JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, G01014, doi:10.1029/2011JG001749, 2012
Linkages between denitrification and dissolved organic
matter quality, Boulder Creek watershed, Colorado
Rebecca T. Barnes,1,2,3 Richard L. Smith,1 and George R. Aiken1
Received 4 May 2011; revised 30 November 2011; accepted 1 December 2011; published 11 February 2012.
[1] Dissolved organic matter (DOM) fuels the majority of in-stream microbial processes,
including the removal of nitrate via denitrification. However, little is known about how
the chemical composition of DOM influences denitrification rates. Water and sediment
samples were collected across an ecosystem gradient, spanning the alpine to plains, in
central Colorado to determine whether the chemical composition of DOM was related to
denitrification rates. Laboratory bioassays measured denitrification potentials using the
acetylene block technique and carbon mineralization via aerobic bioassays, while organic
matter characteristics were evaluated using spectroscopic and fractionation methods.
Denitrification potentials under ambient and elevated nitrate concentrations were strongly
correlated with aerobic respiration rates and the percent mineralized carbon, suggesting that
information about the aerobic metabolism of a system can provide valuable insight
regarding the ability of the system to additionally reduce nitrate. Multiple linear regressions
(MLR) revealed that under elevated nitrate concentrations denitrification potentials were
positively related to the presence of protein-like fluorophores and negatively related to
more aromatic and oxidized fractions of the DOM pool. Using MLR, the chemical
composition of DOM, carbon, and nitrate concentrations explained 70% and 78% of the
observed variability in denitrification potential under elevated and ambient nitrate
conditions, respectively. Thus, it seems likely that DOM optical properties could help to
improve predictions of nitrate removal in the environment. Finally, fluorescence
measurements revealed that bacteria used both protein and humic-like organic molecules
during denitrification providing further evidence that larger, more aromatic molecules
are not necessarily recalcitrant in the environment.
Citation: Barnes, R. T., R. L. Smith, and G. R. Aiken (2012), Linkages between denitrification and dissolved organic matter
quality, Boulder Creek watershed, Colorado, J. Geophys. Res., 117, G01014, doi:10.1029/2011JG001749.
1. Introduction
[2] Humans have significantly altered reactive nitrogen
cycling within the biosphere [Galloway et al., 2008].
Anthropogenic addition of excess reactive nitrogen to the
environment has several unintended consequences that
directly affect our society, including contamination of
groundwater and estuarine eutrophication [e.g., National
Research Council, 2000]. Despite high anthropogenic loadings, the results from mass balance studies show that on
average only 10%–30% of added nitrogen is transported to
coastal environments [Howarth et al., 2006; Seitzinger et al.,
2006]. These same mass balance studies point to denitrification, the microbial reduction of nitrate (NO
3 ) to gaseous
nitrogen (N2O and N2), as one of the processes responsible
1
U.S. Geological Survey, Boulder, Colorado, USA.
Department of Geological Sciences, University of Colorado at Boulder,
Boulder, Colorado, USA.
3
Now at Bard Center for Environmental Policy, Bard College,
Annandale-on-Hudson, New York, USA.
2
Copyright 2012 by the American Geophysical Union.
0148-0227/12/2011JG001749
for removal of the “missing” fixed nitrogen [van Breemen
et al., 2002]. However, denitrification remains a poorly
characterized process due to measurement difficulties and
environmental heterogeneities [Groffman et al., 2006], and it
is challenging to accurately assess the importance of the
process on a watershed, regional, or global scale [Johnes and
Butterfield, 2002]. Recently, model estimates of whole
stream network denitrification have been improved through
the addition of reaction rate constants aimed at incorporating
the nonlinear nature of biological activity [Alexander et al.,
2009; Mulholland et al., 2008; Wollheim et al., 2008]. By
increasing our understanding of fundamental controls on
denitrification we can continue to improve model estimates
enabling better predictions for how watersheds will respond
to global change pressures such as enhanced nitrogen
deposition and land use change.
[3] Organic carbon fuels the majority of microbial processes, including those that regulate in-stream nitrogen
constituents via denitrification [Seitzinger, 1994], and is an
important link between energy and nutrient dynamics within
streams. Dissolved organic carbon (DOC) influences stream
ecosystem functions by controlling biogeochemical reactions and microbial food webs. Extensive work has been
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Figure 1. Map of the Boulder Creek, Colorado, watershed. Sampling took place at 7 sites throughout the
watershed. Additional information about the sites can be found in the text and in Table 1. Site BE is a firstorder stream entering the Boulder Creek main stem from the north, below the ORO sampling location.
done to understand the factors controlling dissolved organic
matter (DOM) quantity and quality (i.e., chemical composition) and the flux of DOM within streams. DOM is made up
of a range of organic compounds, each with varying degrees
of reactivity and ecological significance. The chemical nature
of DOM is highly dependent on its source [McKnight et al.,
2001] and in situ transformations by microbes [Cole et al.,
2007], resulting in both spatial and seasonal variability
within a watershed. The bioavailability of DOM, and hence
its microbial utilization, is correlated with its chemical
nature. For example, organic acids (a dominant component of
freshwater DOM) with lower C:N ratios are generally more
bioavailable to microbial communities [e.g., Hunt et al.,
2000]. Thus by measuring the quantity and the quality of
DOM, researchers may be able to better constrain the spatiotemporal patterns of in-stream nitrogen processes.
[4] The reliance of many inorganic nitrogen transformations on the availability of oxidizable organic carbon ultimately links the removal of nitrate (NO
3 ) to carbon cycling.
Several studies in riparian and hyporheic sediments [e.g.,
Hedin et al., 1998; Sobczak et al., 1998] and in streams
[Inwood et al., 2005] have linked denitrification rates and
NO
3 concentration to DOC availability. It should be noted
that several studies have found no significant relationship
between DOC concentrations and denitrification rates [e.g.,
Arango and Tank, 2008; Bernhardt and Likens, 2002]. In
general, these studies examined how denitrification rates
responded to predefined carbon additions or DOC concentrations. Fewer studies have measured denitrification
rates and characterized the chemical nature of the in situ
organic matter pool. An exception is work by Baker and
Vervier [2004], who found a positive relationship between
the fractional contribution of acetate, formate, lactate, and
butyrate to total DOC and rates of denitrification within the
hyporheic zone of the Garonne River. By examining a natural gradient of DOM quality and quantifying denitrification
potentials in relation to seasonal and spatial changes in the
ambient organic matter pool this study provides an important
link between these previous field experiments and observations.
[5] The Colorado Front Range receives elevated inorganic
nitrogen (N) deposition due to its proximity to the Denver
metropolitan area and agricultural activities in eastern Colorado, with N deposition increasing with elevation [e.g.,
Williams et al., 1996]. High elevation watersheds are particularly sensitive to increased N deposition because of their
short growing seasons, poorly developed soils, and sparse
vegetation. In response to this chronic deposition, the
structure and function of both aquatic and terrestrial ecosystems have shifted [e.g., Baron et al., 2000; Williams and
Tonnessen, 2000]. The Boulder Creek watershed (Figure 1)
has been subjected to this human perturbation and spans an
elevation gradient that produces multiple ecosystem types
with varied nutrient characteristics. Specifically, as vegetative cover increases at decreasing elevation, soil C:N ratios
increase and stream DOC concentrations increase, while
stream NO
3 concentrations decrease [Hood et al., 2003].
