Data from: ABC inference of multi-population divergence with admixture from unphased population genomic data

Rapidly developing sequencing technologies and declining costs have made
it possible to collect genome-scale data from population-level samples in
non-model systems. Inferential tools for historical demography given these
datasets are, at present, underdeveloped. In particular, approximate
Bayesian computation (ABC) has yet to be widely embraced by researchers
generating these data. Here, we demonstrate the promise of ABC for
analysis of the large datasets that are now attainable from non-model taxa
through current genomic sequencing technologies. We develop and test an
ABC framework for model selection and parameter estimation given histories
of three-population divergence with admixture. We then explore different
sampling regimes to illustrate how sampling more loci, longer loci, or
more individuals affects the quality of model selection and parameter
estimation in this ABC framework. Our results show that inferences
improved substantially with increases in the number and/or length of
sequenced loci, while less benefit was gained by sampling large numbers of
individuals. Optimal sampling strategies given our inferential models
included at least 2000 loci, each approximately 2kb in length, sampled
from five diploid individuals per population, although specific strategies
are model- and question-dependent. We tested our ABC approach through
simulation-based cross-validations and illustrate its application using
previously analyzed data from the oak gall wasp, Biorhiza pallida.