Molecular Phylogenetics and Evolution 71 (2014) 249–260

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Molecular Phylogenetics and Evolution journal homepage: www.elsevier.com/locate/ympev

Concordance of seven gene genealogies compared to phenotypic data reveals multiple cryptic species in Australian dermocyboid Cortinarius (Agaricales) Franck O.P. Stefani a,1, Rodney H. Jones b, Tom W. May a,⇑ a b

Royal Botanic Gardens Melbourne, Birdwood Ave, South Yarra, Victoria 3141, Australia School of Botany, The University of Melbourne, Victoria 3010, Australia2

a r t i c l e

i n f o

Article history: Received 2 November 2012 Revised 20 May 2013 Accepted 22 October 2013 Available online 1 November 2013 Keywords: DNA barcoding Cortinarius Dermocyboid fungi Gene genealogies Species delimitation Splendidi

a b s t r a c t This study aims to delimit species of Australian dermocyboid fungi (Cortinarius, Agaricales) using genealogical concordance on well-characterised phenotypic species and to assess the utility of seven loci for DNA barcoding Australian Cortinarius taxa. Eighty-six collections of dermocyboid Cortinarius were sampled from across southern Australia. Phenotypic species were first recognised by performing clustering analyses on a comprehensive phenotypic dataset including morphological, colour and pigment data. Then phylogenetic species were delimited from the concordance of seven locus genealogies (ITS, nLSU, gpd, mcm7, rpb1, rpb2 and tef1). Seventeen phenotypic species were recognised while the concordance of gene genealogies recovered 35 phylogenetic species. All loci except for LSU recovered most phylogenetic species, although only rpb1 correctly identified all phylogenetic species. The ITS region is confirmed as an effective barcode for Cortinarius and a standard pairwise distance threshold of 2.0% is proposed to DNA barcode Australian Cortinarius taxa. Australian dermocyboid fungi belong in separate clades to the boreal clade Dermocybe, mostly in the clade Splendidi. This study provides a solid foundation for future ecological, taxonomic and systematic research on one of the most diverse genera of mushrooms worldwide. Ó 2013 Elsevier Inc. All rights reserved.

1. Introduction Studies of biodiversity at all levels from individuals to ecosystems depend on an objective and rigorous delimitation of species. However, drawing a static line to establish boundaries between entities subjected to an irregular but permanent evolution (i.e. speciation) across geological time still represents a major issue in modern systematics (Sites and Marshall, 2003; Wiens, 2007). Moreover, the many definitions of species have generated as many ways to delimit species, leading to conflicting conclusions about species boundaries and consequently about diversity (De Queiroz, 2007). Species delimitation has historically relied on the comparison of physical attributes (phenotypic species), ecological characteristics (ecological species), breeding tests (biological species) or on various combinations of the above (Donoghue, 1985). Difficulties in consistent application of such concepts have gradually led taxonomists to use molecular data to frame species limits and to ⇑ Corresponding author. Fax: +61 3 9252 2413 E-mail address: [email protected] (T.W. May). Present address: Institut de Recherche en Biologie Végétale, Université de Montréal and Jardin botanique de Montréal, 4101 Rue Sherbrooke Est, Montréal (Québec) H1X 2B2, Canada. 2 Current address: 2/2 East Gordon St, Mackay, Queensland 4740, Australia. 1

1055-7903/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ympev.2013.10.019

recognize species as independent evolutionary lineages (De Queiroz, 2007; Wiens, 2007; Padial et al., 2010), hereafter referred to as phylogenetic species. For fungi, delimitation of species based on reproductive distinctiveness is possible in some lineages, but there are many where growth in pure culture is impossible or very slow, especially among biotrophic ectomycorrhizal fungi. Concordance of gene genealogies (Avise and Ball, 1990; Baum and Shaw, 1995) and DNA barcoding (Hebert et al., 2003) are increasingly being used in molecular fungal taxonomy, to assist recognition of species. Concordance of gene genealogies recognises phylogenetic species based on exclusive monophyly observed across independently reconstructed gene trees. This method has proven a powerful way to resolve species complexes within Ascomycota (Taylor et al., 2000; Koufopanou et al., 2001; Dettman et al., 2003; O’Donnell et al., 2011) and Basidiomycota (Geml et al., 2006; Kauserud et al., 2006; Hedh et al., 2008; Jargeat et al., 2010; Van de Putte et al., 2012). DNA barcoding allows discrimination of species according to their level of nucleotide divergence using easily amplifiable and sequenceable molecular markers showing non-overlapping intra and inter-specific distance distributions (a barcode gap). In contrast to the concordance of gene genealogies, DNA barcoding is a

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very practical approach for quickly recognising species but no universal threshold of nucleotide divergence can be defined to delimit species across broad taxonomic lineages (Nilsson et al., 2008; Hughes et al., 2009; Begerow et al., 2010; Schoch et al., 2012). Therefore the threshold to assess sequence conspecificy needs to be a priori calibrated using rigorously delimited species and including sister taxa. Despite the internal transcribed spacers (ITS) of the nuclear ribosomal RNA cistron being the best candidate as a primary fungal barcode marker (Schoch et al., 2012), other loci, such as rpb1, rpb2 and tef1, have been targeted as potential secondary fungal barcode markers for taxonomic groups with poor ITS sequence divergence (Gazis et al., 2011; Brazee et al., 2011). To date, genealogical concordance has never been applied to delimit species in Cortinarius from Australia. In addition, the utility of different loci for DNA barcoding Cortinarius species has never been rigorously assessed. Cortinarius is the most species-rich mushroom genus known today (Soop and Gasparini, 2011) and the most diverse genus of macrofungi recorded in Eucalyptus and Nothofagus forests in Australia (May et al., 2012) and New Zealand (McKenzie et al., 2000). Species delimitation in the genus has traditionally utilized fruitbody morphology, sometimes augmented by pigment composition as analysed by thin layer chromatography (Høiland, 1983; Keller et al., 1987; Gill, 1995b; Jones and May, 2008). Cortinarius is ecologically important due to ectomycorrhizal associations with a large range of shrubs and forest trees (Høiland, 1983; Horak, 1987). In addition, some species of Cortinarius possess chemical compounds that have significant biological properties, such as antibiotics (Beattie et al., 2010) or toxins (Keller-Dilitz et al., 1985; Cantin et al., 1989). As a case study for the application of genealogical concordance and DNA barcoding to delimit species in Cortinarius, we focus on species with the macro-morphology of Cortinarius subgenus Dermocybe sensu lato (hereafter referred to as dermocyboid species). Dermocyboid Cortinarius are readily recognisable in the field by the brightly coloured (often red, yellow or orange) fruit-bodies (Fig. 1) with generally dry, silky, non-hygrophanous pileus in combination with a cylindrical to subclavate stipe with distinct zones or bands of universal veil remnants below the cortina remnant (Høiland, 1983; Jones, 2003). In Australia, around ten species of Cortinarius have been assigned to Dermocybe based on morphology (Grgurinovic, 1997; Gasparini and Soop, 2008). Cortinarius subgenus Dermocybe was originally circumscribed for boreal species sharing anthraquinone pigments and generally brightly coloured fruit-bodies (Moser, 1972; Høiland, 1983). The subgenus is monophyletic as far as northern hemisphere species (Liu et al., 1997; Chambers et al., 1999; Høiland and Holst-Jensen, 2000; Peintner et al., 2004; Garnica et al., 2005). However, chemical structures of pigments (Gill, 1995a,b) and the few molecular data available for Australian dermocyboid taxa (Peintner et al., 2004; Garnica et al., 2005) suggest Dermocybe sensu lato to be polyphyletic. The main objectives of this study were to rigorously delimit species within Australian dermocyboid Cortinarius and to assess the effectiveness of DNA barcoding in recognising these species. Firstly we utilised multivariate analysis of morphological, colour and pigment composition data to recognise phenotypic species. Secondly, we compared the phenotypic species so delimited against phylogenetic species recognised by concordance of gene genealogies from analysis of a comprehensive multi-loci molecular dataset (ITS, nLSU, gpd, mcm7, rpb1, rpb2 and tef1). DNA barcoding analyses were then performed to assess the distance threshold of each locus that best recognises the newly identified phylogenetic species. In addition, we used the ITS sequences to place Australian dermocyboid fungi within Cortinarius.

