The evolutionary advantage of haploid versus diploid microbes in nutrient-poor environments

Kazuhiro Bessho, Yoh Iwasa, Troy Day

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Sexual eukaryotic organisms are characterized by haploid and diploid nuclear phases. In many organisms, growth and development occur in both haploid and diploid phases, and the relative length of these phases exhibits considerable diversity. A number of hypotheses have been put forward to explain the maintenance of this diversity of life cycles and the advantage of being haploid versus that of being diploid. The nutrient-limitation hypothesis postulates that haploid cells, because they are small and thus have a higher surface area to volume ratio, are advantageous in nutrient-poor environments. In this paper, we examine this hypothesis theoretically and determine the conditions under which it holds. On the basis of our analysis, we make the following predictions. First, the relative advantages of different ploidy levels strongly depend on the ploidy-dependent energy conversion efficiency and the scaling of mortality with cell size. Specifically, haploids enjoy a higher intrinsic population growth rate than diploids do under nutrient-poor conditions, but under nutrient-rich conditions the intrinsic population growth rate of diploids is higher, provided that the energy conversion efficiency of diploids is higher than that of haploids and the scaling of mortality with cell size is weak. Second, differences in nutrient concentration in the inflowing medium have almost no effect on the relative advantage of ploidy levels at population equilibrium. Our study illustrates the importance of explicit modeling of microbial life history and population dynamics to understand the evolution of ploidy levels.

Original languageEnglish
Pages (from-to)116-129
Number of pages14
JournalJournal of Theoretical Biology
Publication statusPublished - Oct 21 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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