Fungi and Algae Co-Occur in Snow: an Issue of Shared Habitat Or Algal Facilitation of Heterotrophs? Author(S): Shawn P

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Fungi and Algae Co-Occur in Snow: An Issue of Shared Habitat or Algal Facilitation of Heterotrophs? Author(s): Shawn P. Brown, Bradley J.S.C. Olson and Ari Jumpponen Source: Arctic, Antarctic, and Alpine Research, 47(4):729-749. Published By: Institute of Arctic and Alpine Research (INSTAAR), University of Colorado DOI: http://dx.doi.org/10.1657/AAAR0014-071 URL: http://www.bioone.org/doi/full/10.1657/AAAR0014-071

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BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research. Arctic, Antarctic, and Alpine Research, Vol. 47, No. 4, 2015, pp. 729–749 Fungi and algae co-occur in snow: an issue of shared habitat or algal facilitation of heterotrophs?

Shawn P. Brown1,3,4 Abstract Bradley J.S.C. Olson1,2 and Late season alpine snows are often colonized by psychrophilic snow algae that may pro- Ari Jumpponen1,2 vide a source of nutrients for microbes. Such late season snows are a harsh environment, but support a diverse and complex fungal community. We used culture independent meth- 1 Division of Biology, Kansas State ods (Illumina MiSeq) to test if the presence of snow algae influences fungal communities. University, 116 Ackert Hall, Manhattan, We compared algae-colonized snows to adjacent (3 m distant) noncolonized snows in a Kansas 66506, U.S.A. 2Ecological Genomics Institute, Kansas paired experimental design. Our data indicate that several fungi are locally enriched in al- State University, 116 Ackert Hall, gae colonized snows. Although many such fungi were basidiomycetous yeasts, our analy- Manhattan, Kansas 66506, U.S.A. ses identified a large number of snow-borne members of phylum Chytridiomycota. While 3 Present address: Department of Plant the ecology and function of these Chytridiomycetes remain unclear, we hypothesize that Biology, University of Illinois at Urbana- their enrichment in the algal patches suggests that they depend on algae for nutrition. We Champaign, 249 Morrill Hall, 505 South Goodwin Avenue, Urbana, Illinois 61801, propose that these chytrids are important components in snow ecosystems, highlighting U.S.A. the underestimation of their diversity and importance. Taken together, our data strongly 4Corresponding author: indicate that fungal communities are heterogeneous in snow even among adjacent sam- [email protected] ples. Further, fungal and algal communities may be influenced by similar environmental drivers resulting in their co-occurrence in snow.

DOI: http://dx.doi.org/10.1657/AAAR0014-071

Introduction Alpine and polar snows that persist through or linger late into the growing season often house snow-borne algal communities, Earth’s cryosphere is composed of solid water persisting for frequently dominated by the red-pigmented algae Chlamydomonas more than one month (Fountain et al., 2012) and includes all snow, nivalis or Chloromonas nivalis, both of whose taxonomies remain permafrost, sea ice, freshwater ice, glaciers, and ice shelves. It currently unresolved. These algae often produce colonies that are plays important roles in global climate (Walsh et al., 2005) and visible to the naked eye as a result of their characteristic red color. terrestrial energy balance by influencing surface albedo (Prestrud, The red color of these algae is a result of the secondary carotenoid 2007). With the predicted increase in the mean global annual tem- astaxanthin (Müller et al., 1998) and its fatty acid ester deriva- perature, the cryosphere is declining and becoming a more endan- tives (Gorton et al., 2001) produced in large quantities during their gered ecosystem (Derksen et al., 2012). The decrease in the annual diploid, zygotic stage that is also characterized by thickened cell persistence of late season snow has exceeded climate model pro- walls that are resistant to harsh environmental conditions includ- jections in the Northern Hemisphere (Derksen and Brown, 2012). ing repeated freezing temperatures (Hoham, 1980; Remias et al., These changes likely impact local watershed dynamics as well as 2005, 2010). During the warm season in small meltwater pools, global biogeochemical cycles (Fountain et al., 2012). In addition the zygotes undergo meiosis, producing haploid offspring that are to the consequences to local and global hydrological cycles, the green, are metabolically active, and multiply asexually. Late in the cryosphere decline sets constraints on the distribution and diversity season, when nitrogen is more limiting, these organisms develop of organisms that depend on these unique habitats (Hoham and Du- into flagellated sexual gametes that mate producing new zygotes val, 2001). The transience of the late season snow coupled with its that can survive the next season’s cold temperatures and snow changing volume highlights the importance of understanding the (Müller et al., 1998). endemic biodiversity residing in late season snow packs. Although the red C. nivalis colonies may be visually domi- Snowpacks are a harsh environment characterized by low nant, snow algal communities often consist of many species repre- temperatures and intense ultraviolet irradiation that may act as senting Chlorophyceae (Fujii et al., 2010; Remias et al., 2010). In mutagenic stressors. Yet, there is evidence for rich and diverse addition, snow colonized by algae houses a broad range of fungi metabolically active microbial life in snow (Carpenter et al., 2000; and bacteria that may be specifically adapted to grow in such envi- Harding et al., 2011; Hell et al., 2013). These metabolically active ronments (Hodson et al., 2008; Naff et al., 2013). Some evidence communities contrast the belief of some that snow is but a pas- suggests syntrophic relationships between the snow algae and bac- sive recipient of aerially dispersed propagules whose metabolism teria or fungi. In the most comprehensive microscopic examination is limited because of minimally available liquid water (Warren and to date, Weiss (1983) described the ultrastructure of the snow alga Hudson, 2003). Yet, microbial activity or at least the presence of C. nivalis and repeatedly found encapsulated gram-negative bacte- diverse microbial communities has been suggested for over a cen- ria on the surface of the zygotic resting stage. Weiss (1983) posited tury (Hersey, 1913). These dichotomous viewpoints emphasize our that the microscopic observations suggested syntrophy, as no simi- present rudimentary understanding of microbial diversity in snow, lar bacteria were present in the adjacent snow without algal colo- particularly so for eukaryotic microbes. Consequently, snow can nization. Similar syntrophisms have been suggested for snow algae arguably be considered as a vast unexplored and undocumented and fungi (Kol, 1968; Hoham et al., 1993). In such syntrophic or ecosystem for microbial diversity (Larose et al., 2013). loose symbiotic relationships, algae-associated microorganisms

© 2015 Regents of the University of Colorado Shawn P. Brown et al. / 729 1523-0430/11 $7.00 may utilize dissolved organic carbon (DOC) excreted by the algae. 1 for locations and dates). Samples from each of the locations This is indirectly supported by recent studies of Chlamydomonas in Washington State were within 150 m of each other between reinhardtii, suggesting that algae excrete large amounts of carbon 2011 and 2012. Additionally, in Colorado we sampled two paired (Kind et al., 2012). Algae, in turn, may benefit from the “shade” sampling locations within Niwot Ridge Long Term Ecological provided by the microorganisms, thus buffering the algae against Research Site (http://culter.colorado.edu/NWT) in August 2011 the harsh environmental conditions (Light and Belcher, 1968; Ho- and in July 2012 at two paired sampling locations within nearby ham and Duval, 2001; Remias et al., 2005). However, Kol (1968) Indian Peaks Wilderness area, Arapahoe and Roosevelt National argued that some of these fungi might simply parasitize the algae Forest. Colorado sites at Niwot Ridge LTER could not be sam- rather than form mutually beneficial associations. pled in 2012 due to lack of snow in areas where 2011 samples To our knowledge, studies of snow-borne fungi and bacteria were collected. The sampling locations were still adjacent: the are rare and limited primarily to select fungi such as “snow molds.” maximum distance between the 2011 and 2012 Colorado sam- Generally, these “snow molds” are filamentous and thrive on or- pling locations was 5.7 km. We sampled all the accessible algae ganic substrates in the snow-soil interphase, but are not active in colonized snows at each location, resulting in 16 paired samples the snow itself (Robinson, 2001; Matsumoto, 2009). Recently, Naff (32 total). Most snow colonized by algae was inaccessible and et al. (2013) suggested that Chytridiomycota are abundant in snow could not be sampled (similar to Müller et al., 1998). At each and significantly influence snow food-web dynamics. Additional sampling site, snows were collected only if the following condi- broad inquiries of snow-borne microbial communities indicate an tions were met: (1) there were no signs of foot traffic or other abundance of microbes in snow (Harding et al., 2011) and suggest anthropogenic disturbance; (2) there was an adjacent patch of that these snow/ice-inhabiting microbes are physiologically adapt- uncolonized snow (based on visual assessment) within 3 m from ed to psychrophilic environments (Gunde-Cimerman et al., 2003). the boundary of algae-colonized patch but within the same snow Our current understanding of the general ecology of fungi in snow pack; and (3) there were no topographical differences between remains rudimentary and is based primarily on soils liberated by algal and nonalgal colonized snows, thus minimizing potential thawing snow (de Garcia et al., 2007) or periglacial soils (Freeman confounding effects of microsites. We confirmed the absence of et al., 2009; Schmidt et al., 2012; Brown and Jumpponen, 2014). algae in the uncolonized snows using flow cytometry. Studies of fungal communities associated with snow algae are usu- ally motivated by the great abundance of fungi observed in the course of microscopic examination (Stein and Amundsen, 1967; SAMPLING PROTOCOL Kol, 1968; Hoham et al., 1993). Snows colonized by algae and paired noncolonized controls Here we present the first high-throughput investigation of 3 m away from the edge of the algal colony were selected (selec- fungal communities associated with the snow algae by targeted tion criteria detailed above) and a total of five 85 cm3 volumetric ITS2 Illumina MiSeq sequencing. The Internal Transcribed surface subsamples were collected using a steel cylinder. Sur- Spacer (ITS) regions are the designated fungal barcode of life face scrapings of the top 5 cm were combined into clean 1-gallon (see Schoch et al., 2012). The ITS2 region in particular has been zip-top plastic bags and allowed to melt at ambient temperature. more frequently utilized because its diminutive and largely con- Once melted, one 100 mL sample was drawn with a sterile syringe served length allows for sequencing the entire region even when (BD 30 mL Syringe, Dickinson and Company, Franklin Lakes, using more recent sequencing technologies that provide rela- New Jersey) and passed through a 1.0 μm Nuclepore Track-Etch tively short reads. We utilized a community-filtering framework Membrane filter (47 mm diameter) encased in a 47 mm Swin- (Diamond, 1975; Keddy, 1992) and explicitly test hypotheses Lok Plastic Filter Holder (Whatman, Kent, U.K.) to collect large on co-occurrence of algae and snow fungi (see Jumpponen and cells (mainly fungi and algae). The flow-through was collected Egerton-Warburton, 2005). We compared paired adjacent sam- into a field-sterilized container (using denatured alcohol) and ples with and without visible snow algae. We utilized this paired passed through 0.22 μm Nuclepore Track-Etch Membrane filter sample design because both snow algae (Müller et al., 1998) and (47 mm diameter) to collect bacterial cells (data not shown here). nutrient loads (Fahnestock et al., 2000) in snow are heterogene- Collection and flow through containers were field sterilized with ous and potentially confound landscape level analyses. Thus, denatured alcohol between sample collections. Filter holders and any shifts in community-wide distributions should be detected syringes were used only once to minimize cross contamination locally (in all or most of the paired samples). Paired sample de- between samples. After filtration, filters were stored in MoBio signs to interrogate community ecology are not new (Welling- UltraClean Soil DNA Isolation Kit bead tubes (Carlsbad, Califor- ton, 1982; Schooley et al., 2000), but are often under-utilized in nia) with reagents S1 and IRS added to aid in sample preservation. microbial community analyses (but see Taylor and Bruns, 1999; In 2012, additional unfiltered samples were collected into sterile Hartmann et al., 2014). In this study, we addressed the follow- 15 mL falcon tubes for flow cytometric cell counts. Samples were ing three questions: (1) do snow algae enrich the communities shipped to Kansas State University and stored at –20 °C until with specific fungi; (2) do snow algae shift fungal community processed. Total proportions of autofluorescent microbial constit- composition; and, (3) will such shifts be consistent across larger uents in snow (cells with chloroplasts relative to all cells) from geographic scales? the 2012 samples were estimated using flow cytometry (Guava Technologies PCA-96, Hayward, California) equipped with a 532 nm (green) excitation laser to confirm the low abundance of algae Materials and Methods in noncolonized snows. Triplicate 10 μL samples were diluted in 250 mL 1X PBS. A combination of fluorescence emission at 675 SAMPLING SITES nm (PM2, measure of chloroplast autofluorescence) and Forward We sampled late season snows at six paired locations in Sep- Scatter (FCS) directly related to particle size were used to gener- tember 2011 and August 2012 in the Glacier Peak Wilderness ate dot-plots. Using the program Flowing Software (v.2.5; www. area, Wenatchee National Forest, Washington, U.S.A. (see Table flowingsoftware.com), boundaries of segregating autoflorescent

730 / arctic, Antarctic, and Alpine Research TABLE 1 Sampling locations of paired algae-colonized and uncolonized snows across consecutive years. WA = Washington State, U.S.A., CO = Colo- rado, U.S.A. All Washington sampling locations were collected at or near the Lyman Glacier Basin.

