impacts of long-term irrigation of domestic treated ... · sought (i) to investigate the impacts of...

Impacts of Long-Term Irrigation of Domestic Treated Wastewater onSoil Biogeochemistry and Bacterial Community Structure

Denis Wafula,a* John R. White,b Andy Canion,c* Charles Jagoe,a,d Ashish Pathak,a Ashvini Chauhana

Environmental Biotechnology and Genomics Laboratory, School of the Environment, Florida A&M University, Tallahassee, Florida, USAa; Wetland and AquaticBiogeochemistry Laboratory, Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana, USAb; Earth, Ocean, andAtmospheric Science Department, Florida State University, Tallahassee, Florida, USAc; NOAA Environmental Cooperative Science Center, Florida A&M University,Tallahassee, Florida, USAd

Freshwater scarcity and regulations on wastewater disposal have necessitated the reuse of treated wastewater (TWW) for soilirrigation, which has several environmental and economic benefits. However, TWW irrigation can cause nutrient loading to thereceiving environments. We assessed bacterial community structure and associated biogeochemical changes in soil plots irri-gated with nitrate-rich TWW (referred to as pivots) for periods ranging from 13 to 30 years. Soil cores (0 to 40 cm) were collectedin summer and winter from five irrigated pivots and three adjacently located nonirrigated plots. Total bacterial and denitrifiergene abundances were estimated by quantitative PCR (qPCR), and community structure was assessed by 454 massively paralleltag sequencing (MPTS) of small-subunit (SSU) rRNA genes along with terminal restriction fragment length polymorphism (T-RFLP) analysis of nirK, nirS, and nosZ functional genes responsible for denitrification of the TWW-associated nitrate. Soil physi-cochemical analyses showed that, regardless of the seasons, pH and moisture contents (MC) were higher in the irrigated (IR)pivots than in the nonirrigated (NIR) plots; organic matter (OM) and microbial biomass carbon (MBC) were higher as a func-tion of season but not of irrigation treatment. MPTS analysis showed that TWW loading resulted in the following: (i) an increasein the relative abundance of Proteobacteria, especially Betaproteobacteria and Gammaproteobacteria; (ii) a decrease in the rela-tive abundance of Actinobacteria; (iii) shifts in the communities of acidobacterial groups, along with a shift in the nirK and nirSdenitrifier guilds as shown by T-RFLP analysis. Additionally, bacterial biomass estimated by genus/group-specific real-timeqPCR analyses revealed that higher numbers of total bacteria, Acidobacteria, Actinobacteria, Alphaproteobacteria, and the nirSdenitrifier guilds were present in the IR pivots than in the NIR plots. Identification of the nirK-containing microbiota as a proxyfor the denitrifier community indicated that bacteria belonged to alphaproteobacteria from the Rhizobiaceae family within theagroecosystem studied. Multivariate statistical analyses further confirmed some of the above soil physicochemical and bacterialcommunity structure changes as a function of long-term TWW application within this agroecosystem.

Rapid population growth across the globe, an increase in percapita water consumption, and, in part, global climate changehave resulted in increased demands on available freshwater re-sources (1–3). Many countries are turning to wastewater recyclingin order to meet these increased freshwater demands (3–5).Therefore, planned and managed reuse of wastewater is increas-ingly practiced not only in arid or semiarid regions but also intemperate and subtropical regions that do not routinely face watershortages (6–9). Regardless of the motivation, large-scale reuse oftreated wastewater (TWW) is now becoming increasingly com-mon worldwide. With proper planning, implementation, andmanagement, land application of treated wastewater can benefitagriculture, water resource management, and the environment(10–13). Therefore, in 1992, the U.S. Environmental ProtectionAgency (U.S. EPA) developed guidelines for the reuse of TWW (8)intended for the irrigation of residential landscapes, parks, schoolyards, highway medians, fodder, and fiber crops, as well as forenvironmental purposes such as creating artificial wetlands, andsustaining stream flows.

However, reuse or disposal of TWW is not totally free of un-desirable impacts. Most notably, land application of TWW has thepotential to transfer heavy metals (14), pharmaceuticals (15), andeven pathogens (16) in the environment and into the food chain(17). In fact, several studies have shown that nutrients, includingtotal carbon (TC), total nitrogen (TN), and soil microbial quo-tient (the ratio of microbial biomass carbon [MBC] to soil total

organic C) remain higher in soils irrigated (IR) with TWW (4, 5, 9,18, 19). Among the nutrients originating from land application ofwastewater, nitrate (NO3

�) is considered to be a ubiquitous con-taminant worldwide (20, 21), threatening aquatic ecosystems andsubsurface aquifers, which are often the major source of potablewater. Thus, it comes as no surprise that over 20% of rural wells insome parts of the United States contain NO3

� concentrationsabove the drinking water limit of 10 mg/liter (22); inputs of even a

Received 7 July 2015 Accepted 23 July 2015

Accepted manuscript posted online 7 August 2015

Citation Wafula D, White JR, Canion A, Jagoe C, Pathak A, Chauhan A. 2015.Impacts of long-term irrigation of domestic treated wastewater on soilbiogeochemistry and bacterial community structure. Appl Environ Microbiol81:7143–7158. doi:10.1128/AEM.02188-15.

Editor: J. E. Kostka

Address correspondence to Ashvini Chauhan, [email protected].

* Present address: Denis Wafula, Department of Cell Biology and MolecularGenetics, University of Maryland, College Park, Maryland, USA; Andy Canion, St.Johns River Water Management District, Palatka, Florida, USA.

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.02188-15.

Copyright © 2015, American Society for Microbiology. All Rights Reserved.

doi:10.1128/AEM.02188-15

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few milligrams/liter of NO3� can have significant and long-lasting

environmental impacts (23).Recent reports from Florida (Tallahassee) and Colorado (Den-

ver and Fort Collins) have shown that groundwater sampleddowngradient from farmlands receiving spray-irrigated TWWcontained elevated levels of sodium (Na), boron (B), phosphorus(P), NO3

�-N, chloride, and even pharmaceutical and personalcare products (PPCPs) (6, 24–27). Studies conducted by the U.S.Geological Survey (USGS) on the same location used to conductthis study showed that land application of TWW is a major con-tributor to eutrophication in Wakulla Springs, FL (28), theworld’s largest freshwater spring, located downgradient from thewastewater-receiving agroecosystem (Fig. 1). In fact, the TWWapplied to the study site contains approximately 16 mg/liter totaldissolved N, with a further breakdown of 8.8 mg/liter of ammoniaplus organic N and 7.6 mg/liter NO3

�-N (27). However, NO3� is

the only dissolved N species found in water from wells monitoredat the southern boundary of the spray field under study, withconcentrations ranging from 3.4 to 4.8 mg/liter N (27). This is inline with previous reports of the conversion of ammonium andorganic nitrogen to nitrate in the upper part of the unsaturatedzone beneath the root zone (28). Because TWW-associatedNO3

�-N has been identified as a major pollutant to the receivingagroecosystem soils and groundwater (23) and is known to persistin the environment for decades (29), our main objective in thisstudy was to investigate the fate of applied TWW-associated ni-trate, which is known to recycle in the soils and groundwater pri-marily via denitrification.

