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Seasonal trends in the biomass and structure of bryophyte-associated fungal communities explored by 454 pyrosequencing

Marie L. Davey1,2, Einar Heegaard3, Rune Halvorsen4, Mikael Ohlson1 and Havard Kauserud2

1Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, PO Box 5003, NO-1432 As, Norway; 2Microbial Evolution Research Group (MERG),

Department of Biology, University of Oslo, PO Box 1066 Blindern, NO-0316 Oslo, Norway; 3Norwegian Forest and Landscape Institute, Fanaflaten 4, NO-5244 Fana, Norway; 4Department

of Botany, Natural History Museum, University of Oslo, PO Box 1172 Blindern, NO-0318 Oslo, Norway

Author for correspondence:Marie Louise Davey

Tel: +47 6496 5347Email: [email protected]

Received: 30 April 2012

Accepted: 23 May 2012

New Phytologist (2012)doi: 10.1111/j.1469-8137.2012.04215.x

Key words: bryophilous fungi, ergosterol,fungal biomass, fungal diversity,Hylocomium splendens, Pleurozium

schreberi, Polytrichum commune,seasonality.

Summary

• Bryophytes are a dominant vegetation component of the boreal forest, but little is known

about their associated fungal communities, including seasonal variation within them.

• Seasonal variation in the fungal biomass and composition of fungal communities associated

with three widespread boreal bryophytes was investigated using HPLC assays of ergosterol

and amplicon pyrosequencing of the internal transcribed spacer 2 (ITS2) region of rDNA.

• The bryophyte phyllosphere community was dominated by Ascomycota. Fungal biomass

did not decline appreciably in winter (P = 0.272). Significant host-specific patterns in seasonal

variation of biomass were detected (P = 0.003). Although seasonal effects were not the

primary factors structuring community composition, collection date significantly explained

(P = 0.001) variation not attributed to locality, host, and tissue. Community homogenization

and a reduction in turnover occurred with the onset of frost events and subzero air and soil

temperatures. Fluctuations in the relative abundance of particular fungal groups seem to

reflect the nature of their association with mosses, although conclusions are drawn with

caution because of potential methodological bias.

• The moss-associated fungal community is dynamic, exhibiting seasonal turnover in compo-

sition and relative abundance of different fungal groups, and significant fungal biomass is

present year-round, suggesting a winter-active fungal community.

Introduction

The boreal forest is the world’s second-largest terrestrial biome,accounting for 8–11% of global landmass and encompassingsignificant areas of Alaska, Canada, Fennoscandia and Siberia(Whittaker, 1975; Melillo et al., 1993). The biome is of risingglobal concern as its soils and vegetation represent major storageand sequestration components of the global carbon cycle, and areexpected to be affected by climate change (Melillo et al., 1993;Gower, 2003). It is becoming increasingly apparent that micro-bial communities not only play vital ecological roles in the borealecosystem and interact with other organisms: they are also ofgreat importance in nutrient and carbon cycling (Hattenschwileret al., 2005; van der Heijden et al., 2008; McGuire & Treseder,2010; Allison & Treseder, 2011). Knowledge of boreal microbialcommunities is largely limited to soil microbes (Hattenschwileret al., 2005; van der Heijden et al., 2008; Allison & Treseder,2011) and those ectomycorrhizal fungi associated with vascularplants (Buscot et al., 2000; Read & Perez-Moreno, 2003; Lehto& Zwiazek, 2011). Bryophytes are a dominant vegetationcomponent of the boreal forest with significant contributions tocarbon and nitrogen cycling (Turetsky, 2003; Lindo & Gonzalez,

2010), and they provide unique microbial habitats as a result oftheir stems being a continuous gradient from dead to senescingto photosynthetic tissues. Despite this, relatively little is knownabout their associated microbial communities. Recent researchsuggests that cyanobacterial moss associates produce the majorityof fixed nitrogen in the boreal forest (DeLuca et al., 2002) anddemonstrates that this fixation rate is affected by precipitation(Jackson et al., 2011) and temperature (Gentili et al., 2005). Bycomparison, the fungal component of the moss-associated micro-bial community and their ecological function have remainedpoorly characterized, although Racovitza (1959) and Davey &Currah (2006) report a variety of fungal pathogens, parasites,and saprophytes to be associated with mosses. Because of the highfungal biomass associated with senescent bryophyte tissues(Davey et al., 2009a) and active bryophyte cell wall degradationexhibited by some fungi (Day & Currah, 2011a,b), it is thoughtthat fungi are the primary decomposers of moss substrates. It hasalso been suggested that bryophytes may act as inoculum reser-voirs for some tree pathogens (Davey et al., 2010), that mosspathogens create micro-disturbances that ultimately alter plantcommunity composition on a local scale (Hoshino et al., 2001),and that ectomycorrhizal fungi associated with trees are able to

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scavenge nutrients from moss substrates (Carleton & Read,1991). Such diverse ecological roles highlight the probable eco-logical significance of this poorly known group of fungi to theboreal forest ecosystem.

