Discordant Pleistocene population size histories in a guild of hymenopteran parasitoids.

Signatures of past changes in population size have been detected in genome-wide variation in many species. However, the causes of such demographic changes and the extent to which they are shared across co-distributed species remain poorly understood. During Pleistocene glacial maxima, many temperate European species were confined to southern refugia. While vicariance and range expansion processes associated with glacial cycles have been widely documented, it is unclear whether refugial populations of co-distributed species have experienced shared histories of population size change. We analyse whole-genome sequence data to reconstruct and compare demographic histories during the Quaternary for Iberian refuge populations in a single ecological guild (seven species of chalcid parasitoid wasps associated with oak cynipid galls). For four of these species, we find support for large changes in effective population size (Ne ) through the Pleistocene that coincide with major climate events. However, there is little evidence that the timing, direction and magnitude of demographic change are shared across species, suggesting that demographic histories in this guild are largely idiosyncratic, even at the scale of a single glacial refugium.

Coalescence times for three genes provide sufficient information to distinguish population structure from population size changes.

The increasing amount of genomic data currently available is expanding the horizons of population genetics inference. A wide range of methods have been published allowing to detect and date major changes in population size during the history of species. At the same time, there has been an increasing recognition that population structure can generate genetic data similar to those generated under models of population size change. Recently, Mazet et al. (Heredity 116(4):362-371, 2016) introduced the idea that, for any model of population structure, it is always possible to find a panmictic model with a particular function of population size-change having an identical distribution of [Formula: see text] (the time of the first coalescence for a sample of size two). This implies that there is an identifiability problem between a panmictic and a structured model when we base our analysis only on [Formula: see text]. In this paper, based on an analytical study of the rate matrix of the ancestral lineage process, we obtain new theoretical results about the joint distribution of the coalescence times [Formula: see text] for a sample of three haploid genes in a n-island model with constant size. Even if, for any [Formula: see text], it is always possible to find a size-change scenario for a panmictic population such that the marginal distribution of [Formula: see text] is exactly the same as in a n-island model with constant population size, we show that the joint distribution of the coalescence times [Formula: see text] for a sample of three genes contains enough information to distinguish between a panmictic population and a n-island model of constant size.

Demographic inference using genetic data from a single individual: Separating population size variation from population structure.

The rapid development of sequencing technologies represents new opportunities for population genetics research. It is expected that genomic data will increase our ability to reconstruct the history of populations. While this increase in genetic information will likely help biologists and anthropologists to reconstruct the demographic history of populations, it also represents new challenges. Recent work has shown that structured populations generate signals of population size change. As a consequence it is often difficult to determine whether demographic events such as expansions or contractions (bottlenecks) inferred from genetic data are real or due to the fact that populations are structured in nature. Given that few inferential methods allow us to account for that structure, and that genomic data will necessarily increase the precision of parameter estimates, it is important to develop new approaches. In the present study we analyze two demographic models. The first is a model of instantaneous population size change whereas the second is the classical symmetric island model. We (i) re-derive the distribution of coalescence times under the two models for a sample of size two, (ii) use a maximum likelihood approach to estimate the parameters of these models (iii) validate this estimation procedure under a wide array of parameter combinations, (iv) implement and validate a model rejection procedure by using a Kolmogorov-Smirnov test, and a model choice procedure based on the AIC, and (v) derive the explicit distribution for the number of differences between two non-recombining sequences. Altogether we show that it is possible to estimate parameters under several models and perform efficient model choice using genetic data from a single diploid individual.

Demographic inference through approximate-Bayesian-computation skyline plots.

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.