2. Material and methods 2.1. Taxon sampling The phenotypic diversity of dermocyboid fungi was captured through two extensive collecting campaigns in autumn and winter of 1999 and 2000 in Victoria and Tasmania, supplemented by fresh collections from collaborators in Western Australia and Tasmania (Table 1). Multiple collections of each putative phenotypic species were sampled based primarily on macro-morphology as observed in the field (Fig. 1). Most of the fruit-bodies were growing in eucalypt-dominated forest areas, with fewer collections from areas of Nothofagus rainforest. Freshly collected fruit-bodies were air- or freeze-dried before being deposited at the National Herbarium of Victoria (MEL). 2.2. Phenotypic data 2.2.1. Morphological characters Macroscopic characters were recorded from at least four fruitbodies of each collection, covering all stages of maturity, and included measurements of pileus diameter, fruit-body height, stipe diameter (at half height), stipe width (at widest point), depth of flesh in sectioned pileus (at mid-point between disc and edge) and depth of lamellae (at mid-point between disc and edge). Microscopic characters were recorded from dried material rehydrated in 3% aqueous KOH (w/v) with 0.5% aqueous Congo Red (w/v) used to enhance contrast for further examination. Spores and basidia were taken from lamellae fragments of mature fruitbodies. Ten to twenty spores were measured from one to two fruit-bodies of each collection. Measurements were to the nearest 0.5 lm and exclude ornamentation and the hilar appendage. Basidia measurements do not include sterigmata. The mean value for spore length and width and the Q-value (ratio of the mean spore lengths divided by the mean spore widths) were calculated. Spore ornamentation texture was recorded in four categories (smooth or very fine, fine, moderate, coarse) while verrucae density was recorded in three categories (sparse, moderate, dense). The ornamentation of spores was examined using Scanning Electron Microscopy (SEM). 2.2.2. Colour attributes Fruit-body colour was recorded from fresh material under combined fluorescent and natural light using the Methuen system (Kornerup and Wanscher, 1978) which utilises three colour attributes (hue, intensity and tone). Colour was recorded from pilei, including any colour gradient from disc to edges, upper and lower stipes and the face of lamellae. For mycelium colour, the hue was recorded in common language terminology. Colour (as hue) and rate of reaction to 3% aqueous KOH (w/v) when applied to pilei and stipes were recorded. For multivariate analyses, intensity was converted from the alphabetical sequence (A–F) of the Methuen system to a numerical sequence (1–6). Where colours were recorded from more than one colour chip for a particular structure, the mean value was calculated for each of the three colour attributes. 2.2.3. Pigment extraction and thin-layer chromatography Dried fungal material (0.05–0.5 g), including sections of the pileus and stipe of at least two fruit-bodies, was macerated and placed into a mortar, suspended in 2 ml methanol for 3 min, prior to pulverisation with a pestle. An additional 2 ml methanol was added and extracts filtered through cotton wool, transferred to vials and evaporated under gaseous nitrogen. Extracted residues

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251

Fig. 1. Plate illustrating the complexity of macro- and micro-morphological characters from dermocyboid taxa and a compilation of their pigment profiles using thin-layer chromatography. Each fruit-body picture is paired with a spore scanning electron micrograph. (A, F) C. austrocinnabarinus; (B, G) C. austrosanguineus; (C, H) C. cramesinus; (D, I) C. magenteiannulatus; (E, J) C. clelandii; (K, P) C. kula; (L, Q) C. basirubescens; (M, R) C. salmoneobasis; (N, S) C. erythrocephalus; (O, T) C. persplendidus; and (U) representative profiles from development of pigment extracts of the dermocyboid taxa sampled in this study (atr: C. atropurpureus; auc: C. austrocinnabarinus; aug: C. austrosanguineus; aus: C. austrovenetus; bas: C. basirubescens; can: C. canarius; chl: C. chloroapicus; cle: C. clelandii; cra: C. cramesinus; ery: C. erythrocephalus; kul: C. kula; mag: C. magenteiannulatus; mel: C. melleipileus; oli: C. aff. olivaceopictus; pal: C. pallidus; sal: C. salmoneobasis; spl: C. persplendidus).

