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.
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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.
Frelich, L. E. Boreal Biome (Oxford Bibliographies Online Datasets, 2013); https://doi.org/10.1093/obo/9780199830060-0085
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
Peng, C. et al. A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat. Clim. Change 1, 467–471 (2011).
Gauthier, S., Bernier, P., Kuuluvainen, T., Shvidenko, A. Z. & Schepaschenko, D. G. Boreal forest health and global change. Science 349, 819–822 (2015).
Gower, S. T. et al. Net primary production and carbon allocation patterns of boreal forest ecosystems. Ecol. Appl. 11, 1395–1411 (2001).
Price, D. T. et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365 (2013).
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).
Martin, F. M., Uroz, S. & Barker, D. G. Ancestral alliances: plant mutualistic symbioses with fungi and bacteria. Science 356, eaad4501 (2017).
Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).
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).
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).
Arnold, A. E. et al. Fungal endophytes limit pathogen damage in a tropical tree. Proc. Natl Acad. Sci. USA 100, 15649–15654 (2003).
Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).
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).
Rodriguez, R. J. et al. Stress tolerance in plants via habitat-adapted symbiosis. ISME J. 2, 404–416 (2008).
Lutzoni, F. et al. Contemporaneous radiations of fungi and plants linked to symbiosis. Nat. Commun. 9, 5451 (2018).
Arnold, A. E. & Lutzoni, F. Diversity and host range of foliar fungal endophytes: are tropical leaves biodiversity hotspots? Ecology 88, 541–549 (2007).
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).
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).
van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).
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).
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
Soininen, J., McDonald, R. & Hillebrand, H. The distance decay of similarity in ecological communities. Ecography 30, 3–12 (2007).
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).
Feurdean, A. et al. Tree migration-rates: narrowing the gap between inferred post-glacial rates and projected rates. PLoS ONE 8, e71797 (2013).
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).
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).
Golan, J. J. & Pringle, A. in The Fungal Kingdom (eds Heitman, J. et al.) 309–333 (ASM Press, 2017); https://doi.org/10.1128/microbiolspec.FUNK-0047-2016
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).
Giauque, H. & Hawkes, C. V. Climate affects symbiotic fungal endophyte diversity and performance. Am. J. Bot. 100, 1435–1444 (2013).
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).
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).
U’Ren, J. M. DNA extraction from fungal mycelium using Extract-n-Amp. protocols.io https://doi.org/10.17504/protocols.io.ga4bsgw (2016).
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).
U’Ren, J. M. & Arnold, A. E. DNA extraction protocol for plant and lichen tissues stored in CTAB. protocols.io https://doi.org/10.17504/protocols.io.fs8bnhw (2017).
U’Ren, J. M. & Arnold, A. E. Illumina MiSeq dual-barcoded two-step PCR amplicon sequencing protocol. protocols.io https://doi.org/10.17504/protocols.io.fs9bnh6 (2017).
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).
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).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).
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).
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
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).
Abarenkov, K. et al. The UNITE database for molecular identification of fungi—recent updates and future perspectives. New Phytol. 186, 281–285 (2010).
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).
Huson, D. H. & Mitra, S. Introduction to the analysis of environmental sequences: metagenomics with MEGAN. Methods Mol. Biol. 856, 415–429 (2012).
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).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Edgar, R. C. UNCROSS2: identification of cross-talk in 16S rRNA OTU tables. Preprint at bioRxiv https://doi.org/10.1101/400762 (2018).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv https://doi.org/10.1101/081257 (2016).
Berger, S. A. & Stamatakis, A. Aligning short reads to reference alignments and trees. Bioinformatics 27, 2068–2075 (2011).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
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).
Clement, M., Posada, D. & Crandall, K. A. TCS: a computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659 (2000).
Oksanen, J. et al. vegan: Community ecology package. R package version 2.4–6 (2018); https://CRAN.R-project.org/package=vegan
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Weiss, S. J. et al. Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data. Preprint at https://doi.org/10.7287/peerj.preprints.1157v1 (2015).
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).
Borcard, D. & Legendre, P. Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93, 1473–1481 (2012).
Nychka, D., Furrer, R., Paige, J. & Sain, S. Fields: tools for spatial data. R package version 9.8-3 (2015); https://CRAN.R-project.org/package=fields
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).
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).
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).
Csardi, G. & Nepusz, T. igraph: Network analysis and visualization. R package version 0.7 (2014).
U’Ren, J. M. et al. Host availability drives distributions of fungal endophytes in the imperiled boreal realm. figshare https://doi.org/10.6084/m9.figshare.c.4327772 (2019).
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).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary methods and Figs. 1–16.
Sampling sites and collection information (sampling date, latitude, longitude, elevation and climate).
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.
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.
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).
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).
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.
Species area relationships (SAR) for fungal endophytes at continental to circumboreal scales.
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.
Fungi included in the mock community, taxonomy and number of reads per replicate.
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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). https://doi.org/10.1038/s41559-019-0975-2
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