vertical distribution of fungal communities in tallgrass ... · ing in their fungal communities....

Vertical distribution of fungal communities in tallgrass prairie soil

Ari Jumpponen1

Division of Biology and Ecological Genomics Institute,Kansas State University, Manhattan, Kansas 66506

Kenneth L. JonesEcological Genomics Institute, Kansas State University,Manhattan, Kansas 66506, and, EnvironmentalHealth Sciences and Georgia Genomics Facility,University of Georgia, Athens, Georgia 30602

John BlairDivision of Biology, Kansas State University,Manhattan, Kansas 66506

Abstract: We used 454 sequencing of the internaltranscribed spacer region to characterize fungalcommunities in tallgrass prairie soils subdivided intostrata 0–10, 10–20, 30–40 and 50–60 cm deep. Thedataset included more than 14 000 fungal sequencesdistributed across Basidiomycota, Ascomycota, basalfungal lineages and Glomeromycota in order ofdecreasing frequency. As expected the communityrichness and diversity estimators tended to decreasewith increasing depth. Although species richness wassignificantly reduced for samples from the deeperprofiles, even the deepest stratum sampled containedrichness of more than a third of that in the topmoststratum. More importantly, nonparametric multidi-mensional scaling (NMS) ordination analyses indicat-ed that the fungal communities differed acrossvertical profiles, although only the topmost anddeepest strata were significantly different when theNMS axis scores were compared by ANOVA. Theseresults emphasize the importance of considering thefungal communities across the vertical strata becausethe deeper soil horizons might maintain a distinctcommunity composition and thus contribute greatlyto overall richness. The majority of operationaltaxonomic units (OTUs) declined in frequency withincreasing depth, although a linear regression anal-ysis indicated that some increased with increasingdepth. The OTUs and BLAST-assigned taxa thatshowed increasing frequencies were mainly uncultur-able fungi, but some showed likely affinities tofamilies Nectriaceae and Venturiaceae or to genusPachnocybe. Although the ecological roles of the fungiin the deeper strata remain uncertain, we hypothesize

that the fungi with preferences for deeper soil haveadequate access to substrates and possess environ-mental tolerances that enable their persistence inthose environments.

Key words: 454 sequencing, pyrosequencing, soildepth, soil fungi, tallgrass prairie

INTRODUCTION

The soil is the host of diverse fungal communities,which derive energy from mutualistic associationswith plants (Smith and Read 2008) and fromconsuming the abundant organic substrates embed-ded in the soil matrix (Cooke and Rayner 1984). Thefungal community in soil has been assessed tradition-ally by isolating fungi in pure culture media. Thesestudies have provided much of the fundamentalunderstanding on the structure of the fungal com-munities in soil (e.g. Domsch et al. 1980) as well asnecessary isolates for physiological studies and exper-imental manipulations of typical, culturable soilfungi. However molecular microbial communityanalyses indicate that culture-dependent methodsare incomplete for describing soil mycobiota becausea large proportion of fungi can escape detection insuch studies (Amann et al. 1995, Borneman andHartin 2000, Jumpponen and Johnson 2005, Porter etal. 2008, Schadt et al. 2003).

Soil microbial diversity has been considered nearlyinnumerable, even in studies using so-called highthroughput molecular tools (see Buee et al. 2009,Roesch et al. 2007). The recent development of suchhigh throughput tools, including massively parallel 454sequencing approaches (Margulies et al. 2005), hasgreat promise for a comprehensive community analysis.Depending on the chosen technology, a singlesequencing run can produce several million base pairs(bp) of sequence reads (Cardenas and Tiedje 2008)thus allowing an unprecedented saturation of organismrichness in complex environmental samples (seeRoesch et al. 2007). Similarly combination of thesetools with DNA-tagging allows simultaneous examina-tion of a large number of samples without an additionalsequencing cost (Hamady et al. 2008, Hoffmann et al.2007, Meyer et al. 2008). Combined with DNA tagging,the application of these tools opens the microbialecologist’s toolbox to adequate replication of experi-mental units and also enables more complex andrevealing experimental designs.

Because most detritus inputs and most microbialactivity and biomass occur in the topmost soil profile

Submitted 21 Dec 2009; accepted for publication 7 Mar 2010.1 Corresponding author. Address: 433 Ackert Hall, Division ofBiology and Ecological Genomics Institute, Kansas State University,Manhattan, Kansas 66506 USA. Phone: 1 785 532 6751. Fax: 1 785532 6653. E-mail: [email protected]

Mycologia, 102(5), 2010, pp. 1027–1041. DOI: 10.3852/09-316# 2010 by The Mycological Society of America, Lawrence, KS 66044-8897

