The molecular clock provides a powerful way to infer evolutionary timescales by integrating information from genomes and fossils. However, traditional molecular clock methods only leverage information from fossil ages. In this talk we will discuss recent advances in which fossil information is explicitly integrate in the form of morphometric data, allow simultaneous inference of speciation times, morphological rates of evolution and ancestral character reconstructions.

This seminar is part of the workshop Leveraging morphological and genomic data to elucidate evolutionary patterns. Mario's seminar is open to the general public with interest in evolutionary biology. Workshop participants are encouraged to attend both Emma and Mario's seminars before the practical sessions. 

About Mario

Work in the dos Reis lab focuses on the development and application of Bayesian methodologies to infer speciation timelines across geological timescales.  Our approaches leverage information from genomic and morphological datasets to unravel evolutionary patterns, for example, by using new digitization technologies, like microCT scans, which are unlocking the potential of morphological data to aid evolutionary inferences. We have a strong focus on developing computational approaches to speed up analyses of the very largest genomic and morphometric datasets now available. We have made important contributions such an understanding the pattern of mammal diversification after the K-PG or the origins of animals near the Cambrian explosion.

Representative publications:

• Álvarez-Carretero, Tamuri, Battini, Nascimento, Carlisle, Asher, Yang, Donoghue and dos Reis M. 2022. A species-level timeline of mammal evolution integrating phylogenomic data. Nature, 602: 263–267.
• Álvarez-Carretero, Goswami, Yang and dos Reis. 2019. Bayesian estimation of species divergence times using correlated quantitative characters. Systematic Biology, 68: 967–986.
• dos Reis, Donoghue and Yang. 2016. Bayesian molecular clock dating of species divergences in the genomics era. Nature Reviews Genetics, 17: 71–80.