quantity&activity relationship of denitrifying bacteria...

Quantity-activity relationship of denitrifying bacteria

and environmental scaling in streams of a forested

watershed

Ben L. O’Connor,1,2,3 Miki Hondzo,1,2 Dina Dobraca,2,4,5 Timothy M. LaPara,6

Jacques C. Finlay,2,7 and Patrick L. Brezonik,6,8

Received 28 June 2006; accepted 25 July 2006; published 30 November 2006.

[1] The spatial variability of subreach denitrification rates in streams was evaluated withrespect to controlling environmental conditions, molecular examination of denitrifyingbacteria, and dimensional analysis. Denitrification activities ranged from 0 and800 ng-N gsed

�1 d�1 with large variations observed within short distances (<50 m) alongstream reaches. A log-normal probability distribution described the range in denitrificationactivities and was used to define low (16% of the probability distribution), medium (68%),and high (16%) denitrification potential groups. Denitrifying bacteria were quantifiedusing a competitive polymerase chain reaction (cPCR) technique that amplified the nirKgene that encodes for nitrite reductase. Results showed a range of nirK quantities from103 to 107 gene-copy-number gsed

�1. A nonparametric statistical test showed no significantdifference in nirK quantities among stream reaches, but revealed that samples with a highdenitrification potential had significantly higher nirK quantities. Denitrification activitywas positively correlated with nirK quantities with scatter in the data that can be attributedto varying environmental conditions along stream reaches. Dimensional analysis was usedto evaluate denitrification activities according to environmental variables that describefluid-flow properties, nitrate and organic material quantities, and dissolved oxygen flux.Buckingham’s pi theorem was used to generate dimensionless groupings and field datawere used to determine scaling parameters. The resulting expressions betweendimensionless NO3

� flux and dimensionless groupings of environmental variables showedconsistent scaling, which indicates that the subreach variability in denitrification rates canbe predicted by the controlling physical, chemical, and microbiological conditions.

Citation: O’Connor, B. L., M. Hondzo, D. Dobraca, T. M. LaPara, J. C. Finlay, and P. L. Brezonik (2006), Quantity-activity

relationship of denitrifying bacteria and environmental scaling in streams of a forested watershed, J. Geophys. Res., 111, G04014,

doi:10.1029/2006JG000254.

1. Introduction

[2] Denitrification is a familiar, yet complex, topic inaquatic sciences and is relevant to the global cycling ofnitrogen. Anthropogenic activities have increased nitrogenloadings and mobility to the environment through agricul-

ture, combustion of fossil fuels, land use changes, andwetland drainage [Vitousek et al., 1997]. This has led tothe eutrophication of many streams and receiving waterswith detrimental results, such as the hypoxia conditions inthe northern Gulf of Mexico [Committee on Environmentand Natural Resources, 2000]. Conversely, nitrogen is anessential nutrient in pristine environments and can limitalgal productivity [Wetzel, 2001]. Denitrification is only oneof several biogeochemical processes that constitutes thenitrogen cycle. The anaerobic bacterial reduction of nitrate(NO3

�) to nitrogen gas (N2) acts as a sink for nitrogen inaquatic ecosystems; however, its significance with respectto nitrogen cycling in streams is not known, becauseestimates of denitrification rates are difficult to resolve.[3] Previous research on nitrogen biogeochemistry in

streams can be organized by the spatial scale examined.Large-scale assessments have primarily used statisticalbased models to estimate nitrogen export from watershedsand focused on quantifying nitrogen inputs and watershedcharacteristics [Alexander et al., 2002; Howarth et al.,1996]. Reach-scale studies have used nutrient and isotope

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, G04014, doi:10.1029/2006JG000254, 2006

1Saint Anthony Falls Laboratory, University of Minnesota-Twin Cities,Minneapolis, Minnesota, USA.

2Also at National Center for Earth-Surface Dynamics, Minneapolis,Minnesota, USA.

3Now at U.S. Geological Survey, Reston, Virginia, USA.4Department of Genetics, Cell Biology, and Development, University of

Minnesota-Twin Cities, Minneapolis, Minnesota, USA.5Now at Saint Anthony Falls Laboratory, University of Minnesota-Twin

Cities, Minneapolis, Minnesota, USA.6Department of Civil Engineering, University of Minnesota-Twin

Cities, Minneapolis, Minnesota, USA.7Department of Ecology, Evolution, and Behavior, University of

Minnesota-Twin Cities, St. Paul, Minnesota, USA.8Now at National Science Foundation, Arlington, Virginia, USA.

Copyright 2006 by the American Geophysical Union.0148-0227/06/2006JG000254

G04014 1 of 13

tracers, along with modeling techniques, to examine thetransport of nitrogen using reach-averaged kinetic parame-ters to determine downstream nitrogen fluxes [Triska et al.,1989; Mulholland et al., 2004; Bohlke et al., 2004; Gooseffet al., 2004]. Microcosm and mesocosm (sediment scale)methods have revealed that reaction rates vary within thereach scale because of the heterogeneity in geomorphology,NO3

� availability, and environmental conditions [Martin etal., 2001; Kemp and Dodds, 2002a; Groffman et al., 2005].Resolving denitrification measurements made at differentscales is difficult because sediment-scale methods are lim-ited in their ability to incorporate fluid-flow characteristicsand heterogeneity in the controlling environmental varia-bles. The net rates determined from reach-scale methodsintegrate fluid-flow and environmental characteristics, butthe results become reach specific and apply only to theenvironmental and fluid-flow conditions occurring duringthe measurement.[4] The problems associated with scale-dependent mea-

surements of denitrification are further complicated becausemost analytical techniques are indirect, as a result of thelarge background interference of the atmospheric N2 endproduct [Seitzinger, 1988]. It is also challenging to decoupledenitrification from other nitrogen cycle processes, such asnitrification and assimilation. A potential remedy for thisproblem is to examine the relationship between denitrifica-tion activity and the abundance of denitrifying organisms.Recent advances in molecular microbiology allow for thequantification of genes that encode for enzymes known tocatalyze certain biogeochemical processes. Experimentalevidence suggests a connection between gene expressionand biogeochemical activity; however, the models devel-oped so far have not been rigorously tested or applied to awide variety of natural systems [Kerkhof, 2003]. Manyenzymes are involved in nitrogen cycle processes, but thereduction of nitrite to nitric oxide catalyzed by nitritereductase is the step that distinguishes denitrification fromthe others [Payne, 1981]. Polymerase chain reaction (PCR)methods have been developed to detect and quantify nitritereductase (nir) type genes which are specific to the knowndenitrifying bacteria [Hallin and Lindgren, 1999; Throbacket al., 2004].[5] The multivariate and scale-dependent nature of deni-

