Biogeochemistry DOI 10.1007/s10533-014-9975-0
Particulate organic matter quality influences nitrate retention and denitrification in stream sediments: evidence from a carbon burial experiment Robert S. Stelzer • J. Thad Scott • Lynn A. Bartsch • Thomas. B. Parr
Received: 8 October 2013 / Accepted: 7 March 2014 Ó Springer International Publishing Switzerland 2014
L. A. Bartsch Upper Midwest Environmental Sciences Center, United States Geological Survey, 2630 Fanta Reed Road, La Crosse, WI 54603, USA e-mail:
[email protected]
denitrification compared to a control of combusted sand. We also determined how POC quality affected the quantity and quality of dissolved organic carbon (DOC) and dissolved oxygen concentration in groundwater. Nitrate and total dissolved nitrogen (TDN) retention were assessed by comparing solute concentrations and fluxes along groundwater flow paths in the mesocosms. Denitrification was measured by in situ changes in N2 concentrations (using MIMS) and by acetylene block incubations. POC quality was measured by C:N and lignin:N ratios and DOC quality was assessed by fluorescence excitation emission matrix spectroscopy. POC quality had strong effects on nitrogen processing. Leaf treatments had much higher nitrate retention, TDN retention and denitrification rates than the wood and control treatments and red maple leaf burial resulted in higher nitrate and TDN retention rates than burial of red oak leaves. Leaf, but not wood, burial drove pore water to severe hypoxia and leaf treatments had higher DOC production and different DOC chemical composition than the wood and control treatments. We think that POC quality affected nitrogen processing in the sediments by influencing the quantity and quality of DOC and redox conditions. Our results suggest that the type of organic carbon inputs can affect the rates of nitrogen transformation in stream ecosystems.
Thomas. B. Parr School of Biology and Ecology, University of Maine, Deering Hall, Orono, ME 04473, USA e-mail:
[email protected]
Keywords Linked biogeochemical cycles Nitrogen processing Groundwater Microbes Decomposition DOC
Abstract Organic carbon supply is linked to nitrogen transformation in ecosystems. However, the role of organic carbon quality in nitrogen processing is not as well understood. We determined how the quality of particulate organic carbon (POC) influenced nitrogen transformation in stream sediments by burying identical quantities of varying quality POC (northern red oak (Quercus rubra) leaves, red maple (Acer rubrum) leaves, red maple wood) in stream mesocosms and measuring the effects on nitrogen retention and Responsible Editor: Jennifer Leah Tank. R. S. Stelzer (&) Department of Biology and Microbiology, University of Wisconsin Oshkosh, 800 Algoma Blvd, Oshkosh, WI 54901, USA e-mail:
[email protected] J. Thad Scott Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 115 Plant Science Building, Fayetteville, AR 72701, USA e-mail:
[email protected]
123
Biogeochemistry
Introduction Organic carbon supply governs many vital rates in ecosystems. Furthermore, the quality of organic carbon is often the rate–limiting step for a variety of ecosystem processes including secondary production, decomposition and respiration (Hobbie 1992; Sterner and Elser 2002; von Elert et al. 2003). Many of these processes involve linked biogeochemical cycles (Taylor and Townsend 2010) and organic carbon quality plays an important role in biogeochemical transformation involving interactions between elemental cycles. For example, particulate organic matter (POC) of higher quality can increase N mineralization rates (Lovett et al. 2004) and may increase the content of heavy metals in sediments (Sanei et al. 2012). Linkages between the carbon and nitrogen cycles have received increased attention recently due to dramatic modifications of the global nitrogen cycle (Galloway et al. 2008), the consequences of elevated nitrogen supply for ecosystems (Diaz and Rosenberg 2008) and the roles of carbon quantity and quality in nitrogen transformation (Baker and Vervier 2004; Arango et al. 2007; Attard et al. 2011). In aquatic ecosystems and riparian zones denitrification can represent a substantial fraction of whole-ecosystem nitrogen removal (Seitzinger et al. 2006; Mulholland et al. 2008; Heffernan et al. 2010). In addition to nitrate availability and redox status, carbon supply is an important driver of denitrification rates in ecosystems. The quantity of organic carbon has been shown to be correlated with denitrification rates in sediments and soils (Hill et al. 2000; Baker and Vervier 2004; Arango et al. 2007) and experimental additions of organic carbon in dissolved (Starr and Gillham 1993; Hill et al. 2000; Sobczak et al. 2003; Zarnetske et al. 2011) and particulate (Schipper and Vojvodic-Vukovic 1998; Stelzer et al. 2015) forms stimulated denitrification or nitrate retention. There is far less known about how organic carbon quality, particularly POC quality, affects denitrification and nitrate retention rates in ecosystems. Several studies have demonstrated that microbial utilization of carbon is favored when POC has low C:N and low lignin:N ratios (e.g. Melillo et al. 1982). It is less clear how denitrifying bacteria will respond to variation in POC quality. Denitrification potential in wetland sediments was inversely related to the C:N ratio of
123
the dominant plant species (Bastviken et al. 2005) and to the percentage of phenolic compounds (Dodla et al. 2008). Denitrification rates have also been shown to be related to DOC quality (Pfenning and McMahon 1996; Baker and Vervier 2004; Barnes et al. 2012). POC quality likely has both direct and indirect effects on denitrification and other types of nitrogen transformation (e.g. assimilatory uptake) in sediments and soils. POC quality could have direct effects on nitrogen processing by influencing the types of microbes (Berg and Smalla 2009) and the carbon availability to microbes that are attached or embedded in the POC matrix. Although carbon must be in dissolved form to pass through the cell membrane of bacteria (Battin et al. 2009) microbes directly associated with POC produce extracellular enzymes that create monomers which can be incorporated into bacterial cells (Sinsabaugh et al. 2002). POC quality could also indirectly affect nitrogen processing by influencing the quantity and quality of DOC that is leached from the POC (Strauss and Lamberti 2002; Yoshimura et al. 2010). Finally, POC quality could affect biological oxygen demand in sediments and soils (Duff et al. 2007), which can affect redox status and thus, the likelihood that nitrate serves as the terminal electron acceptor during respiration. Sediments in streams are often active areas of denitrification and nitrate retention, particularly where nitrate and organic carbon supply and redox conditions are favorable (Duff et al. 2008; Puckett et al. 2008; Zarnetske et al. 2011; Stelzer et al. 2011a; Stelzer and Bartsch 2012). We showed previously that POC burial in sediments increased denitrification rates and groundwater nitrate retention in a gaining stream in Central Wisconsin (Emmons Creek) (Stelzer et al. 2015) which was predicted from our conceptual model for nitrate removal in deep sediments (Stelzer and Bartsch 2012). The main objective of the current study was to determine how the quality of buried POC affected denitrification rates, nitrate retention, and the oxygen status of pore water in sediments of Emmons Creek. We characterized the chemical composition of the POC and DOC to aid in the analysis of potential mechanisms by which POC quality could affect nitrogen processing. To our knowledge there have been no prior experimental studies that have addressed how POC quality affects denitrification and nitrate retention in field settings.
