Phylogeographic model selection leads to insight into the evolutionary history of four-eyed frogs

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 113; no. 29; pp. 8010 - 8017
• Main Authors: Thomé, Maria Tereza C., Carstens, Bryan C.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 19.07.2016
• Subjects: Amphibians; Animals; Anura - genetics; Biological Sciences; Evolution, Molecular; Evolutionary biology; Frogs; Information theory; Mathematical models; Models, Genetic; Phylogeography; Polymorphism; Polymorphism, Single Nucleotide; Sackler on In the Light of Evolution X: Comparative Phylogeography; Brazil; model selection; site frequency spectrum; Pleurodema; information theory; Caatinga
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1601064113

Phylogeographic research investigates biodiversity at the interface between populations and species, in a temporal and geographic context. Phylogeography has benefited from analytical approaches that allow empiricists to estimate parameters of interest from the genetic data (e.g., θ = 4Neμ, population divergence, gene flow), and the widespread availability of genomic data allow such parameters to be estimated with greater precision. However, the actual inferences made by phylogeographers remain dependent on qualitative interpretations derived from these parameters’ values and as such may be subject to overinterpretation and confirmation bias. Here we argue in favor of using an objective approach to phylogeographic inference that proceeds by calculating the probability of multiple demographic models given the data and the subsequent ranking of these models using information theory. We illustrate this approach by investigating the diversification of two sister species of four-eyed frogs of northeastern Brazil using single nucleotide polymorphisms obtained via restriction-associated digest sequencing. We estimate the composite likelihood of the observed data given nine demographic models and then rank these models using Akaike information criterion. We demonstrate that estimating parameters under a model that is a poor fit to the data is likely to produce values that lead to spurious phylogeographic inferences. Our results strongly imply that identifying which parameters to estimate from a given system is a key step in the process of phylogeographic inference and is at least as important as being able to generate precise estimates of these parameters. They also illustrate that the incorporation of model uncertainty should be a component of phylogeographic hypothesis tests.