The chemical composition of DOM in streams also changes
in space and time. For example, Hood et al. [2005] found
that DOM sources changed significantly throughout the year
within the alpine and subalpine regions, dominated by
allochthonous sources during snowmelt and algal production
from upstream lakes in the late summer.
[6] Here we utilize the nitrogen and carbon gradients
created through the nexus of atmospheric deposition and
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Table 1. Boulder Creek Watershed Sampling Sites and Collection Datesa
Site
Collection Dates
GL4
ALB
WF
GG
ORO
BE
BCA
6/09, 8/09
6/09, 8/09
6/09, 8/09
11/08, 3/09, 7/09
11/08, 2/09, 7/09
5/09
11/08, 2/09
Elevation
3535
3250
2963
2680
1775
1934
1562
m
m
m
m
m
m
m
Climatic Zone
Description
Discharge (L s1)
alpine
subalpine
subalpine
montane
foothills
foothills
plains
1st-order tributary of Boulder Creek
1st-order tributary of Boulder Creek, downstream from GL4
2nd-order stream, North Boulder Creek
1st-order tributary of Boulder Creek
4th-order stream, Boulder Creek main stem
1st-order tributary of Boulder Creek
4th-order stream, Boulder Creek main stem
155 / 109
267 / 200
1644b / 475
na / 5 / 3
396 / 425 / 2689
na
226 / 453
a
Sites GL4, ALB, and WF are located within the Niwot Long-Term Ecological Research (Niwot LTER) site, and sites GL4, GG, and BE are part of the
Boulder Creek Critical Zone Observatory (BcCZO).
b
The gauge was malfunctioning on the date of sampling, this value is interpolated from surrounding dates; na indicates that discharge measurements are
not available for all dates.
elevation-controlled ecosystems in the Boulder Creek
watershed to explore the relationships between organic
matter characteristics and denitrification. We measured the
denitrification potential, DOM quality, and stream chemistry
at seven stream sites along an elevation gradient in the
Boulder Creek watershed. Laboratory bioassay experiments
were repeated 2–3 times at each site in an effort to determine
if DOM characteristics, quantity, and denitrification potential changed through time. Our primary objective was to
determine if DOM quality is a significant control over
denitrification rates and, if so, what DOM quality measures
provide the greatest explanatory power within a predictive
model.
2.3. Analysis of Stream Solutes
[9] Anions (including NO
3 N) and cations (including
NH+4 N) were analyzed by ion chromatography [Smith
et al., 2006]. Dissolved organic carbon (DOC) was analyzed with an O.I. Analytical Model 700 TOC Analyzer
(O.I. Analytical, College Station, Tex.) via the platinum
catalyzed persulfate wet oxidation method [Aiken et al.,
1992], except in the case of samples collected at BE,
which were analyzed via high temperature combustion with
a Shimadzu TOC analyzer (Shimadzu, Columbia, Md.).
Quality assurance and control included analyzing samples
in duplicate in addition to using instrument replication, reference standards, and blanks.
2. Methods
2.4. Dissolved Organic Matter Characterization
[10] Following the methods of Aiken et al. [1992], one
liter of filtered stream water DOM was fractionated using
resin columns into three groups: larger molecular weight
hydrophobic acids (HPOA), smaller molecular weight
hydrophilic molecules (HPI), and transphilic acids (TPIA)
using Amberlite XAD-8 and XAD-4 resins. The amount of
organic matter within each fraction was calculated using the
DOC concentration and the sample mass of each fraction
and are presented as the percentage of total DOC. Stream
water samples were fractionated in duplicate and average
values are presented. The standard deviation of these fractions is ≤2%.
[11] Stream water and sediment extract samples were
analyzed for UV-Vis absorption using a Hewlett Packard
Model 8453 photo-diode array spectrophotometer (l = 200–
800 nm) and a 1-cm path length quartz cell. The instrument
blank consisted of deionized water. Samples were diluted
by weight to be within the range of the instrument. Specific
UV absorbance (SUVA254) was determined by dividing
the decadal absorption coefficients (m1) measured at l =
254 nm by DOC concentration (mg C L1). SUVA254 has
been used as a measure of DOC aromaticity [Weishaar et al.,
2003]; replicate SUVA254 measurements indicated measurement uncertainty (one standard deviation) of 0.1 L mg
C1 m1.
[12] Stream water and sediment extract samples were also
characterized by three-dimensional (3-D) fluorescence, a
measure of the fluorescing portion of the DOM pool, using a
Fluoromax-3TM fluorometer as described by Cory et al.
[2010]. Briefly, the resulting 3-D fluorescence intensity
results, excitation-emission matrices (EEMs), must be corrected for a number of factors including: blank subtraction,
2.1. Site Description
[7] The Boulder Creek watershed is located within the
Front Range of the Rocky Mountains and drains 1160 km2.
The watershed encompasses a large altitudinal gradient,
ranging from 4120 m (Continental Divide) to 1480 m (eastern plains) (Figure 1). The large elevation gradient within the
watershed results in five climatic zones: alpine, subalpine,
montane, foothills, and plains. The sampling locations span
this gradient allowing us to sample across a small region yet
capture a range of ecosystem types (Table 1, Figure 1). Sampling events also spanned a range of discharge conditions
(3 L s1 at GG in July 2009 to 2689 L s1 at ORO in July
2009) given the variation in sampling date and stream order
(Table 1).
2.2. Sample Collection
[8] Water and sediment samples for experiments were
collected from stream channels at all 7 sampling sites in
2008 and 2009 (Table 1). Stream water and sediment samples were collected in 1 L HDPE bottles and baked (475°C)
mason jars, respectively. Surface sediment (approximately
the top 5 cm) was collected from multiple locations within
the stream with a trowel to account for the heterogeneity of
material in the channel, i.e., each jar consisted of sediment
from multiple locations within the stream. Samples were
transported back to the laboratory on ice. Water for chemical
analysis was filtered through a Gelman 0.45 mm capsule
filter, and preserved by freezing (anions), chilling at 4°C
(organic carbon analyses, pH, alkalinity), and acidification
with H2SO4 (pH ≈ 2) and chilling at 4°C (cations). Unfiltered water and sediment were stored at 4°C until the incubation experiments commenced (<2 days).
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Raman normalization, and instrument specific corrections.
The EEMs were subsequently analyzed using three methods:
calculation of the fluorescence index, “peak picking,” and
Parallel Factor Analysis (PARAFAC). The fluorescence
index (FI), introduced by McKnight et al. [2001], provides
information about the source of the organic material by
analyzing the slope of an emission curve at an excitation
wavelength of 370 nm; with steeper slopes (>1.8) correlated
with microbial or algal end-members and lower FIs (1.3–
1.4) correlated with terrestrial sources. The FI ratio was
calculated as the ratio of emissions intensities at excitation
wavelength of 370 nm and emission wavelengths of 470 and
520 nm [Cory et al., 2010]; the standard deviation of replicate measures and subsequent calculated FIs was 0.004.
