2009/02/04 by Rasmus Nielsen, Mark Beaumont · 6 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Genetic diversity and population structure #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Phylogeography #Biology #Evolutionary biology #Context (archaeology) #Inference #Statistical inference #Phylogenetic tree #Data science #Computer science #Artificial intelligence #Statistics #Gene #Genetics #Paleontology #Mathematics
paper · pdf · doi:10.1111/j.1365-294x.2008.04059.x
openalex publication_date 2009/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods.