Genetic methods are routinely used to estimate contemporary effective population size (N_e) in natural populations, but the vast majority of applications have used only the temporal (two‐sample) method. We use simulated data to evaluate how highly polymorphic molecular markers affect precision and bias in the single‐sample method based on linkage disequilibrium (LD). Results of this study are as follows: (1) Low‐frequency alleles upwardly bias , but a simple rule can reduce bias to <about 10% without sacrificing much precision. (2) With datasets routinely available today (10–20 loci with 10 alleles; 50 individuals), precise estimates can be obtained for relatively small populations (N_e < 200), and small populations are not likely to be mistaken for large ones. However, it is very difficult to obtain reliable estimates for large populations. (3) With ‘microsatellite’ data, the LD method has greater precision than the temporal method, unless the latter is based on samples taken many generations apart. Our results indicate the LD method has widespread applicability to conservation (which typically focuses on small populations) and the study of evolutionary processes in local populations. Considerable opportunity exists to extract more information about N_e in nature by wider use of single‐sample estimators and by combining estimates from different methods.

Use of single-sample genetic methods to estimate effective population size has skyrocketed in recent years. Although the underlying models assume discrete generations, they are widely applied to age-structured species. We simulated genetic data for 21 iteroparous animal and plant species to evaluate two untested hypotheses regarding performance of the single-sample method based on linkage disequilibrium (LD): (1) estimates based on single-cohort samples reflect the effective number of breeders in one reproductive cycle (N_b), and (2) mixed-age samples reflect the effective size per generation (N_e). We calculated true N_e and N_b, using the model species’ vital rates, and verified these with individual-based simulations. We show that single-cohort samples should be equally influenced by N_b and N_e and confirm this with simulated results: ${\widehat{N}}_{\mathrm{b}}$ was a linear (r² = 0.98) function of the harmonic mean of N_e and N_b. We provide a quantitative bias correction for raw ${\widehat{N}}_{\mathrm{b}}$ based on the ratio N_b/N_e, which can be estimated from two or three simple life history traits. Bias-adjusted estimates were within 5% of true N_b for all 21 study species and proved robust when challenged with new data. Mixed-age adult samples produced downwardly biased estimates in all species, which we attribute to a two-locus Wahlund effect (mixture LD) caused by combining parents from different cohorts in a single sample. Results from this study will facilitate interpretation of rapidly accumulating genetic estimates in terms of both N_e (which influences long-term evolutionary processes) and N_b (which is more important for understanding eco-evolutionary dynamics and mating systems).

Testing for Hardy–Weinberg proportions (HWP) is routine in almost all genetic studies of natural populations, but many researchers do not demonstrate a full understanding of the purposes of these tests or how to interpret the results. Common problems include a lack of understanding of statistical power and the difference between statistical significance and biological significance, how to interpret results of multiple tests, and how to distinguish between various factors that can cause statistically significant departures. In this perspective, which focuses on analysis of genetic data for nonmodel species, I 1) review factors that can cause departures from HWP at individual loci and linkage disequilibrium (LD) at pairs of loci; 2) discuss commonly used tests for HWP and LD, with an emphasis on multiple-testing issues; 3) show how to distinguish among possible causes of departures from HWP; and 4) outline some simple steps to follow when significant test results are found. Finally, I 5) identify some issues that merit particular attention as we move into an era in which analysis of genomics-scale datasets for nonmodel species is commonplace.