Changes in soil carbon cycling across a nitrogen pollution gradient in the San Bernardino Mountains, California
Gloria JimenezSenior Integrative Exercise
9 March, 2007
Submitted in partial fulfillment of the requirements for a Bachelor of Arts degree from Carleton College, Northfield, Minnesota.
Table of ContentsAbstractIntroduction………………………………………………………………………………1Methods…………………………………………………………………………………...5 Study area and site comparison Field and laboratory work Modeling
Determinationofturnovertimefrom∆14CsignaturesModelparameters
Results…………………………………………………………………………………...15 ∆14C data and turnover times
OihorizonOahorizonAhorizon
Discussion……………………………………………………………………………….22Effects of N on SOM ∆14C signatures and turnover times
OihorizonOahorizonAhorizon
Evaluation of model assumptionsMechanisms of N-induced change
Conclusions……………………………………………………………………………...30Acknowledgments………………………………………………………………………31References Cited………………………………………………………………………...32
Changes in soil carbon cycling across a nitrogen pollution gradient in the San Bernardino Mountains, California
Gloria JimenezCarleton College
Senior Integrative Exercise9 March, 2007
Advisors:Nicole Nowinski, University of California, Irvine
Mary E. Savina, Carleton CollegeAlexander Barron, Carleton College
ABSTRACTThe western San Bernardino Mountains have received one of the highest loads of atmospheric nitrogen (N) deposition in North America over the past 50 years because of fossil fuel combustion in the adjacent Los Angeles Basin. Further to the east, areas with the same vegetation, soil type, and climate remain less polluted, creating an ideal setting in which to observe the effects of N fertilization on storage and turnover of soil carbon (C). In this study, I examined the response of soil carbon cycling to N deposition by measuring the ∆14C signature of soil fractions from two sites along an air pollution gradient in the San Bernardino Mountains. The polluted site’s labile fraction turnover times were decreased on average by 20 years in the O horizon and by 50 years in the A horizon in comparison to the unpolluted site. Recalcitrant fraction turnover times increased by 50 years in the O horizon and 80 years in the A horizon. These results suggest that high level, long term N fertilization accelerates decomposition in labile soil fractions and slows decomposition of recalcitrant fractions. Additionally, N fertilization may be responsible for an order of magnitude reduction in C storage of multidecadal-cycling pools observed at the polluted site.
Keywords: Radiocarbon, soil organic matter, soil carbon cycling, soil respiration, N deposition, San Bernardino Mountains
1
INTRODUCTION
Soils account for two thirds of near-surface terrestrial carbon (C) storage,
containing twice as much C as the atmosphere, and half of this soil C is stored in near-
surface, fast-cycling pools with annual to multidecadal turnover times (Post et al., 1982;
Schimel et al., 1997; Trumbore et al., 1996; Vitousek et al., 1997). In light of the current
focus on CO2 as a driver of global climate change, it is important to understand how soil
carbon storage responds to changing environmental factors. One important but unclear
area is the vulnerability of soil C to nitrogen (N) additions, as human activities have more
than doubled the transfer of nitrogenous gases from the atmosphere to terrestrial pools
(Vitousek et al., 1997; Fenn et al., 2003).
Many studies show that N fertilization increases aboveground ecosystem C
storage, but considerable uncertainty remains about the response of belowground C
stocks, particularly in the long term (Aber et al., 1995; Townsend et al., 1996; Cao and
Woodward, 1998; Korner, 2000; Neff et al., 2002, Fenn et al., 2003; Bowden et al., 2004;
Mack et al., 2004). First, it is often difficult to distinguish the impacts of N additions
from other environmental factors that have substantial effects on soil C cycling such as
temperature, moisture regime, vegetation, and substrate quality (Davidson, 1998; Hooper
and Johnson, 1999; Berg, 2000; Berg et al., 2000; Trumbore, 2000; Neff et al., 2002).
For example, estimated turnover times can vary over two orders of magnitude because
of locally different environmental variables such as soil drainage (Trumbore and Harden,
1997; Trumbore, 2000). Also, Hooper and Johnson’s (1999) review demonstrates that
various ecosystem responses to N depend on yearly water availability but others show
evidence of co-limitation between N and water, so that the impact of N fertilization may
differ unpredictably between two sites with different moisture regimes. Another source
of ambiguity is the disparity between the effects of N fertilization on soils in the long and
short term. Bowden et al. (2004) demonstrate that soil microbial respiration in hardwood
stands at Harvard Forest, MA initially increased in response to added N, but had
2
decreased overall after 13 years. In contrast, the authors found that soil respiration in red
pine stands consistently declined through time, again demonstrating how local variations
may confound the response of soil C to N fertilization.
Such ambiguity can be explained in part by the fact that soil organic matter does
not behave as a homogenous unit but rather a mixture of physically and chemically
distinct fractions cycling on various timescales (Trumbore, 2000; Gaudinski et al., 2000).
When and to what extent a soil fraction will respond to N fertilization depends on the
amount of C in the fraction and how long it takes to decompose (Trumbore, 1997; cf.
Mack et al., 2004). For example, Neff et al. (2002) illustrate this varying response with
radiocarbon analyses of alpine tundra soil pools, showing that young, low density soil
carbon pools experienced increased decomposition and faster turnover in response to 10
years of N additions, but turnover times were longer in older, dense, mineral-associated
C pools associated with soil minerals (dense fraction). Thus, discriminating between the
responses of various soil fractions in this manner is necessary to resolve soil C dynamics.
Radiocarbon dating is the only quantitative method with which it is possible
to investigate this differential cycling of soil carbon fractions and their response to
environmental factors over timescales greater than 10 years (Trumbore, 2000; cf.
Gaudinski et al., 2000; Neff et al., 2002; Cisneros-Dozal et al., 2006; Schuur and
Trumbore, 2006). Radiocarbon (14C) has a half-life of approximately 5370 years; it is
produced naturally by cosmic ray spallation in the stratosphere as well as by nuclear
weapons (Godwin, 1965). Until the test ban treaty in 1963, nuclear weapons testing
during the 1950s and 1960s nearly doubled the atmospheric 14C/12C ratio, so that any C
fixed from atmospheric sources after this time became enriched with 14C with respect to
pre-1963 levels (Levin and Kromer, 1997). Atmospheric 14C levels have decreased in
the last decades because of dilution from 14C-poor fossil fuel emissions and C uptake by
terrestrial and oceanic reservoirs, giving rise to what is known as the “bomb curve” for
atmospheric ∆14C (Levin and Kromer, 1997; Trumbore, 2000). Knowing the historical
3
atmospheric concentration of 14CO2 and the amount of 14C in soil organic matter (SOM)
allows determination of the material’s approximate age and turnover time, or the rate at
which a soil reservoir would empty if inputs were stopped (Fig. 1; Gaudinski et al., 2000;
Trumbore, 2000; Trumbore, 2006).
