Open AccessAccounting for spatial sampling patterns in Bayesian phylogeography
Author(s) -
Stéphane Guindon,
Nicola De Maio
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2105273118
Subject(s) - inference , phylogeography , sampling (signal processing) , bayesian probability , bayesian inference , computer science , statistical inference , spatial analysis , geography , data mining , ecology , statistics , artificial intelligence , biology , phylogenetics , mathematics , remote sensing , biochemistry , filter (signal processing) , computer vision , gene
Significance Statistical phylogeography has led to substantial progress in our understanding of the pace and means by which organisms colonize their habitats. Yet, inference from these models often relies on implicit assumptions pertaining to spatial sampling design, potentially leading to biased estimation of key biological parameters. While sampled locations sometimes convey signal about the processes that shape spatial biodiversity, they do not always do so. We present a statistical approach that permits accurate estimation of dispersal rates, even in cases where spatial sampling is driven by practical motivations unrelated to the outcome of the evolutionary process. The proposed framework paves the way to further developments in phylogeography with key applications, including the efficient monitoring of pandemics and invasive species during the course of their evolution.