trification is not unique, but is common to all biogeochem-ical cycles in the environment. Stream ecosystems can bedescribed as spatially distributed habitats of varying degreesof environmental and hydrologic conditions connected bymaterial and hydrological flow paths [Fisher et al., 2004].These habitats can be categorized by their ability to enhancebiogeochemical processes, termed hot spots [McClain et al.,2003]. Flow paths represent the directional transport relativeto ecological process that makes rivers and streams uniqueto other ecosystems. Incorporating the conceptual models offlow paths and hot spots decreases the amount of inherentvariability of controlling factors and facilitates for a moremechanistic understanding of biogeochemical processes,such as denitrification, in streams.[6] Addressing the patchwork nature of denitrification in

streams at the reach scale requires the use of sediment-scalemeasurements and a methodology to explain the observedvariability, which is a major challenge in the field ofbiogeochemistry [Fisher et al., 2004]. One approach has

been to measure denitrification rates with respect to sub-strata types and then use a weighted average according tosubstrata mass estimates [e.g., Kemp and Dodds, 2002b].This approach is labor intensive and does not represent allcontrolling factors, such as fluid-flow conditions. It is alsodifficult to quantify substrata amounts for biofilm-associateddenitrification for streams with coarse, low organic contain-ing sediments.[7] An alternative approach for addressing the variability

in denitrification, which was utilized in this study, is to usedimensional analysis to scale (develop a power law rela-tionship) it with respect to the environmental variables thatcontrol it. At the core of dimensional analysis is the conceptof dynamic similarity, which is the condition that functionalrelationships, expressed in dimensionless form, are con-served regardless of the scale examined [Petersen andHastings, 2001]. Dimensional analysis works on the prin-ciple that any physical law must be able to be represented indimensionless form because it cannot depend upon thearbitrarily chosen units of measure [Barenblatt, 2003].Therefore denitrification rates should be able to beexpressed in dimensionless form according to its controllingfactors. Not all factors can be included in a model todescribe a phenomenon, so only the most significant factorscan be incorporated, and the model becomes based on anidealization of the phenomenon [Barenblatt, 2003]. Theremaining challenge for applying dimensional analysis todenitrification in streams is to develop a proper idealizationof the phenomenon that can explain the observed variabilityin field and experimental data. The relevant controllingfactors for addressing the reach-scale variability in denitri-fication relate to transient storage, organic carbon and NO3

�

supply, dissolved oxygen (DO) concentrations, and sedi-ment characteristics. There are several variables that can beused to describe these controlling factors; however, theiterative use of dimensional analysis with field and exper-imental data will lead to a parametric reduction of the modelto describe the variability in denitrification rates [Petersenand Hastings, 2001].[8] The present study evaluated denitrification in streams

with respect to the high degree of spatial variabilityof measured denitrification rates (activities) according tomicrobial and environmental conditions, and used dimen-sional analysis as a scaling methodology. Results showed askewed distribution in denitrification activities throughoutthe study watershed, and regions of high denitrificationpotential, or hot spots, were defined using properties of alog-normal probability distribution. We compared denitrifi-cation activity measurements to the quantity of denitrifyingbacteria to examine correlations between denitrifier biomassand biogeochemical activity. Furthermore, dimensionalanalysis was used to develop a power law relationshipbetween denitrification and its controlling environmentalvariables.

2. Site Description

[9] Study stream reaches were located in or near to theAngelo Coast Range Reserve (ACRR, Figure 1), Mendo-cino County, California, USA (39�440N, 123�390W). TheNature Conservancy acquired the ACRR in 1959 to protectit from development and it has been operated by the

G04014 O’CONNOR ET AL.: QUANTITY-ACTIVITY OF DENITRIFIERS

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University of California Natural Reserve System since1994. The old-growth forest ecosystem consists of steephill slopes, a mixed forest dominated by Douglas fir, an Oakwoodland understory, and vegetated riparian banks of alderand maple. The region’s Mediterranean-type climate has anannual precipitation of �215 cm that falls almost entirely inthe winter months [Mast and Clow, 2000]. Stream dis-charges are high in the winter and decline to stable summerbase flows.[10] The headwaters of the S. Fork Eel River are located

near the ACRR and is the main river flowing north throughthe reserve (Figure 1). During summer base flows, thestreams within the ACRR are clear, have high DO con-centrations, low to moderate nutrient and dissolved organiccarbon (DOC) concentrations, and high productivity in

sunlit areas [Power, 1992; Finlay, 2004]. This studyfocused on the tributaries of the S. Fork Eel River withinthe ACRR and their confluence regions during spring (endof winter flooding) and summer base flow conditions. Theconfined alluvial valley streams vary in size and physicalcharacteristics (Table 1) with patches of exposed bedrockand small, intermittent hyporheic zones. The tributariescontain step-pool to riffle-pool sequences while the S. ForkEel River is an entrenched channel comprised of shallowruns, long riffles, and several deep pools (>3 m). Thesediment substrata within the streams of the ACRR con-tains coarse particles ranging from sand (<2 mm) toboulders (>256 mm). There are distributed patches ofperiphyton, fine benthic organic material (FBOM), andleaf litter throughout the streams of the ACRR and the

Figure 1. Map of S. Fork Eel River and tributaries within the ACRR, along with a corresponding mapof denitrification potentials. The inset on the denitrification potential map shows a closer view of ElderCreek. The samples are labeled according to sampling locations within the cross section (C, center; R,right bank; and L, left bank).

Table 1. Physical Data for Study Stream Reachesa

Stream Drainage Area, km2 Study Reach Length, m Average Slope Discharge, m3 s�1

S. Fork Eel RiverJack of Hearts 124.8 400 0.005 0.482Elder 142.8 300 0.003 0.513Fox 148.9 100 0.007 0.520Ten Mile 275.5 300 0.001 0.548

Jack of Hearts Creek 10.0 145 0.025 0.031Skunk Creek 0.5 - 0.137 0.003Elder Creek (upper/lower) 17.0 60/152 0.013/0.026 0.147McKinley Creek 0.6 - 0.049 0.004Fox Creek 2.8 60 0.085 0.022Ten Mile Creek 140.0 120 0.070 0.434

aThe values for the S. Fork Eel River are separated by tributary confluence. Drainage areas are taken downstream of confluence, study reach length spansthe confluence, and discharge values are from upstream of the confluence during the spring 2004 sampling period.


