Comparative evaluation of a new effective population size estimator based on approximate bayesian computation.
We describe and evaluate a new estimator of the effective population size (N(e)), a critical parameter in evolutionary and conservation biology. This new "SummStat" N(e) estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N(e). Simulations of a Wright-Fisher population with known N(e) show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, and N(e) values. We also address the paucity of information about the relative performance of N(e) estimators by comparing the SummStat estimator to two recently developed likelihood-based estimators and a traditional moment-based estimator. The SummStat estimator is the least biased of the four estimators compared. In 32 of 36 parameter combinations investigated using initial allele frequencies drawn from a Dirichlet distribution, it has the lowest bias. The relative mean square error (RMSE) of the SummStat estimator was generally intermediate to the others. All of the estimators had RMSE > 1 when small samples (n = 20, five loci) were collected a generation apart. In contrast, when samples were separated by three or more generations and N(e) < or = 50, the SummStat and likelihood-based estimators all had greatly reduced RMSE. Under the conditions simulated, SummStat confidence intervals were more conservative than the likelihood-based estimators and more likely to include true N(e). The greatest strength of the SummStat estimator is its flexible structure. This flexibility allows it to incorporate any potentially informative summary statistic from population genetic data.
Tallmon
David A
DA
Laboratoire d'Ecologie Alpine, UMR Centre National de la Recherche Scientifique 5553, Université Joseph Fourier, F38041 BP 53 Cedex 09, Grenoble, France. dtallmon42@yahoo.com
Luikart
Gordon
G
Beaumont
Mark A
MA
D001499:Bayes Theorem; D003198:Computer Simulation; D016018:Least-Squares Analysis; D008957:Models, Genetic; D011156:Population Density; D015203:Reproducibility of Results