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We also explored the ability of Bayesian model testing to select the best-performing priors. We confirmed the applicability of our results to empirical data sets from cetaceans, phocids, and coregonid whitefish. Estimates of node times were generally robust to the choice of tree prior, but some combinations of tree priors and sampling schemes led to large differences in the age estimates.

In particular, the pure-birth tree prior frequently led to inaccurate estimates for data sets containing a mixture of inter- and intraspecific sampling, whereas the birth—death and skyline coalescent priors produced stable results across all scenarios. Model testing provided an adequate means of rejecting inappropriate tree priors.

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Our results suggest that tree priors do not strongly affect Bayesian molecular dating results in most cases, even when severely misspecified. However, the choice of tree prior can be significant for the accuracy of dating results in the case of data sets with mixed inter- and intraspecies sampling. Bayesian phylogenetic methodsmodel testingmolecular datingnode timetree prior Developments in studying the timescale and dynamics of evolution are increasingly blurring the traditional distinction between the phylogenetic and population genetic domains.

The rise of methods for molecular biogeography and species delimitation, along with the advances in high-throughput sequencing technology, have led to a greater focus on data sets that comprise multiple individuals across multiple species e. As a consequence, molecular dating methods are frequently applied to data sets containing multiple individuals per species.

This might be done, for example, as an initial stage in investigations of groups with unknown or disputed species boundaries e. The behavior of mixed inter- and intraspecies data sets in molecular dating analysis is generally not well understood. One area of particular concern is the effect of the tree prior under the many popular Bayesian dating frameworks. Bayesian phylogenetic methods are among the most widely used tools for estimating evolutionary divergence times.

These methods can estimate posterior probabilities for evolutionary histories given available genetic data, and account for uncertainty in date estimates through co-estimation with and integration over other model parameters Dos Reis et al. Common to all Bayesian inference is the requirement to supply a prior probability distribution over the space of hypotheses being considered, representing our beliefs regarding their probabilities before any new observations are made.

Accordingly, Bayesian phylogenetic methods require the specification of prior beliefs for substitution model parameters, evolutionary rates, node times, and tree topology. The priors on the topology and node times are normally specified as a joint density, referred to as the tree prior Heath and Moore The problem of specifying tree priors is one of the central challenges in Bayesian molecular dating. This was first done by using a result for the joint density of labeled phylogenetic histories and node times produced by a reconstructed constant-rates birth—death process Rannala and Yang Birth—death priors used in molecular dating give the density of a time-resolved phylogeny reconstructed from a sample of extant species, given per-lineage rates of speciation birth and extinction death that are constant through time Feller ; Kendall ; Thompson ; Nee et al.

The birth and death rates, or functions thereof, are themselves typically given prior distributions, resulting in a hierarchical Bayesian framework.

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A special case of the birth—death process, the pure-birth or Yule process Yule ; Edwardsis often used for cases in which extinction rates are expected to be negligible. Further research has led to substantial refinement and extension of the original framework of the birth—death model of speciation. Yang and Rannala initially extended the model to account for incomplete taxon sampling, based on the work by Nee et al. Later developments have included priors for birth—death trees with sequentially sampled tips Stadler and Yang ; approaches to relaxing the assumption of constant diversification and extinction rates reviewed by Stadler ; tree priors conditioned on calibration densities Heled and Drummond ; Heled and Drummond ; and explicit incorporation of fossilization in the branching process Heath et al.

Alongside these developments, the Bayesian dating framework has also been adapted to population genetic studies through the use of tree priors based on the coalescent Kingman ; Kuhner et al.

Coalescent tree priors are parameterized with respect to a model of demographic change through time. The simplest such models are those that assume a deterministic process of population change, such as a constantsize Kuhner et al. Later developments, such as the Bayesian skyline model Drummond et al. Mixed inter- and intraspecies data sets of the kind used in species-delimitation methods pose difficulties for the existing range of tree priors because they possess characteristics of both species-level and population-level data sets, meaning that both the speciation and coalescent priors are misspecified.

In these cases, the practical question arises of whether the choice of tree prior has any effect on the accuracy and precision of resulting date estimates and, if so, whether there is any way of determining which prior is most appropriate to a given data set. Mixed data sets could also provide an interesting test case for studying the potential of tree priors to affect Bayesian divergence-time estimation in general.

There have been few investigations of the impact of tree priors on divergence-time estimation in comparison with other aspects of Bayesian methods, probably because conventional choices are generally limited to the constant-rate birth—death prior for species-level data sets and the various coalescent priors for population-level data. The first study to make use of tree priors Rannala and Yang reported that results obtained using a birth—death tree prior were robust to variation of the birth rate.

