1 Supporting Information

2 Article title: : Host-generalist fungal pathogens of seedlings may maintain forest diversity via 3 host-specific impacts and differential susceptibility among 4 Authors: Erin R. Spear and Kirk D. Broders 5 6 The following Supporting Information is available for this article:

7 Fig. S1 Examples of disease symptoms in the forests of .

8 Fig. S2 Details of shadehouse-based inoculation experiments.

9 Fig. S3 Rank abundance plot and OTU accumulation curve.

10 Fig. S4 Overlap in fungal OTUs among sampling years, methods used to obtain symptomatic

11 seedlings, isolation media, and tissue sampled.

12 Fig. S5 Correlation between OTU host range and isolation frequency.

13 Table S1 Taxonomic assignments, traits, sampling effort, and observed OTUs for tree species

14 evaluated in our survey and experiments.

15 Table S2 Methodological details pertaining to the multi-year collection of symptomatic

16 seedlings, and microbial isolation and sequencing.

17 Table S3 Average light levels, air temperatures, and relative humidities of the shadehouses

18 used for inoculation experiments versus ambient conditions.

19 Table S4 Estimated taxonomic placement, isolation frequency, number of observed hosts,

20 estimated host specialization, and phylogenetic pattern of host use of the OTUs.

21 Table S5 Overlap in seedling-associated OTUs among tree species.

22 Table S6 Results of the beta-binomial generalized linear regression with the proportion of

23 diseased seedlings as a function of size and shade tolerance.

24 Table S7 Average estimates based on the best-ranked beta-binomial generalized linear

1

25 regressions with the proportion of diseased seedlings as a function of seed size and spatial

26 distribution relative to annual rainfall.

27 Methods S1 Methods used to estimate the taxonomic placement of the 66 OTUs and assign

28 nomenclature.

2

29

30 Fig. S1 Disease symptoms on the (a,g-j,m,o,p-t,v) stems, (b-f,k,l,n,o) leaves, and (u) root of

31 seedlings in the forests of Panama. In some panels, arrows direct the viewer’s attention to

32 disease.

3

33 34 Fig. S2 (a) Inoculation experiments were conducted in Smithsonian Tropical Research Institute

35 shadehouses in Gamboa, Panama. (b) Surface-sterilized were germinated in flats of

36 autoclave-sterilized commercial soil. (c,f) Seedlings were transplanted to individual pots

37 containing autoclaved commercial soil and (d,e) either rice visibly colonized by one of the

38 fungal isolates or inoculum-free, autoclave-sterilized rice. Disease was documented every 3 d

39 and was categorized as mortality, (g-o) stem damage, (p,q) wilting, and (r,s) stunting.

4

40 41 Fig. S3 (a) The 66 observed OTUs are ranked from most to least abundant on the horizontal

42 axis, with the total number of isolates per OTU plotted on the vertical axis (full dataset)

43 (BiodiversityR package; Kindt & Coe, 2005). Most of the OTUs are rare (50% singletons),

44 indicated by the steep shape of the curve. The four common OTUs (observed >10 times and

45 comprising 35% of the isolates) are named. (b) Non-asymptotic accumulation of OTUs isolated

46 from 124 symptomatic seedlings (full dataset) (vegan package; Oksanen et al., 2019). The curve,

47 derived from the observed richness and representing the mean accumulation of OTUs over 999

48 randomizations of seedling order, indicates incomplete sampling and a diverse community. 49 References: 50 Kindt R, Coe R. 2005. Tree diversity analysis. A manual and software for common statistical methods for ecological 51 and biodiversity studies. Nairobi, Kenya: World Agroforestry Centre (ICRAF). 52 Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'Hara RB, Simpson GL, 53 Solymos P et al. 2019. vegan: Community Ecology Package. R package version 2.5-6. [WWW document] 54 URL https://CRAN.R-project.org/package=vegan [accessed 2 June 2020]. 5

55 56 Fig. S4 Venn diagrams depicting the overlap in non-singleton fungal operational taxonomic

57 units (OTUs) among the (a) five sampling years, (b) three methods used to obtain seedlings with

58 disease, (c) four media used for isolation, and (d) three tissues sampled (data subset A)

59 (VennDiagram package; Chen, 2018). (a) Fungi, and two oomycetes, were isolated from

60 symptomatic seedlings in Panama over five years. (b) Symptomatic seedlings were obtained in

61 three ways: (i) opportunistic collection of naturally occurring seedlings, (ii) seedlings

62 germinated in a shadehouse and then transplanted to forest sites, and (iii) surface-sterilized

63 seeds planted directly in forest sites. (c, d) The advancing margin(s) of diseased area(s) 6

64 was/were excised, and the excised tissue piece(s) (leaf, stem, and/or root) was/were surface

65 sterilized (Gilbert & Webb, 2007) and plated on (i) Water Agar (WA), (ii) Pimaricin, Ampicillin,

66 Rifampicin, and Pentachloronitrobenzene (PARP); and/or Malt Extract Agar (MEA) amended

67 with antibiotic to prevent bacterial growth, either (iii) chloramphenicol or (iv) rifampicin. See

68 Table S2 and Spear (2007) for additional methodological details. (a) Of the 33 non-singleton

69 OTUs, 19 were observed in more than one year. While no OTUs were observed across all five

70 years, three OTUs were observed across four of the sampling years. The greatest number of

71 unique, non-singleton OTUs was observed in 2019, the year we collected the greatest number

72 of seedlings. (b) Eighteen non-singleton OTUs were isolated from seedlings obtained using

73 more than one method. Four non-singleton OTUs were isolated from seedlings obtained using

74 all three methods. We isolated the greatest number of unique, non-singleton OTUs from

75 naturally occurring seedlings, the most common sampling method. (c) Nineteen non-singleton

76 OTUs were isolated on multiple media. One non-singleton OTU was isolated from tissue pieces

77 plated on all four media. We isolated the greatest number of unique, non-singleton OTUs on

78 MEA+rifampicin, the medium used for the greatest number of seedlings and tissue pieces. (d)

79 Twenty-two non-singleton OTUs were isolated from multiple tissues. Four non-singleton OTUs

80 were isolated from all three tissues. We isolated the greatest number of unique, non-singleton

81 OTUs from leaves, the best-sampled tissue. 82 References: 83 Chen H. 2018. VennDiagram: Generate High-Resolution Venn and Euler Plots. R package version 1.6.20. [WWW 84 document] URL https://CRAN.R-project.org/package=VennDiagram [accessed 25 November 2020]. 85 Gilbert GS, Webb CO. 2007. Phylogenetic signal in pathogen-host range. Proceedings of the National 86 Academy of Sciences, USA 104: 4979–4983. 87 Spear ER. 2017. Phylogenetic relationships and spatial distributions of putative fungal pathogens of seedlings 88 across a rainfall gradient in Panama. Fungal Ecology 26: 65–73

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89 90 Fig. S5 The observed host range of an OTU is positively correlated with isolation frequency

91 (survey-based assessment of host range: blue points, one-tailed Spearman's rank correlation

92 rho = 0.96, P < 0.001; host range observed during the inoculation experiments: green points,

93 one-tailed Spearman's rank correlation rho = 0.72, P = 0.053), suggesting that multi-host fungi

94 may be common in this system.