In this study, I examine the response of soil carbon cycling to N loading by
measuring the ∆14C signature of soil fractions from two sites at either end of an air
pollution gradient in the San Bernardino Mountains, Camp Paivika and Barton Flats.
Twentieth century fossil fuel combustion in the adjacent Los Angeles Basin has caused
one of the highest rates of atmospheric N deposition in North America to occur in the
San Bernardinos (Miller et al., 1977; Arkley, 1981; Miller et al., 1989; Fenn et al., 2003;
Grulke et al., 2005). N loading in the mountains decreases away from Los Angeles, with
high levels in the west, near Camp Paivika and lower levels in the east, near Barton Flats
(Miller et al., 1977; Takemoto et al., 2001). The pollutant load to the western mountains
also includes tropospheric ozone, but ozone effects on mixed conifer forests are well-
studied and separable from those of N (Miller et al., 1977; Grulke and Balduman, 1998;
Takemoto, 2001).
The location of these otherwise similar sites along a pollution gradient sets up an
excellent “natural experiment” in which to investigate the question of soil C response to
N deposition while eliminating the confounding factors of climate regime, vegetation,
and substrate type. Furthermore, this pollution gradient has been in place for over 50
years, so that soil fractions at Camp Paivika, the more polluted site, demonstrate the
effects of long term N fertilization where most other studies cannot represent response on
such a timescale. Thus, in conjunction with measurements of soil respiration (Nowinski,
2006), this study will attempt to resolve how differently cycling soil fractions respond to
long-term, high level N fertilization.
4
-100
100
300
500
700
900
1940 1950 1960 1970 1980 1990 2000
Year
AtmosphericSOM with 10 yr TTSOM with 50 yr TT
∆14 C
(‰)
Figure 1. Relationship between turnover time and ∆14C signature of soil organic matter (SOM; after Gaudinski et al., 2000). The peak in atmospheric 14C, or the bomb curve (solid line) in the 1960s allows determination of how fast a soil fraction is incorporating new material and hence its turnover time, because detectable amounts of 14C are incor-porated into SOM fixed after 1963. As the atmosphere became enriched with respect to 14C, SOM reservoirs began to incorporate detectable amounts of 14C: dotted curves show how the yearly 14C content of SOM would change each year given a turnover time (TT) of 10 versus 50 years, assuming that the fractions are homogenous, cycling at steady state, and have no time lag between fixation of C and incorporation into a fraction.
5
METHODS
Study area and site comparison
Camp Paivika (34º 14’05’’N, 117º 19’12’’W) and Barton Flats (34º 09’42’’N,
116º 51’00’’W) are located 42 km apart (Fig. 2). The two sites were chosen for
uniformity of tree species and soils and have a similar climate regime (Miller et al.,
1977). Camp Paivika is at 1600 m elevation and Barton Flats at 1946 m, which
influences their vegetation: both are characterized as mixed conifer forests, but Camp
Paivika is dominated by ponderosa pine, and Barton Flats has mixed ponderosa and
Jeffrey pine (Fenn and Dunn, 1989; Miller et al., 1989). Climate in the San Bernardino
Mountains is Mediterranean, characterized by warm, dry summers with winter snows as
the major source of moisture (Miller et al., 1977). Barton Flats and Camp Paivika have a
similar amount of yearly precipitation: averaged over 1980-1997, the two sites received
89.7 cm/yr and 98 cm/yr respectively (data for Barton Flats comes from nearby Camp
Osceola; see Fig. 2; Grulke and Balduman, 1999). Grulke and Balduman (1999) report
cooler temperatures and hence a shorter growing season at Barton Flats than at Camp
Paivika over 1993-1995. However, there is a large interannual variation in growing
season length at the sites, and the authors use few data points to measure this difference
and do not provide specific temperature data. In addition, it is likely that moisture affects
soil respiration and decomposition to an equal or greater degree than temperature (cf.
Trumbore, 1996; Trumbore and Harden, 1997; Hooper and Johnson, 1999; Davidson,
2000).
The soils at both sites are generally similar: they are formed on coarse-grained
weathered granitic parent material or colluvium derived from granites, and are well
drained with a low water holding capacity (Miller et al., 1977). Camp Paivika has
predominantly Shaver series soils (coarse loamy, mixed, superactive, mesic Humic
Dystroxerepts), and Barton Flats has Crouch and Cahto variant soils (coarse-loamy or
loamy-skeletal, mixed, superactive or active, mesic Humic Dystroxerepts; Arkley, 1981;
6
Hol
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ood
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Figu
re 2
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catio
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f the
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n th
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ount
ains
(map
afte
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198
9).
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urce
in L
os A
ngel
es th
an B
arto
n Fl
ats (
BF)
. So
me
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dat
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pola
ted
from
Cam
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~3 k
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.
Key
Map
Los
Ang
eles
San
Ber
nard
ino
Mou
ntai
ns A
rea
CO
N
7
Soil Survey Staff, 1998).
Atmospheric pollutant deposition to the sites is primarily caused by fossil fuel
combustion in the Los Angeles Basin, which creates hydrocarbons and nitric oxide
(NO), and these products in turn generate tropospheric ozone (O3) (Miller et al., 1977;
Solomon et al., 1992; Fenn and Kiefer, 1999). Deposition decreases throughout the
mountains from west to east, along the direction of prevailing surface winds, due to rising
elevation and distance from the source of pollution (Fig. 3; Miller et al., 1977; Miller et
al., 1989; Takemoto et al., 2001). Nitrogen deposition levels show an especially dramatic
change because N species have a high depositional velocity, particularly nitric acid
(HNO3). Camp Paivika, in the western mountains, receives 35-45 kg N/ha/yr, whereas
Barton Flats, in the east, has N deposition of 5-9 kg/ha/yr (Bytnerowicz and Fenn, 1996;
Takemoto et al., 2001). N is deposited mostly as dry particulate nitrate (NO3-) and
HNO3, which adsorb onto foliage and later are mobilized in throughfall, though some
wet deposition of NO3- also occurs (Bytnerowicz and Fenn, 1996; Fenn and Kiefer, 1999;
Fenn et al., 2003; Grulke et al., 2005). Both sites also have higher than background
levels of ozone: Camp Paivika has 24-hour average O3 concentrations of ~0.09 ppm,
while Barton Flats has O3 concentrations of ~0.06 ppm (clean, background sites have O3
levels of ~0.015-0.04 ppm; Fenn and Dunn, 1989; Takemoto et al., 2001).
