Abstract
The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.
Highlights
Inferring the historical demography of populations by means of genetic data is key to many studies addressing the ecological and evolutionary dynamics of natural populations
Skyline plots obtained from PODs are congruent with the true underlying demography simulated (Fig. 1), except in the less favorable scenarios with very recent or very small changes in population size
Recent or small magnitude events leave a weak signal in the genetic data and are hard to identify for alternative methods
Summary
Inferring the historical demography of populations by means of genetic data is key to many studies addressing the ecological and evolutionary dynamics of natural populations. Population genetics inference, with appropriate dating, can identify the likely factors (such as climatic events) determining the demography of a species. This can be done with outstanding detail (e.g., in humans, reviewed in Nielsen et al, 2017). Demographic inference can be used to generate null models for the detection of loci under selection (as discussed in Hoban et al, 2016). Most of the methods to estimate demography from genetic data are based on the coalescent. The coalescent (see Wakeley, 2008, for a review) is a mathematical model that describes the rate at which genetic lineages coalesce (i.e., join in a common ancestor)
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