8

95 Table S1 Tree species from which putative pathogens were isolated (original host = OH, 26 tree spp.) and/or for which vulnerability

96 to pathogens was assessed (target = T, 35 tree spp.) via inoculation experiments. For each tree species, the following is listed: a two-

97 or three-letter code (for Tables 1, S2, and S5), taxonomic assignments, and the number of seedlings collected, sites from which

98 seedlings were collected, unique isolates observed, and OTUs observed. Average seed dry mass (mg), shade tolerance, and spatial

99 distribution relative to annual rainfall are listed for the tree species used to explore the relationship between disease susceptibility

100 and plant life history traits. The imperfect match between original hosts and targets was driven by space, time, and seed availability

101 constraints. Additionally, several tree species that were not original hosts were included in the inoculation experiments as

102 phytometers (measures of isolate pathogenicity) because of previously observed disease susceptibility (e.g., L. seemannii).

3 Seedlings collected

Role Species Code Order Seed Shade Dist. Isolates OTUs mass tol.2 Sites (mg)1

OH, T Anacardium excelsum (Bertero ex Kunth) Skeels ANE Anacardiaceae 1507 dry 20 5 41 24 OH, T Dalbergia retusa Hemsl. DR 130 tol dry 18 2 20 10 OH Pouteria reticulata (Engl.) Eyma PR Sapotaceae Ericales 11 1 30 18 OH, T Virola surinamensis (Rol. ex Rottb.) Warb VS Myristicaceae Magnoliales 11 3 15 11 OH Faramea occidentalis (L.) A.Rich. FO 7 1 13 7 OH panamense (Rose) I.M.Johnst. PP Sapindales 7 1 18 11 OH Protium tenuifolium (Engl.) Engl. PT Burseraceae Sapindales 6 2 10 9 OH, T longifolium Willd. CL Calophyllaceae 5 1 14 9 OH, T Castilla elastica Cerv. CE Moraceae Rosales 203.4 dry 5 2 6 4 OH, T Hymenaea courbaril L. HC Fabaceae Fabales 5 3 6 5 OH Cassia moschata Kunth CAM Fabaceae Fabales 4 3 5 4 OH, T panamensis (Woodson) Markgr. LAP Gentianales 237.4 tol wet 4 3 7 5 OH Nectandra cuspidata Nees & Mart. NC Lauraceae Laurales 4 2 5 5 OH, T Cochlospermum vitifolium (Willd.) Spreng. CV Bixaceae 26 intol 3 1 4 4 OH Swietenia macrophylla King SWM Meliaceae Sapindales 2 1 2 2 OH, T Trichilia tuberculata (Triana & Planch.) C. DC. TT Meliaceae Sapindales 151 tol 2 2 2 2 OH, T Brosimum utile (Kunth) Oken BU Moraceae Rosales 1763.5 wet 1 1 2 2 OH, T rufescens (Benth.) Britton & Rose CR Fabaceae Fabales 236.2 tol dry 1 1 2 2 OH Dipteryx oleifera Benth. DO Fabaceae Fabales 1 1 1 1

9

OH, T L. GA Rubiaceae Gentianales 123 tol dry 1 1 1 1 OH Mouriri myrtilloides (Sw.) Poir. MM Melastomataceae Myrtales 1 1 1 1 OH coccinea (Aubl.) Jacks. OC Fabaceae Fabales 1 1 1 1 OH, T Ormosia macrocalyx Ducke OM Fabaceae Fabales 379 tol dry 1 1 1 1 OH Randia armata (Sw.) DC. RA Rubiaceae Gentianales 1 1 1 1 OH Spondias mombin L. SPM Anacardiaceae Sapindales 1 1 2 2 OH, T Tetragastris panamensis (Engl.) Kuntze TEP Burseraceae Sapindales 295 tol 1 1 1 1 T Annona glabra L. AG Annonaceae Magnoliales 229 intol wet T Coccoloba manzinellensis Beurl. COM Polygonaceae Caryophyllales T Copaifera aromatica Dwyer CA Fabaceae Fabales 893.6 tol dry T Eugenia nesiotica Standl. EN Myrtaceae Myrtales 346.5 tol T intermedia (Pittier) Hammel GI Malpighiales 541 tol T Guapira standleyana Woodson GS Nyctaginaceae Caryophyllales 61.2 tol T Inga goldmanii Pittier IG Fabaceae Fabales T Inga sapindoides Willd. IS Fabaceae Fabales 406.4 intol T Jacaranda copaia (Aubl.) D.Don JC Bignoniaceae Lamiales 5 intol T Lacistema aggregatum (P.J.Bergius) Rusby LA Lacistemataceae Malpighiales 10.7 intol T platypus (Hemsl.) Fritsch LIP Malpighiales T Luehea seemannii Triana & Planch LS Malvales 3 intol dry T Pachira quinata (Jacq.) W.S.Alverson PQ Malvaceae Malvales 40 intol dry T Posoqueria latifolia (Rudge) Schult. POL Rubiaceae Gentianales 196.5 tol T Psychotria limonensis K.Krause PSL Rubiaceae Gentianales 6.6 tol T Psychotria marginata Sw. PM Rubiaceae Gentianales 7.5 tol T asterolepis Pittier QA Malvaceae Malvales 335 tol T Siparuna pauciflora (Beurl.) A. DC. SP Siparunaceae Laurales T Swartzia simplex (Sw.) Spreng. SS Fabaceae Fabales 1025 tol T Symphonia globulifera L.f. SG Clusiaceae Malpighiales 2334 tol wet T pittieri (Standl.) Standl. TOP Rubiaceae Gentianales 956.2 tol wet 103 1Seed mass sources: (1) Daws MI, Garwood NC, Pritchard HW. 2005. Traits of recalcitrant seeds in a semi‐deciduous tropical forest in Panama: some ecological 104 implications. Functional Ecology 19: 874–885. (2) Myers JA, Kitajima K. 2007. storage enhances seedling shade and stress tolerance in a 105 neotropical forest. Journal of Ecology 95: 383–395. (3) ER Spear, unpublished data. (4) Svenning JC, Wright SJ. 2005. Seed limitation in a Panamanian 106 forest. Journal of Ecology 93: 853–862. (5) Wright SJ, Kitajima K, Kraft NJB, Reich PB, Wright IJ, Bunker DE, Condit R, Dalling JW, Davies SJ, Díaz S et al. 2010. 107 Functional traits and the growth–mortality trade‐off in tropical . Ecology 91: 3664–3674. 108 2Shade tolerance sources: (1) Augspurger CK. 1984. Light requirements of neotropical tree seedlings: a comparative study of growth and survival. Journal of 109 Ecology 72: 777–795. (2) Brown SH, Mark S. 2013. Fact sheet: Annona glabra. USDA, Cooperative Extension Service, University of Florida, IFAS, Florida A. & M.

