Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Host availability drives distributions of fungal endophytes in the imperilled boreal realm


Boreal forests represent the world’s largest terrestrial biome and provide ecosystem services of global importance. Highly imperilled by climate change, these forests host Earth’s greatest phylogenetic diversity of endophytes, a hyperdiverse group of symbionts that are defined by their occurrence within living, symptomless plant and lichen tissues. Endophytes shape the ecological and evolutionary trajectories of plants and are therefore key to the function and resilience of terrestrial ecosystems. A critical step in linking the ecological functions of endophytes with those of their hosts is to understand the distributions of these symbionts at the global scale; however, turnover in host taxa with geography and climate can confound insights into endophyte biogeography. As a result, global drivers of endophyte diversity and distributions are not known. Here, we leverage sampling from phylogenetically diverse boreal plants and lichens across North America and Eurasia to show that host filtering in distinctive environments, rather than turnover with geographical or environmental distance, is the main determinant of the community composition and diversity of endophytes. We reveal the distinctiveness of boreal endophytes relative to soil fungi worldwide and endophytes from diverse temperate biomes, highlighting a high degree of global endemism. Overall, the distributions of endophytes are directly linked to the availability of compatible hosts, highlighting the role of biotic interactions in shaping fungal communities across large spatial scales, and the threat that climate change poses to biological diversity and function in the imperilled boreal realm.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Geographical location, climate and host information for 498 individual host collections sampled for endophytes at seven boreal sites.
Fig. 2: Host identity structures endophyte communities at a circumboreal scale.
Fig. 3: Networks reveal host affiliations of endophyte OTUs at local and circumboreal scales.
Fig. 4: Evolutionary context of endophyte–host associations revealed by phylogenetic analyses of the most species-rich fungal phylum (Ascomycota).

Similar content being viewed by others

Data availability

Raw sequencing data and metadata are deposited in at DDBJ/EMBL/GenBank (BioProject PRJNA514023: SRA BioSamples SAMN10718335–SAMN10718821; Sanger Targeted Locus Study project accession numbers KCRE01000001–KCRE01010802). All of the sequencing data, metadata and other types of data used in this study are publicly available at figshare69.


  1. Frelich, L. E. Boreal Biome (Oxford Bibliographies Online Datasets, 2013);

  2. Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Peng, C. et al. A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat. Clim. Change 1, 467–471 (2011).

    Article  Google Scholar 

  4. Gauthier, S., Bernier, P., Kuuluvainen, T., Shvidenko, A. Z. & Schepaschenko, D. G. Boreal forest health and global change. Science 349, 819–822 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Gower, S. T. et al. Net primary production and carbon allocation patterns of boreal forest ecosystems. Ecol. Appl. 11, 1395–1411 (2001).

    Article  Google Scholar 

  6. Price, D. T. et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365 (2013).

    Article  Google Scholar 

  7. Lau, J. A. & Lennon, J. T. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc. Natl Acad. Sci. USA 109, 14058–14062 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Martin, F. M., Uroz, S. & Barker, D. G. Ancestral alliances: plant mutualistic symbioses with fungi and bacteria. Science 356, eaad4501 (2017).

    Article  PubMed  Google Scholar 

  9. Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Treseder, K. K., Mack, M. C. & Cross, A. Relationships among fires, fungi, and soil dynamics in Alaskan boreal forests. Ecol. Appl. 14, 1826–1838 (2004).

  11. Arnold, A. E. et al. A phylogenetic estimation of trophic transition networks for ascomycetous fungi: are lichens cradles of symbiotrophic fungal diversification? Syst. Biol. 58, 283–297 (2009).

    Article  PubMed  Google Scholar 

  12. Arnold, A. E. et al. Fungal endophytes limit pathogen damage in a tropical tree. Proc. Natl Acad. Sci. USA 100, 15649–15654 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Müller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50, 211–234 (2016).