were resuspended in a minimal amount of dichloromethane and then subjected to thin-layer chromatography (TLC) on POLYGRAM SIL G/UV254 pre-coated plastic, 10  20 cm plates with a silica layer thickness of 0.25 mm. Depending on extract concentration, 2–10 ll of extract was spotted on a base line 1.5 cm from the bottom edge of the plate. Plates were developed at room temperature in a standard TLC chamber with one main inner face lined with filter paper and the lid sealed using petroleum jelly. The mobile phase solvent was toluene:ethyl formate:formic acid (50:49:1, v/ v/v). Saturation of the chamber was achieved by adding 50 ml of eluent to the chamber at least half an hour prior to the development of a plate. An extract of C. persplendidus was included as a standard on each plate. Development of TLC plates was terminated when the solvent front reached 7.5 cm from the base line. Fifty-two

pigment characters for inclusion in phenetic analyses were scored as in Fig. S1 (Supplementary material). Each character had a unique combination of spot position, colour and relative intensity (Supplementary material, Table S1). Samples from each collection were usually included in multiple TLC plates, in different combinations, to ensure consistent coding of pigment characters. 2.3. Phenotypic species recognition A hierarchical clustering analysis was performed on a matrix including 86 collections scored for 11 multivariate morphological characters, 13 fruit-body colour attributes and 52 spots scored as present or absent from the pigment analysis (Table S1). An association matrix was calculated using the Gower metric. A cluster

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Table 1 Collections analysed in this study. Phenotypic species are listed in alphabetical order. a

Collection designation

Mol ID

MEL number

Collection date

Collection

State

C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C. C.

df039 df080 df081 df017 df040 df041 df042 df043 df044 df045 df046 df047 df048 df049 df050 df051 df052 df053 df097 df098 df099 df036 df096 dk025 dk026 df025 df027 df100 dk013 dk021 dk022 df054 df055 df056 df057 df058 df059 df060 df061 df062 dk001 dk002 dk023 df063 df064 df065 df066 df067 df090 df091 df101 dk027 df068 df069 df070 df071 df072 df073 df074 df103 df105 dk009 dk010 d2k2 d4k2 df075 df076 df077 df078 df079 df104 df082 df083 df084

2120743 2120741 2120749 2120791 2089707 2089708 2089672 2089674 2120784 2120787 2120745 2120750 2120748 2120746 2089685 2089687 2120788 2089663 2089665 2089666 2089668 2089702 227855 2089700 2089699 2060416 2089669 2089670 2120747 2120782 2120756 2120760 2089703 2120763 2120767 2120776 2089676 2120780 2089677 2120771 2120742 2120744 2120786 2089684 2120752 2120753 2120764 2089686 2089681 2089680 2089683 2089679 2089689 2089690 2120789 2120740 2089688 2089692 2089691 2120768 2120783 2120755 2120765 2063361 2089704 2089705 2089706 2120775 2120778 2120779 2120777 2120758 2120759 227477

21.v.1999 18.v.1999 05.vi.1999 18.iv.1987 08.iv.2000 09.iv.2000 25.vi.2000 21.vii.2000 30.vi.1985 05.vi.2000 28.v.1999 07.vi.1999 05.vi.1999 28.v.1999 24.vi.1999 03.vii.2000 07.vi.2000 15.vi.1999 08.vii.1999 03.vi.2000 22.vi.2000 11.vi.2000 07.v.1989 27.v.2000 11.iv.2000 25.iv.1992 08.iv.2000 11.iv.2000 05.vi.1999 11.v.1999 27.vii.1999 02.v.2000 15.v.1999 03.vi.2000 25.vi.2000 29.vii.2000 29.vii.2000 30.vii.2000 30.vii.2000 13.vii.2000 02.vi.1999 21.v.1999 25.v.1997 07.vi.1999 04.vii.1999 04.vii.1999 09.vi.2000 16.vi.2000 16.vi.2000 03.vi.2000 30.vii.2000 03.vi.1999 22.vi.1999 22.vi.1999 26.vi.2000 08.v.1999 22.vi.1999 09.vi.2000 31.vii.1999 13.vii.2000 20.v.1999 27.vii.1999 16.vi.2000 25.vi.1999 29.vi.1999 27.v.2000 09.vii.2000 29.vii.2000 29.vii.2000 30.vii.2000 29.vii.2000 10.iv.2000 11.iv.2000 03.xi.1986

RH Jones 25 RH Jones 21 RH Jones 48 TW May T87-266 RH Jones 91 & P Catcheside RH Jones 94 & K Syme RH Jones 150 RH Jones 167 TW May B365 & BA Fuhrer K Syme 1055/00 RH Jones 33 & WA Worboys RH Jones 51 RH Jones 47 RH Jones 35 & WA Worboys RH Jones 65 RH Jones 152 K Syme 1057/00 & R Robinson RH Jones 56 & C Sheehan RH Jones 75 RH Jones 125 RH Jones 147 & K Beattie RH Jones 135 TW May B610 & BA Fuhrer RH Jones 122 & H Crapper RH Jones 100 et al. TW May 757 RH Jones 90 & G Gates RH Jones 99 & K Syme RH Jones 45 SJM McMullan-Fisher 223 RH Jones 83 G Gates & DA Ratkowsky [RH Jones 114] TW May [RH Jones 20] RH Jones 128 RH Jones 149 RH Jones 171 et al. RH Jones 172 et al. RH Jones 178 & T Lebel RH Jones 180 & T Lebel RH Jones 162 RH Jones 41 RH Jones 28 K Syme 906 RH Jones 53 RH Jones 73 RH Jones 74 RH Jones 132 RH Jones 140 RH Jones 138 RH Jones 124 RH Jones 169 & T Lebel RH Jones 43 et al. RH Jones 62 RH Jones 64 & P Sexton K Syme 1072/00 RH Jones 15 & BA Fuhrer RH Jones 60 & P Sexton RH Jones 129 RH Jones 84 RH Jones 158 SJM McMullan-Fisher 230 RH Jones 81 & N Polikarpowski RH Jones 139 T Lebel 92 RH Jones 69 & K Ralston RH Jones 123 & H Crapper RH Jones 156 RH Jones 170 et al. RH Jones 174 et al. RH Jones 176 & T Lebel RH Jones 173 et al. RH Jones 98 RH Jones 102 TW May B181 & KE Geering

