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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.

SUBMITTER: Culshaw V

PROVIDER: S-EPMC6767073 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Culshaw Victoria V Stadler Tanja T Sanmartín Isabel I

Evolution; international journal of organic evolution 20190509 6

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 a ...[more]

PMID: 31017656

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Project description:The estimation of diversification rates is one of the most vividly debated topics in modern systematics, with considerable controversy surrounding the power of phylogenetic and fossil-based approaches in estimating extinction. Van Valen's seminal work from 1973 proposed the "Law of constant extinction," which states that the probability of extinction of taxa is not dependent on their age. This assumption of age-independent extinction has prevailed for decades with its assessment based on survivorship curves, which, however, do not directly account for the incompleteness of the fossil record, and have rarely been applied at the species level. Here, we present a Bayesian framework to estimate extinction rates from the fossil record accounting for age-dependent extinction (ADE). Our approach, unlike previous implementations, explicitly models unobserved species and accounts for the effects of fossil preservation on the observed longevity of sampled lineages. We assess the performance and robustness of our method through extensive simulations and apply it to a fossil data set of terrestrial Carnivora spanning the past 40 myr. We find strong evidence of ADE, as we detect the extinction rate to be highest in young species and declining with increasing species age. For comparison, we apply a recently developed analogous ADE model to a dated phylogeny of extant Carnivora. Although the phylogeny-based analysis also infers ADE, it indicates that the extinction rate, instead, increases with increasing taxon age. The estimated mean species longevity also differs substantially, with the fossil-based analyses estimating 2.0 myr, in contrast to 9.8 myr derived from the phylogeny-based inference. Scrutinizing these discrepancies, we find that both fossil and phylogeny-based ADE models are prone to high error rates when speciation and extinction rates increase or decrease through time. However, analyses of simulated and empirical data show that fossil-based inferences are more robust. This study shows that an accurate estimation of ADE from incomplete fossil data is possible when the effects of preservation are jointly modeled, thus allowing for a reassessment of Van Valen's model as a general rule in macroevolution.

Dataset Information

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

Publications

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

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