|Document Type:||Journal Article|
|Title:||Effects of overlapping generations on linkage disequilibrium estimates of effective population size|
|Author:||Robin S. Waples, Tiago Antao, G. Luikart|
|Keywords:||Effective population size,overlapping generations,age structure,computer simulations,bias|
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 (Nb); and 2) mixed-age samples reflect the effective size per generation (Ne). We calculated true Ne and Nb using the model species’ vital rates and verified these with individual-based simulations. We show that single-cohort samples should be equally influenced by Nb and Ne and confirm this with simulated results: Nb^ was a linear (r2 = 0.98) function of the harmonic mean of Ne and Nb. We provide a quantitative bias correction for raw Nb^ based on the ratio Nb/Ne, which can be estimated from two or three simple life history traits. Bias-adjusted estimates were within 5% of true Nb 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 Ne (which influences long-term evolutionary processes) and Nb (which is more important for understanding eco-evolutionary dynamics and mating systems).
|Theme:||Recovery and rebuilding of marine and coastal species|
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.
Develop methods to use physiological, biological and behavioral information to predict population-level processes.