Phylogeographic model selection leads to insight into the evolutionary history of four-eyed frogs
Proceedings of the National Academy of Sciences of the United States of America, ISSN: 1091-6490, Vol: 113, Issue: 29, Page: 8010-8017
2016
- 42Citations
- 201Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations42
- Citation Indexes42
- 42
- CrossRef37
- Captures201
- Readers201
- 201
Article Description
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., è = 4Neì, 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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84978884617&origin=inward; http://dx.doi.org/10.1073/pnas.1601064113; http://www.ncbi.nlm.nih.gov/pubmed/27432969; https://pnas.org/doi/full/10.1073/pnas.1601064113; https://dx.doi.org/10.1073/pnas.1601064113; https://www.pnas.org/content/113/29/8010
Proceedings of the National Academy of Sciences
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