Introduction
Soil respiration (RSOIL) is a major component of the terrestrial carbon
cycle (Raich and Potter, 1995; Schimel, 1995) and is
30–60 % greater than net primary productivity globally (Raich and Potter,
1995). Estimates of annual soil carbon emissions range from 68 to 100 Pg of
carbon per year (Schlesinger, 1977; Raich and Schlesinger, 1992;
Bond-Lamberty and Thomson, 2010). Temperate systems contribute approximately
20 % of the annual global RSOIL (Bond-Lamberty and Thomson, 2010)
but have been shown to be recent carbon sinks, averaging 0.72 Pg of C uptake
per year from 1990 to 2007 (Pan et al., 2011). RSOIL can be estimated
in the field by measuring soil CO2 efflux (FSOIL) – the direct
rate of CO2 crossing the soil surface over a period of time (Raich and
Schlesinger, 1992). FSOIL can vary spatially and temporally within and
across systems as a result of the varied and complex interactions of
controlling mechanisms (Drewitt et al., 2002, Trumbore, 2006; Vargas et al.,
2010). The edaphic controls on FSOIL on the landscape scale include
soil temperature, soil moisture, root biomass, microbial biomass, soil
chemistry, and soil physics (Fang et al., 1998; Davidson et al., 1998; Kang
et al., 2000; Xu and Qi, 2001; Epron et al., 2004). These factors do not
simply elicit additive or monotonic responses but, rather, create complex
responses of FSOIL across spatial and temporal scales (Dilustro et al.,
2005; Pacific et al., 2009).
Soil temperature is quite commonly a primary driver of FSOIL (e.g., Fang
and Moncrieff, 2001), and in complex terrain, temperature regimes can be
mediated by elevation, slope, and aspect (Wu et al., 2013). The effects of
elevation and topography on soil temperature can in turn affect carbon
cycling (Schindlbacher et al., 2010) either directly or through indirect
processes (Murphy et al., 1998). Soil water content (SWC), however, often
serves as an important secondary control on FSOIL. At high SWC values,
CO2 transport through the soil pore space is limited (Davidson and
Trumbore, 1995; Jassal et al., 2005). Production of soil CO2 can also
become limited at high SWC values due to anoxia and decreased microbial
aerobic respiration (Oberbauer et al., 1992). At low SWC values, FSOIL
is decreased as well due to microbial desiccation and concomitantly reduced
microbial activity (Van Gestel et al., 1993), resulting in decreased
CO2 production (Scanlon and Moore, 2000).
In topographically complex landscapes, precipitation gradients that exist as
a function of elevation affect decomposition rates, CO2 production, and
movement of CO2 through the soil (Schuur and Matson, 2001). The complex landscape
structure and heterogeneity of mountain catchments also directly affect
local soil moisture regimes through the lateral redistribution of soil
water, adding to the spatial heterogeneity of these biogeochemical and
physical processes. FSOIL therefore varies across landscape positions
as a function of this soil water redistribution (Riveros-Iregui and McGlynn,
2009). In subalpine forested systems for example, soil water content has
been shown to be a strong driver of the spatial (Scott-Denton et al., 2003)
and temporal (Pacific et al., 2008) variability of FSOIL.
In addition to meteorological variables, vegetation (itself controlled by
the spatial heterogeneity of micrometeorology) can influence carbon cycling
within a watershed. Vegetation affects carbon cycling directly through
photosynthesis (Raich and Schlesinger, 1992; Ekblad and Högberg, 2001;
Högberg et al., 2001), above- and belowground tissue allocation (Chen
et al., 2013), and litter production (Prevost-Boure et al., 2010).
Vegetation therefore controls the quantity and quality of soil organic
matter (SOM) within systems, which in part will determine decomposition
rates and soil CO2 production (e.g., Berg, 2000). However, the role of
belowground plant and microbial processes in the dynamics of SOM has become
increasingly more apparent, showing that root and rhizosphere contributions
to SOM are substantive (e.g., Schmidt et al., 2011). Vegetation also exerts
controls on production of CO2 through root respiration in the soil and
through complex mycorrhizal associations that can mediate the response of
soil CO2 production to rain pulse events (Vargas et al., 2010).
Finally, vegetation also elicits feedbacks on the abiotic aspects of a
system, including the soil moisture and soil temperature regimes, further
impacting biogeochemical cycling (Wullschleger et al., 2002; Metcalfe et
al., 2011; Vesterdal et al., 2012).
Interannual variation in RSOIL within systems can be high and exceed
the interannual variation in net ecosystem exchange (NEE) of carbon (Savage
and Davidson, 2001); this interannual variation can be driven in large part
by the dynamics of precipitation (Raich et al., 2002). Current climate
models project potentially dramatic changes in precipitation in the coming
years (Kirtman et al., 2013), and presently the controls on interannual
variation in RSOIL in response to changing precipitation regimes are
poorly understood on spatial scales ranging from landscapes to regions. The
interactions among topography, vegetation cover, and climate are therefore
an important and complicated area of study.
Interannual climate variability in mountainous, subalpine catchments,
however, has been shown to alter the spatiotemporal heterogeneity of carbon
dynamics within those systems (Riveros-Iregui et al., 2011, 2012). In a subalpine watershed in
Montana, Riveros-Iregui et al. (2012) found that areas with low upslope
accumulated area (generally uplands and drier areas) showed FSOIL
increases during wet years, while poor-drainage areas (riparian areas)
showed FSOIL decreases during wet years. This resulting bidirectional
response is a function of the landscape heterogeneity of the system, soil
biophysics, and interannual climate variability (Riveros-Iregui et al.,
2012).