Biogeochemistry
Methods Study site Emmons Creek is a third order, predominantly groundwater-fed low-gradient stream located in the Central Sand Ridges Ecoregion in Central Wisconsin. The terrain is flat to gently rolling and soils are sandy and well drained. Discharge at base flow is approximately 400 L s-1. Surface water temperature averages 13 °C in the summer months. The most common substrate in the wetted channel is sand, followed by silt and gravel. Groundwater and surface water tend to be nitrate-rich (Stelzer et al. 2011b). The Emmons Creek watershed has a heterogeneous land cover which includes forest, row-crop agriculture, small dairy farms, prairie, and wetlands. The study took place in a 700-m reach of Emmons Creek in southeastern Portage County (089.24320°, 44.29667°). Experimental design We established four POC quality treatments in sediments within mesocosms in Emmons Creek as follows: northern red oak (hereafter red oak) (Quercus rubra) leaves, red maple (Acer rubrum) leaves, red maple wood and a control (combusted sand) (Table 1). Mesocosms consisted of stainless steel pipes (16 cm diameter 9 36 cm length) that were partially inserted into the sediments within upwelling locations (Fig. 1). We used a randomized block design and each block
consisted of a cluster of four mesocosms to which treatments were randomly assigned. Clusters of mesocosms were placed about 20 m apart. Each treatment was replicated 11 times. Whole red oak and red maple leaves were collected at abscission in autumn and small red maple twigs were collected from live trees and cut into small pieces (3–5 cm in length). The POC, which was collected from the Emmons Creek watershed, was allowed to air dry for at least three months and then was placed in plastic mesh bags (0.02 cm2 mesh) on the top of the sediment surface in a run of Emmons Creek for 25 days for microbial colonization. After colonization the POC was rinsed in stream water to remove sediment and examined for macroinvertebrates, which were also removed. Next, leaves were cut into approximately 5 cm2 pieces and the POC was added to the mesocosms (Day 1, June 4, 2012). A layer of combusted sand (heated at 450 °C for 3 h to remove organic matter) was placed below and on top of the added POC such that the POC was buried to a sediment depth of about 12 cm (Table 1; Fig. 1). The control mesocosms only received combusted sand. A piezometer was installed adjacent to each mesocosm cluster at a mean sediment depth of 44 cm for measuring vertical hydraulic gradient and for collecting groundwater for the acetylene block denitrification incubations (see below). Piezometers were constructed of CPVC (1.2 cm inner diameter) with the terminal 4.5 cm screened (3 mm holes covered with 100 lm Nitex mesh, Fig. 1).
Table 1 Amounts of particulate organic matter (POC) (as dry mass) and combusted sand added to the mesocosms
Hydrology
Treatment
Vertical Hydraulic gradient was measured on Day 24 of the experiment to confirm that upwelling was occurring at each mesocosm cluster. The static head in each piezometer was measured with a Solinst level tape and these data and stream surface levels were used to calculate vertical hydraulic gradient as described in Dahm et al. (2006). Salt injections were used in 32 of the 44 mesocosms at the end of the experiment to measure advection rates. Sixty mL of concentrated NaCl solution were added with a syringe to a Minipoint (modified after Duff et al. 1998) at a sediment depth of 18–20 cm (Fig. 1). The Minipoints consisted of 0.2 cm inner diameter stainless steel tubing, perforated with slits (ca. 0.05 cm wide) in the terminal 1 cm which were covered with 100 lm mesh
Material Added
Nutrient Concentrations (mg L-1) at 25 cm Depth
POC (g)
Sand (ml)
NO3N
NH4N
DON
DOC
Control
–
3,700
2.41
\0.01
0.11
0.71
Red maple wood
15
3,300
2.60
\0.01
0.09
0.66
Red maple leaves
15
3,300
2.52
\0.01
0.10
0.72
Red oak leaves
15
3,300
2.64
\0.01
0.09
0.70
Mean concentrations of nitrate, ammonium, dissolved organic nitrogen (DON) and dissolved organic carbon (DOC) in groundwater at 25 cm sediment depth in the mesocosms based on samples from Days 4, 12 and 19
123
Biogeochemistry Fig. 1 Mesocosm configuration for particulate organic carbon (POC) treatment. The locations of Minipoint samplers at 5 and 25 cm sediment depths and adjacent piezometer are indicated. The mesocosms were arranged in clusters of four (inset)
Nitex cloth. Pore water was sampled at 5 cm depth from a Minipoint about every 20 min after the injection for several hours. Specific conductivity of the pore water was measured using a YSI Model 30 or 85 conductivity meter and the time at which peak or plateau conductivity occurred was used to determine the mean travel time. Advection rate was calculated by dividing the distance between the injection and sampling depths by the mean travel time. Diffusion tests in the laboratory suggested that diffusion was negligible (an order of magnitude lower) in the sediments relative to advection.
Organic carbon quality We assessed the quality of the POC and dissolved organic carbon (DOC). POC quality was described by the C:N ratio and %N (based on dry mass) of initial POC (prior to microbial colonization), of POC on Day 1 of the experiment (after microbial colonization), and of POC on Day 24 when sediments were collected from the mesocosms for the acetylene block incubations. The quality of POC collected on Day 1was also assessed by percent lignin (based on dry mass) and
123
lignin:N ratios. POC was dried at 60 °C, homogenized and analyzed for total C and N using a CE Instruments NC 2100 elemental analyzer (ThermoQuest Italia, Milan, Italy). POC was analyzed for lignin using the acid detergent method (AOAC 1990) at the University of Wisconsin Soil and Forage Analysis Laboratory. DOC quality was assessed using fluorescence excitation emission matrix (EEM) spectroscopy coupled with Parallel Factor Analysis (PARAFAC). An EEM contains excitation, emission, and intensity data for all of the fluorescent compounds in a sample. Many of these compounds will have overlapping peaks that are difficult to distinguish based on the results from a single sample. PARAFAC analysis offers a method for statistically decomposing these EEMs into components resembling chemical fluorophores (Cory and McKnight 2005). EEM regions (Coble 1996; Stedmon 2000) and subsequently PARAFAC components (Cory and McKnight 2005; Stedmon and Markager 2005; Fellman et al. 2010) have been linked to different pools of organic matter and environmental processes (Cory and McKnight 2005; Wilson and Xenopoulos 2008). Although a component may still represent hundreds or thousands of individual compounds, its environmental behavior can be similar
Biogeochemistry
across studies (Ishii and Boyer 2012). Pore water samples for DOC quality were collected from 28 to 32 mesocosms at 5 and 25 cm using Minipoints (Fig. 1) on Days 8 and 22 and syringe-filtered through 0.22 lm filters into combusted glass scintillation vials. Samples were shipped cold overnight to the University of Maine and analyzed within 24 h of receipt. Fluorescence excitation emission spectra (EEMs) were measured on a Hitachi F-4500 between Ex. 220–535 nm and Em. 250–535 nm in 3 nm increments using a scan rate of 2,400 nm-1, with an excitation and emission slit width of 5 nm, and a response of 0.5 s using a 0.5 cm inner diameter quartz cell. EEMs were corrected for instrumental bias according to the manufacturer’s method. Correction and calibration were confirmed using Suwanee River fulvic acid and Pony Lake fulvic acid (IHSS 2012) as well as Starna fluorescence reference set 6BF. EEMs were then blank-subtracted, Raman normalized, and inner-filter corrected. Inner-filter effect was corrected using the following equation (Fery-Forgues and Lavabre 1999; Ohno 2002): I ¼ I0 10bðAex þAem Þ ð1Þ where, I is the detected fluorescence intensity, I0 is the fluorescence in the absence of self-absorption, b is 0.25 cm, the path length to the center of the cell for both excitation and emission, Aex is absorbance at the excitation wavelength, Aem is absorbance at the emission wavelength. Absorbance values for inner-filter correction and specific ultra violet absorbance at 254 nm were collected with a Cary 50 UV–Vis Dual beam spectrophotometer by scanning from 190–800 nm in 0.5 nm intervals with a 0.1 s integration time.
