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Forest Ecology and Management 242 (2007) 587–597
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Soil Profile CO2 concentrations in forested and clear cut sites in
Nova Scotia, Canada
Asfaw Bekele a,*, Lisa Kellman b, Hugo Beltrami b
b
a
Imperial Oil Resources, 3535 Research Road NW, Calgary, Alberta T2L 2K8, Canada
Environmental Sciences Research Center, St. Francis Xavier University, Antigonish NS B2G 2W5, Canada
Received 21 July 2006; received in revised form 21 January 2007; accepted 23 January 2007
Abstract
Subsurface soil CO2 concentration is an important component of the terrestrial C budget and site specific information on the spatial and
temporal variability and how it responds to forest management is needed for accurately estimating ecosystem C budgets. The objectives of this
study were to examine the within site spatial and seasonal variability, and differences between sites of subsurface CO2 concentrations as affected by
microtopography, clear cut harvesting, and soil texture. To address these objectives, we used two paired forested and clear cut sites of contrasting
soil texture in Nova Scotia, Canada. The soil texture at the Lakevale pair (LF = intact forest and LCC = clear cut) was sandy while that of Pomquet
(PF: intact forest and PCC = clear cut) was clayey. Two and half-years after clear cut harvesting, data were collected from each site on an
approximately monthly time interval for 1 year from four mineral soil depths (0, 5, 20 and 35 cm) and 10 microsites separated by approximately
10 m and representing three local topographic features (level, trough and hump). We also monitored soil temperature and moisture with depth at a
representative location at each site. Soil CO2 showed high within site variability and ranged between 346 and 28,086 ppmv (median = 2835) for LF,
319–29,135 (median = 2802) for LCC, 364–29,016 (median = 2345) for PF, and 407–81,053 (median = 5690) for PCC. Differences due to
microtopographic positions were not statistically significant ( p > 0.05). Median CO2 concentration and its variability generally increased with
depth. Seasonally aggregated data indicated a distinct pattern with median CO2 concentrations as high as 8646 ppmv (95% confidence
interval = 6937–12,142) during summer at PCC and as low as 1570 ppmv (95% confidence interval = 1290–1920) at LCC during winter. Despite
the high within site variability, PCC showed significantly higher median CO2 concentration than PF. No significant difference in subsurface CO2
concentration was found between LF and LCC. Subsurface CO2 concentration showed significant quadratic correlation (R2 = 0.32–0.85, p < 0.05)
with soil temperature and volumetric water content only for the Lakevale sites, suggesting the presence of strong soil texture control on subsurface
CO2 concentration dynamics at these sites.
# 2007 Elsevier B.V. All rights reserved.
Keywords: CO2 concentration; Forest management; Soil temperature; Soil moisture; Soil texture
1. Introduction
Subsurface processes exert a significant control on soil C
dynamics that affect ecosystem carbon balance (Valentini et al.,
2000). Studies examining soil C dynamics in ecosystems are
increasingly employing the use of subsurface CO2 sampling
techniques to assess in situ processes through depth in soils
(Fernandez et al., 1993; Certini et al., 2003; Welsch and
Hornberger, 2004;). These techniques are relatively labor
intensive and it is often not possible to adequately investigate
the spatial variability of gas concentrations at each depth of
* Corresponding author. Tel.: +1 403 284 7541; fax: +1 403 284 7589.
E-mail address: [email protected] (A. Bekele).
0378-1127/$ – see front matter # 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2007.01.088
interest at a given site. Variability in subsurface CO2
concentration represents differences in rates of CO2 production
and transport caused by the complex interactions between
biotic and environmental factors.
Soil air generally exhibits much higher CO2 levels relative to
the atmosphere. Plant root and microbial respiration (source
factors), which are influenced largely by soil temperature and
moisture content, and soil physical characteristics (transport
factors) mainly determine the variability in subsurface soil CO2
concentration (Hamada and Tanaka, 2001; Jassal et al., 2004;
Oh et al., 2005). Previous research showed that forest soil CO2
concentrations vary significantly as a function of depth, season,
soil moisture and/or temperature, and forest type and
management (Hamada and Tanaka, 2001; Certini et al.,
2003; Pumpanen et al., 2003). These studies, however, were
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A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
based on a few and often single profile measurements and did
not account for the spatial variability of CO2 concentrations in
evaluating treatment effects. Although the existence of
pronounced spatial variation in subsurface soil CO2 concentration was acknowledged (Kursar, 1989; Rayment and Jarvis,
2000; Welsch and Hornberger, 2004), only a few studies have
examined the magnitude of spatial variability of subsurface
CO2 concentration within forest soils. Detailed characterization
of CO2 spatial and temporal variability at various depths and
response to forest management is useful for at least two
reasons: (1) it provides clues about processes controlling CO2
production and where in the soil profile it is produced (Welsch
and Hornberger, 2004); (2) it allows for the more accurate
prediction of forest-related feedbacks on the global C cycle
(Wiseman and Seiler, 2004). Further, research aimed at
evaluating the impact of forest management on CO2 production
in soil profiles must consider both the spatial and temporal
variability of CO2 concentration in the soil.