1027

(Fritze et al. 2000, Lenz and Eisenbeis 1998, Snajdr etal. 2008), soil microbial communities, their diversityand richness have rarely been evaluated in the deepersoil (Fierer et al. 2003). In addidtion most studies onvertical distribution of fungal communities in soilfocus on mycorrhizal associates of plants (Dickie et al.2002, Egerton-Warburton et al. 2003, Oehl et al. 2005,Rillig and Field 2003). Studies on ectomycorrhizal orsoil fungal community composition show verticalseparation of the fungal communities in boreal forestecosystems (Baier et al. 2006, Dickie et al. 2002,Rosling et al. 2003). From a functional standpointLindahl et al. (2007) suggested that saprobic fungiare confined largely to recent plant litter on the forestfloor whereas ectomycorrhizal fungi tend to dominatethe underlying litter and humus. Studies in arable orgrassland ecosystems similarly show vertical pattern-ing in their fungal communities. Arbuscular mycor-rhizal (AM) root colonization declines with depth( Jakobsen and Nielsen 1983, Rillig and Field 2003) asdo the number of AM propagules (An et al. 1990), thenumber of AM spores (Oehl et al. 2005, Zajicek et al.1986), the amount of AM hyphae (Kabir et al. 1998)or the number of observed species (Oehl et al. 2005,Zajicek et al. 1986). Although AM spore densitiestend to decline with soil depth, Oehl et al. (2005)observed that soil up to 70 cm deep can maintainconsiderable AM species richness thus supportingother findings that deeper soil can contributesubstantially to the microbial diversity in soil (Fiereret al. 2003).

Our primary goals in this study were to apply 454sequencing and DNA tagging to explore the verticaldistribution of the fungal communities in a nativetallgrass prairie soil and to test whether they differ inrichness, diversity or composition along the verticalgradient. In addition we also aimed to pinpoint taxathat show a clear preference for deeper soil; weexpected that a majority of fungi would be limited tothe topmost profile. Oehl et al. (2005) identified AMspecies that increased in relative abundance with

increasing depth (e.g. Scutellospora calospora and S.castanea), one of which (S. castanea) was detectedonly below 20 cm. Our data show that, while fungalrichness and diversity do decrease with increasingdepth, the profiles below 50 cm maintain consider-able diversity and some taxa seem near exclusive tothese deeper soils.

MATERIALS AND METHODS

Site.—The study was conducted at the Konza PrairieBiological Station (KPBS, 39u059N, 96u359W), a long-termecological research (LTER) site representative of nativetallgrass prairie in the Flint Hills of eastern Kansas. KPBSspans 3487 ha, and the majority of the site remainsundisturbed by agriculture. The vegetation is dominatedby native grasses: big bluestem (Andropogon gerardii Vit-man), indian grass (Sorghastrum nutans (L.) Nash.), littlebluestem (Schizachyrium scoparium (Michx.) Nash.) andswitch grass (Panicum virgatum L.) (see Towne 2002 for acomplete list of vascular plants at the KPBS). The Flint Hillsare generally characterized by shallow soils overlaying chert-bearing limestone and shale (Ransom et al. 1998).Topographic relief divides the landscape into uplandplateaus with shallow soils, slopes with outcrops oflimestone and lowlands with deeper alluvial and co-alluvialsoils. January mean temperature is 23 C (range 29 to 23 C)and the July mean is 27 C (range 220 to 233 C). Annualprecipitation averages 835 mm, 75% of which falls in thegrowing season. In the present study we sampled soilprofiles in the unsheltered control plots of an ongoingclimate change study (Fay et al. 2000). These plots arelocated on relatively deep Irwin silt-like clay loams (fine,mixed, mesic Pachic Argiustolls) with slopes less than 4%.Depth to limestone varies approximately 75 cm–1.2 m. Soiltexture and bulk density vary with depth, as do percent Cand N (TABLE I). The experimental site is managed byannual spring burning (typically late March), a commonpractice that controls woody vegetation and generallyenhances productivity of the dominant perennial grasses.

Sampling, DNA extraction, PCR amplification and sequenc-ing.—All soil cores were collected 25–30 Nov 2007 with aGeoprobe hydraulic soil corer (Geoprobe, Salina, Kansas)

TABLE I. Selected physical and chemical properties of soil cores by depth stratum

DepthBulk density

(g/cm3)a % Sanda,b % Silta,b % Claya,b % Ca,c % Na,c

0–10 cm 0.95 6 0.01 19 6 2 59 6 2 21 6 1 4.29 6 0.28 0.34 6 0.0210–20 cm 1.15 6 0.02 21 6 5 53 6 3 26 6 3 2.80 6 0.07 0.24 6 0.0130–40 cm 1.24 6 0.05 14 6 1 46 6 1 40 6 1 1.94 6 0.07 0.17 6 0.0150–60 cm 1.32 6 0.04 19 6 2 44 6 1 37 6 1 1.21 6 0.10 0.12 6 0.01

a Values are means (6 1 SE) based on six soil cores, except for texture, which was based on three samples formed bycompositing soil samples from 2 cores.

b Soil texture was determined with the hydrometer method following dispersal of soils with sodium hexametaphosphate.c Soil C and N content were determined by coupled combustion and gas chromatography with a Flash EA1112 analyzer.