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downstream reaches of the S. Fork Eel River containpatches of large floating algal mats.

3. Methods

3.1. Sampling and Analyses

[11] The reach-scale sampling of water and sedimentswas designed to represent the variability in the spatiallydistributed habitats relating to denitrification. We did notattempt to classify this spatial distribution based on sedi-ment substrata (e.g., leaf litter, algal mats, and FBOM),instead we used measurements relating to geomorphology,hydrology, and benthic organic composition to quantitativelycharacterize these habitats at regular intervals along thestreams. The stream reaches that were examined rangedbetween 100 to 500 m in length and were divided into threeto eight cross sections where water and sediments werecollected. For the larger streams (S. Fork Eel River, ElderCreek, and Ten Mile Creek), each cross section wassampled at the center and at each stream bank. For thesmaller streams (Jack of Hearts, Skunk, McKinley, and FoxCreeks), the center of the cross section was sampled.[12] Water and sediment sampling at the ACRR occurred

during July–August 2003 and May–June 2004, whichincluded measurements of DO, stream temperature, conduc-tivity, pH, nutrients, and fluid-flow velocities. Water sam-ples were collected along stream reaches, filtered using0.45 mm membrane syringe filters, and stored at 4�C untilanalyzed. Composite sediment samples (two to five samplestaken over an area �2 m2 and mixed) were collected usingpolyvinyl chloride (PVC) core tubes (2.5 cm diameter) to anaverage depth of 2.5 cm. Water and sediment collectionbottles were washed overnight in 10% hydrochloric acidand rinsed thoroughly with deionized water.[13] DO, stream temperature, conductivity, and pH mea-

surements were collected using Hydrolab Datasonde 4a(Hach Company, Loveland, Colorado) multiprobes, whichwere placed at the upstream and downstream extents of astream reach during sample collection. Water samples wereanalyzed for NO3

�, ammonium (NH4+), and soluble reactive

phosphorus (SRP) concentrations using the Cd-reduction,phenate, and ascorbic acidmethods 4500NO3

�-F, 4500NH3-G,4500 P-G [American Public Health Association, 1998]respectively, on a Lachat QuickChem

1

8000 flow injectionanalysis (FIA) automated ion analyzer (Hach Company,Loveland, Colorado).[14] Flow and sediment topography measurements were

made using a StreamPro acoustic-Doppler current profiler,ADCP (RD Instruments, San Diego, California, USA), inpool and run habitats. Point velocity profiles were made inriffles using a Flow Tracker acoustic-Doppler velocimeter,ADV (Sontek YSI Inc., San Diego, California). A push-point pore water sampler (MHE Products, East Tawas,Michigan) was used to collect hyporheic zone water sam-ples and to measure vertical hydraulic gradients (VHG) atdepths between 12 to 20 cm into the sediments.

3.2. Denitrification Activity Measurements

[15] The denitrification activity was measured using theacetylene inhibition method [Sørensen, 1978]. Denitrifica-tion activity rates were calculated by the mass accumulationof nitrous oxide (N2O) over the incubation time and

normalized by the sediment dry weight (gsed). The totalmass of N2O was calculated using the measured N2Oconcentration in the headspace and the calculated N2Odissolved concentration using solubility coefficients deter-mined by Weiss and Price [1980]. Experiments wereconducted using water containing 10 mg L�1 of NH4

+,20 mg L�1 of SRP, and 2 mg L�1 of DOC representingambient concentrations, as well as 250 mg L�1 of NO3

�,which represents concentrations where NO3

� is not limitingdenitrification.[16] Incubations were carried out in 44 mL amber glass

vials containing approximately 10 g of wet sediment and30 mL water so that the total volume of sediment and waterslurry was 37 mL. The sample water was saturated withpurified acetylene (sparged in 0.1 N phosphoric acid) andthen purged of DO using high purity N2. Vials were sealedwith a Teflon septum, shaken vigorously for 5 min, andequilibrated to 1 atm pressure. The sample vials wereincubated at room temperature for between 8 to 14 hours.After incubation, samples were shaken to release anytrapped N2O and a 250 mL headspace sample was takenusing a gas-tight syringe for analysis.[17] Headspace N2O concentrations were measured using

a Hewlett Packard 5890 Series II (Agilent TechnologiesInc., Palo Alto, California) gas chromatograph equippedwith a 63Ni electron capture detector operated at 380�C.High purity N2 carrier gas was set to a flow rate of 17 mLmin�1 through a Poropak Q (80/100 mesh packing), stain-less steel, 3 m long by 0.3175 cm diameter columnincubated at 60�C. Initial experiments (data not shown)indicated that the accumulation of N2O in the headspacewas linear with time up to 24 hours and that there was nodetectable N2O present at the start of the incubations. Afterincubations, the sediment dry weight was determined bydrying at 105�C.

3.3. Quantification of Denitrifying Bacteria

[18] The quantity of denitrifying bacteria in sedimentswas measured using cPCR to amplify gene fragments thatencode for nitrite reductase. This in vitro method uses smallnucleotide fragments as primers to amplify the target genesequence to detectable levels [Innis et al., 1999]. Twofunctionally redundant nir type genes were targeted, nirSand nirK, using the primer pairs F1aCu:R3Cu for nirK,along with cd3aF:R4cd and cd3aF:R3cd for nirS amplifi-cation (Hallin and Lindgren [1999], Michotey et al. [2000],and Throback et al. [2004], respectively). The quantitativeaspect of cPCR involves the coamplification of a competitorgene template (same end sequences as target DNA butsmaller in size) of known concentration that acts as aninternal standard.[19] Sample DNAwas extracted from �0.5 g of sediment

using a FastDNA1

Spin Kit for Soil and a FastPrep1

Instrument (Qbiogene Inc., Carlsbad, California) accordingto manufacturer’s protocols. PCR for nirK and nirS wasperformed in a total volume of 50 mL containing 5 mL of10X PCR buffer, 0.1% BSA, 125 nmol MgCl2 (250 nmolwas used for nirS), 4 nmol deoxynucleoside triphosphates(Amersham Bioscience, Piscataway, New Jersey), 25 pmolof forward and reverse primers, 1.25 U Amplitaq