Nitrogen retention Water samples for nitrate, ammonium, dissolved organic nitrogen (DON), total dissolved nitrogen (TDN; nitrate ? ammonium ? DON) and DOC concentrations were collected using Minipoint samplers inserted in each mesocosm to sediment depths of 5 and 25 cm on Days 4, 12 and 19 of the experiment (Fig. 1). Pore water was withdrawn from Minipoints using syringes and filtered through 25-mm Whatman GF/F filters in the field. Nitrate and TDN retention and DOC production were determined two different ways. First, we determined the differences in nitrate, TDN and DOC concentrations in pore water between 5 and 25 cm sediment depths. We also compared ammonium and DON concentrations between 5 and 25 cm sediment depths. A decrease in concentration between 25 and 5 cm was considered retention and an increase in concentration was considered production. Second, for a subset of mesocosms (25 total based on those mesocosms in which we were able to estimate advection rate), we calculated the nitrate (mg NO3– N day-1), TDN (mg TDN day-1) and DOC (mg DOC day-1) fluxes at 5 and 25 cm sediment depths using the following equation: F ¼V AC
ð2Þ
where, F is solute flux (mg day-1), V is advection rate (cm day-1), A is cross sectional area of the mesocosms (cm2), C is solute concentration (mg L-1). We then determined the net retention rates for nitrate and TDN and the net production rates for DOC by calculating the differences between the nitrate, TDN and DOC fluxes (F) at 5 and 25 cm. These rates were expressed on an areal basis by dividing them by the cross-sectional area of the mesocosm.
Dissolved oxygen Denitrification Dissolved oxygen in the pore water was measured at 5 cm sediment depth on Days 5 and 16 in each mesocosm with a Microelectrodes dissolved oxygen electrode that was pushed into the sediment. The probe was calibrated with 0 and 100 % O2-saturated water in the lab at the temperature of ambient sediment. Pore water temperature at 5 cm sediment depth was measured with a handheld thermometer so that oxygen data could be converted from % O2 to concentration.
Denitrification was measured in situ with membraneinlet mass spectrometry (MIMS) (Kana et al. 1994) and denitrification potential was measured in the laboratory using the acetylene block method (Groffman et al. 2006). Pore water samples were collected on Days 8 and 22 of the experiment at 5 and 25 cm depths from the Minipoints with syringes attached to 3-way Luer lock fittings for the MIMS-based denitrification
123
Biogeochemistry
measurements (Fig. 1). Pore water was collected slowly from mesocosms (5–7 replicates per treatment) and transferred to 24 mL test tubes fitted with groundglass stoppers. The test tubes were filled from the bottom up to minimize exposure to air, preserved using 0.3 mL of 50 % (w/w) ZnCl solution, and the stoppers were immediately secured. The stoppers were tightly wrapped with Parafilm and the tubes were submerged in bottles filled with stream water to minimize gas exchange. Samples were express shipped on ice the following day to the University of Arkansas for gas analysis. N2 and argon concentrations were measured using MIMS, which allows the rapid and precise determination of dissolved gas concentrations in environmental water samples (Kana et al. 1994). Dissolved gases were introduced to a high-vacuum Pfieffer Prisma mass spectrometer by pumping water samples through a permeable membrane capillary housed within the vacuum system. N2/ Ar ratios were estimated by MIMS using a single point calibration with air-saturated deionized water at near ambient temperature. N2 concentrations at each depth were calculated from this ratio assuming Ar was conserved in water moving through the sediments. N2 fluxes at 5 and 25 cm sediment depths were calculated based on Eq. 1. Denitrification rate was calculated as net N2 production which was determined as the difference in N2 flux between 5 and 25 cm depths. Acetylene block denitrification assays were performed according to the methods described in Richardson et al. (2004). Sediment was collected from each mesocosm on Day 24 with a 7.6 cm diameter polycarbonate corer. The section that was retained from each core consisted of about 500–1,000 mL of sediment centered on the location of the buried POC or at an equivalent depth for the controls. Core sections were returned to the laboratory in a cooler and then placed at ambient sediment temperature in an incubator. On the following day, twenty-five mL of sediment, 20 mL of groundwater (pumped from the piezometer adjacent to each mesocosm cluster), and 5 mL of chloramphenicol solution (1 mg mL-1) were added to vessels (246 mL glass canning jars) fitted with grey butyl septa. Incubations lasted for 90 min and were carried out in a Fisher Isotemp Model 307C incubator at the temperature of ambient groundwater. Anoxia in the vessels was established by a series of air evacuation by vacuum (-25 psi) and helium addition to the head space. Immediately after addition of
123
20 mL of atomic absorption-grade acetylene (time zero), vessels were placed on a shaker (Innova Model 2000) at 175 rpm in the incubator. Head space gas was sampled with a syringe at 30-min intervals during the incubations and was immediately transferred to evacuated 2 mL serum vials. Nitrous oxide (N2O) in the vials was analyzed on a Hewlett-Packard Model 5890 gas chromatograph fitted with a Porapak R 3.18 mm diameter 2 m stainless steel column and a 63Ni electron capture detector (ECD). N2O concentration in the head space was converted to total N2O (water plus gas phase) using the equations in Groffman et al. (1999). Denitrification potential was calculated as the rate of N2O production, based on simple linear regression models fit to the N2O time series and reported per sediment ash-free dry mass (AFDM). Sediments and POC from the vessels were stored at -20 °C for determination of AFDM, which was measured as mass loss after samples were dried at 80 °C and then combusted for 2 h at 500 °C. Solute analysis Nitrate concentration was measured with a Dionex ICS-1000 ion chromatograph equipped with an IonPac AS14A column. Ammonium concentration was measured colorimetrically with a Thermo Spectronic Aquamate spectrophotometer based on Solarzano (1969). TDN and dissolved organic carbon (DOC) concentrations were measured with a Shimadzu TOCV Carbon Analzyer with TNM Nitrogen Module using high temperature catalytic oxidation with chemiluminiscent N detection. Dissolved organic nitrogen concentration was estimated as the difference between TDN and the sum of nitrate and ammonium concentrations. Statistics We used repeated measures ANOVA to assess the effects of POC treatment, time, and the treatment x time interaction on solute (nitrate, DON, ammonium, TDN, DOC) concentration differences between 5 and 25 cm sediment depths. Repeated measures ANOVA was also used to assess the effects of POC treatment, time, and the treatment x time interaction on nitrate and TDN retention rates and DOC production rates. Compound symmetry was evaluated based on the
Biogeochemistry
Greenhouse-Geisser Epsilon and P-values were adjusted when compound symmetry was violated. One-way ANOVA was used to assess the effects of POC treatment on in situ denitrification rates, denitrification potential and pore water dissolved oxygen concentrations. One-way ANOVA was used to assess differences among treatments in the C:N ratio and %N of POC on Day 24 of the experiment (statistical comparisons were not performed for initial and Day 1 POC samples because true replicates did not exist). In cases where there was a significant treatment effect, means were compared among the four treatments using Tukey’s Honestly Significant Difference Test. The statistical analyses described above were performed using Systat v. 13. For DOC quality assessment, the EEMs (n = 120) were modeled from Ex. 259–535 nm and Em. 289–535 nm with Parallel Factor Analysis (PARAFAC) in Matlab R2012b (MathWorks 2012) using DOMFluor v1.7. Prior to PARAFAC, EEMs were normalized to the maximum value in each EEM. After several preliminary modeling runs, Ex. (\259) and Em. (\289) wavelengths were removed due to noise interference. All EEM corrections were scripted in [R] 2.15.2 ([R] Core Team, 2012). A three component model was determined to provide the optimal fit based on Stedmon and Bro (2008). The model was validated using randomly initialized split-half validation (Stedmon and Bro 2008) and the core consistency diagnostic (Bro and Kiers 2003). Average residuals were less than 5–10 % of the data. Although leverage plots indicated several outlier samples, their removal did not alter the model fit and thus they were left in the final model.
