Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees

By D. Rabosky. PLoS ONE, 2014

1,070 citations recorded by the source corpus.

A number of methods have been developed to infer differential rates of species diversification through time and among clades using time-calibrated phylogenetic trees. However, we lack a general framework that can delineate and quantify heterogeneous mixtures of dynamic processes within single phylogenies. I developed a method that can identify arbitrary numbers of time-varying diversification processes on phylogenies without specifying their locations in advance. The method uses reversible-jump Markov Chain Monte Carlo to move between model subspaces that vary in the number of distinct diversification regimes. The model assumes that changes in evolutionary regimes occur across the branches of phylogenetic trees under a compound Poisson process and explicitly accounts for rate variation through time and among lineages. Using simulated datasets, I demonstrate that the method can be used to quantify complex mixtures of time-dependent, diversity-dependent, and constant-rate diversification processes. I compared the performance of the method to the MEDUSA model of rate variation among lineages. As an empirical example, I analyzed the history of speciation and extinction during the radiation of modern whales. The method described here will greatly facilitate the exploration of macroevolutionary dynamics across large phylogenetic trees, which may have been shaped by heterogeneous mixtures of distinct evolutionary processes.

Subfields: Genetics, Molecular Biology, Paleontology

View Paper: https://www.semanticscholar.org/paper/a5f0773b499a1c37f4c21f8f440ab80c7d85c008

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Citations in the corpus

A Bayesian approach for detecting the impact of mass‐extinction events on molecular phylogenies when rates of lineage diversification may vary (2016, methodology): “Schnitzler & Zizka 2011; Stadler 2011a; Etienne & Haegeman 2012; Stadler et al. 2013; H€ohna 2014; Morlon 2014; Rabosky 2014).”
A tale of worldwide success: Behind the scenes of Carex (Cyperaceae) biogeography and diversification (2019, methodology): “Transitions in lineage diversi ﬁ cation rates were estimated on the constrained singletons tree calibrated with 10 fossils using the speciation ‐ extinction model implemented in Bayesian analysis of macroevolutionary mixtures (BAMM; Rabosky, 2014).”
Out‐of‐Tibet: the spatio‐temporal evolution of Gentiana (Gentianaceae) (2016, methodology): “We used a reversible jump Markov chain Monte Carlo (rjMCMC) to discover the most plausible scenarios of diversification rate shifts, as implemented in bamm 2.5.0 (Rabosky, 2014).”
Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates? (2019, background): “29 Rabosky, D.L. & Lovette, I.J. (2008). Explosive evolutionary radiations: decreasing speciation or 619 increasing extinction through time? Evolution, 62, 1866–1875. 620 Rabosky, D.L., Chang, J., Title, P.O., Cowman, P.F., Sallan, L., Friedman, M., Kaschner, K., 621 Garilao, C., Near, T.J., Coll, M”
Reproductive isolation and the causes of speciation rate variation in nature (2016, methodology): “Figure 1 shows a pattern of speciation rate variation across a phylogeny of living birds (Jetz et al., 2012), as inferred using a recently-developed method for quantifying speciation rate heterogeneity (Rabosky, 2014).”
A new method for testing evolutionary rate variation and shifts in phenotypic evolution (2018, methodology): “…evolutionary rates (Thomas & Freckleton, 2012), many of them do not work with fossil phylogenies (e.g. Rpanda Morlon et al., 2016; BayesTRaiTs Pagel & Meade, 2014; Bamm Rabosky, 2014; Rabosky, Donnellan, Grundler, & Lovette, 2014), or are computationally very intensive (Thomas & Freckleton, 2012).”
Continuous traits and speciation rates: Alternatives to state‐dependent diversification models (2017, methodology): “values among diversification rate regimes inferred using BAMM (Rabosky, 2014) or potentially other multiprocess diversification models, but the power of this approach is limited by the number of distinct rate regimes present in a given phylogeny.”
RPANDA: an R package for macroevolutionary analyses on phylogenetic trees (2016, neutral): “A number of packages are available to handle and utilize phylogenetic trees (e.g. ‘ape’ Paradis, Claude & Strimmer 2004), in order to understand, for example, community assembly (e.g. ‘picante’ Kembel et al. 2010; ‘DAMOCLES’ Pigot & Etienne 2015), trait evolution (e.g. ‘Coevol’ Lartillot & Poujol 20”
BAMMtools: an R package for the analysis of evolutionary dynamics on phylogenetic trees (2014, methodology): “BAMMtools is a companion package to the BAMMprogram (Bayesian analysis of macroevolutionary mixtures; Rabosky et al. 2013; Rabosky 2014) and is oriented entirely around the analysis and visualization of complex evolutionary dynamics on phylogenetic trees.”
phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things) (2023, background): “…and Arnold 2008; Revell and Collar 2009; Morlon et al. 2010; Stadler 2011; Etienne and Haegeman 2012; Goldberg and Igić 2012; Beaulieu et al. 2013; Rabosky 2014; Uyeda and Harmon 2014; Beaulieu and O’Meara 2016; Revell 2021; MacPherson et al. 2022, and many others; reviewed in OMeara 2012;…”
Ecological Opportunity and Adaptive Radiation (2016, background): “Discussion of this topic has focused on the evolution of so-called key innovations—features that allow a lineage to interact with the environment in a novel way and thus may provide the ability, hitherto unavailable, to utilize available resources (Hunter 1998, Galis 2001, Rabosky 2014).”
Intrinsic inference difficulties for trait evolution with Ornstein‐Uhlenbeck models (2014, methodology): “Rabosky (2014) used a similar approach to infer differential rates of species diversification or shifts in trait evolutionary rate, using a compound Poisson prior process for the location and number of shifts.”