PUBLICATIONS
Journal of Geophysical Research: Biogeosciences
RESEARCH ARTICLE
10.1002/2015JG003261
Key Points:
• We adapted dissolved N2 methods
and a novel modeling approach to
estimate denitrification in rivers
• The water column accounted for
0–85% and 39–85% of combined
denitrification and respiration
• Rivers can remove nitrogen via
denitrification at equivalent or higher
rates than headwaters
Sediment, water column, and open-channel denitrification
in rivers measured using membrane-inlet
mass spectrometry
Alexander J. Reisinger1,2, Jennifer L. Tank1, Timothy J. Hoellein3, and Robert O. Hall Jr.4
1
Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA, 2Now at Cary Institute of
Ecosystem Studies, Millbrook, New York, USA, 3Department of Biology, Loyola University Chicago, Chicago, Illinois, USA,
4
Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming, USA
Abstract
Supporting Information:
• Supporting Information S1
• Data Set S1
• Data Set S2
• Table S1
Correspondence to:
A. J. Reisinger,
[email protected]
Citation:
Reisinger, A. J., J. L. Tank, T. J. Hoellein,
and R. O. Hall Jr. (2016), Sediment,
water column, and open-channel
denitrification in rivers measured using
membrane-inlet mass spectrometry,
J. Geophys. Res. Biogeosci., 121,
1258–1274, doi:10.1002/2015JG003261.
Received 27 OCT 2015
Accepted 22 APR 2016
Accepted article online 27 APR 2016
Published online 12 MAY 2016
Riverine biogeochemical processes are understudied relative to headwaters, and reach-scale
processes in rivers reflect both the water column and sediment. Denitrification in streams is difficult to
measure, and is often assumed to occur only in sediment, but the water column is potentially important in
rivers. Dissolved nitrogen (N) gas flux (as dinitrogen (N2)) and open-channel N2 exchange methods avoid many
of the artificial conditions and expenses of common denitrification methods like acetylene block and 15N-tracer
techniques. We used membrane-inlet mass spectrometry and microcosm incubations to quantify net N2 and
oxygen flux from the sediment and water column of five Midwestern rivers spanning a land use gradient.
Sediment and water column denitrification ranged from below detection to 1.8 mg N m2 h1 and from below
detection to 4.9 mg N m2 h1, respectively. Water column activity was variable across rivers, accounting for
0–85% of combined microcosm denitrification and 39–85% of combined microcosm respiration. Finally, we
estimated reach-scale denitrification at one Midwestern river using a diel, open-channel N2 exchange approach
based on reach-scale metabolism methods, providing an integrative estimate of riverine denitrification.
Reach-scale denitrification was 8.8 mg N m2 h1 (95% credible interval: 7.8–9.7 mg N m2 h1), higher than
combined sediment and water column microcosm estimates from the same river (4.3 mg N m2 h1) and
other estimates of reach-scale denitrification from streams. Our denitrification estimates, which span habitats
and spatial scales, suggest that rivers can remove N via denitrification at equivalent or higher rates than
headwater streams.
1. Introduction
Reactive nitrogen (N) in the biosphere has been rising following the industrial and agricultural revolutions
[Green et al., 2004; Gruber and Galloway, 2008], increasing global fluxes of riverine N to oceans [Boyer et al.,
2006; Seitzinger et al., 2010]. Excess N in coastal ecosystems, both marine [Rabalais et al., 2002] and freshwater
(e.g., Lake Erie) [North et al., 2007], can cause nuisance algal blooms, and subsequent decomposition of algal
biomass results in periodic hypoxic conditions at river outlets worldwide [Diaz and Rosenberg, 2008]. Streams
transform and retain a portion of N prior to downstream export [Peterson et al., 2001; Mulholland et al., 2008],
but N retention in rivers is understudied relative to headwater streams [Ensign and Doyle, 2006; Tank et al.,
2008]. Reduced contact between sediment and the water column is often assumed to reduce N retention
in rivers [e.g., Alexander et al., 2009] despite nutrient uptake also occurring in the water column of streams
and rivers [Reisinger et al., 2015], and assimilation or dissimilatory reduction of N by water column biota in
larger rivers may offset any decrease in N removal by the sediment. If water column processes offset reductions in benthic N removal, riverine N removal may be higher than previously considered in watershed models [Alexander et al., 2007; Wollheim et al., 2008].
©2016. American Geophysical Union.
All Rights Reserved.
REISINGER ET AL.
Riverine denitrification rates have been estimated by modeling studies parameterized using limited empirical
data. Some key relationships have been developed to explain denitrification as a function of hydrology and
geomorphology [Seitzinger et al., 2006]. For example, the SPARROW model describes denitrification as a firstorder process with N removal decreasing with stream depth [Smith et al., 1997; Alexander et al., 2007],
whereas the Riv-N model estimates denitrification as a function of water residence time [Seitzinger et al.,
2002]. Neither model separates hydrological and biological processes, making it difficult to partition biological pathways for N removal [Wollheim et al., 2006]. A more recent model of watershed-scale N retention found
that hydrologic and biogeochemical processes contribute equally to reducing N export [Alexander et al.,
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2009]. However, the mechanisms responsible for N retention in rivers are either assumed to be identical to
headwater streams [Wollheim et al., 2006, 2008] or are unknown.
Nitrogen uptake in streams and rivers reflects a combination of assimilatory uptake by autotrophs (i.e., algae and
macrophytes) and heterotrophs (i.e., fungi and bacteria), abiotic sorption to sediments, and removal via dissimilatory transformations such as denitrification and other N2-producing processes such as anammox. Although
each of these mechanisms delays the export of dissolved inorganic N to downstream ecosystems, denitrification
represents a permanent N removal from aquatic habitats and has been the subject of much research [PiñaOchoa and Álvarez-Cobelas, 2006; Mulholland et al., 2008; Roley et al., 2012]. Denitrification requires reducing conditions and is typically limited by the availability of NO3 and labile carbon [Smith and Tiedje, 1979; Knowles,
1982]. Favorable conditions for denitrification commonly occur in benthic sediments of headwater streams,
where denitrification has been studied extensively [Mulholland et al., 2008; Findlay et al., 2011]. In rivers, denitrification may also occur within anaerobic microsites on suspended particles in the water column [Ploug et al.,
1997], as occurs in marine environments [Michotey and Bonin, 1997]. Indeed, anaerobic microsites capable of
supporting denitrification are present even in oxygen-rich environments such as shallow bed sediments of
the hyporheic zone [Harvey et al., 2013; Briggs et al., 2015] and on the surface of macrophytes [Schaller et al.,
2004]. Elevated suspended sediment concentrations have been linked with increasing water column denitrification [Liu et al., 2013], yet information on water column denitrification under natural conditions is lacking.
Common methods for quantifying denitrification either suffer from artificial conditions or are prohibitive
both in terms of time and cost [Groffman et al., 2006]. Acetylene inhibition requires artificially manipulating
environmental conditions and substrate availability while also inhibiting coupled nitrification-denitrification,
leading to reduced NO3-N availability [Groffman et al., 2006]. In contrast to acetylene inhibition, reach-scale
tracer additions of 15N are considered the most realistic method for estimating ambient denitrification rates
in streams [Mulholland et al., 2008, 2009]. Unfortunately, 15N tracer costs limit the application of this technique in large rivers, and the approach also does not address coupled nitrification-denitrification processes
[Mulholland et al., 2009] which could be important for denitrification in low-nutrient ecosystems.
Improving methods to quantify habitat-specific and reach-scale denitrification in rivers will provide an
improved understanding of nutrient dynamics in larger streams and rivers that is needed in stream and river
ecology [Mulholland and Webster, 2010].
Technological developments have facilitated several denitrification estimates using dissolved N2 measurements made with membrane-inlet mass spectrometry (MIMS) [Kana et al., 1994] for high-precision estimates
of dissolved N2, oxygen (O2), and argon (Ar). The MIMS approach has most commonly been used in estuarine
studies [Kana et al., 1998; An et al., 2001; Gardner and McCarthy, 2009] but is increasingly used in freshwater
ecosystems both at mesocosm [Smith et al., 2006; Fork and Heffernan, 2014; Turek and Hoellein, 2015] and
reach scales [Laursen and Seitzinger, 2002; Baulch et al., 2010]. Denitrification estimates from previous
reach-scale approaches [Laursen and Seitzinger, 2002; McCutchan et al., 2003; Pribyl et al., 2005] are high compared to other aquatic ecosystems, but these high rates may be due to uncertainty involved in parameters
needed for reach-scale denitrification models [Baulch et al., 2010].
To quantify denitrification in rivers and partition the contribution of sediments and the water column to ecosystem denitrification, we used MIMS to measure dissolved N2 concentrations and calculate net N2 flux in sediment
and water column microcosms across five rivers representing a human land use gradient. We predicted that (1)
denitrification would be higher in the sediment than the water column, (2) sediment and water column denitrification would increase with NO3 availability, and (3) water column denitrification would increase with suspended sediments. Finally, we estimated denitrification at the reach scale using an open-channel N2 technique
concurrent with ecosystem metabolism models to provide an estimate of reach-scale denitrification, allowing
us to contextualize microcosm estimates and to compare denitrification rates across aquatic ecosystems.
2. Methods
2.1. Study Sites
We performed this study in five rivers spanning a human land use gradient in the Midwestern United States,
ranging from northern Michigan (Lower Peninsula) to southern Indiana (Table 1). We characterized land use
in the watershed of each river with ArcGIS 10.0 (Environmental Systems Research Institute Corporation,
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Table 1. Sampling Date, Location, and Environmental Parameters of Study Rivers
River
Manistee
Muskegon
St. Joseph
Tippecanoe
c
Tippecanoe-Diel
White
Date
Latitude, Longitude
o
o
1/8/2014 44 16′3″N, 86 0′59″W
o
o
30/7/2014 43 42′20″N, 85 49′42″W
o
o
24/7/2014 41 43′43″N, 85 48′53″W
o
o
22/7/2014 41 1′26″N, 86 35′6″W
16/9/2014
o
o
28/7/2014 39 5′5″N, 85 51′35″W
WC
Sediment Agricultural Developed
Land
AFDM
AFDM
Land
Q
Depth Width NO3 -N
DOC
WS Area
a
a
b
b
2
3 1
1
1
3
2
Use (%)
Use (%)
(m) (mg L ) (mg L ) (g m ) (g m )
(km ) (m s ) (m)
3600
4500
4800
2500
4500
44.5
46.7
28.3
10.7
21.3
17.3
1.3
1.1
1.2
0.6
0.6
0.8
52.5
67.0
50.0
50.6
50.6
47.9
0.12
0.32
1.63
0.58
1.53
1.67
6.7
10.8
8.6
10.8
d
6.7
0.27
1.03
0.28
0
0.08
0.04
0.05
0.12
0.1
0.08
10
24
74
61
77
5
7
8
9
11
a
Widths
b
and depths are from Hall et al. [2015] and Tank et al. unpublished data.
Agricultural land use is the sum of pasture and cropland; developed land use is the sum of all developed land use classifications (minimal, low, medium, and
high) from the National Map. The sum of agricultural and developed land uses was used for analysis.
c
Diel sampling location and land use are the same as the Tippecanoe microcosm sampling.
d
Dissolved organic carbon (DOC) and ash-free dry mass (AFDM) were not collected during diel sampling.
Redlands, CA, USA), using digital elevation models (30 m2 resolution) and the 2006 National Land Cover Data
Set accessed via the National Map [Dollison, 2010]; we expressed human land use in the watershed as the sum
of all agricultural and developed land use classifications, with agriculture being the predominant human land
use (Table 1). We collected sediment and water for our microcosm study at each river during summer 2014,
and we estimated river discharge (Q) on each sampling date using nearby U.S. Geological Survey gages. We
performed diel sampling for our open-channel approach 50 days after microcosm sampling; Q during the
sampling of in situ, diel denitrification in the Tippecanoe R was approximately 2 times higher than during
microcosm sampling (Table 1).
2.2. Sediment and Water Column Microcosm Incubations
Sediment in the five rivers was primarily silt, sand, and pebble, representing over 70% of total benthic habitat in
each river (Tank et al., unpublished data). At each river, we collected 25 sediment cores to a depth of 5 cm using
a 3.6 cm diameter polyvinyl chloride (PVC) corer, representing different benthic habitats (e.g., low and high
flows and different substrate sizes). We pooled cores into three separate plastic bags and stored composite
sediment samples on ice until we returned to the laboratory. With some methods, sediment cores are kept
intact for denitrification rate measurements; we pooled cores because sediments are unconsolidated in
these rivers and keeping cores intact is difficult [Turek and Hoellein, 2015]. This approach is often used for rate
measurements in unconsolidated sediments commonly found in streams [Arango and Tank, 2008], although
we recognize that sediment homogenization likely altered sediment redox conditions and may have resulted
in an underestimation of denitrification rates. Nevertheless, we were able to conduct incubations on a large
number of samples over a short period of time. We also collected unfiltered river water by slowly moving
two 20 L plastic carboys vertically in the water column until filled. This sampling technique allowed us to
capture any potential gradients associated with depth, although based on our previous research all rivers were
turbulent and well mixed. Upon returning to the laboratory, we stored sediment and water samples in a refrigerator at 4°C overnight and performed microcosm incubations within 24 h of field collection.
Prior to initiating microcosm incubations, we homogenized sediment from the plastic bags in a plastic tray,
and slowly transferred water from carboys into 20 L buckets, minimizing bubble formation. For water column
microcosms, we filled sterile, 50 mL centrifuge tubes (VWR International, Radnor, PA, USA; n = 60) with river
water from the buckets, capping tubes underwater to insure that there was no headspace in each tube.
For sediment microcosms, we first added 10 mL of sediment to sterile, 50 mL centrifuge tubes (n = 50), and
then gently added river water from the buckets using a syringe to slowly fill each tube, capping underwater
to prevent any headspace. Prior to sampling from the buckets, and throughout microcosm setup, we resuspended any materials that settled by gently stirring the water within the bucket. Sediment and water column
microcosms acclimated to the incubation temperature (20°C) by the time all microcosms had been filled.