Given the possible interactions among precipitation, topography, and
vegetation, we examined how FSOIL varies as a function of landscape
position and vegetation cover in response to interannual variation in
precipitation within a complex, humid watershed. To do this we used a
plot-based approach with repeated measures sampling to account for spatial
and temporal variation in the biophysical controls on FSOIL within
our study watershed. The empirical nature of this study design, coupled with
the use of portable infrared gas analyzers (IRGAs) to measure soil CO2
efflux, is a robust and proven way of quantifying the seasonal dynamics of
FSOIL and allows for greater consideration of the spatial variability
of FSOIL (Riveros-Iregui et al. 2008; Riveros-Iregui and McGlynn, 2009)
on the watershed scale. We attempted to answer the following questions:
How does FSOIL respond to interannual variation in precipitation in
a humid, complex watershed?
How do landscape position and vegetation heterogeneity affect FSOIL,
and how do they interact with interannual variation in precipitation?
Methods
Site description
The Weimer Run watershed (374 ha) is located in the Allegheny Mountain
range in northeastern West Virginia within the Little Canaan Wildlife
Management Area near Davis, WV (39.1175, -79.4430), and is a sub-watershed of
the Blackwater River, a tributary of the Cheat River. The watershed has an
elevation range of 940 m (confluence of Weimer Run and the Blackwater River)
to 1175 m (Bearden Knob; Fig. 1). For the climate period 1980–2010, mean
annual precipitation (MAP) for the watershed was 1450 mm yr-1 (PRISM
Climate Group, 2004). The mean daily maximum July temperature is 18.8 ∘C, and the mean daily maximum January temperature is -3.9 ∘C
(National Climate Data Center (NCDC), Station ID DAVIS 3 SE, Davis, WV). Precipitation varied during the
study period, producing a relatively dry year in 2010 (1042 mm), a wet year
in 2011 (1739 mm), and a mesic year in 2012 (1244 mm, MesoWest, University of Utah, from station BDKW2, 2013; Fig. 5a).
Above: Weimer Run watershed (374 ha) with elevation levels
indicated on map. Below: conceptual diagram showing vegetation classes.
Images courtesy of the Integration and Application Network, University of
Maryland Center for Environmental Science (http://ian.umces/edu/symbols).
The Weimer Run watershed is adjacent to the Canaan Valley in West
Virginia, which exists in a transitional zone between the Appalachian
Valley and Ridge and the Appalachian folded plateau (Matchen, 1998). The
surrounding ridge tops and the study site are underlain by Pennsylvanian
sandstone from the Pottsville formation (Allard and Leonard, 1952). The
overstory vegetation within the watershed is a mixed northern
hardwood coniferous forest, consisting of yellow birch (Betula alleghaniensis), red maple (Acer rubrum), red
spruce (Picea rubens), and black cherry (Prunus serotina; Allard and Leonard, 1952; Fortney, 1975).
The understory is comprised of Rhododendron maximum, Kalmia latifolia, Osmundastrum cinnamomeum, and Osmunda claytoniana (Fortney, 1975).
Vegetation and elevation classes
Three elevation classes were established along the northeastern aspect of
the watershed to form an elevation gradient: “Low” (975 m), “Mid” (1050 m), and
“High” (1100 m). Site elevations were determined using a digital elevation map
(DEM) derived from 1/9 arc second elevation data from the Shuttle Radar
Topography Mission (SRTM) (USGS 2006) processed with ArcGIS®
software (ESRI; Redlands, CA). In order to address the effects of vegetation
cover on FSOIL, three vegetation cover classes were established:
“Canopy” – closed canopy, forest interior with no shrub layer; “Shrub” – closed
canopy, forest interior, with dense shrub layer; “Open” – forest gap with no
canopy closure, within the forest interior. Differences among vegetation
classes were confirmed using a plant area index (PAI) which was measured for
each plot in June 2010 with a LAI-2000 plant canopy analyzer (LI-COR
Lincoln, Nebraska). PAI was strongly statistically significantly different
among vegetation cover types (F= 13.39; p value = 0.0003). Shrub plots
were the greatest (3.46 m-3 m3), followed by Canopy plots (2.14 m-3 m3) and then Open plots (1.75 m-3 m3; Table A1 in the Appendix).
At each elevation level in the watershed, three 2 × 2 m plots of each
vegetation class were established – for a total of 27 plots across the
entire watershed (Fig. 1). One of the Open replicate plots at the Low
elevation had to be removed from analysis due to inundation during the
summer of 2011. Data from the remaining 26 plots were analyzed.
Environmental variables
Soil CO2 efflux
An EGM-4 portable infrared gas analyzer (IRGA) with an attached SRC-1 soil
chamber (PP Systems, Amesbury, MA) was used to measure soil CO2 efflux
rates. The EGM-4 has a measurement range of 0–2000 ppm (µmol mol-1), with an accuracy of better than 1 % and linearity better than
1 % throughout the range. The SRC-1 has a measurement range of
0–9.99 g CO2 m-2 h-1. Plots were sampled approximately weekly
(every 5–10 days) from the middle of May until the end of September, from
2010 to 2012. For March until mid-May, and during October and November,
plots were measured approximately every 2 weeks (12–21 days) during times
when they were snow-free. FSOIL was measured 1–3 times at different
locations within the plot at each measurement interval and averaged for a
plot-level estimation of FSOIL. Plots were sampled between 09:00 and 16:00
EST, and the sequence of plot measurements was varied to avoid a time-of-day
bias in the results and account for diurnal variation in soil CO2 flux
over time. Our sampling followed a rotating scheduling where for one
sampling period we would start at, say, the High elevation, then proceeding to
work down the mountain (Mid, then Low), and the next week we would start at
the Mid, then working down to the Low finishing with the High, and the
next week we would then start at the Low, then High, then Mid, and so on. This
method was followed through the experiment.