State Site Landmark Latitude Longitude Date Elevation (m) 2011 CO Niwot 1 Near Soddie Laboratory 40°02′56″N 105°34′51″W 10 Aug. 3368 CO Niwot 2 Saddle 40°03′30″N 105°35′20″W 10 Aug. 3514 WA Glacier Peak 1 Cloudy Pass 48°12′09″N 120°55′28″W 13 Sept. 1961 WA Glacier Peak 2 Terminal moraine 48°10′59″N 120°54′11″W 13 Sept. 1802 WA Glacier Peak 3 Lyman Glacier 48°10′21″N 120°53′50″W 14 Sept. 1880 WA Glacier Peak 4 Spider Gap N 48°10′14″N 120°52′55″W 14 Sept. 2135 WA Glacier Peak 5 Spider Gap S 48°10′10″N 120°52′53″W 14 Sept. 2123 WA Glacier Peak 6 Lower Spider snowfield 48°09′42″N 120°52′42″W 14 Sept. 1897 2012 CO Indian Peaks 1 E of Shoshoni Peak 40°04′02″N 105°37′44″W 15 July 3407 CO Indian Peaks 2 S shore Lake Isabelle 40°04′01″N 105°04′01″W 15 July 3358 WA Glacier Peak 1 Cloudy Pass 48°12′10″N 120°55′27″W 2 Aug. 1966 WA Glacier Peak 2 Terminal moraine 48°10′58″N 120°54′11″W 2 Aug. 1794 WA Glacier Peak 3 Lyman Glacier 48°10′24″N 120°53′49″W 2 Aug. 1866 WA Glacier Peak 4 Spider Gap N 48°10′14″N 120°52′55″W 2 Aug. 2173 WA Glacier Peak 5 Spider Gap S 48°10′10″N 120°52′53″W 2 Aug. 2137 WA Glacier Peak 6 Lower Spider snowfield 48°09′41″N 120°52′35″W 2 Aug. 1893

cells along the FCS axis were manually generated based on visual plate at 2 ng μL–1 DNA concentration. PCR amplicons for Il- clustering. The proportions of snow algae were estimated by the lumina MiSeq sequencing were generated using fungus specific proportion of counts that autofluoresced but were larger than bac- primers to amplify the Internal Transcribed Spacer 2 (ITS2) teria (to discriminate against cyanobacteria). Pollen grains may region of the fungal rRNA gene repeat with primers fITS7 (Ihr- autofluoresce using these cytometric filters (gymnosperms have mark et al., 2012) and ITS4 (White et al., 1990). Unique molec- paternal ctDNA transmission and may be included) potentially ular identifier tags (MIDs) were incorporated to the ITS4 primer overestimating the algal counts. However, pollen grain deposi- (MID-ITS4). MIDs were selected from the published Illumina tion in snow is generally spatially homogenous (Bourgeois et al., MID list (Caporaso et al., 2012) and each MID-ITS4 combina- 2001) at the local scale and unlikely to impact the efficacy of tion was tested in silico (OligoAnalyzer 3.1; Integrated DNA the analyses of algal abundance. We used a paired t-test to test if Technologies, Coralville, Iowa, http://www.idtdna.com/ana- the proportion of algae in the visibly colonized and uncolonized lyzer/Applications/OligoAnalyzer) for possible hairpins and/or snows differed. The autofluorescent particles were on average 35 primer dimers at melting temperatures above 40 °C. Primers times more abundant within the algal snows than in the adjacent that passed this rubric were synthesized. PCR amplicons were noncolonized snows (t = 3.25, P = 0.007). In conclusion, the al- generated in 50 μL reactions under the following conditions: gae were functionally negligible in the noncolonized snows. 1 μM forward and reverse primers, 10 ng template DNA, 200

μM of each deoxynucleotide, 2.5 mM MgCl2, 10 μL 5x Green GoTaq Flexi Buffer (Promega, Madison, Wisconsin), 14.6 μL DNA EXTRACTION AND AMPLICON GENERATION molecular biology grade water, and 2 U GoTaq Hot Start Poly- Total genomic DNA was extracted using MoBio extraction merase (Promega, Madison, Wisconsin). PCR cycle parameters kits according to the manufacturer’s protocol with the following consisted of 94 °C initial denaturing step for 4 min, followed by modifications: (1) filters were sonicated for 10 minutes (FS20; 30 cycles of 94 °C for 1 min, 54 °C annealing for 1 min, and 72 ThermoFisher Scientific, Waltham, Massachusetts) in DNA ex- °C extension step for 2 min, followed by a final extension step traction tubes to dislodge any cells adhering to the filters; (2) at 72 °C for 10 min. All PCR reactions were done in triplicate the filter was removed and two 2.4 mm zirconia beads (Bio- to control for PCR stochasticity. Negative PCR controls (sterile Spec Products, Bartlesville, Oklahoma) were added into the molecular grade water in place of DNA template) were included bead tubes; and (3) particles were homogenized in a FastPrep in each run; these controls remained free of observable ampli- instrument (FP120; ThermoFisher Scientific, Waltham, Massa- fication. Triplicate PCR products were pooled by experimental chusetts) at setting 4.0 for 60 s. The extracts were quantified unit (total of 32) and cleaned with Agencourt AmPure cleanup (ND1000 spectrophotometer; NanoDrop Technologies, Wilm- kit using a SPRIplate 96-ring magnet (Beckman Coulter, Bev- ington, Delaware) and each sample was aliquoted to a 96-well erly, Massachusetts) following the manufacturer’s protocol, ex-

Shawn P. Brown et al. / 731 TABLE 2 The most abundant OTUs and sequence abundance for each observed phylum are provided. Nested within phylum, the most abundant order and family (genera where Incertae sedis at the family level are represented parenthetically) are provided in subsequent columns. Taxonomic representations of OTUs based on best BLASTn matches across accessioned fungi deposited in GenBank. Purported ecologies at the family (genus) level are also reported. Number of OTUs representing each taxonomic rank are provided (number of OTUs at phylum level sum to 200, and OTU counts within phylum are a subset of the total OTUs shown for phylum).

Percentage Number Phyla Orders Families/(Genera) Ecology of sequences of OTUs Ascomycota 12.6% 67 Dothideales 5.9% 13 Dothiorceae Biotrophic or Necrotrophic 4.8% 8 (Celosporium) Uncertain (BFM) 1.0% 5 Chaetothyriales 2.3% 12 Herpotrichiellaceae 0.9% 8 (Sarcinomyces) Uncertain (BFM) 1.3% 4 Pleosporales 1.5% 7 Pleosporaceae Necrotrophic or Saprobic 1.4% 5 Basal fungal lineages 1.1% 6 Mucorales 0.7% 2 (Umbelopsis) 0.7% 2 Mortierellales 0.4% 4 Mortierellaceae Saprobic 0.4% 4 Basidiomycota 77.3% 99 Incertae sedis 32.3% 15 (Rhodotorula) Saprobic/(Pathogen?) 32.3% 15 Kriegariales 20.5% 17 Kriegeriaceae Saprobic/(Pathogen?) 20.5% 17 Leucosporidiales 7.3% 9 (Leucosporidiella) Uncertain (non-phytoparasitic)1 4.1% 4 (Leucosporidium) Uncertain (non-phytoparasitic)1 3.2% 5 Chytridiomycota‡ 9.4% 26 Rhizophydiales 6.6% 10 Incertae sedis Uncertain 4.3% 2 Rhizophydiaceae Saprobic 2.3% 7 Polychytriales 1.1% 4 (Polychytrium) Saprobic 1.1% 4 Incertae sedis 0.2% 11 Glomeromycota‡ 0.5% 2 Glomerales 0.5% 2 Glomeraceae 0.5% 2

‡Best BLASTn match to phyla Chytridiomycota and Glomeromycota are extremely dissimilar to any accessioned taxa (query coverage ≤ 25% and BLAST score ≤ 90; see Appendix Table A2). Thriving Glomeromycetes are unlikely in absence of host, thus these likely represent unknown taxa. BFM = black meristematic fungi, polyphyletic group, primarily anamorphic, known to be resistant to harsh environments. 1These genera are defined as nonphytoparasitic (Sampaio et al., 2003) but ecologies remain uncertain.

cept we used a 1:1 bead solution to amplicon ratio to better AmPure cleanup kit once more as above. Illumina MiSeq se- discriminate against small nontarget DNA. Barcoded samples quencing linkers were ligated onto the library and paired-end were equimolarly combined so that each experimental unit was sequenced on a MiSeq Personal Sequencing System (Illumina, equally represented. This final pool was cleaned with Agencourt San Diego, California) using a MiSeq Reagent Kit v2 with 500