Seasonality of fungal reproduction has long been observed,particularly for macrofungi (Kauserud et al., 2010; Gates et al.,2011). However, fungal communities have also been found toexhibit seasonal variations in total biomass (Berg et al., 1998;Bjork et al., 2008; Artigas et al., 2009; Randhawa et al., 2011),community composition and diversity (Schadt et al., 2003; deRoman & de Miguel, 2005; Kennedy et al., 2006; Zinger et al.,2009; Dumbrell et al., 2011; Jumpponen, 2011), and functionalactivity (i.e. decomposition and mycorrhizal nutrient transfer)(Bentivenga & Hetrick, 1992; Buee et al., 2005; Uchida et al.,2005; Capps et al., 2011). Factors such as temperature, precipita-tion, snow cover, and nutrient availability have been identified aspossible driving factors behind temporal variation in fungal com-munities (Steinaker & Wilson, 2008; Artigas et al., 2009; Zingeret al., 2009; Kauserud et al., 2011). While seasonal patterns havebeen well characterized in ectomycorrhizal (de Roman & deMiguel, 2005; Steinaker & Wilson, 2008; Jumpponen et al.,2010) and arbuscular mycorrhizal fungi (Mandyam &Jumpponen, 2008; Dumbrell et al., 2011), aquatic hypho-mycetes (Barlocher, 2000; Artigas et al., 2009; Anderson &Shearer, 2011), and soil fungal communities (Berg et al., 1998;Schadt et al., 2003; Bjork et al., 2008; Zinger et al., 2009), rela-tively few studies have examined the seasonality of fungi associ-ated with the phyllosphere of plants (Cabral, 1985; Osono &Mori, 2005; Osono, 2008; Albrectsen et al., 2010; Jumpponen& Jones, 2010). More specifically, seasonal variation in commu-nity composition and the fungal biomass associated with thephyllosphere of terrestrial bryophytes have not been previouslyinvestigated. However, decomposition rates of dead and senescentbryophyte material are known to be temperature dependent(Nakatsubo et al., 1997), which suggests that, at a minimum, sea-sonal variation in microbial community activity associated withsenescent tissues occurs. Given the importance of bryophytes andtheir associated microbial communities in carbon and nitrogencycling (Turetsky, 2003; Lindo & Gonzalez, 2010), understandingseasonal changes in moss-associated fungal communities is ofparticular relevance to understanding the functioning of theboreal forest and predicting its responses to global change.

Next-generation sequencing techniques have led to a revolu-tion in microbial ecology by providing opportunities to generateunprecedented numbers of sequences and detect very rare orlow-abundance organisms (Begerow et al., 2010; Ekblom &Galindo, 2011). These techniques have been successfully appliedto examine seasonality of fungal communities in several instances(Jumpponen & Jones, 2010; Jumpponen et al., 2010; Dumbrellet al., 2011). In this study, 454 amplicon pyrosequencing andfungal biomass assays were used to investigate seasonal variationin fungal communities associated with the photosynthetic andsenescent tissues of three common boreal mosses. The specificeffects of host and tissue type on community composition are tobe addressed in a forthcoming manuscript (M. L. Davey et al.,unpublished) and will be mentioned only cursorily here.

Materials and Methods

Study sites and sampling

Five 1-m-diameter circular plots containing the mossesHylocomium splendens (Hedw.) Schimp., Pleurozium schreberi(Brid.) Mitt., and Polytrichum commune Hedw. were establishedin each of two mature secondary Picea abies (L.) Karst. spruceforests near As in south-east Norway (Myrvoll: 59�39Æ687¢N10�50Æ703¢E; Nordskogen: 59�40Æ463¢N 10�45Æ706¢E). Vegeta-tion in the two forests was of the bilberry–Norway spruce foresttype (Kielland-Lund, 1982) and the area exhibited a typicalboreal climate with cold winters, relatively warm summers, andan average yearly temperature of 5.9�C (Supporting InformationFig. S1). In each plot, one colony of the three moss species wasselected and marked, and representative shoots were collected forbiomass and genetic analyses every 8 wk between late April 2009and early January 2010. At the time of collection, all plant mate-rial was cleaned of coarse debris and rinsed in running water.Individual stems of each bryophyte species were separated intogreen photosynthetic and brown senescent sections, and theintervening parts discarded. For ergosterol analyses, shoot frag-ments were freeze-dried overnight, crushed to a powder using aRetsch ball mill (Retsch, Dusseldorf, Germany), and stored at)80�C until analysis. For sequencing, one shoot was chosen tobe representative for each host colony at each sampling date. Theresultant 150 shoots were individually rinsed in running water,washed with agitation in 0.01% Triton-X, and then rinsed threetimes in sterile distilled water before being divided into fragmentsas described above and freeze-dried. Dried shoot fragments werecrushed using a Retsch ball mill, and stored in 2 · cetyl-trimethylammonium bromide (CTAB) extraction buffer at)80�C until further analysis.

Ergosterol extraction and quantification

Free and esterified ergosterol was extracted with saponificationfrom all bryophyte tissues. Approximately 200 mg ofpowdered tissue was combined with 20 ml of 3 M KOH in etha-nol and incubated at 80�C in a water bath for 1 h. Extractionmixtures were diluted with 5 ml of distilled water, and ergosterolwas extracted from the supernatant by two successive applicationsof 7.5 ml of hexane. The organic fractions were then combined,evaporated, and resuspended in 1 ml of methanol. Extracts wereanalysed on an 1100 series HPLC (Agilent Technologies, Wald-bronn, Germany). Ergosterol was separated on a Luna� 5-lmparticle size 4.6 · 150 mm column (Phenomenex, Værløse,Denmark) with methanol as the mobile phase at a flow rate of 2ml min)1. The total analysis time was 10 min, and the detectionwavelength was 282.4 nm. Ethanol blanks were analysed afterevery tenth sample, and two samples were used to calculate thestandard error by performing five successive injections andanalyses of the sample. The identification of ergosterol was basedon retention time, online UV spectra and co-chromatographywith a commercial ergosterol standard (Sigma, St Louis, MO,USA).