1

(AppliedBiosystems, Foster City, California), 1 mL of competitorinternal standard, and �1 ng of sample template DNA. PCR


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was performed using a PTC 100 thermal cycler (MJResearch Inc., Watertown, Massachusetts). The PCRcycling procedure for nirK included an initial denaturationat 94�C for 3 min; then 35 cycles of denaturing at 94�C for30 s, annealing at 57�C for 60 s, and extension at 72�C for60 s; followed by a final extension at 72�C for 10 min. ThePCR cycling procedure for nirS was similar to nirK exceptthe initial denaturation at 94�C lasted 5 min; then 35 cyclesof denaturing at 94�C for 60 s, annealing at 50�C for 60 s,and extension at 72�C for 90 s; followed by a final extensionat 72�C for 10 min.[20] The nirK and nirS competitor DNA templates were

synthesized from bacterial isolates known to contain eachgene sequence (Achromobacter cycloclastes ATCC 21921for nirK, and Pseudomonas fluorescens ATCC 33512 fornirS) using internal primers that reduced the nirK sequencefrom 474 to 415 nucleotides and the nirS sequence from 422to 320 nucleotides [Cole et al., 2004]. The nirK and nirScompetitor templates were inserted into a pGEM

1

-T Easycloning vector (Promega, Madison, Wisconsin) and clonedin E. coli DH5a cells. The DNA of the extracted plasmidswas quantified by staining with Hoechst 33258 dye and

measured on a TD-700 fluorometer (Turner Designs, Sun-nyvale, California) using calf thymus DNA standards.[21] All cPCR products were resolved on 2% agarose gels

(Bio-Rad, Hercules, California) stained with ethidium bro-mide in a 1X tris-acetate-EDTA buffer [Sambrook et al.,2001]. Photographic analysis of the gel image (Figure 2a)was performed using LabWorks Image Acquisition software(UVP, Upland, California) to determine the relative bandintensities of the competitor and sample DNA. The com-petitor band intensity was multiplied by a correction factor(474/415 for nirK, 422/320 for nirS) to account for the sizedifference with the sample DNA. The band intensity ratio(sample:corrected-competitor) and the quantity of the com-petitor DNA in the four to six replicate (with varyingcompetitor DNA quantities) cPCR reactions were used togenerate an internal standard curve for each sample. Aregression of the replicate cPCR reactions was used todetermine the sample nirK quantity as the equivalence pointwhere the band intensity ratio was equal to one (Figure 2b).[22] The specificity of the nirK PCR was verified by

developing a clone library and sequencing unique clones.This analysis was not performed for nirS because of its lowdetection in ACRR sediment samples. Two representativesamples (high and low nirK quantities) were amplified byPCR methods as described previously. The PCR productswere purified using GENECLEAN

1

(Qbiogene Inc., Carls-bad, California), ligated into a pGEM

1

-T Easy cloningvector (Promega, Madison, Wisconsin), and cloned inE. coli DH5a cells. Thirty clones were randomly selected foreach sample and their plasmids were extracted using thealkaline lysis procedure [Sambrook et al., 2001]. A restric-tion enzyme digestion was performed with RsaI and theresulting fragment lengths were resolved on a 1.5% agarosegel. Clones with unique restriction band patterns weresequenced in both directions at the BioMedical GenomicsCenter at the University of Minnesota-Twin Cities. Theconsensus of this bidirectional sequence information wasthen compared against known nirK sequences within theGenBank database (http://ncbi.nlm.nih.gov) using BLASTx[Altschul et al., 1997]. This procedure generated sevenunique clones, all of which contained nucleotide sequencesthat closely matched known nirK gene sequences (GenBankaccession numbers DQ450885–DQ450891).

3.4. Statistical Analyses

[23] Statistical analyses of denitrification activity andnirK quantity values were performed using the statisticstoolbox in MATLAB

1

7.0.1 R14 (Mathworks, Natick,Massachusetts). The distribution fitting tool was used tofit a log-normal distribution to denitrification activity val-ues. The Kruskal-Wallis test followed by a multiple com-parison procedure was used to compare nirK quantity valuesamong stream reaches of the ACRR and among denitrifi-cation potential groupings.

3.5. Dimensional Analysis

[24] The variables chosen for dimensional analysis rep-resent the controlling environmental conditions of denitri-fication in streams, which include fluid-flow characteristics,organic carbon and NO3

� supply, DO flux, and geomor-phology (Table 2). It should be noted that the processdescribed below represents the final idealization of the

Figure 2. Competitive polymerase chain reaction (cPCR)for nirK from S. Fork Eel River sediment sample takendownstream of Jack of Hearts Creek. (a) Photograph ofcPCR products separated by electrophoresis; lane onecontains a 100 nucleotide ladder marker, lane two is anegative control, and lanes three through eight containsample replicates along with a serial dilution of competitortemplate. (b) The relative band intensities (sample:corrected-competitor) are used for quantification and com-pared to the gene-copy-number (quantity) of the competitorstandard.


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denitrification phenomenon. Many iterations of this processwere performed using combinations of other environmentalvariables (e.g. discharge, VHG, DOC, SRP, and NH4

+), whichwere excluded from the final expression as they did notfacilitate correlation in the experimental data. The streamswithin the ACRR contain rocky sediments with low organiccontent and no well-defined hyporheic flow paths, so thevariables chosen relate to biofilm-associated denitrification.[25] Buckingham’s pi theorem [Buckingham, 1914] was

used to generate dimensionless groupings for denitrification.The functional dependence of the environmental variables onNO3

� flux can be written as

JNO3 ¼ f u*;H ;CNO3; n; ks;BOM ;B; JDO� �

; ð1Þ

where the variables are described in Table 2. The dimen-sional matrix was generated as described by Kundu andCohen [2002]. The rank of the dimensional matrix was threeand nine environmental variables were included in theanalysis (equation (1)), which resulted in 9 � 3 = 6 possibledimensionless groupings. The repeating variables for dimen-sional analysis were chosen as the shear stress velocity (u*),stream depth (H), and NO3

� concentration (CNO3). Nondi-mensional groups were computed according to the Bucking-ham’s pi theorem, and six dimensionless groupings werecombined to reduce the resulting dimensionless functionalrelation to

