scholarly journals Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7917 ◽  
Author(s):  
Rafael S. Marcondes
Keyword(s):  
Parameter Estimation ◽  
Model Selection ◽  
Phylogenetic Signal ◽  
Comparative Methods ◽  
Phylogenetic Comparative Methods ◽  
Trait Evolution ◽  
Continuous Trait ◽  
Starting Point ◽  
Almost All ◽  
Future Work

Model-based analyses of continuous trait evolution enable rich evolutionary insight. These analyses require a phylogenetic tree and a vector of trait values for the tree’s terminal taxa, but rarely do a tree and dataset include all taxa within a clade. Because the probability that a taxon is included in a dataset depends on ecological traits that have phylogenetic signal, missing taxa in real datasets should be expected to be phylogenetically clumped or correlated to the modelled trait. I examined whether those types of missing taxa represent a problem for model selection and parameter estimation. I simulated univariate traits under a suite of Brownian Motion and Ornstein-Uhlenbeck models, and assessed the performance of model selection and parameter estimation under absent, random, clumped or correlated missing taxa. I found that those analyses perform well under almost all scenarios, including situations with very sparsely sampled phylogenies. The only notable biases I detected were in parameter estimation under a very high percentage (90%) of correlated missing taxa. My results offer a degree of reassurance for studies of continuous trait evolution with missing taxa, but the problem of missing taxa in phylogenetic comparative methods still demands much further investigation. The framework I have described here might provide a starting point for future work.

scholarly journals Is horizontal transmission really a problem for phylogenetic comparative methods? A simulation study using continuous cultural traits

2010 ◽  
Vol 365 (1559) ◽  
pp. 3903-3912 ◽  
Author(s):  
Thomas E. Currie ◽  
Simon J. Greenhill ◽  
Ruth Mace
Keyword(s):  
Cultural Evolution ◽  
Horizontal Transmission ◽  
Comparative Methods ◽  
Phylogenetic Comparative Methods ◽  
Ancestral State ◽  
Regression Analyses ◽  
Trait Evolution ◽  
Phylogenetic Methods ◽  
Cultural Traits ◽  
Future Work

Phylogenetic comparative methods (PCMs) provide a potentially powerful toolkit for testing hypotheses about cultural evolution. Here, we build on previous simulation work to assess the effect horizontal transmission between cultures has on the ability of both phylogenetic and non-phylogenetic methods to make inferences about trait evolution. We found that the mode of horizontal transmission of traits has important consequences for both methods. Where traits were horizontally transmitted separately , PCMs accurately reported when trait evolution was not correlated even at the highest levels of horizontal transmission. By contrast, linear regression analyses often incorrectly concluded that traits were correlated. Where simulated trait evolution was not correlated and traits were horizontally transmitted as a pair , both methods inferred increased levels of positive correlation with increasing horizontal transmission. Where simulated trait evolution was correlated, increasing rates of separate horizontal transmission led to decreasing levels of inferred correlation for both methods, but increasing rates of paired horizontal transmission did not. Furthermore, the PCM was also able to make accurate inferences about the ancestral state of traits. These results suggest that under certain conditions, PCMs can be robust to the effects of horizontal transmission. We discuss ways that future work can investigate the mode and tempo of horizontal transmission of cultural traits.

Typological profile of the Transeurasian languages from a quantitative perspective

2020 ◽  
pp. 144-160
Author(s):  
Nataliia Hübler
Keyword(s):  
Phylogenetic Signal ◽  
Structural Features ◽  
Comparative Methods ◽  
Phylogenetic Comparative Methods ◽  
Data Set ◽  
Bayesian Tree ◽  
Language History

This chapter provides an overview of the typological features of the Transeurasian (Turkic, Mongolic, Tungusic, Japonic, Koreanic) languages, including brief descriptions of the phonology and morphosyntax of these languages. Through the application of phylogenetic comparative methods, a set of structural features with a high phylogenetic signal is delimited. These features can be assumed to be genealogically stable. The trees achieved by Bayesian tree-sampling based on all 226 features are compared with those derived via the 97 structural features with a high phylogenetic signal and the conclusion reached is that the data set with presumably stable structural features does not provide a tree that is compatible with the language history assumed by classical historical linguists. Neither the full nor the reduced feature set provides a reliable internal classification of the Turkic, Mongolic, Tungusic, and Japonic language families.