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110 [WWW document] URL http://www.doc-developpement-durable.org/file/Arbres-Fruitiers/FICHES_ARBRES/Cachiman-cochon-mammier-Annona- 111 glabra/Pond%20Apple%20-%20Lee%20County%20Extension%20-%20University%20of%20Florida.pdf. [accessed 21 May 2020]. (3) Comita LS, Aguilar S, Pérez 112 R, Lao S, Hubbell SP. 2007. Patterns of woody plant species abundance and diversity in the seedling layer of a tropical forest. Journal of Vegetation Science 18: 113 163–174. (4) Hall JS, Ashton MS. 2016. Guide to early growth and survival in plantations of 64 tree species native to Panama and the Neotropics. Balboa, 114 Ancón, República de Panamá: Smithsonian Tropical Research Institute. (5) Kitajima K, Llorens AM, Stefanescu C, Timchenko MV, Lucas PW, Wright SJ. 2012. 115 How cellulose‐based leaf toughness and lamina density contribute to long leaf lifespans of shade‐tolerant species. New Phytologist 195: 640–652. (6) Martin 116 WA, Flores EM. 2002. Copaifera aromatica Dwyer. In Vozzo JA, ed. Tropical tree seed manual. Washington DC, USA: USDA Forest Service, 405–407. (7) 117 Molofsky J, Augspurger CK. 1992. The effect of leaf litter on early seedling establishment in a tropical forest. Ecology 73: 68–77. (8) Paul GS, Montagnini F, 118 Berlyn G P, Craven DJ, van Breugel M, Hall JS. 2012. Foliar herbivory and leaf traits of five native tree species in a young plantation of Central Panama. New 119 Forests 43: 69–87. (9) Pearcy RW, Valladares F, Wright SJ, De Paulis EL. 2004. A functional analysis of the crown architecture of tropical forest Psychotria 120 species: do species vary in light capture efficiency and consequently in carbon gain and growth? Oecologia 139: 163–177. 121 3Distribution sources: (1) Engelbrecht BM, Comita LS, Condit R, Kursar TA, Tyree MT, Turner BL, Hubbell SP. 2007. Drought sensitivity shapes species 122 distribution patterns in tropical forests. Nature 447: 80–82. (2) Condit R, Pérez R, Daguerre N. 2010. Trees of Panama and Costa Rica. Princeton, NJ, USA: 123 University Press. (3) Perez R, Condit R. 2020. Tree Atlas of Panama. [WWW document] URL http://ctfs.si.edu/webatlas/maintreeatlas.php. [accessed 21 May

124 2020]

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125 Table S2 Methodological details pertaining to the multi-year collection of symptomatic seedlings, and microbial isolation and

126 sequencing. Seedlings were collected from the lowland tropical forests of Panama in 2007, 2010-2012, and 2019 by E. R. Spear and

127 T. Brenes-Arguedas (column 1). Symptomatic seedlings were obtained by opportunistically collecting naturally occurring seedlings

128 and by baiting pathogens from the soil by planting seedlings or surface-sterilized seeds directly in the forest sites (col. 2). The forest

129 sites from which seedlings were collected included: Buena Vista Peninsula (BV) and Barro Colorado Island (BCI) in Barro Colorado

130 Nature Monument, Gunn Hill in Ciudad del Saber (formerly Fort Clayton; FC), private property on Santa Rita Ridge (SRR), Parque

131 Natural Metropolitano (PNM), and Sendero Camino de Cruces (CC) and Sendero del Charco in Parque Nacional Soberanía (SC) (col. 3;

132 see Fig. S1 in Spear (2017) for a map and additional site details). In total, 124 seedlings of 26 tree species were collected, but the tree

133 species collected varied by site and year (col. 4; see Table S1 for full species names). Seedlings were collected during the rainy

134 season (col. 5 provides date ranges). For all 124 collected seedlings, the advancing margin(s) of diseased area(s) was/were excised,

135 and the excised symptomatic tissue piece(s) was/were surface sterilized following Gilbert & Webb (2007) prior to plating. Fungi (and

136 two oomycetes) were isolated on four media: Water Agar (WA; 20 isolates); Pimaricin, Ampicillin, Rifampicin, and

137 Pentachloronitrobenzene (PARP†; 11 isolates); and Malt Extract Agar (MEA) amended with antibiotic to prevent bacterial growth,

138 either chloramphenicol (chloram; 59 isolates) or rifampicin (rifamp; 121 isolates) (col. 6). Morphologically unique isolates were

139 subcultured into pure culture, and a piece of mycelium was excised from each pure culture for molecular analysis (col. 6). The DNA

140 extractions, PCR amplifications, and bidirectional Sanger sequencing‡ of either the nuclear ribosomal internal transcribed spacer

141 (ITS) region (149 isolates) or the ITS plus an adjacent portion of the large subunit (ITS+LSU) (62 isolates) were completed over

142 multiple years and by multiple labs: the lab of A. E. Arnold at the University of Arizona (methods followed lab's protocols; e.g.,

143 Sandberg et al., 2014); the International Cooperative Biodiversity Groups (ICBG) lab at the Smithsonian Tropical Research (STRI)

144 (methods followed lab's protocols; e.g., Higginbotham et al., 2014); the molecular research lab at STRI Naos Marine Laboratories;

145 the lab of K. D. Broders at STRI; and Macrogen, Inc. (col. 7). The primers ITS1F, ITS5, ITS4, and LR3 (Vilgalys & Hester, 1990; White et

12

146 al., 1990; Gardes & Bruns, 1993) were used for amplification and sequencing (col. 8). Edited DNA sequences for all, but one, of the

147 fungal isolates collected from 2007-2012 have been deposited in NCBI GenBank (col. 9). Edited DNA sequences for one fungal isolate

148 collected in 2011, and 119 fungal isolates and two oomycetes collected in 2019 will be deposited upon manuscript acceptance (col.

149 9). All 211 isolates (from 2007-2019) were used for our survey-based assessment of the host associations and ranges of putative

150 pathogens (col. 10). Twenty-seven of the isolates collected in 2010 and 2011 were used for our experimental assessments of

151 pathogenicity and host range conducted in 2011 and 2012 (col. 11).