732 / arctic, Antarctic, and Alpine Research cycles. Ligation and sequencing were performed at the Integrat- iterations. Nonmetric Multidimensional Scaling (NMDS), based ed Genomics Facility at Kansas State University. on a subsampled Bray-Curtis dissimilarity matrix with 1000 iterations (at 1500 subsampling depth) was used to examine fungal community composition for the first three axes (73.3% commu- SEQUENCE ANALYSIS nity variation, 3D stress = 0.198). To determine if the Washington Sequence data were processed using MOTHUR (v. 1.29.1; sites possessed different fungal communities than the Colorado Schloss et al., 2009). The two obtained fastq (bidirectional reads) sites or if the 2011 and 2012 Washington samples differed, we were contiged and the resultant fasta and qual files used as in- used Analysis of Molecular Variance (AMOVA; PERMANOVA puts for further sequence processing that we briefly describe be- in Anderson, 2001). Diversity estimates and NMDS generation low. We screened contiged sequences and required the following and analyses based on iterative subsampling were implemented for inclusion: exact match to the MIDs (see Appendix Table A1 in MOTHUR. Across the resolved three-dimensional NMDS for complete list of MID sequences), at most 1 bp difference in space, linear (Euclidian) distances were calculated between match to both forward and reverse primers, and with a quality paired algae colonized and nonalgae colonized samples and these score of ≥35 over a 50 bp sliding window for the sequence. In values were analyzed using Student’s t-test to test if Colorado and other words, a sequence was culled if the average Q-score fell Washington paired samples differ in their community similari- below 35 for any 50 bp window (with a 1 bp slide) or if the se- ties. To test for OTU enrichment between paired algae-colonized quences did not match both primers or MID. Additionally, se- and uncolonized snows, we used a nonparametric paired test of quences were culled if they had homopolymers longer than 8 count data. Because of our paired design, our richness, diversity, bp or contained ambiguous nucleotides. This ensured that only NMDS axes scores, and OTU abundance were analyzed using a high-quality full-length ITS2 reads remained. ITS2 sequences nonparametric two-tailed Wilcoxon signed-rank test (H0: Malgae = were truncated to 250 bp for further analysis removing any con- Mnon-algae, H1: Malgae ≠ Mnon-algae) and any significant responses were served 5.8S regions, sequences shorter than 250 bp culled, and corrected for multiple comparison effects using a liberal False putative chimeras removed (UCHIME, Edgar et al., 2011). Re- Discovery Rate (FDR = 0.50). Data were also analyzed using a maining sequences were pair-wise aligned (Needleman-Wunsch) parametric paired t-test. These analyses were consistently con- and the resulting distance matrix was clustered at 97% similarity gruent with the nonparametric tests. As a result, we only report using the average-neighbor algorithm. The rare OTUs (opera- the nonparametric test statistics, as those rely on no assumptions tional taxonomic units) (OTUs not among the 200 most abundant on data distribution or variance homogeneity. All statistics were OTUs) were eliminated to focus on only the most abundant and performed using a combination of MOTHUR and JMP (v. 10.0.2, presumably also the metabolically active taxa in these analyses. SAS Institute, Cary, North Carolina). The 200 most abundant fungal OTUs represented 97.12% of all The taxon assignment of OTUs to phylum Chytridiomy- fungal sequences, therefore likely minimizing the impact of resi- cota (26—or 13%—of the top 200 OTUs) was challenging due dent dormant propagules in these analyses. Randomly selected to their low similarity to any vouchered or uncultured/environ- sequences representing each of the 200 most abundant OTUs mental accessions. Since the low coverage and low similarity were queried (BLASTn nr/nt with the exclusion of uncultured to any accessioned sequence made these BLASTn assignments and environmental samples against GenBank) and best BLAST to Chytridiomycota tentative, we further explored these data matches were recorded with full taxonomic string (see Appen- through a confirmatory Maximum Likelihood (PhyML) analy- dix Table A2). Despite the use of fungal specific primers, many sis. The hypervariable ITS gene regions are difficult to align common OTUs were classified to nonfungal phyla: Chlorophyta globally at the phylum level and thus considered poor at dis- (15 OTUs), Streptophyta (3 OTUs), Ciliphora (1 OTU), and su- cerning taxonomic relationships in phylogenetic analyses at pergroup Rhizaria (1 OTU). We omitted these nonfungal OTUs more inclusive taxonomic ranks (order/family). Even though we and appended our analyses to include only the most abundant used the designated barcode for identifying fungal taxa, these 200 fungal OTUs. Of note is that the most abundant OTU was putative Chytridiomycota consistently failed to be assigned algal (best BLASTn match to Coenochloris sp.) and seven times with satisfactory affinities. As a result, we utilized the PhyML more abundant than the next most abundant OTU, suggesting that approach as a method to confirm the phylum-level placement primer bias was not adequate to discriminate against algal tar- of the OTUs tentatively assigned within Chytridiomycota. gets in samples highly enriched with phylum Chlorophyta under ITS2 reads for representatives of our chytridiomycetous en- the reaction conditions that we used. Richness and diversity esti- vironmental reads (26 OTUs) were aligned with a number of mates were calculated iteratively—OTU richness = Sobs, Good’s accessioned and vouchered Chytridiomycota and two closely –1 coverage = 1 – n1 N where n1 is number of local singletons and related basal phyla (Blastocladiomycota and Monoblephari- N is number of sequences within each sample, complement of domycota) with full-length ITS2 gene regions in GenBank as Simpson’s Diversity = 1 – D = 1 – ∑pi2 (where pi is the propor- well as the ascomycetous yeast Saccharomyces cerevisiae, as tion of individuals in the ith species), and Simpson’s Evenness = an outgroup. Reference sequences were selected across phylum Ed = (1 D–1) S–1 (where S is the OTU richness at each sample and Chytridiomycota to include all available orders of Chytridio- D is the Simpson’s diversity index)—using 1000 iterations at a mycota (orders Cladochytriales and Polychytriales were not in- subsampling depth of 1500 sequences per experimental unit to cluded because no full-length ITS2 sequences from vouchered control for biases due to unequal sampling (Gihring et al., 2011), specimens were available in GenBank at the time of analysis). the average of each estimator used in our analyses. This itera- Sequences were aligned using MUSCLE (1000 iterations) as tive approach controls for potential false positives on diversity implemented in GENEIOUS (v. 5.3.4, BioMatters, Auckland, estimates due to single subsample-based (rarefying) diversity New Zealand). The alignment was trimmed manually to only estimates as detailed by McMurdie and Holmes (2014) by dimin- include full-length ITS2 regions and a Maximum Likelihood ishing the importance of false positives as one extreme measure- tree was generated (100 bootstrap iterations, substitution model ment has little impact on community-wide metrics after multiple = HKY85) in GENEIOUS with Saccharomyces cerevisiae as an

Shawn P. Brown et al. / 733 outgroup. The fasta file of the representative sequences of OTUs 2. Cosmopolitan polyphyletic yeasts in the genus Rhodotorula used for this confirmatory PhyML analysis along with reference (46 OTUs—23% of all OTUs and 53.30% of total sequences) sequences used are available as a Appendix File A1 (available dominated among Basidiomycetes, as also observed in other as an open access file accompanying the online version of this snow and glacier fungi surveys (de Garcia et al., 2007, 2012). article). The four most abundant OTUs were classified asRhodotorula and the next most abundant OTU was classified asCryptococ - cus saitoi, a commonly encountered snow-dwelling yeast with Results uncertain ecology. OTUs assigned to Lyophyllaceae were also common (8 OTUs) and included 2 OTUs with close BLASTn SEQUENCE DATA CHARACTERIZATION affinities to accessioned sequences of Asterophora—a genus with We obtained more than 11 × 106 sequences from our MiSeq members known to be parasites. The remaining 6 OTUs in the library (paired-end fastq files are deposited in Sequence Read family were marginally similar to genus Lyophyllum, members Archive at NCBI: SRR1104197). Of these, 1,466,702 full-length of which are often described as soil-borne saprobic or mycor- ITS2 sequences were retained after quality control. After the rhizal macrofungi. Chytridiomycota were surprisingly frequent removal of nontarget, mainly algal OTUs (363,005 sequences) (26 OTUs—13% and ~10% of total sequences). Of these OTUs, and OTUs that were not among the 200 most abundant, 466,243 none had a strong similarity to any accessioned sequences based sequences remained with unequal sequence counts per experi- on BLASTn alignments (coverage ranged from 15% to 60% mental unit (range 1971–24,964). Among the total of 7835 with very low total BLAST scores; see Table A2). Because of OTUs are OTUs that are exceedingly uncommon (3949 global the low similarity for known accessioned Chytridiomycota, we singletons and an additional 3686 nonsingleton rare OTUs) and reanalyzed these data in queries that also included uncultured/ most likely represent dormant organisms and aerially deposited environmental sequences. These BLASTn analyses consistently spores or hyphal fragments that unlikely contribute to ecosys- failed to match closely any accessioned sequences. Despite their tem function, or methodological artifacts of uncertain origin low similarity to sequences in the combined global genetic data- (Brown et al., 2015). For this reason, we limited our analysis to bases, our confirmatory Maximum Likelihood (PhyML) analysis the 200 most abundant OTUs (see Appendix Fig. A1) that repre- supported placement of the 26 OTUs with 99% bootstrap support sent more than 97% of all fungal sequences in our data set (see within Chytridiomycota. Additionally, our environmental OTUs Appendix File A2 for OTU × Sample community matrix) (File were most closely related to the soil-borne chytridiomycetous or- A2 available as an open access file accompanying the online der Lobulomycetales (Appendix Fig. A2). Yet, bootstrap support version of this article). within the environmental Chytridiomycota clade remains low, a likely result of use of the hypervariable ITS2 region that tends to perform poorly in analyses in more inclusive taxonomic ranks TAXONOMIC DISTRIBUTION (e.g., order or family). As a result, the placement of these OTUs below phylum cannot be deduced from our PhyML analysis. Yet, The fungal communities were dominated by Basidiomy- most of our snow Chytridiomycota OTUs form a distinct clade cota (see Table A2 for complete taxonomic assignments for suggesting that these taxa may represent a monophyletic group the 200 most abundant OTUs and Appendix File A3 [available of snow-borne Chytridiomycetes with very little ITS2 sequence as an open access file accompanying the online version of this similarity to anything known. Unfortunately, our phylogenetic article] for representative sequences of the 200 most abundant analyses suffered from the poorly populated databases that in- OTUs). Sequence and OTU counts of the most abundant orders clude no vouchered reference ITS2 sequences similar to the ob- and families with inferred ecological roles are presented in Table served environmental chytrids.

FIGURE 1. Operational taxonomic unit (OTU) richness in the algae-colonized snows are higher than in the adjacent paired uncolonized snows (Wilcoxon Sign-Rank test). This analysis is based on 1000 iterations to generate average richness estimates (1500 sequences subsampled per iteration) based on the 200 most abundant OTUs. Uncolonized fungal OTU richness estimates are solid symbols and algae colonized richness estimates are open symbols. Dashed lines connect paired samples.