1028 MYCOLOGIA

with a 1 to one-half inch (3.8 cm) inner diam coring tool toa depth of 1 m, or until rock was encountered. The coreswere taken encased in plastic sleeves, which were removed,capped and placed in a 220 C freezer within 5 h ofcollection. Cores were sectioned into 10 cm depthincrements, and individual sections were thawed in arefrigerator as they were processed and subsampled forvarious biological, physical and chemical analyses. In thepresent study we used six replicate cores taken from control(unsheltered) plots that represent the native tallgrassvegetation in absence of any experimental manipulations.Two approximately 0.5 g subsamples from each 0–10, 10–20, 30–40 and 50–60 cm deep strata from each of the sixcores were sampled for genomic DNA extraction andtransferred into MoBio Bead Tubes (UltraClean Soil DNA,MoBio Laboratories, Carlsbad, California). Because weexpected that the fungal richness and diversity woulddecline with soil depth we reduced the sampling intensityin deeper strata. This also reduced the sample numbers andincreased the expected sequencing depth per sample. Thetotal DNA was extracted as instructed by the manufacturerand eluted in 100 mL of buffer S5. All templates werequantified with an ND1000 spectrometer (NanoDropTechnologies, Wilmington, Delaware) and adjusted to afinal 2.5 ng mL21 concentration for PCR amplification.

In direct 454 sequencing of internal transcribed spacer 2(ITS2) amplicons we synthesized primer constructs thatincorporated the 454 primers (Margulies et al. 2005) andITS primers (ITS5 and ITS4, White et al. 1990) as describedin Jumpponen and Jones (2009). Initial tests with theprimer combination ITS1 and ITS4 (White et al. 1990) wereunsuccessful and produced no detectable amplicons. Theselected primer constructs combined 454 sequencingprimer (A-primer) and the reverse primer (ITS4) with afive base-pair (bp) DNA-tag for postsequencing sampleidentification in between, or the DNA capture bead annealprimer (B-primer) for the emulsion PCR (emPCR) and theforward primer (ITS5). This primer choice resulted inreverse sequence across the ITS2 region.

After optimization for template concentration, MgCl2concentration and annealing temperature the ITS regionwas PCR-amplified in 25 mL reactions. Each subsample wasamplified in three reactions to account for heterogeneousamplification from the environmental template; eachstratum from each of the six cores was represented by atotal of six amplicons. Each reaction contained finalconcentrations or absolute amounts of reagents as follows:200 nM each of forward and reverse primers, 5 ng templateDNA, 200 mM of each deoxynucleotide triphosphate, 2.5 mMMgCl2, 1 unit GoTaq Hot Start DNA polymerase (Promega,Madison, Wisconsin) and 5 mL Green GoTaq Flexi PCRbuffer (Promega, Madison, Wisconsin). PCR cycle parame-ters consisted of an initial denaturation at 94 C for 3 min,then 25 cycles of denaturation at 94 C for 1 min, annealingat 54 C for 1 min and extension at 72 C for 2 min, followedby a final extension step at 72 C for 10 min. All PCRreactions were performed on MasterCyclers (Eppendorf,Hamburg, Germany). Possible amplification of contami-nants was determined with blank samples run through theextraction protocol simultaneously with the actual samples

and a negative PCR control in which the template DNA wasreplaced with sterile H2O. These remained free of visibleamplicons.

Ten microliters each of the six amplicons for eachstratum from each of the cores was combined and purifiedwith an AmPure SPRI (AgenCourt Bioscience, Beverly,Massachusetts) magnetic PCR clean-up system. The cleanedPCR products again were quantified with the ND1000spectrometer, 75 ng clean DNA per sample combined forsequencing and this pool adjusted to 10 ng mL21. Thepooled products were sequenced in a GS FLX sequencer(454 Life Sciences, Branford, Connecticut) at the Interdis-ciplinary Center for Biotechnology Research at University ofFlorida. Raw data are available at the Genome Short ReadArchive (SRA) at the National Center for BiotechnologyInformation (NCBI) under accession number SRA009960.

Bioinformatics and OTU designation.—The sequence anal-ysis follows that described in Jumpponen and Jones (2009)and Jumpponen et al. (2010). In brief, sequences wereremoved if they contained no valid primer sequence orDNA tag, contained ambiguous bases or were shorter thanour threshold (200 bp). The remaining reads were alignedwith CAP3 (Huang and Madan 1999) and assigned tooperational taxonomic units (OTUs) at 89–99% similarity at2% intervals using a minimum overlap of 100 bp. Anexample sequence for each OTU at 99% similarity isavailable at GenBank (accession numbers GU306179–GU317357). The data were parsed by sample to calculatethe OTU frequencies for each sample. From this output SAS(SAS Institute Inc., Cary, NC) calculated richness anddiversity indices.

Diversity indices.—Overall OTU richness (S) was calculatedby summing the number of OTUs, including singletons,within each sample. Simpson’s dominance (D 5 Spi

2) andShannon’s diversity (H9 5 2Spi[ln{pi}]) were calculated foreach sample, where pi is the frequency of occurrence ofeach OTU. Evenness was calculated as the ratio ofShannon’s diversity and richness (H9/ln[S]). A final indexof diversity, Fisher’s alpha log-series (Fisher et al. 1943) wascalculated by iterating the equation S/N 5 ([1 2 x]/x)(2ln[1 2 x]), where S is richness and N is the totalnumber of sequences within the sample. To exploreorganism coverage, species accumulation (rarefaction)curves were generated with EstimateS (Colwell 2006).

OTU frequency.—OTU frequencies were analyzed based onOTUs assigned at 95% similarity threshold. We chose 95%

sequence identity because our analyses have indicated thatthe total number of OTUs tends to assume a steep, nearexponential increase above 95% similarity (Jumpponen andJones 2009), either attributable to fine scale intraspecificpolymorphisms or sequencing errors inherent to thepyrosequencing platform (Quince et al. 2009). We usedtwo strategies to identify taxa that might have differed acrossthe strata. First, nonsingleton OTUs were analyzed as aproxy for species-level resolution for effects of samplingdepth (stratum) using JMP 7.0.1 (SAS Institute, Cary, NorthCarolina). Second, all nonsingleton OTUs were manuallyassigned to genera, families or orders based on the top-

JUMPPONEN ET AL.: VERTICAL DISTRIBUTION OF FUNGAL COMMUNITIES 1029

ranked BLAST matches (Zhang et al. 2000, SUPPLEMENTAL

TABLE I), OTU frequencies summed for each core andstratum and taxon and analyzed for effects of depth(stratum). We acknowledge that the inaccuracies indatabase annotations (Nilsson et al. 2009, Vilgalys 2003) aswell as the separate accessioning of teleomorph andanamorph genera present potential problems. Nonethelessbecause unambiguous sequence alignment across the ITS2region is difficult this let us better summarize results andreduce the data volume. To minimize the number ofenvironmental ‘‘unculturable fungus’’ matches to ourqueries we additionally applied an Entrez limit (Eukar-ya[ORGANISM] NOT environmental samples[FILTER]NOT unculturable[ALL FIELDS]). To improve the reliabil-ity of our taxon assignment through BLAST queries weremoved reads whose assignment was based on shortoverlap (, 80% coverage) or low sequence identity(, 90% similarity) among the query and match sequences.

Phylogenetic assignment of short MPS sequence reads.—Toimprove the BLAST-based taxon assignment of the short454 sequences we selected example sequences representingthe most abundant OTUs that responded positively tosampling depth. The sequences within an OTU wereselected so that where possible they represented differentcores and different strata within a core. The orientation ofthese sequences was corrected and they were aligned withreference reads obtained from GenBank (http://www.ncbi.nlm.nih.gov/) in Sequencher (.4.6, GeneCodes Corp., AnnArbor, Michigan). The alignments were analyzed in MEGA4(Tamura et al. 2007) with neighbor joining (NJ) andmaximum parsimony (MP). All positions containing align-ment gaps and missing data were eliminated in pairwisesequence comparisons (pairwise deletion option) in the NJanalyses. All alignment gaps were treated as missing data inthe MP analyses. The robustness of the acquired topologieswas assessed by 1000 bootstrap replicates in both NJ and MPanalyses.

Statistical analyses.—We considered each soil core torepresent an independent vertical sequence. The depen-dent variables were analyzed with linear regression againstthe depth in JMP 7.0.1 (SAS Institute, Cary, NorthCarolina). To examine community differences among thestrata genus, family and order frequencies were analyzed inPC-ORD (4.1, McCune and Mefford 1999). We chose theseanalyses because we were concerned that the dispropor-tionate effect of low frequency OTUs might have driven thecommunity differences. Pairwise community distances wereestimated with Sørensen (Bray-Curtis) index and analyzedby nonparametric multidimensional scaling (NMS, Mather1976). The optimal number of dimensions (k) was selectedbased on Monte Carlo test of significance at each level ofdimensionality comparing 40 runs with empirical data against50 randomized runs with a step-down in dimensionality from6 to 1 and a random seed starting value. For all levels oftaxonomic resolution, the k $ 2 dimensional solutionsyielded similar results and produced solutions with stressvalues smaller than those in randomized runs (P 5 0.0196).Accordingly the two-dimensional solutions were selected andthe data re-ordinated with k 5 2 configuration.

FIG. 1. Species accumulation curves for soil coresdivided into A. 0–10 cm; B. 10–20 cm; C. 30–40 cm; D.50–60 cm strata. Horizontal lines indicate the number ofOTUs (mean 6 1 SD; n 5 6), vertical lines indicate thenumber of sequences obtained (mean 6 1SD; n 5 6), solidlines are the grand means across the entire dataset, dashedlines are the means for a particular stratum.

1030 MYCOLOGIA

RESULTS

General data characterization.—After quality controlwe retained 14 578 high quality sequences across the24 samples for an average of 633 6 227 (mean 6

1SD) sequences per sample. The number of sequenc-es was invariable among the strata (one-way ANOVA:F3,24 5 0.79, P 5 0.5148) and cores (one-way ANOVA:F5,24 5 1.27, P 5 0.3198), indicating that ourpresequencing pooling was accurate across thesamples. Similarly the total number of sequencesdid not correlate with the sampling depth (linearregression: slope 5 22.55 6 2.53; t 5 21.01; P 5

0.3247), indicating that any observed patterns thatcorrelated with sampling depth were not driven byunequal sequencing effort.

At 95% sequence similarity our data represented597 nonsingleton OTUs and 554 singletons. On asample level the observed richness estimators rangedon average from nearly 150 distinct OTUs in thetopmost soil to 57 in soil 50–60 cm deep (see FIG. 1for means and standard errors). Despite our sequenc-ing depth of up to . 1000 reads per sample, we didnot saturate the richness in any of our samples, noteven in those with lowest observed richness (FIG. 1A–D). Similarly extrapolative richness estimators (ACE,

FIG. 2. Proportional distribution of fungal taxa identified from tallgrass prairie soil cores. A. Sequence assignment ofbasidiomycetes by order. B. assignment of ascomycetes by order.