While similar microcosm approaches are commonly used for sediment metabolism measurements, we note
that redox patterns may be altered in microcosms relative to in situ conditions.
We placed microcosms in the dark in an environmental chamber held constant at 20°C. In addition to denitrification (DN), we quantified ecosystem respiration (ER) from these same incubations as a separate ecosystem
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function and a potential predictor of DN. We sacrificially sampled sediment and water column microcosms at 0,
1, 2, 4, and 8 h after starting incubations (n = 10 at each time point). Due to expectations of lower DN and ER in
the water column, we added a 12 h time point for water column microcosms for all rivers, but this additional
time point was dropped at three of five rivers due to nonlinear N2 dynamics at the final time point (i.e., either
N2 production ceased or declined). These nonlinear dynamics in some samples suggest that incubations lasting
>8 h may result in incubation artifacts with changes in denitrification potentially due to substrate limitation.
At each time point, we collected a dissolved gas sample from each sacrificed microcosm taking care to limit
any atmospheric interaction. We transferred the water sample to a 12 mL Exetainer© sample vial (Labco Ltd.,
Lampeter, UK), overfilling vials 3× the sample vial volume from the bottom-up and discarding the first ~5 mL
of water in the centrifuge tube that had interacted with the atmosphere within the syringe. After filling each
sample vial, we added 0.2 mL of 50% ZnCl2 to preserve the sample, which we stored at 4°C submerged upside
down prior to analysis on the MIMS.
We quantified dissolved N2, O2, and Ar using MIMS (Bay Instruments, Easton, MD, USA) [Kana et al., 1994,
1998]. Briefly, for each sample, a peristaltic pump pulled water from the sample vial, and the dissolved gases
in the sample water diffused across a membrane under vacuum, and the mass spectrometer measured the
abundance of 28N2, 32O2, and 40Ar in these dissolved gases. Every five samples, we also analyzed a standard
consisting of purified water (18 M Ω resistance; E-Pure, Barnstead International, Dubuque, IA) maintained at
20.0°C using a circulating water bath (VWR International, Radnor, PA, USA) equilibrated with atmospheric
gases by continuously stirring at low speed (Lab Egg RW11 Basic, IKA Works, Inc., Wilmington, NC, USA).
We calculated concentrations of dissolved O2 and N2 gases in each sample by multiplying the O2:Ar and
N2:Ar ratios provided by the MIMS for each sample by the equilibrium concentration of Ar using equations
from Hamme and Emerson [2004] based on temperature and barometric pressure in the laboratory. We used
O2:Ar and N2:Ar ratios from the MIMS rather than direct O2 or N2 concentrations from the MIMS because ratios
are 10× more precise than concentrations [Kana et al., 1994, 1998]. Temperature was held constant in the
standards and samples, and barometric pressure was corrected for elevation of the MIMS [Colt, 2012]. We corrected measurements for instrument drift using standards interspersed every five samples throughout the
run. Additionally, the MIMS contains an ionization source that produces O+ ions, leading to the production
of nitric oxide (NO) when both N2 and O2 are present, and this NO production may lead to a “pseudoincrease” in N2 as incubations proceed [Eyre et al., 2002]. The production of NO needs to be corrected for
when using isotope pairing approaches which analyze 30N2 [Kana and Weiss, 2004], but we were only interested in 28N2 and therefore did not correct for NO production. A previous study carried out on our MIMS used
the isotope pairing approach and found no mass 30 production in control cores and assumed that NO production in this machine was negligible [Hoellein et al., 2015].
We also measured a suite of environmental variables as potential controls on DN and ER following standard
methods (see supporting information for details). These variables included temperature, pH, and dissolved
O2 measured using a Hydrolab MS5 minisonde (Hach Hydromet, Loveland, CO, USA); background ammonium (NH4+-N), NO3-N, and soluble reactive phosphorus (SRP) measured using automated colorimetric
analysis [Murphy and Riley, 1962; Solorzano, 1969; APHA, 1995]; and sediment organic matter (as ash-free
dry mass (AFDM)) and chlorophyll a (chl a), and water column chl a, AFDM, inorganic matter, and total
suspended solids measured using standard loss upon ignition and fluorometric analysis [Wetzel and Likens,
2001; Steinman et al., 2006].
2.3. Estimating Denitrification and Respiration Using Microcosms
We used simple linear regression (SLR) to quantify ER and DN (as net O2 or N flux) from sediment and water
column chambers from each river, with incubation time as the independent variable and dissolved O2 or N as
the dependent variable, calculated as
MassN ¼
N2
Areq V micro MWN2
Ar
(1)
where MassN is the mass of N in a microcosm (mg microcosm1), N2/Ar is the molar ratio measured on the
MIMS, Areq is the equilibrium Ar concentration (mmol L1), Vmicro is the volume of water in the microcosm
(L), and MWN2 is the molecular weight of N2 (28.014 mg mmol1). The slope of the regression line provided
the microcosm estimate for ER and DN (mg O2 or N h1). A decrease in O2 over time is indicative of ER,
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whereas an increase in N over time is indicative of DN. While we assumed that N fixation would not occur, a
decrease in N over time suggests N fixation. For this study, we were only interested in denitrification; thus, we
treated any DN incubations without a significant regression relationship over time or with a negative relationship as having DN below our limit of detection. We treated each sacrificial microcosm as a replicate for the
regression, resulting in one estimate each for sediment and water column ER and DN per river, for a total
of n = 5 for comparisons across the five rivers. To estimate sediment-specific ER and DN, we accounted for
the activity of the overlying water by subtracting water column ER and DN (corrected for differences in
volume) from combined sediment and water column incubations.
Next we converted per microcosm estimates to areal fluxes (mg N m2 h1) for sediment microcosms as
DNareal ¼
DNmicro
Amicro
(2)
where DNareal is the areal denitrification flux; DNmicro is the microcosm-based denitrification flux (mg N h1);
and Amicro is the surface area represented by sediment in the microcosm (m2), which is calculated as
Amicro ¼ Acores V micro
V cores
(3)
where Acores is the total surface area of cores collected in the river (m2), Vmicro is the volume of water in each
microcosm (m3), and Vcores is the total volume of sediment collected in cores at each river (m3). For water
column estimates, we converted per microcosm estimates to areal fluxes as
DNareal ¼
DNmicro
z
V micro
(4)
where Vmicro is the volume of water in each water column microcosm (m3) and z is the mean river depth (m).
We did not estimate mean river depth during this study but used depth estimated in these rivers under similar flow conditions (i.e., at summer base flow [Hall et al., 2016]; Tank et al., unpublished data). To convert per
microcosm ER estimates to areal fluxes (mg O2 m2 h1), we substituted ER for DN in equations (2) and (4).
We also calculated ER and DN per gram DM, as an estimate of denitrification scaled for total suspended solids
in the water column or total material in the benthic sediments. Although ER is conventionally reported as a
negative dissolved oxygen (DO) flux, we report ER as the absolute value for microcosm incubations (|ER|) in
order to simplify the interpretation of relationships between ER and DN.