    Article  PubMed  Google Scholar 

  15. Rodriguez, R. J. et al. Stress tolerance in plants via habitat-adapted symbiosis. ISME J. 2, 404–416 (2008).

    Article  PubMed  Google Scholar 

  16. Lutzoni, F. et al. Contemporaneous radiations of fungi and plants linked to symbiosis. Nat. Commun. 9, 5451 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Arnold, A. E. & Lutzoni, F. Diversity and host range of foliar fungal endophytes: are tropical leaves biodiversity hotspots? Ecology 88, 541–549 (2007).

    Article  PubMed  Google Scholar 

  18. U’Ren, J. M., Lutzoni, F., Miadlikowska, J., Laetsch, A. D. & Arnold, A. E. Host and geographic structure of endophytic and endolichenic fungi at a continental scale. Am. J. Bot. 99, 898–914 (2012).

    Article  PubMed  Google Scholar 

  19. Zimmerman, N. B. & Vitousek, P. M. Fungal endophyte communities reflect environmental structuring across a Hawaiian landscape. Proc. Natl Acad. Sci. USA 109, 13022–13027 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).

    Article  PubMed  Google Scholar 

  21. Schoch, C. L. et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. Proc. Natl Acad. Sci. USA 109, 6241–6246 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).

    Article  PubMed  Google Scholar 

  23. Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Soininen, J., McDonald, R. & Hillebrand, H. The distance decay of similarity in ecological communities. Ecography 30, 3–12 (2007).

    Article  Google Scholar 

  25. Yeoh, Y. K. et al. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat. Commun. 8, 215 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Feurdean, A. et al. Tree migration-rates: narrowing the gap between inferred post-glacial rates and projected rates. PLoS ONE 8, e71797 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Poulin, R., Krasnov, B. R., Mouillot, D. & Thieltges, D. W. The comparative ecology and biogeography of parasites. Proc. R. Soc. B 366, 2379–2390 (2011).

    Google Scholar 

  28. Salgado-Salazar, C., Rossman, A. Y. & Chaverri, P. Not as ubiquitous as we thought: taxonomic crypsis, hidden diversity and cryptic speciation in the cosmopolitan fungus Thelonectria discophora (Nectriaceae, Hypocreales, Ascomycota). PLoS ONE 8, e76737 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Golan, J. J. & Pringle, A. in The Fungal Kingdom (eds Heitman, J. et al.) 309–333 (ASM Press, 2017);

  30. Carbone, I. et al. T-BAS: Tree-Based Alignment Selector toolkit for phylogenetic-based placement, alignment downloads and metadata visualization: an example with the Pezizomycotina tree of life. Bioinformatics 33, 1160–1168 (2017).

    CAS  PubMed  Google Scholar 

  31. Giauque, H. & Hawkes, C. V. Climate affects symbiotic fungal endophyte diversity and performance. Am. J. Bot. 100, 1435–1444 (2013).

    Article  PubMed  Google Scholar 

  32. Treseder, K. K., Marusenko, Y., Romero-Olivares, A. L. & Maltz, M. R. Experimental warming alters potential function of the fungal community in boreal forest. Glob. Change Biol. 22, 3395–3404 (2016).

    Article  Google Scholar 

  33. U’Ren, J. M. et al. Tissue storage and primer selection influence pyrosequencing-based inferences of diversity and community composition of endolichenic and endophytic fungi. Mol. Ecol. Resour. 14, 1032–1048 (2014).

    PubMed  Google Scholar 

  34. U’Ren, J. M. DNA extraction from fungal mycelium using Extract-n-Amp. (2016).

  35. Higgins, K. L., Coley, P. D., Kursar, T. A. & Arnold, A. E. Culturing and direct PCR suggest prevalent host generalism among diverse fungal endophytes of tropical forest grasses. Mycologia 103, 247–260 (2011).

