Navigating the unknown: model selection in phylogeography. Models of population structure: tools for thinkers.
Despite the widespread use and obvious strengths of model-based methods for phylogeographic study, a persistent concern for such analyses is related to the definition of the model itself. The study by Peter et al. (2010) in this issue of Molecular Ecology demonstrates an approach for overcoming such hurdles. The authors were motivated by a deceptively simple goal; they sought to infer whether a population has remained at a low and stable size or has undergone a decline, and certainly there is no shortage of software packages for such a task (e.g., see list of programs in Excoffier & Heckel 2006). However, each of these software packages makes basic assumptions about the underling population (e.g., is the population subdivided or panmictic); these assumptions are explicit to any model-based approach but can bias parameter estimates and produce misleading inferences if the model does not approximate the actual demographic history in a reasonable manner. Rather than guessing which model might be best for analyzing the data (microsatellite data from samples of chimpanzees), Peter et al. (2010) quantify the relative fit of competing models for estimating the population genetic parameters of interest. Complemented by a revealing simulation study, the authors highlight the peril inherent to model-based inferences that lack a statistical evaluation of the fit of a model to the data, while also demonstrating an approach for model selection with broad applicability to phylogeographic analysis.
Department of Biological Sciences, Louisiana State University, 202 Life Sciences Building, Baton Rouge, LA 70808, USA. carstens@lsu.edu
Bryan C
BC
Carstens
L L
LL
Knowles
D001499:Bayes Theorem; D003198:Computer Simulation; D005828:Genetics, Population; D018895:Microsatellite Repeats; D008957:Models, Genetic; D058974:Phylogeography; D012984:Software