After estimating areal ER and DN separately from sediment and water column microcosms, we calculated
combined sediment and water column ecosystem respiration (|ER|sed + wc; mg O2 m2 h1) and combined
sediment and water column denitrification (DNsed + wc; mg N m2 h1) as the sum of sediment and water column areal fluxes. We partitioned the role of the water column out from the sediment by calculating the percent of |ER|sed + wc and DNsed + wc due to the water column (hereafter referred to as the water column
contribution to |ER|sed + wc or DNsed + wc). We used SLR to investigate relationships between habitat-specific
|ER| and DN (sediment, water column, and wc + sed) or water column contributions to |ER| and DN and measured physicochemical variables. For these statistical analyses, we set our critical value to α = 0.1 because our
modest sample size reduces our statistical power, and due to the novelty of this approach we were more willing to make a type 1 error than a type 2 error. However, we report p values below for all tests of significance.
2.4. Open-Channel River Denitrification and Metabolism
To model reach-scale DN, we first modeled reach-scale metabolism to provide an independent estimate of
the gas exchange rate (K; d1), because K strongly influences reach-scale DN estimates [Baulch et al., 2010].
To estimate reach-scale metabolism in the Tippecanoe R, we coupled a DO concentration time series to a
model of DO fluxes. We deployed a Hydrolab MS5 minisonde (Hach Hydromet, Loveland, CO, USA) to log
temperature, DO, and turbidity every 10 min for the 24 h sampling duration. We also deployed a LI-1400
Datalogger equipped with a LI-190SA Quantum sensor on the riverbank near our sampling site to log photosynthetically active radiation (PAR) every 10 min. We used barometric pressure corrected for altitude [Colt,
2012] from a weather station located approximately 30 km from our study site, which we feel is representative due to consistent weather conditions and minimal elevation change (<2 m).
To estimate reach-scale DN, we collected three dissolved gas samples every hour over a 24 h period using a
dissolved gas sampler constructed using a PVC pipe (60 cm long with a 4 cm inner diameter) with clear tubing
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(3.18 mm ID) inserted into the sampler through a hole 4 cm above the bottom of the sampler (sealed with
silicone to prevent leakage) and a rubber stopper to seal the bottom of the sampler (H. Madinger and R. O.
Hall, personal communication, 2014). To collect a dissolved gas sample, we submerged the sampler below
the surface of the river, parallel to flow, allowing river water to flow through the pipe for 10 s before we
sealed the sampler underwater with the rubber stopper. This sampling technique insures that water
transferred to sample bottles never interacted with the atmosphere. We transferred sample water to prelabeled, 12 mL Exetainer® sample vials, filling with 3× the void from the bottom-up. We then preserved
and stored gas samples as described above for the microcosm dissolved gas samples. We also collected
triplicate water chemistry samples at the same time we collected dissolved gas samples. After returning
to the laboratory, dissolved gas samples were analyzed for N2:Ar and O2:Ar using MIMS, and water chemistry samples were analyzed for NH4+, NO3, and SRP on a Lachat Flow Injection Analyzer (see supporting
information for details).
We first modeled gross primary production (GPP), ER, and K using a model based upon the one-station, openwater O2 exchange method [Odum, 1956]. Using a mass balance model, we estimated GPP (g O2 m2 d1), ER
(g O2 m2 d1), and K (d1) as
0
1
Xt
PAR
O
þ O2;equilðt1Þ O2ðtÞ þ O2ðt1Þ
GPP
t1
A þ ER Δt þ K t;oxy Δt 2;equilðtÞ
O2ðtÞ ¼ O2ðt1Þ þ @
(5)
z
z
PARtotal
2
2
where O2(t) is the modeled DO at time t (g O2 m3), z is the mean river depth (m), Δt is the length of time
between time points (day), O2,equil(t) is the equilibrium concentration of DO (g O2 m3) for the temperature
and pressure at time t [Garcia and Gordon, 1992], and Kt,oxy is the gas exchange rate (d1) for O2 at time t calculated from K600 based on Schmidt number scaling [Jähne et al., 1987; Van de Bogert et al., 2007; Hotchkiss
and Hall, 2014] using Schmidt numbers from Wanninkhof [1992]. We used K600 to normalize K because
K600 is comparable across temperatures and gases [Jähne et al., 1987]. We use light to drive this model, with
the amount of light available for GPP in any time step represented by the sum of PAR accumulated between
times t 1 and t divided by the daily total PAR (PARtotal). Accounting for the accumulation of PAR between
time steps and using the average saturation deficit between time points provides us with a more accurate
representation of processes occurring between times t 1 and t, rather than a snapshot of conditions at time t.
Equation (5) has O2(t) on both sides; to compare modeled O2(t) with our measured DO we algebraically converted equation (5) [Hall et al., 2016]:
Xt
!
!
ER
PAR
O
þO
O
2;equil
ð
t
Þ
2;equil
ð
t1
Þ
2
ð
t1
Þ
GPP
t1
O2ðt1Þ þ z PAR
þ z Δt þ K t;oxy Δt
2
total
O2 ðt Þ ¼
1þ
K t;oxy Δt
2
(6)
This model assumes that GPP is a linear function of light intensity, ER is constant throughout the day, and that
the average of the O2 saturation deficit between times t 1 and t represents the entire span of time between
measurements. In contrast to microcosm ER described above, here we report ER as a negative flux to depict
O2 consumption.
We used Bayesian parameter estimation to simultaneously estimate the posterior probability distributions of
GPP, ER, and K600 from equation (6) [Holtgrieve et al., 2010; Hotchkiss and Hall, 2014; Hall et al., 2015] using
JAGS coupled with the rjags package and Markov chain Monte Carlo (MCMC) sampling from weakly informative prior distributions (Table 2) [Plummer, 2013; R Development Core Team, 2014]. We will refer to this traditional, open-channel O2 exchange metabolism as the “base model” for the remainder of the paper. Using a
Bayesian framework allowed us to use the posterior distribution for K600 from the base model to inform
our gas ratio-based denitrification and metabolism models (see below).
We estimated net denitrification from the Tippecanoe R using an approach based on the metabolism
model described above. We calculated the equilibrium ratio of N2:Ar in the river at each dissolved gas
sampling time point using equations from Hamme and Emerson [2004]. Using JAGS coupled with rjags
and MCMC sampling, we estimated posterior distributions for denitrification and K600 by simultaneously
modeling dissolved N2, Ar, and the N2:Ar ratio at each time point using mass balance equations based
on equation (6) above:
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Table 2. Estimated Mean, 2.5% and 97.5% Quantiles of the Posterior Distributions for GPP, ER, K, and DN Modeled Using Separate Metabolism and
Denitrification Models
Parameter
Model
2 1
GPP (g O2 m
ER (g O2 m
K600 (d
d
)
2 1
d
)
1
)
2 1
DN (mg N m
h
)
Base model
O2:Ar model
Base model
O2:Ar model
Base model
O2:Ar model
N2:Ar DN model
N2:Ar DN model
Prior
Posterior 2.5% Quantile
Posterior Mean
Posterior 97.5% Quantile
0.68
0.54
2.86
2.66
3.17
3.21
3.24
7.83
0.71
0.58
3.05
2.83
3.44
3.45
3.49
8.75
0.74
0.62
3.25
3.00
3.72
3.69
3.74
9.68
a
unif(0,100)
a
unif(0,100)
a
unif(100,0)
a
unif(100,0)
a
unif(0,100)
b
norm(3.25,0.02)
b
norm(3.25,0.02)
a
unif(0,100)
a
Uniform
b
distribution with the range specified within the parentheses.