In order to study the effects of N on soil C cycling, it is necessary to discriminate
between the influences of N and O3. Many studies have focused on these pollutants’
impacts on the aboveground mixed conifer forest ecosystem and particularly ponderosa
pines because of their sensitivity to oxidant air pollution (Miller et al., 1983; Grulke
and Balduman, 1999). Elevated ozone levels have been demonstrated to cause foliar
injury and abscission as trees absorb ozone directly through stomates as well as lowering
root biomass, and greater N availability increases branchlet and bole growth, and also
contributes to lower root biomass (Miller et al., 1977; Miller et al., 1982; Gower et al.,
1996; Grulke et al., 1998; Grulke and Balduman, 1998; Miller and McBride, 1999;
8
San
Ber
nard
ino
Mou
ntai
ns
E
W
NS
CP
BF
Pollu
ted
air fl
ow
Figu
re 3
. To
pogr
aphy
and
dis
tanc
e fr
om L
os A
ngel
es c
reat
e th
e po
llutio
n gr
adie
nt o
bser
ved
in th
e Sa
n B
erna
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oun-
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s (fig
ure
afte
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er e
t al.,
197
7).
Atm
osph
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dep
ositi
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f N is
hig
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t Cam
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Flat
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amp
Paiv
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also
exp
rerie
nces
hig
her o
zone
pol
lutio
n.
9
Takemoto et al., 2001; Fenn et al., 2003).
The different effects of ozone on ponderosa pines at each site have been well-
documented. At Camp Paivika, ozone-injured ponderosa pines drop their needles
approximately once a year, but at Camp Osceola, in the area of Barton Flats, the trees
drop their needles once every 3 years; at background ozone levels ponderosa pines may
retain up to 6 years of needles (Grulke and Balduman, 1999; Takemoto et al., 2001).
Also, ponderosa pine root biomass at Camp Paivika is up to 6-14 times less than at Camp
Osceola (Grulke et al., 1998). Consequently, the influence of high ozone on soils at the
sites is probably indirect and limited to increasing litterfall inputs and lowering root
biomass. Little data is available concerning the direct impact of ozone on soils, so in
the absence of contradictory data this study assumes that all other differences in soil C
cycling observed between the sites are responses to N.
Field and laboratory work
The National Forest Service defined study plots at Camp Paivika and Barton Flats
in the 1970s, selecting for relative homogeneity of vegetation type and cover (Miller et
al., 1977, Fenn and Dunn, 1989). For this study, three ponderosa pines were chosen from
a plot at each site, and soil samples were collected 0.5 m from the northeast side of each
tree and homogenized. A 15 cm square portion of litter (Oi horizon) was cut out and a 5
cm diameter core was taken through the Oa horizon to approximately 10 cm depth in the
A horizon (Fig. 4).
Soil C storage (g C/m2) for each replicate soil horizon was calculated as bulk
density multiplied by g C/g soil (determined by CO2 purification on a vacuum line), and
the C storage within each separated soil fraction was extrapolated using a mass balance
scheme. C storage for each fraction is reported as the mean of the three replicates
analyzed, except that at Barton Flats the presence of a rock in the A horizon beneath one
tree prevented sampling, so only two replicates were used.
10
15 cm square of Oi horizon cut out
Sieved
5 cm diameter core takenthrough Oa and A horizons
Centrifuged with Na polytungstate solu-tion (2.0 g/cm3)
Ground Oi fraction
Oa coarse fraction
Oa fine material
> 1mm
Oa light fraction
A light fraction
Oa heavy fraction
A heavy fraction
}}
floatingmaterial
pellet
< 1mm
Figure 4. Schematic of the process of collecting soil samples and separating them into fractions; thick boxes indicate fractions that were analyzed for ∆14C. The resulting fractions have distinct physical and chemical characteristics that reflect their general stage of decomposition and turnover time. The Oi (litter) fraction contains undegraded pine needles, bark, and wood. The Oa coarse fraction is a mixture of Oi and Oa light material; the Oa and A light fractions are composed of labile organic matter that has been visibly decomposed. The Oa and A heavy fractions have humified or mineral-associated organic matter (Schulten and Leinweber, 1999; Gaudinski et al., 2000; Trumbore, 2000). Note that depths of soil horizons vary between sites.
Oi(litter)
Oa
A
Oa samples
A samples
11
The replicate samples from each horizon were homogenized for ∆14C analysis.
The Oi samples were ground, and soil samples from the Oa and A horizons were further
separated by size and density into fractions with different general turnover times, after
Gaudinski et al. (2000). The Oa samples were sieved to 1 mm, and the material smaller
than 1 mm was separated by density into light and heavy fractions by centrifuging with
a Na polytungstate solution (d = 2.0) (Fig. 4). ∆14C signatures for these fractions were
analyzed on an accelerator mass spectrometer (AMS) at the University of California,
Irvine. Sample targets were prepared using the zinc reduction technique with a Fe
catalyst (Vogel, 1992; Gaudinski et al., 2000). The AMS instrument used in this study
has a 2‰ precision, allowing age determination to within 1 year for samples made from
C fixed since the bomb curve peak in 1963.
Modeling
Determination of soil fraction turnover times from 14C values
A non-steady state accumulation model was used to calculate the turnover time
of the younger, lighter soil fractions (from the Oi and Oa horizons), which are more
dynamic and cycle on a faster timescale. For the A horizon and heavier Oa fraction, a
steady state model was used, given that these fractions cycle on longer timescales so
that their turnover time, age and residence time are approximately equal (Gaudinski et
al., 2000). Radiocarbon values were reported in the model as ∆14C, the amount of 14C in
‰ relative to the amount in an oxalic acid standard, OX1, in 1950, corrected for mass-
dependent fractionation to a δ13C value of -25‰ (Stuiver and Polach 1977):
∆14C = [(14C/12Csample)/(0.95 • 14C/12COX1 • e(y-1950)/8267) – 1]/1000 (1)
This notation is similar to the δ (“del”) notation used for stable isotopes in that it
denotes the deviation in ‰ from an absolute standard; however, ∆ (“delta”) corrects for
14C decay of the OX1 standard since 1950 so that the ∆14C value of a particular sample
does not change through time.
12
High positive ∆14C values (>100‰) indicate that a sample has a radiocarbon value
higher than the 1950 atmosphere and must have had atmospheric contact since nuclear
weapons testing in order to include bomb-enriched material. Negative ∆14C values
(<0‰) signify that a sample has been undisturbed by recent atmospheric C long enough
to have experienced significant radioactive decay. Intermediate values (0-100‰) are
ambiguous and site-specific data must be used to resolve their correct age; they could
either represent a mixture of slow and fast cycling carbon from both pre- and post-bomb
reservoirs, or older material cycling on a longer timescale (see Fig. 1).
In both the steady state and non-steady state models, SOM ∆14C was calculated
from approximated atmospheric ∆14CO2 curves and experimentally measured C storage.
Los Angeles Basin ∆14CO2 is lower than global atmospheric ∆14CO2 because it receives
high fossil fuel emissions, which contain C that was fixed millions of years ago and
consequently all its 14C has decayed radioactively (∆14C = -1000‰). Local atmospheric
∆14C curves were generated by calculating the mixing between Los Angeles basin fossil
fuel CO2 emissions over the past century and global atmospheric CO2 (N. Nowinski,
unpublished data).