Results Hydrology Vertical hydraulic gradient was positive (mean 0.092, range 0.024–0.203) at all mesocosm clusters. Advection rate measurements were obtained for 25 mesocosms and ranged from 1.3 to 3.3 cm h-1 among mesocosms with a mean of 2.3 cm h-1. In seven mesocosms advection rate was unable to be estimated because plateaus in the conductivity breakthrough curves did not occur before 8–10 h.
Organic carbon The POC types differed in chemical composition. As expected, leaves had a lower mean C:N ratio and higher mean %N than wood on Day 24 (one-way ANOVA P \ 0.001, Tukey P \ 0.001, Table 2). Red oak leaves had slightly higher %N on Day 24 than red maple leaves (Tukey P = 0.005) but leaf types did not differ in C:N ratio (Tukey P = 0.99). The data suggest that the C:N ratio of leaves decreased and %N increased during microbial colonization prior to the start of the experiment (Table 2). Percent lignin and lignin:N ratios were 24 and 47 for wood, 39 and 29 for red maple leaves and 38 and 30 for red oak leaves on Day 1. POC quality had strong effects on net DOC production when based on change in concentrations (rmANOVA, F3,40 = 8.84, P \ 0.001, Fig. 2a) and change in fluxes (rmANOVA, F3,21 = 13.31, P \ 0.001, Fig. 2b). Based on both ways to estimate DOC production, the leaf treatments had higher DOC production than the control (Tukey P \ 0.01) and the red maple leaf treatment had higher DOC production than the red oak leaf treatment (Tukey P \ 0.01). The red maple leaf treatment had higher DOC production than the wood treatment regardless of how DOC production was calculated (Tukey P \ 0.001, Fig. 3) and the red oak leaf treatment had higher DOC production than the wood treatment when DOC production was calculated as a rate (Tukey P \ 0.01, Fig. 2b). PARAFAC analysis of EEMs generated from deep (25 cm sediment depth) and shallow pore water samples (5 cm sediment depth) resolved three distinct components (SP1, SP2, and SP3; Fig. 3). Biplots of SP1 and SP2 showed no differences among treatments at 25 cm depth (Fig. 4a). However at 5 cm depth, after groundwater had passed through the POC layer, the leaf treatments had lower average proportions of SP1 and higher average proportions of SP2 than the wood and control treatments (Fig. 4b). Absolute changes in fluorescence intensity of SP1 and SP2 in the leaf treatments between 25 and 5 cm (data not shown) were consistent with the relative changes (Fig. 4a, b). From 25 cm to 5 cm, SP3 increased in the leaf and wood treatments. In the control, there was no or little change in absolute or relative fluorescence for the three components.
123
Biogeochemistry Table 2 Mean (?SD) C:N ratios and %N of particulate organic matter before instream microbial colonization (Initial) and after microbial colonization at the beginning (Day 1) and end (Day 24) of the experiment Treatment
C:N
%N
Initial
Day 1
Day 24
Initial
Day 1
Day 24
Red maple wood
80 (5)
92 (2)
83 (19)
0.6 (0.04)
0.5 (0.02)
0.6 (0.12)
Red maple leaves
69 (3)
35 (1)
42 (1)
0.7 (0.05)
1.3 (0.09)
1.0 (0.09)
Red oak leaves
49 (2)
38 (1)
41 (4)
1.0 (0.05)
1.3 (0.04)
1.1 (0.09)
Nitrogen retention
Fig. 2 a Mean (?SE) differences in DOC concentrations between 5 and 25 cm sediment depths and b Mean (?SE) net DOC production rates for the control, red maple wood, red maple leaf and red oak leaf treatments on Days 4, 12, and 19 of the experiment
Dissolved oxygen POC quality strongly affected dissolved oxygen concentration at 5 cm sediment depth (one-way ANOVA F3,84 = 26.91, P \ 0.001, Fig. 5). Burial of red maple and red oak leaves led to severe hypoxia (means of 0.11 and 0.29 mg O2 L-1 respectively) in the shallow sediments. The wood treatment had oxygen concentrations that were marginally not statistically different from the control (Tukey P = 0.062, Fig. 5).
123
Concentrations of nitrate, ammonium, DON and DOC at 25 cm in the mesocosms did not differ among treatments (1-way ANOVAs P [ 0.59, Table 1) which suggests that the quality of groundwater entering the mesocosms from deep flow paths was similar. POC quality strongly affected nitrate retention when expressed as the difference in concentrations between 5 and 25 cm depths (rmANOVA F3,40 = 32.15, P \ 0.001, Fig. 6a). The two leaf treatments had higher nitrate retention than the wood treatment, which in turn had higher nitrate retention than the control (Tukey P \ 0.05, Fig. 6a). Net DON production occurred in most of the mesocosms and production was higher in those receiving red maple and red oak leaves than in the wood and control mesocosms (rmANOVA F3,40 = 12.17, P \ 0.001, Tukey P \ 0.001, Fig. 6b). Ammonium concentrations were very low at both 5 and 25 cm depths (less than 0.02 mg NH4–N L-1 in most cases) and the difference in ammonium concentration between depths was not affected by the treatments (rmANOVA F3,40 = 1.43, P = 0.249). Collectively, N retention (as nitrate) was much higher than N production (as DON and ammonium), particularly in the mesocosms receiving leaves, resulting in net TDN retention. POC quality strongly affected TDN retention when expressed as the difference in concentrations between 5 and 25 cm depths (rmANOVA F3,40 = 32.08, P \ 0.001). Net nitrate retention (rmANOVA F3,21 = 33.50, P \ 0.001) and net TDN retention (rmANOVA F3,21 = 27.35, P \ 0.001) rates were also affected by POC quality (Fig. 7). Red maple and red oak leaf treatments had higher net nitrate and TDN retention rates than the wood and control treatments (Tukey P \ 0.001). In addition, the mesocosms receiving red maple leaves had higher net nitrate retention rates (mean 1,406 mg NO3-N m-2 day-1) and higher net
Biogeochemistry Fig. 3 The components identified by PARAFAC analysis of EEMs measured from pore water samples collected at 25 and 5 cm sediment depths. a– c Excitation and emission contour plots of components SP1-3. Lighter shading indicates greater fluorescence. d–f Excitation and emission plots of components SP1-3 with relative peak intensity on the y-axis. Split-half results are plotted in gray while the full model is plotted in black
TDN retention rates (1074 mg TDN m-2 day-1) than the red oak leaf mesocosms (965 mg NO3–N m-2 day-1 and 685 mg TDN m-2 day-1) (Tukey P \ 0.001, Fig. 7). Denitrification Denitrification potential measured by the acetylene block method was elevated relative to the control for the leaf treatments but not for the wood treatment (one-way ANOVA F3,38 = 6.34, P = 0.001, Tukey
P \ 0.05 for comparisons between control and leaf treatments, Table 3) and did not differ between the leaf treatments (Tukey P [ 0.79). Similarly, POC quality affected in situ denitrification rate as measured by MIMS (one-way ANOVA F3,42 = 15.57, P \ 0.001, Fig. 7). Red maple and red oak leaf treatments had higher net N2 production than the control and wood treatments (Tukey P \ 0.01), which had net N2 uptake. There was no difference in in situ denitrification rates between the red maple leaf and red oak leaf treatments (Tukey P = 0.54).
