Modeling the velocity of evolving lineages and predicting dispersal patterns P. Bastide, P. Rocu, J. Wirtz, G. W. Hassler, F. Chevenet, D. Fargette, M. A. Suchard, S. Dellicour, P. Lemey and S. Guindon. Proceedings of the National Academy of Sciences, USA. 121, 2024. In this article, we present a new class of models in phylogeography -- the so-called Phylogenetic Integrated Models (PIV) -- which provides a framework for inferring the speed at which evolving lineages disperse throughout their habitat. We show that the speed estimates obtained with PIV models are superior to that deriving from the standard (Relaxed Random Walk) approach. PIV models are then applied to the difficult problem of predicting dispersal. The analysis of West Nile Virus data, including incidence data along with genetic sequences, indicate that predicting patterns of dispersal at a one-year time horizon is feasible indeed.

On the connections between the spatial Lambda–Fleming–Viot model and other processes for analysing geo-referenced genetic data J. Wirtz, S. Guindon. Theoretical Population Biology 158, 2024. This article is a contribution to a special issue in TPB dedicated to celebrate Alison Etheridge's contribution to mathematical population biology. We show that the spatial Lambda–Fleming–Viot tree generating process converges to that of a birth-death model when the spatial diffusion parameter gets small. We also show convergence to the relaxed random walk model in phylogeography whereby habitat boundary effects are reflected onto the random walk.

Accounting for spatial sampling patterns in Bayesian phylogeography S. Guindon, N. De Maio. Proceedings of the National Academy of Sciences, USA. 118(52), 2022. We propose a new statistical approach to accommodate for preferential sampling in phylogeography. This new technique distinguishes between the case where the density of samples in particular areas reflects the underlying population density and the case where it does not. Read this "research highlight" in Nature Computational Science.

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk A. Kalkauskas, U. Perron, Y. Sun, N. Goldman, G. Baele, S. Guindon, N. De Maio. PLOS Computational Biology. 17 (1), e1008561. 2021. In this article, we study the impact that spatial sampling schemes have on the inference of model parameters under standard approaches in continuous phylogeography.

Rates and rocks: strengths and weaknesses of molecular dating methods. S. Guindon. Frontiers in Genetics. 11. 2020. I give here an overview of some of the technical aspects of molecular dating methods, including models of rate variation along lineages and the different ways to calibrate a dating analysis using fossil data.

Accounting for ambiguity in ancestral sequence reconstruction. A. Oliva, S. Pulicani, V. Lefort, L. Brehelin, O. Gascuel & S. Guindon. Bioinformatics. 35: 4290-4297. 2019. We propose a new criterion that better accomodates for ambiguity in ancestral (nucleotide or protein) sequence reconstruction. In particular, more than one nucleotide or amino-acid are inferred whenever multiple states have similar marginal posterior probabilities. The proposed approach is computationally efficient and does not rely on arbitrarily set tuning parameters.

Accounting for calibration uncertainty: Bayesian molecular dating as a ``doubly intractable'' problem. S. Guindon. Systematic Biology. 67: 651-661. 2018. I describe a technique for molecular dating that accomodates for uncertainty in the placement of calibration constraints as defined by an expert. The method relies on the so-called ``exchange algorithm'' for sampling from doubly intractable distributions Cover of July issue!