A simulation-based investigation by Ho et al. However, so far, there hasn't been clear evidence for this hypothesis. Also, even if this turned out to be the case, we would effectively be "kicking the can down the road" on our question of how life arose. Even if life came to Earth from elsewhere in the universe, we would still be left wondering how it arose to begin with wherever it did arise. Summary How life originated on our planet is both a fascinating and incredibly complex question.

We know roughly when life began, but how remains a mystery. Miller, Urey, and others showed that simple inorganic molecules could combine to form the organic building blocks required for life as we know it. Once formed, these building blocks could have come together to form polymers such as proteins or RNA. Other ideas include the pre-RNA world hypothesis and the metabolism-first hypothesis.

Organic compounds could have been delivered to early Earth by meteorites and other celestial objects. These are not the only scientific ideas about how life might have originated, nor are any of them conclusive. Keep your ears and your mind open as new information becomes available and new scientific ideas are proposed concerning life's origins.

The first 3 billion years of evolution. Palaeontology, 2 11 Microfossils of sulphur-metabolizing cells in 3. Nature Geoscience, 4, Retrieved May 22, from Wikipedia: In Origin of life: Retrieved May 22, from Wikibooks: In Kimball's biology pages. What have learned about prebiotic organic synthesis reactions in the past 60 years?

Synthesis of activated pyrimidine ribonucleotides in prebiotically plausible conditions. Proteins and metabolism first: In The origins of life and the universe pp. Montmorillonite-catalysed formation of RNA oligomers: To assess the performance of the test, we calculated the number of type I and type II errors for both criteria.

In our simulation framework, we consider that a data set has sufficient temporal signal if the estimate of the substitution rate is accurate. Accuracy of Rate Estimates in Simulations The substitution rate used in our simulations was an important determinant of the reliability of rate estimates.

The axes are in log10 scale. The columns A—C represent data simulated using different mean substitution rates: The rows correspond to different levels of rate variation among branches used in the simulations, defined as the standard deviation of rates in the lognormal relaxed-clock model: Solid horizontal lines indicate the mean rate used in the simulations, whereas the dashed horizontal lines correspond to 1 standard deviation on either side of the mean.

Symbols in black represent rate estimates that passed the date-randomization test according to criteria CR1 and CR2. We conducted 20 randomizations for the date-randomization test for all data sets. Rate estimates that failed the date-randomization are shown in gray. Squares denote rate estimates that failed the test according to both CR1 and CR2, whereas triangles denote those that failed according to CR2 only.

Numbers of type I and type II errors are shown for each rate treatment. A type I error occurs when the estimate from a data set with no temporal signal passes the test.

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A type II error occurs when the estimate from a data set with sufficient temporal signal fails the test. Analyses of the data sets with narrow calibration windows produced estimates that were less accurate and precise than those with wide calibration windows fig.

This pattern was most pronounced for the data simulated using medium and low substitution rates. In these cases, analyses of the data sets with narrow calibration windows produced mean rate estimates that were up to eight times as high as those used to generate the data fig. The degree of rate variation among branches did not appear to have a strong impact on the accuracy of the estimates, suggesting that our choice of a lognormal relaxed-clock model performed well under the simulation conditions.

Performance of the Date-Randomization Test The performance of the date-randomization test depended on the criterion used. Many data sets with no temporal signal, and with correspondingly inaccurate rate estimates, passed the test according to CR1, with a large number of type I errors. CR2 was preferable in the sense that it resulted in few type I errors.

However, the improved detection of inaccurate estimates when using CR2 came at the expense of many accurate estimates failing the test, with higher numbers of type II errors than when using CR1. Our simulation study illustrates the trade-off between the two types of errors for CR1 and CR2. We report the number of errors per rate treatment, each of which includes the rate estimates from 40 data sets, each with 20 randomizations for the date-randomization test.

In the simulations with low mean rate and medium rate variation, six inaccurate rate estimates passed the test for CR1, whereas all of the inaccurate estimates failed the test according to CR2. In contrast, only one accurate estimate failed the test according to CR1, but six accurate estimates failed the test according to CR2 fig. We do not consider type I errors for the data simulated with high rates because the estimates always included the true rate fig.

In most of our analyses of simulated data, the use of CR2 led to zero type I errors. The exceptions were three data sets simulated with low mean rate and zero rate variation fig. The mean rate estimated from these data sets was up to 7. The largest number of type II errors was 14, for CR2 in the data simulated with a high mean rate and high rate variation fig.