Results from other studies suggest both an increase (Lytle
and Cronan, 1998) and a decrease (Striegl and Wickland, 1998)
of soil CO2 concentration in response to forest harvesting.
However, these conclusions were based on data collected
immediately following forest harvesting. Only a few studies
(Fleming et al., 2006; Peng and Thomas, 2006) evaluated
longer-term CO2 concentration response to forest harvesting.
Our data were collected two and half-years after clear cutting
and provided information on the period beyond initial
treatment effects. The primary objectives of the study were
to (1) examine whether (shallow) CO2 concentrations were
controlled by microtopography, and determine the variability
in subsurface concentrations within each site; (2) determine
how concentration profiles differed between sites and
treatments (clear cut versus intact forest); (3) determine
seasonal patterns in concentration profiles; (4) explore the
relationship between subsurface CO2 concentration and
volumetric soil water content and soil temperature. Consequently, the following null hypotheses were tested: (1) median
subsurface CO2 concentrations did not differ among microtopographic positions and season, and between sites and
treatments; (2) there was no correlation between median CO2
concentration, and volumetric soil water content and soil
temperature. We expected to see greater concentrations at a
given depth in clear cut than forested sites because CO2
generated via root decay and decomposition of detritus in clear
cut sites often far exceed that of root respiration in forested
sites (Lytle and Cronan, 1998).
2. Materials and methods
2.1. Study area
The study sites were located in Lakevale (458450 600 N,
618560 4900 W) and Pomquet (458390 2200 N, 618500 3200 W), Antigonish county, Nova Scotia, Canada and were separated by
approximately 13 km. Two pairs of adjacent forested and clear
cut sites were selected for the study. The Lakevale paired sites
(LF: intact forest and LCC: clear cut) have an elevation of
approximately 60 m. The soils at Lakevale are classified as
Millbrook soil with brown loam over reddish brown gravely
clay loam (Cann and Hilchey, 1954). The trees at LF were
approximately 85-year-old and consist of Balsam fir (Abies
balsamea (L.) Mill, 38%), red spruce (Picea rubens Sarg., 35%)
and white spruce (Picea glauca (Moench) Voss, 11%). The
LCC site was clear cut in the spring of 2002 and sprayed with a
herbicide (Vision1 glyphosate (N-phosphonomethyl glycine),
Monsanto Corp., St. Louis, MO) in late summer 2003 to hinder
the growth of deciduous plants. The new growth consists of a
mixture of wild red raspberry (Rubus idaeus L.), red maple
(Acer rubrum L.), and trembling aspen (Populus tremuloides
Michx.).
The Pompquet paired sites (PF: intact forest and PCC: clear
cut) are at a relatively lower elevation, approximately 5 m. The
soils at Pomquet are classified as Queens soil with light brown
clay loam over reddish brown clay loam (Cann and Hilchey,
1954). Distinct gray and yellow mottles occured in this soil,
indicating some fluctuation in depth to water table throughout
the year. The Queens soil is clayey in texture and has less gravel
and stone than the Millbrook soil (Table 1). This heavy textured
soil drains very slowly compared with the Millbrook soil. Soils
from the two sites were free of carbonates and, therefore, there
was no CO2 contribution from carbonate dissolution. The trees
at PF were relatively younger, approximately 55 yr old, and
consisted of mainly red spruce (86%). Other trees at PF
included trembling aspen (5%), sugar maple (Acer saccharum
Marsh, 4%) and paper birch (Betula papyrifera Marsh., 4%).
The PCC site was clear cut in the spring of 2002. Roots at
Pomquet and Lakevale reach maximum depths of 40–50 cm
and 50–60 cm, respectively.
Table 1
Selected soil characteristics for Millbrook (Lakevale site) and Queens (Pomquet site) soils (Cann and Hilchey, 1954)
Soil type
Depth (cm)
pH (H2O)
Gravel (%)
Sand (%)
Silt (%)
Clay (%)
Texturea
Millbrook
0–5
5–8
8–25
25–38
3.7
3.8
4.5
4.6
–
8.0
20.0
32.0
–
14.2
25.2
27.6
–
54.0
42.0
41.0
–
31.8
32.8
31.4
–
SCL
CL
GCL
Queens
0–5
5–20
20–36
36–51
4.7
4.6
4.7
6.3
–
21.0
18.0
8.5
–
20.0
19.2
15.2
–
38.8
31.6
29.2
–
41.2
49.2
55.6
–
C
C
C
a
SCL: Silty clay loam; CL: Clay loam; GCL: Gravely clay loam; C = Clay.