Different regions of an EEM have been linked to various
pools of DOM (e.g., humics) and thus by determining the
fluorescence intensity within each region or “peak” the 3-D
data can provide compositional information of the fluorescing DOM pool [Coble, 1996, 2007; Kraus et al., 2008;
Stedmon et al., 2003]. Peaks were determined from the literature [e.g., Kraus et al., 2008, and references therein] and
the fluorescence intensity at that location was determined
using MATLAB 7.9 (The Mathworks Inc., 2009, Natick,
Mass.). Finally, the EEMs were analyzed via comparison to
the PARAFAC model developed by Cory and McKnight
[2005]. This model attempts to predict the EEMs of DOM
on the basis of 13 components, 7 of which have spectra
similar to known quinones and 2 of which are similar to
amino acid spectra. The results of this analysis are reported
as the percent of total fluorescence explained by each component, e.g., the weight or loading of each component to the
total measured fluorescence. The residuals of this comparison were always less than 5% of the total fluorescence for all
the stream samples, indicating that the thirteen components
successfully modeled the majority of fluorophores in the
sample.
2.5. Stream Sediment Characteristics
[13] Duplicate stream sediment samples were weighed and
dried in a 60°C oven to determine the percent dry weight by
mass. After drying, sediment was sieved through a 2 mm
screen and the <2 mm size fraction was ground in a shatter
box for 20 s. The ground sediment was analyzed for carbon
and nitrogen content using an Exeter Analytical CE-440
CHN elemental analyzer; duplicate samples indicate an
average standard deviation of 0.04% and 0.01% for the
percent carbon and nitrogen measures, respectively.
2.6. Denitrification Assays
[14] The denitrification potential of the stream sediments
was determined using the acetylene block technique
[Yoshinari and Knowles, 1976] by measuring N2O accumulation over 24 h in the headspace of sealed anoxic
bottles. While there are limitations with the acetylene block
technique [see Groffman et al., 2006, for discussion], this
approach allowed us to examine the spatial and temporal
variability of denitrification potentials throughout the
watershed. The experiments consisted of two treatments
( added NO
3 ) done in triplicate at ambient stream temperature (4°C) with no organic matter additions. The
November 2008 bioassays did not include an ambient treatment. Briefly, approximately 1 L of unfiltered river water
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was deoxygenated in a large, baked beaker on ice via equilibration with the atmosphere of an anoxic glove box for 24 h.
Bottles for denitrification assays were prepared within the
glove box to ensure an anoxic environment. Sediment was
pooled from multiple (2–3) mason jars within the glove box
for the analytical triplicates in order to minimize sediment
heterogeneity. It should be noted that, despite the compositing of sediment samples, in the field and in lab, it is
unlikely that the sediment in each replicate was exactly the
same. Approximately 30 g of wet sediment and 30 g of
deoxygenated unfiltered river water were added to six 60 mL
baked serum bottles, which were then sealed with autoclaved rubber stoppers and removed from the glove box.
Bottles were flushed with He for 20 min on ice, after which
5 mL of calcium carbide generated C2H2 and 15 mL of He
were added to each bottle. After the bottles rotated for 1 h in
an incubator (4°C), 0.75 mL of 5 mM anoxic NaNO3 was
added to three of the six bottles, bringing the [NO
3 ] in the
water to 100 mM. The headspace of each bottle was then
immediately analyzed for N2O on a gas chromatograph
(HNU GC301) equipped with an electron capture detector
and a back-flush valve and then reanalyzed at 30–60 min
intervals. The experiments continued until it appeared that
the entire nitrate pool had been reduced to N2O, usually
between 24 and 36 h. Due to repeated sampling, we
accounted for changes in headspace pressure when calculating the N2O concentrations in the bottles. The reported
denitrification rates were determined using a linear regression of N2O produced over the first 4 h of the experiment to
minimize bottle effects and the ability of the microbes to
adjust to the anoxic environment. In several cases the rate of
N2O production in the ambient denitrification potential
assays became nonlinear after 2–3 h; in these instances rates
were determined over a shorter period of time (as noted in
Table 3). Rates of N2O production are expressed as denitrification potentials by converting N2O to N and normalizing
by sediment dry mass (mmoles N (g DM)1 h1).
[15] Incubations to assess changes in the fluorescing DOM
pool during the denitrification experiments were conducted in
July and August 2009. Following the same procedure as
above, 125 mL baked serum bottles were filled with approximately 60 mL of sediment and deoxygenated unfiltered river
water. In this case, 10 mL of C2H2 and 30 mL of He were
added to each bottle, with 1.25 mL of 5 mM NaNO3 added
for the NO
3 treatment. After the first N2O measurement
(T0) three bottles were sacrificed; the sediment slurry was
centrifuged at 10,000 RPM (Beckman Model J2–21 centrifuge, Beckman Coulter, Brea, Calif.) for 15 min and the
supernatant filtered (0.45 mm) for organic matter analyses.
This measurement was made to determine if the DOM pool
changed substantially due to increased contact with sediment
in the bottles. Similarly, after N2O was no longer being produced (TF) triplicate bottles were sacrificed for organic matter
concentration and characterization measurements.
2.7. Dissolved Organic Matter Changes
and Denitrification
[16] Using the fluorescence EEMs in conjunction with the
denitrification experiments, we characterized the types of
organic matter used by the bacteria in the reduction of added
nitrate. The utilized DOM (DOMdenit) was characterized by
comparing the EEMs measured on sediment water slurries
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Table 2. Water Chemistry and Sediment Characteristics in the
Stream at the Time of Sampling
Whole Water, Filtered
Site
Month
GL4
June 09
Aug 09
June 09
Aug 09
June 09
Aug 09
Nov 08
Mar 09
July 09
May 09
Nov 08
Feb 09
July 09
Nov 08
Feb 09
May 09
ALB
WF
GG
BE
ORO
BCA
NO
3
(mM)
21.41
35.36
1.88
4.14
1.61
5.90
2.38
11.34
4.03
0.74
4.74
11.62
5.84
3.29
10.30
0.48
Stream Sediment
DOC (mM)
Avg %C
Avg %N
C:N
142
83
250
208
258
167
650
550
458
367
175
150
192
275
208
375
6.287
8.749
2.304
0.609
0.334
0.307
2.215
2.762
0.479
1.344
0.151
0.160
0.208
0.533
0.591
0.517
0.519
0.715
0.117
0.030
0.015
0.027
0.119
0.134
0.036
0.045
0.012
0.008
0.012
0.030
0.026
0.013
14.1
14.3
23.0
23.4
25.7
13.3
21.7
24.1
15.3
35.1
14.5
23.4
19.9
20.9
26.9
45.0
from the beginning of the assays (T0) and at the end of N2O
production in the assays (TF) from both the ambient and
elevated nitrate experiments (equation (1)).
ðTFAM T0AM Þ ðTFNO3 T0NO3 Þ ¼ DOMdenit
ð1Þ
where AM and NO3 represent the EEMs collected from the
ambient nitrate concentration and 100 mM nitrate denitrification assays, respectively. The fluorescing pool of DOM
within the experimental bottles could change for three reasons: (1) use by microbes, (2) production by microbes, and
(3) leaching or sorption of DOM from/to the sediment. By
subtracting T0 EEMs from TF EEMs changes associated
with sediment-DOM interactions should be minimized.