    Article  PubMed  Google Scholar 

  36. U’Ren, J. M. & Arnold, A. E. DNA extraction protocol for plant and lichen tissues stored in CTAB. (2017).

  37. U’Ren, J. M. & Arnold, A. E. Illumina MiSeq dual-barcoded two-step PCR amplicon sequencing protocol. (2017).

  38. Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes—application to the identification of mycorrhizae and rusts. Mol. Ecol. 2, 113–118 (1993).

    Article  CAS  PubMed  Google Scholar 

  39. White, T. J. et al. in PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A., Gelfand, D. H., Sninsky, J. J. & White, T. J.) 315–322 (New York Academic Press, 1990).

  40. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  PubMed  Google Scholar 

  41. Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 4, 914–919 (2013).

    Google Scholar 

  43. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  PubMed  Google Scholar 

  44. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Abarenkov, K. et al. The UNITE database for molecular identification of fungi—recent updates and future perspectives. New Phytol. 186, 281–285 (2010).

    Article  PubMed  Google Scholar 

  46. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  47. Huson, D. H. & Mitra, S. Introduction to the analysis of environmental sequences: metagenomics with MEGAN. Methods Mol. Biol. 856, 415–429 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Edgar, R. C. UNCROSS2: identification of cross-talk in 16S rRNA OTU tables. Preprint at bioRxiv (2018).

  51. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv (2016).

  53. Berger, S. A. & Stamatakis, A. Aligning short reads to reference alignments and trees. Bioinformatics 27, 2068–2075 (2011).

    Article  CAS  PubMed  Google Scholar 

  54. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. U’Ren, J. M. et al. Contributions of North American endophytes to the phylogeny, ecology, and taxonomy of Xylariaceae (Sordariomycetes, Ascomycota). Mol. Phylogenet. Evol. 98, 210–232 (2016).

    Article  PubMed  Google Scholar 

  56. Clement, M., Posada, D. & Crandall, K. A. TCS: a computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659 (2000).

    Article  CAS  PubMed  Google Scholar 

  57. Oksanen, J. et al. vegan: Community ecology package. R package version 2.4–6 (2018);

  58. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  59. Weiss, S. J. et al. Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data. Preprint at (2015).

  60. Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).

    Google Scholar 

  61. Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).

    Article  Google Scholar 

  62. McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).

    Article  Google Scholar 

  63. Borcard, D. & Legendre, P. Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93, 1473–1481 (2012).

    Article  PubMed  Google Scholar 

  64. Nychka, D., Furrer, R., Paige, J. & Sain, S. Fields: tools for spatial data. R package version 9.8-3 (2015);

  65. Borcard, D. & Legendre, P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51–68 (2002).

    Article  Google Scholar 

  66. Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).

    Article  Google Scholar 

  67. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Csardi, G. & Nepusz, T. igraph: Network analysis and visualization. R package version 0.7 (2014).

  69. U’Ren, J. M. et al. Host availability drives distributions of fungal endophytes in the imperiled boreal realm. figshare (2019).

Download references


We thank S. Irwin, L. Taylor, J. Stenlid, R. Andronova, A. Knorre, A. Dutbyeva, M. Zhurbenko, K. Arendt, E. Lefèvre, B. Ball, V. Wong, R. Oono, T. Gleason, J. Gonzales III, J. Riddle and K.-H. Chen for field and laboratory assistance; G. Hestmark, B. Hodkinson, S. LaGreca, J. Lendemer, B. McCune, L. Myllys, S. Stenroos and C. Truong for lichen identifications; B. Hurwitz, R. Steidl and G. Burleigh for helpful discussions; K. Youens-Clark and T. O’Connor for computational assistance; staff at the University of Arizona Genetics Core and D. New and A. Gerritsen at the University of Idaho IBEST Genomics Core for technical assistance; and M. Miller for deploying tools and databases used in T-BAS on CIPRES. This study was funded by the US National Science Foundation (NSF) Dimensions of Biodiversity program (A.E.A., DEB-1045766; I.C., DEB-1046167; G.M., DEB-1045608; F.L., DEB-1046065) and the Huron Mountain Wildlife Foundation (A.E.A.). N.B.Z. was supported by the Gordon and Betty Moore Foundation through grant number GBMF 2550.03 to the Life Sciences Research Foundation. The CIPRES RESTful API is supported by the National Institutes of Health (NIH; 5 R01 GM1264635), NSF (DBI-1759844) and an award (TG-DEB090011) of computer time and development support from the XSEDE project (also sponsored by NSF). Data collection that was performed by the IBEST Genomics Resources Core at the University of Idaho was supported in part by NIH (COBRE grant P30GM103324).