VIC – Wallaby Creek VIC – Kinglake VIC – Marysville TAS – Mt Field National Park TAS – 20 km SE of Maydena TAS – Maydena - Strathgordon Rd VIC – Narbethong - Marysville Rd VIC – Union Jack Reserve VIC – Kinglake WA – Lochart Forest Block VIC – Cranbourne RBG VIC – Kinglake National Park VIC – Marysville VIC – Cranbourne RBG VIC – Warrandyte State Park VIC – Warrandyte State Park WA – Walpole - Nornalup National Park VIC – c. 15 km S Colac VIC – Marginal Rd, Glenburn VIC – Daylesford - Ballan Rd VIC – Murrindindi Scenic Reserve VIC – Kinglake National Park VIC – Narbethong district, Acheron Way VIC – Paradise Road, Blakeville TAS – Lake St Clair, Cradle Mountain National Park TAS – Great Western Tiers TAS – National Park TAS – Cradle Mt - Lake St Clair, National Park VIC – Marysville TAS – Hartz Mountains VIC – Kinglake National Park TAS – Mt Field National Park VIC – West Barwon Dam VIC – Werribee Creek Picnic Ground VIC – Narbethong - Marysville Rd VIC – Little Desert National Park VIC – Little Desert National Park VIC – Western Highway, near Dadswells Bridge VIC – Western Highway, near Dadswells Bridge VIC – Gisborne, Pyrete State Forest VIC – Warrandyte State Park VIC – Kinglake National Park WA – Denmark VIC – Kinglake National Park VIC – Wombat State Forest VIC – Wombat State Forest VIC – Kinglake National Park VIC – Wombat State Forest VIC – Wombat State Forest VIC – Wombat State Forest VIC – Western Highway, near Dadswells Bridge VIC – Cranbourne RBG VIC – Kinglake National Park VIC – Marginal Rd, Glenburn WA – Shannon National Park VIC – Kinglake National Park VIC – Marginal Rd, Glenburn VIC – Kinglake National Park VIC – Kinglake National Park VIC – Gisborne, Pyrete State Forest TAS – Mt Wellington VIC – Kinglake West VIC – Wombat State Forest VIC – Otway Ranges VIC – Bambra Bushland Reserve VIC – Blackwood VIC – Wombat State Forest VIC – Little Desert National Park VIC – Little Desert National Park VIC – Little Desert National Park VIC – Little Desert National Park TAS – Styx River TAS – Cradle Mt - Lake St Clair, National Park NSW – Tinderry Range

aff. olivaceopictus aff. olivaceopictus aff. olivaceopictus atropurpureus atropurpureus atropurpureus austrocinnabarinus austrocinnabarinus austrocinnabarinus austrocinnabarinus austrosanguineus austrosanguineus austrosanguineus austrosanguineus austrosanguineus austrosanguineus austrosanguineus austrovenetus austrovenetus austrovenetus austrovenetus basirubescens basirubescens basirubescens basirubescens canarius canarius canarius chloroapicus chloroapicus chloroapicus clelandii clelandii clelandii clelandii clelandii clelandii clelandii clelandii clelandii clelandii clelandii clelandii cramesinus cramesinus cramesinus cramesinus cramesinus erythrocephalus erythrocephalus erythrocephalus erythrocephalus kula kula kula kula kula kula kula kula kula kula kula magenteiannulatus magenteiannulatus magenteiannulatus magenteiannulatus melleilpileus melleilpileus melleilpileus melleilpileus pallidus pallidus pallidus

– Region

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a

a

Collection designation

Mol ID

MEL number

Collection date

Collection

State

C. C. C. C. C. C. C. C. C. C. C. C.

df085 df092 df093 df094 df095 dk028 df086 df087 df088 df089 df102 df106

2120790 2089697 2089695 2089694 2089693 2089696 2120754 2120761 2120762 2120769 2120766 2120785

20.vii.2000 16.vi.2000 22.vi.1999 07.vi.1999 30.v.1999 09.vi.2000 12.vii.1999 03.vi.2000 03.vi.2000 13.vii.2000 20.vi.2000 06.v.1989

K Syme 1083/00 & R van der Waag RH Jones 137 RH Jones 61 & P Sexton RH Jones 54 RH Jones 37 RH Jones 131 SJM McMullan-Fisher [RH Jones 76] RH Jones 126 RH Jones 127 RH Jones 160 RH Jones 144 TW May B618 & BA Fuhrer

WA – Dryandra VIC – Wombat State Forest VIC – Marginal Rd, Glenburn VIC – Kinglake National Park VIC – Lerderderg State Park VIC – Kinglake National Park VIC – Jumping Creek, Warrandyte VIC – Wombat State Forest VIC – Wombat State Forest VIC – Gisborne, Pyrete State Forest TAS – Hobart, Mt Wellington VIC – Jumping Creek, Warrandyte

pallidus persplendidus persplendidus persplendidus persplendidus persplendidus salmoneobasis salmoneobasis salmoneobasis salmoneobasis salmoneobasis salmoneobasis

– Region

NSW, New South Wales; TAS, Tasmania; VIC, Victoria; and WA, Western Australia.

analysis was performed using the agglomerative method of Ward. The optimal number of clusters was validated with a partitioning around medoids (PAM) analysis and cluster uncertainty was assessed using the approximately unbiased (AU) p-values (Shimodaira, 2002). Clustering analyses were also performed separately on each of the morphological characters, the colour attributes and the data from pigment profiles. All statistical analyses were performed using the R statistical language v.2.15.0 (R Core Team, 2013).

into the 25 ll PCR mixture containing the vector primers M13F20 and M13R for amplification under the conditions previously described. PCR products were visualized on GelRed-stained 1.5% agarose gels. DNA purification and sequencing (ABI 3730XL sequencer, Applied Biosystems) were performed at the DNA synthesis and sequencing facility Macrogen Inc. (Seoul, South Korea).

2.4. Molecular data

Sequences were edited, cleaned and assembled in Geneious Pro v.5.5.6 (Biomatters Ltd.). All sequences are available in GenBank (Table S2). Consensus sequences from the nLSU rDNA and the coding genes were aligned with MUSCLE v.3.5 (Edgar, 2004) with eight iterations. The ITS sequences were aligned with SATé-II (Liu et al., 2012) and ambiguously aligned regions were removed. The DNA substitution model was determined using the hierarchical likelihood ratio test implemented in MODELTEST v.3.06 (Posada and Crandall, 1998). Substitution saturation within introns was assessed with the entropy-based index as implemented in DAMBE (Xia et al., 2003). Introns showing saturation or poor phylogenetic signal were not considered for phylogenetic analyses. Bayesian phylogenetic analyses were performed using the parallel version of MrBayes v.3.1.2 (Huelsenbeck and Ronquist, 2001; Ronquist and Huelsenbeck, 2003), running four Markov Chain Monte Carlo (MCMC) for 5 million generations. The value of the temperature parameter for heating chains was adjusted to keep the acceptance rate of swaps between 10% and 70%. Two simultaneous and independent runs were evaluated for each analysis. Convergence of MCMC chains and the values of effective sample size (ESS) of parameters were assessed with Tracer v.1.5 (http://tree.bio.ed.ac.uk/software/tracer). The number of trees saved was set to 50,000 and the first 10,000 trees were excluded before computing consensus trees with Bayesian posterior probabilities (PPs). Maximum likelihood analyses were performed using the parallel version of RAxML v.7.0.4 (Stamatakis, 2006) under the GTRGAMMA model for tree inference. Bootstrap support (BS) was estimated by running 1000 likelihood bootstrap replicates with the rapid bootstrapping algorithm under the GTRCAT model of evolution. Sequences of Laccaria bicolor were used as outgroups. Phylogenetic trees were edited using the R statistical language v.2.15.0 (R Core Team, 2013) with the package ape v.2.6-2 (Paradis et al., 2004). Independent evolutionary lineages were recognized according to the concordance of gene genealogies i.e. if they formed terminal exclusive monophyletic groups strongly supported by both PPs and BS values (P0.95 and P75, respectively) in the majority of the single-locus phylogenies. Collections that were consistently recovered as singletons across the majority of the seven gene genealogies were recognised as phylogenetic species. The NCBI GenBank database was queried using blastn (Altschul et al., 1990) to match the