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Pyrosequencing

Genomic DNA was extracted from each of the 300 shootfragments using a modified CTAB-based extraction protocol(Murray & Thompson, 1980; Gardes & Bruns, 1993) and allextracts were cleaned and purified using the Wizard�SV Gel andPCR Clean-Up System (Promega, USA). A nested PCRapproach was used to amplify and tag the internal transcribedspacer 2 (ITS2) region of rDNA. The ITS2 region has beenidentified as suitable for fungal taxon assignment (Mello et al.,2011), and has been successfully used in other comparativeecology studies where it gives results that are convergent with, ifnot comparable to, those for other markers (Mello et al., 2011;Arfi et al., 2012; R. Blaalid et al., unpublished). The nestedapproach was chosen as recommended by Berry et al. (2011) togenerate highly replicable results (see Kauserud et al., 2012) andto avoid the ligation bias known to occur in barcode primer-freeapproaches (Gillevet et al., 2010). The entire ITS region of fun-gal DNA was first amplified using the fungal-specific primersITS1-F and ITS4 (White et al., 1990; Gardes & Bruns, 1993),and then a second PCR using the primers ITS3 and ITS4 (Whiteet al., 1990) targeted the ITS2 region and added an emulsionPCR adaptor and one of 19 different 10-bp tags to each sampleto allow for individual sample recognition in downstream analy-ses after pooling. PCR reactions were conducted in 20-llvolumes and contained 0.4 units of Phusion polymerase(Finnzymes Oy, Vantaa, Finland), 0.5 lM of each primer, 1.7lM of dNTP, and 1 · Phusion HF PCR-buffer (FinnzymesOy). In the first PCR, 2 ll of a 10 · dilution of extracted geno-mic DNA was used as a template, while in the second 4 ll of a50 · dilution of the first PCR product was used as a template.PCR conditions were as follows: initial heating to 98�C for 30 s,30 cycles of denaturation at 98�C (10 s), annealing at 53�C (20s) and extension at 72�C (10 s), followed by a 7-min extension at72�C before storage at 4�C. Conditions in the second PCR wereidentical to those in the first, with the exception that the denatur-ing-annealing-extension cycle was repeated only 10 times, whichwas determined to be the minimum number of cycles required toreliably detect successful amplification using gel electrophoresis.Amplicons from the second PCR were cleaned and purified usingthe Wizard�SV Gel and PCR Clean-Up System (Promega)according to the manufacturer’s instructions. Of the 294 samplesthat were successfully amplified, seven were selected at randomto be sequenced twice to determine the validity of readabundance data. Amplicons were then quantified and pooled into16 equimolar libraries, each containing 19 uniquely taggedsamples. The amplicon libraries were subsequently sequencedon a full plate split into 16 lanes using the Roche GS FLXTitanium Series 454 sequencing platform at the NorwegianHigh-Throughput Sequencing Centre (University of Oslo, Oslo,Norway).

Bioinformatics and statistical analyses

Raw sequences are publicly available at the MG-RAST metage-nomics analysis server (Project Name: Seasonality of Bryophilous

Fungi). Sequences were quality-filtered, denoised, and clusteredusing QIIME v. 1.3.0 (Caporaso et al., 2010). Reads with length< 200 bp, an average Phred quality score of < 25, or errors in thetags were discarded. Those sequences with homopolymers oflength > 10 bp, more than one ambiguous base call (N ), andmore than one error in the forward primer sequence were also fil-tered from the data set. In addition, a 50-bp sliding window wasused to identify regions of low sequence quality (average qualityscore < 25) and truncate the sequence at the beginning of thelow-quality window. Those truncated sequences still meeting theminimum length requirement (200 bp) were retained in the dataset. The resulting 327 273 reads were subject to denoising usingDENOISER v. 0.91 (Reeder & Knight, 2010) as implemented inQIIME v 1.3.0 (Caporaso et al., 2010). Denoised sequences wereclustered into operational taxonomic units (OTUs) using a 97%similarity threshold and the uclust algorithm with optimal uclustsettings as implemented in QIIME v. 1.3.0 (Caporaso et al., 2010;Edgar, 2010). Those clusters represented globally by a singlesequence were discarded as likely sequencing errors, as suggestedby Tedersoo et al. (2010). The most abundant sequence in eachcluster was designated as the representative sequence and was sub-jected to BLAST searches against the NCBI-nr ⁄ nt database(downloaded November 2011) and a customized databasecombining the UNITE and INSDC databases (downloadedNovember 2011). Taxonomy was assigned at the order level, asOvaskainen et al. (2010) suggest only a moderate fraction of 454reads can be reliably identified to genus using BLAST. Amongthose sequences successfully assigned to the order level, the twodatabases agreed in 92% of cases; however, 62% of sequencescould only be identified to the level of kingdom or phylum usingthe UNITE + INSDC database, and an additional 11% wereonly identified to the class level. Thus, all final taxonomic assign-ments were made at the level of order and based on criticalreviews of the top 10 BLAST matches to the NCBI-nr ⁄ nt data-base. All OTUs with BLAST matches to nonfungal organismswere removed, as were those that could not reliably be identifiedas fungi (best BLAST match < 80% sequence similarity or< 50% coverage). When the top BLAST match was to an unclas-sified or uncultured fungus, the top 10 matches were screened forconcordance and taxonomy assigned based on the most parsimo-nious lineage among those matches meeting the minimumthresholds of > 80% sequence similarity and > 50% coverage.

ESTIMATES v. 8.2.0 (Colwell, 2009) was used to calculaterarefaction curves for the entire data set. The seven samples thatwere sequenced twice were used to assess the validity ofbetween-sample abundance comparisons. A global nonmetricmultidimensional scaling (GNMDS) ordination (see theMethods section) coupled with vector fitting (Fig. S2) demon-strates that sample pairs display greater inter-sample variationthan within-sample pair variation. A paired t-test of rarifiedabundances in each of the seven pairs of resequenced controlsamples indicates that there is no statistically significant differ-ence between the abundance measures of individual OTUsbetween sequencing replicates of the same sample (P = 0.582).Although read abundance cannot be used for direct quantifica-tion without calibration and does not allow for valid

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between-taxa comparisons, it has been suggested to provide reli-able measures for within-taxon comparisons (Amend et al.,2010). Abundance-based comparisons were therefore made solelywithin selected taxonomic groups (ectomycorrhizal families, Exo-basidiales, Chaetothyriales, Mycena, Pleosporales, and Helotiales)using an OTU table that was rarified in QIIME v 1.3.0 (Caporasoet al., 2010) to an even sampling depth of 500 sequences persample in order to minimize effects of unequal sequencing depthbetween samples. Chi-square tests were used to determine ifabundances at the different collection dates varied significantlyfrom one another.

Linear mixed effects models and general linear mixed effectsmodels were fitted using the lme4 and nlme packages of R (Bateset al., 2011; Pinheiro et al., 2011) to examine the response offungal biomass (i.e. ergosterol) and OTU richness to host species,tissue type, and sampling date. For fungal biomass we assume anormal distribution, whereas for OTU richness we assume aPoisson distribution. In both cases, models were nested in accor-dance with the experimental design. We recognized randomcontributions by the components forest and plot (for biomassdata) or shoot (for OTU richness). For the biomass analysis,the inclusion of a level-specific residual variance at level combina-tions of host and forest was required to compensate fordifferences in variance. The analysis of OTU richness requiredthe inclusion of a specific term accounting for number of reads(Zuur et al., 2009) and an observation-specific random termaccounting for potential overdispersion. Log-likelihood ratio testswere used to determine the overall significance of the collectiondate as a fixed effect in the model and to determine whetherinteraction terms should be included in the final model.

The data set was transformed using the Hellinger equation toaccount for so-called ‘blind sampling’ and large numbers ofabsences in the data sets, as suggested by Ramette (2007), andthen split into host ⁄ tissue-type subsets, and a subset containingonly the seven samples that were sequenced twice. The VEGAN

v.2.0.3 package implemented in R v.2.15.0 (Oksanen et al.,2011; R Development Core Team 2011) was used to conductGNMDS (Kruskal, 1964a,b; Minchin, 1987) with the followingoptions (following recommendations by Økland, 1996a and Liuet al., 2008): distance measure = Bray–Curtis distance, dimen-sions = 2 and 3, initial configurations = 100, maximum itera-tions = 200, and convergence ratio for stress = 0.9999. Becausethe underlying gradients in our study are unknown, detrendedcorrespondence analysis (DCA) (Hill, 1979; Hill & Gauch,1980) was also run with default options on all data subsets andall ordinations were inspected for known artefacts such as thearch effect (in GNMDS), tongue effect and other patterns(Økland, 1990; Økland & Eilertsen, 1993). Similar resultsobtained using the two methods and absence of visual artefacts isinterpreted as a strong indication that a reliable gradient structurehas been found (Økland, 1996b) and this was confirmed bycalculating correlations between DCA and GNMDS axes usingKendall’s rank correlation coefficient s for GNMDS ordinationswith both two and three dimensions (Table S1). The larger num-ber of dimensions was only accepted when correlations betweencorresponding DCA and GNMDS axes were better at three

dimensions than at two. The envfit function in VEGAN was used tofit the variables of sampling date, precipitation, minimum airtemperature, and soil temperature at 2-cm depth to the ordina-tion diagrams. The effect of collection date on the complete dataset was investigated using a partial canonical correspondenceanalysis (CCA) (ter Braak, 1986) in which the effects of location,host species, and tissue type were partialled out of the constrainedordination. The significance of collection date as an explanatoryvariable for the remaining variation in the data set was assessedby conducting 999 random permutations of the communitydata.

Results

Data characteristics

Of the 491 280 reads generated, 327 273 were retained after thefiltering and denoising steps, and subsequently clustered into9367 OTUs. After removal of singletons (> 60% of OTUs) andnontarget organisms, the final data set included 2676 OTUsencompassing 78% of the reads retained after filtering and deno-ising (Table S2). The average number of OTUs detected persample was 91 (range: 20–184) with a mean of 1031 reads gener-ated per sample (range: 502–2423). On average, each OTUoccurred in 8.4 samples (range: 1–224) and contained 96 reads(range: 1–11 716), with the majority of OTUs having a freq-uency of < 10 (83%) and containing < 10 reads (62%). Neitherthe species accumulation curves (excluding global singletonOTUs) for the entire data set nor those for each sampling dateapproached an asymptote (Fig. S3). OTUs representing 67orders of fungi were detected. Fifty per cent of OTUs (56% ofreads) belonged to the Ascomycota, while 37% (38% of reads)belonged to Basidiomycota, and a small proportion (4%; 1.6%of reads) were assigned to the Chytridiomycota, Glomeromycota,and Mucormycotina. Dominant orders among the Ascomycotawere the Helotiales, Chaetothyriales, Pleosporales and Capnodi-ales. Dominant basidiomycete groups included the Agaricales,Tremellales, and Sporidiobolales (Table 1).

Seasonal variations in community biomass

Ergosterol was detected in variable amounts in all species andtissues throughout the year, although large variations inergosterol content often occurred within a single host-tissue class.Ergosterol content per gram DW moss tissue was higher insenescent tissues than in photosynthetic tissues. Ergosterolcontent also varied among the three hosts with P. schreberi >H. splendens > Pol. commune, regardless of the host tissue beingexamined (Fig. 1). The linear mixed effects model identified: aneffect of both tissue type and host species on the amount of ergos-terol present; no effect of tissue type on ergosterol variationbetween collection dates; and a significant difference in ergosterolvariation between sampling dates among the three hosts: ergos-terol levels remained relatively constant throughout the year inboth Pol. commune and H. splendens, while they peaked in Augustin P. schreberi (Fig. 1, Table 2).

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Seasonal variation in fungal diversity

Per-sample OTU richness showed large variation within a singlesampling event, but also varied between collection dates (Fig. 2).Collection date was a statistically significant component(P < 0.001) of the generalized linear mixed model, which was con-ditioned on a term for the number of reads per sample and identifiedan effect of both tissue type and month on the number of OTUsdetected; and no differences in variation between collection datesfor the different hosts and tissue types (Fig. 2, Table 3).

Specific fungal groups detected in the mosses also exhibited sea-sonal variations in abundance that were often tissue-type specific(Fig. 3) but were generally not host-specific (data not shown). Ec-tomycorrhizal (ECM) fungi exhibited a peak in relative abun-dance (measured as the proportion of ECM reads per samplingevent) during August in senescent moss tissues, while this peakoccurred in October in photosynthetic tissues. The Exobasidialeswere detected in very low numbers in senescent tissues, but weremore abundant in green tissues, exhibiting a clear seasonality, withabundance peaking in August. The Pleosporales were present inboth tissue types and exhibit a summer abundance peak in

photosynthetic tissues. Mycena, a basidiomycete saprophyte, wasmore abundant in senescent tissues than photosynthetic tissues,and peaked during the summer months, a trend that also occurredin the ascomycete order Chaetothyriales. The most abundantgroup present in both tissues was the Helotiales, which exhibited aslight decline in relative abundance during August.

Seasonal variation in community structure

GNMDS ordination of Hellinger-transformed abundance datapartitions from all species–tissue combinations showed similartrends: vector fitting indicated that all the data are structured tosome extent by sampling date, precipitation, minimum air

Table 1 Summary of the distribution of operational taxonomic units(OTUs) among fungal lineages including the 10 most abundant ordersdetected

Taxnomic affinity Percent of OTUs Percent of reads

Ascomycota 49.6 56.0Dothideomycetes 10.0 6.1

Capnodiales 3.2 1.2Pleosporales 4.5 4.4

Eurotiomycetes 6.4 6.5Chaetothyriales 5.4 5.5

Lecanoromycetes 3.0 1.7Lecanorales 2.4 1.6

Leotiomycetes 15.4 30.0Helotiales 13.8 27.5

Orbiliomycetes 0.6 < 0.1Pezizomycetes 0.9 0.1Saccharomycetes 0.6 0.1Sordariomycetes 3.7 0.7Taphrinomycetes 0.4 < 0.1Unassigned Ascomycota 8.6 10.4

Basidiomycota 37.2 38.3Agaricomycetes 19.4 24.3

Agaricales 9.8 13.9Atheliales 2.1 2.9

Agaricostilbomycetes 0.1 0.1Cystobasidiomycetes 1.5 0.8Microbotryomycetes 3.4 0.9

Sporidiobolales 3.0 0.8Pucciniomycetes 1.6 1.1Tremellomycetes 7.1 3.8

Tremellales 5.0 3.3Unassigned Basidiomycota 4.0 7.4

Chytridiomycota 1.5 1.1Glomeromycota 0.3 0.3Mucormycotina 2.4 0.2

Mortierellales 2.1 0.2Unassigned 9.0 4.0

Fig. 1 Linear mixed model-estimated ergosterol levels for photosynthetic(green) and senescent (brown) tissues of Hylocomium splendens,Pleurozium schreberi, and Polytrichum commune from April to January.The fixed effects of tissue type and host, and the interactions betweentissue and host, and host and month were found to be significant (seeTable 2).

Table 2 Summary statistics for the best linear mixed model fitted to theamount of ergosterol detected in moss tissues with the fixed effects oftissue type, host, month, and the interactions between tissue and host,and host and month

numDF denDF F-value P-value

Intercept 1 243 254.9653 < 0.0001Tissue 1 243 1139.0169 < 0.0001Host 2 243 436.1965 < 0.0001Month 4 243 1.2971 0.2718Tissue : host 2 243 5.7311 0.0037Host : month 8 243 3.0691 0.0026

numDF, numerator degrees of freedom; denDF, denominator degrees offreedom.Forest type and sampling plot were included as random factors. Anadditional contribution of difference in variance was included at thecombined levels of host and forest. The standard deviations of randomcomponents were as follows: plot : forest = 80.3113; forest = 0.02576.Statistically significant values are indicated in bold text.






temperature, and soil temperature at 2-cm depth (Fig. 4). In theCCA ordination in which the effects of locality, host, and tissuetype were removed (3% of total variation in species composi-tion), 2% of the remaining total variability in species composi-tion was significantly explained by seasonal effects (P = 0.001)(Fig. 5). The samples distributed along the axis CCA1 largelyaccording to minimum air temperature and soil temperature at 2cm, and low CCA axis 1 scores represented minimum air temper-atures above the freezing point and soil temperatures > 5�C.Samples were distributed along CCA axis 2 somewhat accordingto precipitation received. High scores along this axis were associ-ated almost exclusively with samples from August, where precipi-tation was nearly three times higher than in any other month.

Discussion

Overall community OTU richness and taxonomic diversity

A large and diverse assemblage of moss-associated fungi wasdetected, and species accumulation curves failed to approach anasymptote, suggesting that sequencing depth was insufficient tocapture the full community diversity. More than 2600 non-singleton OTUs were recovered, which is an order of magnitudemore than in other published phyllosphere studies (Jumpponen& Jones, 2009; Arfi et al., 2012), studies of agricultural soils(Xu et al., 2012), and studies of mycorrhizal root systems(Jumpponen et al., 2010; Tedersoo et al., 2010; Blaalid et al.,2012). The OTU richness in mosses is of comparable magnitudeto the diversity reported in natural soils (Buee et al., 2009;Lentendu et al., 2011). Possible explanations for this highdiversity are the perennial life history strategy of the mosseswhich allows for continual fungal growth and colonization, andthe close physical proximity of the senescent portions of the mossto the soil fungal spore bank. However, the validity ofbetween-study comparisons is questionable, as sequencing depthis generally insufficient to capture complete community diversity.Furthermore, methodologies are inconsistent among existingfungal diversity pyrosequencing studies, and OTU detection andrichness estimations are dependent on experimental methodo-logy, sequencing depth, and library size, as well as the bioinfor-matic processing of the raw data (ex ⁄ denoising) (Quince et al.,2009; Kunin et al., 2010; Schloss, 2010; Gihring et al., 2012).

The bryophyte-associated fungal community detected wasdominated by ascomycetes. This may partly be a product of pref-erential ascomycete amplification by the ITS3 ⁄ ITS4 primer pair(Bellemain et al., 2010). However, other studies of vascular plantphyllosphere communities using different or multiple primercombinations also recover ascomycete-dominated communities(Jumpponen & Jones, 2009; Arfi et al., 2012). Furthermore, themost prevalent orders detected (Helotiales, Chaetothyriales,Agaricales, and Tremellales) were consistent with those describedfrom the same host species by Kauserud et al. (2008), who used acloning and Sanger sequencing-based approach with a primerpair not specifically biased to ascomycete amplification. It there-fore seems likely that our recovery of an ascomycete-dominatedcommunity is not an artefact. Despite insufficient sampling andsequencing depth, the pyrosequencing-based approach detectedan order of magnitude more OTUs than the clone-basedapproach, further highlighting the usefulness of this technologyin complex systems.

Seasonal variation in fungal community biomass

As observed by Davey et al. (2009a), significant amounts offungal biomass are associated with each moss host, and biomassvaries between hosts and tissue types, with brown tissues contain-ing more fungal biomass than green, and Pol. commune hostingless fungal biomass than P. schreberi and H. splendens. Notably,in all hosts and tissue types, winter measurements of fungal bio-mass (October ⁄ January), during which soil and air temperatures

Fig. 2 Generalized linear mixed model-estimated per sample operationaltaxonomic unit (OTU) richness for photosynthetic (green) and senescent(brown) tissues of Hylocomium splendens, Pleurozium schreberi, andPolytrichum commune from April to January. See Table 3 for the signifi-cance of fixed and random effects.

Table 3 Fixed effects table for the generalized linear mixed model fittedto the number of operational taxonomic units (OTUs) detected in mosstissues with the fixed effects of tissue type, host, and month, and a termrepresenting the number of reads per sample

Estimate SE z value Pr(> |z|)

Intercept 1.79331 0.25126 7.137 < 0.001Log(reads) 0.33510 0.03694 9.071 < 0.001Host2 0.06485 0.04482 1.447 0.1479Host3 0.19236 0.04447 4.326 < 0.001Month2 0.26909 0.05828 4.617 < 0.001Month3 )0.01673 0.05867 )0.285 0.7754Month4 0.22698 0.05792 3.919 < 0.001Month5 0.19180 0.05778 3.319 < 0.001Tissue2 0.25300 0.03495 7.24 < 0.001

Forest locality, shoot number (shootID) and observation number (obs)were included as nested random factors (the standard deviation of therandom components are as follows: obs:(shootID : forest) = 0.2745;shootID : forest = 0.0650; forest < 0.0001).Statistically significant values are indicated in bold text.

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were subzero and the hosts were beneath the snowpack, did notfall significantly below levels observed when soil and air temper-atures were above zero (April ⁄ June sampling dates). Given thatergosterol is considered a proxy for living fungal biomass (Ekbladet al., 1998), our findings are consistent with a growing body ofresearch that indicates that fungal cells not only survive subzerotemperatures, but actively grow beneath the winter snowpack(Schadt et al., 2003; Schmidt et al., 2008; Matsumoto, 2009;Haei et al., 2011). Although hyphal mats have been reportedgrowing on soil and plant litter beneath snow cover (Schmidtet al., 2008; Matsumoto, 2009), such mats were not observed onwinter-collected bryophtyes, and there was no detectable abun-dance increase in the psychrotolerant Mucormycotina whichoften form such mats (Schmidt et al., 2008). The presence of asignificant, living, winter fungal community suggests that borealbryophyte-associated fungi are similar to those associated withvascular plant litter and soil in alpine ecosystems, which areimportant in carbon and nitrogen cycling, and decompositionduring winter months (Bardgett et al., 2005). This points tomoss-associated fungi possibly playing a significant role in wintercarbon flux in the boreal forest.

Fluctuations in fungal biomass between sampling dates wereinconsistent between hosts: biomass associated with Pol.commune and H. splendens was relatively constant between sam-pling dates, while P. schreberi exhibited a significant biomass peakin August. This biomass increase was not associated with anabundance or diversity increase in a particular OTU or group ofOTUs. However, bryophytes are known to release bursts of aque-ous nutrient leachates with repeated drying–wetting cycles thatare useable by ectomycorrhizal fungi (Carleton & Read, 1991;

Wilson & Coxson, 1999) and presumably other fungi. It is possi-ble that P. schreberi releases more nutrients than H. splendens andPol. commune during these cycles, and consequently is able tosustain a larger phyllosphere community during the Augustcollection date, when precipitation is maximal. Tissue type didnot affect seasonal variation of ergosterol content, suggesting thatthose factors controlling seasonal biomass fluctuations act equallyon photosynthetic and senescent tissues. Although different fun-gal groups produce varying amounts of ergosterol and the com-pound is absent in some groups (i.e. Chytridiomycota andGlomeromycota) (Ruzicka et al., 2000; Weete et al., 2007), weassume that biomass comparisons are valid because few fungilacking ergosterol were detected (< 5% of OTUs) and communi-ties had similar order-level composition across all sampling dates.