Year & Source of Forest No. of Date No. of unique Facilities where Primers used NCBI Isolates used for Isolates used collector symptomatic site(s) seedlings range of isolates, media extractions, GenBank survey-based for seedlings & tree seedling used, plant tissue amplifications, & Accession assessment of the host experimental spp. collection sampled sequencing were numbers associations & ranges assessments of collected completed of putative pathogens pathogenicity & host range 2007§ Seeds were (1) BV 22 Jul. 21- 28 fungal isolates (1) Arnold Lab ITS5, ITS1F, ITS4, KY413686- Yes No Brenes- germinated in (2) FC Nov.13, LR3 KY413775 Arguedas a STRI (3) SRR Tree spp: 2007 Media: shadehouse (1) BU, (1) WA, and then (2) CAM, (2) PARP† seedlings were (3) CV, planted in (4) HC, Plant tissue: forest sites (5) LAP, (1) stem, (6) NC, (2) root (7) OC, (8) OM (9) PT, (10) SWM 2010§ (1) Surface- (1) SRR 28 Jul 20- 34 fungal isolates (1) Arnold Lab, ITS1F, ITS4, LR3 Yes Yes Spear sterilized (2) PNM Nov. 16, (2) STRI ICBG lab, seeds planted Tree spp: 2010 Media: (3) STRI mol. directly in (1) ANE, (1) MEA+chloram research lab forest sites (2) CE, (2) PARP† (see Spear et (3) CR, al. 2015 for (4) GA, Plant tissue: additional (5) HC, (1) stem, details) (6) PT, (2) root, (2) Naturally (7) RA, (3) leaf occurring (8) TEP, seedings (9) TT, (10) VS

13

2011§ Naturally (1) PNM 8 May 21- 8 fungal isolates (1) STRI ICBG lab, ITS5, ITS1F, ITS4, Yes Yes Spear occurring (2) BCI Jun. 9, (2) STRI mol. LR3 seedings (3) CC Tree spp: 2011 Media: research lab, (4) SC (1) ANE, (1) MEA+chloram (3) Broders Lab, (2) CL, Plant tissue: (4) Macrogen, Inc. (3) DO (1) stem, (2) root, (3) leaf 2012§ Surface- (1) SRR 18 Jul. 5-19, 20 fungal isolates (1) Arnold Lab ITS5, LR3 Yes No Spear sterilized (2) PNM 2012 seeds were Tree sp: Media: planted (1) DR (1) MEA+chloram directly in forest sites Plant tissue: (1) stem, (2) root, (3) leaf 2019 Naturally (1) BCI 48 Sept. 27- 119 fungal (1) STRI mol. ITS5, ITS4 Will be Yes No Spear occurring Oct. 14, isolates & 2 research lab, deposited seedings Tree spp: 2019 oomycetes (2) Broders Lab¶, upon (1) ANE, (3) Macrogen, Inc. manuscript (2) CL, Media: acceptance (3) FO, (1) MEA+rifamp (4) LP, (5) MM, Plant tissue: (6) PP, (1) stem, (7) PR, (2) root, (8) PT, (3) leaf (9) SPM, (10) VS 152 † While PARP contains antifungals and was used with the intention of isolating oomycetes, only fungi were cultivated. We believe there was a problem with the antifungals or medium preparation. All 153 non-singleton fungal operational taxonomic units isolated on PARP were also isolated on MEA+rifamp, MEA+chloram, and/or WA (Fig. S4c). 154 ‡ For 17 isolates, paired-end reads were not possible due to a low quality read in one direction. 155 § Methods and sequences previously published in Spear (2007). 156 ¶ Amplified DNA was generated by either direct colony polymerase chain reaction (DC-PCR; following Walch et al., 2016) or PCR of DNA extracted in TE (Tris-EDTA) buffer. A T100™ Thermal Cycler 157 (Bio-Rad Laboratories, Inc, Hercules, CA, USA) was used for amplification: 3 min of initial denaturation at 95 °C, followed by 36 cycles of 95°C for 30 s, 54°C for 30 s, and 72 °C for 1 min, and a final 158 extension step of 72°C for 10 min (modified from U’Ren et al., 2010). Amplification was verified with gel electrophoresis and GelRed® Nucleic Acid Gel Stain (Biotium, Inc., Fremont, CA, USA). 159 References: 160 Gardes M, Bruns TD. 1993. ITS primers with enhanced specificity for basidiomycetes – application to the identification of mycorrhizae and rusts. Molecular 161 Ecology 2: 113–118. 162 Gilbert GS, Webb CO. 2007. Phylogenetic signal in plant pathogen-host range. Proceedings of the National Academy of Sciences, USA 104: 4979–4983.

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163 Higginbotham S, Wong WR, Linington RG, Spadafora C, Iturrado L, Arnold AE. 2014. Sloth hair as a novel source of fungi with potent antiparasitic, anti-cancer 164 and anti-bacterial bioactivity. PloS ONE 9: e84549. 165 Spear ER. 2017. Phylogenetic relationships and spatial distributions of putative fungal pathogens of seedlings across a rainfall gradient in Panama. Fungal 166 Ecology 26: 65–73. 167 U’Ren JM, Lutzoni F, Miadlikowska J, Arnold AE. 2010. Community analysis reveals close affinities between endophytic and endolichenic fungi in mosses and 168 lichens. Microbial Ecology 60: 340–353. 169 Vilgalys R, Hester M. 1990. Rapid genetic identification and mapping of enzymatically amplified ribosomal DNA from several Cryptococcus species. Journal of 170 Bacteriology 172: 4238–4246. 171 Walch G, Knapp M, Rainer G, Peintner U. 2016. Colony-PCR is a rapid method for DNA amplification of Hyphomycetes. Journal of Fungi 2: 12. 172 White TJ, Bruns T, Lee S, Taylor J. 1990. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis N, Gelfand D, Sninsky J, 173 White T, eds. PCR protocols: A guide to methods and applications. New York, NY, USA: Academic Press, 315–322.

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174 Table S3 Average light levels, air temperatures, and relative humidities of the two shadehouses

175 used for the inoculation experiments (Fig. S2) versus the forest understory. The screened-in

176 shadehouses are located in Gamboa, Panama (9°7'9.87"N, 79°42'4.96"W). The

177 photosynthetically active radiation (PAR, in µmol of photons s-1 m-2) reaching shadehouse

178 seedlings was measured during the afternoon of a uniformly overcast day (Oct. 12, 2011).

