Dispersal is a critical yet poorly understood factor underlying macroecological patterns in microbial communities1. Airborne microbial transport is assumed to occupy a central role in determining dispersal outcomes2,3, and extra-range dispersal has important implications for predicting ecosystem resilience and response to environmental change4. One of the most pertinent biomes in this regard is Antarctica, given its geographic isolation and vulnerability to climate change and human disturbance5. Here, we report microbial diversity in near-ground and high-altitude air above the largest ice-free Antarctic habitat, as well as that of underlying soil microbial communities. We found that persistent local airborne inputs were unable to fully explain Antarctic soil community assembly. Comparison with airborne microbial diversity from high-altitude and non-polar sources suggests that strong selection occurs during long-range atmospheric transport. The influence of selection during airborne transit and at sink locations varied between microbial phyla. Overall, the communities from this isolated Antarctic ecosystem displayed limited connectivity to the non-polar microbial pool, and alternative sources of recruitment are necessary to fully explain extant soil diversity. Our findings provide critical insights into the role of airborne transport limitation in determining microbial biogeographic patterns.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $5.17 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data that support the findings of this study are available from the corresponding author upon request. All sequence data generated by this study have been submitted to the EMBL European Nucleotide Archive under BioProject PRJEB27416 with accession numbers ERS2573837 to ERS2573946.
Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).
Burrows, S. M., Elbert, W., Lawrence, M. G. & Pöschl, U. Bacteria in the global atmosphere—part 1: review and synthesis of literature data for different ecosystems. Atmos. Chem. Phys. 9, 9263–9280 (2009).
Kellogg, C. A. & Griffin, D. W. Aerobiology and the global transport of desert dust. Trends Ecol. Evol. 21, 638–644 (2006).
Wilson, J. R. U., Dormontt, E. E., Prentis, P. J., Lowe, A. J. & Richardson, D. M. Something in the way you move: dispersal pathways affect invasion success. Trends Ecol. Evol. 24, 136–144 (2009).
Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522, 431–438 (2015).
De Wit, R. & Bouvier, T. ‘Everything is everywhere, but, the environment selects’; what did Baas Becking and Beijerinck really say? Environ. Microbiol. 8, 755–758 (2006).
Finlay, B. J. & Clarke, K. J. Ubiquitous dispersal of microbial species. Nature 400, 828 (1999).
Mayol, E. et al. Long-range transport of airborne microbes over the global tropical and subtropical ocean. Nat. Commun. 8, 201 (2017).
Favet, J. et al. Microbial hitchhikers on intercontinental dust: catching a lift in Chad. ISME J. 7, 850–867 (2013).
Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).
Wang, J. et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes. ISME J. 7, 1310–1321 (2013).
Lowe, W. H. & McPeek, M. A. Is dispersal neutral? Trends Ecol. Evol. 29, 444–450 (2014).
Pointing, S. B. et al. Highly specialized microbial diversity in hyper-arid polar desert. Proc. Natl Acad. Sci. USA 106, 19964–19969 (2009).
Chan, Y., Van Nostrand, J. D., Zhou, J., Pointing, S. B. & Farrell, R. L. Functional ecology of an Antarctic dry valley. Proc. Natl Acad. Sci. USA 110, 8990–8995 (2013).
Bahl, J. et al. Ancient origins determine global biogeography of hot and cold desert cyanobacteria. Nat. Commun. 2, 163 (2011).
Jungblut, A., Lovejoy, C. & Vincent, W. Global distribution of cyanobacterial ecotypes in the cold biosphere. ISME J. 4, 191–202 (2010).
Vyverman, W. et al. Evidence for widespread endemism among Antarctic micro-organisms. Polar Sci. 4, 103–113 (2010).
Fraser, C. I., Terauds, A., Smellie, J., Convey, P. & Chown, S. L. Geothermal activity helps life survive glacial cycles. Proc. Natl Acad. Sci. USA 111, 5634–5639 (2014).