734 / arctic, Antarctic, and Alpine Research DIVERSITY ESTIMATORS nonalgal snows (W = 21, P = 0.298; W = 23, P = 0.252, respectively). When the paired Axis 2 values were analyzed separately Coverage and rarefaction (see Appendix Fig. A3) estima- for Colorado and Washington, an interesting pattern emerged. Our tors indicated that the fungal community was adequately captured Colorado locations showed no discernable difference between the (Good’s Coverage for colonized and noncolonized snow 0.972 ± fungal communities of the paired samples across all three axes (see 0.003 and 0.986 ± 0.001, respectively) in our sequencing. We find insert in Fig. 2). The Washington samples show a contrast; a shift that a greater proportion of the 200 most abundant OTUs occur in in fungal communities in the paired algae colonized/uncolonized the algal-colonized snow than in the snow without algae. Estimated snows (W = 27, P = 0.034 for Axis 2; see insert in Fig. 2). Further, OTU richness in the algae-colonized snow (74.98 ± 11.72; mean our AMOVA suggested no difference across years (F = 1.135, ±1 SD) was greater than in the adjacent, noncolonized snow (64.54 1,30 P = 0.286) in the Washington sites but clearly distinguished the ± 11.72) across both sites and years (Fig. 1; paired two-tailed Wil- snow inhabiting fungal communities from Colorado and Washing- coxon Sign-Rank test; W = 58, P = 0.0013). In contrast, neither di- ton (F = 8.654, P < 0.001). These analyses suggest that although versity (complement of Simpson’s Diversity, 1 – D: 0.890 ± 0.051 1,30 our sites located in Colorado and Washington are distinct, the com- for the algae-colonized and 0.865 ± 0.067 for noncolonized snows; munities remain fairly stable over time—likely as a result of local W = 30, P = 0.130) nor evenness (E : 0.146 ± 0.053 for algae- D and/or regional fungal propagule inputs. colonized and 0.145 ± 0.073 for noncolonized snows; W = 10, P We also analyzed Euclidian distances between the paired = 0.632) differed between the paired colonized and noncolonized samples from algae colonized and uncolonized snows to test if snow samples. they differed in the magnitude of fungal community-wide shifts between Colorado and Washington. The Euclidian distances across the three resolved axes between paired algae-colonized and uncol- COMMUNITY DIFFERENCES onized snow-fungal communities was greater in the Washington Fungal communities were resolved optimally on three NMDS snows than in Colorado (t = 2.47, P = 0.0267; Appendix Fig. A4). axes (stress = 0.198, r2 = 0.733). Our community-wide AMOVA This difference may be the result of a constrained shift in the fun- (PERMANOVA in Anderson, 2001) analyses indicated regional gal community associated with sampling locations or dates. (Colorado vs. Washington) but not temporal differences in the In all, 13 of the 200 most abundant OTUs were more common snow fungal communities. These analyses failed to distinguish fun- in algae-colonized snow (see Table 3), whereas none were enriched in the uncolonized snow as determined by paired Wilcoxon Sign- gal communities in the colonized and noncolonized snow (F1,30 = 1.247, P = 0.227) globally. However, paired analyses of the Axis 2 Rank analyses of OTU abundance. An additional 3 OTUs differed ordination loading scores (representing 57.16% of community var- between paired algal-colonized and uncolonized snows but were iability) indicated that fungal communities in the algae-colonized no longer significant after controlling for multiple comparisons. snow were distinct from those in the paired uncolonized snows The colonized snow was enriched for saprobic and putatively (Fig. 2; W = 48, P = 0.011). In contrast, neither Axis 1 (r2 = 6.38%) pathogenic OTUs. Several Rhodotorula OTUs were enriched in nor Axis 3 (r2 = 9.85%) distinguished between the paired algal and the algal-colonized snows, suggesting the opportunistic utilization

TABLE 3 Fungal operational taxonomic units that are enriched in algal colonized snow compared to paired nonalgal colonized snow based on Wil- coxon Sign-Rank test after correction for multiple comparisons (P |W| is the two-tailed P-value between colonized and paired uncolonized snow). Best BLASTn matches and putative ecologies are also reported (EcM = ectomycorrhizal). The symbol ‘‡’ represents taxa whose best BLASTn match is extremely dissimilar to any accessioned taxa (query coverage ≤ 25% and BLAST score ≤ 90; see Appendix Table A2) that are likely novel fungal taxa whose ecologies remain uncertain.

OTU Wilcoxon Sign-Rank P-value number test statistic (W) (P |W|) Ecology Best BLASTn match 2 47 0.0126 Saprobic Rhodotorula sp. 4 41.5 0.03 Saprobic Rhodotorula psychrophenolica 9 43 0.0248 Unknown Rhizophydiales sp. ‡ 37 30 0.0059 Unknown Chytridiomycota sp. ‡ 40 14 0.0156 Unknown Chytridiomycota sp. ‡ 43 23.5 0.0156 Saprobic Rhodotorula sp. 45 32 0.0103 Saprobic Rhodotorula sp. 48 10.5 0.0313 EcM (‡Unknown) Tylopilus formosus ‡ 59 47 0.0122 Pathogenic Ilyonectria macrodidyma 119 22.5 0.0234 Saprobic Rhodotorula sp. 163 14 0.0156 Saprobic (‡Unknown) Lyophyllum sp. ‡ 195 14 0.0156 Saprobic Rhodotorula sp. 199 10.5 0.0313 Saprobic Leucosporidium scottii

Shawn P. Brown et al. / 735 FIGURE 2. Non-Metric Multi-Dimensional Scaling (NMDS) plot of snow-borne fungi in algal colonized snow (solid symbols) and uncolonized snow (open symbols). Dashed lines connect paired algal colonized and adjacent uncolonized samples. Analysis of Molecular Variance (AMOVA) indicates that Colorado (circles) and Washington (squares) fungal communities are distinct. Insert represents paired Wilcoxon Signed-Rank test across Axis 2 (57.12% of community variability) and indicates that snow algae colonized snows possess different fungal communities than paired uncolonized snows (W = 48, P = 0.011); dashed lines connect paired samples.

of increased organic matter associated with these snow algae. Par- of knowledge about snow-borne life or microbial communities and ticularly interesting are OTUs 9, 37, and 40 that—based on our their function in snow, particularly so for fungi. Our analyses sug- PhyML analyses—represent novel chytrids whose functions re- gest strongly coupled snow-borne fungi/algae co-occurrence pat- main unknown (Fig. A2). OTUs 48 and 163 were more common terns. in algal-colonized snow but are highly dissimilar to any known Our data indicate that the presence of snow algae may act accessioned fungi (see Table A2 for full BLAST scores). Best as an environmental filter (Jumpponen and Egerton-Warburton, BLASTn matches identify these OTUs within the genera Tylopilus 2005) that structures the snow-borne fungal community. Alterna- (ectomycorrhizal) and Lyophyllum (saprobic or parasitic). Given tively, the snow algae and co-occurring fungal communities share the great dissimilarity to any accessioned taxa, these OTUs most some yet unidentified environmental variable that determines their likely represent novel taxa and/or taxa that are underrepresented in greater frequency in those samples. This co-occurrence is best evi- the global nucleotide repositories. Thus, further and more detailed denced by the shift in the Axis 2 loading scores between the paired investigation is needed to better understand the fungal communi- colonized and uncolonized samples (Fig. 2). Interestingly, this shift ties in this environment. only was significant in the Washington samples in our conserva- tive nonparametric tests. There are two primary reasons that may underlie the lack of significance in Colorado samples. (1) Our sam- Discussion pling in Colorado was more superficial than that in Washington and included only two algae patches that we were able to locate We present one of the very first deep-sequencing studies of within the Niwot Ridge LTER or Indian Peaks Wilderness. With snow-borne fungi. The late season alpine snow-packs are a declin- so few samples, we may lack the statistical power to resolve those ing ecosystem; climate change predictions suggest that the earth’s trends. (2) Alternatively, for logistical reasons, the Colorado snows cryosphere will dramatically decline in volume (Derksen and were sampled about one month before the Washington snows in Brown, 2012). As a result, assessment of the biodiversity in these both years. This temporal difference may explain this observation; “endangered” ecosystems is timely and critical. There is a dearth perhaps the shifts in communities are not as strong in Colorado

736 / arctic, Antarctic, and Alpine Research because the communities have not had as much time to diverge making direct comparisons impossible. Naff et al. (2013) posited early in the growing season. We speculate that the establishment that these snow Chytridiomycetes parasitize snow algae because of greater algal abundance later in the growing season or similar they were common in clone libraries from algal snows. However, environmental tolerances may facilitate a component of the extant these hypotheses were not explicitly tested, nor is parasitism the snow fungal community. However, our Colorado sampling preced- only reasonable nutritional hypothesis. The present study also dif- ed this community shift. Our ordination analyses partially support fers from Naff et al. (2013) in a very important way, Naff and co- this explanation: the Euclidian distance between the paired sam- authors only collected snow that was colonized by snow algae and ples in ordination space is smaller in Colorado than in Washington sequenced shallowly, whereas we utilized paired samples with and (Fig. A4), suggesting that sampling later in the season may per- without algae. Given that Chytridiomycota also were abundant in mit a further divergence among the compared snow communities. the snows free of algae, these Chytridiomycota may be saprobes or Naturally, we cannot distinguish between the temporal effects of facultative syntrophs. Nevertheless, to capture a high abundance of sampling and the confounding spatial effects in this case. Chytridiomycota is striking; Chytridiomycota tend to be infrequent Alpine snow supports diverse fungal communities dominated in most locus-targeted community sequencing studies. This may be by basidiomycetous yeasts whose ecologies and taxonomies are a result of the highly divergent and difficult to amplify ITS regions poorly understood. Yeast-dominated systems have been reported of these basal fungi (Schoch et al., 2012), and analyses of environ- on glacier surfaces (de Garcia et al., 2012) and in periglacial soils mental DNA may get overwhelmed by more easily amplified tem- (Schmidt et al., 2012; Brown and Jumpponen, 2014). In our analy- plates (Anderson and Cairney, 2004; but see Taylor et al., 2008). ses, the most common fungal OTU was assigned to genus Rhodo- Thus, even the relatively high estimates of the chytrid abundance torula (46 OTUs in all; 6 of which are enriched in algal snows). observed here might be an underestimation. Of note, solely rely- These yeasts are polyphyletic, understudied, and their generic de- ing on OTU abundance as a proxy for organismal abundance may lineation is historically morphological, not phylogenetic (Toome et be ill advised as there is no 1:1 relationship between copy num- al., 2013). Additionally, many OTUs were placed into taxa grouped bers and organism abundance (Amend et al., 2010). Also, different as black meristematic fungi (BMF), another polyphyletic group- fungal lineages may be differentially abundant in environmental ing based on anamorph phenotypes. Although BMFs are overall sequences due to a myriad of factors including primer bias and poorly understood, they are suspected to play a large role in min- differential ITS copy number (Pukkila and Skrzynia, 1993; Porter eral transformations and often are resilient in harsh environments and Golding, 2012). Yet, despite the potential poor amplification (Onofri et al., 2007). These cosmopolitan BMFs most likely utilize of Chytridiomycota, we found that chytrids were abundant and di- allochthonous organic matter such as wind-blown particulate mat- verse, highlighting that they both are common and likely important ter common on the snow surface. These results reiterate that there in snows. is a dearth of information on psychrophilic/tolerant fungi and high- It is tempting to speculate on whether these chytrids parasitize lights the importance for future studies into these systems. or prey on snow algae (Naff et al., 2013) as such associations are We expected that OTUs enriched in algae-associated snows common in algae-dominated freshwater systems (Hoffman et al., would provide the most valuable clues on the ecology of these 2008; Gutman et al., 2009; Rasconi et al., 2011). Snow chytrids fungi. We initially hypothesized that the algal cells and/or their may also act as facultative mutualists or have obligate syntrophic nutrient-rich exudates provide substrates that potentially facilitate relationships; there is a precedence of such relationships in other syntrophy or presence of opportunistic saprobes and algal patho- aquatic systems (Picard et al., 2013). Although our data suggest the gens. Many of the observed enriched OTUs are suspected to have enrichment of these communities with such fungi, they do not al- saprobic/parasitic ecologies and thus support our latter hypotheses. low specific statements about their ecology or life strategies. How- Additionally, several taxa that were more abundant in algae-colo- ever, it is most likely that these novel chytrids are major players in nized snow were either macrofungi or form ectomycorrhizal as- snow-borne fungal communities. It is also clear that snow fungi sociations. The presence of an active community of macrofungi in are a product of establishment from local propagule pools as the snow is unlikely but may represent snowbank fungi that fruit at the snow-borne fungal communities were compositionally distinct in periphery of snowpacks (Cooke, 1955; Cripps, 2009). Addition- Washington and Colorado. In contrast, both locations had stable ally, there may be an abundance of fungal spores representing an fungal communities over two sampling years. It is probable that allochthonous introduction from surrounding areas. Unfortunately, snow fungi initially establish from local propagules and the pres- our analyses do not permit assessing whether these yeasts or other ence of snow algae facilitate their growth and metabolic activity. observed fungi are metabolically active in this substrate. Many small-scale biotic and/or abiotic factors that vary spatially The abundance of Chytridiomycota in our study was surpris- can select for cryotolerant communities differing in their ecologi- ing. This suggests that these enigmatic fungi may be more abun- cal and functional attributes. Further and more detailed investiga- dant and diverse than previously thought. We have just recently tions of the snow fungal metabolic activity and community dynam- begun to appreciate the hidden diversity of Chytridiomycota. ics are needed to better understand them in the cryosphere. Freeman et al. (2009) demonstrated that Chytridiomycota domi- Overall, our results indicate that snow algae and snow fungi nate high altitude periglacial soils. The snow chytrids in our study co-occur and either share similar environmental tolerances or algae were abundant and dissimilar to any sequences accessioned to the may act as an environmental filter in fungal community assembly. nucleotide repositories. Our confirmatory phylogenetic analyses In the latter case, this community filtering is potentially facilitated suggested that these fungi might represent a novel clade of snow by enrichment of saprobic and pathogenic fungi that are able to uti- Chytridiomycota (Fig. A2). Placement of these novel chytrids from lize snow algae directly or indirectly through their exudates. Alter- this study at levels below phylum remains uncertain but they may natively, the enrichment of specific fungal community constituents belong to the early divergent snow Chytridiomycetes identified as may be an outcome of facultative syntrophic associations between ‘Snow Clades’ from North American and European snows (Naff et algae and fungi that are engaged in loose symbioses. Further in- al., 2013). However, this cannot be determined because our study depth studies on the life history strategies and ecology of snow- and that of Naff et al. (2013) targeted different rRNA gene regions, inhabiting fungi are required to shed light into these unresolved