179 Measurements were taken inside and directly outside the shadehouses with a LI-250 light

180 meter, a LI-190 quantum sensor, and a one-meter LI-191 line quantum sensor (LI-COR, Lincoln,

181 NE, USA). The mean (± SD) wet season forest understory light value was obtained from Brenes-

182 Arguedas et al. (2011). In 2006 and 2007, Brenes-Arguedas et al. (2011) measured

183 instantaneous light 0.5 m above the forest floor with LI-190 quantum sensors (LI-COR), and QSO

184 sensors (Apogee Instruments, Logan, UT, USA) and CR200 and CR1000 data-loggers (Campbell

185 Scientific, Inc., Logan, UT, USA) in a nearby (within c. 16 km) forest in Barro Colorado Nature

186 Monument (BCNM), Buena Vista Peninsula (9°11'N, 79°49'W). In each shadehouse, air

187 temperature and relative humidity (RH) were measured at 10-min intervals (CS500 probe,

188 Campbell Scientific). Hourly mean temperature and minimum and maximum RH were recorded

189 on a CR200 datalogger (Campbell Scientific). Measurements were taken November 3-5 and 5-

190 11, 2011 for shadehouses 2 and 1, respectively. We obtained forest understory air temperature

191 and RH data for the same time period (Nov. 3-11, 2011) from the Physical Monitoring Program

192 of the Smithsonian Tropical Research Institute (Paton, 2019a,b) Forest understory air

193 temperature and RH data were measured at 15-min intervals with a CS215 Temperature and

194 Relative Humidity Probe (Campbell Scientific) at a nearby (within c. 16 km) forest in BCNM, the

195 Lutz Tower (sensor height 1m) on Barro Colorado Island (9°9'42.06"N, 79°50'15.83"W). We

196 calculated hourly means from STRI’s timestamped 15-min interval data to allow for comparison

197 with our air temperature and RH data. Both shadehouses were used for the inoculation

198 experiments in 2011 and only shadehouse 1 was used in 2012.

Location Mean ± SD Mean ± SD air Mean min. ± SD Mean max. ± SD % of full PAR temperature‡ RH‡ RH‡

Shadehouse 1 1.4% † 26.1 ± 1.9ºCns 85.1 ± 8.6%*** 88.1 ± 6.4%***

16

Forest understory 1.3 ± 0.8% 25.4 ± 0.9ºC 95.6 ± 0.5% 98.3 ± 1.2%

Shadehouse 2 1.7% † 25.6 ± 1.3ºCns 87.6 ± 6.9%*** 90.5 ± 4.6%***

Forest understory 1.3 ± 0.8% 25.3 ± 0.5ºC 96.7 ± 0.6% 99.0 ± 0.4% 199 †Because inside and outside PAR measurements were not taken simultaneously, we calculated mean % of full PAR 200 as the average of the PAR values recorded inside (shadehouse 1: n = 13, shadehouse 2: n = 8) divided by the 201 average of the PAR values recorded outside (shadehouse 1: n = 9, shadehouse 2: n = 8) and we could not calculate 202 SD. For this reason, and because we did not have access to the raw data summarized in Brenes-Arguedas et al. 203 (2011), we did not use statistical tests to compare shadehouse and forest understory light levels. 204 ‡ The air temperature and relative humidity data are not normally distributed (assessed by variable and 205 shadehouse with Shapiro-Wilk tests of normality [stats package; R Core Team, 2020], all P < 0.01). Therefore, two- 206 tailed Wilcoxon rank-sum tests (stats package; R Core Team, 2020) were used to compare the air temperature and 207 RH conditions of the two shadehouses to those of the forest understory (ns, not significant; ***, P <0.001; Quinn & 208 Keough, 2002). n = 150 per group for shadehouse 1 and its corresponding understory data. n = 46 per group for 209 shadehouse 2 and its corresponding understory data. 210 References: 211 Brenes-Arguedas T, Roddy AB, Coley PD, Kursar TA. 2011. Do differences in understory light contribute to species 212 distributions along a tropical rainfall gradient? Oecologia 166: 443–456. 213 Paton S. 2019a. Barro Colorado Island, Lutz tower 1m_Air Temperature. The Smithsonian Institution. Dataset. 214 https://doi.org/10.25573/data.10042394.v7 215 Paton S. 2019b. Barro Colorado Island, Lutz tower 1m_Relative Humidity. The Smithsonian Institution. Dataset. 216 https://doi.org/10.25573/data.10042400.v7 217 Quinn GP, Keough MJ. 2002. Experimental design and data analysis for biologists. New York, NY, USA: Cambridge 218 University Press. 219 R Core Team. 2020. R: a language and environment for statistical computing. R Foundation for Statistical 220 Computing, Vienna, Austria.

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221 Table S4 For each of the 66 operational taxonomic units (OTUs), we report its estimated taxonomic placement and the UNITE

222 database accession code(s) associated with the reference sequence(s) used to assign nomenclature (Kõljalg et al., 2013), the number

223 of times it was isolated, and the number of tree species from which it was isolated. For the 33 non-singleton OTUs (data subset A),

224 we report estimated host specialization based on the d' index (value and category; low (L): 0–0.33, moderate (M): 0.34–0.67)

225 (bipartite package; Dormann et al., 2008). To determine if there is a phylogenetic signal to the host range of the 31 OTUs isolated

226 from multiple tree species, we used the ses.mpd function (picante package; Kembel et al., 2010). In that analysis, the observed mean

227 phylogenetic distance (MPD) between tree species infected by a given OTU is compared to the MPD expected under a null model

228 with random host-OTU associations (full dataset; 999 permutations). Negative and positive standardized effect sizes indicate

229 phylogenetic clustering and phylogenetic overdispersion of host use, respectively (i.e., a given OTU associates with hosts more

230 closely [Obs. MPD < Exp. MPD] or more distantly [Obs. MPD > Exp. MPD] related than expected by chance) (Kembel et al., 2010). P-

231 values <0.05 and >0.95 indicate significant phylogenetic clustering and overdispersion, respectively (Kembel, 2010). Significant and

232 marginally significant P-values are in bold text and marked with a letter indicating the phylogenetic pattern of host use (clustering, C;

233 overdispersion, O). No. of Est. host Null Null obs. specialization: model model No. of host d' index & Obs. mean SD of Standardized OTU–Est. taxonomic placement - UNITE code(s)† isolates spp. category MPD MPD MPD effect size P A–Colletotrichum xanthorrhoeae - SH1543705 (C. 35 9 0.225 - L 219.3 233.0 21.2 -0.645 0.34 xanthorrhoeae), SH1543739 (unidentified) B– sp. - SH1610517 (Nectriaceae), SH1610162 15 8 0.292 - L 278.8 233.0 23.3 1.969 0.97O (Cylindrocladium buxicola) C–Mycoleptodiscus suttonii - SH1562616 13 7 0.538 - M 262.4 233.8 26.8 1.068 0.85 F–Diaporthe tulliensis - SH1540611 11 5 0.347 - M 223.3 234.7 34.7 -0.328 0.49 I– sp. - SH1541118 7 6 0.402 - M 230.6 235.0 29.2 -0.149 0.56 G–Clonostachys rosea - SH1522825 6 4 0.274 - L 280.0 232.4 39.4 1.209 0.81 D–Cylindrocladiella variabilis - SH1610166 6 3 0.350 - M 307.8 230.5 48.6 1.591 0.85