Volumetric water content
Volumetric water content (Θfield) was measured using a Campbell
HydroSense CD 620 (Campbell Scientific) set to water content measure mode
with 12 cm probes (Campbell Scientific; ±3.0 % m-3 m3, with
electrical conductivity < 2 dS m-1; sampling volume using 12 cm
rods was ca. 650 cm3). A minimum of three measurements was
taken in each plot per sampling event and averaged to make a plot-level
estimation of Θfield.
Measurements taken by the Campbell HydroSense CD 620 have a known bias in
soils where bulk density is outside of the 1–1.7 g cm3 range, where
organic matter is > 10 %, and where clay content is
> 40 %. (Campbell Scientific). In order to calibrate field
measurements, a calibration procedure from Kelleners et al. (2009) was
followed where P, the period, which is the square wave output from the probe
in milliseconds, is converted to Ka, the relative soil permittivity
(unitless). P is related to Θfield as shown in Eq. (1):
P=-0.3385⋅θfield2+0.7971⋅θfield+0.7702.
Equation (2) converts P to Ka.
Ka=P-PairPwater-Pair⋅Kwater-1+1,
where Pair is the period in air and Pwater is the period in
deionized water. Pair was calculated empirically at 0.79 ms. Pwater
was calculated at 1.37 ms following the procedure outlined in Kelleners et al. (2009) by placing the probes of the Campbell Hydrosense CD 620 in
deionized water in an 18.92 L acid-washed container, with total vessel
conductivity measured at 0.47 µS.
Soil samples were taken in conjunction with HydroSense measurements in 2012
(depth: 12 cm; volume: 56.414 cm3; n= 37), and actual VWC (volumetric water content, m-3) m3)) (Θlab) was calculated using Eq. (3) from Rose (2004), where w is the
gravimetric water content of the soil sample (g-3 g3), ρb is the soil bulk density (g cm-3), and ρ (g cm-3) is
the density of water:
θlab=wρbρ.
In order to calibrate field measurements of VWC (Θfield),
Ka values were then regressed against Θlab to
create an Eq. (4), relating Ka to Θ (R2= 0.74) such that field measurements of VWC (Θfield) could be
converted to Θ in order to account for discrepancies in organic
matter, soil bulk density, and clay content:
θ=7.0341⋅Ka+0.0806.
Θ was then converted to water-filled pore space (WFPS; m-3 m3) using the soil porosity (Φ; m-3 m3):
WFPS=θ⋅Φ.
WFPS provides a more mechanistic variable that takes into account the bulk
density and porosity of the soil, which influence the transport and storage
capacity of the soil with regard to soil CO2.
Soil temperature
During each field sampling session, soil temperature (TSOIL;
∘C) was measured at 12 cm using a 12 cm REOTEMP soil thermometer
(REOTEMP San Diego, CA) at a minimum of two locations within the plot. These
measurements were averaged to create a plot mean temperature for each
sampling event.
Soils
Soil pH was determined using a 1 : 1 measure of soil (from 0 to 5 cm depth)
with deionized water and measured with a Fieldscout SoilStik pH Meter
(Spectrum Technologies, Inc. Plainfield, IL) with an accuracy of ±0.01 pH and ±1 ∘C.
Soil samples were taken from 0–5, 0–12, and 0–20 cm profiles within
the soil. Soil bulk density (ρs), total bulk density (ρt), soil particle density, and soil porosity (Φ) were also
calculated for each sample (Grossman and Reinsch, 2002; Flint and
Flint,
2002). Soil bulk density (ρs) is defined as the bulk density of
the soil fraction, where the soil fraction consists of soil that has been
sieved to less than 2 mm and all gravel and root material has been removed.
Total bulk density (ρt) is defined as the absolute density of the
sampled soil, including soil, roots, and gravel, and is simply the sample dry
mass over the sample volume. Total soil carbon and nitrogen were assessed
using an NA 2500 elemental analyzer (CE instruments; Wigan, United Kingdom).
Soil organic matter (SOM) content was estimated using the loss-on-ignition method at
500 ∘C (Davies, 1974).
Data analysis
We chose to parse our data at 11 ∘C rather than strictly by
growing or dormant seasons in order to develop a more functional understanding
of the controls on FSOIL. The 11 ∘C threshold was chosen for
multiple reasons. (1) Mean measured soil temperature at 12 cm across our
watershed during our three years of observations exceeded 11 ∘C
for the period 6 May to 13 October. This period coincides with the growing
season and allows for slight variance with a buffer on either end. (2)
Piecewise regression (using the segmented package in R) identifies an
estimated break point of 11.58 ∘C ± 0.47 standard error when
the ln(FSOIL) is regressed against soil temperature. Based on our
observations, we opted for the more conservative threshold of 11 ∘C. (3) Below 11 ∘C, the FSOIL values are tightly coupled to
temperature, while above 11 ∘C there is increasing variance in
FSOIL that we feel warrants exploration. All analyses and means
presented are for measurement periods where soil temperatures are above
11 ∘C, unless otherwise noted.
We employed a mixed-model analysis of variance (ANOVA) with repeated
measures to identify main and interactive effects of elevation and
vegetation on soil CO2 efflux, soil temperature, and water-filled
pore space using the proc mixed procedure in SAS 9.3 (SAS Institute, North
Carolina, USA). All means presented are least-squares means calculated using
a Tukey–Kramer adjustment.
To decouple the effects of soil temperature and soil moisture on FSOIL,
linear regressions of soil temperature against the natural log of FSOIL
were done by year (2010, 2011, 2012), by vegetation cover type (Open,
Canopy, Shrub), by elevation (Low, Mid, High), by year and vegetation (Open
2010, Canopy 2010, etc.), and by year and elevation (Low 2010, Mid 2010,
etc.). The residuals from each model were then regressed against WFPS by
each combination. All linear regressions use the lm function in R 3.0.1 (R
Core Team, 2013).
