Nitrate amendment reduces biofilm biomass and shifts...

Nitrate amendment reduces biofilm biomass and shiftsmicrobial communities in remote, oligotrophic ponds

Carmella Vizza1,5, Jennifer L. Pechal2,6, M. Eric Benbow3,7, Jennifer M. Lang4,8, Dominic T. Chaloner1,9,Stuart E. Jones1,10, and Gary A. Lamberti1,11

1Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana 46556 USA2Department of Entomology, Michigan State University, East Lansing, Michigan 48824 USA3Department of Entomology, Department of Osteopathic Medical Specialties, Ecology, Evolutionary Biology, and Behavior Program,

Michigan State University, East Lansing, Michigan 48824 USA4Department of Biology, University of Dayton, Dayton, Ohio 45469 USA

Abstract: Humans have increased the amount of reactive N available in the environment by over an order of mag-nitude since the industrial revolution. Most studies have been conducted in ecosystems with pervasive anthropo-genic nutrient inputs, so little is understood about how naïve biofilm communities respond to elevated nutrients.Our nutrient-diffusing substrate (NDS) experiments, which were conducted in Alaskan freshwater ponds with verylittle anthropogenic nutrient inputs, suggest that P limits biofilm photoautotrophs. However, despite low water-column nutrient concentrations, overall biofilm biomass was not enhanced by the addition of N or P. Rather,we observed an ~60% biomass reduction with NO3

– amendment in 15 oligotrophic ponds across 2 y. This wide-spread biomass reduction was accompanied by changes in microbial communities, but these trends were not ob-served with NH4

1 or P amendment. Nonamended communities (i.e., no nutrient amendment other than lysogenybroth agar) were characterized by anaerobic heterotrophs and purple nonsulfur bacteria, whereas NO3

–-amendedcommunities were characterized by aerobic heterotrophs and facultatively aerobic heterotrophs (e.g., denitrifiers).These community patterns suggest that NO3

– can strongly affect microbial interactions during biofilm formationby altering redox conditions. The effect of NO3

– on microbial biomass may be caused by an NO3– toxicity effect or

competitive shifts in taxa, both of which may shape biofilm formation and community assembly. Our results revealpossible consequences for low-NO3

–, aquatic environments after novel exposure to anthropogenic NO3– inputs,

suggesting that a legacy of anthropogenic NO3– inputs may have fundamentally changed microbial community

assembly and biogeochemical cycling in aquatic ecosystems.Key words: nutrient-diffusing substrate, oligotrophic, nitrate inhibition, biofilms, microbial community composi-tion, high-throughput sequencing, redox

From 1860 to the present, humans have increased theamount of reactive N available by more than 10� (Gallo-way et al. 2004). We have dramatically transformed theglobal N cycle via fossil fuel combustion and heavy use ofN in agriculture and industry (Galloway et al. 2008). Thistransformation can have profound effects on terrestrialand aquatic food webs (Meunier et al. 2016). Long-termfertilization studies in terrestrial ecosystems have shownthat N enrichment can strongly alter soil microbial com-

E-mail addresses: [email protected]; [email protected]; [email protected]; 8PGeffen School of Medicine, University of California, Los Angeles, California.edu; [email protected]

DOI: 10.1086/697897. Received 10 May 2017; Accepted 28 January 2018; PublFreshwater Science. 2018. 37(2):251–263. © 2018 by The Society for Freshwate

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munities and processes (Ramírez et al. 2012, Francioli et al.2016) and lead to biodiversity loss (Isbell et al. 2013), butthe long-term effects of anthropogenic N inputs on aquaticecosystems and their microbial communities are less under-stood.

Primary producers in aquatic ecosystems are equallylikely to be limited by N or P (Francoeur 2001, Elser et al.2007). However, atmospheric N deposition (Elser et al.2009) may shift nutrient limitation from primary N limita-

resent address: Department of Medicine/Division of Cardiology, David90095 USA, [email protected]; [email protected]; 10sjones20@nd

ished online 5 April 2018.r Science. 251

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252 | NO3– amendment reduces biofilm biomass C. Vizza et al.

tion to secondary P limitation. In addition, researchers havedemonstrated that ambient water-column chemistry andN∶P stoichiometry (e.g., deviations from the Redfield ra-tio) can be used to predict nutrient limitation (Keck andLepori 2012, Cooper et al. 2015). Most studies document-ing relationships between water chemistry and nutrient-limitation patterns were conducted in aquatic ecosystemsthat have been receiving regular anthropogenic N and P in-puts for close to a century. Thus, primary producers mightbe biologically primed to respond to enhanced nutrient in-puts (Reisinger et al. 2016).

Not all freshwater ecosystems are limited by N or P.Some are limited by light availability (Karlsson et al. 2009),whereas some systems fail to show any limitation (Johnsonet al. 2009). A few investigators have even found that nu-trient addition can inhibit biofilm growth (reviewed byFrancoeur 2001). Here, inhibition of biofilms through Nand P amendment occurred in 1.7 and 3.8% of the studies,respectively (Francoeur 2001). In general, inhibition pat-terns are so scarce that they fall within the type 1 errorrange and, thus, are often ignored (Francoeur 2001). Theseinhibition patterns are usually documented at a single site(e.g., Biggs et al. 1998) or at particular times (e.g., Bernhardtand Likens 2004) and, therefore, are difficult to recreate. Forexample, Bernhardt and Likens (2004) documented N inhi-bition in a heterotrophic stream outside the growing seasononly. Their hypothesis for this pattern was that, in environ-ments with ample organic C sources, nutrients could stim-ulate bacterial heterotrophs, which then inhibit periphytongrowth by outcompeting them for space or other resources.If inhibition of periphyton is the result of competitive shiftsinmicrobial taxa, then examining the effects of nutrients onmicrobial communities is the key to understanding inhibi-tion patterns in aquatic ecosystems.