Shawn P. Brown et al. / 737 questions. Interestingly and congruently with other studies (Naff et Derksen, C., Smith, S. L., Sharp, M., Brown, L., Howell, S., Copland, al., 2013), our data identified potentially novel groups of Chytridi- L., Mueller, D. R., Gauthier, Y., Fletcher, C. G., Tivy, A., Bernier, omycota, some of which were enriched in algae-colonized snows M., Bourgeois, J., Brown, R., Burn, C. R., Duguay, C., Kushner, P., and of undetermined functions. From these studies, it is clear that Langlois, A., Lewkowicz, A. G., Royer, A., and Walker, A., 2012: we are barely scratching the surface of the nearly unexplored Variability and change in the Canadian cryosphere. Climate Change, 115: 59–88. cryosphere. To put it simply, snow is an ecosystem that maintains Diamond, J. M., 1975: Assembly of species in communities. In unique communities that may vanish with the declining cryosphere Cody, M. L., and Diamond, J. M. (eds.), Ecology and Evolution of before we have an opportunity to understand them. Communities. Cambridge, Massachusetts: Harvard University Press, 342–344. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., and Knight, Acknowledgments R., 2011: UCHIME improves sensitivity and speed of chimera The authors thank Vera Brown, Ashlyn Jumpponen, Corin detection. Bioinformatics, 27: 2194–2200. Fahnestock, J. T., Povirk, K. L., and Welker, J. M., 2000: Ecological White, and Kale Lothamer for assistance in sampling. We thank significance of litter redistribution by wind and snow in Arctic anonymous reviewers of previous versions of this contribution for landscapes. Ecography, 23: 623–631. providing useful suggestions that improved this manuscript. We Fountain, A. G., Campbell, J. L., Schuur, E. A. G., Stammerjohn, S. are indebted to those anonymous strangers who assisted Brown E., Williams, M. W., and Ducklow, H. W., 2012: The disappearing during a field injury. We are extremely appreciative to the Douglas cryosphere: impacts and ecosystem responses to rapid cryosphere County, Washington, Sheriff’s Office for not confiscating our sam- loss. BioScience, 62: 405–415. ples when dispatched about suspicious people in a park with gloves Freeman, K. R., Martin, A. P., Karki, D., Lynch, R. C., Mitter, M. S., and syringes. Brown was supported by a National Science Founda- Meyer, A. F., Longcore, J. E., Simmons, D. E., and Schmidt, S. K., tion (NSF) GK-12 fellowship (DGE-0841414 to C. Ferguson) and 2009: Evidence that chytrids dominate fungal communities in high- the Kansas Academy of Science. elevation soils. Proceedings of the National Academy of Sciences, USA, 106: 18315–18320. Fujii, M., Takano, Y., Kojima, H., Hoshino, T., Tanaka, R., and Fukui, M., 2010: Microbial community structure, pigment composition, References Cited and nitrogen source of red snow in Antarctica. Microbial Ecology, Amend, A. S., Seifert, K. A., and Bruns, T. 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Shawn P. Brown et al. / 739 APPENDIX

TABLE A1 Primer sequences (upper) and multiplex Molecular Identifier sequence tags (lower) used in this study.

Gene Primers ITS7f 5′-GTGARTCATCGAATCTTTG-3’ ITS4 5′-TCCTCCGCTTATTGATATGC-3’ Tags Samples TCCCTTGTCTCC Lyman-5-2011-Algae ACGAGACTGATT Lyman-1-2011-Algae TACCGCTTCTTC Niwot-3-2011-Algae ATCACCAGGTGT Lyman-3-2011-Algae TGGTCAACGATA Lyman-4-2011-Algae ATCGCACAGTAA Niwot-1-2011-Algae GTCGTGTAGCCT Lyman-6-2011-Algae GATTATCGACGA Niwot-2-2011-Algae ATCCTTTGGTTC Lyman-2-2011-Algae GCCTAGCCCAAT Lyman-6-2011-Non_Algae ACCGGTATGTAC Lyman-2-2011-Non_Algae GATGTATGTGGT Lyman-4-2011-Non_Algae TGCATACACTGG Lyman-5-2011-Non_Algae AGTCGAACGAGG Niwot-1-2011-Non_Algae ACCAGTGACTCA Niwot-2-2011-Non_Algae GAATACCAAGTC Lyman-3-2011-Non_Algae GTAGATCGTGTA Niwot-3-2011-Non_Algae TAACGTGTGTGC Lyman-1-2011-Non_Algae ACTCCTTGTGTT IndianPeaks-2-2012-Algae CCAATACGCCTG IndianPeaks-1-2012-Algae ACTTGGTGTAAG Lyman-3-2012-Algae TCACCTCCTTGT Lyman-4-2012-Algae CAAACAACAGCT Lyman-6-2012-Algae GCAACACCATCC Lyman-5-2012-Algae GCACACCTGATA Lyman-2-2012-Algae CGAGCAATCCTA Lyman-1-2012-Algae AGTCGTGCACAT IndianPeaks-1-2012-Non_Algae GCGACAATTACA IndianPeaks-2-2012-Non_Algae CGAGGGAAAGTC Lyman-2-2012-Non_Algae TCATGCTCCATT Lyman-6-2012-Non_Algae AGATTGACCAAC Lyman-3-2012-Non_Algae AGTTACGAGCTA Lyman-4-2012-Non_Algae GCATATGCACTG Lyman-1-2012-Non_Algae CAACTCCCGTGA Lyman-5-2012-Non_Algae

740 / arctic, Antarctic, and Alpine Research Accession JF805370.1 AB474394.1 EF151247.1 JF805370.1 JX976323.1 JQ857032.1 HM036644.1 EF634250.1 JN400812.1 JQ780656.1 JX014242.1 JN400812.1 HM036644.1 AB474394.1 EF151247.1 JN040515.1 HQ622100.1 DQ389663.1 JQ857032.1 EU552113.1 NG_027613.1 DQ485621.1 AB474394.1 EU715662.1 JN943815.1 FJ744598.1 FJ997287.1 AJ972462.1 AB474394.1 DQ485665.1 FR838002.1 AY843154.1 EU873018.1 JQ272411.1 JN197600.1 EU873018.1 AB474394.1 Max 89% 96% 99% 87% 74% 88% 93% 91% 92% 100% 85% 90% 85% 94% 82% 100% 99% 91% 88% 99% 97% 96% 94% 83% 97% 98% 98% 90% 91% 74% 84% 99% 93% 89% 83% 93% 88% Identity E value 1.00E-82 3.00E-110 3.00E-122 4.00E-77 1.00E-25 9.00E-79 2.00E-100 3.00E-14 4.00E-95 8.00E-124 4.00E-70 3.00E-90 6.00E-68 9.00E-104 4.00E-51 8.00E-124 3.00E-122 4.00E-07 1.00E-77 4.00E-121 4.00E-07 1.00E-14 9.00E-104 1.00E-07 4.00E-07 1.00E-114 3.00E-116 1.00E-07 2.00E-92 5.00E-12 3.00E-66 8.00E-105 3.00E-08 4.00E-83 4.00E-51 3.00E-08 2.00E-80 Query 100% 100% 100% 100% 96% 100% 100% 25% 100% 100% 100% 100% 100% 100% 95% 100% 100% 18% 100% 100% 15% 21% 100% 28% 15% 100% 98% 20% 100% 60% 100% 87% 18% 100% 89% 18% 100% Coverage 307 Total Total Score 315 407 446 297 125 302 374 87.8 356 452 273 340 266 385 210 452 446 64.4 298 443 64.4 89.7 385 66.2 64.4 421 426 66.2 347 80.6 260 389 68 316 215 68 315 407 446 297 125 302 374 87.8 356 452 273 340 266 385 210 452 446 64.4 298 443 64.4 89.7 385 66.2 64.4 421 426 66.2 347 80.5 260 389 68 316 215 68 307 Max Score Species psychrophenolica saitoi glacialis polyspora glacialis psychrophenolica crustaceus sublividus glacialis leucospermi aggregatum ramanniana chlorogonii pullulans larixicola diaphanum nigricans Genus Rhodotorula Rhodotorula Rhodotorula Rhodotorula Cryptococcus Rhodotorula Asterophora Leucosporidiella Sydowia Leucosporidium Rhodotorula Asterophora Rhodotorula Rhodotorula Sarcinomyces Stemphylium Polyporoletus Rhodotorula Coniozyma Polychytrium Rhizophydium Rhodotorula Umbelopsis Rhizophydium Aureobasidium Celosporium Glomus Rhodotorula Rhizophydium sistotrems Phaeococcomyces Leucosporidium Leucosporidiella Rhodotorula Family Kriegeriaceae Kriegeriaceae Lyophyllaceae Dothiorceae Kriegeriaceae Lyophyllaceae Kriegeriaceae Pleosporaceae Albatrellaceae Kriegeriaceae Dothiorceae Rhizophydiaceae Rhizophydiaceae Dothiorceae Glomeraceae Rhizophydiaceae Corticiaceae Herpotrichiellaceae Order TABLE A2 TABLE Kriegariales Filobasidiales Kriegariales Agaricales Rhizophydiales Leucosporidiales Dothideales Leucosporidiales Kriegariales Agaricales Kriegariales Chaetothyriales Pleosporales Russulales Kriegariales Dothideales Polychytriales Rhizophydiales Mucorales Rhizophydiales Dothideales Dothideales Glomerales Rhizophydiales Corticiales Chaetothyriales Leucosporidiales Leucosporidiales Accession numbers for the closet match. Accession numbers for Class Tremellomycetes Agaricomycetes Chytridiomycetes Microbotryomycetes Dothideomycetes Microbotryomycetes Agaricomycetes Eurotiomycetes Dothideomycetes Agaricomycetes Dothideomycetes Chytridiomycetes Chytridiomycetes Chytridiomycetes Dothideomycetes Dothideomycetes Glomeromycetes Chytridiomycetes Agaricomycetes Eurotiomycetes Microbotryomycetes Microbotryomycetes Subphylum Agaricomycotina Pucciniomycotina Agaricomycotina Pucciniomycotina Pezizomycotina Pucciniomycotina Pucciniomycotina Agaricomycotina Pezizomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Pezizomycotina Mucoromycotina Pezizomycotina Pezizomycotina Agaricomycotina Pezizomycotina Pucciniomycotina Pucciniomycotina Phylum Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Chytridiomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Ascomycota Basidiomycota Basidiomycota Ascomycota Chytridiomycota Chytridiomycota Basidiomycota (Zygomycota) Chytridiomycota Ascomycota Ascomycota Glomeromycota Basidiomycota Chytridiomycota Basidiomycota Ascomycota Chytridiomycota Basidiomycota Basidiomycota Chytridiomycota Basidiomycota Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Kingdom sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. Species Rhodotorula Rhodotorula Rhodotorula psychrophenolica Rhodotorula Cryptococcus saitoi Rhodotorula glacialis Asterophora Rhizophydiales Leucosporidiella fragaria Sydowia polyspora Leucosporidium Rhodotorula glacialis Asterophora Rhodotorula Rhodotorula psychrophenolica crustaceus Sarcinomyces Stemphylium sublividus Polyporoletus Rhodotorula glacialis Coniozyma leucospermi aggregatum Polychytrium Rhizophydium Rhodotorula Umbelopsis ramanniana Rhizophydium chlorogonii pullulans Aureobasidium Celosporium larixicola Glomus diaphanum Rhodotorula Rhizophydium cf.Sistotrema Phaeococcomyces nigricans Chytridiomycota Leucosporidium Leucosporidiella Chytridiomycota Rhodotorula

