Korarchaeota diversity, biogeography, and abundance in Yellowstone and Great Basin hot springs and ecological niche modeling based on machine learning

Robin L. Miller-Coleman, Jeremy A. Dodsworth, Christian A. Ross, Everett Shock, Amanda J. Williams, Hilairy Hartnett, Austin I. McDonald, Jeff R. Havig, Brian P. Hedlund

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Over 100 hot spring sediment samples were collected from 28 sites in 12 areas/regions, while recording as many coincident geochemical properties as feasible (>60 analytes). PCR was used to screen samples for Korarchaeota 16S rRNA genes. Over 500 Korarchaeota 16S rRNA genes were screened by RFLP analysis and 90 were sequenced, resulting in identification of novel Korarchaeota phylotypes and exclusive geographical variants. Korarchaeota diversity was low, as in other terrestrial geothermal systems, suggesting a marine origin for Korarchaeota with subsequent niche-invasion into terrestrial systems. Korarchaeota endemism is consistent with endemism of other terrestrial thermophiles and supports the existence of dispersal barriers. Korarchaeota were found predominantly in >55°C springs at pH 4.7-8.5 at concentrations up to 6.6×106 16S rRNA gene copies g-1 wet sediment. In Yellowstone National Park (YNP), Korarchaeota were most abundant in springs with a pH range of 5.7 to 7.0. High sulfate concentrations suggest these fluids are influenced by contributions from hydrothermal vapors that may be neutralized to some extent by mixing with water from deep geothermal sources or meteoric water. In the Great Basin (GB), Korarchaeota were most abundant at spring sources of pH<7.2 with high particulate C content and high alkalinity, which are likely to be buffered by the carbonic acid system. It is therefore likely that at least two different geological mechanisms in YNP and GB springs create the neutral to mildly acidic pH that is optimal for Korarchaeota. A classification support vector machine (C-SVM) trained on single analytes, two analyte combinations, or vectors from non-metric multidimensional scaling models was able to predict springs as Korarchaeota-optimal or sub-optimal habitats with accuracies up to 95%. To our knowledge, this is the most extensive analysis of the geochemical habitat of any high-level microbial taxon and the first application of a C-SVM to microbial ecology.

Original languageEnglish (US)
Article numbere35964
JournalPLoS One
Volume7
Issue number5
DOIs
StatePublished - May 4 2012

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Korarchaeota
Hot springs
Hot Springs
hot springs
artificial intelligence
Learning systems
niches
biogeography
Genes
basins
Support vector machines
Sediments
Carbonic Acid
ribosomal RNA
Water
Ecology
Alkalinity
national parks
thermophilic microorganisms
indigenous species

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Korarchaeota diversity, biogeography, and abundance in Yellowstone and Great Basin hot springs and ecological niche modeling based on machine learning. / Miller-Coleman, Robin L.; Dodsworth, Jeremy A.; Ross, Christian A.; Shock, Everett; Williams, Amanda J.; Hartnett, Hilairy; McDonald, Austin I.; Havig, Jeff R.; Hedlund, Brian P.

In: PLoS One, Vol. 7, No. 5, e35964, 04.05.2012.

Research output: Contribution to journalArticle

Miller-Coleman, Robin L. ; Dodsworth, Jeremy A. ; Ross, Christian A. ; Shock, Everett ; Williams, Amanda J. ; Hartnett, Hilairy ; McDonald, Austin I. ; Havig, Jeff R. ; Hedlund, Brian P. / Korarchaeota diversity, biogeography, and abundance in Yellowstone and Great Basin hot springs and ecological niche modeling based on machine learning. In: PLoS One. 2012 ; Vol. 7, No. 5.
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