2.8. Aerobic Metabolism Bioassays
[17] The bioavailability of the organic matter in the stream
water and sediment was determined by measuring the evolution of CO2 in sealed bottles over time. The aerobic
incubations were done in conjunction with all 2009 denitrification assays. For each site there were three treatments,
river water (two replicates), river water and sediment (three
replicates), and deionized water. Baked 60 mL serum bottles
were used for these assays, with 45 mL of unfiltered water
added to the river water only treatment and 30 g of sediment
and 30 mL of unfiltered water added to the water and sediment treatment. All bottles were sealed with autoclaved
stoppers and 25 mL of air was added via syringe to ensure an
aerobic environment. The bottles were then incubated for
five to seven days on a rotator at 4°C and the headspace was
periodically sampled for CO2 using a LICOR Infrared
detector (Model LI-6262) after an equilibration period of 5–
6 h. Rates of CO2 evolution were determined using linear
regression. Rates of sediment CO2 production were determined by subtracting the blank normalized results of the
river water only treatment from the sediment and river water
treatment.
[18] Measurements of oxygen concentrations in replicate
bottles for February through June 2009 experiments provided evidence that the bottles remained aerobic in all cases
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with the exception of the GL4 in June 2009. Rates of O2
consumption averaged 0.07 mmoles O2 (g DW)1 h1,
ranging from 0.01 mmoles O2 (g DW)1 h1 (ORO)
to 0.35 mmoles O2 (g DW)1 h1 (GL4). Given that the
bottles start with oxygen levels ranging from 300 to
320 mmoles L1, only the GL4 experiment went anoxic
over the 5–7 day experiment.
2.9. Data Analyses
[19] Pearson’s correlation (r) statistics were reported for
all correlations, along with the associated p-value. In cases
where data were not normally distributed it was transformed
to meet the requirements of the statistical test, in all cases
using a logarithm (base 10). Analysis of Variance (ANOVA)
with a Tukey’s Family Error rate of 5% was used to determine statistical differences across the watershed, i.e., to test
differences between average site values. Two-tailed t-tests
were used to calculate whether experimental rates differed
significantly through time at a given site. Given that many of
the stream nitrogen and carbon chemistry variables were
correlated, multiple linear regressions (MLR) were used to
determine the relative predictive importance of each characteristic; i.e., by using statistics associated with MLR
analysis the relative strength of each relationship can be
ascertained because the model accounts for the variation of
each predictor. Best subsets regression was used to explore
which predictors should be entered into the MLRs. Best subsets determines the “best” set of predictors by using a variety
of statistics including adjusted R2, Mallows’ CP (which should
be close to the number of predictors in the model plus the
constant), and S (standard deviation of the error term in the
model) on four different groups of variables, each group
contained log (DOC) concentration, log (NO
3 ) concentration
and log sediment C content: (1) summary spectral characteristics (log (SUVA), log(FI), and log(absorbance at
254 nm)), (2) fractionation data (%HPOA, %TPIA, and
%HPI), (3) PARAFAC components, and (4) log of the peak
intensities for both ambient and nitrate denitrification
potentials. The predictors chosen by best subsets were then
put into a MLR and only kept if their coefficients were statistically significant. In addition, when an outlier was present
the analysis was repeated without the outlier data, when
present, differences are noted in the text. Relationships were
significant if the p-value was <0.05.
3. Results
3.1. Water Chemistry
[20] GL4 had the greatest (p < 0.001) nitrate-nitrogen
concentrations throughout the sampling period (21.9 mM 9.5 mM, Table 2). The remaining sites, all below the tree
line, had average nitrate concentrations of 5.3 mM 3.2 mM
with concentrations increasing at the onset of snowmelt.
At the time of sampling, all the sites had nondetectable
ammonium concentrations (detection limit ≈2 mM). Dissolved organic carbon concentrations were greatest at GG
(550 mM 100 mM, p < 0.001) and lowest at GL4, ALB,
and WSF (191.7 mM 58.3 mM, p < 0.001). Given the
frequency of sampling, we compared our results with data
obtained from the Boulder Creek Critical Zone Observatory
(BcCZO, http://czo.colorado.edu/) and Niwot Long-term
Ecological Research (Niwot LTER, http://culter.colorado.
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edu/NWT/index.html) programs. When comparisons were
possible (GL4, ALB, WF, GG, and ORO) the average
measured nitrate, ammonium, and DOC concentrations of
our samples were always within one standard deviation of
2008 and 2009 means. Seasonal variability of DOC and
inorganic nitrogen is well documented in the alpine and
subalpine sites, with the greatest concentrations coinciding
with the snowmelt peak and lowest concentrations occurring
during the recession limb in late summer [Hood et al., 2005;
Williams et al., 2001]. Organic carbon and inorganic nitrogen seem to follow similar patterns within the montane and
foothills with the exception of increases in NO
3 concentration following spring precipitation events (unpublished data,
BcCZO). While sampling did not capture peak snowmelt,
samples were taken on both the ascending (e.g., February
2009 ORO and June 2009 GL4) and descending limbs of
hydrographs (e.g., Nov 2008 ORO and August 2009 GL4).
This seasonal variation in sampling adds to the variability in
inorganic nitrogen and DOC conditions captured by the
laboratory bioassay experiments.
3.2. Dissolved Organic Matter Characteristics
[21] Organic matter characteristics varied with both space
and time within the watershed (see Tables S1 and S2 in the
auxiliary material).1 SUVA254 was greatest at GG (4.4
L mg1 m1 1.3 L mg1 m1, p < 0.01), and tended to be
lowest at GL4 (2.3 L mg1 m1 0.5 L mg1 m1) though
there was no statistical difference between sites other than
GG (Table S1). The average percent of HPOA and TPIA at
each site did not vary significantly across the watershed;
HPOA ranged from 28% at GL4 in August 2009 to 52% at
GG in July 2009 and TPIA ranged from 13% in GG in
March 2009 to 24% at ALB in August 2009 (Table S1).
Average FI, calculated from the 3-D excitation-emission
matrices, was greatest (p < 0.05) at BE (1.5) and BCA
(1.43 0.06) and ranged from 1.27 at ALB in June 2009
to 1.51 at BCA in February 2009 (Table S1). In general,
FI data indicated that the majority of DOM originated in
the terrestrial environment (FI < 1.4), with the exception of
four sampling events: BE 5/09, GL4 8/09, and BCA 11/08
and 2/09 (Table S1). Absorbance at l = 254 nm was highly
correlated with the amount of HPOA in the sample (R =
0.672, p = 0.004), and there was an inverse relationship
between SUVA and FI (R = 0.553, p = 0.026) reflecting
the aromatic nature of terrestrial sources of DOM, similar to
what has been observed in other studies [e.g., Hood et al.,
2005] (Table S3).
[22] Examination of different regions or peaks within the
fluorescence EEMs provided further information about the
chemical composition of the stream DOM pool. The maximum intensity of the “A” peak, associated with humic acids
[Kraus et al., 2008, and references therein], showed the
greatest variability between samples (0.13 to 2.36) as well as
the highest intensity at all sites over the sampling period
contributing between 12% and 15% of the total fluorescence
in the samples (Table S1). The maximum intensity of the
“B” peak, often associated with amino acids [Coble, 1996],
ranged from 0.05 to 1.07 between sites, however, it contributed minimally to the total DOM fluorescence, between
1
Auxiliary materials are available in the HTML. doi:10.1029/
2011JG001749.
