Influence of the tree prior and sampling scale on Bayesian phylogenetic estimates of the origin times of language families

2019 ◽  
Vol 4 (2) ◽  
pp. 108-123 ◽  
Author(s):  
Andrew M Ritchie ◽  
Simon Y W Ho
Keyword(s):  
Evolutionary Biology ◽  
Language Evolution ◽  
Model Parameters ◽  
Data Sets ◽  
Cultural Groups ◽  
Phylogenetic Methods ◽  
History Of ◽  
Erroneous Estimate ◽  
Coalescent Tree ◽  
Lexical Data

Abstract Bayesian phylogenetic methods derived from evolutionary biology can be used to reconstruct the history of human languages using databases of cognate words. These analyses have produced exciting results regarding the origins and dispersal of linguistic and cultural groups through prehistory. Bayesian lexical dating requires the specification of priors on all model parameters. This includes the use of a prior on divergence times, often combined with a prior on tree topology and referred to as a tree prior. Violation of the underlying assumptions of the tree prior can lead to an erroneous estimate of the timescale of language evolution. To investigate these impacts, we tested the sensitivity of Bayesian dating to the tree prior in analyses of four lexical data sets. Our results show that estimates of the origin times of language families are robust to the choice of tree prior for lexical data, though less so than when Bayesian phylogenetic methods are used to analyse genetic data sets. We also used the relative fit of speciation and coalescent tree priors to determine the ability of speciation models to describe language diversification at four different taxonomic levels. We found that speciation priors were preferred over a constant-size coalescent prior regardless of taxonomic scale. However, data sets with narrower taxonomic and geographic sampling exhibited a poorer fit to ideal birth–death model expectations. Our results encourage further investigation into the nature of language diversification at different sampling scales.

scholarly journals Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

2016 ◽  
Author(s):  
Kassian Kobert ◽  
Alexandros Stamatakis ◽  
Tomáš Flouri
Keyword(s):  
Evolutionary Biology ◽  
Likelihood Function ◽  
Simulated Data ◽  
Evolutionary Model ◽  
Identical Result ◽  
Model Parameters ◽  
Data Sets ◽  
Efficient Detection ◽  
Novel Method ◽  
Computational Bottleneck

The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory saving attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 10-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.

Bayesian Tip-Dated Phylogenetics in Paleontology: Topological Effects and Stratigraphic Fit

2020 ◽  
Author(s):  
Benedict King
Keyword(s):  
Phylogenetic Analysis ◽  
Bayesian Methods ◽  
Diversification Rate ◽  
Evolutionary Relationships ◽  
Data Sets ◽  
Tree Model ◽  
Phylogenetic Methods ◽  
History Of ◽  
Tree Topologies ◽  
Birth Death

Abstract The incorporation of stratigraphic data into phylogenetic analysis has a long history of debate but is not currently standard practice for paleontologists. Bayesian tip-dated (or morphological clock) phylogenetic methods have returned these arguments to the spotlight, but how tip dating affects the recovery of evolutionary relationships has yet to be fully explored. Here I show, through analysis of several data sets with multiple phylogenetic methods, that topologies produced by tip dating are outliers as compared to topologies produced by parsimony and undated Bayesian methods, which retrieve broadly similar trees. Unsurprisingly, trees recovered by tip dating have better fit to stratigraphy than trees recovered by other methods under both the Gap Excess Ratio (GER) and the Stratigraphic Completeness Index (SCI). This is because trees with better stratigraphic fit are assigned a higher likelihood by the fossilized birth-death tree model. However, the degree to which the tree model favors tree topologies with high stratigraphic fit metrics is modulated by the diversification dynamics of the group under investigation. In particular, when net diversification rate is low, the tree model favors trees with a higher GER compared to when net diversification rate is high. Differences in stratigraphic fit and tree topology between tip dating and other methods are concentrated in parts of the tree with weaker character signal, as shown by successive deletion of the most incomplete taxa from two data sets. These results show that tip dating incorporates stratigraphic data in an intuitive way, with good stratigraphic fit an expectation that can be overturned by strong evidence from character data. [fossilized birth-death; fossils; missing data; morphological clock; morphology; parsimony; phylogenetics.]

