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dc.contributor.advisorChairperson, Graduate Committee: David M. Warden
dc.contributor.authorWood, Jason Michaelen
dc.contributor.otherEric D. Becraft, Danny Krizanc, Frederick M. Cohan, and David M. Ward were co-authors of the article, 'Ecotype simulation 2: an improved algorithm for efficiently demarcating microbial species from large sequence datasets' submitted to the journal 'BMC bioinformatics' which is contained within this thesis.en
dc.contributor.otherJason M. Wood, Frederick M. Cohan and David M. Ward were co-authors of the article, 'Biogeography of American Northwest Hot Spring A/B'-lineage Synechococcus populations' submitted to the journal 'Frontiers in microbiology' which is contained within this thesis.en
dc.description.abstractThe identification of closely related, ecologically distinct populations within microbial communities is paramount to understanding the structure and function of these communities. Microbial systematists have long used differences in DNA sequence relatedness to categorize the observed diversity in a community of microbes without including ecological theory to identify whether or not the identified groups are ecologically distinct. Ecotype Simulation, an evolutionary simulation algorithm based on the Stable Ecotype Model of microbial species and speciation, has been used successfully to study the diversification of thermophilic A/B'-lineage Synechococcus living in the effluent channels of alkaline-siliceous hot springs in Yellowstone National Park. However, Ecotype Simulation is an extremely slow program that is unable to handle the quantity of data produced by modern DNA sequencing technologies. I introduce a new version of this algorithm, called Ecotype Simulation 2, that permits the rapid analyses of microbial diversity from very large DNA sequence datasets. Results from this new version of the Ecotype Simulation algorithm compare favorably with results from the old version, but with analyses performed much more quickly on a much greater quantity of sequences sampled. The new algorithm was used to analyze three datasets. First, the biogeography of thermophilic A/B0-lineage Synechococcus living in hot springs of the American Northwest was analyzed. Results suggested a surprising amount of endemism among springs sampled, as well as implications for adaptations to physical and chemical environmental features not seen before. Second, Ecotype Simulation 2 was used to study the history of change in Synechococcus populations, seasonally (winter to summer) and over a twenty-five year period. Results suggested changes in population abundances and distribution seasonally, but stability in population genetic structure over many years. Finally, Ecotype Simulation 2 was used to study the populations of other predominant phototrophic microbes living along temperature and depth gradients in the same microbial mat community. Results suggested that the algorithm and the Stable Ecotype Model can successfully predict ecological diversity within all predominant mat taxa. Ecotype Simulation 2 provides the means for other microbiologists to base their understanding of the communities they study on evolutionary and ecological principals.en
dc.publisherMontana State University - Bozeman, College of Agricultureen
dc.subject.lcshHot springsen
dc.subject.lcshMicrobial matsen
dc.titleTheory-based demarcation of hot spring microbial mat species from large DNA sequence datasetsen
dc.rights.holderCopyright 2018 by Jason Michael Wooden, Graduate Committee: William P. Inskeep; Ross Carlson; Matthew Fields; Donald A. Bryant; William R. Cannon.en Resources & Environmental Sciences.en
mus.relation.departmentLand Resources & Environmental Sciences.en_US

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