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0.3% to 1.8% (Table S1). Seven of the thirteen PARAFAC
components identified by the Cory and McKnight [2005]
model varied significantly (p < 0.05) between sites (Table S2).
In particular, DOM at GG had significantly different component loadings compared to the other sites; suggesting it has
a greater amount of reduced (C4) and less oxidized (C11)
quinones [Cory and McKnight, 2005] in the fluorophore pool
(Table S2). Component 4, identified as a reduced quinone
[Cory and McKnight, 2005], had the greatest loading of the
thirteen components, contributing between 19% and 36% to
the modeled fluorophore pool (Table S2). Components 8 and
13, with spectra similar to amino acids [Cory and McKnight,
2005], did not vary significantly between sampling sites and
had combined loadings ranging from 3% (GG, July 2009) to
10% (GL4, August 2009, Table S2).
3.3. Sediment Characteristics
[23] The carbon and nitrogen contents of the stream sediment did not vary significantly across the watershed with
the exception of GL4 (Table 2). The sediment collected at
GL4 had significantly (p < 0.001) more carbon (7.5% 1.7%) and nitrogen (0.6% 0.1%) than the sites at lower
elevations. The C:N of sediment ranged from 13.3 (WF,
August 2009) to 45.0 (BCA, May 2009). Temporal variation
at GG and ALB in sediment carbon and nitrogen content
(Table 2) may be due to bed sediment movement during
high flow events or sampling differences, despite efforts to
remove variability in sediment at a given site.
3.4. Stream Chemistry Patterns
[24] Stream water nitrogen and carbon characteristics
co-varied throughout the watershed. In general, watershed
sites with greater amounts of nitrate had lower amounts of
organic carbon (e.g., GL4). Pearson correlations (Table S3)
on transformed variables revealed statistically significant
negative correlations between nitrate concentrations and
DOC concentrations (R = 0.595, p = 0.015) and %HPOA
(R = 0.526, p = 0.036). Dissolved organic carbon concentrations were significantly related to a number of organic
matter characteristics, namely %HPOA (R = 0.659, p =
0.005) and absorbance at 254 nm (R = 0.929, p < 0.0001).
Furthermore, the various PARAFAC components and fluorescence peak intensities were significantly correlated with
each other as well as with other organic matter descriptors
(Table S3). Two PARAFAC components (C11 and C2),
as defined by Cory and McKnight [2005], were negatively
correlated with peak intensities indicative of amino acids
(B and T peaks), while these same peak intensities were
positively correlated with another PARAFAC component
(C4). Similarly, Component 8 from the PARAFAC model,
associated with trypotophan-like molecules, was positively
correlated with FI, reflecting the association between
autochthonous produced DOM with more labile molecules
such as amino acids.
3.5. Denitrification Potentials
[25] The average denitrification potentials at ambient
nitrate concentrations (here after referred to as ambient
denitrification potentials) did not vary significantly across
sites, though there were significant differences at ALB (p =
0.03) and BCA (p = 0.003) between experiments (Table 3).
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Table 3. Denitrification Potentials as Determined by N2O Production Rates Measured in Laboratory Bioassays After 4 h of Incubation, Except Where Otherwise Noted
N2O Production Rates (mmoles N (g DM)1 h1)
@ Ambient NO
3
Concentrations
@ 100 uM NO
3
Concentrations
Site
Month
Average
SD
Average
SD
GL4
June 09
Aug 09
June 09
Aug 09
June 09
Aug 09
Nov 08
Mar 09
July 09
May 09
Nov 08
Feb 09
July 09
Nov 08
Feb 09
May 09
0.0601
0.4117
0.0132
0.0003a
0.0109a
0.0093a
—
0.0063a
0.0000b
0.0000c
—
0.1118a
0.0001a
—
0.0214
0.0004
0.2179
0.0909
0.0038
0.0002
0.0039
0.0005
—
0.0052
0.0002
0.0001
—
0.1830
0.0001
—
0.0021
0.0005
0.7237
1.5943
0.1073
0.2048
0.0734
0.1454
0.9078
0.9166
0.1267
0.1154
0.1192d
0.2465
0.1051
0.4237
0.6268
0.6419
0.1460
0.2173
0.0255
0.0395
0.0152
0.0116
0.0688
0.0533
0.0092
0.0273
0.0175
0.0724
0.0113
0.1576
0.0476
0.0462
ALB
WF
GG
BE
ORO
BCA
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chemical nature of the DOM was significantly related to both
ambient and nitrate denitrification potentials. Relationships
between nitrate and carbon stream chemistry and ambient
denitrification potentials (see Table S4) were driven by the
results of the August 2009 GL4 experiment, with the exception of a negative relationship with DOC which remained
significant after the outlier was removed (R = 0.579, p =
0.048). Nitrate denitrification potentials were significantly
correlated with four fluorophore components identified by
the Cory and McKnight PARAFAC model. There was a
positive relationship with component 8 (R = 0.626, p =
0.009) and negative correlations with loadings on component
a
Rates calculated based on first 2–3 h of experiment. All weights are
normalized to dry mass (DM) of sediment. See text for further details.
b
Average rate = 5.37 106 mmoles N (g DM)1 h1.
c
Average rate = 3.46 105 mmoles N (g DM)1 h1.
d
Rate calculated based on 6 h of experiment.
Ambient denitrification potentials were greatest at GL4
(0.23 mmoles N2O N (g DM)1 h1 0.25 mmoles N2O N (g DM)1 h1) and lowest at BE (3.4 105 mmoles
N2O N (g DM)1 h1 7 105 mmoles N2O N (g DM)1 h1). Average denitrification potentials measured under conditions of elevated nitrate concentrations
(100 mM, hereafter referred to as nitrate denitrification
potentials) varied significantly in space (GL4 had the
highest rates, p < 0.001) as well as at a given site through
time (GL4, ALB, WF and GG, p < 0.05) (Table 3).
Denitrification potentials measured under the different
nitrate conditions were significantly correlated with each
other (R = 0.765, p = 0.002); with nitrate denitrification
potentials 0.393 mmoles N2O N (g DM)1 h1 higher,
on average, than ambient denitrification rates. The
November 2008 experiments (at GG, ORO, and BCA)
included replicates without sediment (i.e., water only)
instead of ambient nitrate concentration sediment slurries.
Denitrification potentials in the stream water were indistinguishable from zero (data not shown).
[26] Ambient and nitrate denitrification potentials were
exponentially related to stream nitrate concentrations (adj
R2 = 0.939, p < 0.0001 and adj R2 = 0.567, p = 0.0005,
respectively) (Figure 2). However, these relationships were
driven in large part by one experiment (GL4, August 2009).
Consequently, the relationships were reanalyzed treating
this result as an outlier. Note that GL4 has significantly
higher nitrate concentrations compared to the other sites
(Table 2). This approach does not suggest that these results
are in error, but rather is taken to determine the robustness
of relationships at lower nitrate concentrations. Repeated
analyses without this experiment indicate that while the
trends remain, in the case of nitrate denitrification potentials,
the relationship is no longer significant (Figure 2b). The
Figure 2. Stream nitrate concentrations were exponentially
related to denitrification potentials as measured in lab under
both (a) ambient and (b) elevated nitrate concentrations.