Normal distribution with the mean and variance specified within the parentheses.
N2ðtÞ ¼
ArðtÞ ¼
DN
z Δt
N
þN
ðt1Þ N2ðt1Þ
þ K t;N Δt 2;equilðtÞ 2;equil
2
K
Δt
1 þ t;N2
Ar
þAr ðt1Þ Arðt1Þ
Arðt1Þ þ K t;Ar Δt equilðtÞ equil
2
1þ
K t;Ar Δt
2
(7)
(8)
where N2,t and Art are the dissolved N2 and Ar at time t (g m3); N2,equil,t and Arequil,t are the equilibrium concentrations of N2 and Ar at time t (g m3), respectively; and Kt,N and Kt,Ar are the gas exchange rates (d1) for
N2 and Ar at time t calculated from K600. The modeled N2:Ar ratios were coupled with three replicate N2:Ar
ratios collected at each time point in the river to estimate DN. We used a weakly informative prior for DN
but an informative prior for K600. Our informative prior for K600 was normally distributed with the mean
and variance of the posterior distribution of K600 from the base model. Our modeling approach assumes that
N2:Ar measurements above the physical equilibrium result from denitrification occurring in the river. We
acknowledge that groundwater can also be out of equilibrium with the atmosphere due to groundwater
flowing through soils at different temperatures than the river. Estimates of the groundwater contribution
to excess N2 and Ar can be made using 222Rn to separate in-stream and groundwater contributions to N2 production, and recent research has shown that groundwater can make significant contributions to excess N2 in
headwater streams [Gardner et al., 2016]. We were unable to separate these contributions with our data, but
we assumed that the importance of groundwater contributions to N2 production decreases with increasing
stream size, as has been shown for CO2 [Hotchkiss et al., 2015]. Therefore, we attribute N2 production from the
Tippecanoe R to riverine denitrification.
As a check of our ratio-based approach for estimating denitrification, we also estimated GPP, ER, and K600
using O2:Ar ratios from the same dissolved gas samples used to estimate denitrification and then compared
these estimates to the base metabolism model. We used the same MCMC ratio-based approach as described
for denitrification, but substituted equation (6) (the base O2 model) for equation (7) (the N2 portion of the gas
ratio model), and estimated posteriors for GPP, ER, and K600 using three O2:Ar ratios collected at each time
point and analyzed on the MIMS. Again, we used minimally informative priors for GPP and ER but the same
informative prior for K600 as in the denitrification model.
For all MCMC models we ran four linked model chains with different starting values within our prior distributions. Each model chain was 100,000 iterations, and we did not thin MCMC chains [Link and Eaton, 2012]. We
insured convergence of the model by visualizing the trace of each model chain. We confirmed convergence
by calculating the Gelman-Rubin statistic, which equals 1 for all model runs. We subtracted the first 1000
iterations of burn-in time based upon visual assessments of MCMC chains before calculating posterior distribution means and credible intervals for each parameter. We used Bayesian methods because, in contrast to
optimization methods which specify a unique solution as an absolute best fit to the data, MCMC provides
parameter values which give reasonable fits to the data and express the certainty of these parameters in
terms of probability [Holtgrieve and Schindler, 2011]. This attribute is particularly beneficial for an openchannel, dissolved N2 exchange denitrification model due to the high degree of uncertainty from previous
methods. An added benefit of Bayesian analysis is that we can incorporate prior information into the analysis,
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Figure 1. Dissolved oxygen (O2) decreased significantly in (a, e, i, m, and q) sediment and(b, f, j, n, and r) water column microcosms from all study rivers. Dissolved
nitrogen gas (N) increased significantly in (c, g, k, o, and s) sediment microcosms at four rivers and in (d, h, l, p, and t) water column microcosms at two rivers. The
1
slope of these regressions represents net O2 and N gas flux (mg h ) which is subsequently scaled areally based upon area:volume relationships. Note that the range
in y axes is consistent for all O2 (or N) microcosms, making slopes comparable for O2 (or N2) fluxes both among rivers and between habitats.
which allowed us to use the posterior distribution for K600 from the base metabolism model as our prior
distribution in the denitrification model thereby incorporating the uncertainty around K600, overcoming a
major issue with previous open-channel denitrification models [Böhlke et al., 2009; Baulch et al., 2010]. All
analyses and models were performed in R (version 3.0.2) [R Development Core Team, 2014].
3. Results
3.1. Respiration and Denitrification in Microcosms
Using the microcosm sampling approach, dissolved O2 declined over time due to microbial activity in both
the sediment and water column in every river (Figure 1). Additionally, N significantly increased in sediment
microcosms from four of five rivers and in water column microcosms from two of five rivers (Figure 1). In
the Manistee R, N decreased over time in the water column. Although this decline in slope suggests net N
fixation, we were exploring denitrification rates for this study; therefore, DNwc of the Manistee R was
designated to be below our limit of detection. We note that the designation of “below detection” for DNwc
does not negate the net N fixation suggested by the negative slope, but because N fixation was not the focus
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Figure 2. Sediment (gray bars) and water column (open bars) (a and b) ecosystem respiration (as the absolute value, |ER|)
and (c and d) denitrification (DN) varied across rivers. When scaled areally (Figures 2a and 2c), sediment and water column
rates were within a factor of 10 of each other, but when scaled per gram DM (Figures 2b and 2d), water column |ER| (|ER|wc)
and DN (DNwc) rates were approximately 10,000× sediment (|ER|sed and DNsed) rates when both habitats had measureable
ER or DN. Water column contributions to |ER| and DN are provided in italics above areal rate bars. Note the secondary y axes
for water column |ER| and DN in Figures 2b and 2d.
of this study, we excluded this result from the Manistee R incubation from subsequent analyses. Nevertheless,
we encourage future work investigating the potential for riverine N fixation measured using the approach
described here, particularly for low N ecosystems. There also was no detectable change in N over time
for sediment and water column microcosms from the St. Joseph R nor in water column microcosms
from the White R (Figure 1). Sites with DN below detection were set equal to zero and not included in regression analyses.
Figure 3. Combined sediment and water column denitrification (DNsed + wc) increased with (a) combined ecosystem
respiration (as the absolute value, |ER|sed + wc) and (b) dissolved organic carbon (DOC). Error bars denote the 95%
confidence intervals.
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Figure 4. The water column contribution (WC) to combined ecosystem respiration (as the absolute value, |ER|sed + wc)
increased with background (a) NO3 and (b) SRP, whereas the WC contribution to combined denitrification (DNsed + wc)
increased with (c) |ER|sed + wc.
