Quantifying separation and similarity in a Saccharomyces cerevisiae metapopulation

Knight, Sarah and Goddard, Matthew R. (2015) Quantifying separation and similarity in a Saccharomyces cerevisiae metapopulation. The ISME Journal, 9 (2). pp. 361-370. ISSN 1751-7362

Full content URL: http://dx.doi.org/10.1038/ismej.2014.132

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Item Type:Article
Item Status:Live Archive


Eukaryotic microbes are key ecosystem drivers; however, we have little theory and few data elucidating the processes influencing their observed population patterns. Here we provide an in-depth quantitative analysis of population separation and similarity in the yeast Saccharomyces cerevisiae with the aim of providing a more detailed account of the population processes occurring in microbes. Over 10 000 individual isolates were collected from native plants, vineyards and spontaneous ferments of fruit from six major regions spanning 1000 km across New Zealand. From these, hundreds of S. cerevisiae genotypes were obtained, and using a suite of analytical methods we provide comprehensive quantitative estimates for both population structure and rates of gene flow or migration. No genetic differentiation was detected within geographic regions, even between populations inhabiting native forests and vineyards. We do, however, reveal a picture of national population structure at scales above ~100 km with distinctive populations in the more remote Nelson and Central Otago regions primarily contributing to this. In addition, differential degrees of connectivity between regional populations are observed and correlate with the movement of fruit by the New Zealand wine industry. This suggests some anthropogenic influence on these observed population patterns.

Keywords:gene-flow, population genetics, yeast, JCOpen
Subjects:D Veterinary Sciences, Agriculture and related subjects > D711 Agricultural Microbiology
C Biological Sciences > C170 Population Biology
C Biological Sciences > C440 Molecular Genetics
C Biological Sciences > C500 Microbiology
Divisions:College of Science > School of Life Sciences
ID Code:18958
Deposited On:09 Oct 2015 08:38

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