Exploring the power of Bayesian birth‐death skyline models to detect mass extinction events from phylogenies with only extant taxa

Abstract

Mass extinction events (MEEs), defined as significant losses of species diversity in significantly short time periods, have attracted the attention of biologists because of their link to major environmental change. MEEs have traditionally been studied through the fossil record, but the development of birth‐death models has made it possible to detect their signature based on extant‐taxa phylogenies. Most birth‐death models consider MEEs as instantaneous events where a high proportion of species are simultaneously removed from the tree (“single pulse” approach), in contrast to the paleontological record, where MEEs have a time duration. Here, we explore the power of a Bayesian Birth‐Death Skyline (BDSKY) model to detect the signature of MEEs through changes in extinction rates under a “time‐slice” approach. In this approach, MEEs are time intervals where the extinction rate is greater than the speciation rate. Results showed BDSKY can detect and locate MEEs but that precision and accuracy depend on the phylogeny's size and MEE intensity. Comparisons of BDSKY with the single‐pulse Bayesian model, CoMET, showed a similar frequency of Type II error and neither model exhibited Type I error. However, while CoMET performed better in detecting and locating MEEs for smaller phylogenies, BDSKY showed higher accuracy in estimating extinction and speciation rates.

Ecological and geological processes impacting speciation modes drive the formation of wide‐range disjunctions within tribe Putorieae (Rubiaceae)

“…We then ran a second analysis using reversible-jump MCMC model selection to estimate MEEs while integrating out rate shifts in diversification and background extinction (May et al, 2016). This was done to avoid issues with parameter non-identifiability, as under the single pulse model, it becomes difficult to distinguish scenarios with constant diversification interrupted by a mass extinction event from another with a significant upturn in the rate of diversification preceded by slow speciation (Culshaw et al, 2019). We set the sampling fraction at present to 0.65, to account for our incomplete taxon sampling (65% of known Putorieae species), and used an empirical Bayesian approach to estimate the hyperprior shapes for speciation (normal distribution: mean = 0.22, SD = 0.20) and extinction rates (lognormal distribution: meanlog = −3.33, SDlog = 1.36).…”

Fossil and phylogenetic analyses reveal recurrent periods of diversification and extinction in dictyopteran insects

“…Mass extinctions have punctuated the history of life, eliminating whole groups of organisms while fostering the subsequent diversification of others (Raup and Sepkoski, 1982;Alroy, 2010). These events caused, in less than one million years (Myrs), more than 75% species loss (Raup and Sepkoski, 1982;Barnosky et al, 2011;Culshaw et al, 2019). Five mass extinction events over the past 542 Myrs, often referred to as the 'Big Five', have been identified (Raup and Sepkoski, 1982;Alroy, 2010): the Ordovician-Silurian extinction event (~443 Myrs ago, ~86% species loss), the Late Devonian extinction event (~376-359 Myrs ago, ~75% species loss), the Permian-Triassic (P-T) extinction event (~252 Myrs ago, ~96% species loss), the Triassic-Jurassic (T-J) extinction event (~201 Myrs ago, ~80% species loss) and the Cretaceous-Paleogene (K-Pg) extinction event (~66 Myrs ago, ~76% species loss).…”

Diversification dynamics of hypermetamorphic blister beetles (Meloidae): Are homoplastic host shifts and phoresy key factors of a rushing forward strategy to escape extinction?

“…Though it is mathematically possible to estimate the extinction rate from a reconstructed time tree including no fossil extinct lineages (Stadler 2011), temporal and clade-specific deviations from constancy in diversification rates leads to inaccurate and often underestimated extinction rates (Rabosky 2010; Morlon 2014). In recent years, different time-dependent and clade-dependent diversification models have been developed to overcome these issues, some assuming continuous time variation while others model change as discrete time steps (Stadler 2011; Morlon et al 2011; Rabosky et al 2014; May et al 2016; Culshaw et al 2019; Höhna et al 2019). Nevertheless, concerns that these models lack statistical power and can generate an infinite array of undistinguishable diversification histories remain (Louca & Pennell 2020, but see Morlon et al 2020).…”