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H–Xylariaceae sp. - SH1541124 6 3 0.242 - L 234.4 235.0 49.2 -0.013 0.60 AAD–Mycoleptodiscus suttonii - SH1562616 5 4 0.272 - L 233.9 232.5 40.0 0.035 0.66 AU–Beltrania pseudorhombica - SH1563660 5 4 0.061 - L 299.2 232.2 38.4 1.744 0.95 O J–Pseudopestalotiopsis theae - SH1552673 5 4 0.206 - L 279.8 233.4 39.9 1.162 0.78 L–Neopestalotiopsis foedans - SH1552672 (N. foedans), 5 4 0.031 - L 217.5 234.1 40.0 -0.416 0.41 SH2700365 (Pezizomycotina) M–Lasiodiplodia gonubiensis - SH1507365 5 3 0.126 - L 320.3 232.1 47.3 1.866 0.94 O S–Colletotrichum citricola - SH1543707 4 4 0.198 - L 233.9 233.1 38.6 0.021 0.66 N–Gliocladiopsis elghollii - SH1546330 4 3 0.669 - M 204.0 235.6 51.0 -0.619 0.30 E–Beltrania pseudorhombica - SH1563660 4 2 0.350 - M 238.3 232.2 68.4 0.090 0.56 O–Fusarium pseudensiforme - SH1546322 (F. 4 2 0.593 - M 235.1 233.9 67.4 0.017 0.36 pseudensiforme), SH1546498 () AV–Cylindrocladiella variabilis - SH1212036 3 3 0.122 - L 320.3 232.9 48.1 1.817 0.97 O AW–Calonectria pseudonaviculata - SH1610162 3 3 0.118 - L 237.2 232.4 47.3 0.103 0.64 AX–Nectriaceae sp. - SH1212162 (Neonectria sp.), SH1610423 3 3 0.511 - M 237.2 234.6 49.4 0.053 0.67 (Cylindrocladiella sp.), SH1212036 (C. variabilis) AY–Beltraniella cf. endiandrae - SH1563659 3 3 0.383 - M 196.6 233.7 50.1 -0.741 0.17 P–Colletotrichum thailandicum - SH1543711 3 3 0.083 - L 237.2 234.8 50.2 0.048 0.76 Q–Diaporthe cf. columnaris - SH1540633 (Diaporthales), 3 2 0.226 - L 119.7 232.1 68.4 -1.644 0.09 C SH1540609 (D. columnaris) AAA–Diaporthe fraxini-angustifoliae - SH1540607 2 2 0.481 - M 235.1 235.3 71.9 -0.003 0.49 AAB–Trichoderma spirale - SH1567965 (T. spirale), 2 2 0.156 - L 215.2 232.8 67.1 -0.262 0.24 SH1552633 (Pezizomycotina) AAC–Xylariaceae sp. - SH1541166 2 2 0.380 - M 361.3 234.9 68.6 1.841 0.87 AZ–Clonostachys cf. miodochialis - SH1522826 (Hypocreales), 2 2 0.530 - M 214.0 233.4 69.5 -0.280 0.18 SH1522827 (C. miodochialis) K–Trichoderma spirale - SH1567965 2 2 0.570 - M 238.3 234.1 71.1 0.059 0.84 R–Diaporthe fraxini-angustifoliae - SH1540607 2 2 0.086 - L 119.7 232.1 68.4 -1.644 0.09 C U–Colletotrichum magnisporum - SH1543718 2 2 0.118 - L 238.3 232.6 70.1 0.082 0.67 Xylaria multiplex V– - SH1541132 2 2 0.134 - L 238.3 233.6 65.6 0.073 0.73 ‡T–Macrophomina - SH1507375 (Botryosphaeriaceae), 2 1 0.343 - M SH1507369 (M. phaseolina) Ceratobasidium ‡X– sp. - SH1551758 2 1 0.164 - L AA–Diaporthe endophytica - SH1540603 1 1

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Colletotrichum thailandicum AAE– - SH1543711 1 1 AAF–Nectriaceae sp. - SH1610517 1 1 Colletotrichum brevisporum AAG– - SH1543708 1 1 Beltraniella endiandrae AAH– - SH1563659 1 1 Diaporthe columnaris AAI– cf. - SH1540609 1 1 Gliocladiopsis elghollii AAJ– - SH1546330 1 1 Cylindrocladiella variabilis AAK– - SH1212036 1 1 AAL–Sordariomycetes sp. - SH1198320 1 1 Talaromyces AAM– - SH1516144 1 1 Cylindrocladiella variabilis AAN– - SH1212036 1 1 Diaporthe siamensis AB– - SH1540610 1 1 AC–Nigrospora cf. oryzae - SH1549605 (unidentified ), 1 1 SH1549606 (N. oryzae) Gliocladiopsis AD– sp. - SH1546383 1 1 Pestalotiopsis AE– sp. - SH1563667 1 1 Fusarium equiseti AF– - SH1610158 1 1 Pestalotiopsis rhododendri AG– - SH1563658 1 1 AH–Oomycota sp. 1 1 Gliocephalotrichum cylindrosporum AI– - SH1546353 1 1 Beltraniopsis neolitseae AJ– - SH1563663 1 1 Diaporthe endophytica AK– - SH1540603 1 1 Digitiseta multidigitata AL– - SH1546391 1 1 Endomelanconiopsis endophytica AM– - SH1507376 1 1 Phytophthora palmivora AN– 1 1 Xylaria AO– sp. - SH1554119 1 1 Diaporthe endophytica AP– - SH1540603 1 1 Ramularia AQ– sp. - SH1209934 1 1 AR–Mycosphaerellaceae sp. - SH1606644 1 1 Fusarium AS– - SH1610159 1 1 AT–Chaetosphaeriaceae sp. - SH1168551 1 1 W–Sordariomycetes sp. - SH1198320 1 1 Y–Talaromyces marneffei - SH1516144 1 1 Z–Colletotrichum brevisporum - SH1543708 1 1

20

†All UNITE Species Hypothesis accession codes, beginning with acronym SH, end in '.08FU' (not shown), denoting the version number and the acronym for Fungi (Kõljalg et al., 2013). Because our OTU grouping strategy (99% sequence similarity) often differed from that of the UNITE database (97–100% similarity), multiple, distinct OTUs in our study sometimes share the same nomenclature based on the species hypothesis (SH) of the best-matching reference sequence in UNITE (e.g., OTUs D, AV, AAK, and AAN). ‡There are no ses.mpd results for T - Macrophomina sp. and X - Ceratobasidium sp. because those OTUs were only isolated from a single host species. 234 References 235 Dormann CF, Gruber B, Fruend J. 2008. Introducing the bipartite Package: analysing ecological networks. R News 8: 8–11. 236 Kembel SW. 2010. An introduction to the picante package. [WWW document] URL https://cran.r-project.org/web/packages/picante/vignettes/picante- 237 intro.pdf [Accessed 21 Feb 2021]. 238 Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blomberg SP, Webb CO. 2010. Picante: R tools for integrating phylogenies and 239 ecology. Bioinformatics 26: 1463–1464. 240 Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, Bates ST, Bruns TD, Bengtsson-Palme J, Callaghan TM et al. 2013. Towards a unified 241 paradigm for sequence-based identification of fungi. Molecular Ecology 22: 5271–5277. 242

