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Dryad

Competition dynamics in long-term propagations of Schizosaccharomyces pombe strain communities

Cite this dataset

Durao, Paulo; Amicone, Massimo; Perfeito, Lília; Gordo, Isabel (2022). Competition dynamics in long-term propagations of Schizosaccharomyces pombe strain communities [Dataset]. Dryad. https://doi.org/10.5061/dryad.280gb5mqt

Abstract

Experimental evolution studies with microorganisms such as bacteria and yeast have been an increasingly important and powerful tool to draw long-term inferences of how microbes interact. However, while several strains of the same species often exist in natural environments, many ecology and evolution studies in microbes are typically performed with isogenic populations of bacteria or yeast. In the present study, we firstly perform a genotypic and phenotypic characterization of two lab and eight natural strains of the yeast Schizosaccharomyces pombe. We then propagated, in a rich resource environment, yeast communities of 2-, 3-, 4- and 5-strains for hundreds of generations and asked which fitness related phenotypes – maximum growth rate or relative competitive fitness – would better predict the outcome of a focal strain during the propagations. While the strain’s growth rates would wrongly predict long-term co-existence, pairwise competitive fitness with a focal strain qualitatively predicted the success or extinction of the focal strain by a simple multi-genotype population genetics model, given the initial community composition. Interestingly, we have also measured the competitive fitness of the ancestral and evolved communities by the end of the experiment (≈370 generations) and observed frequent maladaptation to the abiotic environment in communities with more than three members. Overall, our results aid establishing pairwise competitive fitness as good qualitative measurement of long-term community composition but also reveal a complex adaptive scenario when trying to predict the evolutionary outcome of those communities.

Funding

Fundação para a Ciência e Tecnologia, Award: PTDC/BIA-EVL/31528/2017