Seq count 49594 45579 41353 39847 32545 29737 21673 20636 16371 13141 12874 7325 6726 6460 6264 5958 5880 5839 5764 5057 4644 4054 3238 2914 2877 2591 2523 2436 2339 2115 2040 1908 1739 1646 1627 1621 1532

OUT number 2 3 4 5 6 7 8 9 10 11 12 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 35 36 37 38 39 40 41 Full taxonomic descriptions of the 200 most abundant fungal OTUs including sequence count, best BLASTn match, Max Score, Total Score, Query Coverage, E-Value, Max Identity and E-Value, Query Coverage, Score, Total including sequence count, best BLASTn match, Max Score, fungal OTUs Full taxonomic descriptions of the 200 most abundant

Shawn P. Brown et al. / 741 Accession JQ857032.1 JF805370.1 JN943815.1 AY474394.1 FJ997287.1 JQ857037.1 HM060320.1 JN400825.1 JN197600.1 AB474394.1 HF558652.1 NR_073247.1 FR717869.1 AB474394.1 EF016382.1 KC311505.1 EU326864.1 JQ857032.1 AF284129.1 JQ857032.1 JQ857032.1 NG_027613.1 EU873018.1 HQ630294.1 JQ857032.1 NG_027613.1 JN943798.1 AB180199.1 EU873018.1 JQ857032.1 NR_073276.1 KC489506.1 JQ354915.1 EU167601.1 JF805370.1 EU873018.1 NG_027613.1 Max 87% 88% 95% 87% 97% 98% 90% 99% 85% 90% 100% 100% 87% 99% 91% 100% 95% 86% 92% 87% 85% 97% 93% 96% 81% 97% 100% 100% 91% 86% 82% 100% 99% 99% 87% 91% 97% Identity E value 2.00E-74 2.00E-81 1.00E-07 9.00E-79 3.00E-110 1.00E-115 9.00E-09 1.00E-121 1.00E-57 3.00E-85 8.00E-124 8.00E-124 9.00E-79 2.00E-119 4.00E-89 8.00E-124 1.00E-107 6.00E-68 7.00E-93 4.00E-76 3.00E-66 1.00E-07 3.00E-08 8.00E-105 1.00E-57 1.00E-07 8.00E-124 8.00E-124 4.00E-07 7.00E-61 1.00E-58 8.00E-124 3.00E-122 2.00E-119 5.00E-75 4.00E-07 1.00E-07 Query 100% 100% 16% 100% 98% 100% 20% 100% 89% 100% 100% 100% 100% 100% 97% 100% 100% 98% 99% 100% 100% 15% 18% 97% 100% 15% 100% 100% 18% 90% 99% 100% 100% 100% 100% 18% 15% Coverage 288 311 66.2 302 407 425 69.8 444 232 324 452 452 302 437 336 452 398 266 349 293 260 66.2 68 389 232 66.2 452 452 64.4 242 235 452 446 437 289 64.4 66.2 Total Total Score 288 311 66.2 302 407 425 69.8 444 232 324 452 452 302 437 336 452 398 266 349 293 260 66.2 68 389 232 66.2 452 452 64.4 242 235 452 446 437 289 64.4 66.2 Max Score Species glacialis chlorogonii larixicola glacialis formosus pullulans podzolicus gamsii muscorum minutissima macrodudyma cantuariensis glacialis glacialis glacialis aggregatum kuhlmanii glacialis aggregatum verticillata coprophilum glacialis fellii nana berberidis aggregatum Genus Rhodotorula Rhodotorula Rhizophydium Rhodotorula Celosporium Rhodotorula Tylopilus Aureobasidium Leucosporidiella Rhodotorula Cryptococcus Trichosporon Leucosporidiella Rhodotorula Cladophialophora Ilyonectria Mycocentrospora Rhodotorula Capronia Rhodotorula Rhodotorula Polychytrium Mortierella Rhodotorula Polychytrium Mortierella Trichosporon Rhodotorula Leucosporidium Umbelopsis Capronia Dothidea Rhodotorula Polychytrium Family Kriegeriaceae Rhizophydiaceae Kriegeriaceae Boletaceae Dothiorceae Herpotrichiellaceae Nectriaceae Mycosphaerellaceae Kriegeriaceae Herpotrichiellaceae Kriegeriaceae Kriegeriaceae Mortierellaceae Kriegeriaceae Mortierellaceae Kriegeriaceae Herpotrichiellaceae Dothiorceae Order Continued TABLE A2 TABLE Kriegariales Rhizophydiales Dothideales Kriegariales Boletales Dothideales Leucosporidiales Filobasidiales Tremellales Leucosporidiales Chaetothyriales Hypocreales Capnodiales Kriegariales Chaetothyriales Kriegariales Kriegariales Mortierellales Kriegariales Mortierellales Tremellales Kriegariales Leucosporidiales Mucorales Chaetothyriales Dothideales Class Microbotryomycetes Chytridiomycetes Dothideomycetes Microbotryomycetes Agaricomycetes Dothideomycetes Microbotryomycetes Tremellomycetes Tremellomycetes Microbotryomycetes Eurotiomycetes Sordariomycetes Dothideomycetes Microbotryomycetes Eurotiomycetes Microbotryomycetes Microbotryomycetes Microbotryomycetes Tremellomycetes Microbotryomycetes Microbotryomycetes Eurotiomycetes Dothideomycetes Subphylum Pucciniomycotina Pezizomycotina Pucciniomycotina Agaricomycotina Pezizomycotina Pucciniomycotina Agaricomycotina Agaricomycotina Pucciniomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pucciniomycotina Pezizomycotina Pucciniomycotina Pucciniomycotina Mortierellomycotina Pucciniomycotina Mortierellomycotina Agaricomycotina Pucciniomycotina Pucciniomycotina Mucoromycotina Pezizomycotina Pezizomycotina Phylum Basidiomycota Basidiomycota Chytridiomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Ascomycota Ascomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Chytridiomycota Chytridiomycota (Zygomycota) Basidiomycota Chytridiomycota (Zygomycota) Basidiomycota Chytridiomycota Basidiomycota Basidiomycota (Zygomycota) Ascomycota Ascomycota Basidiomycota Chytridiomycota Chytridiomycota Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Kingdom sp. sp. sp. sp. sp. sp. sp. sp. sp. Species sp. sp. Rhodotorula glacialis Rhodotorula Rhizophydium chlorogonii Rhodotorula Celosporium larixicola Rhodotorula glacialis formosus Tylopilus pullulans Aureobasidium Leucosporidiella Rhodotorula Cryptococcus podzollcus gamsii Trichosporon Leucosporidiella muscorum Rhodotorula minutissima Cladophialophora Ilyonectria macrodidyma cantuariensis Mycocentrospora Rhodotorula glacialis Capronia Rhodotorula glacialis Rhodotorula glacialis aggregatum Polychytrium Chytridiomycota kuhlmanii Mortierella Rhodotorula glacialis aggregatum Polychytrium verticillata Mortierella coprophilum Trichosporon Chytridiomycota Rhodotorula glacialis Leucosporidium fellii Umbelopsis nana Capronia Dothidea berberidis Rhodotorula Chytridiomycota aggregatum Polychytrium

Seq count 1464 1457 1394 1338 1212 1142 1105 1066 1036 996 976 967 934 902 898 862 858 840 834 802 769 709 692 682 661 643 612 608 600 593 593 577 576 567 566 556 551

OUT number 42 43 44 45 46 47 48 49 50 52 53 54 55 57 58 59 60 61 62 63 65 67 68 70 71 72 73 74 75 76 77 78 79 80 81 82 83

