact streams
(from 3402 to 9969 lux, corresponding to about 14 to 41 W m-2; Kalff,
2002) and showed a “large” effect size. This effect was, however, not
significant because of high variability across impact streams (“large” effect
in Struptjärn, no change in Lillsjölidtjärnen; stream-specific
data not shown). In the impact lakes, whole-lake temperatures increased from
10.7 ∘C in the before period to 11.5 ∘C in the after
period, but this increase was 0.4 ∘C less than the increase in the
control lakes. Mixing depth decreased from 1.7 to 1.5 m in impact lakes,
while it remained at 1.8 m in control lakes. These thermal effects were
significant but of “small” size (Table 2).
Effect size of forest clear-cutting on DIC and CH4
concentrations [µM] in groundwater in the impact catchments as shown
in Fig. 3. Given are linear mixed-effects model slope estimates (mean), their
standard errors (SEs), degrees of freedom (df), t and p values, and Cohen'
D. Statistically significant p values (<0.05) are highlighted bold.
Figure
Substance
Soil depth [cm]
Effect size (slope)
Cohen's D
mean
SE
df
t
p
3a
DIC
37.5–42.5
533.3
175.7
68
3.0
0.00
0.63
3c
DIC
5–105
458.0
605.8
69
0.8
0.45
0.30
3b
CH4
37.5–42.5
93.4
44.4
66
2.1
0.04
1.62
3d
CH4
5–105
139.0
182.2
69
0.8
0.45
0.71
Forest clear-cuts did not affect concentrations of O2, DOC, and DIN
in groundwater, stream water, or lake water. Epilimnion and hypolimnion O2
concentrations were around 8 and 1–2 mg L-1, respectively (Table 2).
Hypolimnetic water did quickly turn anoxic during summer stratification
(Fig. S4). The DOC concentrations ranged from 63 to 77 mg L-1 in
groundwater, 25 to 29 mg L-1 in streams, and 18 to 21 mg L-1 in
lakes (Table 2). Concentrations of DIN ranged from 467 to
538 µg L-1 in groundwater, 21 to 32 µg L-1 in
stream water, and 14 to 20 µg L-1 in lake water.
Concentrations of TN decreased in impact streams from 595 to
505 µg L-1; this is a significant “medium” size effect
relative to the increase in control streams from 498 to
531 µg L-1. However, TN remained unaffected in groundwater
(1572–1958 µg L-1) and lake water (367 to
446 µg L-1). Spectral absorbance at 420 nm ranged from 12 to
15 m-1 in streams and 9 to 13 m-1 in lakes and was not affected
by clear-cutting. However, pH showed a significant BACI effect and increased
more in control systems compared to impact systems in the after period relative to
the before period: from 3.9 to 4.8 in the control stream and from 4.4 to 4.6
in the impact streams, and from 4.2 to 5 in control lakes and from 5.1 to 5.4
in the impact lakes (Table 2).
Most hydrological and physicochemical parameters remained unaffected by the
treatment even after site preparation (Table S4). The only significant BACI
effects concerned “medium” size decreases in stream pH and increases
in stream DIN concentrations, and “small” or “medium” size
decreases in hypolimnetic and whole-lake temperatures and mixing depths and
increases in Schmidt stability.
Concentrations of DIC and dissolved CH4 in groundwater at
depth-specific locations (37.5–42.5 cm; a–b) and depth-integrated
locations (5–105 cm; c–d) before and after clear-cutting at
impact sites, and the respective differences between before and after
(ΔAfter, shown in the same units). Each bar represents mean values
(±propagated standard errors) of repeated observations over time.
Significant (p<0.05) effect sizes are marked by an asterisk. Abbreviations:
n is the number of observations.
Time series of lake–atmosphere CO2 fluxes based on daily
means of 2-hourly concentration measurements (grey lines), biweekly spot
measurements (blue dots), and the k model by Cole and Caraco (1998). Given
are absolute fluxes and differences (ΔCO2) between impact
and control lakes. Shadings and error bars show propagated standard errors
(see Fig. S3 and Text S6). Gap-filled data are colored in red. Bars show the
lake ice cover duration with uncertainties expressed by grey scale
(dark = minimum duration, light = maximum duration). Dashed lines
mark the timing of forest clear-cutting (2013) and site preparation (2014).
Units are consistent across all panels. Abbreviations:
SR is Stortjärn, OB is Övre Björntjärn,
ST is Struptjärn, and LL is Lillsjölidtjärnen.
Response of GHG concentrations
Groundwater DIC and CH4 concentrations increased in response to
forest clear-cutting. Specifically, in shallow groundwater (37.5–42.5 cm),
DIC concentrations increased from 992 to 1345 µM at control sites
but from 957 to 1846 µM at impact sites; this is a significant
“medium” effect size of +533 µM or +56 % relative to
the impact sites in the control year (Fig. 3a, Table 3). Whole-soil profile
(5–105 cm) DIC concentrations increased at similar rates “medium”
effect size of +458 µM), yet this change was not statistically
significant (Fig. 3c, Table 3). CH4 concentrations in shallow
groundwater decreased from 24 to 16 µM in control sites but
increased from 11 to 94 µM at impact sites; this is a significant
“large” effect size of +93 µM or +822 % relative to
the impact sites in the control year (Fig. 3b, Table 3). Whole-soil profile
CH4 concentrations increased at even larger absolute rates (+139 µM), but this change was only of “medium” size and not
statistically significant due to high variability (Fig. 3d, Table 3).
Effect size of forest clear-cutting on fluxes of CO2,
CH4, and N2O across the interface between lakes or streams and
the atmosphere as shown in Figs. 6 and 7. Shown are linear mixed-effects
model slope estimates, their standard errors (SEs), degrees of freedom (df),
t and p values, and Cohen' D, summarized as arithmetic means over ten
bootstrap runs that take uncertainty from gap-filling and gas flux models
into account (see Fig. S3). This uncertainty is expressed as bootstrap
standard errors (bse) of p values. For lake–atmosphere fluxes, estimates
based on three different k models are shown. Note that parameter estimates
are based on log+n transformed data, where n is the smallest number
that, when added, leads to positive normal values. Abbreviations:
Logger is the daily mean of 2-hourly measurements,
Chamber is the floating chamber, and Spot is the spot measurement.
Figure
Gas
Flux type
System
Method
k model
Effect size (slope)
Cohen's D
Mean
SE
df
t
p
bse
6a
CO2
Diffusion
Lake
Logger
Cole
0.13
0.11
965
1.25
0.23
0.03
0.02
–
CO2
Diffusion
Lake
Logger
Vachon
0.15
0.10
965
1.45
0.17
0.03
0.02
–
CO2
Diffusion
Lake
Logger
Heiskanen
0.09
0.07
965
1.33
0.31
0.06
-0.03
6d
CO2
Diffusion
Stream
Logger
This study
0.16
0.23
982
0.77
0.47
0.07
0.08
6b
CH4*
Diffusion
Lake
Spot
Cole
0.00
0.21
72
0.00
0.93
0.02
0.14
–
CH4*
Diffusion
Lake
Spot
Vachon
-0.01
0.22
72
-0.07
0.91
0.02
0.16
–
CH4
Diffusion
Lake
Spot
Heiskanen
-0.01
0.22
72
-0.06
0.88
0.03
0.31
7
CH4*
Diffusion + ebullition
Lake
Chamber
–
-0.02
0.24
33
-0.20
0.49
0.09
-0.13
6e
CH4*
Diffusion
Stream
Spot
This study
0.04
0.08
74
0.51
0.62
0.05
0.07
6c
N2O
Diffusion
Lake
Spot
Cole
-0.08
0.05
48
-1.45
0.17
0.02
-0.03
–
N2O
Diffusion
Lake
Spot
Vachon
-0.09
0.06
48
-1.45
0.16
0.01
-0.04
–
N2O*
Diffusion
Lake
Spot
Heiskanen
-0.11
0.07
48
-1.56
0.13
0.02
-0.03
6f
N2O*
Diffusion
Stream
Spot
This study
-0.01
0.10
47
-0.05
0.87
0.03
-0.07
* Assumption on nonadditivity of paired differences in
before period not met.
Time series of stream–atmosphere CO2 fluxes based on daily
means of 2-hourly concentration measurements (dark grey lines) and biweekly
spot measurements ±standard errors (blue dots and error bars). Given
are absolute fluxes and differences (ΔCO2) between impact
and control streams. Shadings and error bars show propagated standard errors
(see Fig. S3 and Text S6). Gap-filled data are colored in red. Dashed lines
mark the timing of forest clear-cutting (2013) and site preparation (2014).
Units are consistent across all panels. Abbreviations:
SR is Stortjärn, OB is Övre Björntjärn,
ST is Struptjärn, and LL is Lillsjölidtjärnen.
Site preparation did not cause any additional effects on groundwater DIC and
CH4 concentrations (Table S5). However, effect sizes remained at
“medium” (+518 to +799 µM) and “large” levels
(+69 to +208 µM), respectively, and DIC in shallow groundwater
was still significantly elevated relative to reference conditions.
In streams and lakes, CO2 concentrations were between 269 and
349, and 95 and 109 µM; CH4 concentrations
between 0.2 and 3.4 and between 0.3 and 1.1 µM; and N2O
concentrations between 16 and 22 nM and between 12 and 17 nM, respectively
(Table S6). Stream and lake water GHG concentrations did not respond to
forest clear-cutting or site preparation, except for stream CO2
that increased less in impact streams (from 314 to 349 µM after
clear-cutting and 329 µM after site preparation) relative to the
control stream (from 269 to 347 and 314 µM, respectively).
Response of GHG emissions from streams and lakes
Lakes and streams emitted CO2, CH4, and N2O to the
atmosphere, but the fluxes did not respond to forest clear-cutting. For
CO2 fluxes, this observation is based on daily averages of 2-hourly
time series shown in Figs. 4 and 5. CO2 fluxes varied synchronously
across all lakes at daily and seasonal timescales, with emission events
during storms and a general increase towards autumn (Fig. 4). Daily means of
2-hourly estimates were validated by estimates based on biweekly spot
measurements (LME, slope = 0.97±0.03, p<0.001, marginal
R2=0.87, residual standard error
(rse) = 9.9 mmol m-2 d-1, n=180). Time series of the
differences between impact and control lakes did not reveal any systematic
change in offset or seasonality between the before and after period.
Depending on the k model chosen, seasonal mean CO2 fluxes varied
between 41 and 99 mmol m-2 d-1. However, consistent for all
models, there was no significant BACI effect associated with forest
clear-cuts (Fig. 6a, Table 4) or site preparation (Table S7).
Fluxes of CO2, CH4, and N2O across the
interface between lakes (a–c) or streams (d–f) and the
atmosphere in control and impact catchments before and after forest
clear-cutting, and the respective differences between before and after
(ΔAfter, shown in the same units). Each bar represents mean values
(±propagated standard errors) of repeated observations over time,
summarized as arithmetic means over ten bootstrap runs that take uncertainty
from gap-filling and gas flux models into account (see Fig. S3 and Text S6).
Data are based on daily means of 2-hourly measurements (CO2) or
biweekly (CH4 and N2O) concentration measurements.
Lake–atmosphere fluxes are here calculated using the k model by Cole and
Caraco (1998). Abbreviations: n is the number of observations.
In streams, 2-hourly time series revealed pronounced CO2 emission
peaks during storm events (Fig. 5). These emission peaks were strikingly
synchronous between streams, but peak amplitudes varied from around
200 to up to 2000 mmol m-2 d-1
(Fig. 5). Between-stream differences did not change in the after period
relative to the before period, indicated by nonsignificant BACI effects
associated to forest clear-cutting (Table 4) and site preparation (Table S7).
Daily means of 2-hourly emission estimates were validated by estimates based
on biweekly spot measurements with excellent agreement (LME,
slope = 1.05±0.01, p<0.001, marginal R2=0.97,
rse = 28.3 mmol m-2 d-1, n=180).
Seasonal means of diffusive CH4 fluxes across the lake–atmosphere
interface also varied depending on the k model chosen (between 0.17 and
0.81 mmol m-2 d-1), but regardless of model choice there was no
significant BACI effect associated with forest clear-cuts (Fig. 6b, Table 4)
or site preparation (Table S7). This result, derived from spot measurements
during June–September at the deepest point of the lake, was also confirmed for total CH4 fluxes (including ebullition) by independent weekly
measurements using floating chambers deployed across the whole lake during
mid-June to late August (Fig. 7, Table 4). Accordingly, total CH4
fluxes integrated over the whole lake surface varied from 0.22 to
0.52 mmol m-2 d-1, of which 72 %–82 % was due to
ebullition and the remainder due to diffusion. Diffusive CH4 fluxes
across the stream–atmosphere interface varied from 1.2 to
1.3 mmol m-2 d-1 in the control stream and from 0.07 to
0.18 mmol m-2 d-1 in the impact streams (Fig. 6e) and remained
unaffected by forest clear-cutting or site preparation (Tables 4, S7).
Fluxes of CH4 by diffusion (shaded) and ebullition
(non-shaded) across the lake–atmosphere interface in control and impact
catchments before and after forest clear-cutting, and the respective
differences between before and after (ΔAfter, shown in the same
units). Fluxes were measured by the use of flux chambers (e.g., independent
approach compared to fluxes calculated from concentrations in Fig. 6). Each
bar represents mean values (±propagated standard errors) of whole-lake
fluxes measured weekly from mid-June to mid-August 2012 and 2014, summarized
as arithmetic means over ten bootstrap runs that take between-chamber
variability into account (see Fig. S3 and Text S6). Whole-lake fluxes are the
area-weighted mean of depth-zone specific fluxes. Abbreviations:
n is the number of observations.
Fluxes of (a) CO2 and (c) CH4
across the stream–atmosphere interface along stream transects in the control
catchment (C) and two impact catchments (I) before and after forest
clear-cutting (OB = Övre Björntjärn, ST = Struptjärn,
LL = Lillsjölidtjärnen,). Effect sizes (ES) defined as the
before–after change in the difference between control and impact streams are
shown in panels (b) and (d). Each point represents seasonal
mean values (±standard errors) of biweekly observations, summarized as
arithmetic means over ten bootstrap runs that take uncertainty from gap-filling and gas flux models into account (see Fig. S3 and Text S6).
Across five sites sampled along 300 m long stream reaches, CO2 and
CH4 fluxes varied from 43 to 465 and from
-0.02 to 5.43 mmol m-2 d-1, respectively (Fig. 8a, c). BACI
effect sizes were “small” but had a large variability ranging from
-53 to 295 and -4.32 to
0.27 mmol m-2 d-1 (Fig. 8b, d, Table S8). These effect sizes
were nonsignificant across the whole length of both impact stream reaches
and did not vary across the clear-cut gradient, with a 5-fold increase in
the areal proportion of the stream reach drainage area affected by forest
clear-cutting (LME, slope = 10.9±5.3 mmol CO2 m-2 d-1 % clear-cut-1, t=2.06,
p=0.08, marginal R2=0.34, and 0.002±0.003 mmol CH4 m-2 d-1 % clear-cut-1,
t=0.54, p=0.61, marginal R2=0.03, respectively).
