H. A. Ogilvie

The impact of model misspecification on phylogenetic network inference

This study evaluates how model misspecification affects phylogenetic network inference using simulated and biological data, finding that gene tree estimation error and substitution rate heterogeneity impact accuracy.

Comparing Inference under the MSC versus the MSC-with-Recombination

This study compares three methods for estimating population parameters under different coalescent models, showing that diCal2 performs worse despite being designed for recombination.

Phylogenetic Network Dissimilarity Measures That Take Branch Lengths Into Account

This paper introduces two phylogenetic network dissimilarity measures that consider both topology and branch lengths, and demonstrates their use in clustering inferred networks.

Novel Integrative Modeling of Molecules and Morphology across Evolutionary Timescales

This paper introduces a new evolutionary model, FBD-MSC, that integrates genealogical and fossilization processes to improve divergence time estimates in Caninae.

Phylogenomic assessment of the role of hybridization and introgression in trait evolution

The paper presents a phylogenomic method to assess hybridization and introgression's role in trait evolution, showing gene flow can lead to misleading trait evolution hypotheses.

Variational Inference Using Approximate Likelihood Under the Coalescent With Recombination

This paper introduces a variational inference method for coalHMMs that learns evolutionary parameters and infers local genealogies with comparable accuracy to existing methods, using simulated data.

Practical Aspects of Phylogenetic Network Analysis Using PhyloNet

This paper demonstrates PhyloNet's functionalities for analyzing phylogenetic networks using simulated data and provides recommendations for data analysis and interpretation.

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

The paper describes new developments in the BEAST 2.5 software platform for Bayesian evolutionary analysis, expanding joint inference over multiple data types and complex phylodynamics.

Bayesian Inference of Species Networks from Multilocus Sequence Data

This paper introduces a Bayesian method to infer species networks and gene trees from multilocus sequence data, demonstrating its ability to detect reticulate evolution in spruce species.