Partitioning the relative contributions of the sediment and the water column showed that the water column
can contribute to both reach-scale O2 and N dynamics, and that the water column can be more biogeochemically active than bed sediments, at least to the depth we sampled. Areal rates of sediment, water column, and
combined sediment + water column DN ranged from 0 to 1.8, 0 to 4.9, and 0 to 5.7 mg N m2 h1, respectively (Figure 2). The water column contribution to DNsed + wc ranged from 0 to 85% and from 39 to 85%
for |ER|sed + wc (Figure 2). In rivers where both water column and sediments contributed to |ER| or DN, water
column |ER| and DN were 4 orders of magnitude higher than sediment |ER| or DN when scaled per gram DM
(Figure 2), with differences attributed to the lower DM in the water column, despite similar biogeochemical
activity between the two habitats.
3.2. Controls on Microcosm Respiration
and Denitrification
We found no significant relationships between
measured environmental factors and |ER| in sediments, |ER| in the water column, or in |ER|sed + wc.
In contrast for denitrification, DNsed + wc was controlled by variables reflecting heterotrophy and
was positively related to both |ER|sed + wc (r2 = 0.83,
p = 0.091; Figure 3a) and DOC concentrations
(r2 = 0.95, p = 0.028; Figure 3b). However, when DN
in the water column or sediment was considered
alone, neither was significantly related to any
environmental predictor.
Factors related to the water column contribution to
|ER|sed + wc were different than those related to the
water column contribution to DNsed + wc. The water
column contribution to |ER|sed + wc increased with
NO3-N (r2 = 0.85, p = 0.027; Figure 4a) and SRP concentrations (r2 = 0.84, p = 0.029; Figure 4b), whereas
the water column contribution to DNsed + wc
increased only with |ER|sed + wc (r2 = 0.82, p = 0.092;
Figure 4c). We also note that NO3-N and SRP were
correlated (Pearson’s r = 0.84), suggesting that some
other factor may be responsible for this relationship.
Figure 5. (a) The base metabolism model predictions (solid
line) fit the dissolved O2 measured with a sonde (gray
points) over the diel sampling period. (b) The gas ratio metabolism model predictions (solid line) fit the observed O2:Ar
(gray points) but not as well as the base metabolism model.
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3.3. Open-Channel Metabolism and
Denitrification Estimates
Both modeling approaches for metabolism indicated that the Tippecanoe R was heterotrophic on
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Figure 6. Posterior probability distributions for (a) gross primary production (GPP), (b) ecosystem respiration (ER), (c) gas
exchange rate (K600), and (d) denitrification (DN) estimated using the base metabolism model (Base Met.; dashed lines),
the O2:Ar gas ratio metabolism model (Ratio Met.; solid gray lines), and the N2:Ar gas ratio denitrification model (Ratio DN;
solid black lines). Gray and black lines are difficult to distinguish in Figure 6c due to the degree of similarity between the
two model outputs for K600.
the day of sampling, and the model fit was comparable for both modeling approaches (Figure 5). The base
model estimated GPP, ER, and K600 to be 0.71 g O2 m2 d1, 3.05 g O2 m2 d1, and 3.44 d1, respectively
(Figure 6 and Table 2). Metabolism estimates for GPP and ER using the gas ratio approach were slightly lower
than those from the base model, with GPP, ER, and K600 estimated as 0.58 g O2 m2 d1, -2.83 g O2 m2 d1,
and 3.45 d1, respectively (Figure 6 and Table 2). Posterior credible intervals of ER overlapped between the
two modeling approaches (Figure 6b), whereas GPP posteriors did not. Differences in GPP between the
two models may be due to differences in accuracy or precision between O2 measurement techniques or
different sampling frequencies between the two O2 measurement techniques.
The ratio of N2:Ar in the Tippecanoe R was higher than the equilibrium ratio throughout the 24 h diel sampling period (Figure 7), and our model estimated DN and K600 as 8.75 mg N m2 h1 and 3.49 d1, respectively (Figure 6 and Table 2). Predicted N2:Ar ratios from the DN model were similar to observed ratios, and
when we turned off DN in our model, model predictions were similar to N2:Ar equilibrium concentrations,
suggesting that the denitrification term in the model accounted for the observed N2:Ar values above
equilibrium (Figure 7). The DN estimate from the reach-scale model was ~2× larger than that estimated
from the combined water column and sediment microcosms in the Tippecanoe R. Although microcosmbased estimates differed from reach-scale estimates, this difference was small given the contrasting approaches. For example, microcosm
approaches incur error through scaling microcosms to the reach-scale. In addition, temporal variation over the 24 h sampling period, and
incorporation of realistic hydrology not accounted
for in microcosms (e.g., groundwater), could also
explain differences. Finally, the microcosm and
reach-scale approaches were performed on separate dates, and DN may not be identical across
dates. Posterior density estimates of K600 were very
similar for all three models (Figure 7), suggesting
that the limited information in the gas ratio models did not update the prior over what we estiFigure 7. The diel N2:Ar denitrification model (black dashed mated using the base model.
line) predicted N2:Ar ratios observed in our dissolved gas
samples (black points) over the diel sampling period. Model
predictions with denitrification set to zero (gray dashed line)
fit N2:Ar ratios expected if the rivers were at equilibrium with
the atmosphere. The black rectangle along the x axis denotes
the photoperiod, with the black region signifying darkness.
N2:Ar is the mass ratio.
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DENITRIFICATION IN RIVERS
4. Discussion
Both microcosm and reach-scale denitrification
methods provided estimates of denitrification in
Midwestern rivers similar to results from other
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methodological approaches (Figure 8).
Although ER and DN varied among five
Midwestern rivers, the water column
contributed substantially to ERsed + wc
in all rivers and to DNsed + wc in two of
five rivers. These measurements provide insight into mechanisms driving N
retention in rivers, which has been
understudied relative to headwater
streams [Tank et al., 2008], and the
application of these data will facilitate
targeted management approaches to
maximize N retention in rivers.
4.1. Partitioning Water Column and
Sediment Processes
Nutrient uptake rates in rivers are often
assumed to decrease with depth
[Alexander et al., 2007; Ye et al., 2012],
and thus, headwater streams drive
Figure 8. (a) Denitrification rates from this study compared with other
nutrient uptake in watershed models
lotic studies using both microcosm and reach-scale methods and (b)
denitrification estimates from multiple aquatic ecosystems. Denitrification [Alexander et al., 2007; Wollheim et al.,
2008]. However, using measurements
estimates were collected from previous reviews of denitrification in lotic
ecosystems [Roley et al., 2012, and references therein] and across aquatic of the relationship between nutrient
ecosystems [Piña-Ochoa and Álvarez-Cobelas, 2006, and references
uptake and flow from small streams to
therein]. Additional N2 flux estimates were collected from Turek and
model nutrient dynamics in rivers may
Hoellein [2015]. Values exceeding 1.5 times the interquartile range above
not be accurate due to the presence
or below the upper and lower quartiles were deemed outliers and
of a free-living potamoplankton in
excluded from the plots. We also excluded questionable open-channel N2
estimates. Numbers above bars indicate the number of study sites each
rivers [Reynolds and Descy, 1996; Hall
bar represents. The asterisk next to the 1 for the sample size of our open et al., 2013]. Using separate microcosm
channel estimate is because the box includes the mean and 95% credible
estimates of ER and DN to partition
interval of our reach-scale DN estimate. See supporting information for a
their relative contributions, our results
list of all studies included in this figure.
show that the water column may be
responsible for a substantial portion of reach-scale ER in some rivers, and a portion of water column N
removal can be attributed to denitrification which permanently removes NO3-N from the ecosystem.