Seasonal means of diffusive N2O fluxes across the lake–atmosphere
interface varied, depending on the k model chosen, between 0.4 and
3.5 µmol m-2 d-1. Consistent for all k models, there
was no significant BACI effect associated with forest clear-cuts (Fig. 6c,
Table 4). The same was true for diffusive N2O fluxes across the
stream–atmosphere interface, ranging from 1.7 to
3.5 µmol m-2 d-1 (Fig. 6f, Table 4).
Discussion
This study is to our knowledge the first experimental assessment of forest
clear-cut and site preparation effects on GHG fluxes between inland waters
and the atmosphere and expands on previous forest clear-cutting experiments
that primarily have focused on effects on hydrology or water chemistry. Our
whole-catchment BACI experiment showed no significant initial effects of
forest clear-cutting and site preparation on GHG fluxes in streams or lakes
despite enhanced potential GHG supply from hillslope groundwater. This
suggests that the generally strong effects of clear-cut forestry on
terrestrial C and nutrient cycling are not necessarily translated to major
effects in GHG emissions from recipient downstream aquatic ecosystems. Our
results are representative for low-productive boreal forest systems
(<3 m3 ha-1 yr-1) in relatively flat landscapes, which
represent the dominant forest type subject to clear-cut forestry in the
boreal biome (Zheng et al., 2004; SFA, 2014).
What caused the contrasting response in GHGs between groundwater and open
water? Open water CO2, CH4, and N2O can result from
bacterial degradation of organic matter (Bogard and del Giorgio, 2016;
Hotchkiss et al., 2015; Peura et al., 2014) and incomplete denitrification
and nitrification (McCrackin and Elser, 2010; Seitzinger, 1988),
respectively. These processes are connected to DOC and DIN dynamics. The lack
of initial responses in catchment-derived DOC and DIN could, therefore,
explain the lack of responses in aquatic GHG fluxes. However, aquatic GHG
fluxes are also fueled by direct catchment inputs of the respective dissolved
gases (Rasilo et al., 2017; Striegl and Michmerhuizen, 1998; Öquist et
al., 2009). Groundwater CO2 and CH4 concentration increased
in response to the clear-cut treatment (Fig. 3), potentially as a consequence
of enhanced organic matter degradation due to enhanced post-clear-cut soil
temperatures (Bond-Lamberty et al., 2004; Schelker et al., 2013a) or reduced
CH4 oxidation (Bradford et al., 2000; Kulmala et al., 2014). Some
CO2 and CH4 may be formed from degradation of logging
residues and litter in the soil (Mäkiranta et al., 2012; Palviainen et
al., 2004). However, it is presently unclear whether this source has
contributed to enhanced groundwater CO2 and CH4
concentrations. Concentration increases were most pronounced in shallow
groundwater, the hotspot for riparian GHG export to headwater streams in our
study region (Leith et al., 2015). Considering that clear-cut areas covered
on average ∼30 % of the stream and lake catchments, but ∼80 % of the subcatchments of the groundwater sampling sites, the
56 % increase in groundwater CO2 concentrations could have caused
an increase of at most 21 % (0.3/0.8⋅0.56) in CO2 concentrations in the impact
streams and lakes. Part of the lack of a response could be due to
difficulties in detecting such subtle changes given the relatively high
natural variability (Table S6). However, the 8-fold increase in groundwater
CH4 concentrations could have supported at most 3-fold increases
(0.3/0.8⋅8) in CH4 concentrations in streams and lakes,
much larger than those observed in our study (Table S6). This mismatch suggests the
following three alternative explanations.
First, groundwater-derived GHGs were transport-limited and, hence,
only a minor source for GHG fluxes in our lakes and streams. Even though
external sources often dominate CO2 and CH4 emissions in
headwater streams (Hotchkiss et al., 2015; Öquist et al., 2009; Jones and
Mulholland, 1998), soil-derived gases may only be a minor source for GHGs in
headwaters during summer low-flow conditions (Dinsmore et al., 2009; Rasilo
et al., 2017). Such conditions were present over extended parts during the
dry post-treatment period (Tables S1, S4).