Differences in soil organic matter (SOM) were examined with a Kruskal–Wallis
rank sum test using the kruskal.test in R 3.0.1 (R Core Team, 2013). A
two-way mixed-model ANOVA using the proc mixed procedure in SAS 9.3 was used
to examine main and interactive effects of elevation, vegetation, and soil
depth on soil bulk density and total bulk density. Soil bulk density, soil
organic matter, total soil carbon, total soil nitrogen, and the plant area index
were individually regressed against the mean plot-level soil CO2 efflux
for each corresponding plot (e.g., High-Canopy 1, High-Open 2). Means
were calculated from all flux data above 11 ∘C for all 3 years
(2010–2012).
Results
Exponential regression of FSOIL measurements against soil temperature
at 12 cm (TSOIL; Fig. 2a) shows a positive relationship (R2= 0.316; y= 0.829+e(0.1149x) ) with increases in temperature
resulting in increased efflux rates. The amount of variance explained by
TSOIL lessens above 11 ∘C (R2= 0.104), with
FSOIL measurements below 11 ∘C showing a much tighter
relationship with temperature (R2= 0.434). To explore this variance,
all data above 11 ∘C were isolated and examined in order to parse
out controls above this apparent temperature threshold for this system.
(a) Soil CO2 efflux (µmol CO2 m-2 s-1)
against soil temperature (∘C) at 12 cm with data split at
11 ∘C. For all data, exponential regression shows an R2= 0.3163. For flux rate values below 11 ∘C, R2= 0.434; for
flux rate values above 11 ∘C, R2= 0.104. (b) Natural log
of soil CO2 efflux (µmol CO2 m-2 s-1) against
soil temperature (∘C) at 12 cm for all data above 11 ∘C. For flux rate values below 11 ∘C, linear regression gives an
R2= 0.1188, with p= < < 0.001. (c) Residuals of the
natural log of soil CO2 efflux (µmol CO2 m-2 s-1)
against water-filled pore space (0–12 cm) for all data above
11 ∘C. R2= 0.0208; p= < < 0.001.
The natural log of flux measurements above 11 ∘C for all years
was regressed against TSOIL (Fig. 2b), showing a significant positive
relationship with soil temperature (R2= 0.119; y=0.096x-0.010). From this linear model, the residuals were then regressed against
WFPS. The residuals from the ln(FSOIL) values above 11 ∘C
show a significant negative relationships with WFPS (Fig. 2c), but this
explains only marginally more of the variance (R2= 0.019).
Soil CO2 efflux (FSOIL)
Repeated measures ANOVA analyses show no significant differences in
FSOIL among years when data are pooled. Significant differences among
years do occur when data are parsed by elevation (F4, 633= 3.17;
p= 0.013) and by vegetation (F4, 633= 2.96; p= 0.019).
Across all data above 11 ∘C, there was a significant effect of
elevation (F2, 633= 3.44; p= 0.032), with plots at High elevation
sites showing the highest FSOIL rates and High sites statistically
differing from Low sites, with Mid elevation sites not differing from either
(Fig. 3a). 2010 was the only year to show a statistically significant
difference in FSOIL among elevation classes within a year, with Low
elevation sites exhibiting significantly lower FSOIL rates (F2, 633= 3.17; p= 0.013).
(a, c, e) Least-squares means of soil CO2 efflux (µmol CO2 m-2 s-1);
WFPS (m3 m-3); and soil temperature
at 12 cm (∘C) by elevation. (b, d, e) Least-squares means of soil
CO2 efflux (µmol CO2 m-2 s-1); WFPS
(m3m-3); and soil temperature at 12 cm (∘C) by
vegetation. Capital letters indicate differences between elevation classes
and lower-case letters indicate differences between
treatment × year interactions. Bars indicate standard error. Colors indicate sampling year.
Differences among vegetation classes were stark (F2, 633= 37.58;
p= < 0.001). Shrub classes across all elevation classes and all
years had higher rates of FSOIL
(6.07 ± 0.42 µmol CO2 m-2 s-1) than Canopy
(4.69 ± 0.42 µmol CO2 m-2 s-1) or Open
(4.09 ± 0.42 µmol CO2 m-2 s-1) plots. This Shrub effect was most notable during 2010, the driest
year during the study, when Shrub plots showed the highest rates of
FSOIL recorded during the study (7.48 ± 0.674). Statistical
differences among vegetation classes among years were complex. Shrub 2010
and Open 2011 were uniquely different among all combinations (Fig. 3b).
Water-filled pore space (WFPS)
WFPS tracked well with precipitation across years, with 2010 having the
lowest values of WFPS and 2011 having the highest rates of WFPS. WFPS in
2011 was significantly greater than either 2010 or 2012 (F2, 633= 17.27; p= < 0.001; Table 2). During 2010, when precipitation was
lower than average, an apparent elevation effect on WFPS is observed, with
High elevation plots exhibiting significantly lower WFPS measurements than
either Low elevation or Mid elevation plots (Fig. 3e). During 2011 and 2012,
under extreme and moderate moisture regimes, this elevation effect is not
evident. During 2010, vegetation treatment types are not significantly
different, but in 2011, when there is more moisture in the system,
statistical differences among vegetation classes are apparent, as Shrub and
Canopy plots exhibit higher WFPS values than Open plots (Fig. 3f).
