ArticleProceedings of the National Academy of Sciences of the United States of America2024
Modeling the velocity of evolving lineages and predicting dispersal patterns.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence.bioRxiv : the preprint server for biology · 2026Article
- Using drift coefficients as a basis for inferring times, effective population sizes, and genetic adaptations.Molecular biology and evolution · 2026Article
- SERAPHIM 2.0: an extended toolbox for studying phylogenetically informed movements.Bioinformatics (Oxford, England) · 2026Article
- Navigating Sampling Bias in Discrete Phylogeographic Analysis: Assessing the Performance of an Adjusted Bayes Factor.Molecular biology and evolution · 2025Article
- Comparative performance of viral landscape phylogeography approaches.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Grains, trade and war in the multimodal transmission of Rice yellow mottle virus: An historical and phylogeographical retrospective.PLoS pathogens · 2025Article
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Abstract
Accurate estimation of the dispersal velocity or speed of evolving organisms is no mean feat. In fact, existing probabilistic models in phylogeography or spatial population genetics generally do not provide an adequate framework to define velocity in a relevant manner. For instance, the very concept of instantaneous speed simply does not exist under one of the most popular approaches that models the evolution of spatial coordinates as Brownian trajectories running along a phylogeny. Here, we introduce a family of models-the so-called Phylogenetic Integrated Velocity (PIV) models-that use Gaussian processes to explicitly model the velocity of evolving lineages instead of focusing on the fluctuation of spatial coordinates over time. We describe the properties of these models and show an increased accuracy of velocity estimates compared to previous approaches. Analyses of West Nile virus data in the United States indicate that PIV models provide sensible predictions of the dispersal of evolving pathogens at a one-year time horizon. These results demonstrate the feasibility and relevance of predictive phylogeography in monitoring epidemics in time and space.
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