Chao1 and first-order Jackknife; not shown) nearlyalways more than doubled the observed richness (S),indicating that the coverage in our assessment was stillinadequate.

We assigned OTUs to taxa with the aid of BLASTand aimed to reduce environmental sequences withEntrez filters. Of the 14 384 sequences (98.67% of allretained sequences) that could be placed with thisstrategy, 14 336 (99.67%) were assigned to kingdomFungi. The few remaining, nonfungal sequencesrepresented Cercozoa, Proteobacteria, Viridiplantae,Alveolata, Eugelenozoa and Metazoa. These nonfun-gal sequences are likely to be either artifacts,erroneously annotated reads or might represent novelorganisms. For example the queries that returnedCercozoan reads were of low coverage (, 35%) andtherefore unlikely to represent true Cercozoanaffinities. In contrast the queries that returned readswithin Viridiplantae represented true nonfungaltargets (OTUs 1746 and 2342 were assigned toArabidopsis thaliana [accession number AC006837]

and Spartina densiflora [AJ489786]) or erroneousannotations (OTU 2412 query returned Begoniaravenii [accession number AJ491250] but has a likelyaffinity within Tilletiopsis as indicated by the numberof Entylomatales accessions that followed Begonia).

Among the fungi basidiomycetes (54%) dominat-ed, followed by ascomycetes (41%), zygomycetes (3%,including those assigned to the basal lineages),glomeromycetes (1%) and chytridiomycetes (1%).Basidiomycetes were distributed across 20 orders andwere dominated by Agaricales, Cantharellales andAtheliales (FIG. 2A). The ascomycetes were distributedrelatively evenly across 35 orders and dominated byOnygenales, Pertusariales, Pleosporales, and Hypo-creales (FIG. 2B). The majority of taxa belonging tothe basal fungal lineages were not assigned to orders.Those that were belonged to Mortierellales, Mucoralesand Endogonales. Glomeromycota were dominated byDiversisporales, followed by Glomerales, Archeospor-ales and Paraglomerales. Chytridiomycota comprisedChytridiales, Spizellomycetales and Rhizophydiales.

TABLE II. Linear regression analyses of total richness (S), OTU singletons and various diversity indices (Fisher’s a, Simpsons1/D, and Shannon’s H9 and dominance Simpson’s D or Evenness H9/lnS)

Estimate St. 6 Err.a Student’s t c Lower 95% CI Upper 95% CI

Richness (S) Qb

Intercept 152.86 6 12.73 12.01*** 126.39 179.33DEPTH 21.56 6 0.35 24.47*** 22.29 20.83

Singletons

Intercept 15.16 6 3.26 4.65*** 8.37 21.94DEPTH 20.17 6 0.09 21.86ns 20.35 0.02

Fisher’s a Qb

Intercept 62.04 6 6.90 8.99*** 47.68 76.39DEPTH 20.76 6 0.19 24.03*** 21.16 20.37

Simpson’s dominance (D)

Intercept 0.08 6 0.04 2.24* 0.01 0.16DEPTH 0.001 6 0.001 1.47ns 20.001 0.003

Simpson’s diversity (1/D) Qb

Intercept 18.91 6 3.74 5.06*** 11.14 26.69DEPTH 20.22 6 0.10 22.11* 20.43 20.002

Shannon’s diversity (H9) Qb

Intercept 3.78 6 0.23 16.45*** 3.30 4.26DEPTH 20.02 6 0.01 23.17** 20.03 20.01

Evenness (H9/lnS)

Intercept 0.75 6 0.04 18.56*** 0.66 0.83DEPTH 20.001 6 0.001 21.74ns 20.004 0.0004

a Each analysis fits a model Y 5 Intercept + Depth X, where Y is the response variable, X is the depth and the Intercept andDepth are the intercept and slope terms with their respective standard errors, 95% confidence intervals and Student’s t-testsdisplayed in the table.

b Arrows indicate the direction of the estimated response with increasing depth.c ns P . 0.05, * 0.01 , P # 0.05, ** 0.001 , P # 0.01, *** P # 0.001.

1032 MYCOLOGIA

Fungal communities across soil profiles.—As we expect-ed, most richness and diversity estimators showed adeclining trend with increasing depth (TABLE II).Richness, Fisher’s a, Simpson’s 1/D and Shannon’sH9 declined (FIG. 3A–D), whereas Simpson’s domi-nance (D) and evenness were not affected (TABLE II).There were no examples of genera, families or ordersthat tended to increase in frequency with greaterdepth. In contrast the relative abundance of mosttaxa, regardless of the taxon assignment (genus,family or order), tended to decline with increasingdepth unless they were too infrequent to analyze. Onthe level of an OTU, 25 showed a significant decliningtrend in abundance whereas eight tended to increasewith the increasing depth (TABLE III). (Examples ofthese responses are shown in SUPPLEMENTAL FIG. 1.)