Author information

Authors and Affiliations



F.L., J.M. and A.E.A. conceived the study with I.C., G.M. and J.M.U. F.L., J.M., A.E.A., J.M.U. and G.M. conducted the fieldwork. J.M.U., J.M., F.L. and A.E.A. collected the data. J.M.U., A.E.A., N.B.Z., I.C. and F.L. developed the analyses. J.M.U., A.E.A. and N.B.Z. analysed the data. J.M.U., A.E.A. and F.L. wrote the paper with comments from all of the authors.

Corresponding author

Correspondence to A. Elizabeth Arnold.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods and Figs. 1–16.

Reporting Summary

Supplementary Table 1

Sampling sites and collection information (sampling date, latitude, longitude, elevation and climate).

Supplementary Table 2

Isolation frequency, species richness and diversity of endophytic fungi from 82 plant collections based on culturing and Illumina NGS for seven sites across the circumboreal belt.

Supplementary Table 3

Isolation frequency, putative species richness and diversity of endophytic fungi from 81 lichen collections based on culturing and Illumina NGS for seven sites across the circumboreal belt.

Supplementary Table 4

Taxonomy and lifestyle of 693 fungal OTUs from soil (of 44,563), as described by ref. 22, that have significant matches to boreal endophytes on the basis of clustering at 99% sequence similarity. Information about ectomycorrhizal lineages (ecm_lineage) and lifestyle are presented in ref. 22 and may reference either fungi with multiple ecological modes or cryptic species that perform various ecological roles. Abbreviations and column titles are from ref. 22 (panel A). Taxonomy, read counts and distribution of 153 endophyte OTUs from temperate plants and lichens as described in ref. 7 with high similarity to endophytes from the boreal biome, as represented by culturing and culture-free methods used in the present study (panel B).

Supplementary Table 5

Relationship of endophyte richness to MAP, host lineage and site. Comparison of −2 log likelihood values between linear mixed models revealed the most likely model is the one containing only host lineage (nested within site; P < 0.0001). Richness was defined for the analysis by calculating the residuals of OTU richness in relation to the square-root of the number of reads22. Results were similar to richness calculated from rarefied data (data not shown).

Supplementary Table 6

Results of PERMANOVA examining the relationship of endophyte community composition to host identity and site. For each taxon set we tested different combinations of the following variables (as appropriate): host genus and host lineage; host genus × site; and host lineage × site. Results from each analysis are presented on a separate row. Host lineage is defined at the phylum level for plants and mycobiont family level for lichens.

Supplementary Table 7

Species area relationships (SAR) for fungal endophytes at continental to circumboreal scales.

Supplementary Table 8

Accession numbers, site, host and collection information for endophyte cultures from this study. Endophytes were archived as living cultures at the Robert L. Gilbertson Mycological Herbarium at the University of Arizona.

Supplementary Table 9

Fungi included in the mock community, taxonomy and number of reads per replicate.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

U’Ren, J.M., Lutzoni, F., Miadlikowska, J. et al. Host availability drives distributions of fungal endophytes in the imperilled boreal realm. Nat Ecol Evol 3, 1430–1437 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing