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LEARNING ABOUT MODES OF SPECIATION BY COMPUTATIONAL APPROACHES
Author(s) -
Becquet Céline,
Przeworski Molly
Publication year - 2009
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.2009.00662.x
Subject(s) - parapatric speciation , allopatric speciation , biology , genetic algorithm , evolutionary biology , spurious relationship , divergence (linguistics) , gene flow , incipient speciation , machine learning , computer science , genetics , population , gene , genetic variation , philosophy , demography , sociology , linguistics
How often do the early stages of speciation occur in the presence of gene flow? To address this enduring question, a number of recent papers have used computational approaches, estimating parameters of simple divergence models from multilocus polymorphism data collected in closely related species. Applications to a variety of species have yielded extensive evidence for migration, with the results interpreted as supporting the widespread occurrence of parapatric speciation. Here, we conduct a simulation study to assess the reliability of such inferences, using a program that we recently developed MCMC estimation of the isolation‐migration model allowing for recombination (MIMAR) as well as the program isolation‐migration (IM) of Hey and Nielsen (2004). We find that when one of many assumptions of the isolation–migration model is violated, the methods tend to yield biased estimates of the parameters, potentially lending spurious support for allopatric or parapatric divergence. More generally, our results highlight the difficulty in drawing inferences about modes of speciation from the existing computational approaches alone.