Those OTUs that showed increasing trends withsoil depth represented mainly uncultured ascomy-cetes (OTUs 426, 1003, 1603, 1954, 2072, 2081, 2142,2292) or were placed within Pucciniomycetes(OTU1710, assigned to genus Pachnocybe) by BLASTqueries. Our limited phylogenetic analyses on themost abundant and most strongly responding OTUssupported these assignments or provided furtherdetails on some OTU assignments. In the MP analyses(FIG. 4A) the environmental 454 reads exemplifyingOTU1710 clustered on a well supported clade withmembers of Septobasidiaceae and Pachnocybaceae,whereas the NJ analyses assigned (80% bootstrapsupport) this OTU as a sister group of Pachnocybeferruginea (Pachnocybaceae). OTU1954 was con-firmed to have ascomycetous affinities, likely withinVenturiaceae (FIG. 4B). The example reads clusteredon a clade that included mitosporic Fusicladiumafricanum in addition to uncultured, unidentifiedfungi. However this clade remained unsupported inthe analyses. OTU2142 was assigned within Nectria-ceae after exclusion of those GenBank sequences thatcontained ‘‘environmental sample’’ as a filter(FIG. 4C). Our MP and NJ analyses supported thisassignment and placed the environmental examplereads on a clade that included mitosporic Cylindro-carpon destructans. Note however that the three C.destructans accessions did not form a monophyleticgroup. The assignment of most OTUs could not beimproved by further analyses. For example OTU2072

FIG. 3. Richness and diversity of fungal communities as afunction of soil depth. Solid lines indicate the linearregression; the dashed lines are the 95% confidenceintervals. A. OTU richness or number of detected species

r

(S). B. Shannon-Wiener diversity index (H9). C. Fisher’salpha (a). D. Evenness (H9/ln(S)). Note that althoughevenness shows a declining trend the slope for this term isnot significant (P 5 0.0968). ns P . 0.05, * 0.01 , P # 0.05,** 0.001 , P # 0.01, *** P # 0.001.


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[EU

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1034 MYCOLOGIA

TA

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.

1036 MYCOLOGIA

could be assigned only among uncultured fungi andamong those the example read placement receivedno support in either MP or NJ analyses (not shown).Similarly OTUs 1003 and 2081 could be alignedamong well accessioned sequences representingHelotiaceae or lichenized families Pertusariaceae,Ramalinaeae and Physciaceae. However these affini-ties were either not supported or the NJ and MPanalyses provided contradictory results (not shown).

In addition to differences in the frequencies amongthe commonly occurring OTUs, our NMS ordinationseparated the communities in different soil depths(FIG. 5) regardless of the chosen rank. The twoordination axes represented 75.0%, 63% and 64.9%

of the variance in the dataset assigned on the level ofan order, family and genus respectively. When theNMS scores for the two axes were analyzed in a one-way ANOVA, the first axis scores were never signifi-cantly different among soil depths (P . 0.7500)whereas the second axis scores were always signifi-cantly or almost significantly different (P 5 0.0445, P5 0.0917, P 5 0.0277 for order, family and genusanalyses respectively). The post-hoc tests (Tukey’sHSD) of the second axis scores indicated that only thetwo extreme strata had different communities where-as the communities detected in 10–20 cm and 30–40 cm deep soil did not differ from either theuppermost or lowest stratum (NMS for the genus levelis an example in FIG. 5).

While causative associations cannot be inferredfrom ordination analyses such as the NMS used here,they do allow examination of correlations betweencommunity members and the environments withinwhich those communities occur. Our NMS analysessuggest that, while the frequencies of most generacorrelate with topmost strata (negative Axis 2 valuesin TABLE IV), relatively few correlate with the samplesfrom deeper soils (positive Axis 2 values in TABLE IV).To illustrate the most strongly correlated genera weplotted five genera with most extreme negative andpositive Axis 2 scores (FIG. 5). These analyses suggestthat OTUs assigned to genera Cudoniella (Helotia-ceae, Helotiales), Cylindrosympodium (Venturiceae,Pleosporales), Malassezia (Malasseziales), Phialoce-phala (mitosporic Helotiales) and Ramichloridium(mitosporic Capnodiales) are correlated with deepersoils.

DISCUSSION

We combined 454 sequencing and DNA tagging tocharacterize soil-inhabiting fungal communitiesacross a vertical gradient. Our analyses detected alarge number of OTUs; at 95% sequence identity wecaptured an estimated 1151 distinct OTUs, half of

which occurred only once in the dataset. Each of ourexperimental units representing a stratum within acore contained on average more than a hundredunique OTUs. While our sequencing depth wassubstantial (more than 600 sequences per sampleon average and more than 1000 in the most deeplysequenced sample) it was inadequate to reach a pointof saturation for the resident taxon richness. Recentdata suggest that sequencing depths substantiallygreater—more than an order of magnitude great-er—might be necessary to approach a plateau in thespecies accumulation curves for soil-inhabiting fungalcommunities (Buee et al. 2009). Similarly for bacterialcommunities in soil a far greater sequencing effort isrequired for thorough community characterization(Roesch et al. 2007). However it presently remainsunclear what these species richness estimators meanbecause the 454 pyrosequencing platform may pro-duce a substantial proportion of artifacts (Quince etal. 2009). We assume these biases to be stochasticallydistributed through the dataset and therefore likely tocontribute to the noise instead of the signal that wewere able to detect. It also is important to note thatour analyses focus on the comparisons on the level ofexperimental unit and aim not to extrapolateecosystem-wide richness estimators.

