Abstract
Understanding and predicting population abundance is a major challenge confronting scientists. Several genetic models have been developed using microsatellite markers to estimate the present and ancestral effective population sizes. However, to get an overview on the evolution of population requires that past fluctuation of population size be traceable. To address the question, we developed a new model estimating the past changes of effective population size from microsatellite by resolving coalescence theory and using approximate likelihoods in a Monte Carlo Markov Chain approach. The efficiency of the model and its sensitivity to gene flow and to assumptions on the mutational process were checked using simulated data and analysis. The model was found especially useful to provide evidence of transient changes of population size in the past. The times at which some past demographic events cannot be detected because they are too ancient and the risk that gene flow may suggest the false detection of a bottleneck are discussed considering the distribution of coalescence times. The method was applied on real data sets from several Atlantic salmon populations. The method called VarEff (Variation of Effective size) was implemented in the R package VarEff and is made available at https://qgsp.jouy.inra.fr and at http://cran.r-project.org/web/packages/VarEff.
Highlights
The results from genetic surveys may be used to infer the demographic history of species and populations, and may help to make conservation management decisions
In order to discuss the effects of gene flow on the results, we developed a simple model to illustrate how permanent immigration may mimic the effect of a recent bottleneck on the distribution of the Most Recent Common Ancestor (TMRCA)
There is a need to develop efficient methods aimed at detecting past variations of effective population size
Summary
The results from genetic surveys may be used to infer the demographic history of species and populations, and may help to make conservation management decisions. Coalescence theory and the development of Bayesian approaches have made it possible to take advantage of the complete information available in samples of alleles drawn in populations and to derive estimates of various parameters. When sequence data are available (Drummond et al 2005) building the coalescence tree of the sampled alleles allows branch lengths of the tree to be estimated, the effective population size from the mutation rate. It provides a ‘Bayesian skyline plot’ estimating past population dynamics through time from a Detecting effective size
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