Despite the above concerns about the land application ofTWW, previous studies have largely focused on the assessment ofsoil biogeochemistry, but the impacts on soil microbiota that un-derpin nutrient biogeochemical cycling have remained largely ig-nored. Even the studies that have attempted to assess the impact ofland application of wastewater have primarily focused on the en-zymatic activity of soil microorganisms, the fate and transport ofpathogenic microorganisms from the irrigated wastewater intothe soils, or the characterization of antibiotic resistance in bacteriaisolated from the irrigated soils (17, 30–32). To address the impactof TWW irrigation on soil microorganisms, Hidri and colleagues(33) utilized automated ribosomal intergenic spacer analysis(ARISA), and more recently Frenk et al. (31) and Broszat et al. (17)used 454 massively parallel tag sequencing (MPTS) of small-sub-unit (SSU) rRNA genes to show that the soil bacterial communi-ties were indeed impacted by wastewater irrigation such that irri-gation caused an increase in the relative abundances of potentiallypathogenic gammaproteobacterial assemblages within the TWW-irrigated soils. However, no attempts were made to correlate thebiogeochemical impacts, such as those from the TWW-associatednitrate (NO3

�) on the soil microbiota, in particular, the bacterialNO3

� reducers, which are likely to be the main sink of N seques-tration in such TWW-impacted environments. Therefore, wesought (i) to investigate the impacts of land application of TWWon soil biogeochemistry along with bacterial abundances, com-munity composition, and diversity and (ii) to obtain a better un-derstanding of the impact of TWW-associated NO3

�-N on thesoil denitrifier microbiota.

FIG 1 Location of the spray-field agroecosystem in Tallahassee, FL. Groundwater in the area flows in a southerly direction toward the Gulf of Mexico. The rightpanel shows an aerial image of the wastewater-receiving plots (pivots) and the nonirrigated control sites sampled for this study. Aerial image data are from theU.S. Department of Agriculture, Farm Service Agency; the map was created using ArcGIS, version 10.1.

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Denitrification is a microbially mediated process in whichNO3

� is reduced sequentially, producing N2 gas (34). It is a majorcause of N loss from agricultural soils, and, moreover, the processalso has the potential to produce nitrous oxide (N2O), a potentgreenhouse gas. Although soil abiotic conditions can affect deni-trification (35–37), microbial factors such as the diversity, com-position, and abundance of bacterial as well as fungal denitrifiersare also critical in regulating the fate of environmental N (38–40).Therefore, it is necessary to obtain a holistic understanding of thecommunity dynamics of soil bacterial denitrifiers as well as oftheir response(s) to human activities, such as the reuse of TWWvia land application. Such studies help further our understandingof the impacts of human managed systems on ecosystem services,which are largely controlled by environmental microorganisms.

To understand the structure and diversity of bacterial denitri-fier communities in the environment, functional genes coding fordenitrification enzymes have been extensively used; these includethe nitrite reductases (nirK and nirS) and the nitrous oxide reduc-tase (nosZ) (38–41). Using these genes, previous studies haveshown that long-term fertilization with different treatments influ-enced the size, composition, and functioning of bacterial denitri-fiers (42). In fact, Zhou et al. (41) demonstrated that, compared toirrigation with clean water, TWW application on agricultural soilsresulted in an increase in the numbers of nirK-containing com-munities and also shifted the composition of the nirS and nirKdenitrifier assemblages.

This study was performed to further enhance our understand-ing of soil biogeochemistry and the response(s) of total bacteriaand the denitrifier bacterial guilds to the long-term application ofsecondary treated wastewater (TWW). Our results show thatTWW irrigation influenced the composition and abundance oftotal bacteria along with shifts in the nirS and nirK denitrifierguilds. Additionally, some of the microbial changes also stronglycorrelated with changes in the irrigated-soil physicochemical sta-tus, most likely brought about by the land application of treatedwastewater.

MATERIALS AND METHODSSite description and sample collection. Since 1982, the City of Tallahas-see, FL, has been reusing secondary treated wastewater by spray irrigationon an approximately 890-ha farm to grow crops (Fig. 1). On average,about 64.5 million liters (approximately 83 thousand liters/ha) of TWWis discharged daily into these fields by the use of 16 computer-controlledirrigators. TWW-receiving areas (termed pivots) range from 14 ha to 80ha although the majority are approximately 59 ha. The irrigated soils aremanaged through no-till farming and do not receive any synthetic ornatural fertilizers. Thus, the spray-irrigated crops, which include canola,corn, soybeans, hay, and sorghum, are sustained solely by the nutrientscontained in the treated wastewater and, upon harvesting, are mainly usedto feed farm animals.

Soil samples used for this study were part of a larger ongoing study andwere collected in December 2010 (winter, W) and August 2011 (summer,S). Triplicate samples were collected from a depth of 0 to 40 cm by a soilauger from five pivots and three adjacent plots, serving as controls. Theplots serving as controls for this study have never been used for agricul-ture, nor have these soils ever received direct application of irrigatedwastewater. An additional nonirrigated (NIR) site (control D) was alsosampled because measurements at the initial nonirrigated site (control C)mirrored the pivots in our preliminary soil biogeochemical analysis, sug-gesting that this site was likely indirectly impacted by the hydrologicalflow of irrigated TWW (Fig. 1), as also shown previously (6). The fivepivots have different TWW exposure histories: pivot 1 and pivot 6 have

received TWW since 1982, pivot 13 was established in 1987, and pivot 15and pivot 16 began receiving TWW in 1999. Plant debris and roots, if any,were removed, and samples were homogenized in Ziplock bags and storedover ice for return to the laboratory at Florida A&M University, Tallahas-see, where they were preserved at �80°C until further analysis.

Soil biogeochemical measurements and statistical analysis. For bio-geochemical analyses, triplicate cores collected from the five pivots andfour control plots were processed and analyzed independently. Soil mois-ture content (MC) was determined using the standard gravimetricmethod (43). Total carbon (TC) and total nitrogen (TN) were determinedon dried, ground subsamples using a Carlo-Erba NA-1500 CNS analyzer(Haak-Buchler Instruments, Saddlebrook, NJ). For total phosphorus(TP), 0.5 g of dried, ground subsample was combusted at 550°C for 4 h ina muffle furnace, followed by dissolution of the ash in 6 M HCl on a hotplate. TP was analyzed in the digested solution using an automated ascor-bic acid method on a Seal AQII discrete analyzer, according to U.S. EPAmethod 365.4 (44). Microbial biomass carbon (MBC) was determinedusing a fumigation and extraction technique (45). The soil biogeochemi-cal data were analyzed by two-way analysis of variance (ANOVA) usingSAS JMP, version 10. Shapiro-Wilk W tests were used to determinewhether data were normally distributed, and log transformations wereapplied where necessary to meet the assumptions of analysis of variance.For this analysis, classification variables were land use (irrigated sites [n �5] or nonirrigated sites [n � 3]) and season (winter or summer). Statisti-cal analysis was performed by averaging replicate samples obtained fromwinter and summer from the irrigated and nonirrigated samples sepa-rately, and the mean values for each plot were used for the two-wayANOVA.