For the non-steady state model, the C stock added in a given year y was calculated
such that the total C storage accumulated by year y was the sum of C stocks from all
preceding years since 1900:
Cy = I • e-k (2006-y) (2)
Ctotal = Σ Cy (from 1900 to y) (3)
This formulation assumes that the C stock was zero in 1900 and for each fraction, k and
I were constant from year to year; C = carbon storage for a soil fraction (g C/m2), I =
amount of C inputs (g C/m2/yr), and k = decay constant (1/yr).
The ratio of 14C contained in SOM is the amount of 14C within the C stock added
every year. Thus, ∆14C values for soil organic matter were calculated as follows:
Ratm(y) = (∆14Catm(y)/1000) + 1 (4)
13
RSOM(y) = Ratm(y) • Cy (5)
∆14CSOM(y) = (Σ RSOM(y) – 1) • 1000 = [(Σ Ratm(y) • Cy)/ Σ Cy – 1] • 1000 (6)
A range of appropriate turnover times (A range of appropriate turnover times (τ = 1/k) for each soil fraction was
ascertained as those values that made C storage and ∆14CSOM calculated in the model
match experimentally determined measurements. Constraints on the variables of input
amount (I), time lag between fixation of C and incorporation into a soil fraction as inputs
(input age), and year when accumulation began (1900) were established for each fraction
and are discussed in the following section.
The preceding calculations for C storage and ∆14CSOM assume that all C within a
given soil fraction decomposes at a constant rate. This assumption did not hold for all
soil fractions and some were calculated within the non-steady state model as the sum of
two pools cycling at different rates.
In contrast to the non-steady state model, the steady-state model calculates the
14C signature of SOM in a given year as dependent on the C storage and 14C value of the
previous year (Gaudinski et al., 2000). The model represents decomposition, the change
in C stock from one year to the next, as:
dC/dt = I – kC (7)
so that the carbon stock in year y is
Cy = I – kCy-1 + Cy-1 (8)
and ∆14CSOM is calculated as follows:
RSOM(y) = [I • Ratm(y) + C(y-1) • RSOM(y-1) • (1 – k - λ)]/ Ct (9)
∆14CSOM(y) = (RSOM(y) – 1) • 1000 (10)
Yearly inputs, I, are calculated as C storage in the fraction divided by turnover time, and
λ is the radioactive decay constant for 14C, or ln(2)/5730, the half-life of 14C (Stuiver and
Polach, 1977).
The steady state model assumes that each C atom in a fraction is equally likely to
decompose at a given moment. During modeling, it was also assumed that C inputs to
14
the soil in a given year would have a ∆14C value representative of that year’s atmosphere
(Trumbore, 2000).
Model parameters
Turnover times, input ages, and input amounts were varied in the model for each
soil fraction until there was a match between the measured and modeled C storage and
∆14C signature. Soil fractions at both sites were assumed to cycle as a homogenous unit
unless a single-pool model could not reproduce both measured C storage and ∆14C, in
which case two pools were assumed. The Oa coarse fraction physically resembled a
mixture of Oi and Oa light material and was therefore modeled as two pools. Several
parameter sets of modeled turnover times, input amounts, and time lags in the non-
steady state model actually match the measured C stock and ∆14C signature for most
fractions. Parameters were chosen based on available site data and whether the model
was internally consistent.
In reality, inputs to each soil fraction include dissolved organic carbon (DOC)
flux, fine root production, and mass flow, or transfer of organic matter from one pool to
the next (from aboveground material, such as litterfall and other plant substances, or from
faster-cycling fractions higher in the profile). However, the only input variable with site-
specific constraints was litterfall; Grulke et al. (1999) report differences in root biomass
but did not measure root inputs to the soil. Consequently, only mass flow data was used
in the model.
Inputs to the Oi fraction were approximated as litterfall and other aboveground
inputs, minus 50 g C/m2/yr of DOC loss (calculated from the model of Neff and Asner,
2001). Litterfall was estimated for Barton Flats from Law et al. (1999), who measure a
rate of 129 g C/m2/yr in a similar, non-polluted ponderosa pine forest in Oregon. Grulke
and Balduman’s (1998) estimates of foliar mass and foliage lost per year were used to
estimate litterfall at Camp Paivika as 290 g C/m2/yr. Because of the large quantity of
15
grasses at Camp Paivika, an additional 300 g C/m2/yr was added to the Oi horizon; no
measurements of aboveground grass biomass were available. Input amounts to the other
soil fractions were established as the values that fit best with the constraints imposed by
other model parameters, particularly measured C stocks.
Time lags were also empirically constrained only for litterfall to the Oi fraction:
the average age of litterfall is approximately 3 years at Barton Flats and 1 year at Camp
Paivika (Grulke and Balduman, 1999). Inputs to the Oi horizon were thus given a time
lag of 3 and 1 years at the two sites, respectively. For inputs to deeper horizons, the input
age was assumed to be the cumulative turnover time of the fractions above (that is, the
age of mass flow down through the soil profile). For instance, given 1 year-old inputs
to the Oi horizon at Camp Paivika followed by an Oi turnover time of 8 years, material
being added to the Oa coarse horizon was given a time lag of 9 years.
Carbon accumulation at each site was assumed to have begun in 1900. With the
advent of fire suppression programs in the San Bernardino Mountains in 1905, the forest
fires typical of forests in the region decreased dramatically in frequency, from occurring
once every 10-12 years to once every 22-29 years (McBride and Laven, 1976). Fire
history data indicate that the study sites at Camp Paivika and Barton Flats did not burn at
all during the twentieth century (file data, San Bernardino National Forest), a conclusion
supported by the absence of charcoal in the soils at either site. In the absence of fire,
there would have been no removal of C stocks from the soils since at least 1900, before
which the amount of stored C would be so small as to have a negligible impact on both
the steady state and non-steady state models relative to the large C stocks of more recent
years.
RESULTS
Carbon storage and dynamics differ significantly between Camp Paivika and
Barton Flats. At both sites, turnover times increase with soil depth and fraction density
16
(Table 1), however, on average Barton Flats’ light fractions have longer turnover times
than Camp Paivika’s, while Camp Paivika’s heavy, mineral-associated fractions have
longer turnover times than Barton Flats’ (Fig. 5). There is also a marked difference in
where C storage occurs between sites: Barton Flats has an order of magnitude greater C
storage in the Oa horizon, whereas Camp Paivika has an order of magnitude more in the
Oi horizon (Fig. 6).
Finding an appropriate turnover time for a given fraction in the non-steady state
model depended largely on the time lag of inputs to the fraction because the time lag
dictates how much bomb-enriched 14C is being added to a fraction (Fig. 7). For example,
litterfall material being added to the Oi fraction at Barton Flats has a known time lag
of 3 years. However, if a time lag of only 1 year had been used, a longer turnover time
would have been necessary to match the Oi fraction’s ∆14C value. Since the Oa heavy
and A fractions modeled in the steady state calculations probably receive a significant
proportion of root inputs relative to their inputs from mass flow (that is, a significant
amount of inputs with ∆14C values representative of the modern atmosphere), they were
given no time lag.