Each data point represents an average rate as calculated by
linear regression for triplicate experiments over 4 h (6–8 time
points), with error bars representing the standard deviation
between the calculated rates of the three replicates. The fitted
lines represent the exponential fit with (solid line) and without (dashed line) the July 2009 GL4 denitrification potential
experiment results. The result from this experiment (gray circle at 35 mM NO
3 ) was identified as an outlier, and the analysis was repeated without this data point. The relationship
between stream nitrate concentrations and ambient denitrification potentials (Figure 2a) remains significant without the
outlier; however, the relationship is no longer significant in
the case of the nitrate denitrification potentials (Figure 2b).
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a weaker yet still significant relationship (R = 0.602, p =
0.018) (Table S4). Similarly, ambient and nitrate denitrification potentials were positively related to the percent of nitrogen in the sediment (R = 0.558, p = 0.047 and R = 0.709, p =
0.002, respectively). Interestingly, ambient and nitrate denitrification potentials were not significantly correlated with
sediment C:N.
[27] Best subsets regression indicates that the best multiple
linear regression (MLR) model to explain ambient denitrification potential variability includes DOC concentrations
and the percent of DOC as HPI (adj R2 = 78.1%, S = 0.0182
mmoles N (g DM)1 h1, Table 4). In addition, basic spectral data (SUVA, FI, absorbance at 254 nm) and DOC concentrations predicted 84.4% (adj R2 = 0.84, S = 0.045
mmoles N (g DM)1 h1) of the variance in ambient denitrification potentials, however, this relationship was heavily
dependent on the data from the GL4 August 2009 experiment and when it was removed the adjusted R2 fell to 44.3%
and none of the coefficients remained significant. Best subsets regression demonstrates that while nitrate denitrification
potentials are better explained by sediment carbon content
(adj. R2 = 0.494, p = 0.001) than stream DOC concentrations, the chemical characteristics of the DOC are able to
explain a greater amount of variability than sediment carbon
content (adj. R2 = 0.676, Table 4). Furthermore, the inclusion of the August 2009 GL4 data does not change the significance of various PARAFAC components or peak
intensities into a MLR when predicting nitrate denitrification
potentials. The inclusion of PARAFAC components 8 and
11 along with the stream DOC concentration predicted
67.6% of variability of the nitrate denitrification potential
(adj. R2 = 0.676, Table 4); removal of the outlier resulted in a
slightly weaker predictive relationship (adj. R2 = 0.538,
Table 4). It should be noted that the amount of carbon in the
stream sediment was significantly negatively related to %C11
(R = 0.502, p = 0.047), this relationship could potentially be
driving the significance of C11 in the MLR. Fluorescence
EEM peak intensities (E, D, A and B) in combination with
stream nitrate concentrations predicted 70.4% of the variability of nitrate denitrification potential (adj R2 = 0.704, S =
0.1566 mmoles N g1 h1) (Table 4) and was not affected by
the inclusion of the GL4 August 2009 data.
Figure 3. Denitrification potential rates under elevated
nitrate concentrations (100 mM NO
3 ) were significantly
related to PARAFAC components as defined by the model
described in Cory and McKnight [2005]. Component 2 (C2)
and Component 11 (C11) are both spectrally similar to
oxidized quinones, while Component 8 (C8) is similar to
tryptophan.
2 (R = 0.593, p = 0.016), component 6 (R = 0.681, p =
0.004), and component 11 (R = 0.562, p = 0.023) (Figure 3).
Nitrate denitrification potentials were positively related to the
percent of carbon in the stream sediment (R = 0.726, p =
0.001); the removal of the GL4 August 2009 data resulted in
3.6. Dissolved Organic Matter Changes
and Denitrification
[28] Any positive values in the resulting EEM (DOMdenit)
from equation (1) can be attributed to use by the microbes
in the presence of elevated nitrate concentrations while
any negative fluorescence intensity values are due to the
production of DOM (Figure 4). Examination of EEMs using
2-D spectra at an excitation wavelength of 270 nm (Figure 5)
provides information about the region commonly associated
with amino acid like molecules (i.e., peaks “B” and “T” and
components 8 and 13 in the Cory and McKnight [2005]
PARAFAC model) as well as phenols (ex: 280 nm; em:
400 nm) [Huguet et al., 2010]. There is a measurable difference between the 2-D spectra for the 5 sites (Figure 5),
indicating consumption of amino acid-like molecules at
GL4, ALB, and WF (em: 300–340 nm). Additional changes
in the fluorescence intensity are observed at 400–480 nm
emissions for ALB, WSF, GG, and ORO. This region of the
EEM is often associated with humic acids and recently
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Table 4. Multiple Linear Regressions Using Ambient Stream Nitrate and Carbon Chemistry to Explain Potential Rates of Denitrification
Measured in the Laboratorya
Multiple Linear Regression Equation
= 0.972 0.122 * log DOC 1.21*%HPI
R2
Adj. R2
S
84.4%
78.1%
0.018
74.1%
63.7%
82.8%
67.6%
53.8%
70.4%
0.245
0.212
0.156
Ambient
+100 mM Nitrate
= 5.17 0.428*logDOC + 13.4*%C8 38.1*%C11
= 2.72 0.138*logDOC + 10.5*%C8 25.8*%C11 (without outlier)
= 0.167 + 0.146*logNO3 3.93*logE + 7.88*logD 4.10*logA
+ 0.525*logB
Explanatory variables are included in equation only if their coefficients were significant (p ≤ 0.05) with the exception of the constant. Further, equations
are included only if they remained significant without the inclusion of the GL4 July 2009 outlier; changes in coefficient values are noted when applicable.
Nitrate and DOC concentrations are in mM, component loadings are entered as percents, and peak intensities are in Raman units. R2 describes the amount of
variation in the observed response variable that is explained by the predictor(s), adjusted (adj.) R2 is a modified R2 that has been adjusted for the number of
terms in the model, and S represents the standard distance data fall from the regression line (in units of the response variable, mmoles N g1 h1); thus the
better the equation predicts the response, the lower the value of S.
a
created DOM [Huguet et al., 2010, and references therein].
Comparison of the DOMdenit EEMs with nitrate denitrification potentials reveals a positive trend between the intensity
of the “B” and “N” peaks and denitrification rates (R = 0.686
and R = 0.761, respectively), however neither trend is significant due to the significantly higher (p < 0.0001) denitrification potentials for the GL4 site. Removal of GL4 from
the data set results in significant relationships between the
fluorescence intensities of the DOMdenit EEMs in the “B,”
“T,” and “A” regions and nitrate denitrification potentials
(R = 0.977, 0.982, and 0.940, respectively; p < 0.05).
3.7. Aerobic Incubations
[29] Average aerobic sediment CO2 production did not
vary significantly across the Boulder Creek watershed
sites ( p = 0.474). The highest rates were measured during
March 2009 at GG (1.38 mmoles CO2 g(DM)1 h1 0.18 mmoles CO2 g(DM)1 h1) and at BCA in February
Figure 4. The excitation-emission matrices illustrating the changes in the fluorophore pool during
the anaerobic denitrification assays, i.e., the result of equation (1). (a) GL4 August 2009, (b) ALB
August 2009, (c) WF August 2009, (d) GG July 2009, and (e) ORO July 2009. The peaks appearing in
Figures 4a–4c in the region defined by ex 270 nm and em 300–350 nm are indicative of protein-like
molecules being consumed over the course of the experiment. The smaller peaks in Figures 4d and 4e
are indicative of the use of humic and recently degraded material; the ex 340 nm, em 450 nm peak
in (d) has been correlated with humic materials [Coble, 1996] and the ex 270 nm, em 450 nm in
Figure 4e has been correlated with humic and recent materials [Coble, 1996]. Note that the color bar
scale changes with each plot to maximize the resolution of the images.
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Figure 5. The 2-D spectra of emissions at an excitation of
270 nm for the DOMdenit EEMs, i.e., the difference in fluorophore pools attributed to the denitrification of the added nitrate
within the bioassays, see equation (1). These 2-D spectra highlight the region of the EEM commonly associated with aminoacid-like or protein-like fluorophores (ex: 270 nm; em: 300–
340 nm). In a comparison of the 5 sites, GL4, ALB, and
WSF all show greater consumption within this range, indicating use of these molecules during the denitrification of the
added nitrate.
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2009 (1.22 mmoles CO2 g(DM)1 h1 0.36 mmoles CO2
g(DM)1 h1). The average amount of CO2 produced over
the course of the incubation and the average percent mineralized carbon, defined as percent of DOC converted to CO2
over the course of the incubation, also did not vary significantly between sites (p = 0.182, 0.442, respectively). While
the total amount of CO2 produced at each site mirrored the
CO2 production rates, the percent mineralized carbon was
greatest at GL4 in August 2009 (16%). Aerobic respiration,
as measured by CO2 production rate, was positively correlated with stream nitrate concentrations (R = 0.621, p =
0.031), the amount of carbon (R = 0.672, p = 0.017) and
nitrogen (R = 0.642, p = 0.024) in the sediment, as well as
the contribution of protein-like fluorophores in the DOM
pool as determined by the Cory and McKnight PARAFAC
model (%C8, R = 0.642, p = 0.02). Ambient denitrification
rates were significantly related to the percent mineralized
carbon (R = 0.690, p = 0.013), while nitrate denitrification
rates were significantly related both to the percent mineralized carbon (R = 0.767, p = 0.004) (Figure 6) and to CO2
production rates (R = 0.648, p = 0.023). It should be noted
that predictor variables were logarithmically transformed to
meet the normal distribution requirement of a linear correlation with the exception of component 8 (%C8).
4. Discussion
4.1. Anaerobic and Aerobic Respiration
and Dissolved Organic Matter
[30] Past research has shown that heterotrophic microbial
activity within the hyporheic zone is limited by the bioavailability
of organic matter [Jones, 1995] and that the availability of
organic substrate may limit denitrification rates in streams
Figure 6. Relationship between aerobic respiration as measured by the amount of CO2 produced relative
to the total DOC present (percent mineralized carbon) and nitrate denitrification rates (i.e., denitrification
potentials under elevated nitrate concentrations). The solid line represents the regression line calculated
with all of the experimental results included, while the dashed line represents the regression without
the inclusion of the outlier (GG July 2009). It should be noted that the aerobic respiration experiments
took place over the course of a week, while the potential rates of denitrification were calculated based
on 4 h of data.
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with high NO
3 concentrations [Arango et al., 2007]. In
Boulder Creek, rates of nitrate denitrification potential were
strongly correlated with aerobic CO2 production rates and the
percent of mineralized carbon, suggesting, that with greater
rates of sediment respiration, the potential for NO
3 reduction
also increases. It is important to note that the CO2 evolution
and denitrification experiments were done under different
oxygen conditions and thus calculated respiration rates cannot be attributed to denitrification. However, this positive
relationship does imply that microbial community functions,
including both anaerobic and aerobic respiration, are dependent on similar characteristics of the substrates available
within the stream environment. This finding is supported by
whole stream ecosystem work conducted by Mulholland
et al. [2008], who found that ecosystem respiration was
positively correlated with denitrification rates. Furthermore,
the significant positive relationships between the amounts of
protein-like fluorescence and both aerobic respiration rates
and denitrification potentials under elevated nitrate conditions are consistent with past work examining the aerobic
biodegradability of DOM [Fellman et al., 2009]. These parallels suggest our understanding of organic matter availability in aerobic environments could be applied to anaerobic
situations when denitrification is active.
4.2. Denitrification Potentials
[31] We compared our measured ambient denitrification
potentials to other studies which use the acetylene-block
method and report rates normalized to sediment mass. In
general, our ambient denitrification potentials (average
0.0497 mmoles N (g DM)1 h1) fall within ranges reported in
the literature. Our results are similar to two studies from agricultural streams in the Midwest; Arango et al. [2007] report
rates ranging from 0.0007 to 0.79 mmoles N (g DM)1 h1 for
sediment, coarse and fine benthic organic matter, biofilm and
sand; and Schaller et al. [2004] reported an average sediment
denitrification potential of 0.027 mmoles N (g DM)1 h1.
Our results were also within the range reported for four streams
in Maryland (0.0002 to 0.3539 mmoles N (g DM)1 h1)
[Groffman et al., 2005], and four streams in Illinois (0 to
0.2357 mmoles N (g DM)1 h1) [Opdyke and David, 2007].
In contrast our nitrate denitrification potential rates (average
6.50 6.11 mmoles N (g DM)1 h1) generally fall above
the reported rates, though this is not surprising given that
the previously mentioned rates were measured at ambient
stream nitrate concentrations. Further, the increased denitrification potentials measured in the NO
3 amended bottles
reflect the findings of Opdyke and David [2007], who also
observed an increase in nitrate denitrification potential in
incubation experiments with fine grained sediments. While a
grain size analysis was not performed on our sediments,
based on visual inspection, sites with finer grained sediment
(GL4, GG, and BCA) generally showed higher nitrate denitrification potentials when compared to sites with coarser
sediment (e.g., ORO, BE).
[32] A positive relationship between nitrate concentrations
and denitrification potentials has been found across different
ecosystem and land use types [e.g., Inwood et al., 2005;
Mulholland et al., 2008] and our results reflect this relationship (Figure 2, Table S4). However, as previously noted
this relationship was driven in large part by the results from
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one set of experiments, GL4 August 2009. For example,
ambient denitrification potentials were positively related to
stream nitrate concentrations, however, below 22 mM nitrate
concentrations only explained 25% of the variability of the
measured denitrification potential in the laboratory
(Figure 2a).
[33] Examining the relationships between denitrification
potentials and stream carbon chemistry provides insight as to
the control of organic carbon quantity and quality on denitrification rates. Using information about the organic carbon
pool (concentration and percent hydrophilic organic acids)
increased the predictive power in the MLR to 78.1%
(Table 4). Interestingly, the relationship between denitrification potentials and DOC concentrations is consistently
negative in the multiple linear regressions. This relationship
could be due to increased competition for available nitrate
by heterotrophs [Taylor and Townsend, 2010]. The result
could be due to a spurious relationship, specifically the
inverse relationship between NO
3 and DOC concentrations
across the Boulder Creek watershed (Table S3). However,
when NO
3 is included as an explanatory variable, while not
significant on its own, the relationship between DOC and
nitrate denitrification potential remains negative. A similar
result was found by Schaller et al. [2004], who report a
negative relationship between denitrification potentials and
DOC concentrations in NO
3 rich streams of the Midwest.
