Graham Jones

TL;DR: A series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release are described.

TL;DR: A Bayesian method for inferring both species delimitations and species trees under the multispecies coalescent model using molecular sequences from multiple loci by using three new operators for sampling from the posterior using the Markov chain Monte Carlo algorithm.

TL;DR: The full range of new tools and models available on the BEAST 2.5 platform are described, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

TL;DR: DISSECT as mentioned in this paper explores the full space of possible clusterings of individuals and species tree topologies in a Bayesian framework and uses an approximation to avoid the need for reversible-jump MCMC, in the form of a prior that is a modification of the birth-death prior for the species tree.

TL;DR: It is argued that phylogenomics stands to benefit by embracing the many heterogeneous genomic signals emerging from the first decade of large-scale phylogenetic analysis spawned by high-throughput sequencing (HTS), and that an integrative cyberinfrastructure linking all steps of the process of building the ToL, from specimen acquisition in the field to publication and tracking of phylogenomic data, are essential for progress.