Extraction of soil gDNA. Genomic DNA (gDNA) was extracted fromeach of the triplicate cores collected from five pivots and four control plotsusing a PowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, CA)according to the manufacturer’s instructions. The extracted DNA fromfive irrigated soils and four nonirrigated soils, respectively, were quanti-fied using an EON Microplate spectrophotometer equipped with a Take 3microvolume plate (BioTek Instruments, Winooski, VT). Soils are knownto be extremely variable, and the best approach is to analyze replicatesindependent of each other. However, to avoid pseudoreplication, wecombined the gDNA extracts based on exposure histories of the pivots towastewater application.

Specifically, triplicate gDNA extracts from each of the irrigated andnonirrigated samples were pooled to obtain three different pivot sampleswith variable periods of TWW exposure and four different control plotsamples. Samples were pooled based on the TWW exposure time suchthat pivots 1 and 6 had received TWW since 1982, pivot 13 was establishedin 1987, and pivots 15 and 16 began receiving TWW in 1999. Thus, gDNAextracts from pivots 1 and 6 were pooled, pivot 15 was analyzed withoutpooling, and extracts from pivots 15 and 16 were pooled. This way, weobtained replicated but pooled samples from three irrigated and fournonirrigated sites in both summer and winter, respectively, which werefurther processed for the quantitative PCR (qPCR), terminal restrictionfragment length polymorphism (T-RFLP), PCR cloning, pyrosequencing,and diversity analyses. Statistical analysis conducted on total bacterialgene copy numbers (see Fig. S1 in the supplemental material) and deni-trifier genes (see Fig. S2) showed that, despite pooling, control measure-ments were distinctly different from those of the pivots.

Total bacterial and denitrifier gene abundance measurements byqPCR. Genes of interest from the irrigated and nonirrigated soil sampleswere quantified by generating calibration curves using SsoAdvancedSYBR green Supermix and a Bio-Rad C1000 thermocycler equipped witha CFX 96 real-time system (Bio-Rad Laboratories, Hercules, CA). Theprimers used for estimating gene copy numbers were from Fierer et al.(46) for total bacteria, Acidobacteria, Actinobacteria, and alphaproteobac-teria along with primers for nirK (47) nirS, and nosZ (48). Further detailsincluding the thermocycling conditions are listed in Table S1 in the sup-plemental material. The qPCR mixtures (20 �l) contained 10 �l of the

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master mix, 1 �l each of 500 nM forward and reverse primers, 20 to 40 ngof template DNA, and nuclease-free water. The cycling conditions in-cluded an initial denaturing step at 98°C for 5 min, followed by 40 cyclesof 98°C for 30 s, annealing at a target-specific temperature for 30 s, and anelongation step at 72°C for 36 s, which included a fluorescent data collec-tion step if appropriate; otherwise the data were collected at 82°C. Thespecificity of our reaction was examined by performing a melt curve anal-ysis at 55°C to 95°C with 0.2°C increments. To ensure robustness of thequantification data, all applicable guidelines for the minimum informa-tion for publication of quantitative real-time PCR experiments (MIQE)were followed (49). For example, a no-template control and a positivecontrol consisting of plasmid DNA with the gene of interest were includedwith each run. Additionally, we also performed inhibition tests using aknown amount of DNA template spiked with plasmid DNA containingthe target gene of interest as a positive control to assess if templates con-tained inhibitors that could potentially affect the quantification process,as shown previously (50). For each calibration curve generated, DNAstandards consisted of plasmid mixtures from 10 to 20 transformantscontaining the target gene of interest as a PCR amplicon. The cloningreaction to obtain plasmids was performed using a TOPO TA cloning kit(Life Technologies, Carlsbad, CA) according to the manufacturer’s in-structions. qPCRs on each of the irrigated and nonirrigated samples wereperformed separately, and the acquired data were initially visualized andanalyzed using CFX Manager, version 2.1 (Bio-Rad Laboratories, Hercu-les, CA). All of the qPCR assays run were shown to possess efficienciesbetween 90 and 110%. Gene copy numbers obtained were analyzed usingtwo-way analysis of variance (ANOVA) using SAS JMP, version 10; Sha-piro-Wilk W tests were used to determine whether variables were nor-mally distributed, and log transformations were applied where necessaryto meet the assumptions of analysis of variance. Classification variablesused for the ANOVA were land use (irrigated sites [n � 5] or nonirrigatedsites [n � 3]) and season (winter or summer). As was done for the bio-geochemical data, replicate qPCR gene copy numbers obtained from theirrigated and nonirrigated samples were averaged, and the mean values foreach plot were used for the two-way ANOVA. Comparison of the ratios ofdenitrifying gene (nirS, nirK, and nosZ) to 16S rRNA gene copy numbersrepresenting the total bacterial population size was also performed usingMicrosoft Excel.

MPTS, heat maps, and statistical analyses. The gDNA was subjectedto massively parallel tag sequencing (MPTS; 454 pyrosequencing) usingpreviously described methods (51). The bar-coding and pyrosequencinganalyses were performed by Research and Testing Laboratories (Lubbock,TX), using forward primer Gray28F (5=-GAGTTTGATCNTGGCTCAG)and reverse primer Gray 519r (5=-GTNTTACNGCGGCKGCTG). Thesequencing reactions were performed using a Roche 454 FLX instrument(Roche, Indianapolis, IN, USA) with titanium reagents, according to themanufacturer’s recommended procedures. After sequencing was com-pleted, sequences that passed the quality controls were uploaded intomothur (52), where tags were removed before the sequences were de-noised, along with the removal of low-quality sequence reads and chi-meras. Sequences that were below 150 bp were discarded from furtheranalysis.

We used a combination of taxonomy-based and taxonomy-indepen-dent approaches to analyze the MPTS data. For the taxonomy-based ap-proach, sequences were analyzed using the Ribosomal Database Project(RDP) Classifier at an 80% confidence level (53). The data obtained in thismanner were analyzed at the bacterial class level. To test for statisticaldifferences in the bacterial community composition, the data were nor-malized for each site using a modification of the procedure applied by Wuet al. (54). Data were transformed by log(x � 1), and Bray-Curtis similar-ities were calculated using Primer, version 6 software (Primer-E, Plym-outh, United Kingdom). The similarity data were then analyzed by clusteranalysis, and nonmetric multidimensional scaling (NMDS) plots weregenerated. In addition, significance of the data was tested using permuta-

tional multivariate analysis of variance (PERMANOVA) with 999 permu-tations, treating land use and season as the two main factors (55, 56).