JNO3

u*�CNO3

� �¼ 1

Re*

� �aBOM

B�CNO3

� �bJDO

u*�CNO3

� �c

; ð2Þ

where h i terms represent dimensionless groupings (Table 2)and Re* is the shear Reynolds number (u*ks n�1). Theexponents a, b, and c were determined from individual, andindependent, correlations between the dimensionless NO3

�

flux (hJNO3/u*CNO3i) and the respective dimensionlessgroupings using field data.[26] Stream width (B), discharge velocity (U), H, and

CNO3 were measured in the field and described previously.Kinematic viscosity (n) was estimated according to stream

temperature. The roughness height (ks) was estimated as theD90 particle size from field observations. The benthicorganic material (BOM) was quantified as ash-free dryweight per area as reported in the work byWarnaars [2005].[27] The NO3

� flux (JNO3) was calculated from denitrifi-cation activity measurements by multiplying values (unitsof ng-N gsed

�1 d�1) by the sediment dry weight and renorm-alizing by the surface area of the sediment in the acetyleneinhibition incubations to obtain flux units (g m�2 d�1).Keulegan’s Law was rearranged to estimate u* from mea-sured U and ks values,

u* ¼ U

1

kln 11

H

ks

� � ; ð3Þ

where k is the von Karman constant taken to be 0.4. Thedissolved oxygen flux (JDO) was estimated from velocityand bulk DO concentrations according to Hondzo et al.[2005],

JDO ¼ D DObulk � DOsedð Þ13:3

nu*

Scð Þ�1=3; ð4Þ

where D is the molecular diffusion coefficient for oxygen inwater, DObulk is the bulk water DO concentration, DOsed isthe DO concentration at the sediment-water interface (fromlaboratory and field microsensor measurements), and Sc isthe Schmidt number.

4. Results

4.1. Physical Data

[28] The 2003 sampling period represented summer baseflow conditions while the 2004 sampling period representedthe end of the winter-flood hydrograph. Discharges in 2003were �40% less than the 2004 discharges were (Table 1).The stream temperatures ranged from 17 to 24�C in 2003and between 11 and 20�C in 2004. Diurnal DO concen-trations ranged between 6.9 to 9.5 mg L�1 in 2003 and 8.5to 11.0 mg L�1 in 2004. VHG measurements were onlymeasurable in Elder Creek and ranged between +0.10 and�0.06 in upwelling and downwelling regions, respectively,along the riffle-pool stream.

4.2. NO3�� and Denitrification Activity

[29] Average stream NO3� concentrations within ACRR

were low, <25 mg L�1 during summer 2003 and <11 mg L�1

during spring 2004. Hyporheic NO3� concentrations were

higher in 2003 than 2004, but the ratio of hyporheic NO3� to

stream water NO3� concentration ranged between 1.5 to 5 in

2003 and between 5 and 10 in 2004 (Tables 3 and 4). Thehigher hyporheic NO3

� concentrations were attributed toshallow groundwater inputs and nitrification. NO3

� concen-trations varied along study reaches with the tributarieshaving increasing NO3

� concentrations moving downstreamto the confluence with the S. Fork Eel River (Figure 3).NO3

� concentrations in the S. Fork Eel River were dynamicover short distances (Figure 3c) because of the variability influid-flow and sediment environmental conditions over thetransitions from deep pools to long riffles, as well as the

Table 2. Dimensional Analysis Variables With Corresponding

Dimensions of Mass, Length, and Time, Along With Dimension-

less Groupings Derived From Buckingham’s Pi Theorema

Variable Description Dimensions

JNO3 nitrate flux ML2T

u* shear stress velocity LT

H stream depth L

CNO3 NO3� concentration M

L3

n kinematic viscosity L2

Tks roughness height L

BOM benthic organic material ML2

JDO dissolved oxygen flux ML2T

B stream width L

h JNO3u*�CNO3

i dimensionless NO3� flux -

h 1Re*

i shear Reynolds number -

h BOMB�CNO3

i dimensionless C:N loading -

h JDOu*�CNO3

i dimensionless DO flux -

aAbbreviations: mass, M; length, L; and time, T.


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patchy distribution of large floating algal mats within theS. Fork Eel River [Power, 1990].[30] The denitrification activity measurements made

using the acetylene inhibition method represent potential,not in situ, denitrification rates because NO3

� was amendedto the samples and chloramphenicol was not added toprevent protein synthesis [Bernot et al., 2003]. Measuringin situ denitrification rates, for the number of samplesneeded to address the spatial variability, would be difficultbecause of the nitrogen limitation throughout the streamsof the ACRR. Small-scale variability in denitrificationactivity was aliased by the composite sediment samplingdesign, so activity values represent a spatial average overthe sampling area (�2 m2). Acetylene inhibition analysisresults were reproducible with analysis replications result-ing in standard deviations ranging between 0.1 and 1.7 ng-Ngsed�1 d�1. Composite samples contained slightly higher vari-ability with replicate (composite samples taken over samesampling area) samples producing standard deviations rang-ing from 2.8 to 14.8 ng-N gsed

�1 d�1.[31] Denitrification activity values were higher in 2003

on average because the late summer sampling periodcoincided with higher stream temperatures and algal DOCproduction resulting in a more established benthic microbialcommunity with increased respiration rates. The 2004sampling period occurred at the early stages of successionafter winter flooding that corresponded to lower denitrifi-cation activity on average but a larger range in values(Tables 3 and 4).[32] A log-normal probability distribution was fitted to

the 2004 denitrification activity values (Figure 4a). Thisresulted in a log-transformed mean (mg, equivalent to thegeometric mean) of 33.6 ng-N gsed

�1 d�1 and a multiplicativestandard deviation (sg, equivalent to the geometric standard

deviation [Limpert et al., 2001]) of 4.7 ng-N gsed�1 d�1 in

denitrification activities for all samples collected within theACRR. The distribution parameters were used to categorizethe denitrification activities into low, medium, and highdenitrification potential groups. Low denitrification poten-tials were determined as activities <mg/sg = 7 ng-N gsed

�1 d�1

(representing 16% of the probability bounded by the log-normal distribution), high denitrification potentials weredetermined as activities >mgsg = 157 ng-N gsed