We note that the microcosm approach excludes the influence of groundwater on sediment DN,
which can be substantial in small streams [Gardner et al., 2016] but likely less influential in our study
rivers. Incorporating these findings into watershed models focused on N uptake or retention [Alexander
et al., 2007; Wollheim et al., 2008; Ye et al., 2012] will improve the accuracy and mechanistic basis of
these models.
The water column contribution to ERsed + wc increased with nutrient availability, potentially driven by increasing autotrophy in the water column, either in the form of plankton or sloughed algae derived from benthic
biofilms. Although we could not partition autotrophic and heterotrophic respiration in this study, water
column chl a increased with anthropogenic land use (r2 = 0.96, p = 0.019; data not shown). In addition, water
column chl a is tightly linked with water column GPP in streams and rivers [Reisinger, 2015], and autotrophic
production drives variation in reach-scale ER in rivers [Hall et al., 2016].
While the water column contribution to DNsed + wc was unrelated to nutrient availability, DNsed + wc was positively related to |ER|sed + wc, with several potential mechanisms driving the relationship. For example, increasing |ER|sed + wc may reduce dissolved O2 and therefore increase denitrification. In contrast, if |ER|sed + wc is
driven by benthic oxygen demand, increased benthic competition for NO3-N may reduce sediment DN
and subsequently increase the relative water column contribution to DNsed + wc. Given the correlation
between rates of these two ecosystem processes, the hypothesized drivers influencing the role of the water
column in rivers warrant further exploration.
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Experiments using short-term 15NO3 tracer additions have shown that whole-stream denitrification rates
increase with NO3-N, but rates of increase eventually slow with increasing concentration [O’Brien et al.,
2007; Mulholland et al., 2008]. In all five study rivers, NO3-N concentrations were high (i.e., >0.1 mg
NO3N L1) and DNsed + wc was not related to NO3-N availability. In contrast to NO3-N availability, water
column DOC and |ER|sed + wc both explained variation in DNsed + wc. Due to high NO3-N concentrations
and the positive relationships between DNsed + wc, DOC, and |ER|sed + wc, carbon availability may drive DN
at the ecosystem level of these high NO3-N Midwestern rivers, even though we did not find carbon
limitation using habitat-specific incubations. It is also possible that delivery of NO3-N to anaerobic sediments controls benthic denitrification rates, even when a river exhibits high concentrations throughout
the water column.
Although MIMS has been used to estimate denitrification in freshwater ecosystems for >10 years, methodological difficulties continue to limit its applicability. One of the more common MIMS methods used to estimate denitrification is the continuous-flow core method. The continuous-flow core method originated in
estuarine sediments [Kana et al., 1998; An et al., 2001; Gardner and McCarthy, 2009] but has since been applied
in freshwater ecosystems [Bernot et al., 2003; Smith et al., 2006; Turek and Hoellein, 2015]. Continuous-flow
cores overcome some of the major limitations for estimating denitrification [Groffman et al., 2006] by using
intact sediment cores with natural substrate availability and no chemical inhibitors. Keeping sediment cores
intact is crucial for marine or lentic studies, where benthic sediments are generally stratified, are not typically
resuspended, and rarely experience subsurface flow. However, we argue that riverine sediments, particularly
in Midwestern rivers with fine, organic-rich sediment, are more likely to be resuspended and transported
downstream. In addition, lotic ecosystems commonly have water flowing through benthic substrata underneath the sediment-water interface, which inhibits a static redox gradient typical of lentic ecosystems.
Therefore, intact cores are critical to generate realistic representations of lentic sediments but potentially less
critical for lotic ecosystems with fine grained, primarily unconsolidated sediments. An additional benefit of
the sacrificial microcosm approach is that it reduces the incubation period from the recommended 3–5 days
for continuous-flow cores to 8–12 h in the microcosm approach. We encourage future studies comparing the
sacrificial microcosm and continuous-flow core approaches. In particular, there is a need for more comparisons focused on the water column of rivers in order to more accurately represent the hydraulics and redox
conditions of suspended particles.
While the sacrificial microcosm approach is fast and cost-effective, it is not without problems. Drawbacks of
the method include the sacrificial nature of sampling, homogenization of samples prior to the incubation, the
large number of samples needed for field-based replication, and potential problems with diffusion of gases
across the sediment-water column boundary layer. The diffusion problem could be overcome by continuously agitating microcosms using a shaker table as is commonly done in acetylene block approaches. For this
study, however, continuous shaking would suspend sediment in the water column of microcosms to a
greater degree than occurs in situ, inhibiting our assessment of the relative role of water column and sediment contributions to N2 and O2 fluxes. Although this microcosm approach does not perfectly represent in
situ denitrification, the ease and cost-effectiveness of the method increase the availability of tools for stream
ecologists to compare denitrification across multiple sites or habitats.
4.2. Open-Channel N2 Estimates of Denitrification
Although microcosm approaches have long been used to estimate DN [Smith and Tiedje, 1979; Inwood et al.,
2005; Reisinger et al., 2013], in situ methods for estimating denitrification are preferable for reach-scale estimates to avoid scaling errors. One in situ method commonly applied in headwater streams is the short-term
15
N tracer addition approach [Mulholland et al., 2008, 2009], which allows for the estimation of nearly ambient
reach-scale denitrification rates without manipulating natural conditions. The open-channel N2 exchange
method has been used as an alternative approach in a variety of streams and rivers [see Baulch et al., 2010,
and references therein], but various problems have limited its widespread application.
To accurately estimate denitrification, the open-channel N2 method requires measurements of dissolved N2
above equilibrium values throughout the day, which is challenging in rivers with high K values, high diel temperature variation [Baulch et al., 2010], or high rates of N fixation. Low-gradient Midwestern rivers like the
Tippecanoe R are ideal for the open-channel approach. Although N2:Ar was supersaturated throughout the
diel sampling in the Tippecanoe R, there was a decrease in N2:Ar near the end of the sampling that
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corresponded to sunrise (Figure 7). This decrease in N2:Ar could be a decline in denitrification, an increase in
N fixation which consumes N2 from the water column, temperature-induced changes in convective mixing at
sunrise altering river hydrodynamics, or a spurious pattern. Denitrification may decline at sunrise either due
to direct competition for NO3 with primary producers [Welsh et al., 2000] or indirectly as higher GPP and
associated increases in dissolved O2 could inhibit denitrification. Alternatively, N fixation by primary producers responding to sunrise may consume dissolved N2, thus reducing N2:Ar in response to sunrise. In our
model we assumed that N fixation was not occurring in the Tippecanoe R, because research has suggested
that N fixation is very low in ecosystems with NO3-N concentrations >0.02 mg L1 [Kunza and Hall, 2014].
We acknowledge that it is possible, although unlikely, that N fixation was occurring in the Tippecanoe R,
but further exploration of these potential interactions will require additional data collection.
An additional constraint on the open-channel N2 method is that low K and relatively stable temperatures
are required for implementation and cannot be achieved in all rivers. As such, the open-channel N2 method
has primarily been applied in low-gradient, nutrient-rich rivers [this study; Laursen and Seitzinger, 2002;
McCutchan et al., 2003; Smith et al., 2008]. A major benefit of modeling metabolism and denitrification
together is that, through the use of a Bayesian statistical framework, we are able to first estimate K600 using
the diel patterns of oxygen [Holtgrieve et al., 2010; Hotchkiss and Hall, 2014; Hall et al., 2016]. By first
modeling oxygen, we estimated K600, avoiding labor-intensive tracer injections which have been used to
estimate K for previous open-channel N2 models [Laursen and Seitzinger, 2002; McCutchan et al., 2003;
Smith et al., 2008]. However, empirical estimates of K could easily be included in our modeling approach,
particularly for ecosystems with higher values of K which would necessitate an empirical, rather than
modeling approach.
Another challenge associated with the open-channel N2 method is that it requires accurate and precise
estimates of in situ dissolved N2. Using MIMS allows for high-precision estimates of N2 in environmental
samples [Kana et al., 1994], but complex equations are still required in order to convert MIMS data to in situ
N2, and potential error is introduced at each step of data conversion. Early open-channel N2 studies
using MIMS resulted in denitrification estimates that were >10× higher than in previous studies using
established methods [Laursen and Seitzinger, 2002; McCutchan et al., 2003; Pribyl et al., 2005]. As an
example of the potential errors during calculations, Böhlke et al. [2009] estimated denitrification using
the original N2 data presented in Laursen and Seitzinger [2002], but incorporated changes in atmospheric
pressure necessary to accurately estimate dissolved gas concentrations, resulting in denitrification ranging
from 2.0 to 28.2 mg N m2 h1, which was a 5× reduction from the original estimate. Our estimate of
open-channel denitrification in the Tippecanoe R fell within these updated estimates from Böhlke et al.
[2009] (Figure 8).
4.3. Comparisons to Other Aquatic Ecosystems
The range of our microcosm denitrification estimates overlapped with other microcosm estimates of denitrification in streams using either the acetylene block or N2 flux techniques (Figure 8 and supporting information). Our estimated 95% credible interval in the Tippecanoe R using the open-channel N2 method also fell
within the range of previous microcosm estimates for denitrification in flowing waters but slightly higher
than denitrification estimates from other ecosystems (Figure 8 and supporting information). Both our diel
study and other open-channel N2 estimates of denitrification were higher than denitrification estimated
using 15N tracer additions, but the open-channel N2 exchange method needs to be applied in more ecosystems in order to generalize about its utility (Figure 8). We would expect open-channel estimates of denitrification to be higher than microcosm estimates due to the inclusion of habitats not incorporated with
microcosm estimates. For example, the subsurface hyporheic zone is a hot spot for denitrification
[Zarnetske et al., 2012], and the open-channel method incorporates hyporheic denitrification in addition to
contributions by the sediment and water column. Indeed, Harvey et al. [2013] showed that denitrification
in the uppermost portion of the hyporheic zone accounted for between 1 and 200% of whole stream denitrification. Additionally, a portion of N2 found in streams may come from groundwater inflows [Gardner et al.,
2016], potentially explaining differences between microcosm and open-channel approaches in the
Tippecanoe R, but the contribution of groundwater to excess N2 in the Tippecanoe R should be minimal compared to headwater streams. In the future, the analysis of a second gas tracer, such as 222Rn [Gardner et al.,
2016], would allow for assessing the groundwater contribution to riverine N2 production. It is unclear why
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denitrification estimates made using 15N tracer additions tend to be lower than other reach-scale estimates
(Figure 8), but one potential reason could be that the 15N-NO3 additions do not include coupled
nitrification-denitrification, which may be particularly important in streams with NO3-N concentrations
<300 μg L1, thus underestimating denitrification rates as a whole [Mulholland et al., 2008, 2009].
Our microcosm estimates were similar to denitrification estimates made using a variety of methods across
aquatic ecosystems [Piña-Ochoa and Álvarez-Cobelas, 2006] (Figure 8). The open-channel estimate of denitrification in the Tippecanoe R was higher than for other ecosystems, but we note that most denitrification rates
were measured using microcosms or mesocosms [Piña-Ochoa and Álvarez-Cobelas, 2006; Roley et al., 2012].
Denitrification was generally higher in freshwater habitats (this study; streams, rivers, and lakes) compared
to marine ecosystems, likely due to increased availability of NO3-N and labile carbon in freshwater ecosystems [Piña-Ochoa and Álvarez-Cobelas, 2006], which are tightly linked to the surrounding landscape. By the
time water reaches the coastal zones and the open ocean, most NO3-N has been removed either via denitrification or other retention mechanisms [Gruber and Galloway, 2008; Wollheim et al., 2008] and terrestrial
carbon has been repeatedly processed resulting in a reduction in lability [Cole et al., 2007; Hotchkiss et al.,
2014]. Given higher NO3-N concentrations and increased carbon quality in freshwaters, it is not surprising
that denitrification is generally higher in freshwaters than in marine ecosystems.
5. Conclusions
The combination of habitat-specific microcosm estimates and open-channel methods provides both an
estimate of denitrification at the ecosystem level and allows partitioning of the habitat contributions and
isolated controls on riverine denitrification. The water column contributed substantially to ER across five
Midwestern rivers and to denitrification in two of five rivers. The inclusion of the water column as a biogeochemically active habitat, particularly in rivers, will improve watershed-scale models of N retention, which
typically assume that all N retention occurs in the benthos. We found that the water column contribution
to denitrification increased with ERsed + wc, and several direct and indirect mechanisms may drive this relationship. In addition to habitat-specific DN estimates, our open-channel N2:Ar modeling approach coupled
with single-station metabolism models provides an integrative estimate of reach-scale denitrification. Overall,
the results of both habitat-specific microcosm incubations and a reach-scale estimate of denitrification
suggest that rivers can remove N via denitrification at equivalent or higher rates than headwater streams.
Acknowledgments
We thank Caleb Bomske for assisting
with microcosm incubations and
Martha Dee, Brittany Hanrahan, and
Lindsey Reisinger who helped with the
24 h diel sampling on the Tippecanoe
R. In addition, we thank Sarah Roley for
supplying the denitrification rates from
previous studies, Mike Brueseke for
analyzing the DOC samples at the Notre
Dame Center for Environmental Science
and Technology (CEST), and Emma
Rosi-Marshall and Michelle Baker for
helping to select the study sites and
designing the larger project on which
this research was based. Hilary
Madinger assisted with gas modeling.
Comments from Matt Cohen and an
anonymous reviewer improved this
manuscript. A.J.R. was partially supported through a CEST-Bayer Fellowship
as well as a National Science Foundation
Doctoral Dissertation Improvement
Grant (DEB-1311319) provided to A.J.R.
and J.L.T., NSF DEB-0922118 to J.L.T.,
and NSF DEB-0921598 to R.O.H. Data
and R code used in this paper are
available in the supporting information.
REISINGER ET AL.
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