123
Biogeochemistry
Fig. 4 The proportion of PARAFAC component SP2 and SP1 for the control, wood, red maple leaf, and red oak leaf treatments based on pore water samples collected at 25 cm (a) and 5 cm (b) sediment depths. Data points are from samples collected on Days 8 and 22 of the experiment
Fig. 5 Mean (?SE) dissolved oxygen concentration at 5 cm sediment depth for the control, red maple wood, red maple leaf and red oak leaf treatments based on measurements on Day 5 and 16 of the experiment. Treatments with different letters denote those with significantly different means based on Tukey’s Honestly Significant Difference Test
123
Fig. 6 Mean (?SE) differences in nitrate (A) and DON (B) concentrations between 5 and 25 cm sediment depths for the control, red maple wood, red maple leaf and red oak leaf treatments on Days 4, 12, and 19 of the experiment
Fig. 7 Mean (?SE) net total dissolved nitrogen (TDN) and NO3–N retention rates and net N2–N production rates for the control, red maple wood, red maple leaf and red oak leaf treatments. Net TDN and NO3–N retention rates are grand means based on Days 4, 12, and 19 of the experiment and net N2–N production rates are grand means based on Day 8 and Day 22. TDN and nitrate retention are shown on a negative axis for consistency with Fig. 6
Biogeochemistry Table 3 Mean (and SD) denitrification potential Treatment
Control
Denitrification potential (lg N2O–N g AFDM-1 h-1) 2.7 (1.0)
Red maple wood
5.5 (2.0)
Red maple leaves
11.7 (10.4)
Red oak leaves
14.5 (8.4)
Discussion POC quality had strong effects on nitrogen processing in that red maple and red oak leaf treatments resulted in higher nitrate and TDN retention rates and higher denitrification rates than the wood treatment. In addition, burial of red maple leaves resulted in higher nitrate and TDN retention rates, but not higher in situ denitrification rate, than burial of red oak leaves. We think that there are several mechanisms that could have caused POC quality to affect nitrogen processing, including direct effects of POC chemical composition on microbes associated with the POC, and indirect effects of POC chemical composition on microbes through changes in the quantity and quality of DOC and changes in redox status. We think that all of these mechanisms are plausible as there were differences in POC chemical composition, DOC production, DOC chemical composition, and pore water dissolved oxygen concentration among the POC treatments. Denitrification accounted for a substantial amount of the nitrate removal (about 60 %) in the leaf treatments. Carbon availability and favorable redox conditions are necessary, but not sufficient, conditions for heterotrophic denitrification (Tiedje 1982). For example, production of labile DOC would not be expected to stimulate denitrification if redox conditions were not favorable for nitrate to serve as the preferred electron acceptor in respiration. We think it is likely that increases in DOC production and severe hypoxia in the leaf treatments both lead to higher nitrogen processing than in the wood treatments. Because both DOC quantity and its chemical composition were different in the leaf treatments than in the wood treatment we cannot determine the relative influence of these two factors on the increased nitrogen processing in the leaf treatments. The higher amount of DOC production in the red maple leaf treatment compared to the red oak leaf treatment (Fig. 2) or
differences in the DOC quality between these treatments (Fig. 4b) may have contributed to the higher nitrate retention rates for the red maple leaves than the red oak leaves. Previous studies have shown that plant species identity can affect rates of organic matter breakdown and nitrogen processing. In particular, several investigators have shown that maple leaves decompose faster than oak leaves (Kaushik and Hynes 1971; Webster and Benfield 1986; Mehring and Maret 2011) and that maple stands in forests tend to promote higher nitrogen cycling than oak stands (Lovett et al. 2004; Alexander and Arthur 2010). The responses of denitrification potential in the lab and in situ denitrification to variation in POC quality were fundamentally similar. However, the control and wood treatments yielded positive denitrification potential in the lab and resulted in net N2 uptake in the field, suggesting that in situ denitrification did not occur in these treatments. This discrepancy may be due to differences in the redox potential between the lab and field measurements. Redox conditions were optimized for denitrification in the laboratory but not in the field, where oxic conditions prevailed in the control and wood treatments (Fig. 5). N2 loss in the field due to nitrogen fixation (Fulweiler et al. 2007) or N2 offgassing as groundwater moved through the sediments are two additional potential causes of the discrepancy between the lab and field denitrification results. If gross N2 loss also occurred in the leaf treatments in the field because of nitrogen fixation or offgassing our estimate of the role of denitrification in nitrate removal may be conservative. Two additional mechanisms may have contributed to the differences between nitrate retention and in situ denitrification rates (Fig. 7) in our study: (1) assimilatory uptake of nitrate and (2) incomplete denitrification, in which N2O is the terminal product of nitrate reduction rather than N2 (Beaulieu et al. 2011). Nitrate and TDN retention and denitrification rates were probably reduced, even when biogeochemical conditions for nitrogen removal were favorable, in those mesocosms with low but undetermined advection rate (mesocosms in which the chloride breakthrough curve did not occur or was not measured). Our estimates of nitrate and TDN retention rates, DOC production rates, and in situ denitrification rates were based on mesocosms for which we could measure advection rates. Therefore, our mean rates of nitrate and TDN retention and DOC production were likely
123
Biogeochemistry
overestimated and our mean rate of situ denitrification may have been overestimated (see above for factors that could have led to underestimation of in situ denitrification rate). This bias due to undetermined advection rate in some of the mesocosms does not change our conclusion that POC quality had strong effects on nitrogen processing in the sediments. We think that allowing the POC to become colonized with microbes in the surface water before burying the material in the sediments was reflective of the sequence of events that occur when allochthonous POC enters freshwater ecosystems. POC typically spends time in the surface water, after which a portion of the material deposited on the sediment surface may become buried by shifting sediments (Metzler and Smock 1990) or by accumulation of additional organic material (Teodoru et al. 2013). Allowing microbial colonization to occur in the stream channel probably resulted in higher microbial biomass than if the POC had been immediately buried (Cornut et al. 2010) and colonization in the channel likely affected the sources of the dissolved organic carbon in the mesocosms after burial occurred. The increases in %N and decreases in C:N ratio of the red maple and red oak leaves during colonization (Table 2) suggest that microbes colonized and proliferated on the leaves which resulted in N immobilization as microbes acquired dissolved N from the water column (Cheever et al. 2012). The surface water is always oxic in Emmons Creek and the microbial community that colonized the POC in the surface water may not have been able to tolerate the hypoxic conditions that formed in the sediments after POC burial (Medeiros et al. 2009). For example, species of obligate aerobic bacteria that colonized the leaves in the stream channel may have not been able to survive the decreased oxygen availability in the sediments. Thus, lysing microbial cells may have been an important source of DOC in the mesocosms (van Oevelen et al. 2006). This may partly explain the high DOC production (Fig. 2) 28–43 days after the POC was placed in the stream channel for colonization. Organic compounds produced from, or modified by, living bacteria associated with the buried leaves, in particular, also may have contributed to the elevated DOC production, especially later in the experiment, as the death of aerobic microbes might occur rapidly after exposure to severe hypoxia. Presumably, much of the soluble materials from the dead plant cells in the leaves and wood were abiotically leached during the
