DOI: 10.5061/dryad.r5f70
title: Data from: Estimating age-dependent extinction: contrasting evidence from fossils and phylogenies

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 age-dependent extinction, 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.
description: Supplementary
 MaterialSupplementary Material
 including Figures and Table.Hagen_et_al_2017_SM.pdfCarnivora Fossil and Pylogenetic Empirical DataThe folder “empirical_data” contains the empirical data used for estimating age-dependent extinction processes. The folder “fossil_ Pires_et_al_2015” stores fossil data of Carnivora from the Northern Hemisphere compiled and cleansed by Pires et al. (2015). Inside the folder, “all_carnivora_SpeciesList.txt” provides a species list with extinct or extant status. The file “all_carnivora.py” is a ready for PyRate input file of the fossil data set, for more information please refer to PyRate help literature provided at https://github.com/dsilvestro/PyRate/wiki. The files “all_carnivora_combined_10_files.log” and “all_carnivora_Neogene_combined_10_files.log” are the outputs from the PyRate runs of the 10 replicated data sets for the entire fossil data set and the data set for the fossils found during the Neogene (see Fig2 for a schematic workflow). The folder “phylogenetic_Nyakatura_Bininda-Emonds_2012” stores the dated phylogeny of 286 species extant carnivores, with crown age at about 65 Ma build by Nyakatura and Bininda-Emonds (2012) References - Pires M.M., Silvestro D., Quental T.B. 2015. Continental faunal exchange and the asymmetrical radiation of carnivores. Proc. R. Soc. Biol. Sci. Ser. B 282. - Nyakatura K., Bininda-Emonds O.R.P. 2012. Updating the evolutionary history of carnivora (mammalia): A new species-level supertree complete with divergence time estimates. Bmc Biol 10.empirical_data.zipFossil SimulationsThe folder “fossil_simulations” contains the R and Python scripts used for the fossils simulation study. The folder “1.simulators” contains the R scripts adapted from (Hagen and Stadler 2014) used to simulate the initial complete phylogenies. “SimulatorScenarioIandII_ConstantOverTime.R” was used for simulating scenarios I and II, “SimulatorScenarioIII_2TimeBin.R” was used for simulating scenario III and “SimulatorScenarioIV_2TimeBin_BIGDROP.R” was used for simulating scenario IV. Please note that these files require the parameters given at “parametersage.csv” and generated by the scripts “Parametersage_Generator.R” found respectively inside each scenario folder. Please change the working directories before running the scripts. To generate fossil data from the phylogenies the script “2.FossilSamplerRunOverFolder.py” was run, evoking “fossil_sampler.py” for all fossils inside the respective scenario. The script “3.GetPyRateEstimatesRunOverFolders.py” automates a PyRate estimation while “4.PlottingScenarios.R” is used for visualizing the result. “SummarySimulations.RData” summarizes the results and analysis conducted. Each scenario folder also contains a “summary_ScenarioX.csv” file recording the number of: sampled species, singletons, simulated extinct species, simulated extant species, fossil occurrences and several other returned values from PyRate. References - Hagen O., Hartmann K., Steel M., Stadler T. 2015. Age-Dependent Speciation Can Explain the Shape of Empirical Phylogenies. Syst Biol. 64 (3): 432-440. doi: 10.1093/sysbio/syv001. - Hagen O., Stadler T. 2017. Treesimgm: Simulating phylogenetic trees under general bellman harris models with lineage-specific shifts of speciation and extinction in r. Methods Ecol. Evol. in press.fossil_simulations.zipSimulation of MorphospeciesScript used to measure the effect of anagenesis on the estimation of true ADE shape parameter (Supplementary Material Fig. S6).AnagenesisExperimentFigS6.R