For the taxonomy-independent analysis, 16S rRNA gene sequenceswere analyzed using the QIIME platform (57). Briefly, all the sequenceswere trimmed to 300 bp, and the sequences were clustered at 97% and95% similarity levels. The 97% similarity clustering data were used tocalculate UniFrac distances (58), while the 95% clustering data were usedfor the canonical correspondence analysis (CCA). Random subsamplingwas conducted to normalize sequence numbers in each sample prior tocalculating the UniFrac distances. The site differences were assessed basedon season and land application and visualized using principal coordinateanalysis (PCoA). To correlate the measured biogeochemical parameterswith the microbial communities, CCA was performed using XLSTAT(Addinsoft, New York, NY), which is a statistical software suite for Mi-crosoft Excel, and/or PAST (PAleontological STatistics) (O. Hammer,D. A. T. Harper, and P. D. Ryan, University of Oslo, Oslo, Norway [http://folk.uio.no/ohammer/past]).

In addition, we analyzed the taxonomic differences between the unas-sembled pyrosequences (environmental gene tags) which were annotatedusing the MG-RAST (Metagenomics Rapid Annotation using SubsystemTechnology) pipeline, version 3.2.5 (http://metagenomics.anl.gov/)(59), with a maximum BLAST E value cutoff of �1 � 10�5 and aminimum alignment length of 15 bp. Bacterial taxonomic profiles atthe phylum and class levels were generated within MG-RAST using thenormalized abundance of phylogenetic identity of sequence matchesto the Ribosomal Database Project and Greengenes, both at a BLAST Evalue cutoff of �1 � 10�5 and a minimum alignment length of 15 bp(60). Heat maps of the frequency of MG-RAST hits to individual taxaacross soils were created after data were normalized (dividing by thetotal number of hits) to remove sequencing bias or differences in theread lengths. Rarefaction analysis of obtained pyrosequences usingboth RDP and MG-RAST showed that enough numbers of sequenceswere obtained from each site because the curves from each samplereached the near-plateau phase at 95% confidence of upper and lowerlimits for each distance, representing good sampling depth (data notshown).

T-RFLP. For nirS amplification, each of the triplicate 20-�l reactionmixtures from the irrigated and nonirrigated samples contained 10 �l ofSsoAdvanced SYBR green master mix (Bio-Rad Laboratories, Hercules,CA), molecular-grade water, approximately 50 ng of template, 500 nM(each) phosphoramidite dye (6-carboxyfluorescein [FAM])-labeled for-ward primer cd3aF and reverse primer R3cd. PCR was carried out in aBio-Rad C1000 Touch thermal cycler (Bio-Rad Laboratories, Hercules,CA) with an initial denaturation at 98°C for 5 min, followed by 34 cycles of98°C for 45 s, 64.2°C for 45 s, and 72°C for 60 s, with a final extension at72°C for 5 min. The enzyme used in this reaction is normally used forqPCR; however, during PCR optimization, we found satisfactoryproducts (sharp bands and no smearing) with only the SYBR greenmaster mix. For nirK amplification, triplicate 25-�l PCR mixtureswere set up consisting of 12.5 �l of GoTaq Hot Start Green master mix(Promega, Madison, WI) with the FAM-labeled forward primernirK1F and reverse primer nirK5R; a denaturing temperature of 95°Calong with an annealing temperature of 64.5°C were used. For nosZamplification, each of the triplicate 25-�l PCR mixtures contained12.5 �l of GoTaq Hot Start Green master mix (Promega, Madison,WI), along with forward hexachlorofluorescein (HEX)-labeled nosZ-Fprimer and nosZ1622R reverse primer; the cycling conditions weresimilar to those of the nirK amplification program.

PCR amplicons were purified using an UltraClean PCR cleanup kit(MoBio), and DNA quantities were spectrophotometrically adjusted us-ing a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies,Inc., Wilmington, DE, USA). Approximately 30 ng of each reaction prod-uct was digested using MspI (Promega, Madison, WI) for 4 h at 37°C. Thedigested DNA was cleaned by ethanol precipitation and dried using aSavant DNA 120 Speedvac (Thermo Scientific). The amplified DNA was

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then suspended along with a 6-carboxy-X-rhodamine (ROX)-labeledstandard in formamide and run on an ABI 310 genetic analyzer (AppliedBiosystems, Foster City, CA). The digested samples were evaluated byGeneScan analytical software (Applied Biosystems, Foster City, CA), andthe resulting T-RFLP peaks were transformed by using the log(x � 1)value to reduce the effect of larger peaks. A resemblance matrix was cre-ated using the Bray-Curtis method in Primer, version 6, software (Primer-E,Plymouth, United Kingdom). To test for differences and visualize therelationships between different sites, PERMANOVA followed by canoni-cal analysis of principal components (CAP) was performed as shown pre-viously (61, 62). Since PERMANOVA is nonparametric, significance isdetermined through permutations and does not require distributionalassumptions such as normality (55).

Identification of the nirK-containing microbiota as a proxy for thedenitrifier communities using PCR cloning and sequencing. PCR wasperformed using the nirK-specific primers and conditions described byHallin and Lingren (63), with a slight modification which involved in-creasing the annealing temperature from 57°C to 60°C, which yielded thebest PCR products from the soils under investigation. PCR amplicons ofnirK genes were visualized to determine correct product size on 2% aga-

rose and stained with GelStar nucleic acid stain (Lonza, MD). The PCRproducts were then purified using an UltraClean PCR purification kit(MoBio) and cloned into TOPO TA cloning vector pCR4 according to themanufacturer’s protocol (Invitrogen, Carlsbad, CA). From each of the IRand NIR clone libraries, 96 clones were screened by PCR amplification ofthe nirK gene. In spite of the use of the nirK gene-specific primers, asizeable number of gene sequences turned out to be non-nirK sequencesafter a BLAST search, especially those from control plot samples and mostof the winter samples. The application of a temperature gradient duringPCR could also not resolve this problem. Thus, several additional librarieswere generated to obtain at least 96 clones each from the IR and NIR soils,and clones were amplified for the nirK gene, digested with HaeIII restric-tion endonuclease enzyme (New England BioLabs, Beverly, MA), andresolved in a 3% agarose gel. Clones were grouped into different opera-tional taxonomic units (OTUs) according to restriction fragment lengthpolymorphism (RFLP) banding patterns. Two representatives from eachOTU were sequenced using a BigDye Terminator sequencing kit (AppliedBiosystems, Foster City, CA) on an Applied Biosystems 3100 genetic an-alyzer prior to phylogenetic analysis. Vector sequences flanking the nirKgene sequences were removed using FinchTV (Geospiza) sequence view-

FIG 2 Box-and-whisker plots are shown for the soil biogeochemical parameters measured from the spray-field agroecosystem soils in Tallahassee, FL, analyzedas a function of land use and season. Box-and-whisker plots give the medians (horizontal lines inside the boxes), interquartile ranges (boxes), and outliers (smallblack dots). †, significant difference in values between seasons; *, significant difference in values between land use types. IR and NIR indicate data obtained fromirrigated pivots and nonirrigated plots, respectively.




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ing and editing software. The BLAST algorithm and GenBank database(64) were used to acquire the nearest known phylogenetic relative of eachsequence, which was aligned using MEGA4 (65). A neighbor-joining treewith a Jukes-Cantor correction with 1,000 bootstrap sampling was gener-ated.

Nucleotide sequence accession numbers. Clone library sequencesgenerated in this study are available in GenBank under accession numbers

KF235417 to KF235419 (nirK), KF235420 to KF235422 (nosZ), andKF235423 to KF235426 (nirS), respectively. Standard sequences gener-ated for qPCR are listed in GenBank under accession numbers KF235427to KF235436. Bar-coded pyrosequences generated in this study are avail-able in the MG-RAST database under the following accession numbers:for IR (S), 4532400.3; IR (W), 4532401.3; NIR (S), 4532554.3, and NIR(W), 4532555.3.

FIG 3 Box-and-whisker plots are shown for the gene copy numbers measured from the spray-field agroecosystem soils in Tallahassee, FL, analyzed as a function of landuse and season. Box-and-whisker plots give the medians (horizontal lines inside the boxes), interquartile ranges (boxes), and outliers (small black dots). Differencesbetween seasons were not significant. *, significant difference between values for land use types. IR and NIR indicate data obtained from irrigated pivots or nonirrigatedplots, respectively.

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FIG 4 (A) Bar plot showing abundances of the predominant phyla identified from the spray-field agroecosystem soils in Tallahassee, FL, as a function of land applicationof domestic TWW relative to those of nonirrigated soils. Identified taxa were categorized at the class level, with the exception of only Acidobacteria, which is shown at thephylum level. (B) Double-hierarchical dendrogram showing distribution at the class level of identified taxa from the spray-field agroecosystem soils in Tallahassee, FL.The phylogenetic tree was calculated using the neighbor-joining method, and the relationship among samples was determined by Bray-Curtis distance and the completeclustering method. The heat map depicts the relative percentage of each identified class (variables clustering on the y axis) within each sample (x-axis clustering). Therelative Euclidean distance values for the bacterial classes identified are depicted by red and green, indicating low and high abundance, respectively, correlating with thelegend at the bottom of the figure. Clusters based on the distance of samples along the x axis and the bacterial classes along the y axis are indicated in the top and left ofthe figure, respectively. Arrows point to the phyla/taxa that were clearly different in the irrigated pivots relative to those in the nonirrigated plots based on Euclideandistances.



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RESULTS

Soil biogeochemistry. To achieve a meaningful statistical com-parison between 15 irrigated and 9 nonirrigated soil samples, wecombined the data obtained into irrigated summer [IR (S)], irri-gated winter [IR (W)], nonirrigated summer [NIR (S)], and non-irrigated winter [NIR (W)] groups and assessed the effect of irri-gation treatment and seasons on soil properties by a two-wayANOVA; differences were considered significant at a P valueof �0.05. As shown in Fig. 2 and Table S2 in the supplementalmaterial, pH and moisture were significantly higher in irrigatedsoils than in the nonirrigated soils. Additionally, organic matter(OM) and MBC were significantly higher in summer but did notdiffer between irrigation treatments. Other measured nutrients,including carbon, nitrogen, and phosphorous, did not differ be-tween seasons or irrigation treatments.

Quantification of bacterial 16S rRNA genes and bar-codedpyrosequence analyses. The standard curves generated for bacte-rial 16S rRNA gene analysis were linear across a scale of 8 orders ofmagnitude (from 100 to 108 copies per reaction), indicating thatthe test reactions were devoid of inhibitors that could have com-promised the quantification process (data not shown). Addition-ally, as is typical, we did not use one cloned cell containing thegene of interest to generate the standard curves; rather, we used apool of plasmid DNA from several cloned cells containing thegene of interest so as to minimize any PCR bias potentially asso-ciated with using DNA from a single clone. Using this approach,the bacterial biomass was estimated using the 16S rRNA genes astracers; a two-way ANOVA showed that the total 16S gene copynumbers were significantly higher in the irrigated pivots, and, byinference, bacterial abundance was higher in the irrigated soilsthan in the nonirrigated plots (Fig. 3; see also Table S3 in thesupplemental material). However, bacterial numbers did not varysignificantly between the two seasons.

Additionally, 454 massively parallel tag sequencing (MPTS)analysis was performed to assess differences in overall communitystructure between the irrigated pivots and the nonirrigated con-trol plots. We found that Proteobacteria, especially Alphaproteo-bacteria, predominated in these soils regardless of season or irri-

gation status, with a relative abundance between 30.8% and40.7%. Proteobacteria were closely followed by Acidobacteria (13%to 31%) and Actinobacteria (6.5% to 25.5%) (Fig. 4A). In addition,specific bacterial responses shown as a function of TWW loadingincluded the following:(i) an increase in the relative abundance ofProteobacteria, especially Betaproteobacteria and Gammaproteo-bacteria, (ii) a decrease in the relative abundance of Actinobacteria,(iii) shifts in the communities of acidobacterial groups, and (iv)no consistent trends in the less abundant phyla. Furthermore, theOTUs and Chao1 species richness estimates calculated from thepyrosequencing data also suggested that the bacterial communi-ties in the TWW-receiving soils were generally more similar toeach other than those in the nonirrigated soils (Table 1).

Additionally, estimation of the 16S gene copy numbers of thepredominant phyla identified by MPTS analyses across the waste-water-receiving agroecosystem were also performed (Fig. 3; seealso Table S3 in the supplemental material). Two-way ANOVA totest for the effects of land use (irrigated or nonirrigated) and sea-son (winter or summer) on the bacterial gene copy numbersshowed that Acidobacteria (P � 0.002), Actinobacteria (P � 0.03),and Alphaproteobacteria (P � 0.001) abundances were signifi-cantly higher in the irrigated pivots than in the nonirrigated plotsand that seasons did not impact the numbers.

Heat map analyses of bacterial communities. We also com-pared the taxonomic affiliations of the bacterial communitiesobtained by pyrosequencing analyses of the irrigated versus thenonirrigated soils at the class, order, and genus levels, using adouble-hierarchical dendrogram (Fig. 4B). Overall, we found thatbacterial communities from the nonirrigated samples clusteredtogether from both of the seasons tested; conversely, the bacterialcommunities from the irrigated sites formed a distinctly separatecluster (Fig. 4B). Moreover, the acidobacterial groups in the irrigatedsoils showed Euclidean distances of 0.41 to 0.47 in the irrigated sum-mer and winter soils, whereas the nonirrigated soils were distinctlyseparate, with Euclidean distances in the range of 0.69 to 0.8 in thesummer and winter seasons, respectively (Fig. 4B). Other taxa thatwere visibly different in the heat map belonged to Chlorophyta, withEuclidean distances of 0.29 in the IR soils and 0.55 in the NIR soils,

TABLE 1 Bacterial diversity measures obtained between irrigated and nonirrigated soil samples used in this studya

Site (season)No. of sequencesretrieved

No. of OTUscalculated Chao1 Coverage (%)

Shannon diversity(H=) Shannon evenness (E)

NIR (S) 7,946 62 63 99.9 2.81 0.68NIR (W) 9,389 66 70 99.9 2.82 0.67IR (S) 59,374 86 91 100 2.81 0.67IR (W) 14,022 73 76 100 2.86 0.63a Alpha diversity measures between different site locations based on the taxonomy-dependent data at the class level.

TABLE 2 Relative abundance of denitrifying gene copy numbers compared to 16S RNA gene copy numbers

Site (season)a

Relative abundance of the indicated gene (no. of gene copies/no. of 16S rRNA gene copies)

nirK nirSb nosZ

NIR (S) 4.49 � 10�4 � 4.28 � 10�4 5.04 � 10�7 � 5.97 � 10�7 1.07 � 10�3 � 9.41 � 10�4

NIR (W) 5.30 � 10�4 � 1.82 � 10�4 1.08 � 10�7 � 1.53 � 10�7 1.17 � 10�3 � 4.66 � 10�4

IR (S) 1.12 � 10�3 � 1.32 � 10�3 6.68 � 10�6 � 8.72 � 10�6 1.26 � 10�3 � 1.50 � 10�3

IR (W) 6.05 � 10�4 � 5.26 � 10�4 1.78 � 10�5 � 1.66 � 10�5 1.37 � 10�3 � 1.02 � 10�3

a S and W, summer and winter seasons, respectively.b It is noteworthy that nirS gene copy numbers were lowest across the control and pivot soils (gene copy number values are reported as the negative powers of the exponent).

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Nitrospira, with Euclidean distances of 0.42 in the IR soils and 0.7in the NIR soils, Bacteroidetes, with Euclidean distances of 0.36 inIR and 0.53 in NIR soils, and Actinomycetes, with Euclidean dis-tances of 0.3 in IR and 0.08 in NIR soils.

Quantification of the denitrifier-associated functional genes.Because the irrigated sites are continuously spray-irrigated withnutrient-rich (mainly NO3

�) wastewater, we hypothesized thatthe wastewater NO3

� would potentially impact the soil denitrifiermicrobiota. To test this, we quantified and compared the abun-dances of denitrifier-associated bacterial nirS, nirK, and nosZgenes at the five irrigated pivots and four nonirrigated sites. Usingtwo-way ANOVA, we found that the nirS gene abundances weresignificantly higher at irrigated sites (P � 0.03) (Fig. 3). Con-versely, abundances of nirK and nosZ genes did not differ signifi-cantly between the irrigated and nonirrigated sites although theirnumbers appeared to be relatively higher in the irrigated soils(Fig. 3; see also Table S3 in the supplemental material). In addi-tion, we found no significant differences in the denitrifier-associ-ated genes between the two seasons. Thus, overall, the denitrifiercommunities mirrored the pattern observed for the total bacterialcommunities such that the bacterial population sizes, as inferredby their gene copy numbers, were higher in the irrigated sites thanin the nonirrigated plots. Moreover, when the ratios of denitrify-ing genes were compared with 16S rRNA gene abundances, wefound that the contributions of the nirS gene relative to total bac-teria (16S rRNA genes), at all locations, were the highest such thattheir gene copy numbers ranged between 10�5 and 10�7, whichare 2- to 3-fold greater than those of nirK (10�3 to 10�4) or of nosZ(10�3 to 10�4), respectively (Table 2). This observation is in linewith a previous study (66).

Studies on the denitrifier bacterial community structure byterminal restriction fragment length polymorphism. T-RFLPanalyses were used to assess the community structure of bacterialdenitrifier guilds, which showed trends similar to those of the totalbacterial communities. More specifically, we found that the nirKand the nirS guilds from the irrigated pivots clustered together andaway from the nonirrigated control plot communities (Fig. 5Aand B). Moreover, in the irrigated pivots, the nirS guilds showedthe strongest similarities at 40%, 60%, and 80% resemblance lev-els while nirK communities had only 60% and 80% levels (Fig. 5Aand B). Conversely, the nosZ guilds did not correlate with eitherthe irrigation status or the tested seasons (Fig. 5C). Additionally,PERMANOVA also showed that a significant relationship existsbetween the irrigation status and both the nirS (P � 0.05) and nirK(P � 0.004) assemblages, but neither irrigation nor season had anysignificant impact on the nosZ guilds.

Identification of the nirK-containing soil microbiota as aproxy for denitrifier communities using PCR cloning and se-quencing. In order to determine the diversity of the bacterialdenitrifier community in the IR and NIR soils, the nirK gene wasused as a proxy, mainly because several previous reports indicatedthat nirK is more amenable than nirS to PCR amplification in soils(67–69). Using PCR cloning, a total of 95 nirK-type gene se-quences were recovered from the IR and NIR samples. Phyloge-netic analysis revealed that many of the nirK-containing microor-ganisms from this agroecosystem were similar to environmentalclones and alphaproteobacteria from the Rhizobiaceae family na-tive to agricultural soils (Fig. 6). These results are in accordancewith previous studies that found numerous functional marker

genes for denitrification in soils clustering with denitrificationgenes of Rhizobiales (67, 70–72).

Correlation between soil biogeochemistry and bacterialcommunity structure using CCA. To further understand whichof the environmental and biogeochemical factors likely caused the

FIG 5 Nonmetric multidimensional scaling ordination of the T-RFLP datafor the nirK gene (A), nirS gene (B), and nosZ gene (C) obtained from thespray-field agroecosystem soils in Tallahassee, FL. Each data point is a mean oftriplicate runs; the data are based on a Bray-Curtis similarity matrix. Open andfilled blue squares and red circles represent the summer (S) and winter (W)seasons, respectively, for the IR and NIR sites, as indicated. Bray-Curtis simi-larity values between irrigated and nonirrigated sample bacterial communitiesare shown at the 20%, 40%, 60%, and 80% levels in the summer (S) and winter(W) seasons.




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differences observed between the bacterial communities of irri-gated and nonirrigated soils, canonical correspondence analysis(CCA) was performed by both taxonomy-dependent and -inde-pendent approaches. As shown in Fig. 7, the first two CCA axesrepresented over 88% of the variance, with Actinobacteria andBacilli in the nonirrigated-site summer samples showing positivecorrelations with TP; conversely, cyanobacteria were negativelycorrelated with the irrigated summer samples. On the second axis,we found positive correlations of Acidobacteria and Proteobacteriafrom the irrigated winter samples with organic matter and totalnitrogen concentrations; moisture content and pH were presentalmost at the borderline of axes 1 and 2, respectively. CCA alsoshowed that nonirrigated winter samples correlated negativelywith total carbon and the microbial biomass carbon (Fig. 7). Cor-relation of soil properties with bacterial OTUs from taxonomy-independent analysis revealed a pattern similar to that observedfor the taxonomy-dependent analysis, albeit with the first andsecond axes describing a reduced (slightly over 50%) source of thecommunity structure variation (data not shown).

In addition, pyrosequencing data were analyzed by principalcoordinate analysis (PCoA) of the weighted UniFrac resemblancematrix, which supported the observations described above, suchthat the wastewater-receiving pivots clustered away from the con-trol sites on the first principal axis, which accounted for 45.12% ofthe variation (data not shown). Additionally, the second axis,which accounted for 21.90% of the variation, was able to separatethe bacterial community of the irrigated sites by season, with win-ter samples clustering together and away from the summer sam-ples. This also suggests seasonal impacts to the soil microbialcommunity within this wastewater-receiving agroecosystem. Ad-ditional ordination of the pyrosequencing data confirmed strongclustering of the samples as a function of both land use andseasons at an 80% similarity level (Fig. 8A and B). Finally,PERMANOVA of the class-level data also revealed significant dif-ferences (P � 0.05) in the bacterial communities as a function ofwastewater irrigation.

DISCUSSION

Historically, environmental controls of microbial communitystructure and their associated functions have been under studiedand hence poorly understood (73–75). However, an increasingbody of evidence garnered from recent studies has shown that soilproperties, vegetation, land use, and even climate change exert asignificant control on the abundance, activities, structure, andfunctions of soil microbial communities (76–79). Using a suite ofapproaches that included measurements of major soil nutrients(TC, TN, and TP), quantitative estimation of total bacterial geneabundances and denitrifier gene abundances, and a detailed as-sessment of the bacterial community shifts using 454 massivelyparallel tag sequencing (MPTS) analyses, T-RFLP, and PCR clon-ing, we depict a holistic understanding of the impact of domestic

FIG 6 Phylogenetic analysis of the denitrifying bacteria obtained from theagroecosystem soils in Tallahassee, FL, based on nirK-type gene sequences. Theneighbor-joining method was used to construct the phylogenetic tree with abootstrap value of 1,000 iterations; only values above that were above 50 areshown at branch points. Accession numbers of the retrieved nirK-type genesequences along with their closest phylogenetic relatives are shown in paren-theses. The Escherichia coli formate dehydrogenase gene (fdoG) (GenBank ac-cession number X87583) was used as an outgroup.

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treated wastewater (TWW) irrigation on soil microbiota that un-derpin biogeochemical cycling, especially of nitrogen, an areaabout which currently little is known.

We found that regardless of seasons, pH and moisture content(MC) were higher in the irrigated (IR) pivots than in the nonirri-gated plots; these findings are in line with previous studies con-ducted by the U.S. Geological Survey (USGS) on the same locationinvestigated in this study (6, 24–26). However, a detailed analysisof bacterial response(s) associated with TWW irrigation was notattempted previously, a knowledge gap which is filled by thisstudy. Note that despite the pooling of irrigated samples for themicrobial analysis based on the period of exposure to TWW ap-plication, we found that excess NO3

� from the TWW irrigationpositively correlated to enhanced bacterial abundances and likelycaused a shift in the community structure of total bacteria as wellas the nirK- and nirS-containing bacterial assemblages associatedwith denitrification of TWW NO3. However, the gene copy num-bers of nosZ guilds did not differ significantly with respect to thevariables of land use and season, suggesting that the nosZ-contain-ing complete denitrifiers were either under different environmen-tal controls than nirK and nirS or that the nosZ assemblages areinsensitive to the TWW application. This is an important findingbecause denitrifier bacterial assemblages lacking the terminal nosZgene produce N2O as a metabolic end product, which is an impor-tant greenhouse gas known to exacerbate global warming pro-cesses. However, the nosZ data obtained in this study should beinterpreted with caution because it is possible that we underesti-mated the population of nosZ-containing denitrifiers. Specifically,after we conducted our study, Sanford et al. used bioinformatics toidentify phylogenetically distinct, atypical nosZ genes in microbialtaxa from both terrestrial and marine environments (80). It wasestablished that due to the conserved sequence features that dis-tinguish the atypical and typical nosZ genes, previously reported

primers for nosZ abundance and diversity analysis, includingthose that we used in this study, are likely to not target the newlydiscovered atypical nosZ genes. In fact, gene copy numbers for alldenitrifiers from this study should be interpreted with cautionbecause the taxonomy of denitrification genes in complex envi-ronmental samples is still not fully known, and thus existing prim-ers may bind to and amplify nonspecific genomic regions, which islikely the reason for the non-nirK-type genes that were found inour study during the clone library sequencing. It can also be thatsome denitrifier members have duplications of the nirK gene.Moreover, hardly any primers are known to amplify the nirS andnosZ genes of Gram-positive denitrifiers; only very recently havenewer primers been developed to target distinctly different nirKand nosZ gene sequences of Gram-positive denitrifiers (81, 82). Toaddress these issues, we performed qPCR assays using SYBRgreen, which is well known to be much more robust than Taq;melting curve analysis was also performed to ensure that qPCRassays produced a single, specific product, as expected for each ofthe denitrifier genes.

We also performed denitrification enzyme assays (DEA) butonly in samples collected in winter that formed part of an earlieranalysis related to this study. DEA rates can serve as an effectiveindicator of in situ denitrification activity (83, 84), and our anal-yses showed that DEA rates were below the detection limit in thenonirrigated plots apart from control C, which is located down-gradient from the spray-field agroecosystem and shows the indi-rect impact from the TWW application; conversely, the irrigatedsoils showed higher rates, ranging between 0.15 and 0.67 �g kg�1

dry soil h�1 (see Fig. S3 in the supplemental material). Thus, takentogether, our results demonstrate that NO3

�-rich wastewater ir-rigation can affect the abundances, diversity, and activities of N-cycling bacterial communities.

It is imperative to note that despite the recent interest in theimpacts of TWW irrigation on soils (17, 31), rarely has theimpact of land application of wastewater been correlated toboth the biogeochemistry and associated soil microbiota thatunderpin biogeochemical processes. This is especially true forthe bacterial NO3� reducers, which likely are one of the mainguilds mediating N sequestration in such TWW-impacted en-vironments. To this end, our study showed that, similar to thenirK- and nirS-containing microbiota, total soil microorgan-isms also responded to the TWW irrigation, indicated espe-cially by the increase in the relative abundances of Betaproteo-bacteria and Gammaproteobacteria (Fig. 4). Moreover, anincrease of as much as 50% in gammaproteobacterial assem-blages from the irrigated soils in this study is similar to findingsfrom Frenk et al. (31) and Broszat et al. (17) from Israel andMexico, respectively. Thus, overall, given the cooccurrence andpredominance of Proteobacteria, Acidobacteria, and Actinobacte-ria in the TWW-irrigated soils investigated in this study, werecommend that future studies specifically focus on thesegroups so that a better understanding of their functional rolesin soil and plant productivity can be obtained and demon-strated. We observed conflicting results on the impact of waste-water on Actinobacteria; i.e., we documented a decrease in rel-ative abundance by using MPTS analysis but an increase inabsolute abundance by using qPCR analysis. As discussed byFierer et al. (46), this discrepancy can potentially be due toDNA extraction bias that can likely change the estimated abun-dances of certain bacterial groups; heterogeneity observed in

FIG 7 Biplot derived from canonical correspondence analysis (CCA) of thebacterial abundances, correlated with soil biogeochemical and environmentalproperties obtained from the spray-field agroecosystem in Tallahassee, FL.Percentages of variation are shown in parentheses on the x and y axes. Nonir-rigated and irrigated sites and sampling seasons are indicated. The filled bluecircles represent bacterial taxa/phyla identified over two seasons in IR and NIRsoils.




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the bacterial ribosomal operon numbers may also affect rela-tive estimates of specific group abundances, and, moreover, theqPCR assays may not amplify rRNA genes from all the mem-bers of each targeted group present in the tested soils.

