Making sense of genetic estimates of effective population size

The last decade has seen an explosion of interest in use of genetic markers to estimate effective population size, N_e. Effective population size is important both theoretically (N_e is a key parameter in almost every aspect of evolutionary biology) and for practical application (N_e determines rates of genetic drift and loss of genetic variability and modulates the effectiveness of selection, so it is crucial to consider in conservation). As documented by Palstra & Fraser ( 2012 ), most of the recent growth in N_e estimation can be attributed to development or refinement of methods that can use a single sample of individuals (the older temporal method requires at least two samples separated in time). As with other population genetic methods, performance of new N_e estimators is typically evaluated with simulated data for a few scenarios selected by the author(s). Inevitably, these initial evaluations fail to fully consider the consequences of violating simplifying assumptions, such as discrete generations, closed populations of constant size and selective neutrality. Subsequently, many researchers studying natural or captive populations have reported estimates of N_e for multiple methods; often these estimates are congruent, but that is not always the case. Because true N_e is rarely known in these empirical studies, it is difficult to make sense of the results when estimates differ substantially among methods. What is needed is a rigorous, comparative analysis under realistic scenarios for which true N_e is known. Recently, Gilbert & Whitlock ( 2015 ) did just that for both single‐sample and temporal methods under a wide range of migration schemes. In the current issue of Molecular Ecology, Wang ( 2016 ) uses simulations to evaluate performance of four single‐sample N_e estimators. In addition to assessing effects of true N_e, sample size, and number of loci, Wang also evaluated performance under changing abundance, physical linkage and genotyping errors, as well as for some alternative life histories (high rates of selfing; haplodiploids). Wang showed that the sibship frequency (SF) and linkage disequilibrium (LD) methods perform dramatically better than the heterozygote excess and molecular coancestry methods under most scenarios (see Fig. 1, modified from figure 2 in Wang 2016 ), and he also concluded that SF is generally more versatile than LD. This article represents a truly Herculean effort, and results should be of considerable value to researchers interested in applying these methods to real‐world situations.