Soil temperature (TSOIL)
Data for all years showed a significant effect of elevation on TSOIL
across elevation classes for all data above 11 ∘C (F2, 633 = 170.76; p= < 0.001). Low elevation sites were warmer
(15.99 ±0.35 ∘C) than Mid sites (14.71 ± 0.35 ∘C) and High (14.94 ± 0.35 ∘C) elevation sites. There was no
statistical difference in soil temperature by elevation within years (Fig. 3c).
Vegetation (Fig. 3d) had a statistically significant effect on TSOIL (F= 52.79; p= < 0001). Shrub plots were the coolest
(14.93 ±0.35 ∘C), Open plots the warmest (15.62 ± 0.35 ∘C), and Canopy plots were in between (15.10 ± 0.35 ∘C). No within-year comparisons were statistically significant.
There were also no differences in temperature among years when data were
pooled and compared by year alone.
Soil physical and chemical characteristics
Soils within the Weimer Run watershed are heavily acidic, with pH ranging
from 3.87 to 4.32 across the sampling area (Table A1). Soil bulk density
(ρs) from 0–12 cm ranges from 0.49 to 1.11 g cm-3 (Fig. 4a and b), with lower values occurring beneath the shrub understory at
lower elevations and higher values found in open, forest gap areas. There is
an effect of elevation (F2, 56= 5.77; p= 0.005) and vegetation
(F2, 56= 10.55; p= 0.001) on ρs for all soil
profiles (0–5, 0–12, and 0–20 cm). Elevation effects on ρs by soil depth are mixed, with statistical differences at
5 cm depth (F2, 12= 4.11; p= 0.044) and at 20 cm depth (F2, 18 = 4.15; p= 0.003). By elevation classes across all vegetation types,
ρs from 0–12 cm is lowest at Low elevations (0.0.65 ± 0.08 g cm-3), highest at Mid elevations
(0.95 ± 0.08 g cm-3), and in between at High elevations
(0.73 ± 0.08 g cm-3). Vegetation shows significant differences at 12 cm (F2, 18 = 3.60; p= 0.048) and 20 cm (F2, 18= 5.15; p= 0.002). By
vegetation classes across all elevations, ρs from 0–12 cm is
lowest in Shrub plots (0.58 ± 0.08 g cm-3), highest in Open plots
(0.92 ± 0.08 g cm-3), and in between at Canopy plots (0.83 ± 0.08 g cm-3). No interactive effects of elevation and
vegetation were evident (Table B1).
(a, c) Means of soil bulk density (g cm-3) and soil organic
matter (%) by elevation treatment. (b, d) Means of soil bulk density
(g cm-3) and soil organic matter (%) by vegetation treatment. Bars
indicate standard error. Colors indicate soil depth profiles.
Soil porosity from 0–12 cm ranges from 0.58 to 0.82 m-3 m3 and
is correlated with vegetation cover, with higher values in the Shrub
plots (0.77 ± 0.03 m3 m-3), medial values in Canopy plots
(0.68 ± 0.03 m3 m-3), and lower values in Open plots
(0.65 ± 0.03 m3 m-3; Table E1). Shrub plots also show the
highest concentrations of total soil carbon (9.35 %), significantly
greater than other vegetation types (F= 9.79; p= 0.0002). Vegetation
also influences total soil nitrogen, with Shrub plots exhibiting higher
proportions of total soil N than other plots (Table E1; F= 6.36; p= 0.0029). Total soil carbon also differed by elevation, with Low and High
classes showing greater proportions of total soil carbon in samples than Mid
elevation sites (Table D1; F= 6.28; p= 0.0031). Mid level plots also
showed lower proportions of total soil nitrogen than other elevation levels
(Table D1) (F= 6.45; p= 0.0027).
Kruskal–Wallis tests show that soil organic matter (SOM) for all soil depths
(0–5, 0–12, and 0–20 cm) varied significantly by vegetation (χ2= 8.21; p= 0.016) and by soil depth (χ2= 36.18;
p= < 0.001) but not by elevation (χ2= 1.82; p= 0.401). Differences in SOM by vegetation treatment through the soil column
were significant for the 0–5 and the 0–20 cm soil profiles (Table D1). The highest rates of SOM were found in the High elevation plots
(40.14 %) compared to the Mid (21.73 %) and Low elevation plots
(33.03 %; Fig. 4c). Shrub plots (33.54 %) and Canopy plots (33.14 %)
had similar SOM values. Open plots were lower (27.76 %; Fig. 4d).
(a) Soil bulk density (g cm-3); (b) soil organic matter
(%); (c) total soil carbon (%); (d) total soil nitrogen (%), and
(e) plant area index (m-3 m3) against mean plot-level soil
CO2 efflux by plot for all measurements across all 3 years in which soil temperature (∘C) was above 11 ∘C. Only soil bulk
density (a) shows a significant relationship (R2= 0.302; p= 0.003)
with mean plot-level soil CO2 efflux.
Regressions of mean plot-level soil FSOIL) against soil bulk
density, soil organic matter, total soil carbon, total soil nitrogen, and
plant area index only yielded a statistically significant relationship between FSOIL) and soil bulk density (R2= 0.302; p= 0.003; Fig. 5).
(a) Hyetographs for 2010, 2011, and 2012 from the Bearden Knob
weather station located within the Weimer Run watershed (BDKW2 MesoWest;
University of Utah). Precipitation totals by year are indicated within each
graph and are in millimeters per year. (b) Precipitation for the years 1970–2013
(mm yr-1) from NCDC station Canaan Valley, WV (461393). Linear
regression shows that mean annual precipitation is increasing by 17.88 mm yr-1 (r= 0.697; F1,42= 39.74;
R2= 0.474; p= < 0.001). The year-to-year variance in precipitation is also
increasing (BP = 8.58; p= 0.003). (c) Number of extreme precipitation
days (EPD) per year (defined as days on which total precipitation exceeded
25.4 mm per day). The number of EPDs are increasing by 0.38 days per year (R= 0.637; F1,42= 28.69; r2= 0.392; p= < 0.001). The
variance is also increasing (BP = 11.12; p= < 0.001).