FIG. 5. Nonmetric multidimensional scaling (NMS) ofgenus frequencies observed across the four strata in thetallgrass prairie soil. The percentages next to the axesindicate the variance represented by that axis. Strata withdifferent letters differ according to Tukey’s HSD at a , 0.05on the second ordination axis. Note that the one-wayANOVA indicated no stratum differences on the firstordination axis. Five genera with the most extreme scoreson the Axis 2 are shown for the inference of taxa withpotential correlation with stratum depth (TABLE IV).


Detected OTUs were broadly distributed across 35ascomycetous and 20 basidiomycetous orders inaddition to a small number of Glomeromycota andorders representing basal fungal lineages. Assignmentof ecological roles to molecular OTUs is problematic.The taxa detected in our sampling cover a widevariety of orders, many of which include membersthat are lichenized (e.g. Lecanorales, Peltigerales,Pertusariales), pathogens (e.g. Capnodiales, Pucci-niales, Ustilaginales), parasites (e.g. Entorrhizales,

TABLE IV. NMS scores for the genera observed in thetallgrass prairie soil

Genusa Axis 1 score Axis 2 score

Omphalina 0.19474 20.99129c

Pochonia 0.19474 20.99129c

Saccharomyces 0.19474 20.99129c

Metarhizium 0.20613 20.92408c

Debaryomyces 0.15368 20.89242c

Beauveriana 0.3411 20.81576Chaetomium 0.3411 20.81576Colletotrichum 0.3411 20.81576Discostroma 0.3411 20.81576Erythrobasidium 0.3411 20.81576Gracilacus 0.3411 20.81576Helicoma 0.3411 20.81576Neurospora 0.3411 20.81576Spizellomyces 0.3411 20.81576Cercophora 0.0803 20.81345Gibberella 0.29687 20.79074Cryptococcus 0.09348 20.77082Articulospora 0.12953 20.72933Tetracladium 0.20886 20.70999Apodus 0.20422 20.70994Entrophospora 20.07733 20.70911Cordyceps 20.14651 20.67894Aspergillus 20.16676 20.67658Spirosphaera 20.09779 20.64637Sordaria 0.07988 20.64579Hygrocybe 20.67796 20.64454Epicoccum 0.12328 20.63745Phoma 20.03209 20.62817Anthostomella 20.19652 20.61063Nectria 0.21914 20.59525Aureobasidium 0.06412 20.59423Panaeolus 20.47954 20.56924Mortierella 20.07319 20.56342Monacrosporium 0.01279 20.55317Cistella 20.03285 20.54689Arnium 20.0446 20.54563Codinaeopsis 20.0446 20.54563Preussia 20.0446 20.54563Pycnidiophora 20.0446 20.54563Stachybotrys 20.05873 20.48639Fonsecaea 0.24197 20.48569Cephalotheca 20.06446 20.48425Morganella 20.05669 20.48064Bullera 20.06722 20.47711Microdiplodia 20.13618 20.46469Periconia 20.13965 20.4428Marasmiellus 0.03872 20.44202Fusarium 0.14157 20.43986Alternaria 0.12675 20.42713Nigrospora 20.08703 20.41707Corynascus 20.18059 20.39851Dactylaria 20.18059 20.39851Dioszegia 20.18059 20.39851Sporisorium 20.18059 20.39851Mycena 20.31713 20.38529

TABLE IV. Continued

Genusa Axis 1 score Axis 2 score

Capronia 20.14031 20.36949Candida 1.86799 20.36794Leptosphaeria 0.19539 20.36729Phialemonium 0.19539 20.36729Ramularia 20.10377 20.35731Podospora 20.04845 20.35666Cladosporium 20.107 20.34644Rhexocercosporidium 20.32203 20.30461Glomus 20.06532 20.27859Hypocrea 20.1785 20.27265Humicola 20.26528 20.26713Phaeosphaeria 20.1515 20.26149Schizothecium 20.04145 20.26102Myrothecium 20.18111 20.26097Lophiostoma 20.14305 20.20788Davidiella 20.36171 20.17937Curvularia 20.19403 20.13942Flagelloscypha 20.548 20.12251Penicillium 0.07197 20.11981Gliomastixb 20.04466 20.06571

Lachnum 20.32875 0.16023Buergenerula 20.36639 0.20486Clitopilus 20.66829 0.2393Geomyces 20.66829 0.2393Schizophyllum 20.66829 0.2393Peziza 20.3641 0.29666Coniosporium 20.39291 0.34826Teratospora 20.06587 0.47766Trichoderma 20.06587 0.47766Ceratobasidium 20.11399 0.52488Cylindrosporum 0.04292 0.60318c

Ramichloridium 20.44623 0.69825c

Phialocephala 0.28039 1.20182c

Cudoniella 0.33191 1.46486c

Malassezia 0.98609 1.84349c

a Taxa are arranged by the Axis 2 scores to emphasizethose likely correlated with the topmost strata (low Axis 2scores) or with deep strata (high Axis 2 scores).

b The cross line midtable emphasizes where the first axisintercepts the second axis.

c Boldfaced taxa are shown in FIG. 5; Axis 2 scores forMalassezia and Cudoniella were modified to illustrate themin FIG. 5.