[34] Given that nitrate denitrification potentials were significantly related to the chemical characteristics of the DOM
pool as defined by fluorescence (Figure 3, Table S4), a significant amount of variability (up to 70%) in nitrate denitrification potentials could be explained by including peak
intensities or PARAFAC components into a MLR (Table 4).
Furthermore, nitrate denitrification potentials were positively related with protein-like fluorophores (e.g., C8, as
identified by the Cory and McKnight [2005] PARAFAC
model, and peak B). In comparison, past studies have shown
mixed results concerning the relationship between stream
DOC and denitrification rates. Inwood et al. [2005] determined that DOC was a secondary influence on measured
denitrification rates, though they postulate that this relationship could be an indicator of sediment carbon’s influence on denitrification, a relationship established in this
(Table S4) and other studies [e.g., Arango et al., 2007].
Others have found no relationship between denitrification
rates and stream carbon concentration [e.g., Herrman et al.,
2008] nor a measured response to the addition of a labile
carbon pool [e.g., Bernhardt and Likens, 2002]. However, as
previously noted, most researchers have examined the relationship between DOC concentrations and denitrification
rates, as opposed to the chemistry of the organic matter.
Further, several studies have shown that DOC concentration
is not related to the measures of carbon quality [e.g., Jaffé
et al., 2008], suggesting that our findings are not necessarily in conflict with these previous studies.
[35] The link between denitrification rates and ambient
carbon quality metrics, coupled with consistently higher
denitrification potentials under elevated nitrate concentrations, suggests that the rate of denitrification could be governed by the quality of the organic matter pool, especially at
higher nitrate concentrations when carbon may be limiting.
It further suggests that if the headwaters of Boulder Creek
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continue to receive chronic inputs of N deposition and thus
remain nitrogen saturated [Williams et al., 1996], the
reduction of added nitrate could be mitigated by the documented seasonal changes in the composition of the DOM
pool [Hood et al., 2005; Miller and McKnight, 2010; this
study].
[36] The results of the multiple linear regression analyses
suggest that significant amounts of the variability in nitrate
denitrification potentials could be explained using ambient
stream nitrate concentrations and carbon chemistry.
Regressions were able to explain up to 70.4% and 78.1% of
the observed variability in the measured nitrate denitrification potentials and ambient denitrification potentials,
respectively (Table 4). Admittedly, these relationships are
from laboratory incubations measuring potential reaction
rates, however they suggest that spectral measurements can
be used to incorporate DOM reactivity into river network
models [e.g., Böhlke et al., 2009; Mulholland et al., 2008;
Seitzinger et al., 2006; Wollheim et al., 2006] in an effort to
improve nitrate removal predictions.
4.3. Utilization of Protein-Like DOM by Denitrifiers
[37] Using the fluorescence measurements in conjunction
with the denitrification experiments, we characterized the
types of organic matter used by the bacteria in the reduction
of added nitrate. Results of these calculations revealed that at
the 2 sites with significantly higher denitrification potentials
(ALB and GL4) the microbial community preferentially
consumed protein-like DOM (Figures 4 and 5) during the
course of denitrification. Sites WF, GG, and ORO, which are
located below tree line, had lower denitrification rates
despite stream nitrate concentrations similar to ALB
(Table 2). While the bacteria in the WF experiment also
appeared to preferentially consume the protein-like fluorophores (Figures 4c and 5) the same was not true for GG and
ORO (Figures 4d–4e and Figure 5), where humic-like DOM
appeared to be consumed instead. The preference of amino
acids and proteins by bacterial populations explains the
positive relationships between the contribution of proteinlike molecules to the DOMdenit fluorophore pool and nitrate
denitrification potentials. The positive relationship between
humic-like DOMdenit fluorophores and nitrate denitrification
potentials suggests that classes of larger molecular weight
DOM are also readily utilized (as reviewed in Findlay and
Sinsabaugh [1999]) and cannot be thought of as recalcitrant. It should be noted that DOM is an exceedingly complex
group of molecules, of which only a small portion fluoresces.
If the assumption is made that changes in the fluorophore
pool mirror the overall pool of DOM, these experiments
provide evidence that both simple and more complex organic
molecules are oxidized during denitrification.
5. Conclusions and Implications
[38] Significant alteration of nitrogen and carbon cycling
in response to human activities has occurred worldwide,
affecting even relatively remote ecosystems lacking human
development. Linkages between the availability of labile
organic matter and nitrogen cycling have long been recognized [e.g., Hedin et al., 1998]; however, past work has
mostly focused on the response of nitrate removal to the
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addition of a labile, homogenous pool of organic matter
(e.g., acetate). This work presents an important step toward
linking the chemical character of the organic matter pool to
the removal of nitrate from the aquatic environment.
Through a series of experiments we were able to show that
organic matter characteristics were important predictors of
nitrate removal potential under ambient and elevated nitrate
concentrations. In addition, aerobic respiration rates and
denitrification rates were strongly correlated with similar
characteristics of the DOM pool as well as with each other,
indicating that much can be learned about the potential of an
ecosystem to reduce and remove nitrate through the examination of aerobic metabolism. Furthermore, the use of
ambient stream chemistry and DOM spectral characteristics
were able to explain over 70% of the variability observed in
laboratory assays. Due to the fact that these results are based
on laboratory experiments, an essential next step is to
examine these relationships in the field, through both
experimental and observational studies.
[39] These findings add to a growing body of evidence
that suggest that, as stream nitrate concentrations increase,
denitrification will likely become carbon limited. This is
compounded by the projected increases in reactive nitrogen
worldwide [Galloway et al., 2008], likely leading to more
and more ecosystems moving toward nitrogen saturation and
thus being carbon limited. This study suggests that the
chemical character of organic matter is important in determining the rates of nitrate removal. Importantly, many of the
DOM optical properties presented in this paper are relatively
easy to measure and require little time and expense, thus
they can provide a quick indicator of reactive potential.
Further, in situ sensors could be designed to measure only
specific excitation/emission pairs, providing real time monitoring of substrate reactivity in a stream environment.
Incorporating this growing data set into studies of nitrogen
transport and removal in aquatic ecosystems should lead to a
greater understanding of where nitrate removal may be
enhanced throughout the stream network.
[40] Acknowledgments. We would like to thank D. Repert and
J. Crawford for assistance with the laboratory experiments; K. Butler and
C. Hart for chemical analyses; and L. Larsen, S. Ewing, and three anonymous reviewers for helpful comments on an earlier versions of this manuscript. This research was done in collaboration with the U.S. Geological
Survey’s National Research Program, Boulder Creek Critical Zone Observatory (NSF-EAR 0724960), and Niwot Long-Term Ecological Research
Site (NSF DEB 0423662) and supported by a National Science Foundation
award (NSF-EAR 0814457) to R.T.B. The use of brand names is for identification purposes only and does not imply endorsement by the U.S. Geological Survey.
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