Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates

Bayesian methods have become extremely popular in molecular ecology studies because they allow us to estimate demographic parameters of complex demographic scenarios using genetic data. Articles presenting new methods generally include sensitivity studies that evaluate their performance, but they tend to be limited and need to be followed by a more thorough evaluation. Here we evaluate the performance of a recent method, bayesass, which allows the estimation of recent migration rates among populations, as well as the inbreeding coefficient of each local population. We expand the simulation study of the original publication by considering multi‐allelic markers and scenarios with varying number of populations. We also investigate the effect of varying migration rates and FST more thoroughly in order to identify the region of parameter space where the method is and is not able to provide accurate estimates of migration rate. Results indicate that if the demographic history of the species being studied fits the assumptions of the inference model, and if genetic differentiation is not too low (FST ≥ 0.05), then the method can give fairly accurate estimates of migration rates even when they are fairly high (about 0.1). However, when the assumptions of the inference model are violated, accurate estimates are obtained only if migration rates are very low (m = 0.01) and genetic differentiation is high (FST ≥ 0.10). Our results also show that using posterior assignment probabilities as an indication of how much confidence we can place on the assignments is problematical since the posterior probability of assignment can be very high even when the individual assignments are very inaccurate.

[1]  G. Evanno,et al.  Detecting the number of clusters of individuals using the software structure: a simulation study , 2005, Molecular ecology.

[2]  Bruce Rannala,et al.  Bayesian inference of recent migration rates using multilocus genotypes. , 2003, Genetics.

[3]  O. Gaggiotti,et al.  Combining demographic, environmental and genetic data to test hypotheses about colonization events in metapopulations , 2004, Molecular ecology.

[4]  Stephen P. Brooks,et al.  Markov chain Monte Carlo method and its application , 1998 .

[5]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[6]  Zaid Abdo,et al.  Evaluating the performance of likelihood methods for detecting population structure and migration , 2004, Molecular ecology.

[7]  O. Gaggiotti,et al.  INVITED REVIEW: What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity , 2006, Molecular ecology.

[8]  Arnaud Estoup,et al.  A Spatial Statistical Model for Landscape Genetics , 2005, Genetics.

[9]  P. Donnelly,et al.  Inference of population structure using multilocus genotype data. , 2000, Genetics.

[10]  M. Whitlock,et al.  Indirect measures of gene flow and migration: FST≠1/(4Nm+1) , 1999, Heredity.

[11]  Alan Hastings,et al.  Complex interactions between dispersal and dynamics: Lessons from coupled logistic equations , 1993 .

[12]  Peter Beerli,et al.  Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[13]  C. Geyer Markov Chain Monte Carlo Maximum Likelihood , 1991 .

[14]  D. Balding,et al.  Significant genetic correlations among Caucasians at forensic DNA loci , 1997, Heredity.

[15]  M. Whitlock,et al.  Indirect measures of gene flow and migration: FST not equal to 1/(4Nm + 1). , 1999, Heredity.

[16]  F. Balloux EASYPOP (version 1.7): a computer program for population genetics simulations. , 2001, The Journal of heredity.

[17]  M. Stephens,et al.  Inference of population structure using multilocus genotype data: dominant markers and null alleles , 2007, Molecular ecology notes.

[18]  Jukka Corander,et al.  BAPS 2: enhanced possibilities for the analysis of genetic population structure , 2004, Bioinform..