123
first few days of POC colonization (Nykvist 1962; Webster and Benfield 1986; Yoshimura et al. 2010). The PARAFAC analysis suggested that DOC chemical composition did not differ among treatments at 25 cm sediment depth (Fig. 4a) but that divergence in DOC chemical composition occurred after the groundwater passed through the layer of POC, especially the leaves (Fig. 4b). In this study, SP1 and SP2 were the predominant components in the DOC. SP1 was spectrally similar to Component 1 in Cory and Kaplan 2012 which they associated with the biological production of humic material from autochthonous sources. SP2 was spectrally similar to Component 2 in Stedmon and Markager (2005) which they described as a fulvic acid fluorophore group that could have terrestrial and autochthonous sources. SP3 occurred predominantly at the depth of 5 cm and was largely absent from the controls. Given its spectral location and presence at only 5 cm, it is likely that this component represents lignin freshly leached from both wood and leaves (Hernes et al. 2009). We think that changes in DOC chemical composition in the leaf treatments between deep and shallow groundwater could be due to cell lysing (described previously) or chemical transformation between pools of dissolved organic carbon. Because leaf treatments resulted in greater denitrification, the decrease in SP1 and increase in SP2 in these treatments could reflect a redox transformation of SP1 to SP2. In this process compounds represented by SP1 are likely donating electrons to the reduction of nitrate resulting in an increase in SP2. Similar redox catalyzed changes in fluorescence have been described in previous work (Cory and McKnight 2005). The notion that SP2 may be derived from autochthonous (microbial in this case) sources (Stedmon and Markager 2005) is consistent with both of these possible explanations. Because the leaves were incubated for 25 days before addition to the mesocosms, it is unlikely that the increase in SP2 at 5 cm in the leaf treatments represents unmodified terrestrial organic matter leached from the mesocosms. Other investigators have shown that organic carbon quality can influence potential or actual denitrification rates. Here, we summarize and interpret this prior work. There have been a few studies that have shown that POC chemical composition or type influences denitrification rates (Bastviken et al. 2005, Greenan et al. 2006). For example, Greenan et al. (2006) showed that in a laboratory experiment involving four
Biogeochemistry
different types of POC (wood chips, wood chips with soybean oil, cardboard fibers, and cornstalks), the addition of cornstalks, which were of highest quality based on C:N ratio, resulted in the highest rate of denitrification. However, the mechanism(s) by which POC chemical composition affected denitrification is unclear in these studies because the effects of POC chemical composition on DOC quantity or quality and redox status were not measured. Anoxia was induced prior to the denitrification measurements in these studies which makes it uncertain if POC chemical composition would have affected denitrification rates under field conditions (Fennel et al. 2009). As the results of our study and other work (Yoshimura et al. 2010) suggest, the chemical composition of POC affects DOC production, which may play a role in nitrogen processing. Investigators have also shown that DOC chemical composition affects (Pfenning and McMahon 1996) or is correlated with (Baker and Vervier 2004; Barnes et al. 2012) denitrification rate. For example, Barnes et al. (2012) showed that denitrification potential of stream sediments was positively related to the quantity of protein-like fluorophores and negatively related to more aromatic fractions. As in the studies assessing the effect of POC quality on nitrogen processing described above, most studies that have examined how DOC chemical composition influences denitrification have been done in the laboratory under favorable redox conditions for denitrification. One notable exception is the study by Baker and Vervier (2004) who showed that denitrification rate measured at the groundwater- surface water interface was best explained by the concentration of low molecular weight organic acids. They found no correlation between denitrification rate and bulk DOC concentration. We think more field-based studies are needed that examine how POC and DOC chemical composition affect denitrification and other forms of nitrogen transformation at ambient redox potential. Our work and those of other investigators suggest that the quantity and quality of organic carbon can influence nitrogen processing, including denitrification, in stream sediments and hyporheic zones (Sobczak et al. 2003; Baker and Vervier 2004; Arango et al. 2007; Zarnetske et al. 2011; Barnes et al. 2012). To better understand how carbon supply is linked to nitrogen processing in fluvial ecosystems, we think further research is needed on POC deposition and burial, the relative importance of carbon-driven
nitrogen transformation in sediments and hyporheic zones compared to that occurring in other ecosystem compartments and the mechanisms by which organic carbon influences nitrogen processing in sediments. Although it is well known that organic carbon drives many types of nitrogen transformations in the sediments of streams and rivers few investigators have quantified organic matter deposition and burial in lotic ecosystems (Metzler and Smock 1990; Jones et al. 2007). In watersheds with high nitrogen availability the supply of organic carbon is probably the ratelimiting step for much of the nitrogen processing in sediments. Further information about the rates of POC deposition in streams (Richardson 1992), the fraction of deposited POC that becomes buried, the fate of the buried POC and the factors that govern these processes could be useful for predicting the capacity for nitrogen transformation in stream sediments. Although several studies have shown that carbondriven nitrogen processing is important in deep sediments and hyporheic zones of streams, we are not aware of any studies that have compared the relative importance of linked compartments of fluvial ecosystems (hyporheic zones, deep sediments, channels) in whole system nitrogen processing (Seitzinger et al. 2006). Several studies of nitrogen processing in streams based on solute releases suggest that assimilatory uptake of nitrogen can be the dominant sink of nitrogen in these systems (Arango et al. 2008; Mulholland et al. 2008). However, these studies were not designed to include nitrogen processing in deep sediments or ‘‘slow-cycling’’ hyporheic compartments. We think studies are needed that compare the magnitude of nitrogen transformation and removal in stream channels to that occurring in other compartments including deep sediments and phreatic groundwater. The heterotrophic denitrification reaction has a much lower C:N ratio than the stoichiometry of carbon and nitrogen assimilation (Fenchel and Blackburn 1979; Sterner and Elser 2002). Therefore, there is the potential for highly efficient removal of nitrate via denitrification in sediments if nitrate and high-quality organic carbon are available at suitable redox conditions. This set of conditions are frequently found in depositional environments of gaining streams, particularly in anthropogenically-influenced watersheds (Arango et al. 2007; Stelzer et al. 2011a; Heppell et al. 2013). Finally, many questions remain about the pathways by which POC quality could affect nitrogen
