Abstract
Genome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the coding joint site frequency spectrum (jSFS), in an approximate Bayesian computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and classes in the jSFS), and show that the number of individuals and loci have little effect on model selection. In contrast, different decompositions of the joint site frequency spectrum strongly affect the results: including classes of low and high frequency shared polymorphisms can more effectively reveal recent migration events. We then take advantage of the flexibility of the ABC to compare more realistic models of speciation including variation in migration rates through time (i.e. periodic connectivity) and across genes (i.e. genome-wide heterogeneity in migration rates). We show that these models consistently outperform the simpler alternatives. This argues that methods that are restricted to simpler models may fail to reconstruct the true speciation history.