742 / arctic, Antarctic, and Alpine Research Accession AB474394.1 HQ829153.1 FJ997287.1 AB267395.1 JQ857032.1 KC180717.1 HQ608097.1 DQ347480.1 JZ076964.1 AB474394.1 DQ421971.1 JQ857032.1 GU327540.1 FJ997287.1 EU1186181 FJ997287.1 AB474394.1 AB509666.1 KC460834.1 HM192933.1 JQ857032.1 JQ857032.1 DQ182502.1 JX462675.1 AY997055.1 NG_027613.1 KF181242.1 AB699672.1 AJ244254.1 EU873018.1 KC455921.1 GQ153222.1 KC455921.1 NG_027613.1 HQ650627.1 EU304350.1 KC897667.1 Max 95% 98% 95% 98% 84% 100% 99% 99% 100% 86% 80% 88% 78% 92% 88% 91% 93% 77% 100% 100% 99% 98% 89% 82% 78% 97% 100% 94% 86% 93% 84% 98% 94% 97% 96% 85% 100% Identity E value 1.00E-107 7.00E-118 3.00E-104 2.00E-117 7.00E-55 8.00E-124 3.00E-122 5.00E-120 8.00E-124 4.00E-76 1.00E-50 9.00E-79 3.00E-46 4.00E-96 4.00E-76 3.00E-90 4.00E-96 1.00E-38 8.00E-124 8.00E-124 4.00E-121 4.00E-115 2.00E-10 1.00E-58 1.00E-07 1.00E-07 8.00E-124 5.00E-12 4.00E-58 3.00E-08 1.00E-57 3.00E-117 6.00E-100 4.00E-07 8.00E-111 1.00E-57 8.00E-124 Query 100% 100% 98% 100% 90% 100% 100% 100% 100% 100% 100% 100% 100% 98% 100% 98% 100% 100% 100% 100% 100% 100% 22% 100% 39% 15% 100% 20% 87% 18% 88% 100% 100% 15% 100% 90% 100% Coverage 398 432 387 430 223 452 446 439 452 293 208 302 194 360 293 340 360 168 452 452 443 423 75.2 235 66.2 66.2 452 80.6 233 68 232 430 372 64.4 408 232 452 Total Total Score 398 432 387 430 223 452 446 439 452 293 208 302 194 360 293 340 360 168 452 452 443 423 75.2 235 66.2 66.2 452 80.6 233 68 232 430 372 64.4 408 232 452 Max Score Species larixicola variabilis glacialis chartarum spirale longisporus glacialis murrhardtense larixicola laeticolor larixicola miyabeana tenuissima glacialis glacialis curvatum aggregatum acuminatum portentosum dematioides punctata spicifera Genus Rhodotorula Aureobasidium Celosporium Typhula Rhodotorula Urocladium Mortierella Malassezia Trichoderma Rhodotorula Lactarius Rhodotorula Fibulobasidium Celosporium Ramariopsis Celosporium Rhodotorula Clavulinopsis Alternaria Ganoderma Rhodotorula Rhodotorula Lyophyllum Aureobasidium Hyaloraphidium Polychytrium Fusarium Tricholoma Phaeosclera Rhodotorula Rhodotorula Polychytrium Amandinea Endosporium Curvularia Family Dothiorceae Typhulaceae Kriegeriaceae Pleosporaceae Mortierellaceae Malasseziceae Hypocreaceae Russulaceae Kriegeriaceae Sirobasidiaceae Clavariaceae Clavariaceae Pleosporaceae Ganodermataceae Kriegeriaceae Kriegeriaceae Lyophyllaceae Dothiorceae Nectriaceae Tricholomataceae Erythrobasidiaceae Erythrobasidiaceae Caliciaceae Pleosporaceae Order Continued TABLE A2 TABLE Dothideales Dothideales Thelephorales Kriegariales Pleosporales Mortierellales Malasseziales Hypocreales Russulales Kriegariales Tremellales Dothideales Agaricales Dothideales Agaricales Pleosporales Polyporales Kriegariales Kriegariales Agaricales Dothideales Hypocreales Agaricales Erythrobasidiales Erythrobasidiales Polychytriales Caliciales Myriangiales Pleosporales Class Dothideomycetes Dothideomycetes Agaricomycetes Microbotryomycetes Dothideomycetes Exobasidiomycetes Sordariomycetes Agaricomycetes Microbotryomycetes Tremellomycetes Dothideomycetes Agaricomycetes Dothideomycetes Agaricomycetes Dothideomycetes Agaricomycetes Microbotryomycetes Microbotryomycetes Agaricomycetes Dothideomycetes Monoblepharidales Sordariomycetes Agaricomycetes Cystobasidiomycetes Dothideomycetes Cystobasidiomycetes Chytridiomycetes Lecanoromycetes Dothideomycetes Dothideomycetes Subphylum Pezizomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Pezizomycotina Mortierellomycotina Ustilaginomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Agaricomycotina Pezizomycotina Agaricomycotina Pezizomycotina Agaricomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Pucciniomycotina Agaricomycotina Pezizomycotina Monoblepharidomycetes Pezizomycotina Agaricomycotina Pucciniomycotina Pezizomycotina Pucciniomycotina Pezizomycotina Pezizomycotina Pezizomycotina Phylum Basidiomycota Ascomycota Ascomycota Basidiomycota Basidiomycota Ascomycota (Zygomycota) Basidiomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Chytridiomycota Chytridiomycota Ascomycota Basidiomycota Ascomycota Chytridiomycota Basidiomycota Ascomycota Basidiomycota Chytridiomycota Ascomycota Ascomycota Ascomycota Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Kingdom sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. Species sp. Rhodotorula Aureobasidium Celosporium larixicola variabilis Typhula Rhodotorula glacialis Ulocladium chartarum Mortierella Malassezia spirale Trichoderma Rhodotorula Lactarius longisporus Rhodotorula glacialis murrhardtense Fibulobasidium Celosporium larixicola Ramariopsis laeticolor Celosporium larixicola Rhodotorula Clavulinopsis miyabeana Alternaria tenuissima Ganoderma Rhodotorula glacialis Rhodotorula glacialis Lyophyllum Aureobasidium curvatum Hyaloraphidium aggregatum Polychytrium Fusarium acuminatum portentosum Tricholoma dermatioides Phaeosclera Chytridiomycota Rhodotorula Dothideomycetes Rhodotorula aggregatum Polychytrium Amandinea punctata Endosporium aviarium Curvularia spicifera

Seq count 509 497 492 486 470 453 450 438 437 417 408 390 380 373 370 367 362 358 350 344 319 314 314 314 308 308 305 299 296 294 287 284 282 274 268 267 267

OUT number 86 87 89 91 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125

Shawn P. Brown et al. / 743 Accession EF596819.1 FJ664851.1 EF585633.1 NR_073276.1 KC455919.1 JX966308.1 AB509796.1 FR837917.1 AY916485.1 KC005769.1 KC333160.1 AJ244242.1 JQ272465.1 AB474394.1 EF585662.1 GU108582.1 HM176528.1 JN569120.1 NR_103569.1 EF585662.1 JN943815.1 AB738883.1 KC455921.1 AF347081.1 KC455883.1 NG_027613.1 JN040515.1 GQ153222.1 KC884769.1 JN400824.1 JX454829.1 KC455921.1 JN129414.1 JX242880.1 DQ182502.1 GQ302682.1 JX669836.1 Max 82% 80% 100% 77% 86% 84% 76% 98% 74% 84% 99% 99% 73% 94% 100% 93% 82% 71% 93% 95% 97% 74% 82% 75% 99% 97% 94% 98% 100% 100% 80% 94% 90% 94% 89% 99% 91% Identity E value 7.00E-61 5.00E-50 2.00E-12 2.00E-43 2.00E-62 3.00E-08 1.00E-25 2.00E-118 2.00E-30 6.00E-62 1.00E-122 8.00E-105 3.00E-28 4.00E-102 4.00E-13 2.00E-92 1.00E-58 3.00E-28 3.00E-103 1.00E-13 4.00E-07 2.00E-30 5.00E-57 5.00E-31 3.00E-122 1.00E-07 6.00E-100 1.00E-115 8.00E-124 8.00E-124 8.00E-48 1.00E-102 3.00E-08 2.00E-88 2.00E-10 4.00E-121 8.00E-86 Query 100% 100% 18% 100% 89% 24% 72% 100% 100% 98% 100% 87% 100% 100% 18% 96% 10% 100% 100% 22% 15% 100% 96% 92% 100% 15% 100% 100% 100% 100% 100% 100% 20% 88% 22% 100% 92% Coverage 242 206 82.4 185 248 68 125 434 141 246 448 389 134 349 84.2 347 235 134 383 86 64.4 141 230 143 446 66.2 372 425 452 452 199 381 68 334 75.2 443 325 Total Total Score 242 206 82.4 185 248 68 125 434 141 246 448 389 134 379 84.2 347 235 134 383 86 64.4 141 230 143 446 66.2 372 425 452 452 199 381 68 334 75.2 443 325 Max Score Species mytilinum fellii glacialis miyabeana arboricola gracile eucalypti pityophila fluminus chlorogonii dulciolens confinis magnus aggregatum crustaceus hamatum minutum hirsutum nigrella Genus Lophium Verrucaria Alphamyces Leucosporidium Rhodotorula Lyophyllum Clavulinopsis Zalerion Helicosproium Phaeococcomyces Preussia Dothichiza Rhodotorula Rhizophydium Paecilomyces Clavulinopsis Phialocephala Rhizophydium Rhizophydium Tricholoma Rhodotorula Trechispora Cryptococcus Polychytrium Sarcinomyces Trichoderma Mrakiella Entoloma Rhodotorula Tomentella Geminibasidium Lyophyllum Mortierella Donadinia Family Mytilinidiaceae Verrucariaceae Alphamycetaceae Kriegeriaceae Lyophyllaceae Clavariaceae Tubeufiaceae Phaeococcomycetaceae Sporormiaceae Dothiorceae Venturiaceae Erythrobasidiaceae Rhizophydiaceae Clavicipitaceae Botryosphaeriaceae Clavariaceae Rhizophydiaceae Rhizophydiaceae Tricholomataceae Erythrobasidiaceae Hypocreaceae Entolomataceae Erythrobasidiaceae Thelephoraceae Lyophyllaceae Mortierellaceae Sarcosomataceae Order Continued TABLE A2 TABLE Mytilinidales Verrucariales Rhizophydiales Leucosporidiales Kriegariales Agaricales Agaricales Lulworthiales Lichenostigmatales Pleosporales Dothideales Venturiales Erythrobasidiales Rhizophydiales Hypocreales Botryosphaeriales Agaricales Helotiales Rhizophydiales Rhizophydiales Agaricales Erythrobasidiales Trechisporales Filobasidiales Polychytriales Chaetothyriales Hypocreales Cystofilobasidiales Agaricales Erythrobasidiales Thelephorales Geminibasidiales Agaricales Mortierellales Pezizales Class Dothideomycetes Eurotiomycetes Chytridiomycetes Microbotryomycetes Microbotryomycetes Agaricomycetes Agaricomycetes Sordariomycetes Dothideomycetes Arthoniomycetes Dothideomycetes Dothideomycetes Dothideomycetes Cystobasidiomycetes Chytridiomycetes Sordariomycetes Dothideomycetes Agaricomycetes Leotiomycetes Chytridiomycetes Chytridiomycetes Agaricomycetes Cystobasidiomycetes Agaricomycetes Tremellomycetes Chytridiomycetes Eurotiomycetes Dothideomycetes Sordariomycetes Tremellomycetes Agaricomycetes Cystobasidiomycetes Agaricomycetes Walleminomycetes Agaricomycetes Pezizomycetes Subphylum Pezizomycotina Pezizomycotina Pucciniomycotina Pucciniomycotina Agaricomycotina Agaricomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pucciniomycotina Pezizomycotina Pezizomycotina Agaricomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Agaricomycotina Agaricomycotina Pezizomycotina Pezizomycotina Pezizomycotina Agaricomycotina Agaricomycotina Pucciniomycotina Agaricomycotina Agaricomycotina Mortierellomycotina Pezizomycotina Phylum Ascomycota Ascomycota Chytridiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Ascomycota Ascomycota Ascomycota Ascomycota Ascomycota Basidiomycota Chytridiomycota Ascomycota Ascomycota Basidiomycota Ascomycota Chytridiomycota Chytridiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Chytridiomycota Ascomycota Ascomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota (Zygomycota) Ascomycota Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Kingdom sp.

sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. Species sp. sp. sp. sp. Lophium mytiilnum Verrucaria Alphamyces chaetifer Leucosporidium fellii Rhodotorula glacialis Lyophyllum Clavulinopsis miyabeana Zalerion arboricola Helicosporium gracile Phaeococcomyces eucalypti Presuuia pityophili Dothichiza Venturiaceae Rhodotorula Rhizophydium sp Paecilomyces Botrysphaeriacese Clavulinopsis Phialocephala fluminis Rhizophydium sp Rhizophydium chlorogonii dulciolens Tricholoma Rhodotorula confinis Trechispora Cryptococcus magnus aggregatum Polychytrium crustaceus Sarcinomyces Dothideomycetes hamatum Trichoderma Mrakiella Entoloma minutum Rhodotorula Tomentella Geminibasidium hirsutum Lyophyllum Mortierella Donadinia nigrella

Seq count 260 258 257 251 244 240 234 233 230 222 222 221 221 219 217 216 213 210 207 206 205 205 203 198 191 191 188 188 185 184 183 179 175 172 170 169 168

OUT number 126 127 128 129 130 131 132 133 134 136 137 138 139 140 141 142 143 144 145 146 147 148 150 151 152 153 154 155 156 157 158 159 160 161 163 164 165