A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
Based on 30 years (1970–2000) data from the nearest
weather station at Collegeville, NS, some 25 km from the
Pomquet sites, both paired sites have mean annual precipitation
of 1384 mm evenly distributed throughout the year. Snow fall
accounted for 15% of the precipitation (Environment Canada
Climate Normals: http://www.climate.weatheroffice.ec.gc.ca/
climate_normals/index_e.html; viewed 5 September 2006).
The sites have a mean annual air temperatures of 5.8 8C. Soil
freezing rarely occured at these sites and only when the snow
cover was thin (Beltrami, 2001). The region is described as
humid to perhumid with deficiency of moisture during the
summer months with potential evapotranspiration rates often
exceeding precipitation from May to August (Cann and
Hilchey, 1954).
As part of a larger project conducted at the Environmental
Sciences Research Center (ESRC) at St. Francis Xavier
University, each site (LF, LCC, PF and PCC) was equipped with
a meteorological station monitoring standard aboveground
meteorological information and detailed subsurface thermal
and moisture budgets. The detail of the functioning of these
meteorological stations was provided in Beltrami and Kellman
(2003). Briefly, soil temperature was measured using six
CS107b soil temperature probes (Cambell Scientific Inc.,
Logan, UT) installed at depths of 0, 5, 10, 20, 50 and 100 cm
along with two CS-615 soil moisture probes (Cambell
Scientific Inc., Logan, UT) at depths of 5 (M1) and 35 cm
(M2) at each site. Soil temperature and volumetric water
content data from these sites were used to evaluate differences
in these soil attributes between sites and treatments and to
explore the relationships between CO2 concentrations versus
volumetric water content and soil temperature at each depth.
Soil temperature data corresponding to the depths of only 0, 5,
20 and 50 cm were used for this study. The 0 cm depth
represented the beginning of the depth of the mineral soil. The
mean thickness of the forest floor for the intact forest and clear
cut sites measured during soil gas sample probes installation
was 6.5 cm and 5.5 cm, respectively. The soil temperature at
50 cm depths was used to evaluate its relationships with CO2
concentration at the 35 cm depth. Measurements of volumetric
soil water content at the shallower depth (M1) represented
integrated volumetric water content within 0–25 cm depth. The
deeper soil moisture (M2) probe provided volumetric soil water
content estimate for the 25–50 cm depth. Therefore, M1 was
used to evaluate its relationship with CO2 concentrations at the
0, 5 and 20 cm depths while M2 was used to evaluate its
relationship with the CO2 concentration at the 35 cm depth.
Due to resource limitation, soil moisture and temperature data
from a single representative spot in a site was used to evaluate
its relationship to CO2 concentration data collected from 10
microsites from that site.
2.2. Soil gas sample probes
Ten subsurface soil air equilibration tubes were installed at
each site with sampling ports at 0, 5, 20, and 35 cm. After the
pits for the gas samplers were dug carefully, the holes for the
samplers were drilled into the side of the pit with a hammer drill
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and the samplers were fully inserted into the drilled holes. The
pits were backfilled with the soil in the same manner that it was
dug out with the tubing used for sample extraction coming up
out of the pit and secured to a post. The 0 cm sampler was
installed at the organic-mineral soil interface at each individual
well, and subsequent samplers were installed at the various
depths based on this reference sampler. These samplers were
constructed of 50 cm long polyvinylchloride (PVC) tubes with
a 1.2 cm inner diameter and an internal volume of 56.5 cm3. A
long narrow perforation in the PVC tube, covered by a water
resistant breathable membrane, allows soil air to diffuse into the
sampler from the surrounding air filled pore space. The ends
were sealed and connected to the surface by microbore tubing
(inner diameter = 0.762 mm; outer diameter = 2.286 mm)
fitted with three-way stopcocks that were used to extract
samples from the sampler.
To assess the within site spatial variability due to
microtopographic differences, 10 locations for gas profile
installation were selected so that three microtopographic
positions (level, trough and hump) were represented, each
separated by approximately 10 m. Thus, a total of 40 probes (10
probes at each site) were installed at the two paired sites.
Samples were collected in evacuated 6 ml exetainers at
approximately midday and monthly intervals for 1 year
between October 2004 and September 2005. Carbon dioxide
concentrations were measured in the laboratory with a LI-7000
CO2/H2O infrared gas analyzer (Li-Cor Inc., Lincoln, NE).
Initial inspection of the data indicated missing CO2 concentration values, which were assigned a value of zero as no CO2 data
were collected at that location. While it was suspected that
these data were a result of saturated conditions inhibiting CO2
production, it was possible that these zero values may have also
arisen in cases of instrument malfunction, sampling error, other
unknown factors or a combination of factors. As a result, data
analyses were made both on the full data set and on the data set
with zeroes excluded. Only minor differences were observed
when comparing results from the two data sets. Consequently,
only results from data with the zeroes excluded were presented
since the zero CO2 concentrations were not easily justifiable.
