title: Critical mutation rate has an exponential dependence on population size in haploid and diploid populations.
abstract: Understanding the effect of population size on the key parameters of evolution is particularly important for populations nearing extinction. There are evolutionary pressures to evolve sequences that are both fit and robust. At high mutation rates, individuals with greater mutational robustness can outcompete those with higher fitness. This is survival-of-the-flattest, and has been observed in digital organisms, theoretically, in simulated RNA evolution, and in RNA viruses. We introduce an algorithmic method capable of determining the relationship between population size, the critical mutation rate at which individuals with greater robustness to mutation are favoured over individuals with greater fitness, and the error threshold. Verification for this method is provided against analytical models for the error threshold. We show that the critical mutation rate for increasing haploid population sizes can be approximated by an exponential function, with much lower mutation rates tolerated by small populations. This is in contrast to previous studies which identified that critical mutation rate was independent of population size. The algorithm is extended to diploid populations in a system modelled on the biological process of meiosis. The results confirm that the relationship remains exponential, but show that both the critical mutation rate and error threshold are lower for diploids, rather than higher as might have been expected. Analyzing the transition from critical mutation rate to error threshold provides an improved definition of critical mutation rate. Natural populations with their numbers in decline can be expected to lose genetic material in line with the exponential model, accelerating and potentially irreversibly advancing their decline, and this could potentially affect extinction, recovery and population management strategy. The effect of population size is particularly strong in small populations with 100 individuals or less; the exponential model has significant potential in aiding population management to prevent local (and global) extinction events.
authors.affiliation: Research Institute for the Environment, Physical Sciences and Applied Mathematics, Keele University, Keele, Staffordshire, United Kingdom.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Aston
authors.affiliation: Research Institute for the Environment, Physical Sciences and Applied Mathematics, Keele University, Keele, Staffordshire, United Kingdom.
authors.forename: Alastair
authors.initials: A
authors.lastname: Channon
authors.affiliation: Research Institute for the Environment, Physical Sciences and Applied Mathematics, Keele University, Keele, Staffordshire, United Kingdom.
authors.forename: Charles
authors.initials: C
authors.lastname: Day
authors.affiliation: Faculty of Life Sciences, The University of Manchester, Manchester, Greater Manchester, United Kingdom.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Knight
mesh_terms: D000465:Algorithms; D004171:Diploidy; D005828:Genetics, Population; D006238:Haploidy; D006801:Humans; D008957:Models, Genetic; D059645:Mutation Rate; D011156:Population Density; D015203:Reproducibility of Results