scholarly journals Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages

2011 ◽  
Vol 278 (1725) ◽  
pp. 3662-3669 ◽  
Author(s):  
Sean Lee ◽  
Toshikazu Hasegawa
Keyword(s):  
Linguistic Diversity ◽  
Language Evolution ◽  
Global Scale ◽  
Striking Similarity ◽  
Agricultural Expansion ◽  
Bayesian Phylogenetic Analysis ◽  
The People ◽  
History Of ◽  
Linguistic Evolution ◽  
Lexical Data

Languages, like genes, evolve by a process of descent with modification. This striking similarity between biological and linguistic evolution allows us to apply phylogenetic methods to explore how languages, as well as the people who speak them, are related to one another through evolutionary history. Language phylogenies constructed with lexical data have so far revealed population expansions of Austronesian, Indo-European and Bantu speakers. However, how robustly a phylogenetic approach can chart the history of language evolution and what language phylogenies reveal about human prehistory must be investigated more thoroughly on a global scale. Here we report a phylogeny of 59 Japonic languages and dialects. We used this phylogeny to estimate time depth of its root and compared it with the time suggested by an agricultural expansion scenario for Japanese origin. In agreement with the scenario, our results indicate that Japonic languages descended from a common ancestor approximately 2182 years ago. Together with archaeological and biological evidence, our results suggest that the first farmers of Japan had a profound impact on the origins of both people and languages. On a broader level, our results are consistent with a theory that agricultural expansion is the principal factor for shaping global linguistic diversity.

scholarly journals The riddle of Tasmanian languages

2012 ◽  
Vol 279 (1747) ◽  
pp. 4590-4595 ◽  
Author(s):  
Claire Bowern
Keyword(s):  
Recent Work ◽  
Evolutionary Biology ◽  
Clustering Algorithm ◽  
Evolutionary Processes ◽  
Phylogenetic Methods ◽  
Linguistic History ◽  
Language Research ◽  
History Of ◽  
Source Materials ◽  
Insight Into

Recent work which combines methods from linguistics and evolutionary biology has been fruitful in discovering the history of major language families because of similarities in evolutionary processes. Such work opens up new possibilities for language research on previously unsolvable problems, especially in areas where information from other sources may be lacking. I use phylogenetic methods to investigate Tasmanian languages. Existing materials are so fragmentary that scholars have been unable to discover how many languages are represented in the sources. Using a clustering algorithm which identifies admixture, source materials representing more than one language are identified. Using the Neighbor-Net algorithm, 12 languages are identified in five clusters. Bayesian phylogenetic methods reveal that the families are not demonstrably related; an important result, given the importance of Tasmanian Aborigines for information about how societies have responded to population collapse in prehistory. This work provides insight into the societies of prehistoric Tasmania and illustrates a new utility of phylogenetics in reconstructing linguistic history.

scholarly journals Inferring the ancestry of everyone

2018 ◽  
Author(s):  
Jerome Kelleher ◽  
Yan Wong ◽  
Patrick K. Albers ◽  
Anthony W. Wohns ◽  
Gil McVean
Keyword(s):  
Dna Sequences ◽  
Evolutionary Biology ◽  
Sequence Data ◽  
Data Sets ◽  
Original Sequence ◽  
Efficient Access ◽  
History Of ◽  
Comparable Accuracy ◽  
Rich Information ◽  
Inference Methods

AbstractA central problem in evolutionary biology is to infer the full genealogical history of a set of DNA sequences. This history contains rich information about the forces that have influenced a sexually reproducing species. However, existing methods are limited: the most accurate is unable to cope with more than a few dozen samples. With modern genetic data sets rapidly approaching millions of genomes, there is an urgent need for efficient inference methods to exploit such rich resources. We introduce an algorithm to infer whole-genome history which has comparable accuracy to the state-of-the-art but can process around four orders of magnitude more sequences. Additionally, our method results in an “evolutionary encoding” of the original sequence data, enabling efficient access to genealogies and calculation of genetic statistics over the data. We apply this technique to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the genealogies we estimate are both rich in biological signal and efficient to process.

scholarly journals Bayesian phylogenetic analysis of linguistic data using BEAST

2021 ◽  
Author(s):  
Konstantin Hoffmann ◽  
Remco Bouckaert ◽  
Simon J Greenhill ◽  
Denise Kühnert
Keyword(s):  
Phylogenetic Analysis ◽  
Language Evolution ◽  
Software Tool ◽  
Theoretical Background ◽  
Linguistic Data ◽  
Bayesian Phylogenetic Analysis ◽  
Phylogenetic Methods ◽  
History Of ◽  
Set Up ◽  
Clock Models