2.3. Statistical methods
Exploratory data analysis (EDA) was performed using
histograms and box plots. EDA as well as the test for normality
using the Shapiro-Wilk statistic (SAS Institute, 2004) indicated
that data were highly skewed and non-normally distributed.
Consequently, a nonparametric test based on ranked data was
used to test differences in CO2 concentration due to
microtopography, season, depth and clear cut harvesting.
The non-normal distributional characteristic of forest soil CO2
concentration has been observed in other studies (Fernandez
et al., 1993; Yavitt et al., 1995).
Data from microsites within a site were assumed independent in testing the hypotheses for this study. This is consistent
with Rayment and Jarvis (2000) where they reported a
separation distance of 10 m between CO2 concentration
samples from forest soils to be independent for a valid
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A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
statistical inference. Separate analyses were made for each site
to evaluate the within site variability of CO2 concentration due
to microtopography, season, and depth using the Kruskal–
Wallis test (Desu and Raghavarao, 2004). To evaluate the effect
of season, data were grouped into four seasons based on air
temperature: winter (December–February), spring (March–
May), summer (June–August), and fall (September–November). For skewed data, the median is the preferred measure of
center or ‘typical value’ than the mean (Zar, 1999). Comparison
of the 95% confidence intervals of the medians was used to
construct error bars and to determine which sample medians did
not differ significantly from each other. The 95% confidence
intervals for the medians were computed as outlined in Desu
and Raghavarao (2004) using the Proc Univariate procedure of
SAS (SAS Institute, 2004). When the 95% confidence intervals
of the medians overlap, the medians being compared were
considered statistically not different at 5% probability level;
otherwise the medians were considered statistically different.
There was significant seasonal variation; so season was used
as a blocking variable to test the effect of clear cutting on CO2
concentration by comparing LF to LCC and PF to PCC
separately using the Friedman’s test (Desu and Raghavarao,
2004). Blocking by season eliminates the season to season
variation in CO2 concentration and allows the determination of
clear cut harvesting effect on CO2 concentration. The
relationship between CO2 concentration versus volumetric
soil water content and soil temperature at each depth was
evaluated using the Proc Reg procedure of SAS (SAS Institute,
2004). Several regression models were evaluated to derive
empirical relationships between CO2 concentration and
volumetric soil water content and soil temperature to determine
which of these two factors were related to CO2 concentration
dynamics within these forest soils. Regression models
evaluated included the subsets of all potential independent
variables (soil temperature, volumetric soil water content, and
their simple linear, interaction and quadratic terms) and CO2
concentration as the dependent variable. The adjusted R2 was
used to judge the goodness of fit of the regression models and
determine the relative contribution of each of these two
environmental factors in explaining the variability in CO2
concentration. The adjusted R2 takes into account the number of
predictors in the model and is useful to compare different
regression models (Zar, 1999). All statistical significance was
judged at the 5% probability level.
significant pattern in CO2 concentration due to microtopography was observed at both paired sites (graph not shown).
While the lack of microtopographic dependence of CO2
concentration variability was unexpected, the result was not
surprising given the myriad of potential factors contributing to
the variability in subsurface CO2 concentration (Welsch and
Hornberger, 2004). Davidson et al. (1998) indicated that
although spatial heterogeneity in soil respiration rates within
the landscape is related to soil drainage (as was expected to
occur at contrasting microtopographies), the effects of drainage
on soil microbial activity might be confounded with differences
in net primary productivity and carbon inputs to the soil. In
addition, the effect of microtopography on CO2 concentration
may be less pronounced in humid regions such as Nova Scotia
than moisture limited regions. In drier regions, differences in
CO2 concentration can be observed since soil moisture and
temperature differences are generally related to microtopographic differences (Epron et al., 2006).
For subsequent data interpretation, the data from the three
microtopographic positions were pooled together to explore the
general seasonal and depth patterns of CO2 concentrations
within a site (Figs. 1 and 2). The seasonal patterns observed in
median CO2 concentrations were similar for both paired sites
except that fall and summer CO2 concentrations were
comparable at LF and LCC while PF and PCC showed
relatively lower, albeit statistically non significant, median CO2
concentrations for fall compared with summer (Fig. 1). Based
on the comparison of the width of the 95% confidence intervals
for the medians, statistically significant seasonal differences
were observed at the Lakevale pairs but not at the Pomquet
pairs. For both LF and LCC, CO2 concentration was
significantly lowest in winter and highest in summer and fall.