∆14C data and turnover times
Oi horizon
Both sites have similar Oi turnover times, between 8 and 9 years, but Camp
Paivika’s Oi C storage is ~3800-4200 g C/m2/yr and Barton Flats stores ~400-500 g C/m2/
yr (p<0.001) (Table 1, Fig. 6). This difference in C storage derives from the difference
in aboveground inputs at both sites: Camp Paivika receives 540 g C/m2/yr and Barton
Flats receives only 80 g C/m2/yr. In order to match the C stock at Camp Paivika, half the
inputs were given a time lag of 0 years (that is, it was assumed they came from 2006),
which is not unreasonable given that trees there drop needles at least once a year and the
grasses are annuals.
17
TABLE 1. MO
DEL PAR
AMETER
S US
ED TO
CALC
ULATE TU
RN
OVER
TIMES
Site and SOM
fraction
C storage
(g C/m
2)†
14C signature (‰
) ‡Input am
ount (g/m
2/yr) Tim
e lag (yr) §
Pool distribution Turnover tim
e (yr) #
B
arton Flats
O
i 430+110*
115-145 80
2003 1
8-9
Oa coarse
5000+1900* 200-220
170 1999;1995
1/2; 1/2 20-23; 37-44
O
a light 580+220*
200-210 20
1995 1
37-44
Oa heavy
10+5 120-130
<1 N
.A.
1 60-70
A light
520+30 40-55
5 N
.A.
1 150-180
A heavy
180+10 5-30
<1 N
.A.
1 210-300
total ~6720
C
amp Paivika
Oi
4010+280* 80-100
540 2006; 2005
1/2; 1/2 8; 8
O
a coarse 100+70*
200-215 15
1997; 1997 1/2; 1/2
11-14; 15-23
Oa light
50+30* 210-245
5 1997; 1985
1 15-23
O
a heavy 10+5
70-80 <1
N.A
. 1
110-120
A light 750+410
60-85 5
N.A
. 1
100-130
A heavy 180+100
5-10 <1
N.A
. 1
270-290
total ~5100
N
ote
s:
† C in
ven
tories are
repo
rted as
+ 1
; asterisks in
dica
te frac
tion
s that h
ave sig
nifican
tly d
ifferent C
storag
e betw
een site
s. ‡
14C
sign
ature
s are sho
wn
as rang
es represen
ting
the av
erag
e valu
e of a
ll thre
e samp
les + 0
.6. It is in
app
rop
riate to co
mp
are b
etween
-site differen
ces
in
14C
sign
atu
re u
sing
statistical te
chn
iqu
es, becau
se atmo
sph
eric
14C
valu
es w
ithin
the sam
e year can
be d
epressed
by
up
to 9
‰ a
t Cam
p P
aivik
a d
ue
to g
reater d
ilutio
n b
y fo
ssil fuel C
O2in
pu
ts. Mo
deled
turn
ov
er times tak
e these d
ifferent atm
osp
heric
14C
O2 cu
rves in
to acco
un
t. § F
raction
s mo
deled
in th
e stead
y sta
te m
od
el w
ere n
ot g
iven
time lag
s, assum
ing
that so
il rece
ived
yearly
atm
osp
heric-ag
e inp
uts.
# Tu
rno
ver tim
es are rep
orted
as the ran
ges o
f valu
es th
at fe
ll with
in 1
of a
fractio
n’s m
ean
14C
sign
atu
re.
18
Oi
Oa
coar
seO
a lig
ht
Oa
heav
y
A lig
ht
A he
avy
Roo
ts
20-2
3 yr
s37
-44
yrs
8 yr
s
60-7
0 yr
s
~1 y
r
150-
180
yrs
Bar
ton
Flat
s
4 yr
s
3 yr
s
210-
300
yrs
Oi
Oa
coar
seO
a lig
ht
Oa
heav
y
A lig
ht
A he
avy
Roo
ts
11-1
4 yr
s15
-23
yrs
8 yr
s
~1 y
r
100-
130
yrs
Cam
p P
aivi
ka
110-
120
yrs
1 yr
Figu
re 5
. D
iagr
am c
ompa
ring
mod
eled
soil
C c
yclin
g pa
thw
ays a
nd ti
mef
ram
es b
etw
een
site
s. D
ashe
d lin
es in
dica
te sc
hem
atic
ho
rizon
bou
ndar
ies (
not t
o sc
ale)
; arr
ows f
rom
box
ed so
il fr
actio
ns sh
ow m
ass f
low
bet
wee
n fr
actio
ns.
The
times
cale
of m
ass f
low
be
twee
n fr
actio
ns is
the
turn
over
tim
e of
the
uppe
r fra
ctio
n (n
umbe
r ran
ges)
, whi
ch c
an b
e co
ncep
tual
ized
as t
he le
ngth
of t
ime
a si
ngle
C a
tom
wou
ld ta
ke to
phy
isca
lly a
nd c
hem
ical
ly jo
in a
new
frac
tion.
Not
e th
at ro
ot in
puts
are
onl
y sh
own
bein
g ad
ded
to th
e O
a he
avy,
A li
ght,
and
A h
eavy
frac
tions
, as w
as a
ssum
ed in
the
mod
el, b
ut th
is a
ssum
ptio
n is
unr
ealis
tic.
Als
o, a
t Bar
ton
Flat
s, th
e O
a co
arse
is sh
own
rece
ivin
g m
ass f
low
with
a ti
me
lag
of o
nly
4 ye
ars b
ecau
se so
me
litte
rfal
l was
ass
umed
to a
dd d
irect
ly to
the
Oa
coar
se w
ithou
t cyc
ling
thro
ugh
the
Oi f
irst.
270-
290
yrs
19
C inventory (g C/m2)
0
1000
2000
3000
4000
5000
6000
7000
8000
CP
BF
Oi
Oa
A
AFigure 6. Carbon storage in each soil layer for C
amp Paivika and B
arton Flats. A: M
ost soil C at C
amp Paivika is stored in the O
i layer, but at B
arton Flats most soil C
is in the Oa layer. B
: Horizon depth and thus C
storage depth differs between sites; m
arkers indicate the m
idpoint of each horizon. Note that C
stocks in Barton Flats’ O
a layer and Cam
p Paivika’s Oi layer are sim
ilar but B
arton Flats’ Oa layer is thinner (7 cm
) than Cam
p Paivika’s Oi layer (13 cm
).