Second, the riparian zone effectively buffered potential clear-cut
and site preparation effects on aquatic GHG fluxes. In part, this could be
because the riparian buffer zones were wide enough to retain their wind
sheltering function. Wind speeds indeed remained unaffected by clear-cutting
on nearby mires (Table 2). This would indicate that riparian buffer zones may
have prevented additional forcing on air–water gas exchange velocities,
assuming that relative changes in wind speeds on the mires were
representative for lake conditions (Text S2). In addition, riparian zones may
have acted as efficient reactors of GHGs and reduced their concentration
during transport from the hillslope to the open water (Leith et al., 2015;
Rasilo et al., 2017, 2012). This applies especially to methane, which can be
efficiently oxidized in the large redox gradients in riparian zones, similar
to inorganic N (Blackburn et al., 2017).
Third, in-stream processing effectively buffered potential clear-cut
and site preparation effects on aquatic GHG fluxes. In boreal headwater
streams, metabolism can strongly regulate CO2 emissions at summer low-flow conditions (Rasilo et al., 2017). Therefore, additional CO2
leaking from clear-cut soils could have been taken up by algae stimulated in
growth by increased light intensities (Kiffney et al., 2003; Clapcott and
Barmuta, 2010). We indeed observed strong algae blooms in the inlet stream of
Struptjärn in response to a tripling in light intensities after forest
clear-cutting (Fig. S5). Increased algal CO2 and N uptake could
explain the observed decrease in stream CO2 and TN concentrations. In
the experimental lakes, however, we did not observe any change in primary
production in response to the treatment (Deininger et al., 2018). Additional
CH4 transported from clear-cut soils could have been efficiently
oxidized. Enhanced in-stream CH4 oxidation in the sediments is likely
primarily an effect of the commonly found substrate limitation of CH4
oxidation (e.g., Bastviken, 2009; Duc et al., 2010; Segers, 1998), i.e.,
CH4 oxidizer communities have a higher capacity than commonly
expressed and will oxidize more CH4 when concentrations increase.
Despite lacking mechanistic understanding of the biogeochemical function of
the riparian zones and headwater streams in our catchments, we can conclude
from groundwater, stream, and lake observations that they must have
effectively prevented the potential increase in aquatic GHG emissions. In
addition, the riparian buffer vegetation left aside could have acted as wind
shelters that prevented potential increases in emissions due to enhanced
near-surface turbulence. However, the biogeochemical processing of GHGs in
the riparian zone–stream continuum should be given special attention in
future clear-cut experiments to resolve the mismatch between responses in
hillslope groundwater and receiving streams and lakes.
Our experiment revealed statistically significant BACI effects on pH and lake
thermal conditions. The relative pH decrease of 0.5 units in impact relative
to control systems is a common clear-cut effect in northern forests (Martin
et al., 2000; Tremblay et al., 2009). However, most relevant for the scope of
this paper, this change did not bias CO2 concentrations because
shifts in the bicarbonate buffer system are minor (≤2 %) at the
observed pH levels of ≤5 (Stumm and Morgan, 1995). Likewise, pH is not a
major control on aquatic CH4 cycling (Stanley et al., 2016). This
applies even to N2O here, because we did not observe any increase in
N2O emissions in the post-clear-cut period that would be expected
from the positive effect of higher pH levels on nitrification (Soued et al.,
2016). Whole-lake temperatures and mixing depths decreased significantly in
impact lakes relative to control lakes. However, these effects were small in
absolute terms (-0.4 ∘C, -0.2 m, respectively) and associated
with relative epilimnion volume changes of about 10 %. Such subtle
changes are unlikely to have had major effects on metabolism and
lake-internal vertical exchange processes as a driver of GHG fluxes.