744 / arctic, Antarctic, and Alpine Research Accession AB213266.1 KC164754.1 DQ341099.1 AF347080.1 JX030294.1 NG_027613.1 NR_103569.1 KC254108.1 KC333170.1 KC595931.1 JN040515.1 DQ182502.1 DQ182502.1 JQ775574.1 AJ504633.1 JN256671.1 FJ873574.1 JQ857036.1 KC180711.1 DQ182502.1 FR670788.1 FM208881.1 FR838002.1 KC455921.1 AB474394.1 AB474394.1 KF305753.1 JX243973.1 KC333170.1 HQ914838.1 KC455919.1 KC183729.1 JX014242.1 EU873018.1 AB509666.1 JN569120.1 AF050256.1 Max 79% 100% 85% 84% 99% 97% 93% 85% 91% 74% 89% 85% 89% 100% 91% 99% 95% 75% 89% 89% 92% 81% 82% 89% 88% 85% 100% 100% 88% 96% 96% 99% 91% 88% 80% 71% 86% Identity E value 6.00E-49 8.00E-124 2.00E-69 4.00E-64 3.00E-122 1.00E-07 2.00E-100 3.00E-08 2.00E-88 2.00E-12 2.00E-80 3.00E-09 2.00E-10 8.00E-124 1.00E-07 3.00E-122 8.00E-105 6.00E-24 3.00E-79 2.00E-10 2.00E-12 7.00E-23 4.00E-58 3.00E-78 6.00E-81 4.00E-70 8.00E-124 8.00E-124 6.00E-81 5.00E-107 5.00E-69 3.00E-122 2.00E-92 1.00E-08 6.00E-37 1.00E-20 1.00E-77 Query 100% 100% 100% 100% 100% 15% 100% 24% 100% 70% 100% 26% 22% 100% 18% 100% 100% 77% 100% 22% 23% 54% 100% 98% 100% 100% 100% 100% 100% 100% 100% 100% 100% 22% 77% 82% 100% Coverage 203 452 271 253 446 66.2 374 68 334 82.4 307 71.6 75.2 452 66.2 446 389 120 304 75.2 82.4 116 233 300 309 273 452 452 309 396 269 466 347 69.8 163 109 298 Total Total Score 203 452 271 253 446 66.2 374 68 334 82.4 307 71.6 75.2 452 66.2 446 389 120 304 75.2 82.4 116 233 300 309 273 452 452 309 396 269 466 347 69.8 163 109 298 Max Score Species penicillioides nigrum isiola subsphaerospora mexiusanum aggregatum fluminus dermatis alni crustaceus anisopliae gelida carneus irrigata glacialis cerevisiae scottii miyabeana pulcherrima Genus Thysanophora Epicoccum Helicoma Trechispora Tuber Polychytrium Phialocephala Trichosporon Rhodotorula Postia Sarcinomyces Lyophyllum Lyophyllum Glomus Metarhizium Cryptococcus Mrakia Paecilomyces Lyophyllum Rhizophydiales Hygrocybe Sistotrema Rhodotorula Rhodotorula Rhodotorula Penicillium Exophiala Rhodotorula Lophiostoma Rhodotorula Saccharomyces Leucosporidium Clavulinopsis Clavulinopsis Capronia Family Aspergillaceae Tubeufiaceae Tuberaceae Erythrobasidiaceae Lyophyllaceae Lyophyllaceae Glomeraceae Clavicipitaceae Cystofilobasidiaceae Clavicipitaceae Lyophyllaceae Hygrophoraceae Corticiaceae Erythrobasidiaceae Erythrobasidiaceae Erythrobasidiaceae Aspergillaceae Herpotrichiellaceae Erythrobasidiaceae Lophiostomataceae Erythrobasidiaceae Saccharomycetaceae Clavariaceae Clavariaceae Herpotrichiellaceae Order Continued TABLE A2 TABLE Eurotiales Trechisporales Pezizales Polychytriales Helotiales Tremellales Erythrobasidiales Polyporales Chaetothyriales Agaricales Agaricales Glomerales Hypocreales Cystofilobasidiales Hypocreales Agaricales Rhizophydiales Agaricales Corticiales Erythrobasidiales Erythrobasidiales Erythrobasidiales Eurotiales Chaetothyriales Erythrobasidiales Pleosporales Erythrobasidiales Saccharomycetales Leucosporidiales Agaricales Agaricales Chaetothyriales Class Eurotiomycetes Dothideomycetes Dothideomycetes Agaricomycetes Pezizomycetes Chytridiomycetes Leotiomycetes Tremellomycetes Cystobasidiomycetes Agaricomycetes Eurotiomycetes Agaricomycetes Agaricomycetes Glomeromycetes Sordariomycetes Tremellomycetes Tremellomycetes Sordariomycetes Agaricomycetes Chytridiomycetes Agaricomycetes Agaricomycetes Cystobasidiomycetes Cystobasidiomycetes Cystobasidiomycetes Eurotiomycetes Eurotiomycetes Cystobasidiomycetes Dothideomycetes Cystobasidiomycetes Saccharomycetes Microbotryomycetes Agaricomycetes Agaricomycetes Eurotiomycetes Subphylum Pezizomycotina Pezizomycotina Pezizomycotina Agaricomycotina Pezizomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Agaricomycotina Pezizomycotina Agaricomycotina Agaricomycotina Pezizomycotina Agaricomycotina Agaricomycotina Pezizomycotina Agaricomycotina Agaricomycotina Agaricomycotina Pucciniomycotina Pucciniomycotina Pucciniomycotina Pezizomycotina Pezizomycotina Pucciniomycotina Pezizomycotina Pucciniomycotina Saccharomycotina Pucciniomycotina Agaricomycotina Agaricomycotina Pezizomycotina Phylum Ascomycota Ascomycota Ascomycota Basidiomycota Ascomycota Chytridiomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Basidiomycota Basidiomycota Ascomycota Glomeromycota Ascomycota Basidiomycota Basidiomycota Ascomycota Basidiomycota Chytridiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Basidiomycota Ascomycota Ascomycota Basidiomycota Ascomycota Basidiomycota Ascomycota Basidiomycota Chytridiomycota Basidiomycota Basidiomycota Ascomycota Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Kingdom sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. Species sp. sp. sp. sp. Thysanophora penicillioides Thysanophora Epicoccum nigrum Helicoma isiola subsphaerospora Trechispora mexiusanum Tuber aggregatum Polychytrium Phialocephala fluminis dermatis Trichosporon Rhodotorula alni Postia crustaceus Sarcinomyces Lyophyllum Lyophyllum Ascomycota Glomus Metarhizium anisopliae Cryptococcus gelida Mrakia carneus Paecilomyces Lyophyllum Rhizophydiales sp irrigata Hygrocybe Sistotrema Rhodotorula Rhodotorula Rhodotorula Penicillium Exophiala Rhodotorula Lophiostoma Rhodotorula glacialis cerevisiae Saccharomyces Leucosporidium scottii Chytridiomycota Clavulinopsis miyabeana Clavulinopsis pulcherrima Capronia

Seq count 168 167 166 160 160 159 159 158 158 155 154 152 150 150 149 147 143 143 143 143 139 139 138 138 137 137 136 131 129 129 128 127 125 124 124 123 122

OUT number 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 195 196 197 198 199 200 201 202 204

Shawn P. Brown et al. / 745 Accession AY843195.1 AB474394.1 AB509666.1 AF284129.1 HG324079.1 JF908409.1 AF050261.1 JN225922.1 JN040515.1 EU280139.1 GQ154588.1 EF596819.1 JQ857031.1 KC455921.1 KC455921.1 Max 82% 88% 78% 93% 100% 82% 81% 82% 86% 99% 91% 100% 100% 94% 85% Identity E value 7.00E-17 2.00E-81 8.00E-48 4.00E-96 8.00E-124 1.00E-64 4.00E-51 9.00E-60 6.00E-75 3.00E-122 1.00E-90 8.00E-124 8.00E-124 1.00E-103 8.00E-67 Query 40% 100% 100% 99% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 98% Coverage 96.9 311 199 360 452 255 210 239 289 446 342 452 452 385 262 Total Total Score 96.9 311 199 360 452 255 210 239 289 446 342 452 452 385 262 Max Score Species miyabeana infectoria oregonensis villosa crustaceus strigosellum prunicola mytilinum watticus Genus Phaeosclera Rhodotorula Clavulinopsis Capronia Alternaria Mycena Capronia Saccharata Sarcinomyces Trichoderma Phaeomoniella Lophium Holtermanniella Rhodotorula Rhodotorula Family Erythrobasidiaceae Clavariaceae Herpotrichiellaceae Pleosporaceae Tricholomataceae Herpotrichiellaceae Botryosphaeriaceae Hypocreaceae Mytilinidiaceae Erythrobasidiaceae Erythrobasidiaceae Order Continued TABLE A2 TABLE Erythrobasidiales Agaricales Chaetothyriales Pleosporales Agaricales Chaetothyriales Botryosphaeriales Chaetothyriales Hypocreales Mytilinidales Holtermanniales Erythrobasidiales Erythrobasidiales Class Cystobasidiomycetes Agaricomycetes Eurotiomycetes Dothideomycetes Agaricomycetes Eurotiomycetes Dothideomycetes Eurotiomycetes Sordariomycetes Dothideomycetes Tremellomycetes Cystobasidiomycetes Cystobasidiomycetes Subphylum Pucciniomycotina Agaricomycotina Pezizomycotina Pezizomycotina Agaricomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pezizomycotina Pezizomycotina Agaricomycotina Pucciniomycotina Pucciniomycotina Phylum Ascomycota Basidiomycota Basidiomycota Ascomycota Ascomycota Basidiomycota Ascomycota Ascomycota Ascomycota Ascomycota Ascomycota Ascomycota Basidiomycota Basidiomycota Basidiomycota Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Fungi Kingdom sp. sp. sp. sp. Species sp. sp. Phaeosclera Rhodotorula Clavulinopsis miyabeana Capronia Alternaria infectoria Mycena oregonenis villosa Capronia Saccharata crustaceus Sarcinomyces strigosellum Trichoderma Phaeomoniella prunicola Lophium mytiilnum Holtermanniella watticus Rhodotorula Rhodotorula

Seq count 120 119 117 114 112 112 111 110 109 108 108 107 105 105 103

OUT number 205 206 207 208 209 210 211 213 214 215 216 217 218 219 220

746 / arctic, Antarctic, and Alpine Research FIGURE A1. Ranked operational taxonomic unit (OTU) abundance distribution plot of all OTUs. The dashed line represents our cutoff of 200 OTUs for analyses and represents greater than 97% of all fungal sequences. Insert represents the 30 most abundant OTUs (notice the change in scale) demonstrating that few OTUs make up most of the fungal community members.

Shawn P. Brown et al. / 747 FIGURE A2. Phylogenetic analysis (Maximum Likelihood) of putative chytrid OTUs with vouchered representative ITS2 sequences within phyla Chytridiomycota, Blastocladiomycota, and Monoblepharidomycota indicates that observed novel OTUs are nested within phylum Chytridiomycota with 99% bootstrap support.

748 / arctic, Antarctic, and Alpine Research FIGURE A3. Rarefaction analysis of observed OTUs for algal colonized and non-algal colonized snow fungi indicate that at the 1500 sequence subsampling point (dashed line), the majority of community members have been observed as this subsample value is well past the inflection point of the curves.

FIGURE A4. Distances of paired algal colonized and non-algal colonized axes loading score across three-dimensional space indicate that Colorado (CO) fungal communities are more similar between paired samples than Washington (WA) paired fungal communities (Euclidian distance between paired samples based on t-test).

Shawn P. Brown et al. / 749