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243 Table S5 Overlap in operational taxonomic units (OTUs) among tree species, considering only the 13 tree species and 22 OTUs with 3

244 or more observations (data subset B). Gray cells contain the number of observed OTUs and, in parentheses, isolates collected for

245 each tree species (original hosts in Table S1). White cells contain the shared richness of OTUs between pairs of tree species and, in

246 parentheses, the estimated OTU community similarity (1 – Chao index, which is abundance-based and adjusted for unseen species),

247 ranging from zero (no similarity) to one (identical communities). Black cells contain the pooled number of OTUs for each pair of tree

248 species. ANE CL CM CE CV DR FO HC LP PR PP PT VS ANE 13 (29) 3 (0.30) 0 (0) 1 (0.03) 2 (0.35) 4 (0.47) 3 (0.34) 0 (0) 2 (0.36) 7 (0.81) 4 (0.36) 2 (0.23) 7 (1) CL 17 7 (12) 0 (0) 0 (0) 1 (0.10) 2 (0.24) 2 (0.35) 0 (0) 2 (0.33) 7 (1) 3 (0.42) 2 (0.32) 2 (0.17) CM 15 9 2 (3) 1 (0.24) 0 (0) 2 (0.54) 0 (0) 1 (0.24) 0 (0) 1 (0.04) 0 (0) 0 (0) 2 (0.18) CE 14 9 3 2 (4) 0 (0) 1 (0.11) 0 (0) 1 (0.21) 0 (0) 0 (0) 0 (0) 0 (0) 1 (0.09) CV 15 10 6 6 4 (4) 1 (0.08) 0 (0) 1 (0.21) 3 (1) 1 (0.08) 0 (0) 0 (0) 1 (0.16) DR 16 12 7 8 10 7 (16) 2 (0.32) 1 (0.11) 1 (0.07) 4 (0.63) 2 (0.23) 1 (0.18) 2 (0.15) FO 14 9 6 6 8 9 4 (10) 0 (0) 0 (0) 2 (0.32) 1 (0.27) 2 (0.61) 1 (0.07) HC 16 10 4 4 6 9 7 3 (4) 1 (0.16) 0 (0) 0 (0) 1 (0.18) 1 (0.09) LP 15 9 6 6 5 10 8 6 4 (6) 2 (0.21) 1 (0.13) 0 (0) 1 (0.08) PR 19 13 14 15 16 16 15 16 15 13 (25) 5 (0.59) 3 (0.42) 6 (1) PP 15 10 8 8 10 11 9 9 9 14 6 (13) 1 (0.21) 1 (0.06) PT 15 9 6 6 8 10 6 6 8 14 9 4 (5) 1 (0.14) VS 15 14 9 10 12 14 12 11 12 16 14 12 9 (13) 249

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250 Table S6 Results of the beta-binomial (logit link) generalized linear regression with the

251 proportion of seedlings with disease as a function of seed size and shade tolerance (26 tree

252 species, 179 observations: 42 for intolerant spp., 137 for tolerant spp.). Estimates and standard

253 errors (SE) are log odds of disease for a one-unit increase in the variable. The intercept

254 represents shade-intolerant tree species at a hypothetical seed dry mass (mg) of zero. Model

255 averaging was not done because no other model had a ΔAICc ≤ 2. P-values indicating statistical

256 significance are in bold (P < 0.05). Variable Estimate SE z value P intercept -0.812 0.342 -2.377 0.018 seed size -0.004 0.001 -3.057 0.002 shade tolerance -1.131 0.385 -2.938 0.003 seed size:shade tolerance 0.004 0.001 -2.457 0.014 257 258

259 Table S7 Average estimates based on the best-ranked (ΔAICc ≤ 2) beta-binomial (logit link)

260 generalized linear regressions with the proportion of seedlings with disease as a function of

261 seed size and spatial distribution relative to annual rainfall (15 tree species, 154 observations:

262 108 for dry-site spp., 46 for wet-site spp.). Estimates and standard errors are log odds of

263 disease for a one-unit increase in the variable. The intercept represents a dry-site tree species

264 at a hypothetical seed dry mass (mg) of zero. P-values indicating statistical significance are in

265 bold (P < 0.05). Variable Estimate Std. error z value P intercept -1.517 0.222 6.829 <0.001 seed size -0.0005 0.0003 1.593 0.111 distribution -1.255 0.619 2.028 0.042 seed size:distribution 0.0008 0.0006 1.286 0.198 266

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267 Methods S1 Methods used to estimate the taxonomic placement of the 66 operational

268 taxonomic units (OTUs) and assign nomenclature.

269 We estimated the taxonomic placement of the fungi isolated from symptomatic

270 seedlings by querying all 209 fungal sequences in our study against the well-curated and

271 annotated Full UNITE+INSD dataset for Fungi (v. 8.2, released 2020-02-04; Abarenkov et al.,

272 2020). Fungal sequences accessioned in the UNITE database have been clustered into what are

273 hypothesized to be species-level groups (Species Hypotheses, SHs), each with a unique

274 accession code. We implemented BLASTN (Altschul et al., 1990) in Python with default

275 parameters (blastn -query Spear.fasta -task blastn -db uniteDB -out

276 Python_blastout_1Oct20.txt -evalue 10 -outfmt ‘6 qseqid pident qcovs evalue bitscore length

277 sallseqid’ -max_target_seqs 5 -num_threads 16). For each query, we considered the top five

278 hits and an OTU was assigned nomenclature based on the reference sequence meeting the

279 following criteria: an alignment length ≥75 bp (range 374–1148 bp, mean 759 bp, median 618

280 bp), an E-value ≤ 10-36 (all <10-150) (as in Radujković et al., 2019), query cover ≥90% (range 91–

281 100%, mean 99.5%, median 100%), and the highest percent identity for the OTU (range 93–

282 100%, mean 99.7%, median 100%) (as suggested by Lücking et al., 2020).

283 Following the aforementioned steps of our initial, Python-based approach, there was

284 taxonomic ambiguity for 18 of the 64 fungal OTUs. These OTUs had multiple (2-3) different SH

285 accession codes among the hits with a percent identity of 100% and/or they were not identified

286 to the level based on the SH of their 'best' hit. In an attempt to resolve ambiguities and

287 achieve the greatest taxonomic resolution possible, we queried one to two representative

288 sequence(s) from each of those 18 OTUs against the web-based UNITE database (v. 8.2,

289 accessed 9-Oct and 16-Nov-2020; Nilsson et al., 2018), reviewed the top 30 hits for each

290 sequence, and, when possible and appropriate (e.g., no taxonomic conflicts among the named

291 matches), revised their nomenclature based on the criteria specified above (e.g., OTU AC was

292 revised from 'unidentified fungus' to Nigrospora cf. oryzae). As a result, we manually assigned

293 the nomenclature for 10 of the 18 OTUs with taxonomic ambiguity following our Python-based

294 approach. For those 10 OTUs, we list all relevant SH accession codes (i.e., the SH of the 'best' hit

295 and the SH(s) of the reference sequence(s) used to assign nomenclature) in Table S4.

