Phylogeography takes a relaxed random walk in continuous space and time.

By Philippe Lemey, Andrew Rambaut, J. Welch, Marc A. Suchard. Molecular biology and evolution, 2010

698 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f11d12b50ddc45775538f2c06feb0f2a72ea563e

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Citations in the corpus

Evolutionary Processes of Diversification in a Model Island Archipelago (2013, background): “Recent developments in statistical phylogeography and landscape genetics (Manel et al. 2003, Knowles 2009, Lemey et al. 2010) provide ample opportunities for new insights into these questions.”
Bayesian Phylogenetics with BEAUti and the BEAST 1.7 (2012, methodology): “These latter data and models include microsatellite markers (Wu and Drummond 2011), phenotypic traits under a multistate stochastic Dollo process (Alekseyenko et al. 2008), discretized geographic diffusion (Lemey et al. 2009), and multivariate continuous re-laxed random walks (Lemey et al. 2010).”
Molecular phylogenetics: principles and practice (2012, background): “The third area of exciting development, which was mentioned at the beginning of this Review, is statistical phylogeography 20,114–116 .”
Integrating influenza antigenic dynamics with molecular evolution (2013, methodology): “Consequently, we seek to flexibly incorporate genetic data by modeling antigenic phenotype as an evolutionary diffusion [12], wherein a virus’s antigenic character state evolves along branches of the phylogenetic tree according to a Brownian motion process (see Materials and methods).”
The early spread and epidemic ignition of HIV-1 in human populations (2014, background): “These processes can be recovered from genetic data using formal statistical inference methods that take into account both the sequences’ shared ancestry and sources of statistical uncertainty (76, 81).”
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis (2018, background): “The very popular discrete trait model and continuous phylogeographic methods from Lemey and colleagues [79, 80] assume that the whole tree was generated under an unstructured model, and that the trait evolved—just like a nucleotide—on that tree.”
Bayesian analysis of biogeography when the number of areas is large. (2013, background): “Lemmon and Lemmon (2008) and Lemey et al. (2009; 2010) proposed stochastic models that treat the distribution of species as continuous variables.”
Emergence and rapid spread of a new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) lineage with multiple spike mutations in South Africa (2020, positive): “To model phylogenetic diffusion of the new cluster across the country, we used a flexible relaxed random walk (RRW) diffusion model that accommodates branch-specific variation in rates of dispersal with a Cauchy distribution 23 .”
Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 (2018, background): “Such continuous models have most frequently been applied to diffusion on a geographical landscape with the traits representing coordinates and the phylogeny reconstructing the epidemiological process within the host population (Lemey et al. 2010).”
Viral Phylodynamics (2013, background): “Phylodynamic approaches have mapped the geographic movement of the human influenza virus [3] and quantified the epidemic spread of rabies virus in North American raccoons [12,13].”
SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes. (2016, background): “Phylogenetic diffusion processes have been proposed to model the evolution of both discrete and continuous traits without the need to assume that the tree is known without error (Ronquist 2004; Sanmart (cid:2) ın et al. 2008; Lemey et al. 2009, 2010).”
Phylogeographic studies of plants in China: Advances in the past and directions in the future (2012, background): “Finally, spatiotemporal reconstructions and tests of the alternative hypotheses of dynamic histories of a species based on allele distributions can be realized through the newly developed approaches (e.g., Maddison & Maddison, 2008; Lemey et al., 2009, 2010).”
Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty. (2012, methodology): “In the current study, we focus on comparing demographic and clock models, but the general implementation allows to calculate marginal likelihoods for any model that can be fitted in BEAST, such as sequence evolution, trait evolution, and phylogeographic models (see, e.g., Lemey et al. 2009, 2010).”
BEAST 2: A Software Platform for Bayesian Evolutionary Analysis (2014, methodology): “A suite of methods available in former versions of BEAST have been ported to the new framework including ancestral state reconstruction and phylogeography [7], continuous phylogeography [8], and the stochastic Dollo substitution model with various character ascertainment schemes [13].”