Abstract Bayesian phylogenetic methods provide a set of tools to efficiently evaluate large linguistic datasets by reconstructing phylogenies—family trees—that represent the history of language families. These methods provide a powerful way to test hypotheses about prehistory, regarding the subgrouping, origins, expansion, and timing of the languages and their speakers. Through phylogenetics, we gain insights into the process of language evolution in general and into how fast individual features change in particular. This article introduces Bayesian phylogenetics as applied to languages. We describe substitution models for cognate evolution, molecular clock models for the evolutionary rate along the branches of a tree, and tree generating processes suitable for linguistic data. We explain how to find the best-suited model using path sampling or nested sampling. The theoretical background of these models is supplemented by a practical tutorial describing how to set up a Bayesian phylogenetic analysis using the software tool BEAST2.

scholarly journals Theory and Applications of the Unit Gamma/Gompertz Distribution

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1850
Author(s):  
Rashad A. R. Bantan ◽  
Farrukh Jamal ◽  
Christophe Chesneau ◽  
Mohammed Elgarhy
Keyword(s):  
Stochastic Ordering ◽  
Real Data ◽  
Rate Function ◽  
The Other ◽  
Likelihood Method ◽  
Model Parameters ◽  
Data Sets ◽  
Gompertz Distribution ◽  
Probability And Statistics ◽  
Analytical Behavior

Unit distributions are commonly used in probability and statistics to describe useful quantities with values between 0 and 1, such as proportions, probabilities, and percentages. Some unit distributions are defined in a natural analytical manner, and the others are derived through the transformation of an existing distribution defined in a greater domain. In this article, we introduce the unit gamma/Gompertz distribution, founded on the inverse-exponential scheme and the gamma/Gompertz distribution. The gamma/Gompertz distribution is known to be a very flexible three-parameter lifetime distribution, and we aim to transpose this flexibility to the unit interval. First, we check this aspect with the analytical behavior of the primary functions. It is shown that the probability density function can be increasing, decreasing, “increasing-decreasing” and “decreasing-increasing”, with pliant asymmetric properties. On the other hand, the hazard rate function has monotonically increasing, decreasing, or constant shapes. We complete the theoretical part with some propositions on stochastic ordering, moments, quantiles, and the reliability coefficient. Practically, to estimate the model parameters from unit data, the maximum likelihood method is used. We present some simulation results to evaluate this method. Two applications using real data sets, one on trade shares and the other on flood levels, demonstrate the importance of the new model when compared to other unit models.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

The New Statistics with R

2021 ◽  
Author(s):  
Andy Hector
Keyword(s):  
Linear Model ◽  
Evolutionary Biology ◽  
Environmental Science ◽  
Research Training ◽  
Model Analysis ◽  
Scientific Data ◽  
Information Criteria ◽  
Reproducible Research ◽  
Data Sets ◽  
R Programming

Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear-model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of overuse of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics, which is rapidly becoming the lingua franca in many areas of science. This second edition adds new chapters, including one discussing some of the complexities of linear-model analysis and another introducing reproducible research documents using the R Markdown package. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.

The Deconstruction of Philology

boundary 2 ◽  
2021 ◽  
Vol 48 (1) ◽  
pp. 17-34
Author(s):  
David Golumbia
Keyword(s):  
Evolutionary Biology ◽  
Human Diversity ◽  
Wilhelm Von Humboldt ◽  
History Of

The history of philology provides an exceptionally rich vein for locating what Derrida came to call deconstructions: nodes or pseudo-events in the development of discourse where it appears that foundations collapse, only to be rebuilt in forms that may or may not have changed. The history of philology engages language, the sciences (especially evolutionary biology), and race, all of which are evidenced in the work of the German philologist Wilhelm von Humboldt. The relationships among these discourses have been repeatedly subject to deconstruction, sometimes so as to enhance appreciation of human diversity, and at other times against it. Understanding the history of philology is critical to understanding our present, but there remains significant work to do to reconstruct its liberatory aspects in the service of a more egalitarian future.

Sign in / Sign up

Export Citation Format

Share Document