Species evolution research

Recently algorithms for estimating species trees, rather than gene trees, have initiated a paradigm shift in evolutionary biology that is clarifying many issues in the study of phylogenetics, but also raising new conceptual challenges.

Our centre has researched this area for several years, mostly based on implementing and elaborating the multispecies coalescent model for embedding gene trees inside a shared species tree.

Genomes, phenotypes and fossils: integrative models of species evolution

How and when species came to be is the fundamental question in macroevolution. Attempts to answer it use a variety of data sources including genome sequences, morphology and fossil discoveries. Yet current methods are unable to exploit all this data, with different data sources often producing conflicting results. This research project aims to create a unifying probabilistic framework that combines genomic, fossil and phenotypic data to give us the best possible understanding of evolutionary history. The research will involve

  1. developing new mathematical models
  2. creating open-source software tools to disseminate new methods widely
  3. using these new methods to address outstanding questions in human, animal and pathogen evolution

The initial focus of the work will be to extend the StarBEAST2 package to allow for sampled ancestral species and their phenotypes in the species tree as well as ancient DNA samples in the embedded gene trees. This is a major software engineering task. There will also be work on developing new trait evolution models that can account for trait variation both within and between species. Current models will be incorporated into the BEAST 2 software and major studies on real and simulated data will be run to assess their strengths and weaknesses. We will also implement new inference methods for correlated continuous trait evolution.

Finally, a third focus of the work will be on incorporating rich fossil data into the phylogenetic framework within BEAST 2. New models will incorporate the variability of sampling over time and space, trait-dependent sampling, and will be able to use multiple fossils from the same morphospecies while accounting for uncertainty in the geologically-derived age of fossils. Both simulated and curated data sets will be used to test and prove the newly developed methods.

This is a three-year research project supported by the Marsden Fund starting 1 March 2017.

About the researchers
Professor Alexei Drummond (School of Biological Sciences)
Dr David Welch (School of Computer Science)
Professor Tanja Stadler (ETH Zurich)
Dr Nicholas J. Matzke (School of Biological Sciences)
Dr Tim Vaughan (ETH Zurich)

Other collaborators
Dr Mana Dembo (Simon Fraser University)
Dr Mark Collard (Simon Fraser University)
Associate Professor Graham Slater (University of Chicago)

Modelling penguin evolution using Bayesian total evidence approach

The total evidence approach to divergence-time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevolutionary parameters in a single coherent framework

The fossilised birth-death process explicitly models tthe diversification, fossilisation, and sampling processes and naturally allows for sampled ancestors. This model was recently applied to estimate divergence times based on molecular data and fossil occurrence dates.

We incorporate the fossilised birth-death model and a model of morphological trait evolution into a Bayesian total-evidence approach to dating species phylogenies. We apply this method to extant and fossil penguins and show that the modern penguins radiated much more recently than has been previously estimated, with the basal divergence in the crown clade occurring at 12.7 Ma and most splits leading to extant species occurring in the last 2 million years.

Our results demonstrate that including stem-fossil diversity can greatly improve the estimates of the divergence times of crown taxa. The method is available in BEAST2 (version 2.3) software www.beast2.org with packages SA (version 1.1.3) and morph-models (version 1.0.1) installed.

Read the full paper

About the researchers
Dr Alexandra (Sasha) Gavryushkina (University of Otago)
Professor Alexei Drummond (School of Biological Sciences)
Dr David Welch (School of Computer Science)