Data from: Using ABC and microsatellite data to detect multiple introductions of invasive species from a single source

The introduction of invasive species to new locations (that is, biological
invasions) can have major impact on biodiversity, agriculture and public
health. As such, determining the routes and modality of introductions with
genetic data has become a fundamental goal in molecular ecology. To assist
with this goal, new statistical methods and frameworks have been
developed, such as approximate Bayesian computation (ABC) for inferring
invasion history. Here, we present a model of invasion accounting for
multiple introductions from a single source (MISS), a heretofore largely
unexplored model. We simulate microsatellite data to evaluate the power of
ABC to distinguish between single and multiple introductions from the same
source, under a range of demographic parameters. We also apply ABC to
microsatellite data from three invasions of bumblebee in New Zealand. In
addition, we assess the performance of several methods of summary
statistics selection. Our simulated results suggested good ability to
distinguish between one- and two-wave models over much but not all of the
parameter space tested, independent of summary statistics used. Globally,
parameter estimation was good except for bottleneck timing. For one of the
bumblebee species, we clearly rejected the MISS model, while for the other
two we found inconclusive results. Since a second wave may provide genetic
reinforcement to initial colonists, help relieve inbreeding among
founders, or increase the hazard of the invasion, its detection may be
crucial for managing invasions; we suggest that the MISS model could be
considered as a potential model in future theoretical and empirical
studies of invasions.