We must study remote ecosystems that receive very lit-tle anthropogenic N and P to understand whether anthro-pogenic nutrient input can fundamentally change micro-bial community taxonomic composition and, as a result,biogeochemical cycling in aquatic ecosystems. Our studywas conducted in Alaskan ponds in the Copper River Delta(CRD). The CRD in southcentral Alaska comprises diversewetland pond habitats, distributed along a gradient of gla-cial and oceanic influences (Vizza et al. 2017b). The CRD isconsidered to be a low-nutrient system because of its geo-logical history and the limited anthropogenic influence inthis remote area (Bryant 1991). The microorganisms inthese ecosystems may be naïve to elevated nutrient supplybecause they have not been subjected to long-term anthro-pogenic nutrient loading. Our basic study objectives wereto: 1) assess the nutrient-limitation status of these pondsusing nutrient-diffusing substrate (NDS) experiments and2) identify howmicrobial biofilm communities were affectedby nutrient amendment based on targeted high-throughputgene amplicon sequencing.


METHODSStudy area

The Copper River in southcentral Alaska is the 8th-largest river in the USA (Kammerer 1990). The CopperRiver drains a large region of the Chugach and WrangellMountains into the Gulf of Alaska, and the river and the sed-iments it deposits have shaped the largest contiguous wet-land on the Pacific Coast of North America. The CRD en-compasses about 283,000 ha of wetland pond habitat andsupports extraordinary biodiversity (Bryant 1991). Withinthe CRD, different wetland habitats can be distinguishedalong a gradient of glacial and oceanic influences (Vizzaet al. 2017b). Ponds were created and modified by theGreat Alaska earthquake in 1964 that elevated the Deltaby 124 m (Thilenius 1995). Our study ponds (n 5 15),which we treated as independent replicates, provided a dis-tinct gradient of habitats differing in biogeochemistry (Ta-ble 1).

Study designWe conducted 2 separate experiments. We designed the

1st experiment, conducted in 2013 (n5 9 ponds) and 2014(n 5 6 ponds), to test nutrient limitation using 4 differentNDS treatments (control, N, P, and N1P). The total num-ber of samples for this experiment was 600 (15 ponds �4 treatments� 10 replicates). We conducted the 2nd exper-iment in 2014 in the same 9 ponds sampled the previousyear to test for the effects of NH4

1 compared toNO3– using

10 replicates of 5 different treatments (control, low NH41,

high NH41, low NO3

–, and high NO3–) for a total of 450

samples (9 ponds � 5 treatments � 10 replicates).

NDSsWe used NDSs to assess nutrient limitation in CRD

ponds (Tank et al. 2017). They were constructed from30-mL plastic cups, which were filled with a 2% lysogenybroth (LB) agar solution (Novagen; EMD Chemicals Inc.,San Diego, California) and topped with glass fritted disks.We constructed different treatments for each of the 2 ex-periments detailed in the study design. For the nutrient lim-itation experiment, the treatments consisted of control(CTL; not amended except for LB agar), N (LB 1 0.5 MKNO3), P (LB 1 0.5 M KH2PO4), and N1P (LB 1 0.5 MKNO3 1 0.5 M KH2PO4). The 2

nd experiment, in whichwe specifically tested for the effects of N form (NH4

1 orNO3

–) and concentration (high or low), consisted of the fol-lowing treatments: CTL (LB), low NO3

– (LB 1 0.05 MKNO3), high NO3

– (LB 1 0.5 M KNO3), low NH41 (LB 1

0.05 M NH4Cl), and high NH41 (LB 1 0.5 M NH4Cl).

After a deployment period of 21 to 28 d, we removed sub-strate disks from ponds, wrapped them in foil, and frozethem until they could be analyzed for chlorophyll a (Chl a)and ash-free dry mass (AFDM), or the total amount of


Table

1.Mean(±SD

)values

forph

ysical

andbiogeochem

ical

variablesat

theCop

perRiver

Delta

(CRD)po

ndssampled

inthesummersof

2013

and2014.W

ater-chemistry

variablesweremeasuredat

thesurfacelayerof

5sitesperpo

nddu

ring

July

andAug

ust(n

510).Allanalytes

werewithindetectionlim

itsexcept

forNO

3–,for

which

allsam-

ples

were<5lgN/L.S

alinitydidno

tvary

withinapo

nd.A

map

ofthepo

ndsalon

gwithdetailedmetho

dsforparameter

measurementwas

publishedby

Vizza

etal.(2017b).

Tem

p5

temperature,S

pC5

specificcond

uctivity,D

O5

dissolvedO

2,D

OC5

dissolvedorganicC,S

RP5

solublereactive

P,T

N5

totalN,T

P5

totalP.

Depth

Daily

Light

SpC

Salin

ity

DO

DOC

NH

41

SRP

TN

TP

Pon

d(m

)temp(7C)

(kilo

lux)

pH(lS/cm

)(PSU

)(m

g/L)

(mg/L)

(lgN/L)

(lgP/L)

(lgN/L)

(lgP/L)

BVN

1.14

±0.02

16.1

±1.7

8.9±18

5.9±0.1

20±1.1

0.01

8.9±0.3

2.7±1.2

1.1±1.0

3.7±1.0

130±62

27±12

BVS

0.86

±0.08

15.5

±1.4

1.8±4.2

6.1±0.2

24±1.1

0.01

6.6±0.3

3.6±0.8

0.9±1.0

3.2±0.9

190±33

34±14

CME

0.83

±0.04

15.4

±1.8

5.4±8.4

5.7±0.1

52±3.1

0.02

2.7±0.7

4.5±0.8

0.8±1.7

2.9±0.8

210±31

18±9

CMW

0.81

±0.04

15.9

±2.3

1.7±3.3

7.7±0.5

47±1.3

0.02

10±0.6

4.5±0.4

0.6±0.6

3.8±1.0

230±32

29±13

EYN

0.52

±0.04

16.6

±2.3

9.3±12

6.3±0.1

9.5±0.3

0.00

7.7±0.5

7.1±0.6

11±2.8

4.8±1.5

270±40

34±8

EYS

0.59

±0.04

17.6

±2.6

14±18

6.6±0.1

8.8±0.2

0.00

8.3±0.4

6.8±0.9

13±5.7

4.8±2.5

280±34

38±13

LIL

0.61

±0.01

15.7

±2.5

6.1±10

7.0±0.1

76±11

0.04

3.7±0.5

4.2±0.5

6.2±0.8

6.9±3.1

130±28

24±7

RHM

0.64

±0.12

13.6

±1.7

7.7±11

7.2±0.1

81±2.4

0.04

3.6±1.0

4.0±0.5

15±5.3

8.6±2.5

120±29

19±8

SCS

0.89

±0.14

16.3

±2.5

11±20

7.4±0.1

53±5.0

0.02

8.2±0.4

2.7±0.4

5.0±1.0

8.7±3.0

110±25

23±7

SME

0.66

±0.02

14.9

±1.2

2.4±4.5

6.3±0.2

44±2.9

0.02

11±0.4

3.8±0.3

0.5±0.5

5.1±3.3

180±32

27±9

SMW

1.04

±0.05

15.6

±1.7

4.7±10

6.7±0.1

70±0.8

0.03

11±0.5

2.9±0.7

0.5±0.9

4.3±2.0

160±44

27±9

STN

0.57

±0.01

19.4

±2.6

13±23

7.6±0.1

42±0.4

0.02

8.5±0.3

8.7±1.6

13±0.9

4.0±0.7

300±98

24±7

STS

0.54

±0.09

19.1

±2.8

13±19

7.6±0.1

63±1.8

0.03

8.1±0.5

5.5±1.7

15±2.6

5.6±2.1

220±89

20±7

TIN

0.60

±0.03

18.3

±2.6

12±21

6.6±0.1

11±0.6

0.00

6.2±1.1

8.0±0.4

14±2.4

5.2±1.0

250±10

31±6

TIS

0.73

±0.04

18.1

±2.7

14±23

6.6±0.1

8.2±0.9

0.00

7.8±0.5

6.4±0.5

11±1.1

4.8±0.9

200±22

27±5

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organic matter. Chl a represents the photoautotrophs ofthe biofilm including both algae and cyanobacteria, whereasAFDM represents both autotrophs and heterotrophs in thebiofilm.

LB agar promotes colonization of heterotrophic biofilmsbecause it is supplemented with yeast extract (5 g/L), pep-tone (10 g/L), and NaCl (10 g/L), whereas the fritted diskpromotes colonization of autotrophic biofilms (Johnsonet al. 2009). Different substrates, such as a cellulose sponge,have been used to promote the heterotrophic community,but our design allowed us to mimic natural organic sub-strates in these ponds, such as macrophyte stems, whichcan structurally and chemically support biofilms composedof microbial autotrophs and heterotrophs (Cattaneo et al.1998, He et al. 2014). The NDS method used can affectthe nutrient limitation patterns detected (Capps et al. 2011).For example, Capps et al. (2011) found slight variations in Nand P colimitation patterns of a single stream dependingon substrate type and diffusion rates. Therefore, diffusionrates and ambient chemistry should be reported when us-ing standardized nutrient limitation methods.

To better understand nutrient release from a controlsubstrate with LB agar relative to agar-agar, we assessedlaboratory diffusion rates of dissolved organic C (DOC;Fig. S1A), total N (TN; Fig. S1B), and total P (TP; Fig. S1C)from these 2 agar types. Our diffusion rates for LB agar(Appendix S1, Fig. S1A–C) were orders ofmagnitude lowerthan rates in other studies (Bernhardt and Likens 2004, Ru-genski et al. 2008), but our C, N, and P release rates tendedto be about anorder ofmagnitude higher for LB agar relativeto agar-agar. Stoichiometry was similar between the agartypes, suggesting that biofilms would experience primarilyC limitation followed by N limitation based on the amend-ments alone (C∶N∶P after 24 h of LB agar diffusion was35∶11∶1 and that of agar-agar was 36∶6∶1; Appendix S1,Fig. S1A–C). We also assessed diffusion rates in high andlow NO3

–-amended LB agar (Appendix S1, Fig. S1A–C) be-cause of the strong inhibition response exhibited by biofilmson NO3

–-amended substrates.

Chl a and AFDM analysesWithin 60 d of collection, we extracted Chl a from disks

overnight in 20 mL of 90% buffered acetone. The next day,we used a fluorometer (TD-700; Turner Designs, San Jose,California; after Steinman et al. 2017) to measure Chl a in asubsample of the extract. We estimated total biofilm bio-mass by measuring AFDM (after Steinman et al. 2017).We air-dried disks and their respective acetone extracts (in-cluding the subsample used for Chl a analysis) for a week,and then oven-dried them for at ≥48 h at 607C, weighedthem, and combusted them at 5007C for 4 h. Last, we re-wetted the disks and dried them at 607C for ≥48 h beforethe final weighing. We used the difference in mass beforeand after combustion to estimate AFDM. We report Chl a


and AFDM in areal units based on the top surface area ofa fritted glass disk (3.9 cm2).

Microbial community study designWe used automated ribosomal intergenic spacing anal-

ysis (ARISA) to generate initial microbial community fin-gerprints from 1 replicate of each treatment per pond in2013 (Lang et al. 2015). ARISA results demonstrated thatmicrobial communities on disks varied among nutrienttreatments (Appendix S2, Fig. S2). Therefore, in 2014, weused a more advanced sequencing platform (MiSeq; Illu-mina, San Diego, California) to obtain more detailed infor-mation on microbial taxa by analyzing 1 replicate of eachtreatment per pond. In total we used samples from 4 treat-ments (CTL, N, P, and N1P) in the 6 ponds sampled in2013/2014 and 5 treatments from 9 other ponds (CTL,low NO3

–, high NO3–, low NH4

1, high NH41) sampled in

2014 to generate a total of 69 samples. In both years, westerilized 2-mL centrifuge tubes in a boiling water bathand rinsed them with 95% ethanol because no autoclavewas available near these remote field sites.We removed bio-film samples from disks with a flame-ethanol-sterilized ra-zor blade and placed them in the 2-mL centrifuge tubes.Wehad no access to a –807C freezer, so we covered the tubeswith sterile glass-fiber filters but left them uncapped to air-dry for ~48 h to prevent mold growth and to preserve sam-ples. Two weeks later, they were transported to MichiganState University and frozen at –807C upon arrival. Studiescomparing preservation methods indicate this approachwas sufficient to enable us to identify community differencesbased on environmental factors (Piggott and Taylor 2003,Lauber et al. 2010).

DNA extractionDNA was extracted from biofilm samples according to

the manufacturer’s protocol using a PowerBiofilm® DNAisolation kit (Mo Bio, Carlsbad, California). Sufficient readswere obtained for only 27 of the 69 samples in the first se-quencing run because of a combination of low quality(probable inhibition) and low quantity (0.206–67.0 ng/lL)of DNA products. Therefore, we used a PowerClean ProDNA clean-up kit (Mo Bio) for the remaining 42 samplesand then sequenced these for a 2nd run. These 2 runs resultedin sequencing all 24 samples from the nutrient limitationexperiment (CTL, N, P, and N1P), and 35 samples fromthe N-form experiment. However, only 5 of the 9 ponds(EYN, EYS, SCS, STN, and TIN) from this experimenthad all treatments (CTL, low NO3

–, high NO3–, low

NH41, high NH4

1) represented; therefore, we included onlythose 25 samples (5 treatments� 5 ponds) in analyses. Eventhough we conducted 2 different sequencing runs with Illu-minaMiSeq,

Volume 37 June 2018 | 255

16S ribosomal RNA (rRNA) gene ampliconhigh-throughput sequencing

Targeting the 16S rRNA gene allowed us to gather phy-logenetic information about Bacteria and Archaea. AfterDNA extraction, we quantified the DNA using a Quant-iT dsDNA HS Assay kit and a Qubit 2.0 (Thermo Fisher,Grand Island, New York) and then stored all samples at2807C. Illumina MiSeq 16S library construction (2 � 250base pair [bp] paired-end reads) and sequencing was per-formed in the Michigan State University Genomics CoreFacility with a modified version of the protocol adaptedfor the Illumina MiSeq described by Pechal and Benbow(2016). Briefly, V4 regions of the 16S rRNA gene ampliconregion were amplified with region-specific primers that in-clude Illumina flowcell adapter sequences (515f [50 GTGCCAGCMGCCGCGGTAA] and 806r [50 GGACTACHVGGGTWTCTAAT]) (Caporaso et al. 2010). All sequenc-ing data were curated using the mothur software package(version 1.37; https://www.mothur.org/) and the proceduredetailed at https://www.mothur.org/wiki/MiSeq_SOP(Kozich et al. 2013). Sequences were classified against theSILVA (version 123) reference taxonomy (Pruesse et al.2007). We assessed the error rate of our sequences (7.25 �10–5) using the mock community described by Kozich et al.(2013). We then performed rarefaction to ensure an evensequence depth of 1000 sequences/sample subsampled1000�; the range in coverage of these rarefied sequenceswas 0.879 to 0.996, which indicates sufficient sampling ofthe microbial communities. Sequence files for all samplesused in this study are deposited in the Sequence Read Ar-chive at the EMBL EuropeanNucleotide Archive (ENA; http://www.ebi.ac.uk/ena): PRJEB19927.

Statistical analysesWe converted the raw Chl a and AFDM data to average

response ratios (Francoeur 2001) at the site level per pond,which resulted in 5 response ratios per treatment per pond.Response ratios were calculated by averaging the 2 repli-cates per treatment at each deployment site within a pond(if applicable) and then dividing the average nutrient treat-ments by the average CTL treatments. For the nutrient-limitationexperiment,weusedablockedanalysisof variance(ANOVA) design where either Chl a or AFDM response ra-tio was the response variable, pond was a blocking variable,and treatment (N,N1P, P)was the factor of interest.Withinthe ANOVA design, we tested 3 potential data distributions(normal, log-normal, and gamma) to determine which dis-tribution had the lowest Akaike’s Information Criterion valueand, therefore, was the best distribution to model each re-sponse variable (Burnham and Anderson 2002). We used alog-normal distribution for the Chl a response ratios of thenutrient-limitation experiment and a gamma distributionfor the AFDM response ratios. For the N-form experiment,we also used a blocked ANOVA design where either Chl a or


AFDM response ratio was the response variable, pond was ablocking variable, and form of N (NH4

1 or NO3–) and con-

centration (0.05 or 0.5 M) were the factors of interest. Weincluded an interaction between N form and concentration.We used a gamma distribution for the Chl a response ratiosfor the N-form experiment, and a normal distribution for theAFDM response ratios. We performed post hoc pairwisecomparisons on all 4 ANOVAs (i.e., Tukey’s Honestly Sig-nificant Difference [HSD]; Zar 2010). For all ANOVAs andTukey’s HSD tests, we set a 5 0.05.

To examine the effects of nutrient amendment on mi-crobial community diversity, we calculated operational tax-onomic unit (OTU) richness and Shannon entropy, an in-dex that accounts for both species richness and evenness(Jost 2007). We then performed 2 blocked ANOVAs (1 perexperiment) on OTU richness, which was log10(x)-trans-formed to ensure normality, with treatment as the factorof interest and pond as a blocking variable. We used 2 sim-ilar blocked ANOVAs to test Shannon entropy.

To analyze microbial community data, we calculatedpairwise Bray–Curtis dissimilarity indices for the relativeabundance data from the 2 experiments separately. To vi-sualize patterns in microbial community composition inour experiments, we used principal coordinates analyses(PCoAs) to ordinate microbial communities. We then eval-uated whether our treatments had statistically significant ef-fects onmicrobial community composition of biofilms usingpermutational multivariate analyses of variance (PERMA-NOVA; Anderson and Walsh 2013). All PERMANOVAmodels included pond and sequencing run as blocking var-iables, and we set a 5 0.05.

Last, we used indicator species analysis as a heuristictool to identify which OTUs were representative of NO3

–

and CTL treatments because these 2 treatments exhibitedthe greatest difference in biomass and microbial communitycomposition. Indicator species are those that are stronglyassociated with a particular habitat (Carignan and Villard2002) or that can be used to reveal evidence for the effectof environmental changes (McGeoch 1998). To identifywhich OTUs were the best indicator “species” for eachtreatment, we calculated indicator value indices (Cáceresand Legendre 2009) for the 1090 OTUs found on the CTLand high-NO3

– treatments from both experiments. We pre-sent only the subset for which the unadjusted p-value was≤0.01. All statistical analyses were conducted in the R soft-ware environment using the base, vegan, and indicspeciespackages (version 3.3.0; R Project for Statistical Computing,Vienna, Austria).

RESULTSFor the nutrient-limitation experiment, biofilm photo-

autotrophs (Chl a) and total biomass (AFDM) respondeddifferently to N and P (Fig. 1A, B, Table 2). PO4

3– (P, N1P)had a positive effect, and approximately doubled the amount



of Chl a relative to the CTL substrates, whereas NO3– alone

had relatively little effect (Fig. 1A). In contrast, NO3– re-

duced biofilm biomass by 60%, but P had relatively little ef-fect (Fig. 1B). N1P substrates had an intermediate amountof biomass relative to the N and P treatments (Fig. 1B).

For the N-form experiment, biofilm photoautotrophswere slightly reduced in the presence of high N concentra-tions, whereas total biofilm biomass was drastically lowerfor NO3

– amendments at both concentrations (Fig. 2A, B,Table 2). Both treatments with lower concentrations ofNH4

1 and NO3– had approximately the same amount of

Chl a as CTL substrates, whereas the higher concentrationsof NH4

1 and NO3– had ~80 and 60% of CTL Chl a, respec-

tively (Fig. 2A). Biofilm biomass exhibited a 40 and 60% re-duction in the presence of low- and high-NO3

– treatments,respectively. In contrast, NH4

1 treatments had approxi-mately the same AFDM as the CTL substrates (Fig. 2B).


Biofilm a diversity did not respond to N or P. The num-ber of observed microbial OTUs ranged from 19 to 157(mean ± SD, 60 ± 37) in the nutrient-limitation experimentand from 16 to 80 in the N-form experiment (38 ± 17).However, nutrient amendments and pond did not signifi-cantly affect the number of OTUs present for either exper-iment (p ≥ 0.17). In addition, species richness/evenness wassimilar between experiments (Shannon entropy for nutri-ent limitation: 2.1 ± 0.6; N form: 1.9 ± 0.6). Treatmenthad no effect on species richness/evenness for either exper-iment (p ≥ 0.16), but pondwas a significant factor in the nu-trient-limitation experiment (p 5 0.042).

In contrast to a diversity, biofilm microbial communitycomposition responded differently to N and P. In thenutrient-limitation experiment, N-amended communitieswere the most different from CTL communities, whereasP-amended communities grouped closer to CTL treatments(Fig. 3A). N1P-amended communities were clustered be-tween the communities associated with N and P treatments(Fig. 3A). Only treatment had a significant effect on biofilmmicrobial communities (PERMANOVA, R2 5 0.57, p 50.001; Fig. 3A). Pond only weakly influenced microbial com-munities (R2 5 0.14, p5 0.08), and sequencing run was notsignificant (R2 5 0.03, p 5 0.18).

Biofilm microbial community composition also re-sponded differently to NH4

1 and NO3–. The CTL commu-

nities grouped with the low- and high-NH41-treated com-

munities, and these communities differed from thosegrown at both concentrations of NO3

– (Fig. 3B). Treatmentand pond had significant effects on the biofilm microbialcommunities (treatment: R2 5 0.38, p 5 0.001; pond: R2 50.24, p 5 0.002), whereas sequencing run had a small andnonsignificant effect (R2 5 0.04, p 5 0.09).

The CTL and high NO3– treatments differed substan-

tially in biomass and microbial community composition,so we identified OTUs that were associated with these dif-ferences. A total of 8 and 6 indicator OTUs for the CTLand high NO3

– treatments, respectively, were diagnostic(Table 3). In general, CTL indicators tended to be anaer-obic chemoorganotrophs or phototrophs, whereas NO3

–

indicators were aerobic or facultatively aerobic chemo-organotrophs with an ability to reduce NO3

– (Table 4).

DISCUSSIONOur study suggests that nutrients can have strong pos-

itive and negative effects on microbial biofilms in low-nutrient aquatic ecosystems. Pond photoautotrophs (mea-sured as Chl a) probably were limited by P, but total biofilmbiomass (measured as AFDM) did not increase in the pre-sence of N or P, but rather experienced a 60% reductionwith the addition of NO3

–. This reduction in biomass withNO3

– addition was observed across 15 remote, oligotrophicponds differing in biogeochemistry across 2 y, a result notobserved with NH4

1 addition. NO3– amendment shifted het-

Figure 1. Mean (±95% CI) response ratios of chlorophyll a(Chl a) (A) and ash-free dry mass (AFDM) (B) on nutrient-amended substrates (N, P, and N1P) relative to the control inthe 15 study ponds in 2013 (n 5 9) and 2014 (n 5 6). Responseratios of 1.0 indicate equal growth relative to the lysogenybroth agar control. Bars with the same lowercase letters are notsignificantly different (p > 0.05).



erotrophic microbial composition from predominantly an-aerobic to aerobic with many of the aerobic taxa capable ofusing both O2 and NO3

– as electron acceptors. These com-munity patterns suggest that NO3

– probably has a signifi-cant effect on microbial interactions during biofilm forma-tion, at least in ecosystems that receive little geologic oranthropogenic inputs of NO3

–.P was the primary nutrient limiting photoautotroph

growth in CRD biofilms as shown by a doubling of Chl a,a widely used surrogate for algal and cyanobacterial bio-mass (Wetzel and Likens 2000). P limitation of photoauto-troph growth is common in both lakes and streams (Elseret al. 1990, Francoeur 2001), and water nutrient concentra-tions and N∶P stoichiometry are generally consideredgood predictors of nutrient limitation (Keck and Lepori2012). In our study, water-column TN∶TP molar ratioswere ~17, which suggests that the ponds could be on theverge of P limitation because the ratio is higher than theRedfield N∶P ratio of 16∶1, which is considered an opti-mal nutrient ratio for oceanic seston (Redfield 1958). Incontrast, Kahlert (1998) found that periphyton N∶P > 32indicates P limitation, which suggests that periphyton as-similating nutrients from the water column in the CRDcould be limited by N instead of P. Nonetheless, we ob-served widespread P limitation of primary producers acrossponds with P amendments significantly increasing Chl a.

In contrast to the photoautotrophs, total biofilmbiomasswas not enhanced by nutrient addition. Instead, AFDMwasstrongly reduced in the presence of NO3

–. Results of somealgal studies tend to show similar trends for Chl a andAFDM (e.g., Wyatt et al. 2010), but Lang et al. (2012) foundthat N appears to be more limiting for photoautotrophs thantotal biofilm biomass. In addition, different nutrient-limitationpatterns for fungi and algae have been identified when usingwood substrates (Tank andDodds 2003).We also found dif-


ferent response patterns between total biofilm biomass andthe photoautotrophic components of biofilms on glass frit-ted disks amended with LB agar. The strong decrease in bio-mass with NO3

– amendment was unexpected given that in-hibition patterns (i.e., reduction in Chl a or AFDM relativeto the CTL) are rare and often difficult to reproduce in timeand space (Francoeur 2001, Tank and Dodds 2003, Bern-hardt and Likens 2004, Sanderson et al. 2009). We are thefirst investigators to document strong NO3

– inhibition oftotal biofilm biomass across multiple sites and years.

NH41 did not limit or reduce biofilm biomass. Differen-

tial response to N form usually is expected because NH41 is

energetically less expensive to assimilate than NO3– (Von

Schiller et al. 2007). Nevertheless, some investigators haveshown that biofilm response does not differ in response toNO3

– or NH41 (Hoellein et al. 2010), whereas biofilms of

midwestern rivers (USA) responded more to NO3– than

to NH41 (Reisinger et al. 2016). An explanation is that or-

ganisms adapt to the most common N form. For example,Reisinger et al. (2016) hypothesized that positive biofilm re-sponses to NO3

– amendment in agriculturally influencedstreams are related to microbial acclimation to elevatedNO3

– levels from fertilizer runoff. In ecosystems where nu-trient concentrations are very low, differences in assimi-lation are unlikely to cause strong reductions in biofilmcaused by NO3

– amendment. However, in the CRD, water-column NO3

– levels were very low (


heterotrophs (e.g., Desulfovibrio and Paludibacter), whichsuggests that biofilms were thick enough to reduce O2. Forexample, Desulfovibrio is a genus known to use SO4

2– asan alternative electron acceptor (Heidelberg et al. 2004). Inaddition, purple nonsulfur bacteria (e.g., Rhodoblastus andRhodocyclus) were also abundant in the CTL communitiesand have extremely flexible metabolisms; they can be pho-toorganotrophic (i.e., use light for energy and organic com-pounds as a source for C and electrons), photolithotrophic(i.e., use light for energy, CO2 as a C source, andH2 or otherinorganic compounds as electron donors), or chemoorgan-otrophic (i.e., use organic compounds for sources of energy,C, and electrons) in dark, oxic conditions (Madigan et al.2014). In contrast, NO3

–-amended communitieswere char-acterized by aerobic heterotrophs (e.g., Janthinobacterium),probably because the biofilms were not as thick, but alsoby facultative aerobic heterotrophs (e.g., Microvirgula andPseudomonas) that generate energy via fermentation pro-


cesses or by using NO3– as an alternative electron acceptor

when O2 is not present (i.e., denitrification). For example,Microvirgula aerodenitrificans has the unique ability to si-multaneously use both O2 and NO3

– as electron acceptorsduring respiration (Patureau et al. 1998). Organisms foundon CTL substrates had different metabolisms than thoseon NO3

–-amended substrates, so differences in redox con-ditions probably contributed to these divergent microbialcommunities. These drastic shifts in microbial communi-ties occurred on an artificial medium supplemented withorganic C and other nutrients. Therefore, direct extrapola-tion of these results or those of any NDS experiment to how

Figure 3. Principal coordinates analysis (PCoA) plots basedon a Bray–Curtis relative abundance distance measure on 16Sribosomal RNA gene amplicon sequencing data from the nutrient-limitation experiment where treatments consisted of control(CTL), N, P, and N1P (A) and the N-form experiment wheretreatments consisted of CTL, low NH4

1, high NH41, low NO3

–,and high NO3

– (B). Each dot represents a nutrient-diffusingsubstrate disk.

Figure 2. Mean (±95% CI) response ratios of chlorophyll a(Chl a) (A) and ash-free dry mass (AFDM) (B) on nutrient-amended substrates (low NH4

1, high NH41, low NO3

–, highNO3

–) relative to the control in 9 ponds in 2014. Response ra-tios of 1.0 indicate equal growth relative to the lysogeny brothagar control. Bars with the same lowercase letters are not sig-nificantly different (p > 0.05).



a natural system would respond would be difficult withoutconducting an ecosystem-scale manipulation.

NO3– could alter redox conditions in ecosystems with

anaerobic conditions (D’Angelo and Reddy 1999) and rela-tively low NO3

– inputs from surrounding geology, agricul-


tural runoff, and urban wastewater. Most NDS studies havebeen conducted in well-aerated streams rather than inlakes and wetlands, which are more likely to experienceanaerobic conditions because of stagnant water. A biofilmnutrient-limitation survey conducted across Great Lakes


Table 4. Taxonomic and metabolic information for the indicator operational taxonomic units (OTUs) from indicator species analysison the control (CTL) and high NO3

– nutrient-diffusing substrate treatments listed in Table 3 including class and the ability to reduceNO3

–. Each taxon is identified to the lowest level possible. UC stands for unclassified and is used to mark different OTUs sharingthe same taxonomic group. Taxa that are nonsulfur purple bacteria, which have flexible metabolisms, are marked with an asterisk (*).References used to compile this table are listed in Appendix S4.

Treatment Taxon Class MetabolismReducesNO3

–?

CTL Desulfovibrio Deltaproteobacteria Obligate anaerobic chemoorganotrophs No

Paludibacter Bacteroidia Obligate anaerobic chemoorganotrophs No

Phaeospirillum* Alphaproteobacteria Anoxygenic photoorganotrophs No

Rhodoblastus* Alphaproteobacteria Anoxygenic photoorganotrophs No

Rhodocyclus* Betaproteobacteria Anoxygenic photoorganotrophs No

Clostridiaceae Firmicutes Obligate anaerobic chemoorganotrophsor chemolithotrophs

Some

Veillonellaceae Firmicutes Obligate anaerobic chemoorganotrophs Some

Bacteroidales Bacteroidia Obligate anaerobic chemoorganotrophs Some

High NO3– Janthinobacterium Betaproteobacteria Obligate aerobic chemoorganotrophs Yes

Microvirgula Betaproteobacteria Facultative aerobic chemoorganotrophs Yes

Paenibacillus Firmicutes Aerotolerant anaerobic chemoorganotrophs Yes

Pseudomonas Gammaproteobacteria Obligate or facultative aerobic chemoorganotrophs Some

EnterobacteriaceaeUC1 and 2

Gammaproteobacteria Facultative aerobic chemoorganotrophs Most

Table 3. Indicator species analysis on the control (CTL) and high NO3– nutrient-diffusing substrate treatments from 11 ponds

(p ≤ 0.01 for all indicator taxa). Each taxon is identified to the lowest level possible. UC (unclassified) is used to mark differentoperational taxonomic units (OTUs) sharing the same taxonomic group. An indicator value is the product of components A and B.Component A is the probability that a sample belongs to the treatment group given that the OTU has been found, whereascomponent B is the probability of finding the OTU in samples belonging to the treatment group. Mean (±SD) sequence reads aregiven for each indicator OTU by treatment.

Treatment Taxon A B Indicator value Reads (CTL) Reads (NO3–)

CTL Clostridiaceae 0.91 1.00 0.96 16 ± 18 1 ± 5

Rhodocyclus 0.99 0.91 0.95 270 ± 220 2 ± 4

Paludibacter 1.00 0.73 0.85 4 ± 6 0 ± 0

Desulfovibrio 1.00 0.73 0.85 54 ± 86 0 ± 0

Phaeospirillum 0.99 0.73 0.85 25 ± 64 0 ± 0

Bacteroidales 0.99 0.73 0.85 37 ± 53 1 ± 1

Rhodoblastus 0.98 0.64 0.79 4 ± 6 0 ± 0

Veillonellaceae 1.00 0.55 0.74 1 ± 2 0 ± 0

High NO3– Enterobacteriaceae UC1 0.98 1.00 0.99 3 ± 5 170 ± 100

Enterobacteriaceae UC2 0.96 1.00 0.98 5 ± 9 120 ± 180

Pseudomonas 0.95 1.00 0.98 8 ± 14 170 ± 160

Microvirgula 0.98 0.82 0.90 4 ± 5 170 ± 130

Paenibacillus 0.98 0.73 0.84 0 ± 1 9 ± 9

Janthinobacterium 0.83 0.82 0.83 2 ± 3 9 ± 8


coastal wetlands demonstrated increased biomass in re-sponse to NO3

– (Cooper et al. 2015), but the communitiesof these wetlands with their high NO3

– (97 ± 220 lg/L) andTN (920 ± 710 lg/L) concentrations may have been adaptedto NO3

