ABSTRACT
Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fisher’s geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.