3. Results and discussion
3.1. Seasonal and within site spatial variability in
subsurface CO2 concentration
Measured CO2 concentration from the four depths (0, 5, 20
and 35 cm) and three microtopographic positions (flat, hump,
trough) for the study period (October 2004–September 2005)
varied widely within a site and ranged (in ppmv units) between
346–28,086 (median = 2835) for LF, 319–29,135 (median = 2802) for LCC, 364–29,016 (median = 2345) for PF,
and 407–81,053 (median = 5690) for PCC. No consistent
Fig. 1. The seasonal variability of subsurface CO2 concentration (0–35 cm
depth; October 2004–September 2005) from two paired intact forest and clear
cut sites of contrasting soil textures and three microtopographic positions. Error
bars represent 95% confidence intervals for median concentrations. When the
error bars overlap, the medians are not significantly different from each other at
5% probability level. LF: Lakevale forest; LCC: Lakevale clear cut; PF:
Pomquet forest; PCC: Pomquet clear cut.
A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
591
Fig. 2. The within site depth profile of CO2 concentration by season (October 2004–September 2005) for two paired intact forest and clear cut sites of contrasting soil
textures.. Error bars represent 95% confidence intervals for median concentrations. When the error bars overlap, the medians are not significantly different from each
other at 5% probability level. LF: Lakevale forest; LCC: Lakevale clear cut; PF: Pomquet forest; PCC: Pomquet clear cut.
Fig. 3. The temporal variability of (a) precipitation, and volumetric soil water content within the 0–25 cm depth, (b) percentile plots of volumetric soil water content
within the 0–25 cm depth, and (c) volumetric soil wate content within the 25–50 cm depth. LF: Lakevale forest, LCC: Lakevale clear cut, PF: Pomquet forest, and
PCC: Pomquet clear cut.
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A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
At LF and LCC, the effect of season became weaker with
increasing depth (Fig. 2).
Concentrations of CO2 significantly increased with increasing depth at LF, LCC and PF regardless of season. For PCC,
CO2 concentration increased up to the 20 cm depth and sharply
decreased at the 35 cm depth to values similar to or less than the
0 cm depth. This sharp CO2 concentration decrease at 35 cm
depth of PCC was most likely due to the characteristics of the
soil at that depth. Field observation indicated that this depth
contained a sandy layer that was not found at similar depth of
PF, LF or LCC. The sudden change in soil texture might have
also caused a seasonal perched water table influencing the soil
moisture dynamics in the upper soil layer. Similar CO2
concentration depth pattern reversal was reported for spodic
boreal forest soils of Sweden characterized by shallow water
table (Magnusson, 1992). In the presence of shallow water
table, the water can act as a CO2 sink thus decreasing the CO2
concentration levels in the soil (Magnusson, 1992).
The CO2 concentrations measured at our sites were within the
range reported (350–70,000 ppmv) from other temperate and
boreal forest soils with lower values in the surface soil and higher
values in deeper soil profiles. The soil CO2 concentration of well
drained soils generally increases with depth because of the
differences in the relative strength of transport and production
factors (Magnusson, 1992; Oh et al., 2005). The upper organic
material rich soil generally has high porosity that result in the
rapid exchange of air with the atmosphere. Below the organic
layer CO2 concentration generally increases because of CO2
accumulation caused by microbial and root CO2 generation with
a much slower rate of gas exchange between the subsoil and the
atmosphere (Fernandez et al., 1993; Jassal et al., 2004). In poorly
drained soils and soils with shallow water table, as was the case
for PCC, CO2 concentration increase with depth may not be
observed (Magnusson, 1992).
Yavitt et al. (1995) observed CO2 concentrations within the
range of 350–19,000 ppmv at a 0.2 m depth for a northern
hardwood ecosystem during mid-summer. Fernandez et al.
(1993) reported a threefold increase in CO2 concentration with
depth, from 1023 ppmv in the O horizon to nearly 3300 ppmv
in the C horizon of forest soil in Maine. For the Lakevale pairs,
the median CO2 concentration at the 35 cm depth was
approximately 4.5 times the CO2 concentration at the shallow
depth. At Pomquet, these gradient factors were approximately
4.0 for each. Note that for PCC, the highest CO2 concentration
was measured at the 20 cm depth and the concentration at this
depth was used for comparison to the shallow depth.
Modeling work by Simunek and Suarez (1993) showed an
increase in CO2 concentration of as much as 15 times in wet soil
as in dry soil. Simunek and Suarez (1993) and Magnusson
(1992, 1995) considered water content and ground water level
as dominating factors controlling soil air CO2 concentrations.
Because our sites are in relatively moist soils, the relatively
high CO2 concentration variability at greater depths (Fig. 2)
may also be related to greater variability in soil moisture
contents driven by water table fluctuations at the deeper depth.
The coefficients of variations (CV) for volumetric soil water
content were higher for the deeper depth (28–61%) than the
shallow ones (20–36%) for all sites except PCC where deeper
soil moisture was less variable (CV = 10%) than shallow
moisture (CV = 19%; Fig. 3a and c). This soil moisture pattern
appears to correlate with the relatively low variability in CO2
concentration at the 35 cm depth compared with shallower
depths of PCC (Fig. 4). Kursar (1989) observed less variation in
soil respiration during the dry season when the soil water
content was uniformly low than the wet season for a lowland
moist forest in Panama. High variability in CO2 concentrations
can occur as a result of rapid changes in concentration during a
rainy season (Kursar, 1989; Certini et al., 2003).