In contrast to many previous boreal forest clear-cut experiments (Schelker et
al., 2012; Nieminen, 2004; Lamontagne et al., 2000; Winkler et al., 2009;
Bertolo and Magnan, 2007; Palviainen et al., 2014), hydrology and water
chemistry remained largely unaffected by our treatments. The absence of
effects is no absolute evidence of an absence of impacts, but the response is
low relative to natural variability and restricted to initial responses
within 3 years after clear-cutting. First, hydrological
responses may have been masked or delayed given that the post-clear-cut
period was much dryer than the pre-clear-cut period (Buttle and Metcalfe,
2000; Schelker et al., 2013b; Kreutzweiser et al., 2008). During the
post-clear-cut period, groundwater levels may have fallen below a threshold
level in both control and impact catchments where any minor clear-cut induced
increase in water levels would not have translated into comparable increases
in stream discharge. This is because stream discharge largely depends on the
transmissivity which typically decreases exponentially with depth in Swedish
boreal soils (Bishop et al., 2011). Second, the proportion of
clear-cuts in our catchments (18 %–44 %) was just around the
threshold level (∼30 %), above which significant effects on
hydrology and water chemistry can be expected in our study region (Ide et
al., 2013; Palviainen et al., 2014; Schelker et al., 2014). These threshold
values can however vary and are highly site-specific (Kreutzweiser et al.,
2008; Palviainen et al., 2015). For example, the relatively high baseline DOC
concentrations in our streams and lakes (20 and 29 mg L-1,
respectively) are potentially less likely to be further enhanced by forest
clear-cuts. Relatively wide riparian buffer strips and gentle catchment
slopes (Table 1) may have further dampened these effects (Kreutzweiser et
al., 2008). Third, the time it takes for the system to respond may
have exceeded the experimental period. For example, it can take 4 to
10 years for groundwater nitrate concentrations to respond to clear-cutting
in low-productive forest ecosystems due to tight terrestrial N cycling
(Futter et al., 2010). Similar delays have been found for responses in boreal
stream and lake water chemistry, often triggered by site preparation
(Schelker et al., 2012; Palviainen et al., 2014). In the first year after
site preparation, only stream DIN concentrations started to increase, but not
DOC or GHG concentrations. However, the absence of initial effects does not
necessarily imply absence of longer-term effects. On decadal timescales,
forestry may change soil C cycling (Diochon et al., 2009), leading to
enhanced terrestrial organic matter exports and lake CO2 emissions
(Ouellet et al., 2012). Clearly, future work should explore how universal our
results are across different hydrological conditions, other types of systems,
and longer timescales.
The particular complexity and multiple controls of catchment-scale GHG fluxes
emphasize the need of large-scale experiments to assess treatment responses
in realistic natural settings (Schindler, 1998). We addressed this challenge
by sampling at high spatial and temporal resolution. However, logistical
challenges forced us to restrict the analysis to 1 June to 30 September, the
period for which we were able to collect consistent data in all years and all
catchments. Hence, we do not account for potential clear-cut effects on
stream–atmosphere fluxes during snowmelt (April–May) or late autumn storms
(October–November), when a large proportion of GHGs in streams can be
supplied from catchment soils (Leith et al., 2015; Dinsmore et al., 2013).
Similarly, we do not account for potential clear-cut effects on
lake–atmosphere fluxes during ice-breakup (mid-May), which can be fueled by
gases directly derived from catchment inputs or be a result of degradation of
catchment-derived organic matter during winter (Denfeld et al., 2015; Vachon
et al., 2017). Peak flow conditions during spring or late autumn are hot
moments of clear-cut effects on C and N export to aquatic systems (Schelker
et al., 2016; Laudon et al., 2009; Ide et al., 2013). Spring can also
contribute disproportionally to annual GHG fluxes of boreal headwater streams
(Dinsmore et al., 2013; Natchimuthu et al., 2017) and lakes (Huotari et al.,
2009; Karlsson et al., 2013). Strong seasonality in CO2 fluxes was
also apparent in our systems (Figs. 4, 5). Hence, future investigations of
clear-cut effects should be based on whole-year sampling.