Before joining the Rothfels lab, I developed statistical methods to infer complex “macroevolutionary” processes from phylogenetic data. These methods focused on understanding how and why rates of speciation, extinction, or character evolution change over time or among lineages. Currently, I am exploring the impact of model specification on inferences of time-calibrated phylogenies using total-evidence dating.
Mass-extinction events have profound consequences on the accumulation of lineages through time. However, the signature of mass-extinction events on molecular phylogenies can be difficult to distinguish from other processes that may be biologically common; for example, increases in net-diversification rates (speciation — extinction) can result in lineage-accumulation curves that are similar to those caused by mass-extinction events. Failure to accommodate diversification-rate variation may therefore cause us to make errors in inferring the number, timing, and magnitude of mass-extinction events. To tackle this problem, I developed a Bayesian method, CoMET, that allows us to robustly infer the impact of mass-extinction events against a background of variation in rates of speciation and extinction.
Selected publications on lineage diversification
May, M. R., S. Höhna, and B. R. Moore, 2016: A Bayesian approach for detecting the impact of mass-extinction events on molecular phylogenies when rates of lineage diversification may
vary. Methods in Ecology and Evolution, 7(8), 947–959.
May, M. R. and B. R. Moore, 2016: How well can we detect lineage-specific diversification-rate shifts? A simulation study of sequential AIC methods. Systematic Biology, 6(65), 1076–1084.
Moore, B. R., S. Höhna, M. R. May, B. Rannala and J. P. Huelsenbeck, 2016: Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures. Proceedings
of the National Academy of Sciences, 34(113), 9569–9574.
Höhna, S., M. R. May, and B. R. Moore, 2015: TESS: an R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates. Bioinformatics,
Many macroevolutionary studies seek to understand how rates of trait evolution depend on the state of some other variable; for example, have reef-dwelling fish—owing to increased habitat complexity and ecological opportunity—experienced increased rates of morphological evolution relative to their non-reef-dwelling relatives? However, we expect rates of trait evolution to vary for many reasons; even within clades that are entirely reef-dwelling, we can easily imagine that rates of continuous-trait evolution may vary owing to a variety of other ecological and life-history factors. During my PhD, I developed a Bayesian method for inferring how rates of continuous-trait evolution vary according to a discrete trait of interest, while controlling for background sources of rate variation. I implemented this method, MuSSCRat, in the Bayesian phylogenetic software RevBayes.
Selected publications on character evolution
May, M. R, and B. R. Moore, 2020: A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation. Systematic Biology in press.
Bayesian total-evidence dating has opened the door to leveraging fossil and molecular data to estimate phylogenies in a single coherent statistical framework. These cutting-edge methods require the investigator to specify a model of morphological trait evolution and a model of lineage diversification (among other things). In both cases, there are a large number of modeling decisions that need to be made, for example: how does the rate and mode of morphological evolution vary among lineages and among characters, and; are rates of speciation, extinction, and/or fossilization allowed to vary over time, and if so, how? I am currently exploring the impact of these modeling decisions on estimates of tree topology and divergence times, using the Marattioid ferns as a case study.