0510152025
01000
20003000
40005000
60007000
8000
CP
BF
Depth (cm)
Soil C storage (g C/m2)
B
20
Oa horizon
The Oa coarse and Oa light fractions at Barton Flats store an order of magnitude
more C than at Camp Paivika (p=0.05 for both fractions) (Figs. 5, 6). Inputs to the Oa
coarse and Oa light at Barton Flats are also higher than at Camp Paivika, at 170 and 20 g
C/m2/yr versus 15 and 5 g C/m2/yr, respectively (Table 1). The longer turnover times and
greater inputs to the Oa coarse and Oa light fractions at Barton Flats account for greater C
storage in those fractions than at Camp Paivika.
Both sites’ Oa coarse fractions were modeled with an Oi-like pool and an Oa
light-like pool, and these pools had longer turnover times at Barton Flats than at Camp
Paivika. At Camp Paivika, the Oa light’s ∆14C signature is higher than the Oa coarse’s,
indicating that it contains slightly older material. At Barton Flats, however, the Oa coarse
fraction has a more variable and overall higher ∆14C signature than the Oa light, denoting
the presence of more old, bomb-enriched material in the Oa coarse (Fig. 7). The Oa
coarse fraction’s ∆14C signature may be elevated at Barton Flats due to the presence of
wood chips in the horizon from logging in 2005 (N. Nowinski, pers. comm.); average tree
age is approximately 50-60 years at Barton Flats, so wood debris would represent a range
of old, bomb-enriched C (Grulke et al., 1998).
Modeling the Oa heavy fractions was problematic because their ∆14C signatures
indicate a mixture of both pre- and post-bomb C. The non-steady state model
approximated Oa heavy dynamics poorly because the fractions cycle on timescales of
decades to centuries and are not subject to much loss from fire, so the fractions begin to
approach steady state. Consequently, the steady state model offered a clearer and more
accurate representation of C cycling in the Oa heavy fraction.
The steady state model shows that the Oa heavy fraction’s turnover time is twice
as long at Camp Paivika as at Barton Flats (Fig. 5), but C storage is not significantly
different between the two sites (p=0.48; Fig. 6). Inputs to the fraction are probably
greater at Barton Flats so that the fractions at either site maintain the same C storage
21
2000
1980
1960
1940
1920
1900
-100100
300500
700900
∆14C
(‰)
Oi 8-9
Figure 7. Relationship betw
een measured SO
M ∆
14C values, turnover tim
es and input time lags,
exemplified by the upper soil fractions of B
arton Flats. Thick colored lines span a range of years equal to the turnover tim
e of a given fraction, indicating how m
uch 14C that fraction should have
accumulated. C
olored lines are located on the bomb curve (black line) based on the youngest
material they w
ere modeled to include (that is, the tim
e lag of inputs). Stars show the average ∆
14Csignature m
easured for each fraction, which falls approxim
ately in the middle of the lines; the O
a light fraction has tw
o stars, indicating that the fraction includes material from
either side of the bom
b curve. Note how
turnover times depend on the assum
ed time lag in order to m
atch ∆14C
signatures. Oa coarse 20-23
Oa light
37-44
22
despite hastened turnover at Barton Flats, though the model is not sensitive enough to
detect such small-scale differences (Table 1).
A horizon
The A light fraction at Barton Flats has a longer turnover time than at Camp
Paivika, while the A heavy fraction has a longer turnover time at Camp Paivika (Fig. 5),
but C storage in the A fractions is similar between sites (Fig. 6). In the A light fraction,
Barton Flats has approximately 510-540 g C/m2/yr and Camp Paivika has 500-1000 g
C/m2/yr (p=0.45), and in the A heavy fraction, Barton Flats has 180-190 g C/m2/yr and
Camp Paivika has 100-200 g C/m2/yr (p=0.99) (Table 1).
DISCUSSION
Effects of N on SOM ∆14C signatures and turnover times
A high level of N fertilization over the past 50 years at Camp Paivika is associated
with shorter turnover times in the Oa and A light soil fractions and longer turnover times
in the Oa and A heavy fractions. This difference suggests that N fertilization destabilizes
low-density, labile soil pools, hastening their decomposition, but stabilizes dense,
mineral-associated recalcitrant pools.
Additionally, although the sites have similar overall C stocks, these stocks are
distributed differently in each soil profile and C cycles through the soil fractions at
different rates (Table 1). At Barton Flats, 80% of C storage is in the Oa light fraction,
which cycles on the scale of 40 years. In contrast, 70% of the C stock at Camp Paivika
is contained in its Oi fraction, with a turnover time of 8 years. The thickness of Camp
Paivika’s Oi horizon derives from the ozone-induced increase in litterfall at the site, and
likely any N-induced changes are subsumed by this effect. However, Camp Paivika’s
Oa horizon is very thin in contrast to that of Barton Flats despite the abundant material
accumulating in the Oi horizon above (Fig. 6). If indeed N fertilization has increased
23
decomposition in labile soil pools, the thinness of Camp Paivika’s Oa horizon may derive
from a long-term higher mass loss rate in the Oa coarse and Oa light fractions than at
Barton Flats.
Knowing the approximate age and turnover time of SOM allows inference of the
effects of N fertilization on SOM cycling, but gives no information about what factors,
such as decomposition rates or microbial substrate choice, caused the observed difference
in SOM ∆14C between sites. However, contrasting the measured ∆14C values at the two
sites can suggest what elements changed to cause the observed differences in SOM ∆14C,
as can comparison between SOM ∆14C and the ∆14C values of heterotrophic respiration,
which have been calculated from incubations of bulk soil horizons (Nowinski, 2006).
Respiration ∆14C values from soil incubations reflect the predominant age of material
being decomposed by soil microbes in each horizon.
Oi horizon
At both sites, respiration ∆14C in the Oi horizon is similar to the Oi horizon’s
SOM ∆14C, indicating that the material being decomposed is of average age for the
fraction. C stocks at the two sites are very different (Fig. 6), but this difference derives
primarily from the increased litterfall caused by foliar ozone injury at Camp Paivika. N
fertilization may also contribute directly to an increase in litterfall (Gower, 1996), as well
as making high litterfall amounts possible by alleviating nutrient limitations (Takemoto et
al., 2001).
Oa horizon
The Oa horizon shows the most striking response to N fertilization because its
dynamics are not obscured by ozone effects as in the Oi horizon, and it contains more
labile C than the A horizon. Even considering the up to 10‰ difference in atmospheric
∆14C values between sites, it is qualitatively apparent that the ∆14C value of the Oa heavy
24
fraction is lower at Camp Paivika, indicating that the fraction contains older C than at
Barton Flats. This discrepancy is confirmed by the model, which shows that the Oa
heavy fraction’s turnover time is roughly twice as long at Camp Paivika (Table 1).
I suggest two changes that could give rise to such a marked difference in the
Oa heavy fraction’s ∆14C signature and turnover time between sites. First, less overall
decomposition might be occurring in the Oa heavy fraction at Camp Paivika, so that the
fraction retains proportionally more old, ∆14C-poor material. Modeled turnover times
support this hypothesis, because the Oa heavy fraction at Camp Paivika has a longer
turnover time than at Barton Flats, implying that decomposition rates are lower at Camp
Paivika. Second, it is possible that more old, ∆14C-poor products are being added from
the Oa light to the Oa heavy fraction at Camp Paivika than at Barton Flats. Since the
∆14C signature of Camp Paivika’s Oa light fraction actually indicates the presence of
more bomb-enriched C than at Barton Flats, any such trend in the ∆14C signature of
inputs would be caused by increased microbial selection of older C, for which no data
are available. The Oa coarse and Oa light fractions, which are the primary mass flow
pathways to the Oa heavy in the model, do not contain young material with a low enough
∆14C signature to affect the Oa heavy (see Fig. 7), and it is doubtful that significant
respiration of pre-bomb is occurring and adding ∆14C-poor inputs, since this material is
more recalcitrant.
Respiration ∆14C data help to evaluate these proposed changes in the Oa heavy
fraction ∆14C signature. At both sites, respiration ∆14C signatures from the Oa horizon are
similar to those of the lighter Oa fractions, which is unsurprising because these fractions
contain the most labile or easily decomposed C in the horizon. However, respiration ∆14C
values from the Oa horizon are lower at Barton Flats than at Camp Paivika (accounting
for error from the difference in atmospheric curves), suggesting that mostly the youngest
SOM from the Oa coarse and light fractions is decomposing and dominating the
respiration signature at Barton Flats. In general, then, a greater relative proportion of
25
intermediate-age, Oa light substrate is being decomposed at Camp Paivika than at Barton
Flats.
Evidence of increased Oa light fraction decomposition at Camp Paivika in the
respiration data supports the decreased Oa light turnover times calculated in the model
(Fig. 5). Also, some disparity in Oa light decomposition between sites would be expected
in order to create the observed difference in Oa horizon C stocks (C storage is mostly in
the Oa coarse and Oa light fractions; see Table 1).
A horizon
At both sites, the ∆14C signature of respiration from the A horizon is ~50‰ higher
than SOM ∆14C, indicating that most material being decomposed is younger than average;
this young material is probably from root inputs. Although it was possible to model the
A horizon’s fractions with one pool, this difference between SOM and respiration ∆14C
values suggests that it may have been more appropriate to model the A horizon fractions
as a mixture of two pools, one with old, mass flow-derived C cycling slowly and one with
young, root-derived C cycling quickly.
Again, respiration ∆14C is higher at Camp Paivika than Barton Flats (despite error
from the sites’ different atmospheric curves). Because the ∆14C signature of A horizon
SOM shows that the substrate is mostly pre-bomb C, high ∆14C respiration values in
this case indicate that younger substrates are being decomposed at Camp Paivika than at
Barton Flats. Although SOM ∆14C differences indicate that the A light fraction at Barton
Flats is composed of older material than at Camp Paivika, and respiration ∆14C also
differs, C storage is very similar (Table 1). The lack of difference in C storage between
sites may derive from the long turnover times of the A fractions, meaning that changes
in cycling would need a longer time to produce a noticeable discrepancy in overall C
storage.
26
Evaluation of model assumptions
The non-steady state model calculations involved several unrealistic assumptions
about input amounts and time lags, but comparison with literature estimates of these
values, as detailed in the following section, indicates that the assumptions made during
modeling should not be problematic. Specifically, input amounts and time lags were
only empirically constrained for the Oi fraction; all other inputs were modeled as
homogenously aged mass flow cycling from upper fractions. This is a simplistic view
of soil carbon cycling because the lower soil fractions also receive inputs from fine root
production and lose dissolved organic carbon (DOC) due to leaching, but field data for
these inputs was unavailable. Thus, the ages of C inputs to lower fractions actually
vary more than assumed during modeling, and the input amounts were derived solely
from what worked best in the model and might differ significantly from true values. It
is important to consider whether the total amount of inputs assumed during modeling is
reasonable in comparison to the probable amount of inputs from roots; additionally, if
root inputs constitute a high enough proportion of the total input amount, their low ∆14C
signature may have an effect on turnover time which was not accounted for in the model.
The accuracy of the input amounts used in the model can be evaluated by
comparison with literature estimates of DOC flux and root biomass; a summary of this
information is presented in Table 2. It seems that mass flow is a much more important
pathway than root inputs or DOC in these soils, except in the lower and heavier fractions.
First, it is unlikely that DOC flux is a significant factor in determining soil turnover times
at Camp Paivika and Barton Flats. Given that the soils at both sites are sandy, DOC flux
should be an important loss pathway but not a source of lingering inputs to soil fractions.
Removal of material would affect modeling of the C storage to some extent, but would
not change the ∆14C signature of each fraction because yearly removal of C is accounted
for in the calculation of turnover time, regardless of the pathway of loss.
The amount of DOC removal from each fraction is calculated after Neff and
27
Asner (2001), who propose that most DOC flux in inceptisols (the soil order at both sites)
would be lost from the upper, organic matter-rich horizons because the soils have medium
to high sorption and low desorption. Flux from various soil horizons probably depends
strongly on texture, though, and since study site soils are sandy, significant flux may also
occur from lower horizons. Lacking site-specific data, DOC flux was modeled after Neff
and Asner (2001), who calculate that leaching from 0 cm depth should be ~50 g C/m2/
yr, ~40 g C/m2/yr from 5 cm depth, and ~20 g C/m2/yr from 10 cm. These amounts are
distributed among fractions in a horizon evenly, except that no significant removal of
bioavailable C is assumed to occur from the Oa heavy and A heavy fractions.
Fine root production (FRP), such as sloughed tissue and exudates, probably
composes a large proportion of C inputs to lower soil fractions, and therefore would
impact modeled turnover times to a larger extent than DOC. Fine root inputs are
estimated in two ways: first, the difference between Grulke et al.’s (1998) estimates of
total root biomass in July and September yields ~45 g C/m2/yr at 0-20 cm of presumable
biomass lost at Camp Osceola, near Barton Flats, and ~1 g C/m2/yr from the same level
at Camp Paivika. These estimated values are very low and are treated as a lower limit to
root inputs.
Root inputs are also approximated from Law et al.’s (1999) estimates of total root
allocation (TRA, which includes root respiration and mortality) as 554 g C/m2/yr, in the
same Oregon ponderosa pine forest from which litterfall was estimated for Barton Flats.