scholarly journals Phylogenetic Comparative Methods on Phylogenetic Networks with Reticulations

2018 ◽  
Vol 67 (5) ◽  
pp. 800-820 ◽  
Author(s):  
Paul Bastide ◽  
Claudia Solís-Lemus ◽  
Ricardo Kriebel ◽  
K William Sparks ◽  
Cécile Ané
Keyword(s):  
Phylogenetic Signal ◽  
Statistical Tests ◽  
Recursive Algorithm ◽  
Species Traits ◽  
Comparative Methods ◽  
Hybrid Vigor ◽  
Phylogenetic Networks ◽  
Phylogenetic Comparative Methods ◽  
Data Set ◽  
Phylogenetic Regression

Abstract The goal of phylogenetic comparative methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species’ traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel’s $\lambda$ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a data set of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

scholarly journals Beyond Brownian motion and the Ornstein-Uhlenbeck process: Stochastic diffusion models for the evolution of quantitative characters

2016 ◽  
Author(s):  
Simon Phillip Blomberg
Keyword(s):  
Brownian Motion ◽  
Stochastic Processes ◽  
Comparative Methods ◽  
Diffusion Models ◽  
Mathematical Framework ◽  
Phylogenetic Comparative Methods ◽  
Uhlenbeck Process ◽  
Trait Evolution ◽  
Ornstein Uhlenbeck Process ◽  
Non Gaussian

AbstractGaussian processes such as Brownian motion and the Ornstein-Uhlenbeck process have been popular models for the evolution of quantitative traits and are widely used in phylogenetic comparative methods. However, they have drawbacks which limit their utility. Here I describe new, non-Gaussian stochastic differential equation (diffusion) models of quantitative trait evolution. I present general methods for deriving new diffusion models, and discuss possible schemes for fitting non-Gaussian evolutionary models to trait data. The theory of stochastic processes provides a mathematical framework for understanding the properties of current, new and future phylogenetic comparative methods. Attention to the mathematical details of models of trait evolution and diversification may help avoid some pitfalls when using stochastic processes to model macroevolution.

scholarly journals A variable-rate quantitative trait evolution model using penalized-likelihood

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11997
Author(s):  
Liam J. Revell
Keyword(s):  
Brownian Motion ◽  
Quantitative Trait ◽  
Penalized Likelihood ◽  
Comparative Methods ◽  
Character Trait ◽  
Likelihood Method ◽  
Rate Variation ◽  
Phylogenetic Comparative Methods ◽  
Trait Evolution ◽  
Rate Of Evolution

In recent years it has become increasingly popular to use phylogenetic comparative methods to investigate heterogeneity in the rate or process of quantitative trait evolution across the branches or clades of a phylogenetic tree. Here, I present a new method for modeling variability in the rate of evolution of a continuously-valued character trait on a reconstructed phylogeny. The underlying model of evolution is stochastic diffusion (Brownian motion), but in which the instantaneous diffusion rate (σ2) also evolves by Brownian motion on a logarithmic scale. Unfortunately, it’s not possible to simultaneously estimate the rates of evolution along each edge of the tree and the rate of evolution of σ2 itself using Maximum Likelihood. As such, I propose a penalized-likelihood method in which the penalty term is equal to the log-transformed probability density of the rates under a Brownian model, multiplied by a ‘smoothing’ coefficient, λ, selected by the user. λ determines the magnitude of penalty that’s applied to rate variation between edges. Lower values of λ penalize rate variation relatively little; whereas larger λ values result in minimal rate variation among edges of the tree in the fitted model, eventually converging on a single value of σ2 for all of the branches of the tree. In addition to presenting this model here, I have also implemented it as part of my phytools R package in the function multirateBM. Using different values of the penalty coefficient, λ, I fit the model to simulated data with: Brownian rate variation among edges (the model assumption); uncorrelated rate variation; rate changes that occur in discrete places on the tree; and no rate variation at all among the branches of the phylogeny. I then compare the estimated values of σ2 to their known true values. In addition, I use the method to analyze a simple empirical dataset of body mass evolution in mammals. Finally, I discuss the relationship between the method of this article and other models from the phylogenetic comparative methods and finance literature, as well as some applications and limitations of the approach.

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