David Alejandro Duchêne

The paper compares phylogenomic inferences from reference mapping and whole-genome alignment across Ursidae species, finding comparable results despite sequence loss.

This study examines evolutionary rates in living-fossil lineages and finds reduced morphological but not molecular rates, suggesting morphological conservatism is a key feature.

This paper presents the first whole-genome data for the giant forest hog, revealing its evolutionary relationship with warthogs and significant genetic differentiation between populations.

This study reconstructs the genomic history of the extinct giant deer, revealing genetic vulnerabilities linked to its extinction.

This study uses deep learning-based tracking to detect behavioral changes in infected poultry, showing reduced activity in E. coli-infected chickens compared to controls.

This study traces the evolutionary origins of acetic acid bacteria, showing they transitioned from plant-associated lifestyles to fermented foods, not from social insect hosts.

This study decomposes evolutionary rates in birds to identify drivers of genomic change, finding clutch size, generation length, and beak shape predict mutation rates, with rapid evolution linked to meiosis, heart function, and ecological diversification.

The paper develops a Bayesian method to infer phylogenetic distances incorporating heterotachy and rate variation, finding evidence for a two-domain theory of eukaryotic origins.

ClockstaRX tests molecular clock hypotheses by analyzing phylogenomic data to identify rate variation patterns and influential loci.

This paper introduces BETS, a Bayesian method to evaluate temporal signal in sequence data, showing it accurately classifies datasets with or without temporal signal across various empirical examples.

This study finds that proportionally linking branch lengths across loci provides the best fit for phylogenetic inference compared to other models.

The paper introduces TreeModelAdequacy, a tool to assess phylodynamic model adequacy using posterior predictive simulations, applied to Ebola and H1N1 influenza outbreak data.

The paper compares fully Bayesian and hybrid methods for inferring demographic parameters in bacterial genomic data, finding hybrid methods are computationally efficient and reliable when data have strong temporal structure.