Genomic DNA was isolated from pieces of lamellae following a modified protocol of the EZNA Forensic DNA kit (OMEGA). Fungal tissue was first pulverised in liquid nitrogen using micropestles and incubated for at least one hour at 65 °C in a lysis solution including 250 ll of STL buffer, 25 ll of proteinase K and 0.8 ll of b-mercaptoethanol per sample. The procedure then followed the standard protocol for isolating DNA ‘from hair, nails and feathers’ except that centrifugation time was increased to at least 3 min and the final elution was done in 50 ll of elution buffer. The primer sets ITS1-F (Gardes and Bruns, 1993)/ITS4 (White et al., 1990) and LR0R (Vilgalys and Sun, 1994)/LR5 (Vilgalys and Hester, 1990) were used to amplify the ITS and large subunit (nLSU) regions, respectively. Nested PCR were required for some collections to amplify the protein-coding genes gpd, mcm7, rpb1, rpb2 and tef1. The first round of amplification was performed with published primers while the second amplification was performed using primer sets specific to dermocyboid fungi (Table 2). The MEME suite web tool server (Bailey et al., 2009) was used to identify potential primer binding sites to amplify mcm7. Primers were designed with Primer3 (Rozen and Skaletsky, 2000). Each PCR mixture contained 1X PCR buffer, 25 lg BSA (Sigma–Aldrich Co.), 1.5 mM MgCl2, 0.2 lM dNTPs (Invitrogen), 0.5 lM of each primer, 1 unit of HotStar Taq DNA polymerase (QIAGEN, Rockville, MD), 1 ll of genomic DNA and sterile distilled water to a total of 25 ll. The thermal cycling conditions were: initial denaturation at 95 °C for 15 min, followed by 38 cycles of 94 °C for 45 s, 52–58 °C for 45 s according to the primer sets used, and 72 °C for 45 s and a final elongation step consisting of 72 °C for 10 min. PCR products that did not provide clean sequences were purified with the QIAquick PCR purification kit and cloned with the PCR Cloning Plus kit (QIAGEN, Rockville, MD) using a modified protocol. Briefly, 2 ll of purified PCR product was incubated for at least 24 h at 4 °C with 0.5 ll of the pdrive cloning vector, 5 ll of distilled water and 2.5 ll of 2X ligation master mix. Five microliters of ligation product was then used to transform 12 ll of competent cells resuspended in 85 ll of SOC medium and the whole solution was plated on LB agar plates. After an overnight incubation at 37 °C, white bacterial colonies were spiked and transferred

2.5. Bioinformatic and phylogenetic analyses

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Table 2 Primer sets used to amplify the 5 coding genes through nested PCR. Locus

Abbreviation

Primer name 0

Nucleotide sequence 50 –30

References

Glyceraldehyde-3-phosphate dehydrogenase

gpd

GPD-f GPD-R0 GPD-Cort28F GPD-Cort735R

BGGTGTYTTCACHACCRTCGAVAA GTARCCCCACTCGTTGTCGTACCA CACTTGAAMGGTGGHGCC GATGAGCTTVACAAASTTGG

Jargeat et al. (2010) Jargeat et al. (2010) This study This study

DNA replication licensing factor

mcm7

MCM7-Cort1bF MCM7-Cort1bR MCM7-Cort43F MCM7-Cort836R

GTSAACGCSTATACNTG GTCRAAYTCRTCAATRCAGC AAACCTATYCTTGACTGCG GGMGCGTTAGTDCTTGCYGA

This This This This

RNA polymerase II largest subunit

rpb1

RPB1-A RPB1_Cort1260R RPB1_Cort119bF RPB1_Cort926bR

GAKTGTCCKGGWCATTTTGG GTBARRATCATCCAYTCNGG TGTGTRAATTGTGGAAAGC GGYYTTCTTGTAYTGAACG

Stiller and Hall (1997) This study This study This study

Second largest subunit of RNA polymerase II

rpb2

bRPB2-6F bRPB2-7.1R RPB2-Cort1F RPB2-Cort7R2

TGGGGYATGGTNTGYCCYGC CCCATRGCYTGYTTMCCCATDGC GGRCTTGTCAAGAAYCTYGC ACYTGRTTGTGATCTGGRAAHGG

Matheny (2005) Matheny (2005) This study This study

Translation elongation factor 1a

tef1

EF1-526F EF1-1567R EF1-983F EF1-Cort33F EF1-Cort901R

GTCGTYGTYATYGGHCAYGT ACHGTRCCRATACCACCRATCTT GCYCCYGGHCAYCGTGAYTTYAT TGGTGGTATCGACAAGCG ATCYTGGAGRGGAAGACGG

Matheny et al. (2007) Matheny et al. (2007) Matheny et al. (2007) This study This study