– inputs. In ecosystems with relatively little anthro-pogenic NO3

– input like the CRD, NO3– amendment could

provide a novel substrate that alters redox conditions in an-aerobic biofilms. If thick biofilms experience O2 depletionand are given a novel electron acceptor, such as NO3

–,denitrifiers could outcompete organisms using other alter-native electron acceptors because Mn41, Fe31, SO4

2–, andCO2 are less energetically profitable than NO3

– (StummandMorgan 1996). This advantagemay explain why we ob-served a shift in microbial communities on NO3

–-amendedsubstrates toward organisms with the ability to reduceNO3

–.The shift in redox conditions can explain the observed

patterns in microbial community composition, but the re-duction in biofilm biomass caused byNO3

– ismore difficultto interpret. Likens et al. (1970) suggested that high con-centrations of NO3

– may be toxic to certain bacterial spe-cies, but did not offer a mechanism or toxicity threshold.NO3

– is used as a preservative to reduce the growth ofClostridium botulinum and other microorganisms (Roberts1975), and NO2

– can retard lipid oxidation (Gray et al.1981). Reduction of NO3

– to NO2– in these pond biofilms

could inhibit microbial growth. We did see a larger reduc-tion in biomass at higher concentrations of NO3

– (>60%at 0.5 M), but still observed a 40% reduction in biofilmbiomass at the log-lower concentration (0.05 M). The Ndiffusion rate of the high NO3

– (0.1 mg/h) and low NO3–

(0.01mg/h) substrates early in deployment wasmuch lowerthan for N substrates used in other studies with comparableconcentrations (Bernhardt and Likens 2004: 10 mg/h,Rugenski et al. 2008: 0.1–1 mg/h). However, NO2

– stillcould have accumulated in the thick CRD biofilms at highenough concentrations to approach toxicity.

An alternative way to explain the reduction in total bio-film biomass is that NO3

– alters microbial interactions byselecting for taxa that produce natural antimicrobial com-pounds. Thus, the reduction in biomass could be caused bycertain bacterial taxa spending their energy on the produc-tion of toxins instead of growth. The toxins, in turn, couldreduce the growth of other microbial competitors. Manyof the indicator OTUs for the NO3

–-amended substrateshave been documented to produce antibiotics. For exam-ple, Janthinobacterium spp., an indicator of our NO3

–-amended communities, produces a violet pigment with an-timicrobial properties called violacein (Pantanella et al.2007, Kim et al. 2012), whereas Janthinobacterium lividumproduces the antibiotic prodigiosin (Schloss et al. 2010). Astrain of Paenibacillus, another indicator of our NO3

–-amended communities can produce polymyxin E1, an anti-biotic active against Gram-negative bacteria, and 2983-Da,


an unknown antibiotic active against Gram-positive bacte-ria (He et al. 2007).We did not find similar evidence of CTLOTUs producing antimicrobial compounds in the litera-ture. We cannot verify whether our related OTUs produceantibiotics without culturing these strains in the laboratoryand testing them under similar conditions to those in theexperiment, but their potential antimicrobial propertiescould help explain the reduction in total biofilm biomass.

ConclusionsWe demonstrated that NO3

– can greatly reduce biofilmbiomass and strongly alter microbial community composi-tion in low-nutrient environments. The reduced biomassobserved on NO3

–-amended substrates may be caused byNO3

– toxicity or shifts in competitive advantages amongtaxa, which affect biofilm formation and community as-sembly. We suggest that other aquatic ecosystems that re-ceive little geologic or anthropogenic NO3

– input may ex-hibit strong changes in microbial community structure andpotentially function. Future researchers should test whethera legacyofanthropogenicNO3

– inputs fundamentallychangesmicrobial community assembly and biogeochemical cyclingin aquatic ecosystems.

ACKNOWLEDGEMENTSAuthor contributions: CV, DTC, and GAL designed the study.

CV performed the fieldwork and water chemistry, Chl a, andAFDM analyses. JML conducted the ARISA analyses. JLP and MEBhelped with IlluminaMiSeq analyses. JML, JLP, MEB, and SEJ pro-vided guidance to CV on the microbial data analyses. CV wrote themanuscript with significant input on the manuscript’s directionfrom SEJ and all authors providing editorial advice.

We thank the Cordova Ranger District of the US Departmentof Agriculture, Forest Service for providing field and logistical sup-port, particularly Deyna Kuntzsch, Andrew Morin, Sean Meade,Luca Adelfio, and Ken Hodges, without whom this work on theCRD would not have been possible. We also thank Gordie Reevesof the Pacific Northwest Research Station for his leadership anddirection in the extensive research being conducted on the CRD.Mike Brueseke, Melanie Runkle, Josephine Chau, and Julia Hartassisted with analyses of water chemistry, Chl a, and AFDM. Dayna(Smith) Evans and Julia Hart helped with summer field and labo-ratory work in 2013 and 2014, respectively. The Center for Envi-ronmental Science and Technology (CEST) at University of NotreDame (UND) provided instrumentation and analytical assistancefor the chemical analyses. Ursula Mahl helped with chemical anal-yses on the Lachat. Funding was provided by the USDA ForestService, the Pacific Northwest Research Station, the National Fishand Wildlife Foundation, UND, and the National Science Foun-dation Graduate Research Fellowship Program. We also thankmembers of the Jones laboratory and the Lamberti laboratory atUND and Jen Tank for their feedback on the manuscript. We aregrateful for the comments of 2 anonymous referees and AssociateEditor Antonia Liess, all of whom made suggestions that improvedthe manuscript.



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