Raich and Nadelhoffer (1989) calculate that FRP is one third of TRA, so that if Barton
Flats is analogous to Law et al.’s (1999) forest, FRP should be 185 g C/m2/yr. Root
biomass at Camp Paivika is at least 6 times less than at Barton Flats (Grulke et al., 1998;
Grulke and Balduman, 1999), so FRP should be around 31 g C/m2/yr. FRP allocations
to the soil were calculated after Neff and Asner (2001), who allocate 40% of root C to
between 0-10 cm of soil starting at the base of the Oi horizon, which has no roots, and
35% between 10-30 cm. Since fine roots are defined as having diameters from 1-5 mm
28
(Nadelhoffer and Raich, 1992), at least half of fine root inputs would separate by size into
the Oa light fraction rather than the Oa coarse, and a negligible amount would go directly
into the mineral-associated Oa heavy fraction. Estimates derived from the Raich and
Nadelhoffer (1989) model probably represent an upper bound for root C inputs (Gower et
al., 1996).
These iterature estimates suggest that both root inputs and flux from DOC have
a negligible contribution at Camp Paivika, so the model’s assumption of inputs from
mass flow is probably fairly accurate there. At Barton Flats, root inputs probably have
a greater impact, but still make up no more than half of total inputs to a fraction. The
only exception to this is the Oa coarse fraction at Barton Flats, which has very high C
storage and therefore required high inputs in the model (higher than the inputs to the
Oi horizon above). This may be due in part to Barton Flat’s comparatively thin Oi
horizon, which probably allows some litterfall inputs to move directly to the Oa coarse
horizon. Alternatively, the estimates of root inputs could be too low for this particular
system; large variability in fine root production has been shown to exist even in forests
with limited geographical range and similar vegetation type, and different measurement
techniques yield inconsistent results (cf. Nadelhoffer and Raich, 1992). In general, then,
literature values for root and DOC contributions show that these inputs are unimportant
compared to mass flow at the two study sites.
The other major assumption made during modeling was that the time lags of root
inputs were assumed to be insignificant, and were not considered in the non-steady state
calculations. The fractions modeled in the steady state model were assumed to have
some amount of yearly atmospheric exchange, which accounts for root inputs to them;
however, time lags for the non-steady state fractions were determined according to the
age of mass flow from upper fractions. Studies do not agree on the age of root inputs to
the soil: some authors find an annual fine root turnover, but others measure older ages for
root biomass and respiration, postulating that they contain older photosynthate allocated
29
from elsewhere in the plant (Fahey and Hughes, 1994; Horwath et al., 1994; Gaudinski et
al., 2000; Loya et al., 2002; Cisneros-Dozal et al., 2006). However, Grulke et al. (1998)
give evidence for significant yearly fine root turnover at Barton Flats, and grasses at
Camp Paivika are annual, so root inputs are probably ~1 year old.
With a time lag of 1 year, root inputs would differ in age by up to 10 years from
the inputs from mass flow assumed in the model, which would have aged as it travelled
through the upper Oi and Oa soil fractions. Such an age difference represents a roughly
50‰ difference in ∆14C signature. This is notable but not probably would not have a
large impact on the ∆14C signatures of the younger fractions, especially considering
the probable amount of root inputs to each site: Camp Paivika has very few roots and
receives negligible inputs, and at Barton Flats mass flow from upper fractions seems to be
a more important contributor to total inputs (see Table 2).
In summary, literature-derived estimates of inputs from fine root production and
DOC flux imply that the non-steady state model’s assumptions of homogenous inputs
and time lags based on mass flow between fractions are not unreasonable. Therefore,
modeled turnover times probably provide a realistic picture of soil C cycling.
Mechanisms of N-induced change
Although this study gives evidence that differences in soil fraction turnover times
occur in association with N fertilization, these results offer no information about the
specific soil mechanisms responsible for these changes. Some studies have addressed
this question: for example, Fenn and Dunn (1989) attribute faster litter decomposition
in the western, N-polluted San Bernardino Mountains to higher substrate quality.
Similarly, Berg (2000) demonstrates that N fertilization increased substrate quality and
humus buildup in Scots pine litter, and Berg and Matzner (1997) report that N stimulates
decomposition of labile material. In other words, previous work has determined that N
additions increase decomposition rates by raising substrate quality in lighter, organic
30
matter-rich fractions, which supports this study’s findings of a faster Oa light fraction
turnover time at N-polluted Camp Paivika.
Berg and Matzner (1997) also note that N slows decomposition of late-stage,
humified material. More specifically, Berg (2002) suggests that high N fertilization
correlates with a low limit value (the point at which decomposition ceases in soils),
so that more recalcitrant material remains in soils with high N concentrations. This
mechanism supports this study’s suggestion that the increase in Oa heavy turnover time at
Camp Paivika is a response to N fertilization.
CONCLUSIONS
Although this study is observational and thus cannot demonstrate causation, there
is a clear association between long term, high level N deposition and differences in soil C
cycling between sites: at the more polluted site, labile fractions had shorter turnover times
and recalcitrant fractions had longer turnover times. These findings are similar to those
of Neff et al. (2002), who found that N additions over a 10 year time period accelerated
decomposition of low-density, labile soil fractions and slowed decomposition in dense,
recalcitrant fractions. Further, it is likely that this change in soil cycling dynamics has
contributed to an order of magnitude less storage of multidecadal-aged C storage at the
more polluted site.
These results show the difference in soil fraction responses to N fertilization over
a long timescale without the confounding factors of climate, vegetation, and soil type.
Although some differences are present between sites, notably temperature and exposure
to ozone, these are relatively minor and have predictable effects. Further work is being
undertaken to investigate the effects of additional N amendment on soil C cycling at these
two sites, which will help resolve inter-site differences and distinguish soil responses to
N from other factors, particularly as ozone pollution.
The results of this study add to a growing body of evidence that soil C cycling
31
exhibits a complex response to N fertilization. It is essential to understand soil organic
matter as a mixture of fractions acting on different timescales in order to be able to
predict how N fertilization will affect soils. Given that most C storage occurs in labile
fractions, this study suggests that N fertilization may lower total C storage and thus
decrease soils’ ability to offset CO2 emissions.
ACKNOWLEDGMENTS
I am deeply grateful to Nikki Nowinski and Sue Trumbore for their tutelage and
mentorship—they challenged me, helped me, and introduced me to the best isotope ever.
In particular, I cannot thank Nikki enough for her patience and continual willingness to
explain concepts, read drafts, and make suggestions. I wouldn’t be anywhere without
her! I also thank Xiaomei Xu and the Trumbore lab for answering my questions, helping
me run my samples this winter; they’ve set the bar high for any lab I work with in the
future. Alex Barron and Mary Savina provided excellent comments and advice on many
preliminary versions of this manuscript and it wouldn’t be the same without them, and
Phil Camill, Sue Trumbore, and Kendra Murray offered helpful suggestions for the final
draft. I also thank the Carleton College Geology Department for providing me with
funding to return to the University of California, Irvine and run more samples—what a
different project I have now! Finally, Richard Minnich and Carl Skinner are wonderful
people for cheerfully and promptly providing a desperate undergraduate with fire history
information. This work was partially completed during the UCI Biogeochemistry and
Climate Change REU last summer, and funded by the National Science Foundation grant
ATM-0453495 and the UCI Earth System Science REU Program.
32
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