123
Biogeochemistry
processing in sediments. Experiments in which DOC chemical composition is manipulated while controlling for DOC quantity or experiments in which redox state is manipulated independently of organic carbon availability may be productive ways to address the relative importance of these pathways. POC and DOC quality tend to decline through time during decomposition as more labile carbon and nutrients are leached and used preferentially by microbes (Moorhead and Sinsabaugh 2006). Long-term experiments could be useful in determining if the impact of buried POC on nitrogen transformation in sediments becomes muted through time as the POC becomes more recalcitrant. Acknowledgments We thank Courtney Heling, Mike Louison, Alyssa McCumber and Erin Grantz for technical assistance. Will Cook at Duke University conducted the CN analysis and Jeff Merriam at the United States Forest Service performed the DOC and DON analysis. We thank two anonymous reviewers for their comments on the manuscript. This research was supported by a grant from the University of Wisconsin Water Resources Institute through the Wisconsin Groundwater Coordinating Council. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
References Alexander HD, Arthur MA (2010) Implications of a predicted shift from upland oaks to red maple on forest hydrology and nutrient availability. Can J For Res 40:716–726. doi:10.1139/X10-029 AOAC-973.18 (1990) Fiber (acid detergent) and lignin in animal feed. Official methods of analysis of the Association of Official Analytical Chemists, 15th edn. Arlington, Virginia Arango CP, Tank JL, Schaller JM, Royer TV, Bernot MJ, David MB (2007) Benthic organic carbon influences denitrification in streams with high nitrate concentration. Freshw Biol 52:1210–1222. doi:10.1111/j.1365-2427.2007.01758.x Arango CP, Tank JL, Johnson LT, Hamilton SK (2008) Assimilatory uptake rather than nitrification and denitrification determines nitrogen removal patterns in streams of varying land use. Limnol Oceanogr 53:2558–2572. doi:10. 4319/lo.2008.53.6.2558 Attard E, Recous S, Chabbi A, De Berranger C, Guillaumaud N, Labreuche J, Philippot L, Schmid B, Le Roux X (2011) Soil environmental conditions rather than denitrifier abundance and diversity drive potential denitrification after changes in land uses. Glob Change Biol 17:1975–1989. doi:10.1111/j. 1365-2486.2010.02340.x Baker MA, Vervier P (2004) Hydrological variability, organic matters supply and denitrification in the Garonne River ecosystem. Freshw Biol 49:181–190
123
Barnes RT, Smith RL, Aiken GR (2012) Linkages between denitrification and dissolved organic matter quality, Boulder Creek Watershed, Colorado. J Geophys Res 117:G01014. doi:10.1029/2011JG001749 Bastviken SK, Eriksson PG, Premrov A, Tonderski K (2005) Potential denitrification in wetland sediments with different plant species detritus. Ecol Eng 25:183–190. doi:10. 1016/j.ecoleng.2005.04.013 Battin TJ, Kaplan LA, Findlay S, Hopkinson CS, Marti E, Packman AI, Newbold JD, Sabater F (2009) Biophysical controls on organic carbon fluxes in fluvial networks. Nat Geosci 1:95–100. doi:10.1038/ngeo101 Beaulieu JJ et al (2011) Nitrous oxide emission from denitrification in stream and river networks. Proc Natl Acad Sci 108:214–219. doi:10.1073/pnas.1011464108 Berg G, Smalla K (2009) Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol Ecol 68:1–13. doi:10.1111/j.1574-6941.2009.00654.x Bro R, Kiers HAL (2003) A new efficient method for determining the number of components in PARAFAC models. J Chemom 17:274–286. doi:10.1002/cem.801 Cheever BM, Kratzer EB, Webster JR (2012) Immobilization and mineralization of N and P by heterotrophic microbes during leaf decomposition. Freshwater Sci 31:133–147. doi:10.1899/11-060.1 Coble PG (1996) Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy. Mar Chem 51:325–346. doi:10.1016/03044203(95)00062-3 Cornut J, Elger A, Lambrigot D, Marmonier P (2010) Early stages of leaf decomposition are mediated by aquatic fungi in the hyporheic zone of woodland streams. Freshw Biol 55:2541–2556. doi:10.1111/j.1365-2427.2010.02483.x Cory RM, Kaplan LA (2012) Biological lability of streamwater fluorescent dissolved organic matter. Limnol Oceanogr 57:1347–1360. doi:10.4319/lo.2012.57.5.1347 Cory RM, McKnight DM (2005) Fluorescence spectroscopy reveals ubiquitous presence of oxidized and reduced quinones in dissolved organic matter. Environ Sci Technol 39:8142–8149. doi:10.1021/es0506962 Dahm CN, Valett HM, Baxter CV, Woessner WW (2006) Hyporheic zones. In: Hauer FR, Lamberti GA (eds) Methods in stream ecology. Elsevier, Boston, pp 119–142 Diaz RJ, Rosenberg R (2008) Spreading dead zones and consequences for marine ecosystems. Science 321:926–929 Dodla SK, Wang JJ, DeLaune RD, Cook RL (2008) Denitrification potential and its relation to organic carbon quality in three coastal wetland soils. Sci Total Environ 407:471–480. doi:10.1016/j.scitotenv.2008.08.022 Duff JH, Murphy F, Fuller CC, Triska FJ, Harvey JW, Jackman AR (1998) A mini drivepoint sampler for measuring pore water solute concentrations in the hyporheic zone of sandbottom streams. Limnol Oceanogr 42:1378–1383 Duff JH, Jackman AP, Triska FJ, Sheibley RW, Avanzino RJ (2007) Nitrate retention in riparian ground water at natural and elevated nitrate levels in North Central Minnesota. J Environ Qual 36:343–353 Duff JH, Tesoriero AJ, Richardson WB, Strauss EA, Munn MD (2008) Whole-stream response to nitrate loading in three
Biogeochemistry streams draining agricultural landscapes. J Environ Qual 37:1133–1144. doi:10.2134/jeq2007.0187 Fellman JB, Hood E, Spencer RGM (2010) Fluorescence spectroscopy opens new windows into dissolved organic matter dynamics in freshwater ecosystems: a review. Limnol Oceanogr 55:2452–2462. doi:10.4319/lo.2010.55.6.2452 Fenchel T, Blackburn TH (1979) Bacteria and mineral cycling. Academic Press Fennel K et al (2009) Modeling denitrification in aquatic sediments. Biogeochemistry 93:159–178. doi:10.1007/s10533008-9270-z Fery-Forgues S, Lavabre D (1999) Are fluorescence quantum yields so tricky to measure? A demonstration using familiar stationery products. J Chem Educ 76:1260. doi:10. 1021/ed076p1260 Fulweiler RW, Nixon SW, Buckley BA, Granger SL (2007) Reversal of net dinitrogen gas flux in coastal marine sediments. Nature 448:180–182. doi:10.1038/nature05963 Galloway JN et al (2008) Transformation of the nitrogen cycle: recent trends, questions, and potential solutions. Science 320:889–892 Greenan CM, Moorman TB, Kaspar TC, Parkin TB, Jaynes DB (2006) Comparing carbon substrates for denitrification of subsurface drainage water. J Environ Qual 35:824–829. doi:10.2134/jeq2005.0247 Groffman PM, Holland EA, Myrold DD, Robertson GP, Zou X (1999) Denitrification. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P (eds) Standard methods for longterm ecological research. Oxford University Press, New York, pp 272–288 Groffman PM et al (2006) Methods for measuring denitrification: diverse approaches to a difficult problem. Ecol Appl 16:2091–2122 Heffernan JB et al (2010) Hydrologic and biotic influences on nitrate removal in a subtropical spring-fed river. Limnol Oceanogr 55:249–263. doi:10.4319/lo.2010.55.1.0249 Hernes PJ, Bergamaschi BA, Eckard RS, Spencer RGM (2009) Fluorescence-based proxies for lignin in freshwater dissolved organic matter. J Geophys Res 114:G00F03 Heppell CM, Heathwaite AL, Binley A, Byrne P, Ullah S, Lansdown K, Keenan P, Trimmer M, Zhang H (2013) Interpreting spatial patterns in redox and coupled water– nitrogen fluxes in the streambed of a gaining river reach. Biogeochemistry. doi:10.1007/s10533-013-9895-4 Hill AR, Devito KJ, Campagnolo S, Sanmugadas K (2000) Subsurface denitrification in a forest riparian zone: the interactions between hydrology and supplies of nitrate and organic carbon. Biogeochemistry 51:193–223 Hobbie SE (1992) Effects of plant-species on nutrient cycling. Trends Ecol Evol 7:336–339. doi:10.1016/01695347(92)90126-V Ishii SKL, Boyer TH (2012) Behavior of reoccurring PARAFAC components in fluorescent dissolved organic matter in natural and engineered systems: acritical review. Environ Sci Technol 46:2006–2017. doi:10.1021/es2043504 Jones JB, Schade JD, Fisher SG, Grimm NB (2007) Organic matter dynamics in Sycamore Creek, a desert stream in Arizona, USA. In: Webster JR, Meyer JL (eds) Stream organic matter budgets. J North Am Benthol Soc 16:78–82 Kana TM, Darkangelo C, Hunt MD, Oldham JB, Bennet GE, Cornwell JC (1994) Membrane inlet mass spectrometer for
