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A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data.

ABSTRACT: Phylogenetic trees are crucial to many aspects of taxonomic and comparative biology. Many researchers have adopted Bayesian methods to estimate their phylogenetic trees. In this family of methods, a model of morphological evolution is assumed to have generated the data observed by the researcher. These models make a variety of assumptions about the evolution of morphological characters, and these assumptions are translated into mathematics as parameters. The incorporation of prior distributions further allows researchers to quantify their prior beliefs about the value any one parameter can take. How to translate biological knowledge into mathematical language is difficult, and can be confusing to many biologists. This review aims to help systematics researchers understand the biological meaning of common models and assumptions. Using examples from the insect fossil record, I will demonstrate empirically what assumptions mean in concrete terms, and discuss how researchers can use and understand Bayesian methods for phylogenetic estimation.

SUBMITTER: Wright AM

PROVIDER: S-EPMC6643758 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data.

Wright April M AM

Insect systematics and diversity 20190501 3

Phylogenetic trees are crucial to many aspects of taxonomic and comparative biology. Many researchers have adopted Bayesian methods to estimate their phylogenetic trees. In this family of methods, a model of morphological evolution is assumed to have generated the data observed by the researcher. These models make a variety of assumptions about the evolution of morphological characters, and these assumptions are translated into mathematics as parameters. The incorporation of prior distributions ...[more]

PMID: 31355348

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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.

Project description:BackgroundThe Approximate Bayesian Computation (ABC) approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years.ResultsWe evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC.ConclusionsWe conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

Dataset Information

A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data.

Publications

A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data.

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