scholarly journals A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution

2018 ◽  
Vol 68 (1) ◽  
pp. 93-116 ◽  
Author(s):  
Julien Clavel ◽  
Leandro Aristide ◽  
Hélène Morlon
Keyword(s):  
New World ◽  
Penalized Likelihood ◽  
Brain Evolution ◽  
Comparative Methods ◽  
High Dimensional ◽  
New World Monkeys ◽  
Phylogenetic Comparative Methods

scholarly journals Phylogenetic Comparative Methods and the Evolution of Multivariate Phenotypes

2019 ◽  
Vol 50 (1) ◽  
pp. 405-425 ◽  
Author(s):  
Dean C. Adams ◽  
Michael L. Collyer
Keyword(s):  
Phylogenetic Analysis ◽  
Evolutionary Biology ◽  
Generalized Least Squares ◽  
Comparative Methods ◽  
High Dimensional ◽  
Phenotypic Integration ◽  
Phylogenetic Comparative Methods ◽  
Evolutionary Trends ◽  
Phenotype Space ◽  
Multivariate Phenotypes

Evolutionary biology is multivariate, and advances in phylogenetic comparative methods for multivariate phenotypes have surged to accommodate this fact. Evolutionary trends in multivariate phenotypes are derived from distances and directions between species in a multivariate phenotype space. For these patterns to be interpretable, phenotypes should be characterized by traits in commensurate units and scale. Visualizing such trends, as is achieved with phylomorphospaces, should continue to play a prominent role in macroevolutionary analyses. Evaluating phylogenetic generalized least squares (PGLS) models (e.g., phylogenetic analysis of variance and regression) is valuable, but using parametric procedures is limited to only a few phenotypic variables. In contrast, nonparametric, permutation-based PGLS methods provide a flexible alternative and are thus preferred for high-dimensional multivariate phenotypes. Permutation-based methods for evaluating covariation within multivariate phenotypes are also well established and can test evolutionary trends in phenotypic integration. However, comparing evolutionary rates and modes in multivariate phenotypes remains an important area of future development.

scholarly journals Brain shape convergence in the adaptive radiation of New World monkeys

2016 ◽  
Vol 113 (8) ◽  
pp. 2158-2163 ◽  
Author(s):  
Leandro Aristide ◽  
Sergio Furtado dos Reis ◽  
Alessandra C. Machado ◽  
Inaya Lima ◽  
Ricardo T. Lopes ◽  
...  
Keyword(s):  
Adaptive Radiation ◽  
New World ◽  
Ecological Factors ◽  
Brain Evolution ◽  
Brain Morphology ◽  
New World Monkeys ◽  
Evolutionary Diversification ◽  
Adaptive Peak ◽  
Brain Shape ◽  
Shape Changes

Primates constitute one of the most diverse mammalian clades, and a notable feature of their diversification is the evolution of brain morphology. However, the evolutionary processes and ecological factors behind these changes are largely unknown. In this work, we investigate brain shape diversification of New World monkeys during their adaptive radiation in relation to different ecological dimensions. Our results reveal that brain diversification in this clade can be explained by invoking a model of adaptive peak shifts to unique and shared optima, defined by a multidimensional ecological niche hypothesis. Particularly, we show that the evolution of convergent brain phenotypes may be related to ecological factors associated with group size (e.g., social complexity). Together, our results highlight the complexity of brain evolution and the ecological significance of brain shape changes during the evolutionary diversification of a primate clade.

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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