MBE Advance Access published online on September 30, 2009
Molecular Biology and Evolution, doi:10.1093/molbev/msp233
Research Article |
How robust are "Isolation with Migration" analyses to violations of the IM model? A simulation study
1 Department of Biology, Indiana University, Bloomington, IN 47405 USA
2 Department of Botany, University of British Columbia, Vancouver, B.C. V6T 1Z4 Canada
Corresponding Author: Jared L. Strasburg, Address: Indiana University, Department of Biology, 915 E. 3rd Street, Myers #150, Bloomington, IN 47405, Telephone: (812) 855-9018, Fax: (812) 855-6705, e-mail: jstrasbu{at}indiana.edu
Received for publication June 9, 2009. Revision received September 17, 2009. Revision received September 25, 2009. Accepted for publication September 25, 2009.
Methods developed over the past decade have made it possible to estimate molecular demographic parameters such as effective population size, divergence time, and gene flow with unprecedented accuracy and precision. However, they make simplifying assumptions about certain aspects of the species’ histories and the nature of the genetic data, and it is not clear how robust they are to violations of these assumptions. Here we use simulated datasets to examine the effects of a number of violations of the "Isolation with Migration" (IM) model, including intralocus recombination, population structure, gene flow from an unsampled species, linkage among loci, and divergent selection, on demographic parameter estimates made using the program IMA. We also examine the effect of having data that fits a nucleotide substitution model other than the two relatively simple models available in IMA. We find that IMA estimates are generally quite robust to small to moderate violations of the IM model assumptions, comparable to what is often encountered in real-world scenarios. In particular, population structure within species, a condition encountered to some degree in virtually all species, has little effect on parameter estimates even for fairly high levels of structure. Likewise, most parameter estimates are robust to significant levels of recombination when datasets are pared down to apparently non-recombining blocks, although substantial bias is introduced to several estimates when the entire dataset with recombination is included. In contrast, a poor fit to the nucleotide substitution model can result in an increased error rate, in some cases due to a predictable bias and in other cases due to an increase in variance in parameter estimates among datasets simulated under the same conditions.
Key Words: historical demography • introgression • divergence time • effective population size • simulations • Isolation with Migration