phylogenetic species identified as described above with the closest ITS sequences from Cortinarius. To infer the phylogenetic placement of Australian dermocyboid fungi within Cortinarius, a Bayesian phylogenetic tree was inferred by combining the representative ITS sequences of each phylogenetic species with the datasets of Liu et al. (1997), Peintner et al. (2004) and Garnica et al. (2005). Sequences were aligned with SATé-II and ambiguously aligned regions were removed. Two independent runs were performed in MrBayes for 20 million generations and 100,000 trees were sampled during the analysis. The likelihood values from each run reach a plateau after 10 million generations. Therefore the last 50,000 sampled trees were used to calculate the consensus tree. ITS sequences of Hebeloma fastibile and H. mesophaum were used as outgroups. 2.6. DNA barcoding analyses Using the phylogenetic species that included at least two collections, the distance threshold which best discriminated species was investigated through analyses of intra- and inter-specific distances. For each locus, uncorrected pairwise distances were calculated between aligned sequences (excluding the singletons) using mothur v.1.23.1 (Schloss et al., 2009). Introns within protein coding genes were considered. Gaps generated by the occurrence of indels were treated as a single event and gaps occurring at the beginning and at the end of alignments were not penalized. The degree of overlap between the distributions of inter- and intra-specific distances was investigated by plotting box-and-whisker-plots. Outliers were plotted if they represented more than 1.5 times the interquartile range from the box. In order to analytically define the distances for which each locus best discriminated species, a false positive and false negative identification analysis was performed across the range of intra and inter-specific distances. False positive identifications occur when sequences from the same species are split into spurious new species because the proposed threshold is inferior to the intra-specific distance of that species. False negative identifications occur when sequences from two different species are fused to the same species or assigned to the wrong species because the proposed threshold is superior to the inter-specific distance of these species. False positive and false negative identifications were calculated using the R package spider v.1.1-1 (Brown et al., 2012). The distance or the median of the

study study study study

range of distances for which the cumulative error (CE, the sum of false positive and false negative identifications) is the lowest was considered as the most appropriate threshold to discriminate species for each locus. 3. Results 3.1. Phenotypic species recognition The clustering analysis performed on the 76 morphological, colour and pigment variables from the 86 collections (Table S1) supported 17 clusters (Fig. 2). This result is confirmed by the partitioning around the medoids that found the optimal number of clusters to be 17. All but one of the collections (df096) formed cohesive clusters conforming to the ten existing species C. atropurpureus, C. austrocinnabarinus, C. austrovenetus, C. basirubescens, C. canarius, C. cramesinus, C. clelandii, C. erythrocephalus, C. kula and C. persplendidus (synonym Dermocybe splendida) and to seven new species denoted by Jones (2003) as C. austrosanguineus, C. chloroapicus, C. magenteiannulatus, C. melleipileus, C. aff. olivaceopictus, C. pallidus and C. salmoneobasis (all under Dermocybe, but here treated in Cortinarius). Collections identified as C. clelandii and C. salmoneobasis showed the most variable phenotypes with at least three subclusters at a dissimilarity level of 0.11, but none of them were statistically supported. The cluster analysis performed on the pigment profiles alone was sufficient to recover the 17 phenotypic species (data not shown) but adding the morphological and colour data to the chemical data provided more resolution within phenotypic species. 3.2. Phylogenetic species recognition and identification Sequences of all seven loci were obtained for 76 of the original 86 collections while the ITS was successfully sequenced for all the collections. Single-locus phylogenies (Figs. 3 and S2A–S2F provided in Supplementary material) recovered the same terminal clades with the exception of the nLSU phylogeny that was less resolved. The analysis of the seven individual phylogenetic trees (Fig. 3 and Table S3) showed that 21 clades were reciprocally monophyletic across most loci and 14 collections were found as singletons. Therefore, a total of 35 phylogenetic species was recognised. Eleven of the 21 clades had significant support across all seven loci

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Querying GenBank with the ITS sequences of the phylogenetic species identified herein showed that the closest matches were with environmental ITS sequences (recovered in Australia and New Zealand) in 19 out of 35 species. Only 10 phylogenetic species showed an ITS sequence similarity equal or superior to 98% with sequences in GenBank, and seven of these matches were with environmental sequences. All the matches with herbarium collections were with ITS sequences from vouchers identified as Cortinarius. 3.3. Barcode analyses

Fig. 2. Ward’s minimum variance clustering based on the morphological and pigment data recorded from 86 collections. The scale of the dendrogram is the squared distance. Values at branches are approximately unbiased (AU) p-values. Tips marked with light gray and dark gray (green and red in web version of this article) circles indicate collections for which, respectively, the ITS and the seven nuclear loci were successfully sequenced.

in both Bayesian and maximum likelihood trees while nine were supported in both analyses by five or six loci. Various subclades within phylogenetic species such as C. clelandii and C. kula were present, but only observed and supported in one or two loci, and consequently were not accepted as phylogenetic species (Table S3). Nine of the 17 phenotypic species were exactly recovered by the phylogenetic analyses, such as C. erythrocephalus and C. kula (Fig. 3). Among the other eight phenotypic species, all were split into two or more phylogenetic species. In C. austrosanguineus, all collections formed a monophyletic group strongly supported, but a subclade consisting of six of the eight collections was discriminated in each of the seven single locus phylogenies and two collections (df049, df052) were recognised as singletons (Table S3). In most cases where phenotypic species consisted of multiple phylogenetic species, the latter were sister taxa. For example, the phenotypic species C. melleipileus was split in two sister clades plus one singleton (df077). Occasionally, a collection from a phenotypic species fell outside of the main clade as for the the phenotypic species C. clelandii (collection df057) and C. salmoneobasis (collection df102). The phenotypic species C. pallidus was the most heterogeneous as the four collections that composed this group were recovered as four non-sister phylogenetic species. Some subclusters recorded inside the phenotypic species (Fig. 2) were congruent with some phylogenetic species (e.g. C. austrosanguinea 1, 2 and 3, C. clelandii 1 (three out of four collections), C. clelandii 2, C. melleipileus 2, C. salmoneobasis 2) but none of these phenotypic subclusters were statistically supported.

The box-and-whisker plots (Fig. 4) showed that the difference between the upper quartile of the intra-specific distances and the lower quartile of the inter-specific distances for the LSU was the smallest of the seven loci analyzed. For the other loci, this difference was at least three to eight times larger and a weak overlap between intraand inter-specific distances was observed due to extreme values, mainly generated by the two phylogenetic species found within C. persplendidus. The analyses of false positive and false negative identifications (Fig. 4) showed a perfect sequence assignment (CE = 0) only for rpb1, for distances ranging from 0.7% to 1.1% (median = 0.9%). The second lowest cumulative error (CE = 2) was recorded for mcm7, for distances ranging from% 0.9 to 1.2% (median = 1.0%). For these distances, 22 species were recovered because the phylogenetic species C. melleipileus 1 was split into two singletons. The locus gpd had a CE of 4 at a distance of 3.0%. For that distance, only false positive identifications were recorded because some collections from each of the two phylogenetic species found in C. persplendidus were not correctly assigned. The loci ITS and tef1 had a CE of 5 over a large range of distances, from 1.6% to 2.9% (median = 2.2%) and 2.1% to 3.3% (median = 2.7%), respectively. For these distances, 20 species were recovered because the two phylogenetic species found within C. persplendidus were not distinguished in both loci. The locus rpb2 had a CE of 7 for distances ranging from 1.0% to 1.1% mainly because of a high level of false positives. Despite the LSU having the worst sequence assignment with a CE of 8, 16 of the 21 phylogenetic species were recovered for distances ranging from 0.4% to 0.5%. The two phylogenetic species found within C. persplendidus were not distinguished while C. melleipileus 1 and C. austrovenetus were each split into two species. In addition, in respect of the 14 singleton phylogenetic species, all were unambiguously recovered by the distance threshold determined for each locus by the analyses of false positive and false negative identifications. 3.4. Phylogenetic placement of Australian dermocyboid taxa within Cortinarius Sixteen of the 35 phylogenetic species fall within the well-supported clade Splendidi (PPs = 0.98, Fig. 5). This clade includes the phenotypic species C. basirubescens, C. clelandii, C. erythrocephalus, C. kula, C. melleipileus, C. persplendidus, C. salmoneobasis, plus C. globuliformis and C. sejunctus from the datasets of Peintner et al. (2004) and Garnica et al. (2005). All the collections of subgenus Dermocybe from the northern hemisphere are in a separate clade (PPs = 1). The collections corresponding to the phenotypic species C. austrosanguineus seem to be most closely related to the clade Dermocybe s.s from northern hemisphere (rather than Splendidi) but the node is not supported (PPs = 0.58). The three phylogenetic species belonging to the phenotypic species C. chloroapicus along with C. magenteiannulatus were affiliated to members of subgenus Leprocybe from Europe and North America (PPs = 1). A previously unknown clade (PPs = 0.96) was recovered made up of three collections from South America and the three phylogenetic species C. pallidus 1, 2 and C. aff. olivacopictus. All the remaining phylogenetic species formed minor clades or were standing alone within Cortinarius.

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Fig. 3. Phylogenetic species recognition based on the concordance of the ITS, nLSU, gpd, mcm7, rpb1, rpb2 and tef1 genealogies. The adjoining table for each phylogenetic species identifies the support of each of the seven loci from the Bayesian and maximum likelihood inferences (first and second line of cells, respectively). Dark gray (green in web version of this article) cells identify terminal clades recovered as monophyletic and strongly supported by Bayesian posterior probabilities (PPs P 0.95) and bootstrap values (BS P 75); black (red in web version) cells identify clades for which PPs and/or BS were found inferior to 0.95 and 75, respectively; white cells identify clades for which monophyly was not observed. Phylogenetic species marked with a star were previously identified as phenotypic species. The ITS Bayesian consensus tree is presented, with PPs (above branch) and BS values (below branch). Only significant PPs and BS values P75 are shown. The scale represents the branch length corresponding to expected substitutions per site. Shortened branches are denoted by ‘//’.

4. Discussion 4.1. Species delimitation The concordance of seven gene genealogies allowed an objective and clear-cut delimitation of species boundaries within Australian dermocyboid fungi. Thirty-five phylogenetic species were recognised which doubles the initial estimation of species richness based on phenotypic attributes despite the use of a very comprehensive phenotypic dataset. This suggests that at least 50% of the Cortinarius species richness in Australia remains to be uncovered. The high concordance observed among six independent loci (ITS and nLSU being linked in the rDNA repeats) emphasises that the

sister taxa embedded within the phenotypic species have been genetically isolated for a period of time sufficiently long to lose ancestral shared polymorphism and to fix mutations. Nevertheless, this period was not sufficient to accumulate obvious synapomorphic morphological characters to discriminate these cryptic species. This paucity of phylogenetic signal in phenotypic attributes seems to be common in macrofungi as independent evolutionary lineages have been found within phenotypic species in Agaricales (Hedh et al., 2008; Jargeat et al., 2010; Sheedy et al., 2013), Boletales (Kauserud et al., 2006; Skrede et al., 2012; Zeng et al., 2012) and Russulales (Van de Putte et al., 2010; 2012). Nonetheless, taxonomy is an iterative process and a careful analysis of phenotypic attributes is required in order to assess if

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Fig. 4. Box-and-whisker plots (above) of the distribution of intra- (light gray) and inter- (dark gray) uncorrected-pairwise distances and histograms (below) showing the distribution of false positive (light gray) and false negative (dark gray) identifications counted for the range of threshold values along the x axis. For each locus, the minimum cumulative error (CE) and the distance (d%) or range of distances at which this minimum occurred are provided.

any subtle morphological differences or other characters such as geographic distribution, host association and microhabitat or climatic requirements, separate any of the phylogenetic species. Some differences might be so subtle that it would only be possible to consistently recognise them after delimiting phylogenetic species. In addition, a larger sample of collections across each phylogenetic species is desirable, especially for the 14 singleton species, and will assist in characterisation of any distinguishing phenotypic characteristics. Genealogical concordance is the most objective and accurate method to enable mycologists to fully account for cryptic species and should significantly contribute to fill the taxonomic deficit, i.e. the ratio of expected taxa to named taxa (Blaxter et al., 2005) in Cortinarius and other macrofungi. However, building a multilocus dataset across a large number of taxa can be laborious and not efficient for environmental surveys. DNA barcoding may overcome that issue, provided the effectiveness of the targeted locus can be rigorously assessed against well-defined phylogenetic species and sister taxa. 4.2. DNA barcoding The 21 phylogenetic species used for the DNA barcoding analyses were only recovered perfectly with the rpb1 sequences. This result was unexpected as Schoch et al. (2012) showed ITS to perform better than rpb1 in correctly identifying species within Basidiomycetes. Nevertheless rpb1 was found to be slightly better at discriminating species than the ITS when all fungal lineages were

considered. None of the other loci analysed showed a perfect assignment of sequences to phylogenetic species. The locus mcm7 was the second most accurate for recognising the phylogenetic species. This is one of the first assessments of mcm7 for DNA barcoding in a major group of Basidiomycetes, using redesigned primers, and it supports mcm7 as a promising locus for fungal systematics (Aguileta et al., 2008; Schmitt et al., 2009; Raja et al., 2011; Eberhardt et al., 2012). As regards the ITS, the median distance that minimized the cumulative error was 2.2%, which for practical purposes can be rounded to 2.0%. Various thresholds ranging from 1.0% to 6.0% of ITS dissimilarity have been used to separate species in other studies based on specimens (Ortega et al., 2008; Harrower, 2010; Harrower et al., 2011) or on environmental sequences (O’Brien et al., 2005; Buée et al., 2009; Tedersoo et al., 2009; Hughes et al., 2009; Begerow et al., 2010). Ortega et al. (2008) found that the inter-specific distances of ITS sequences of nine closely related species of Cortinarius (section Calochroi) to vary from 2.1% to 5.7%. Based on the analysis of 2463 Cortinarius ITS sequences, Harrower (2010) observed that multiple species per barcode similarity group may be found at a threshold of 3.0–5.0%. Similarly, in a study investigating the diversity of Cortinarius species from British Columbia based on ITS sequences, Harrower et al. (2011) found that a 3% threshold failed to discriminate closely related but phenotypically different species. Tedersoo et al. (2009) observed that the ITS region for Cortinarius is relatively conserved and applied a cut-off of 2.0% to assess ITS sequence conspecificity. In the present study, the two phenotypic species C. persplendidus and C. erythrocephalus