24

296 For the two oomycetes (OTUs AH and AN), their sequences were queried against

297 GenBank (accessed 8-Oct-2020; Benson et al., 2012) and the curated database Phytophthora-ID

298 (v. 2.0, accessed 8-Oct-2020; Grünwald et al., 2011).

299 When annotating OTUs based on reference sequences named to the species level, we

300 considered: (i) 99–100% percent identity a positive match between the query and reference

301 sequence; (ii) 97–98.99% percent identity a close match, the observed differences may fall

302 within the variability of the species (5 OTUs; annotated as cf.); and 90–96.99% percent identity

303 to be a positive match at the genus, but not species, level (3 OTUs: T, AS, AAM).

304 Dual nomenclature for pleomorphic fungi (i.e., different scientific names for the asexual

305 and sexual forms of single species) has been replaced with a single name for a fungal species

306 (Hawksworth et al., 2011). For each OTU, we verified the current accepted name by reviewing

307 Index Fungorum (www.indexfungorum.org), Mycobank (Crous et al., 2004), and published

308 literature, and we revised the nomenclature for OTU AF (from Gibberella intricans to Fusarium

309 equiseti; Xia et al., 2019), OTU AQ (from Mycosphaerella to Ramularia; Wijayawardene et al.,

310 2014), and OTU AW (from Cylindrocladium buxicola to Calonectria pseudonaviculata; Lombard

311 et al., 2010). Additionally, we followed the nomenclature of Hernández-Restrepo et al. (2019)

312 for the taxonomic ranks of Mycoleptodiscus.

313 All assigned nomenclatures represent estimates due to several limitations associated

314 with our sequence-based approach (Kang et al., 2010; Hofstetter et al., 2019; Lücking et al.,

315 2020). While the nuclear ribosomal internal transcribed spacer (ITS) region is the formal

316 barcode for the molecular identification of fungi (Schoch et al., 2012). It is well established that

317 the ITS region is an unreliable barcode for species discrimination for certain taxa (Lücking et al.,

318 2020); for example, Calonectria (Liu et al., 2020), Diaporthe (Santos et al., 2017), Colletotrichum

319 (Marin-Felix et al., 2017), and Penicillium (Seifert et al., 2007). A multi-locus phylogenetic

320 evaluation is required for accurate species identification (sensu Santos et al., 2017).

321 Furthermore, precision is limited by the incomplete taxonomic and geographic coverage of

322 existing databases, even well-curated ones like UNITE (Kõljalg et al., 2013; Lücking et al., 2020).

323 Additionally, fungal sequences accessioned in the UNITE database have been clustered into SHs

324 based on sequence similarity thresholds ranging from 97–100% (Kõljalg et al., 2013; Robbertse

25

325 et al., 2017; Nilsson et al., 2018). While there is no single threshold that appropriately

326 addresses lineage-specific intra- versus interspecific variability for all fungi (Nilsson et al., 2008),

327 we used a threshold value of 99% sequence similarity to designate OTUs because: (1) 97–98.5%

328 sequence similarity is too relaxed for species delimitation for some taxonomic groups (Garnica

329 et al., 2016), and (2) we are making statements about host specificity so we adopted a stringent

330 similarity threshold that splits rather than lumps, but that accounts for a small amount of

331 sequencing error. Because our grouping strategy (99% sequence similarity) often differed from

332 that of the UNITE database (97–100% similarity), multiple, distinct OTUs in our study

333 sometimes share the same nomenclature based on the SH of the best-matching reference

334 sequence in UNITE (e.g., OTUs D, AV, AAK, and AAN are all annotated as Cylindrocladiella

335 variabilis). It should also be noted that, for certain groups of fungi (e.g., Trichoderma), UNITE

336 SHs erroneously include distinct species (Robbertse et al., 2017; Lücking et al., 2020). 337 References: 338 Abarenkov K, Zirk A, Piirmann T, Pöhönen R, Ivanov F, Nilsson RH, Kõljalg U. 2020. Full UNITE+INSD dataset for 339 Fungi. UNITE Community. 10.15156/BIO/786372 340 Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. Basic local alignment search tool. Journal of Molecular 341 Biology 215: 403–410. 342 Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. 2012. GenBank. Nucleic 343 Acids Research 41: D36-D42. 344 Crous PW, Gams W, Stalpers JA, Robert V, Stegehuis G. 2004. MycoBank: an online initiative to launch mycology 345 into the 21st century. Studies in Mycology 50: 19–22. 346 Garnica S, Schön ME, Abarenkov K, Riess K, Liimatainen K, Niskanen T, Dima B, Soop K, Frøslev TG, Jeppesen TS 347 et al. 2016. Determining threshold values for barcoding fungi: lessons from Cortinarius (Basidiomycota), a 348 highly diverse and widespread ectomycorrhizal genus. FEMS Microbiology Ecology 92: fiw045. 349 Grünwald NJ, Martin FN, Larsen MM, Sullivan CM, Coffey MD, Hansen EM, Parke JL. 2011. Phytophthora-ID.org: 350 A sequence-based Phytophthora identification tool. Plant Disease 95: 337–342. 351 Hawksworth DL. 2011. A new dawn for the naming of fungi: impacts of decisions made in Melbourne in July 2011 352 on the future publication and regulation of fungal names. IMA Fungus 2: 155–162. 353 Hernández-Restrepo M, Bezerra JDP, Tan YP, Wiederhold N, Crous PW, Guarro J, Gené J. 2019. Re-evaluation of 354 Mycoleptodiscus species and morphologically similar fungi. Persoonia 42: 205–227. 355 Hofstetter V, Buyck B, Eyssartier G, Schnee S, Gindro K. 2019. The unbearable lightness of sequenced-based 356 identification. Fungal Diversity 96: 243–284.

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392 Wijayawardene NN, Crous PW, Kirk PM, Hawksworth DL, Boonmee S, Braun U, Dai D-Q, D’souza MJ. Diederich P, 393 Dissanayake A et al. 2014. Naming and outline of Dothideomycetes–2014 including proposals for the 394 protection or suppression of generic names. Fungal Diversity 69: 1–55. 395 Xia JW, Sandoval-Denis M, Crous PW, Zhang XG, Lombard L. 2019. Numbers to names–restyling the Fusarium 396 incarnatum-equiseti species complex. Persoonia 43: 186–221.

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