Seasonal variation in community structure

Bryophyte-associated fungal communities are primarily struc-tured by host identity and tissue type (M. L. Davey et al., unpub-lished); however, effects of collection date were also detected.CCA analysis found 2% of variation remaining after partiallingout of the effects of host, forest, and tissue, which is statisticallysignificant (P = 0.001). This variation is explained by the collec-tion date (note that the total variation as given by the total inertiaof CCAs is inflated by lack-of-fit-of-data-to-model ‘variation’ ofunknown quantities (Økland, 1999) so that the fraction of‘explainable’ variation in species composition is therefore likelyto be 1.5–2.5 times > 2%). This demonstrates that, like soils andvascular plant roots and shoots (Osono & Mori, 2005; Courtyet al., 2008; Zinger et al., 2009; Jumpponen & Jones, 2010;

Fig. 3 Seasonal trends in the relative abundance of selected taxonomic groups in photosynthetic (green lines) and senescent (brown lines) moss tissues.Groups in which fluctuations in abundances are statistically significantly larger than expected at random are indicated by a ‘*’ at the end of the line. ECM,ectomycorrhizal.






Jumpponen et al., 2010), bryophytes host a seasonally dynamicfungal community. Precipitation, minimum air temperature andsoil temperature at 2-cm depth significantly structure ordinationanalyses, with individual collection dates tending to cluster,

indicating that compositional turnover takes place in the fungalcommunity. This is consistent with vascular plant-associatedcommunities and soils (Schadt et al., 2003; Walker et al., 2008;Jumpponen & Jones, 2010; Jumpponen et al., 2010; Dumbrell

Fig. 4 Ordination diagrams for global nonmetric multidimensional scaling (GNMDS) ordinations of the fungal operational taxonomic unit (OTU) composi-tion for each combination of host and tissue type. Arrows representing the direction of maximum increase for minimum air temperature (AirTemp), soiltemperature at 2-cm depth (SoilTemp), and monthly precipitation (Precipitation) are black when statistically significant (P < 0.05), and grey when non-significant. Centroids for each collection date are marked with a black ‘X’ when significantly different from one another (P < 0.05) and a grey ‘+’ when thedifference is nonsignificant. Stress values for each ordination are as follows: Hylcomium green: 23.99957; Hylocomium brown: 26.29371; Pleurozium

green: 20.54731; Pleurozium brown: 24.62266; Polytrichum green: 16.47877; Polytrichum brown: 25.96876.

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et al., 2011), where significant community composition turnovercan occur in a matter of months. However, soil (Schadt et al.,2003) and mycorrhizal (Koide et al., 2007; Dumbrell et al.,2011) communities exhibit more dramatic seasonal and temporalturnover than bryophyte-associated communities, with nearlycomplete turnover occurring in some cases (i.e. Schadt et al.,2003; Dumbrell et al., 2011). In CCA analysis it becomes clearthat the winter samples (October ⁄ January) represent a commu-nity that is distinct from other sampling dates, and that composi-tional turnover is reduced during months experiencing subzerotemperatures. Zinger et al. (2009) observed similar winterhomogenization of alpine soil communities that was concomitantwith temperature and soil pH. It is unclear whether the observedhomogenization phenomenon can be attributed to the existenceof a fungal assemblage that is strongly favoured by its tolerance tofreezing events (Henis et al., 1987), or whether other seasonallyvariable environmental factors (i.e. pH) are the driving factorreducing community turnover. The observed changes in commu-nity composition are concurrent with fluctuations in soil and airtemperatures, and precipitation, suggesting that these communi-ties are likely to be sensitive to the environmental perturbationsexpected from global climate change. While bryophyte-associatedfungi are clearly responsive to seasonal environmental changes,season is not the dominant factor structuring the communitycomposition and other factors (i.e. host and tissue type; M. L.Davey et al., unpublished) play a more important role. Neverthe-less, further research is warranted to determine to what extent theecological function of this community shifts in conjunction with

the observed seasonal changes in community composition, and todetermine which environmental factors drive these shifts.

GLMM modelling of OTU richness indicated significant dif-ferences between hosts and tissue types, as well as significantchanges in OTU richness between collection dates. Seasonal fluc-tuations in alpha diversity and species richness have also beenreported in mycorrhizal fungi (de Roman & de Miguel, 2005;Jumpponen et al., 2010; Dumbrell et al., 2011) soil fungi(Toberman et al., 2008) and phyllosphere fungi (Osono & Mori,2005; Osono, 2008; Jumpponen & Jones, 2010). Although ourdata suggest that similar variation occurs in moss-associatedfungal communities, the magnitude of the observed differences waslow (10–15% of total OTUs), and given the insufficient sequencingdepth of this study and its impact on OTU detection, we cannotconclusively determine that OTU richness varies seasonally.

Seasonal variation within taxonomic groups

The relative abundance of particular fungal groups variedbetween sampling dates in some cases, and remained constant inothers. For some groups, these patterns provide additional insightinto the interactions between the fungi and their moss hosts. Forexample, ECM fungi peaks in abundance in moss tissue duringlate summer or autumn, as does their biomass and abundance inboreal forest soils (Wallander et al., 2001). This corroboratesreports of ECM fungi actively colonizing moss tissues, as demon-strated by Carleton & Read (1991), and that moss species inaddition to P. schreberi (Carleton & Read, 1991) can act as sub-strates for ECM fungi. Similarly, the Exobasidiales, a group ofEricaceous plant pathogens, are associated primarily with photo-synthetic tissues and peak in abundance during August. Thiscoincides with a late summer dispersal and reproduction peak ofExobasidium, which infects the abundant Vaccinium species inthe plots (Pehkonen et al., 2002; Pehkonen & Tolvanen, 2008).It seems likely the Exobasidiales are a casual phyllosphere mem-ber of the fungal community that are ostensibly deposited on themosses by nearby infected Ericaceous plants. Although mosseshave been reported as potential alternate hosts of vascular plantpathogens (Davey et al., 2010; M. L. Davey, unpublished), itappears unlikely that mosses represent an overwintering host orinoculum reservoir for Exobasidium pathogens. Pleosporaleanfungi exhibit the same gradual abundance increase in greentissues throughout the growing season followed by a decreaseconcomitant with the onset of frost events. However, given thatsome members of the Pleosporales are known to be moss patho-gens (Racovitza, 1959; Davey et al., 2009b; Stenroos et al.,2010; M. L. Davey, unpublished) it seems unlikely that thesefungi are casual epiphyllous members of the phyllosphere like theExobasidiales, and their seasonal abundance variation ratherrepresents a typical Northern Hemisphere fungal disease cyclewith an epidemiological peak and dispersal occurring in summer.

