Phylogeographic ancestral inference using the coalescent model on haplotype trees.
Phylogeographic ancestral inference is issue frequently arising in population ecology that aims to understand the geographical roots and structure of species. Here, we specifically address relatively small scale mtDNA datasets (typically less than 500 sequences with fewer than 1000 nucleotides), focusing on ancestral location inference. Our approach uses a coalescent modelling framework projected onto haplotype trees in order to reduce computational complexity, at the same time adhering to complex evolutionary processes. Statistical innovations of the last few years have allowed for computationally feasible yet accurate inferences in phylogenetic frameworks. We implement our methods on a set of synthetic datasets and show how, despite high uncertainty in terms of identifying the root haplotype, estimation of the ancestral location naturally encompasses lower uncertainty, allowing us to pinpoint the Maximum A Posteriori estimates for ancestral locations. We exemplify our methods on a set of synthetic datasets and then combine our inference methods with the phylogeographic clustering approach presented in Manolopoulou et al. (2011) on a real dataset from weevils in the Iberian peninsula in order to infer ancestral locations as well as population substructure.
Department of Statistics, Duke University, Durham, NC 27708, USA. ioanna.manolopoulou@duke.edu
Ioanna
I
Manolopoulou
Brent C
BC
Emerson
D000818:Animals; D004272:DNA, Mitochondrial / Q000145:classification / Q000235:genetics*; D030561:Databases, Nucleic Acid; D019143:Evolution, Molecular; D049810:Genetic Speciation; D006239:Haplotypes; D008957:Models, Genetic; D010802:Phylogeny; D058974:Phylogeography; D011174:Portugal; D013030:Spain; D048889:Weevils / Q000145:classification / Q000235:genetics*
D004272:DNA, Mitochondrial