A new approach to estimate parameters of speciation models with application to apes

Abstract

How populations diverge and give rise to distinct species remains a fundamental question in evolutionary biology, with important implications for a wide range of fields, from conservation genetics to human evolution. A promising approach is to estimate parameters of simple speciation models using polymorphism data from multiple loci. Existing methods, however, make a number of assumptions that severely limit their applicability, notably, no gene flow after the populations split and no intralocus recombination. To overcome these limitations, we developed a new Markov chain Monte Carlo method to estimate parameters of an isolation-migration model. The approach uses summaries of polymorphism data at multiple loci surveyed in a pair of diverging populations or closely related species and, importantly, allows for intralocus recombination. To illustrate its potential, we applied it to extensive polymorphism data from populations and species of apes, whose demographic histories are largely unknown. The isolation-migration model appears to provide a reasonable fit to the data. It suggests that the two chimpanzee species became reproductively isolated in allopatry approximately 850 Kya, while Western and Central chimpanzee populations split approximately 440 Kya but continued to exchange migrants. Similarly, Eastern and Western gorillas and Sumatran and Bornean orangutans appear to have experienced gene flow since their splits approximately 90 and over 250 Kya, respectively.

Figure 1.

The “isolation-migration” model, in which two populations diverged T generations ago from a common ancestral population. The parameters θ₁, θ₂, and θ_A are the population mutation rates per base pair for populations 1 and 2 and the ancestral population, respectively. The split time in generations is T, and m is the symmetrical migration rate between populations per generation (for details, see Methods).

Figure 2.

Smoothed marginal posterior distributions estimated by MIMAR from bonobo and common chimpanzee polymorphism data (for details, see Methods). The range of the X-axis corresponds to the support of the prior. The distributions are for the analyses of bonobos and Western chimpanzees (A), bonobos and Central chimpanzees (B), and bonobos and Eastern chimpanzees (C).

Figure 3.

Smoothed marginal posterior distributions estimated by MIMAR from the common chimpanzee subpopulation polymorphism data (for details, see Methods and legend of Fig. 2). The distributions are for the analyses of Western and Central chimpanzees (A), Western and Eastern chimpanzees (B), and Central and Eastern chimpanzees (C).

Figure 4.

Smoothed marginal posterior distributions estimated by MIMAR from the gorilla (A) and orangutan (B) subspecies polymorphism data (for details, see Methods and legend of Fig. 2). (A) Distributions for the analysis of Western and Eastern gorillas. The apparent multimodality of the marginal posterior distribution estimated for the split time was also noted by Thalmann et al. (2006). (B) Distributions for the analysis of Sumatran and Bornean orangutans. Note that the posterior distribution for the split time is rather flat, suggesting that the data do not carry much information about this parameter.