Discussion
The threshold approach employed in this paper allows for a quantification of
the controls on soil CO2 efflux during periods when fluxes are not
temperature limited. This threshold was chosen empirically after analyzing
the data. While the exact threshold of 11 ∘C may not be applicable
to all watersheds, if similar or related methods for threshold determination
(e.g., piecewise regression or Bayesian change-point analysis) are used,
this approach offers potential for comparisons and insights into controls on
fluxes. If varying thresholds are found, it would be of research interest to
examine the variance.
Vegetation effects
Significantly greater CO2 fluxes from plots with shrub cover are
apparent in our data, despite consistently lower soil temperatures in these
plots. We propose that increases in soil CO2 efflux from beneath shrubs
are related to the observed differences in soils beneath plots with shrub
cover compared to our other vegetation plots in this watershed. Soil bulk
density, soil porosity, soil carbon, and other soil properties have been
shown to drive the spatial variability of carbon fluxes (Jassal et al.,
2004; Fiener et al., 2012; Luan et al., 2011).
Here we see shrubs decrease soil bulk density (Fig. 4b; Table E1) and
increase soil porosity (soil porosity (Φ) for Shrub plots averaged
0.77 m3m-3 from 0–12 cm depth, compared to 0.65 m3 m-3
for Open plots and 0.68 m3 m-3 for Canopy plots; Table E1),
allowing for greater diffusivity within the soil matrix and increased
transportation potential of soil CO2 through the soil. While soils
in Shrub plots have higher concentrations of SOM and soil C, soil bulk
density is lower, which results in overall lower values of SOM and
comparable values of soil C by volume. The increased soil porosity in soils
beneath shrub cover likely results in increased oxidation of labile
soil C. It should be considered that Shrub plots, to 20 cm soil depth, had
the highest mean values of SOM (18.13 %), higher soil C (9.35 %),
higher soil N (0.47 %), higher C : N ratios (19.36), and lower ρs
(0.39 g cm-3) compared to Canopy (SOM = 12.48 %; soil C = 6.35 %;
soil N = 0.37 %; soil C : N = 16.30) and Open plots (SOM = 12.48 %; soil C = 5.14 %; soil N = 0.31; soil
C : N = 15.76; Table D). The high C : N ratios for Shrub plots possibly indicate lower
amounts of available, labile carbon and lower rates of
decomposition than other areas of the watershed. This is corroborated by
early results from a 2-year litterbag experiment conducted in this
watershed (Atkins et al., 2015). This indicates that root respiration
contributions from shrubs may be substantive and may also be influenced by
varying soil moisture and precipitation regimes. The effect of the soil
microbial community on the temperature sensitivity of soil respiration can
also be enhanced in soils with high soil C : N ratios (Karhu et al., 2014).
Interactions of vegetation and interannual climate variability
While Shrub plots exhibit greater rates of soil CO2 fluxes than other
classes in this watershed during the course of this study, the magnitude of
these fluxes is also influenced by the interannual variability in
precipitation. Across the 3 study years, there is evidence of an
intrinsic link between the movement of carbon and water in this watershed in
response to landscape heterogeneities (i.e., vegetation and elevation) and
interannual climate dynamics. During 2010, our comparatively dry year, we
see increased rates of FSOIL across the watershed but more pronounced
increases in fluxes from Shrub plots. Conversely, during 2011, the
relatively wet year, vegetation-level differences in FSOIL are
statistically unapparent. When changing precipitation regimes are
considered, along with future projections of warming and carbon dynamics,
the importance of this coupling among water, carbon, and vegetation within
humid watersheds cannot be understated. Changes in the distribution,
variability, and amount of rainfall, as a result of climate change, are
expected to have a major effect on carbon cycling (Borken et al., 2002). The
magnitude of this effect, however, remains uncertain (Wu et al., 2011;
Ahlström et al., 2012; Reichstein et al., 2013).
Interactions of interannual climate variability and topography
During 2010 (driest year), we see a strong effect of elevation on
water-filled pore space (WFPS). During 2011 and 2012, however, there is no
apparent effect of elevation on WFPS. When precipitation decreases across
the watershed, as is the case during 2010, a different soil moisture regime
manifests itself at higher elevations, with lower values of WFPS that contribute,
in the case of this watershed, to increased rates of FSOIL. During
periods of increased precipitation, the watershed exhibits a more uniform
soil moisture regime. The difference in the magnitude of carbon fluxes
across elevation levels decreases during years with higher precipitation.
During periods of higher precipitation and increased soil moisture, air
space within the soil remains filled and transportation of CO2 through
the soil is limited, resulting in decreased rates of FSOIL. The production
of CO2 in the soil is also decreased due to the increased incidence of
anoxic conditions as a function of increased WFPS. Our Low elevation plots
were statistically similar in wetness to the Mid plots, both of which were
wetter than the High plots during the study. The Low elevation plots were
also the warmest for each year of the study, yet exhibited the lowest rates
of FSOIL for the entire study period. One consideration not explicitly
detailed in our study is the effect of topographic aspect on soil water
redistribution as plots in our study all had an east-northeasterly aspect.
Landscape positions with varying aspect can have differing soil water
contents while having similar soil temperature regimes (Kang et al., 2003)
that still result in varied soil carbon fluxes. Another contributor to the
magnitude of carbon fluxes can be the amount of upslope accumulated area or
the connectivity of varying landscape positions to flow paths within
watersheds (McGlynn and Seibert, 2003; Pacific et al., 2011). During our wet
year, however, we see a diminished effect of these topographic
heterogeneities.
Enhanced fluxes during years of decreased precipitation suggest that soil
respiration in humid mountain watersheds is strongly controlled by soil
water and, to a lesser extent, soil temperature. During average and
above-average precipitation years, soil respiration values are lower due to
limited CO2 production and/or diffusion through the soil. During
years where precipitation is below average, soil respiration values
increase. However, what is not considered here are the cumulative effects of
interannual variability in precipitation. Would consecutive dry or
consecutive wet years result in increases or decreases following the second
year?
Least-squares means of dynamic environmental variables. Error terms
indicate standard error.
Year
Class
FSOIL (µmol CO2 m-2 s-1)
WFPS (m3 m-3)
TSOIL (∘C)
2010
Low
4.69 ± 0.687
0.189 ± 0.014
16.29 ± 0.656
2010
Mid
6.13 ± 0.691
0.184 ± 0.014
14.90 ± 0.656
2010
High
6.32 ± 0.668
0.141 ± 0.014
15.30 ± 0.654
2011
Low
4.75 ± 0.571
0.247 ± 0.012
16.61 ± 0.520
2011
Mid
4.82 ± 0.561
0.250 ± 0.012
15.31 ± 0.519
2011
High
4.76 ± 0.551
0.249 ± 0.012
15.54 ± 0.518
2012
Low
4.45 ± 0.722
0.184 ± 0.014
15.08 ± 0.659
2012
Mid
4.04 ± 0.702
0.206 ± 0.014
13.93 ± 0.658
2012
High
4.71 ± 0.681
0.183 ± 0.014
13.98 ± 0.656
2010
Open
4.54 ± 0.685
0.164 ± 0.014
15.67 ± 0.656
2010
Shrub
7.48 ± 0.674
0.187 ± 0.014
15.42 ± 0.655
2010
Canopy
5.11 ± 0.674
0.167 ± 0.014
15.39 ± 0.655
2011
Open
4.02 ± 0.562
0.225 ± 0.012
16.31 ± 0.519
2011
Shrub
5.63 ± 0.559
0.270 ± 0.012
15.38 ± 0.518
2011
Canopy
4.68 ± 0.557
0.251 ± 0.012
15.76 ± 0.518
2012
Open
3.77 ± 0.698
0.188 ± 0.014
14.86 ± 0.656
2012
Shrub
5.12 ± 0.705
0.188 ± 0.014
13.98 ± 0.658
2012
Canopy
4.31 ± 0.697
0.198 ± 0.014
14.15 ± 0.657
Low
4.61 ± 0.431
0.207 ± 0.010
15.99 ± 0.356
Mid
4.99 ± 0.427
0.214 ± 0.009
14.71 ± 0.356
High
5.25 ± 0.418
0.191 ± 0.009
14.94 ± 0.355
Open
4.09 ± 0.425
0.191 ± 0.009
15.61 ± 0.355
Shrub
6.07 ± 0.424
0.214 ± 0.009
14.93 ± 0.355
Canopy
4.69 ± 0.423
0.206 ± 0.009
15.10 ± 0.355
2010
5.71 ± 0.634
0.172 ± 0.013
15.50 ± 0.652
2011
4.78 ± 0.525
0.248 ± 0.011
15.82 ± 0.516
2012
4.36 ± 0.647
0.192 ± 0.013
14.36 ± 0.653
Implications of vegetation dynamics
The most dominant shrub species in this watershed is Rhododendron maximum, an ericaceous
understory shrub that has been shown to increase SOM and soil N in forests
where it is present (Boettcher and Kalisz, 1990; Wurzberger and Hendrick,
2007). R. maximum occurs most commonly in forest coves and on north-facing slopes with
mesic to moist soil water regimes (Lipscomb and Nilsen, 1990). Ericaceous
litter also contributes to declines in soil fertility, lower N
mineralization rates, and lower decomposition rates due to higher
concentrations of foliar polyphenols (Hättenschwiler and Vitousek, 2000;
DeLuca et al., 2002; Côté et al., 2000; Wurzberger and Hendrick, 2007).
Ericaceous plants have ericoid mycorrhizae that provide a competitive
advantage to breaking down organic N over ectomycorrhizae associated with
many deciduous and coniferous species (Bending and Read, 1997), which leads
to the inhibition of overstory species regeneration (Nilsen et al., 2001).
The areal extent of R. maximum has increased in some areas of southern and central
Appalachia (Phillips and Murdy, 1985; Rollins et al., 2010; Brantley et al.,
2013; Elliott et al., 2014). Shrub cover in the region is expected to
continue to increase given fire suppression, lack of grazing, and forest
canopy die-off from infestations (Nowacki and Abrams, 2008; Ford et al.,
2012). If precipitation increases in this area in accordance with climate
projections, the accompanying increase in soil moisture availability may
further the expansion of R. maxiumum. The loss of previously dominant foundational
species in these systems (e.g., Picea rubens in West Virginia due to logging and fire in
the late 1800s and early 1900s; Tsuga canadensis die-off from hemlock woolly adelgid across
the Appalachians and eastern USA) may result in possible, multiple
stable states (Ellison et al., 2005).
Increase in shrub cover has the potential to further impact ecosystem fluxes
and biogeochemical cycling and may contribute strongly to future forest
community dynamics. However, conversely, if the variance in interannual
precipitation continues to increase, drought years may serve as a possible
control on shrub expansion.
