Literature DB >> 27671356

Making sense of genetic estimates of effective population size.

Robin S Waples1.   

Abstract

The last decade has seen an explosion of interest in use of genetic markers to estimate effective population size, Ne . Effective population size is important both theoretically (Ne is a key parameter in almost every aspect of evolutionary biology) and for practical application (Ne 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 (), most of the recent growth in Ne 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 Ne 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 Ne for multiple methods; often these estimates are congruent, but that is not always the case. Because true Ne 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 Ne is known. Recently, Gilbert & Whitlock () 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 () uses simulations to evaluate performance of four single-sample Ne estimators. In addition to assessing effects of true Ne , 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 ), 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.
© 2016 John Wiley & Sons Ltd.

Keywords:  bias; computer simulations; linkage disequilibrium; precision; siblings

Mesh:

Substances:

Year:  2016        PMID: 27671356     DOI: 10.1111/mec.13814

Source DB:  PubMed          Journal:  Mol Ecol        ISSN: 0962-1083            Impact factor:   6.185


  12 in total

1.  Estimation of contemporary effective population size and population declines using RAD sequence data.

Authors:  Schyler O Nunziata; David W Weisrock
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Authors:  Claire R Peart; Sergio Tusso; Saurabh D Pophaly; Fidel Botero-Castro; Chi-Chih Wu; David Aurioles-Gamboa; Amy B Baird; John W Bickham; Jaume Forcada; Filippo Galimberti; Neil J Gemmell; Joseph I Hoffman; Kit M Kovacs; Mervi Kunnasranta; Christian Lydersen; Tommi Nyman; Larissa Rosa de Oliveira; Anthony J Orr; Simona Sanvito; Mia Valtonen; Aaron B A Shafer; Jochen B W Wolf
Journal:  Nat Ecol Evol       Date:  2020-06-08       Impact factor: 15.460

3.  Expansion history and environmental suitability shape effective population size in a plant invasion.

Authors:  Joseph Braasch; Brittany S Barker; Katrina M Dlugosch
Journal:  Mol Ecol       Date:  2019-05-21       Impact factor: 6.185

4.  Consequences of severe habitat fragmentation on density, genetics, and spatial capture-recapture analysis of a small bear population.

Authors:  Sean M Murphy; Ben C Augustine; Wade A Ulrey; Joseph M Guthrie; Brian K Scheick; J Walter McCown; John J Cox
Journal:  PLoS One       Date:  2017-07-24       Impact factor: 3.240

5.  Genetic analysis of African lions (Panthera leo) in Zambia support movement across anthropogenic and geographical barriers.

Authors:  Caitlin J Curry; Paula A White; James N Derr
Journal:  PLoS One       Date:  2019-05-31       Impact factor: 3.240

6.  Single nucleotide polymorphism-based analysis of the genetic structure of Liangshan pig population.

Authors:  Bin Liu; Linyuan Shen; Zhixian Guo; Mailing Gan; Ying Chen; Runling Yang; Lili Niu; Dongmei Jiang; Zhijun Zhong; Xuewei Li; Shunhua Zhang; Li Zhu
Journal:  Anim Biosci       Date:  2020-05-12

7.  A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes.

Authors:  Matthew Carl Yates; Thais A Bernos; Dylan J Fraser
Journal:  Evol Appl       Date:  2017-07-04       Impact factor: 5.183

8.  Using spatial genetics to quantify mosquito dispersal for control programs.

Authors:  Igor Filipović; Hapuarachchige Chanditha Hapuarachchi; Wei-Ping Tien; Muhammad Aliff Bin Abdul Razak; Caleb Lee; Cheong Huat Tan; Gregor J Devine; Gordana Rašić
Journal:  BMC Biol       Date:  2020-08-20       Impact factor: 7.431

9.  Measures of effective population size in sea otters reveal special considerations for wide-ranging species.

Authors:  Roderick B Gagne; M Timothy Tinker; Kyle D Gustafson; Katherine Ralls; Shawn Larson; L Max Tarjan; Melissa A Miller; Holly B Ernest
Journal:  Evol Appl       Date:  2018-05-17       Impact factor: 5.183

10.  Do estimates of contemporary effective population size tell us what we want to know?

Authors:  Nils Ryman; Linda Laikre; Ola Hössjer
Journal:  Mol Ecol       Date:  2019-04-26       Impact factor: 6.185

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