�1 d�1 (16%of the probability), and medium potentials were betweenthese two bounds (68% of the probability). The 2003denitrification activities were not fitted to a log-normalprobability distribution because of their smaller sample size(n = 25 versus n = 95 in 2004) and they were sampled in abiased manner towards habitats with expected higher deni-trification rates (lower velocities with smaller sedimentparticles and more organic substrata).[33] The validity of using a log-normal probability distri-

bution to describe denitrification hot spot activity was testedagainst a comparable data set from an agriculturallyimpacted watershed in southern Minnesota (B. L. O’Connoret al., manuscript in preparation, 2006). A histogram ofdenitrification activities demonstrated a log-normal distribu-tion for sediment samples collected within the Seven MileCreek (SMC) watershed (Figure 4b). The resulting denitri-fication activities ranged from 0 to 3500 ng-N gsed

�1 d�1.The log-normal mean (mg) for SMC was 299.0 ng-N gsed

�1 d�1

and sg was 4.5 ng-N gsed�1 d�1. The higher mg value for SMC

can be explained by stream NO3� concentrations (�5 to

10 mg L�1), which were an order of magnitude greater thanin the ACRR. The sg values are almost identical between theACRR and the SMC, and when the denitrification activitiesare normalized by mg for each system, the resulting log-normal

Table 3. Stream and Hyporheic Zone NO3� Concentrations and Denitrification Activity Values From Summer 2003 Sampling Period

2003 Data Stream NO3� Water, mg L�1 NO3

� Hyporheic, mg L�1 Denitrification Activity, ng-N gsed�1 Denitrification Activity, ng-N gsed

�1

S. Fork Eel RiverJanes Reach 18.5 26.2 86.3 50.6–183.0Fox 13.6 22.8 163.0 -Ten Mile 12.7 31.0 309.5 -

Elder Creek (lower) 10.4 47.4 227.0 75.5–515.2Fox Creek 21.2 45.1 205.5 118.7–304.3Ten Mile Creek 6.0 24.7 141.9 -

Table 4. Stream and Hyporheic Zone NO3� Concentrations and Denitrification Activity Values From Spring 2004 Sampling Period

2004 Data StreamNO3

� Water,mg L�1

NO3� Hyporheic,mg L�1

Denitrification Activity,ng-N gsed

�1Denitrification Activity,

ng-N gsed�1

S. Fork Eel RiverJack of Hearts 1.6 - 37.2 -Elder 2.7 - 82.5 -Janes Reach 0.9 5.2 93.8 40.3–179.6Fox 1.6 - 276.0 -Ten Mile 5.7 - 47.9 -

Jack of Hearts Creek 1.2 8.3 87.6 1.2–464.9Skunk Creek 2.7 - 13.0 -Elder Creek (upper) 1.5 14.1 29.1 1.3–159.0Elder Creek (lower) 1.4 15.3 32.7 2.6–219.0McKinley Creek 1.1 - 6.2 -Fox Creek 10.7 - 145.1 -Ten Mile Creek 1.8 - 191.1 125.3–314.8


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probability distribution fits are nearly identical for the ACRRand SMC (Figure 4c).[34] A map of denitrification potentials (Figure 1) shows

the high degree of spatial variability in denitrification ratesmeasured within the ACRR, as well as within individualstream reaches. The stream reach variability is shown forJack of Hearts Creek and Elder Creek (Figure 5) wheredenitrification activities varied by over 100 ng-N gsed

�1 d�1

within 50 m. This intra-reach variability is also shown inthe photograph of Elder Creek (Figure 6, between 100 and150 m downstream on Figure 5b). Denitrification potentialvalues are labeled on the photograph showing a highpotential in the side-stream pool with medium and lowpotentials scattered throughout the reach.

4.3. Quantification of Denitrifying Bacteria

[35] The presence or absence of nirK and nirS wasexamined by PCR prior to quantitative cPCR analysis.The nirK gene was detected in all samples tested; however,nirS was only detected in six samples (all from the S. ForkEel River), all of which were not quantifiable by cPCR. Theprimer pair cd3aF:R3cd was the most general (able to detectacross the diversity in nirS sequences) of the publishedprimer pairs used to amplify nirS and was the only one toconsistently detect nirS in environmental samples accordingto the work of Throback et al. [2004]. We attemptedchanging the PCR cycling procedure (annealing tempera-ture and number of cycles), along with using another primerpair cd3aF:R4cd [Michotey et al., 2000], but we were notable to consistently detect or quantify nirS in ACRRsediment samples.

[36] The nirK quantities varied over a range from 103 to107 gene-copy-number gsed

�1 as illustrated in the box-plot ofnirK quantities within each stream reach (Figure 7). AKruskal-Wallis test resulted in a P value of 0.027 betweenstream reaches, which suggested that at least one groupmedian was significantly different from the others. However,a multiple comparison procedure indicated that no streamreach was statistically significant from another, even thoughit does appear that the nirK quantities in Elder Creek werelower than in the other streams. The nirK quantities rangedbetween 2 and 3 orders of magnitudes over short streamwisedistances on the order of 50 m (Figure 5).

4.4. Quantity Versus Activity

[37] The correlation between nirK quantity and denitrifi-cation activity for all samples taken in the ACRR (Figure 8b)indicated a significant increasing trend (R = 0.44, P = 0.011)of denitrification activity with increasing nirK quantities,but with much scatter in the data. When the nirK quantityvalues were grouped according to their respective denitrifi-cation potentials, the medium denitrification potential groupcontained the broadest range in nirK values (from 103 to

Figure 4. Log-normal probability distribution (line)and histogram (bars) of denitrification activities for(a) Angelo Coast Range Reserve-ACRR, (b) Seven MileCreek-SMC Watershed in southern Minnesota, and (c) acomparison of the ACRR and SMC. The high, medium,and low denitrification potential ranges were determinedusing the distribution parameters mg/sg and mgsg as limits(high >mgsg, mgsg > medium > mg/sg, and low < mg/sg).

Figure 3. NO3� concentrations along (a) Jack of Hearts

Creek, May 2004; (b) Elder Creek, August 2003; and(c) S. Fork Eel River (from Janes Reach to Fox Creek),July 2003.


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106, Figure 8a). A Kruskal-Wallis test resulted in a P valueof 0.009 among the denitrification potential groups. Themultiple comparison procedure indicated the high potentialgroup, which represents denitrification hot spots, had sig-nificantly higher denitrifying biomass quantities than themedium and low potential groups.