Burrows, S. M. et al. Bacteria in the global atmosphere—part 2: modeling of emissions and transport between different ecosystems. Atmos. Chem. Phys. 9, 9281–9297 (2009).
Kleinteich, J. et al. Pole-to-pole connections: similarities between Arctic and Antarctic microbiomes and their vulnerability to environmental change. Front. Ecol. Evol. 5, 137 (2017).
Cox, F., Newsham, K. K., Bol, R., Dungait, J. A. J. & Robinson, C. H. Not poles apart: Antarctic soil fungal communities show similarities to those of the distant Arctic. Ecol. Lett. 19, 528–536 (2016).
Kobayashi, F. et al. Atmospheric bioaerosols originating from Adélie penguins (Pygoscelis adeliae): ecological observations of airborne bacteria at Hukuro Cove, Langhovde, Antarctica. Polar Sci. 10, 71–78 (2016).
Pearce, D. A., Hughes, K. A., Lachlan-Cope, T., Harangozo, S. A. & Jones, A. E. Biodiversity of air-borne microorganisms at Halley Station, Antarctica. Extremophiles 14, 145–159 (2010).
Bottos, E. M., Woo, A. C., Zawar-Reza, P., Pointing, S. B. & Cary, S. C. Airborne bacterial populations above desert soils of the McMurdo Dry Valleys, Antarctica. Microb. Ecol. 67, 120–128 (2013).
Rao, S. et al. Low-diversity fungal assemblage in an Antarctic Dry Valleys soil. Polar. Biol. 35, 567–574 (2011).
Brown, S. P. & Jumpponen, A. Phylogenetic diversity analyses reveal disparity between fungal and bacterial communities during microbial primary succession. Soil Biol. Biochem. 89, 52–60 (2015).
Cowan, D. A., Makhalanyane, T. P., Dennis, P. G. & Hopkins, D. W. Microbial ecology and biogeochemistry of continental Antarctic soils. Front. Microbiol. 5, 154 (2014).
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA 109, 21390–21395 (2012).
Atkins, C. B. & Dunbar, G. B. Aeolian sediment flux from sea ice into Southern McMurdo Sound, Antarctica. Glob. Planet. Change 69, 133–141 (2009).
Pointing, S. B., Fierer, N., Smith, G. J. D., Steinberg, P. D. & Wiedmann, M. Quantifying human impact on Earth’s microbiome. Nat. Microbiol. 1, 16145 (2016).
Dybwad, M., Skogan, G. & Blatny, J. M. Comparative testing and evaluation of nine different air samplers: end-to-end sampling efficiencies as specific performance measurements for bioaerosol applications. Aerosol Sci. Technol. 48, 282–295 (2014).
Šantl-Temkiv, T. et al. High-flow-rate impinger for the study of concentration, viability, metabolic activity, and ice-nucleation activity of airborne bacteria. Environ. Sci. Technol. 51, 11224–11234 (2017).
Archer, S. D. J., McDonald, I. R., Herbold, C. W., Lee, C. K. & Cary, C. S. Benthic microbial communities of coastal terrestrial and ice shelf Antarctic meltwater ponds. Front. Microbiol. 6, 485 (2015).
Metagenomic Sequencing Library Preparation Part # 15044223 Rev. B (Illumina, 2013).
Warren-Rhodes, K. et al. Subsurface microbial habitats in an extreme desert Mars-analogue environment. Preprint at https://www.biorxiv.org/content/10.1101/269605v4 (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).
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
Callahan, B. J. et al. DADA2: high resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Deshpande, V. et al. Fungal identification using a Bayesian classifier and the Warcup training set of internal transcribed spacer sequences. Mycologia 108, 1–5 (2016).
Maki, T. et al. Variations in the structure of airborne bacterial communities in Tsogt-Ovoo of Gobi desert area during dust events. Air Qual. Atmos. Health 10, 249–260 (2017).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome. Biol. 15, 550 (2014).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).
Pointing, S. B. et al. Biogeography of photoautotrophs in the high polar biome. Front. Plant Sci. 6, 692 (2015).
Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
Ulrich, W., Almeida-Neto, M. & Gotelli, N. J. A consumer’s guide to nestedness analysis. Oikos 118, 3–17 (2009).
Fruchterman, T. M. J. & Reingold, E. M. Graph drawing by force-directed placement. Software Pract. Exper. 21, 1129–1164 (1991).
Almeida-Neto, M., Guimarães, P., Guimarães, P. R., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).
Ulrich, W. et al. A comprehensive framework for the study of species co-occurrences, nestedness and turnover. Oikos 126, 1607–1616 (2017).
Oksanen, J. et al. Vegan: Community Ecology Package. v. 2.5-3 (2017); https://cran.r-project.org/web/packages/vegan/index.html
Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, New York, 2014).
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
Paradis, E. Ape: analyses of phylogenetics and evolution v. 5.2 (2018); https://www.rdocumentation.org/packages/ape/versions/5.2
Michonneau, F. Phylobase: base package for phylogenetic structures and comparative data v. 0.8.4 (2018); https://www.rdocumentation.org/packages/phylobase/versions/0.8.4
Dray, S. Adephylo: exploratory analyses for the phylogenetic comparative method. v. 1.1-11 (2018); https://www.rdocumentation.org/packages/adephylo/versions/1.1-11
Zhang, J. Phylotools: phylogenetic tools for eco-phylogenetics v. 0.2.2 (2018); https://besjournals.onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00169.x
Höhl, M., Rigoutsos, I. & Ragan, M. A. Pattern-based phylogenetic distance estimation and tree reconstruction. Evol. Bioinform. Online 2, 359–375 (2007).
Choi, J. & Kim, S.-H. A genome tree of life for the fungi kingdom. Proc. Natl Acad. Sci. USA 114, 9391–9396 (2017).
Ebersberger, I. et al. A consistent phylogenetic backbone for the fungi. Mol. Biol. Evol. 29, 1319–1334 (2012).
Boyle, E. E. & Adamowicz, S. J. Community phylogenetics: assessing tree reconstruction methods and the utility of DNA barcodes. PLoS ONE 10, e0126662 (2015).
Kembel, S. W. & Hubbell, S. P. The phylogenetic structure of a neotropical forest tree community. Ecology 87, 86–99 (2006).
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).
Horner-Devine, M. C. & Bohannan, B. J. M. Phylogenetic clustering and overdispersion in bacterial communities. Ecology 87, S100–S108 (2006).
Field and logistical support was provided by Antarctica New Zealand. The research was funded by a grant from the New Zealand Ministry of Business, Innovation and Employment (UOWX1401) and Yale-NUS College Start-Up Fund. F.T.M. is supported by the European Research Council (BIODESERT project; ERC grant agreement number 647038).
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 Figure 1, legends for Supplementary Figures 2–4, Supplementary Figure 5, Supplementary legends for Figures 6–8, Supplementary Figure 9 and legends for Supplementary Datasets 1–6.
Taxonomic identity of bacteria by phylum in each air and soil sample. This raw data file accompanies Supplementary Figure 2.
Taxonomic identity of bacteria by class in each air and soil sample. This raw data file accompanies Supplementary Figure 3.
Taxonomic identity of bacteria by genus in each air and soil sample. This raw data file accompanies Supplementary Figure 4.
Taxonomic identity of fungi by phylum in each air and soil sample. This raw data file accompanies Supplementary Figure 6.
Taxonomic identity of fungi by class in each air and soil sample. This raw data file accompanies Supplementary Figure 7.
Taxonomic identity of fungi by genus in each air and soil sample. This raw data file accompanies Supplementary Figure 8.
About this article
Frontiers in Microbiology (2019)
Nature Reviews Microbiology (2019)
Characterization of fungal biodiversity and communities associated with the reef macroalga Sargassum ilicifolium reveals fungal community differentiation according to geographic locality and algal structure
Marine Biodiversity (2019)
Science of The Total Environment (2019)