In contrast to results with the irrigated soils, we observedthat the nonirrigated soils generally contained a higher diver-sity of Acidobacteria, with the exception of only control C site,where Acidobacteria group 1 represented more than 50% of thetotal bacterial assemblages. Irrigated sites showed two distincttrends for Acidobacteria; in summer, approximately 50% of allAcidobacteria were members of group 1, but in winter, this

group was only 10% of Acidobacteria communities, with groups4 and 6 representing at 80% to 90% of this community. Thesetwo groups were generally predominant in winter, and theyalso made up 20% of the total bacterial communities in theirrigated sites. Acidobacteria ecology continues to be poorly un-derstood even though these bacteria can represent as much as80% of the total microbial communities in a variety of differentsoil types (85–87). Recently, 26 Acidobacteria subdivisions wereproposed, and whole-genome sequencing of some Acidobacteriaphylotypes has shed light on their physiological and metabolicsuperiority, which likely facilitates their survival in dry, low-

FIG 8 (A) Nonmetric multidimensional scaling ordination plots of the bar-coded pyrosequences showing the bacterial community structure at the class level.Bray-Curtis similarity values between irrigated and nonirrigated sample bacterial communities are shown at the 50%, 60%, 70%, and 80% levels in the summer(S) and winter (W) seasons. (B) Dendrogram based on cluster analysis of the NMDS of the total bacterial community analyzed by 454 massively parallel tagsequencing. The relative abundance data were transformed by log(x � 1), and the bacterial communities were grouped using the complete linkage option.

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pH, and k-type or oligotrophic environments (88), such as thenonirrigated sites from this study. In a previous study con-ducted by our group to assess the effects of bauxite mining onsoil microbial communities in Jamaica, we found Acidobacteriagroup 6 to predominate, followed by groups 4, 5, 7, and 17(89),suggesting the ubiquitous presence of Acidobacteria in a varietyof differently managed soils.

Some of the bacterial shifts described above strongly suggestthe possibility of changes in soil properties brought about by theTWW irrigation. Specifically, previous studies performed by theCity of Tallahassee have shown pH shifts in the top 0 to 30 cm ofsoils irrigated with TWW. The background pH of these soils istypically 5.20 � 0.20 (0 yr). The pH increased to 6.7 � 0.1 after 3.5years of TWW application, 7.1 � 0.1 after 7.9 years, and 7.3 � 0.2after 17.6 years (90). We are tempted to speculate that this increasein soil pH might have caused suppression of some acidobacterialgroups (Fig. 4; see also Table S3 in the supplemental material)because these bacteria thrive at pH optima of 5.0 to 6.5 (91). Inaddition, the nutrient-rich wastewater likely caused some groupsof Acidobacteria to be outcompeted in the TWW-irrigated soilsbecause most Acidobacteria perform better in oligotrophic k-typeenvironments (73, 88). Therefore, in all likelihood, the irrigationof nutrient-rich TWW favored the r-type microbial communitiesin these soils. In fact, an overall increase in the proportion ofProteobacteria and, more specifically, the 2-fold increase of be-taproteobacterial populations in the TWW-irrigated soils are aclear indication of an increased r-type behavior or copiotrophybecause these bacteria prefer high-nutrient environmentalconditions (73).

When CCA was used to identify the environmental param-eters having significant linkages to the soil microbial commu-nity at the taxonomic or functional gene level, we observed apositive correlation of total nitrogen, organic matter, moistureconcentration, and pH with Proteobacteria and Acidobacteria,communities that jointly constituted approximately 50% of thetotal bacteria in the irrigated sites (Fig. 7). This is strong evi-dence that changes in soil biogeochemistry occurred due toTWW irrigation, which, in turn, likely influenced the soil mi-crobial assemblages. However, these findings should be inter-preted with caution. For example, it can be argued that thebiogeochemical and microbial differences observed in thewastewater receiving soils can also be due to the inherent prop-erties of the supplied wastewater. In fact, some of the microor-ganisms identified from the irrigated pivots are likely native tothe wastewater holding tanks where TWW is stored prior tobeing used in irrigation and so were likely transported into thesoils from the wastewater. This is a critical point, given that thesurvival of wastewater microorganisms under environmentalconditions can vary from a few days to 3 months (92, 93).However, a detailed examination of our data clearly shows thatmost of the predominant bacteria from the irrigated soils are,in fact, native to soils rather than wastewater, with the excep-tion of only cyanobacteria (Fig. 4B) and some members ofGammaproteobacteria, which made up only a minor fraction ofthe total bacterial communities identified in the TWW-receiv-ing soils. Hence, we believe that the contributions of the waste-water-native communities to the overall bacterial shifts ob-served in the irrigated soils were minimal, if any. One can alsoargue that the differences between the irrigated and nonirri-gated soils were due to the farming practices being used in the

agroecosystem. However, it seems unlikely that the farmingpractices, which include no-till farming and no application offertilizers or pesticides, would have caused the irrigated soils todiffer in their biogeochemical and microbial properties relativeto those of the nonirrigated soils. Finally, pH and moisturewere significantly higher in the irrigated soils, likely broughtabout by the TWW irrigation. Specifically, moisture contentsignificantly differed in only the nonirrigated winter soil sam-ples. Therefore, soil moisture was not a significant driver of theobserved bacterial trends in the TWW-receiving sites. If higherpH and moisture were not brought about by irrigation, then,clearly, these two inherent soil parameters alone could havebeen responsible for the bacterial shifts observed. This, how-ever, does not seem to be the case, and most likely, TWWapplication brought about the soil changes we demonstrate inthis study.

In conclusion, multiple lines of evidence clearly showed thatthe soil microbial community dynamics and their functionalroles, such as denitrification, are influenced by the land appli-cation of domestic treated wastewater. Specifically, TWW irri-gation influenced the composition and abundance of total bac-teria, along with shifts in the nirS and nirK denitrifier guilds;statistically, some of these changes also strongly correlated withphysicochemical differences measured within the irrigatedsoils. To our knowledge, such a comprehensive evaluation ofTWW-irrigated soils has not been presented before. An exten-sion of this study would be to investigate whether microbialchanges brought about by the TWW irrigation are advanta-geous or detrimental to the maintenance of soil productivityand associated microbially mediated ecosystem services suchthat environmental risk analysis of wastewater reuse by landapplication can be properly evaluated.

ACKNOWLEDGMENTS

We acknowledge the financial support provided by the School of Gradu-ate Studies, Florida A&M University, and by partial funding obtainedfrom Department of Defense (DoD) grants W911NF-10-1-0146 andW911NF-10-R-0006.

Support provided by Jamie Shakar, Water Quality Manager, and Cityof Tallahassee Underground Utilities for access to the spray-field site isgreatly appreciated. We also thank Caitlin Van Dyke for help with samplecollection, Anthony Nguyen and Nathan Nguyen for assistance with bio-geochemical analyses, and Drew Seminara for help with the geographicinformation systems mapping of the sampled site locations. We also ex-tend our sincere appreciation to the reviewers for providing critical inputwhich greatly facilitated better presentation of this study.

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