Biology subjects

Milkey, A.

Publications and source records attributed to Milkey, A..

3 recordsLinked to original sources

bioRxiv2026

How much information is there for inferring species trees?

AO_SCPLOWBSTRACTC_SCPLOWAs modern phylogenomics datasets become increasingly large, it is useful to develop recommendations for how to subsample datasets for best species tree inference. Here we apply a new measure of phylogenetic information content that estimates the reduction in tree space occupied by a posterior sample of inferred trees relative to a prior sample in order to assess the effects of gene tree parameters on species tree estimation. We find that, consistent with earlier studies, when data are informative, more data result in better species tree inference. However, when data are uninformative, subsampling a dataset to include only the most informative loci may produce a better species tree sample. We perform analyses on a variety of simulated and empirical datasets.

evolutionary biology↗

bioRxiv2026

Estimating Bayesian phylogenetic information content using geodesic distances

AO_SCPLOWBSTRACTC_SCPLOWA new Bayesian measure of phylogenetic information content is introduced based on geodesic distances in treespace. The measure is based on the relative variance of phylogenetic trees sampled from the posterior distribution compared to the prior distribution. This ratio is expected to equal 1 if there is no information in the data about phylogeny and 0 if there is complete information. Trees can be scaled to have the same mean tree length to avoid dominance by edge length information and focus on topological information. The method scales well, requiring only that a valid sample can be obtained from both prior and posterior distributions. We show how dissonance (information conflict) among data sets can also be estimated. Both simulated and empirical examples are provided to illustrate that the new approach produces sensible and intuitive results.

evolutionary biology↗

bioRxiv2025

The Sequential Multispecies Coalescent

The multispecies coalescent (MSC) model applies coalescent theory to gene evolution within and among reproductively isolated populations ("species") to estimate a species tree in the face of gene tree conflict resulting from deep coalescence. Sequential Monte Carlo (SMC) uses particle filtering to sample a posterior distribution, providing a fully-Bayesian and easily parallelized alternative to traditional MSC tree inference approaches. The method we propose samples first from the joint posterior distribution of gene and species trees, then samples species trees conditional on gene trees sampled previously, employing SMC for both rounds. Analyses of simulated and empirical datasets yield results comparable to state-of-the-art Bayesian MCMC approaches. Sampling the multispecies coalescent using SMC retains the advantages of fully Bayesian methods and is parallelizable in ways that Bayesian MCMC methods are not but also adds unique challenges. We demonstrate the performance of SMC compared to other commonly-used species tree methods using two empirical datasets and 400 simulated datasets.

evolutionary biology↗