high-precision determination of N2, O2, and Ar in environmental water samples. Anal Chem 66:4166–4170 Kaushik NK, Hynes HBN (1971) The fate of dead leaves that fall into streams. Arch Hydrobiol 68:465–515 Lovett GM, Weathers KC, Arthur MA, Schultz JC (2004) Nitrogen cycling in a northern hardwood forest: do species matter? Biogeochemistry 67:289–308 MATLAB version 8.0.0.783 (2012) Natick, Massachusetts: The MathWorks Inc Medeiros AO, Pascoal C, Graca MAS (2009) Diversity and activity of aquatic fungi under low oxygen conditions. Freshw Biol 54:142–149. doi:10.1111/j.1365-2427.2008.02101.x Mehring AS, Maret TJ (2011) Red maple dominance enhances fungal and shredder growth and litter processing in temporary ponds. Limnol Oceanogr 56:1106–1114. doi:10. 4319/lo.2011.56.3.1106 Melillo JM, Aber JD, Muratore JF (1982) Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology 63:621–626 Metzler GM, Smock LA (1990) Storage and dynamics of subsurface detritus in a sand- bottomed stream. Can J Fish Aquat Sci 47:588–594 Moorhead DL, Sinsabaugh RL (2006) A theoretical model of litter decay and microbial interaction. Ecol Monogr 76:151–174. doi:10.1890/0012-9615(2006)076[0151:ATMOLD]2.0.CO;2 Mulholland PJ et al (2008) Stream denitrification across biomes and its response to anthropogenic nitrate loading. Nature 452:202–205. doi:10.1038/nature06686 Nykvist N (1962) Leaching and decomposition of litter V. Experiments on Leaf Litter of Alnus glutinosa Fagus silvatica and Quercus robur. Oikos 13:232–248. doi:10.2307/ 3565087 Ohno T (2002) Fluorescence inner-filtering correction for determining the humification index of dissolved organic matter. Environ Sci Technol 36:742–746. doi:10.1021/ es0155276 Pfenning KS, McMahon PB (1996) Effect of nitrate, organic carbon, and temperature on potential denitrification rates in nitrate-rich riverbed sediments. J Hydrol 187:283–295 Puckett LJ, Zamora C, Essaid H, Wilson JT, Johnson HM, Brayton MJ, Vogel JR (2008) Transport and fate of nitrate at the ground-water/surface-water interface. J Environ Qual 37:1034–1050. doi:10.2134/jeq2006.0550 Richardson JS (1992) Coarse particulate detritus dynamics in small, montane streams in southwestern British Columbia. Can J Fish Aquat Sci 49:337–346. doi:10.1139/f92-038 Richardson WB, Strauss EA, Bartsch LA, Monroe EM, Cavanaugh JC, Vingum L, Soballe DM (2004) Denitrification in the Upper Mississippi River: rates, controls, and contributions to nitrate flux. Can J Fish Aquat Sci 61:1102–1112 Sanei H, Outridge PM, Dallimore A, Hamilton PB (2012) Mercury-organic matter relationships in pre-pollution sediments of thermokarst lakes from the Mackenzie River Delta, Canada: the role of depositional environment. Biogeochemistry 107:149–164. doi:10.1007/s10533-010-9543-1 Schipper LA, Vojvodic-Vukovic M (1998) Nitrate removal from groundwater using a denitrification wall amended with sawdust: field trial. J Environ Qual 27:664–668 Seitzinger S et al (2006) Denitrification across landscapes and waterscapes: a synthesis. Ecol Appl 16:2064–2090. doi:10. 1890/1051-0761(2006)016[2064:DALAWA]2.0CO;2
123
Biogeochemistry Sinsabaugh RL, Carreiro MM, Repert DA (2002) Allocation of extracellular enzymatic activity in relation to litter composition, N deposition, and mass loss. Biogeochemistry 60:1–24. doi:10.1023/A:1016541114786 Sobczak WV, Findlay S, Dye S (2003) Relationships between DOC bioavailability and nitrate removal in an upland stream: an experimental approach. 62:309–327. doi:10. 1023/A:1021192631423 Solarzano L (1969) Determination of ammonia in natural waters by the phenolhypochlorite method. Limnol Oceanogr 14:799–801 Starr RC, Gillham RW (1993) Denitrification and organic carbon availability in two aquifers. Groundwater 31:934–947. doi:10.1111/j.1745-6584.1993.tb00867.x Stedmon CA (2000) Optical properties and signatures of chromophoric dissolved organic matter (CDOM) in Danish coastal waters. Estuar Coast Shelf Sci 51:267–278. doi:10. 1006/ecss.2000.0645 Stedmon CA, Bro R (2008) Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnol Oceanogr Methods 6:1–6 Stedmon CA, Markager S (2005) Resolving the variability of dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnol Oceanogr 50:686–697. doi:10.4319/lo.2005.50.2. 0686 Stelzer RS, Bartsch LA (2012) Nitrate removal in deep sediments of a nitrogen-rich river network: a test of a conceptual model. J Geophys Res Biogeosci 117:G02027. doi:10.1029/2012JG001990 Stelzer RS, Bartsch LA, Richardson WB, Strauss EA (2011a) The dark side of the hyporheic zone: depth profiles of nitrogen and its processing in stream sediments. Freshw Biol 56:2021–2033. doi:10.1111/j.1365-2427.2011.02632.x Stelzer RS, Drover DR, Eggert SL, Muldoon MA (2011b) Nitrate retention in a sand plains stream and the importance of groundwater discharge. Biogeochemistry 103:91–107. doi:10.1007/s10533-010-9449-y Stelzer RS, Scott JT, Bartsch LA (2015) Buried particulate organic carbon stimulates denitrification and nitrate retention in stream sediments at the groundwater-surface water interface. Freshw Sci (in press)
123
Sterner RW, Elser JJ (2002) Ecological stoichiometry: the biology of elements from molecules to the biosphere. Princeton University Press, Princeton Strauss EA, Lamberti GA (2002) Microbial decomposition of leaf leachates and effect of organic carbon quality on nitrification rates in stream sediments. Freshw Biol 47:65–74 Taylor PG, Townsend AR (2010) Stoichiometric control of organic carbon-nitrate relationships from soils to the sea. Nature 464:1178–1181. doi:10.1038/nature08985 Teodoru CR, del Giorgio PA, Prairie YT, St-Pierre A (2013) Depositional fluxes and sources of particulate carbon and nitrogen in natural lakes and a young boreal reservoir in Northern Quebec. Biogeochemistry 113:323–339. doi:10. 1007/s10533-012-9760-x Tiedje JM (1982) Denitrification. In: Methods of soil analysis, part 2. chemical and microbiological properties-agronomy monograph no. 9 (2nd edn). ASA-SSSA, Madison, WI, pp. 1011–1026 van Oevelen D, Middelburg JJ, Soetaert K, Moodley L (2006) The fate of bacterial carbon in an intertidal sediment: modeling an in situ isotope tracer experiment. Limnol Oceanogr 51:1302–1314 von Elert E, Martin-Creuzburg D, Le Coz JR (2003) Absence of sterols constrains carbon transfer between cyanobacteria and a freshwater herbivore (Daphnia galeata). Proc R Soc B Biol Sci 270:1209–1214. doi:10.1098/rspb.20033.2357 Webster JR, Benfield EF (1986) Vascular plant breakdown in freshwater ecosystems. Ann R Ecol Syst 17:567–594 Wilson HF, Xenopoulos MA (2008) Ecosystem and seasonal control of stream dissolved organic carbon along a gradient of land use. Ecosystems 11:555–568. doi:10.1007/s10021008-9142-3 Yoshimura C, Fujii M, Omura T, Tockner K (2010) Instream release of dissolved organic matter from coarse and fine particulate organic matter of different origins. Biogeochemistry 100:151–165. doi:10.1007/s10533-010-9412-y Zarnetske JP, Haggerty R, Wondzell SM, Baker MA (2011) Labile dissolved organic carbon supply limits hyporheic denitrification. J Geophys Res Biogeosci 116:G04036. doi:10.1029/2011JG001730