[38] For Elder Creek and Jack of Hearts Creek, thecorrelation between nirK quantity and denitrification activ-ity showed good agreement in streamwise profiles (Figure 5).In Jack of Hearts Creek (Figure 5a), the high denitrificationpotential observed around the 50 m downstream pointcontained an in-stream sedge patch, which provided asheltered, organic rich environment accounting for theelevated nirK quantity. In Elder Creek (Figure 5b), theupstream segment showed very little denitrification activitywith varying nirK quantities. The downstream segment(between 100 and 150 m downstream) was where the nirKquantity and denitrification activity values coincided. Theside-stream pool (Figure 6) located at 150 m downstreamcontained soft sediments with a recirculating flow, whichaccounted for the elevated nirK quantity and denitrificationvalues.

4.5. Dimensional Analysis

[39] The NO3� flux values (JNO3) exhibited no clear

correlations between individual environmental variables(Figure 9). Some general trends observed included increas-ing NO3

� flux with decreasing fluid-flow velocity, increas-ing NO3

� concentrations, and increasing BOM. Theseobservations are consistent with previous studies on deni-trification [e.g., Duff and Triska, 2000].[40] Dimensional analysis using Buckingham’s pi theo-

rem with the variables in equation (1) generated the dimen-sionless NO3

� flux hJNO3/u* � CNO3i, shear Reynolds numberh1/Re*i, C:N loading hBOM/B � CNO3i, and the DO fluxhJDO/u* � CNO3i groups. The dimensionless NO3

� fluxrepresents the ratio of denitrification to the advective NO3

�

flux to sediments; the shear Reynolds number describes theratio of advective to diffusive fluxes at the sediment-waterinterface; organic carbon and NO3

� availability are describedby the C:N loading group; and dimensionless DO flux

Figure 5. Reach profiles of nirK quantity and denitrifica-tion activity measurements for (a) Jack of Hearts Creek and(b) Elder Creek.

Figure 6. Photograph of Elder Creek showing denitrifica-tion potential values (H, high; M, medium; and L, low).

Figure 7. Range of nirK quantities measured in streamreaches of the ACRR. The boxes have lines at the lower(25%), median (50%), and upper (75%) quartile values ofthe data. The whiskers represent the range in data except foroutliers (shown by plus sign), which were determined asvalues greater than 1.5 times the interquartile range (thedistance between the inner and outer quartile values).


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depicts the oxygen and NO3� fluxes at the sediment-water

interface.[41] The dimensionless groupings were compared indi-

vidually against the dimensionless NO3� flux group in

order to determine the exponents a, b, and c inequation (2), which resulted in a = 1/4, b = 1/6, and c =2/3. The resulting expression was plotted against thedimensionless NO3

� flux according to denitrification poten-tials, with the majority of the data (80%) collapsing on theequation y = 10�2.1x2 with a slope of two and an R2 = 0.60on a log-log plot (Figure 10). The outlying data above andbelow the regression equation appeared to have similarscaling (slope) with respect to the controlling factors as theregression equation. The outlying data were fitted toequations of the form y = cx2, where c is the intercept.The upper line included five samples collected on theS. Fork Eel River downstream of Janes Reach withmedium to high denitrification potentials, while the lowerline contains eight samples collected from Jack of HeartsCreek and Elder Creek with low denitrification potentials.[42] The dimensional analysis determined the functional

relation and dimensionless groupings described by

equation (2). The combined expression of dimensionlessgroupings plotted with field data had a slope of two on a log-log plot (Figure 10). This was used to rewrite the dimension-less functional relation for NO3

� flux in equation (2) as

JNO3

u�CNO3

� �¼ c

1

Re*

� �1=2BOM

B�CNO3

� �1=3JDO

u*�CNO3

� �4=3

; ð5Þ

where c is equal to 10�2.1 for the majority of the data.

5. Discussion

[43] The main objective for this study was to evaluatethe variability in stream denitrification by quantifyingthe controlling physical, chemical, and microbiologicalconditions within the reach scale. Our approach was to(1) examine the spatial variability in denitrification,which resulted in the use of a log-normal probabilitydistribution; (2) compare the acetylene inhibition mea-surement to a relatively new microbiological approach(cPCR); and (3) to use dimensional analysis with thecontrolling environmental factors in order to develop apower law relationship between denitrification and itscontrolling environmental factors.[44] The log-normal probability distribution has been

used in a wide-range of disciplines to describe phenomena

Figure 8. Comparison of denitrification activity and nirKquantity throughout the ACRR. (a) The nirK quantityranges categorized by high, medium, and low denitrificationpotential groups. The boxes have lines at the lower (25%),median (50%), and upper (75%) quartile values of the data.The whiskers represent the range in data except for outliers(shown by plus signs), which were determined as valuesgreater than 1.5 times the interquartile range. (b) Thecorrelation between denitrification activity and nirKquantity.

Figure 9. Variability of NO3� flux (calculated from

acetylene inhibition measurements) with respect to mea-sured and estimated environmental variables (a) streamdischarge, (b) mean velocity, (c) shear stress velocity,(d) DO flux, (e) stream NO3

� concentration, and (f) benthicorganic material.


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with a skewed distribution and a large variance from meanvalues. The fact that two systems with different environ-ments (forested mountain versus open-field agricultural,Figure 4) would exhibit similar distributions and almostidentical sg values is not uncommon as noted by the workof Limpert et al. [2001], which showed that sg valuesranged between 1.1 and 33 across sciences from geologyto medicine. The similar sg values imply that a smallnumber of measurements could be used with a log-normaldistribution model to describe the spatial variability anddefine hot spots for a system. This result, if continued to beproven valid in future studies, suggests that more sophisti-cated techniques for measuring in situ denitrification rates(e.g., membrane inlet mass spectrometry, or novel molecularmicrobiological techniques) could be utilized with lowersampling frequency and still resolve the spatial variability ofthe system.[45] The novel use of cPCR to quantify denitrifying

bacteria provides a microbiological basis for interpretingnitrogen biogeochemical processes. Denitrification hotspots, defined by the log-normal distribution of denitrifica-tion activity measurements, had significantly higheramounts of denitrifier biomass (Figure 8a). The scatter inthe quantity-activity relationship (Figure 8b) indicates deni-trifier biomass is not the sole determinant of denitrificationand that the spread in nirK quantities and denitrificationactivities could be explained by varying environmentalconditions. The large fluctuations in denitrification activitiesand nirK quantities over short downstream distances(Figure 5) is consistent with the changing environmentalconditions caused by the riffle-pool-run transitions.[46] The use of cPCR to examine denitrification in

streams has some limitations that need consideration. Theprimers used to amplify nirK and nirS must compromise

between generality (include diversity of gene sequences)and specificity (exclusion of non-targeted gene sequences)in detecting gene sequences. Genes encoding for nirK weredetected in all ACRR sediment samples and the PCRproducts closely matched known nirK nucleotide sequen-ces. The lack of nirS detection in ACRR sediments may bethe result of the PCR design; however, nirS was detected ina laboratory channel sediments that were inoculated withnirS-type denitrifying bacteria (unpublished data), with themethodology used in this study. Evidence from otherinvestigations suggest that nirS and nirK detection inenvironmental samples may be segmented with respect tothe environmental conditions (oxygen content, organiccarbon richness, and pH) that the denitrifying bacteriaencounter [Prieme et al., 2002; Throback et al., 2004; Coleet al., 2004]. The ability to detect nirS in inoculatedlaboratory channel sediments and not in ACRR sedimentsis most likely a result of the low organic carbon content andhigh DO concentration environment occurring in thestreams within the ACRR generating a preferential nichefor nirK-type denitrifying bacteria.[47] The functional relationship between denitrification

flux and environmental conditions determined by dimen-sional analysis will have a degree of universality if allcontrolling variables are present. In this study, the dimen-sionless functional relationship described in equation (5)produced consistent scaling of denitrification with environ-mental factors with a respectable amount of collapse in thedata. As mentioned in the introduction, the real challenge inusing dimensional analysis is developing the correct ideal-ization of the phenomenon. The variables chosen for dimen-sional analysis in equation (5) did not include a termrepresenting surface-hyporheic water exchange as streamsof the ACRR have limited hyporheic zones. Instead,transient storage was accounted for indirectly through B,H, ks, and u*. The use of an areal measure of organicmaterial (BOM) was chosen because the denitrificationwithin the ACRR was primarily biofilm associated. Theabundance of denitrifying bacteria should also be incor-porated into a functional relationship describing denitrifi-cation. However, the units of gene-copy-number gsed

�1 donot lend their use in dimensional analysis, which requiresthe use of fundamental dimensions (mass, length, andtime) or derived dimensions (combinations of fundamentaldimensions).[48] The separation between the three scaling relation-

ships (Figure 10) seems to be related to the quality of theorganic material (algal versus detritus), which was notrepresented in the dimensionless groupings of environmen-tal variables. The top line of outlying data, described by theequation y = 10�2.8x2, represents S. Fork Eel River samplesdownstream of Janes Reach. The denitrification in thisregion is enhanced by the high quality DOC supply fromthe large mats of filamentous algae not found in thetributaries [Holmes et al., 1996; Finlay, 2004]. The lowerline of outlying data, described by y = 10�1.4x2, containssamples from Jack of Hearts Creek and Elder Creek withlow denitrification potential. These stream reaches have lowalgal DOC inputs because of shading. Another possibleexplanation is the low abundance of denitrifier biomass forthe Elder Creek samples as shown in Figure 7, which is notrepresented in the controlling factors in equation (5).

Figure 10. Correlations between dimensionless NO3� flux

and the scaled dimensionless environmental groupingsaccording to equation (2). The middle solid line represents80% of the data bounded by the upper and lower dashedlines. The upper solid line represents S. Fork Eel Riversamples downstream of Janes Reach and the lower solid linerepresents Jack of Hearts Creek and some of Elder Creek’ssamples with low denitrification potential.


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[49] Previous studies on stream denitrification haveshown the importance of NO3

� availability and the amountof organic matter control the variability in denitrificationrates [Martin et al., 2001; Groffman et al., 2005]. Theresults from this study are compatible, but also point to theimportance of boundary layer fluid-flow and DO flux ondenitrification (equation 5). Data on u* and DO flux are notoften reported in conjunction with denitrification, so com-parison of our results to other studies is difficult. This matteris further complicated by the fact that DO flux (commonlyreferred to sediment oxygen demand or SOD) is not an easyvariable to measure as it is a function of mass transferconditions and microbial respiration [Jørgensen and Boudreau,2001]. Future studies on stream denitrification should focuson (1) simultaneous measurements of the controlling phys-ical, chemical, and microbiological conditions; (2) estab-lishing the relevant environmental variables for differenttypes of stream systems; and (3) developing methodologiesthat accurately measure these environmental variables thatare compatible with dimensional analysis, all of which canbe used to develop a more mechanistic understanding ofdenitrification in stream ecosystems.[50] The spatial variability of biogeochemical activity,

including denitrification, provides the basis for the hot spotparadigm in aquatic environments [McClain et al., 2003].Stream ecosystems are heterogeneous with respect tohabitats at all scales [Frissell et al., 1986], and these hotspot regions are connected spatially by material andhydrologic flow paths according to the conceptual modelproposed by Fisher et al. [2004]. The use of a log-normalprobability distribution provides a quantitative approach todefine what is the magnitude of a hot spot for theparticular system of interest. Connecting denitrificationactivity to denitrifier biomass provides a microbiologicalbasis for measurement rates and an indication to thedegree to which environmental factors are limiting deni-trification. Using dimensional analysis to develop anidealization to model denitrification with respect to itscontrolling environmental variables provides an analyticalmethodology for quantifying material and hydrologic flowpaths. Collectively, these tools can be used to predict theextent and location of hot spot activity for denitrificationand other biogeochemical process of interest, which isessential for basic research, as well as management andrestoration practices.

[51] Acknowledgments. This work was supported by the NationalCenter for Earth-surface Dynamics (NCED), a Science and TechnologyCenter funded by the Office of Integrative Activities of the NationalScience Foundation (under agreement EAR-0120914). We would like tothank Peter Steel, Manager at the Angelo Coast Range Reserve, for hisassistance with the logistics in conducting field investigations and MaryPower for hosting us at the Angelo Coast Range Reserve. We would alsolike to thank Diane McKnight and two unknown reviewers for helpfulcomments with regards to the preparation of this manuscript.

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quantity&activity relationship of denitrifying bacteria...

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