Linkage disequilibrium and effective population size when generations overlap
Article first published online: 8 AUG 2012
© 2012 The Authors. Evolutionary Applications published by Blackwell Publishing Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Volume 6, Issue 2, pages 290–302, February 2013
How to Cite
Robinson, J. D. and Moyer, G. R. (2013), Linkage disequilibrium and effective population size when generations overlap. Evolutionary Applications, 6: 290–302. doi: 10.1111/j.1752-4571.2012.00289.x
- Issue published online: 18 FEB 2013
- Article first published online: 8 AUG 2012
- Manuscript Accepted: 25 JUN 2012
- Manuscript Revised: 14 JUN 2012
- Manuscript Received: 3 MAY 2012
- United States Fish and Wildlife Service
- age structure;
- computer simulations;
- effective number of breeders;
Estimates of effective population size are critical for species of conservation concern. Genetic datasets can be used to provide robust estimates of this important parameter. However, the methods used to obtain these estimates assume that generations are discrete. We used simulated data to assess the influences of overlapping generations on the estimates of effective size provided by the linkage disequilibrium (LD) method. Our simulations focus on two factors: the degree of reproductive skew exhibited by the focal species and the generation time, without considering sample size or the level of polymorphism at marker loci. In situations where a majority of reproduction is achieved by a small fraction of the population, the effective number of breeders can be much smaller than the per-generation effective population size. The LD in samples of newborns can provide estimates of the former size, while our results indicate that the latter size is best estimated using random samples of reproductively mature adults. Using samples of adults, the downwards bias was less than approximately 15% across our simulated life histories. As noted in previous assessments, precision of the estimate depends on the magnitude of effective size itself, with greater precision achieved for small populations.