1038 MYCOLOGIA

Erysiphales), saprobes (e.g. Orbiliales, Sporidiobo-lales, Xylariales), or mutualistic root symbionts (e.g.Glomerales, Diversisporales, Archeosporales, Paraglo-merales). Many of the observed taxa are likely presentonly as dormant propagules (e.g. foliar inhabitantssuch as Erysiphales and Mycosphaerellaceae) andprobably do not participate in soil ecosystem func-tions. To account for only the actively functioningtaxa approaches that more specifically target theactive microbial communities are necessary. Suchapproaches include the reverse transcription of theribosomal RNA or its precursors (see Anderson andParkin 2007, Anderson et al. 2008).

Our results support our hypotheses and earlierobservations that the majority of fungal speciesrichness and diversity are concentrated in thetopmost horizons in soil (Oehl et al. 2005, Roslinget al. 2003, Zajicek et al. 1986). However it isnoteworthy that substantial community richness wasmaintained in the deeper soil. More importantly, ourordination analyses show that while adjacent soilstrata may differ only slightly, distinct fungal commu-nities occupy the topmost and deep soil strata. Thesebroad community level differences are further sup-ported by our analyses at the level of individual OTUs,particularly with respect to the fungi whose relativefrequencies increased with sampling depth. While thefunctions of these fungi remain elusive and aredifficult to pinpoint, our results underline theimportance of including vertical strata in a commu-nity characterization (Fierer et al. 2003).

We attempted to assign sequences to taxa in thecase of fungi that appeared more frequently indeeper soil strata. At the OTU level the linearregression analyses suggested that it is mainlyuncultured fungi that showed preferences for deeperstrata. The OTUs with affinities to well accessionedtaxa represented mainly saprobes, parasites or path-ogens. Venturiaceae (OTU1954) are mainly endo-phytes and weak parasites of plants, infecting usuallyaerial plant tissues. Fusicladium africanum thatappears closely related to these environmental se-quences has been isolated from Eucalyptus litter(Crous et al. 2007), suggesting a saprobic habit.Nectriaceae (represented by OTU2142) similarlyinfect plant tissues but are often pathogens or weakparasites. The OTU (OTU1710) with affinities withinPucciniomycetes was assigned within Septobasidialesand had likely affinities to either Pachnocybe orSeptobasidium. The former is found on wood, whereasthe latter include obligate parasites of insects (Henkand Vilgalys 2007).

When the OTUs were grouped by genus, the NMSanalyses suggested a few taxa that positively correlatedwith the deeper soil (TABLE IV). As with the OTUs

that were inferred from the regression analyses, thereasons why these taxa are detected in the deeper soilstrata remain unclear; for example two genera,Malassezia and Cudoniella, present a puzzle. Inaddition to our study Malassezia has been detectedin the fungal communities from deep-sea sedimentsin the South China Sea (Lai et al. 2007). Its presencethere, beyond anthropogenic contamination, remainsa mystery. However this taxon is commonly associatedwith mammals as well as nematodes (Renker et al.2003) and therefore can inhabit even deeper soil ifappropriate hosts are present. Cudoniella is consid-ered an aero-aquatic genus (Wang et al. 2005) amonga number of other aquatic Helotiales (Goos 1987). Itspresence in deeper soil may be attributable to long-maintained water saturation at these depths or simplyspore transport to these strata with water infiltrationthrough the soil profile. These correlations inferredfrom the regression and ordination analyses shouldbe considered with caution. However our resultsemphasize the importance of further and moreextensive sampling in the deeper soil horizons.

In conclusion we characterized fungal communitiesacross a vertical gradient in tallgrass prairie soil with454 pyrosequencing. While we fell short of saturationof species richness, we successfully tested hypotheseson species richness, diversity and community compo-sition as a function of soil depth. Our results showthat fungal community richness and diversity declinewith soil depth. In addition our data show that thecommunities are distinct among the strata. Althoughmany community members decline with soil depth,we were able to pinpoint some that increase. Theecology and nutritional modes of the fungi that aremore abundant in deeper soil strata remain elusive.However we speculate that those fungi have access totheir primary substrates in those strata and possess theenvironmental tolerance necessary to survive in thoseenvironments.

ACKNOWLEDGMENTS

This research was financially supported by Kansas StateUniversity, Division of Biology (BRIEF-program), EcologicalGenomics Institute (SEED-program) and the U.S. Depart-ment of Energy’s Office of Science (BER) through theMidwestern Regional Center of the National Institute forClimatic Change Research at Michigan TechnologicalUniversity. Konza Prairie LTER provided access to thetallgrass prairie sites. We thank Patrick O’Neal, Jeff Taylorand Rose Philips for assistance with collection andprocessing of soil cores. Lorena Gomez assisted in thesample processing from DNA extraction to 454 sequencing.Regina Shaw, Interdisciplinary Center for BiotechnologyResearch at University of Florida, performed 454 sequenc-ing at University of Florida Genomics Core Facility.


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