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Fig. 5. Consensus tree inferred from the Bayesian phylogenetic analysis of the ITS sequences of 34 Dermocybe collections from North America (Liu et al., 1997), 85 collections belonging to various Cortinarius subgenera from southern hemisphere (Garnica et al., 2005), 123 collections representing the classical subgenera recognized in Cortinarius from northern and southern hemisphere (80 and 43 sequences, respectively) (Peintner et al., 2004) and representative sequences (labelled in bold; or in red in web version of this article) of each of the 35 phylogenetic species identified in this study.

were merged into the same taxonomic unit using a threshold of 3.0%. Therefore, there is an array of evidence from independent studies to conclude that the best cut-off to assess conspecificity in Cortinarius using ITS sequences is closer to 2.0% rather than 3.0%, even if applying a threshold of 2.0% to our dataset did not discriminate one out of the 21 phylogenetic species. Despite three protein coding genes having lower cumulative errors, the ITS is clearly of most utility as a barcode in Cortinarius due to the ease of amplifying this ribosomal region across distantly related taxa and the prevalence of fungal ITS sequences in public databases compared to other loci (Nilsson et al., 2008; Hughes et al., 2009; Begerow et al., 2010; Schoch et al., 2012). 4.3. Characterization of the Splendidi clade Exploration of the relationships of dermocyboid fungi from Australia to the currently recognized subgenera of Cortinarius clearly shows that Dermocybe species from northern hemisphere and dermocyboid fungi from Australia are distinct entities, despite morphological similarity and the presence of readily extractable pigments. Sixteen of the phylogenetic species identified among Australian dermocyboid fungi belong to the monophyletic and strongly supported clade Splendidi. This clade was recovered as monophyletic in previous studies although with poor taxon

sampling (two to four species; Peintner et al., 2001; 2004; Garnica et al., 2005). Likewise, Gill (1995b) had suggested that the novelty of pigments in species such as C. persplendidus, C. basirubescens and C. erythrocephalus, warranted ‘a new group in which tetrahydroanthraquinones are the major pigments that are otherwise without affinities to other known taxa’. Our data expand Splendidi as a species-rich clade restricted to Australia and New Zealand containing both epigeous agaricoid and hypogeous sequestrate species (Danks et al., 2010). Peintner et al. (2004) hypothesized that Dermocybe s.str. from the northern hemisphere may be derived from southern hemisphere taxa, in particular taxa belonging to Splendidi. Surprisingly, the three phylogenetic species identified within the phenotypic species C. austrosanguineus appear to be more closely related to the Dermocybe clade from northern hemisphere rather than Splendidi. However, due to poor resolution of the ITS at deeper nodes, the relationships between C. austrosanguineus and these two clades cannot be elucidated. All other Australian dermocyboid fungi are unrelated to sections Splendidi or Dermocybe. Importantly, although Australian dermocyboid fungi are widely distributed within Cortinarius, there are sufficient sister taxa and other groups of closely related taxa, especially within the Splendidi clade, to make our dataset a robust test of the effectiveness of the different loci in discriminating at the species level.

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4.4. Cryptic diversity in herbarium collections

References

Sequences belonging to Cortinarius are frequently recovered in soil samples from Eucalyptus and Nothofagus dominated forests in Australia and New Zealand (Tedersoo et al., 2008; 2009; Horton, 2011) but until now very few have a conspecific match to herbarium collections. Most of the phylogenetic species recognised in the present study had a conspecific match with environmental sequences. This suggests that many environmental sequences generated in specimen-independent studies belong to described species not yet represented in GenBank, which corroborates the observation of O’Brien et al. (2005). The paucity of GenBank in sequences from well-annotated herbarium collections is also demonstrated by Brock et al. (2009) who sequenced the ITS of 279 herbarium collections representing 127 fungal species and showed that only 30% of them had homologous sequences in GenBank. We consider it likely that a significant portion of Cortinarius diversity could have been already deposited in herbaria and awaits sequencing. Such collections form an important resource in fleshing out phylogenetic species of dermocyboid Cortinarius, especially the singletons, and in improving information on host and geographic distribution, and are also a source of new taxa throughout the remainder of the genus.

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5. Conclusion The in-depth analyses of phenotypic and molecular data performed here have enabled rigorous delimitation of phylogenetic species in Australian dermocyboid fungi. Many species of Cortinarius cannot be detected even from analyses of a comprehensive phenotypic datasets and the comparison with genealogical concordance reveals that the use of phenotypic attributes in Cortinarius taxa underestimates species richness by half. Furthermore, the rigorous phylogenetic species delimitation allowed critical assessment of the performance of seven loci in DNA barcoding, confirming that the ITS region, although not the best-performing region, is an effective barcode region for this genus. The present study also provides the first comprehensive overview of the systematic placement of Australian dermocyboid fungi and clearly demonstrates the disconnection between Australian dermocyboid fungi (most of which belong to the Splendidi clade) and northern hemisphere taxa in the Dermocybe clade. Finally, this study provides a solid foundation for future ecological, taxonomic and systematic research on one of the most diverse genera of mushrooms worldwide. Acknowledgments RHJ thanks the Australian Biological Resources Study for provision of a Postgraduate Scholarship; Pauline Ladiges, School of Botany, University of Melbourne, for advice and encouragement; current and former staff of the School of Chemistry, The University of Melbourne, in particular the late Melvyn Gill, for assistance with chromatography; and Bruce Fuhrer, Genevieve Gates, David Ratkowsky and Katrina Syme for specimens and information about collecting sites. Collecting in Victoria was carried out under permit from the Department of Sustainability and Environment. This research was supported by a Victorian Life Sciences Computation Initiative (VLSCI) grant number VR0165 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ympev.2013. 10.019.

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