A large proportion of the taxonomic groups detected areprimarily saprophytic and present in varying abundance through-out the year. Although temporal shifts in the relative importanceof bacterial vs fungal decomposers have been documented in soils(Berg et al., 1998; Bardgett et al., 2005; Bjork et al., 2008),

Fig. 5 Canonical correspondence analysis (CCA) ordination of the fungaloperational taxonomic unit (OTU) composition with the effects of forestlocality, host, and tissue partialed out. Arrows point in the direction ofmaximum increase of minimum air temperature (AirTemp), soil tempera-ture at 2-cm depth (SoilTemp), and monthly precipitation (Precipitation),while ‘X’ marks the centroid for each collection date. All variables andfactors had significant effects (P < 0.05) on the ordination configuration.Conditioned and constrained variables represented 3.0% and 2.2% of thetotal variation, respectively, while 94.8% remained unconstrained.






similar shifts within the fungal community are not well character-ized. The dominant order in the bryophyte-associated commu-nity, Helotiales, experiences a slight summer decline inabundance. Conversely, other saprophytic groups found in senes-cent tissues, including the basidiomycete Mycena and the Chae-tothyriales, experience spring and summer abundance climaxes.This may reflect shifts in the relative importance of differentgroups in decomposition throughout the season, although it isunclear whether this could be attributable to competitive ability,or responses to abiotic climatic factors such as temperature andmoisture availability. Seasonal changes in saprophyte abundancemay be particularly pertinent to understand boreal ecosystemfunctioning. Despite the importance of bryophytes and their asso-ciated microbial communities in carbon and nitrogen cycling(Turetsky, 2003; Lindo & Gonzalez, 2010) and the temperaturedependence of bryophyte decomposition rates (Nakatsubo et al.,1997), the bryophyte component of the ecosystem is largely over-looked in models predicting ecosystem responses to global change,if only because of a lack of knowledge (Hobbie et al., 2000;Cornelissen et al., 2007; Lindo & Gonzalez, 2010). Although theseasonal abundance shifts detected herein appear both biologicallyfeasible and ecologically relevant, all conclusions drawn are tenta-tive, as insufficient sequencing depth and the inherent uncertain-ties of taxonomic identification based on existing sequencedatabases (Nilsson et al., 2006; Ovaskainen et al., 2010) add sig-nificant uncertainty to the reliability of these comparisons.

Conclusions

The bryophyte phyllosphere hosts a large fungal community com-parable in magnitude to soil fungal communities, and dominatedby the Helotiales (Ascomycota) and the Agaricales (Basidiomy-cota). Fungal biomass does not decline significantly in winter,which may reflect a fungal community that is active at temper-atures near and below 0�C. Variation of fungal biomass betweencollection dates differed between host species. Although seasonaleffects were not the primary factors structuring communities,composition and OTU richness changed between sampling datesconcomitantly with changes in precipitation and soil and airtemperatures. Some community homogenization and reductionin turnover were detected in conjunction with frost events andsubzero temperatures. Fluctuations in the relative abundance ofparticular fungal groups appear to reflect the nature of their associ-ation with the mosses. Saprophytic fungi experienced abundancepeaks at different collection dates, highlighting the complexity ofcarbon cycling in the bryophyte component of the boreal forestecosystem. It is increasingly evident that the fungal communitiesassociated with mosses are both dynamic and complex systems.

Acknowledgements

This research was supported by a Miljø-2015 grant from theResearch Council of Norway to M.O., and by a post-doctoral fel-lowship from the Natural Sciences and Engineering ResearchCouncil of Canada to M.L.D. The authors thank Tor Carlsenand MERG colleagues for discussion of 454 set-up and data

analysis. The University of Oslo is acknowledged for providinglaboratory facilities for molecular analyses.

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Supporting Information

Additional supporting information may be found in the onlineversion of this article.

Fig. S1 Monthly precipitation, and air and soil temperatures forthe two focus forests in southeastern Norway.

Fig. S2 Global nonmetric multidimensional scaling (GNMDS)ordination of Hellinger-transformed abundance data of samples

that were each sequenced twice in different lanes of the 454pyrosequencing plate.

Fig. S3 Operational taxonomic unit (OTU) rarefactions of allnonsingleton OTUs for the complete data set and each individualcollection date.

Table S1 Kendall’s tau correlation tests between detrended corre-spondence analysis (DCA) and global nonmetric multidimen-sional scaling (GNMDS) axes for GNMDS ordinations run intwo and three dimensions

Table S2 Abundance, frequency, and taxonomic distribution ofnonsingleton operational taxonomic units (OTUs) identifiedagainst a custom database of UNITE + INSDC sequences andthe NCBI-nr ⁄ nt database

Please note: Wiley-Blackwell are not responsible for the contentor functionality of any supporting information supplied by theauthors. Any queries (other than missing material) should bedirected to the New Phytologist Central Office.






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