Implications of dynamic precipitation
Data from the NCDC's station in Canaan
Valley, WV (Station ID 461393), show that precipitation in this region of WV
is increasing, notably so since 1993 (Fig. 6b). This increase in
precipitation appears to be driven by a notable increase in the number of
extreme precipitation days (EPDs), defined here as days on which precipitation
exceeds 25.4 mm (Fig. 6c). While precipitation is generally increasing in
the Weimer Run watershed, and similar areas across West Virginia, the
year-to-year variance is increasing as well. A Breusch–Pagan test, which
tests for the presence of heteroscedasticity in linear regression models,
shows that NCDC precipitation data from Canaan Valley since 1970 exhibit a
statistically significant increase in interannual variance (BP = 8.58;
p= 0.003). This means that the low-precipitation years are trending much lower
than the mean, while the high-precipitation years are trending much higher
than the mean, with fewer overall “average” precipitation years. This
increased variance appears to again be driven by the increased variance in
EPDs from year to year (Fig. 6b and c) and has been attributed to changes
in the North Atlantic Subtropical High and anthropogenic climate change (Li
et al., 2011). As soils are subject to year-to-year wet–dry cycles,
cumulative effects on carbon cycling and carbon fluxes are likely. It is
beyond the scope of this study to answer the question posed above; however,
with the observed dynamics in precipitation for the region, this may be an
important line of future research. These relative extremes in rainfall
amounts that occurred during this study resulted in significant differences
in soil moisture regimes (measured as WFPS) across the entire watershed and
between both our elevation and vegetation cover classes (Sect. 3.2; Tables 1
and 2). During 2011, there were 34 EPDs, whereas in 2010 there were only 11
and in 2012 only 9. Precipitation also affected the variance in WFPS within
the watersheds by year, as measured by the coefficient of variation (CV),
with 2011 showing decreased variance in WFPS (CV = 27.85) compared to
either 2010 (CV = 41.11) or 2012 (CV = 29.48). Increased precipitation
and increased numbers of EPDs changes the soil moisture regime within the
watershed and that in turn affects CO2 fluxes.
Statistical table from repeated measures mixed-model ANOVA. For all
comparisons by elevation, vegetation, and year, n= 633 and df = 2633. For
elevation by year and vegetation by year comparisons, n= 633 and df = 4633.
Elevation
F
p
Fsoil
3.44
0.0326
WFPS (0–12 cm)
11.13
< 0.001
Soil temp. (12 cm)
170.76
< 0.001
Vegetation
Fsoil
37.58
< 0.001
WFPS (0–12 cm)
11.20
< 0.001
Soil temp. (12 cm)
52.79
< 0.001
Elevation by vegetation
Fsoil
2.47
0.0436
WFPS (0–12 cm)
24.48
< 0.001
Soil temp. (12 cm)
9.55
< 0.001
Year
Fsoil
1.40
0.2464
WFPS (0–12 cm)
17.27
< 0.001
Soil temp. (12 cm)
1.66
0.1918
Elevation by year
Fsoil
3.17
0.0134
WFPS (0–12 cm)
6.05
< 0.001
Soil temp. (12 cm)
1.02
0.3945
Vegetation by year
Fsoil
2.96
0.0192
WFPS (0–12 cm)
4.08
0.0034
Soil temp. (12 cm)
5.46
0.0003
Theoretical contributions
Our findings indicate that for this relatively humid watershed, increased
precipitation may result in decreased soil water heterogeneity and decreased
fluxes of carbon from the soil surface, while decreased precipitation may
result in increased soil water heterogeneity and increased carbon
fluxes – especially from areas of higher elevation and/or with greater shrub
coverage. This study adds to a growing body of literature that deals
theoretically with the effects of topography and vegetation on water and
carbon cycling, and more specifically on carbon cycling across watersheds
with varying degrees of moisture availability.
Similar studies in drier watersheds have found that increases in soil water
availability largely result in increases in soil carbon fluxes. Pacific et al. (2008) showed that for the Stringer Creek watershed, a subalpine,
montane watershed in Montana, the spatial variability of soil CO2
efflux was controlled by the input of soil water driven by seasonal
snowmelt. Fluxes at riparian areas lower in the watershed were suppressed at
high levels of soil water early in the growing season, but as soil water
decreased, fluxes increased. Pacific et al. (2009) further compared a wet
and a dry year in the same watershed, finding that cumulative fluxes were
33 % higher in riparian areas during the dry year but 8 % lower at
landscape positions higher in the watershed. Decreased moisture inputs for
Stringer Creek resulted in significant responses in fluxes across landscape
positions, but the riparian areas respond similarly to the entirety of the
Weimer Run watershed in our study, with dry years resulting in increases in
carbon fluxes. It has been shown in previous studies (Clark and Gilmour,
1985; Davidson et al., 2000; Sjogersten et al., 2006; Pacific et al., 2008)
that a production optimality of surface CO2 efflux exists in response
to soil water content such that peak rates of surface CO2 efflux
coincide with medial values of soil water content, with soil water varying
both temporally and spatially (with elevation). Our study adds the
dimension of vegetation to this model, demonstrating that vegetation
heterogeneity can have significant effects on surface CO2 efflux within
humid watersheds, particularly during periods of below-average soil water
availability.
There are other possible avenues of carbon loss not considered here that may
be affected by interannual climatic variability. It is possible that
dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) fluxes
from the watershed are increased during wet years due to increased flow in
the system. Fluxes from these pools may be significant, but are difficult to
measure and often carry a high degree of uncertainty. DIC and DOC fluxes are
highly variable spatially, coinciding with preferential flow paths within
watersheds as a function of runoff (McGlynn and McDonnell, 2003; Kindler et
al., 2011). Manipulative experiments have shown that simulated drought
decreases DOC leaching across an elevation gradient by as much as 80–100 % (Hagedorn and Joos, 2014), indicating that these fluxes are also
responsive to interannual climate variability.