3.2. Effect of clear cutting on subsurface CO2
concentration
The effect of clear cutting on subsurface CO2 concentration
was not identical for the two paired sites. After adjusting for
seasonal variations, CO2 concentration was significantly higher
at PCC than PF within the 0, 5 and 20 cm depths. But CO2
concentrations at PF were significantly higher than that of PCC at
the 35 cm depth. On the contrary, CO2 concentrations were not
statistically different when LF and LCC were compared at each
Fig. 4. The effect of clear cut harvesting on subsurface CO2 concentration
(October 2004–September 2005) for (a) the sandy site and (b) the clayey site.
Error bars represent 95% confidence intervals for median concentrations. When
the error bars overlap, the medians are not significantly different from each
other at 5% probability level. LF: Lakevale forest; LCC: Lakevale clear cut; PF:
Pomquet forest; PCCL: Pomquet clear cut.
A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
depth (Fig. 4). The decrease in CO2 concentration within the
35 cm depth at PCC was likely caused by shallow water table
(Magnusson, 1992). The difference in response in subsurface
CO2 concentration to clear cut harvesting at the two paired sites
may be attributed to the differences in the interactive effects
among soil moisture, temperature and soil texture (Smith et al.,
2003).
The results from Lakevale sites agree with those reported by
Fleming et al. (2006) for jack pine forest soils in Ontario,
Canada 3 years after harvesting. The soil at their sites was
coarse textured as Lakevale. Fleming et al. (2006) indicated that
forest harvesting effects on CO2 efflux were site specific and
largely driven by soil moisture and temperature trends. For our
sites, soil moisture within the upper 25 cm depth was
significantly greater at the clear cut than the forested sites
for both pairs but the magnitude of the difference was higher for
Pomquet pairs than Lakevale pairs (Fig. 3a). The differences in
soil moisture are highlighted with the aid of percentile plots
(Fig. 3b). These moisture plots (Fig. 3a and b) also reveal that
the soil at the clear cut sites may have been saturated with water
more frequently during the study period with the exception of
the early and later part of the study period (after June 2005) that
occurred during the relatively high summer temperatures and
low precipitation. This is based on the assumption that for
sandy and clayey soils, soil water content of 0.4 and as high as
0.6, respectively, represent saturation (Hillel, 1998).
For the clear cut sites (LCC and PCC), approximately 50%
of the data show soil water content of at least 0.40 with recorded
593
maximums of 0.54 at LCC and 0.50 at PCC (Fig. 3b). On the
contrary, the forested sites (LF and PF) indicated identical and
lower soil water content maximum of only 0.40. An increase in
soil water content and a decrease in depth to water table usually
occur after clear cutting because of decreased evapotranspiration (Barg and Edmonds, 1999). Gas transport limitation can
occur following clear cutting because of the increased soil
moisture. The diffusion of CO2 in water is about 10,000 times
slower than in air (Hillel, 1998). This slow diffusion can
maintain high CO2 concentration even under low respiration. A
gas diffusion coefficient of zero has been reported at an air filled
pore space of less than 10% for soils of contrasting texture (Xu
et al., 1992). The observation of zero gas diffusion coefficients
even for relatively unsaturated soils was ascribed to the
presence of air as isolated pockets and a discontinuity in the
pathways of air-filled pores for gas diffusion (Glinski and
Stepniewski, 1985; Xu et al., 1992). The phenomenon of gas
transport limitation and CO2 accumulation may be more
prominent in poorly drained and fine textured soils which
explain the relatively higher CO2 concentration at PCC. This is
in agreement with data from Bouma and Bryla (2000) who
reported a much more restricted efflux of CO2 from fine
textured soils than sandy soils after watering.
The surface soil temperature differences measured at the top
of the mineral soil at the two paired sites (Fig. 5) were not as
great as the soil moisture differences (Fig. 3). Soil temperature
differences were apparent only in the summer. During the
summer period, LCC had lower surface temperatures than LF
Fig. 5. Soil temperature at the two paired sites. Broken lines are for clear cut sites and solid lines for intact forest sites; and thick and thin lines are for Pomquet and
Lakevale sites, respectively.
594
A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
but at Pomquet, the clear cut site exhibited higher temperatures
than the forested site. These two factors, that is, the wider range
of difference in moisture between PF and PCC and the
relatively higher summer surface temperature of PCC
compared with PF may be confounding the effect of clear
cut harvesting on subsurface CO2 concentration at the two
paired sites.
3.3. Soil moisture and soil temperature effects on CO2
concentration
The correlation of subsurface CO2 concentration to soil
temperature and volumetric water content for each depth was
statistically significant only for the Lakevale (sandy soil) paired
sites (Fig. 6 and Table 2). Quadratic regression models of CO2
concentration as dependent variable and soil temperature and
moisture as independent variables explained between 32% and
84% of the variation in CO2 concentration with consistently
higher R2 values for the forested site than the clear cut site
(Table 2). The significant regression models with the highest R2
values were obtained with the exclusion of the soil temperature
by volumetric soil water content interaction term and when
only either (not both) of the independent variables was in the
quadratic term (Table 2). When both independent variables
were included in the quadratic terms, the regression parameter
estimates (coefficients) became statistically non significant
( p > 0.05) with no improvements in R2 values. The inclusion of
both quadratic terms did not improve the regression models
perhaps due to the strong correlation between volumetric soil
water content and soil temperature (Fig. 7).
When CO2 concentration data were fit to regression models
that contained volumetric soil water content or soil temperature
separately, the resulting regression models were significant but
the R2 values were lower than the models that contained both
independent variables in one model. For example, a quadratic
polynomial model with soil temperature or volumetric soil
water content as the only independent variable explained 60%
and 48% of the CO2 concentration variation, respectively, for
the 0 cm depth of LF. For LCC at the same depth, the
corresponding proportions of variances explained were 58 and
Fig. 6. Relationship of CO2 concentration versus soil temperature and volumetric soil water content for Lakevale intact forest (dots and solid line) and clear cut
harvest (circle and broken lines) sites. Note that the scale for temperature decreased and for CO2 concentration increased with increasing depth. The CO2
concentrations are median values from 10 microsites representing three microtopographic positions for October 2004–September 2005.
A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
595
Table 2
Polynomial equations relating subsurface CO2 concentration to soil temperature and volumetric soil water content
Site
Depth (cm)
LF
0
5
20
35
2
(R = 0.78,
(R2 = 0.85,
(R2 = 0.74,
(R2 = 0.55,
LCC
0
5
20
35
(R2 = 0.68,
(R2 = 0.32,
(R2 = 0.54,
(R2 = 0.42,
[CO2] = a + bT + cW + dT2 + eW2
a
a
b
c
p = 0.0001)
p < 0.0001)
p = 0.0002)
p = 0.0055)
*
3744
5801*
13,737*
2971
*
68
195*
520*
2159*
p = 0.0007)
p = 0.05)
p = 0.006)
p = 0.023)
4086*
5117
25,665*
1017
48 *
81
430*
893
d
e
36,308
50,429*
10,5402*
8168
0
0
0
145*
67,212*
90,039*
16,7088*
0
27,400*
35,414
130,474*
5765
0
0
0
22
35,281*
45,327
153,134*
0
*
T: temperature (8C); W: volumetric soil water content (v/v); a, b, c, d and e are regression coefficients.
*
Statistically significant model component ( p < 0.05).
a
The reported R2 is the adjusted R2 as it takes into account the number of predictors in the model and useful to compare different regression models (Zar, 1999).
n = 16 in all cases.
52%, respectively. The improvement in the R2 values by using
both independent variables in the same model was particularly
significant for LF (Table 2). Borken et al. (2006) obtained
similar R2 value (0.60) for the Oe/Oa horizon when soil
temperature was used as a single independent variable with an
exponential model to explain the variation in soil respiration in
a prolonged field drought experiment in Harvard Forest,
Massachusetts, U.S.A. When soil moisture and soil temperature
were in the model, the R2 they obtained (0.82) was only slightly
higher than what we found for LF within a similar depth
(Table 2). Similar to the results from our study, Dilustro et al.
(2005) found no relationship between volumetric soil water
content and CO2 efflux for clayey soils while reporting
significant relationships for sandy forest soils of southwestern
Georgia, U.S.A.
For all depths, CO2 concentration increased with increasing
temperature, reached a maximum and declined at higher
temperatures. Note that for the 0 cm depth, almost identical
maximum median CO2 concentration was measured at
Fig. 7. Relationship between soil temperature and volumetric soil water content
for LF (Lakevale forest; filled circles) and LCC (Lakevale clearcut; open
circles) at 0, 5, 20 and 35 cm depths; October 2004–September 2005. Sizes
of circles indicate relative CO2 concentration. The correlations for each site and
depth are statistically significant (R2 = 0.60–0.81, p < 0.05).
approximately equal soil temperatures (15 8C) but at different
soil moisture levels for the intact forest (around 0.21) versus
clear cut sites (around 0.35) to reach to the same maximum
median CO2 concentrations (Fig. 6). While the relative
positions of the regression lines relating CO2 concentration
and soil moisture for the clear cut and forested Lakevale sites
stayed unchanged for the 0, 5 and 20 cm depths, the regression
lines and the soil temperature values corresponding to
maximum median CO2 concentration for clear cut sites
progressively shifted to the right (higher soil temperatures)
relative to the regression lines of the intact forest.
The influence of soil water content and soil temperature on
soil CO2 production or concentration has been shown to be nonlinear and site specific (Davidson et al., 1998; Fang and
Moncrieff, 1999; Hamada and Tanaka, 2001; Smith et al., 2003;
Borken et al., 2006). As can be seen from our data from
Lakevale (Fig. 6), CO2 concentration decreased at both very
low and very high soil water contents. Others (Davidson et al.,
1998; Bowden et al., 1998; Jassal et al., 2004) have reported a
similar trend. Low soil water content inhibits microbial and root
metabolic activity and very high soil water content depletes
oxygen in the soil air as a result of pore spaces saturated with
water (Glinski and Stepniewski, 1985; Jassal et al., 2004). It is
also true that low soil temperature (<5 8C) inhibits microbial
and root metabolic activity. Efforts to isolate the confounding
effects of soil temperature and soil water content on soil
respiration and determine the most important factor controlling
soil respiration have recently been undertaken (Davidson et al.,
1998; Bowden et al., 1998; Borken et al., 2006).
Similar to our observation, Davidson et al. (1998) reported a
decrease in soil respiration both at low and high volumetric soil
water content for a mixed deciduous forest of the Harvard
Forest, Massachusetts, U.S.A. but using a piece-wise simple
linear regression model. The similarity in the results from the
Lakevale site and the Harvard Forest may be related to the
similarity in the seasonal soil moisture and temperature patterns
(warm, dry summer; and wet, cold winter and spring) as well as
soil texture (fine sandy loam with significant rock contents up to
40%) and vegetation types at the two sites. A field soil drought
experiment in the summer at the Harvard Forest sites indicated
596
A. Bekele et al. / Forest Ecology and Management 242 (2007) 587–597
a decrease in heterotrophic respiration in the O horizon (Borken
et al., 2006). An empirical exponential model that incorporated
both soil moisture (gravimetric) and soil temperature indicated
that changes in soil moisture at high soil temperatures had large
effects on soil respiration (Borken et al., 2006).
4. Conclusion
This study showed that subsurface CO2 concentration had
high within site spatial variability that was not related to
microtopographic differences. This implies that other physical
and biological properties were involved at each microsite, such
as water table depth, soil water content, soil temperature,
organic C content, root distribution, and microbial activity.
Within site variability in soil CO2 concentration was more
pronounced at the clayey sites than sandy sites.
Soil texture control on CO2 dynamics at these sites was
inferred from (1) the inconsistent response of subsurface CO2
concentration to clear cut harvesting from the two paired sites
(i.e. significantly higher median CO2 concentration was
measured at the clear cut than the forested site only for the
clayey sites while no significant change was detected at the
sandy site); (2) the inconsistent relationship between CO2
concentration versus soil temperature and volumetric water
content: soil CO2 concentration was related to soil temperature
and moisture only at the sandy sites.
Consistent with results from other field and computer
modeling studies, concentrations of CO2 increased with
increasing depth except at PCC where median CO2 concentration was the highest within the 20 cm depth. While these
general increases in CO2 concentration with depth were
unaffected by season, the highest and the lowest median CO2
concentrations were measured during summer and winter,
respectively, but with much greater variability with increasing
depth at the clayey than the sandy sites. Clear cutting had a
significant effect on volumetric soil water content at both paired
sites, but with the effect more pronounced at the clayey than the
sandy sites. The effect of clear cutting on soil temperature was
smaller and limited to the summer months. Quadratic multiple
regression models relating CO2 concentration to soil temperature and volumetric soil water content explained up to 84% of
the data variation for each depth but only for the sandy paired
sites.
Our data confirm the dependence of the variability of soil
CO2 concentration on soil texture and local hydrology; and that
soil texture differences and depth to water table and their
interaction with forest management must be considered when
modeling ecosystem CO2 budgets. Future gas sampling at these
and similar sites should consider collecting samples based on
genetic horizons instead of sampling based on fixed depth
intervals so as to reduce the within site variability and increase
the power of detecting differences amongst sites and land
treatments.
We point out two limitations to this study that could be
useful in designing similar studies in the future. First, we did
not have adequate replication to reliably evaluate the simple
effect of forest harvesting on soil CO2 concentration without the
confounding effect of soil texture. The difficulty of finding
adequate replication and representative controls is a major
drawback of most ecological studies. Secondly, we did not have
pre-harvesting data on soil CO2 concentration and other
physical factors at each microsite. Where replication is
difficult, a pretreatment data should be obtained and data
interpreted as a before-after/control-impact (BACI) design
(Bennett and Adams, 2004).
Acknowledgements
Amanda Diochon provided the vegetation information; and
other members of the Environmental Sciences Research Centre
at StFX University collected the data reported in this
manuscript. We thank the two anonymous reviewers for
improving the contents of this manuscript.
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