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Genetic data have been widely used to reconstruct the demographic history
of populations, including the estimation of migration rates, divergence
times and relative admixture contribution from different populations.
Recently, increasing interest has been given to the ability of genetic
data to distinguish alternative models. One of the issues that has plagued
this kind of inference is that ancestral shared polymorphism is often
difficult to separate from admixture or gene flow. Here, we applied an
Approximate Bayesian Computation (ABC) approach to select the model that
best fits microsatellite data among alternative splitting and admixture
models. We performed a simulation study and showed that with reasonably
large data sets (20 loci) it is possible to identify with a high level of
accuracy the model that generated the data. This suggests that it is
possible to distinguish genetic patterns due to past admixture events from
those due to shared polymorphism (population split without admixture). We
then apply this approach to microsatellite data from an endangered and
endemic Iberian freshwater fish species, in which a clustering analysis
suggested that one of the populations could be admixed. In contrast, our
results suggest that the observed genetic patterns are better explained by
a population split model without admixture.
265 views reported since publication in 2011.