Brain shape convergence in the adaptive radiation of New World monkeys

Aristide, Leandro; Reis, Sérgio Furtado dos; Machado, Aryane Flauzino; Lima, I.; Lopes, Ricardo Tadeu; Pérez, S. Iván

doi:10.1073/pnas.1514473113

Cited by 67 publications

(134 citation statements)

References 60 publications

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“…For example, coincident with our results for cluster 1, the anterior frontal region would have diversified early into discrete trait optima, while convergent changes would be mostly associated with other areas of the frontal region, in agreement with our cluster 3. Overall, our results support the idea that there has been differential selection on different brain regions in New World monkeys, due both to an early adaptive radiation and convergence on ecologically relevant traits (Rosenberger 1992; Gavrilets and Losos 2009; Aristide et al 2015, 2016).…”

Section: Discussionsupporting

confidence: 83%

Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum

Lewitus

Aristide

Morlon

2019

Preprint

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The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no non-parametric comprehensive framework for characterising and comparing patterns of phenotypic evolution. Here we build on a recently introduced approach for using the phylogenetic spectral density profile to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for non-parametric analysis of phylogenetic trait data. We show how to construct the spectral density profile of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.Phylogenetic trait data are essential to understanding the evolution of biodiversity. 1 They have been used to identify adaptive radiations (Harmon et al. 2010), infer stabilizing 2 selection (Hansen 1997; Butler and King 2004), measure the phenotypic effects of species 3 interactions (Drury et al. 2018) and environmental fluctuations (Clavel and Morlon 2017), 4 and generally to estimate the role of the phylogeny in how traits evolve over time 5 ( Felsenstein, 1973). They are critical to connecting microevolutionary processes of natural 6 selection to macroevolutionary patterns of phenotypic evolution (Hansen and Martins 7 1996). 8A wide range of approaches, reflecting the general interest of trait evolution among 9 evolutionary biologists, have been developed to infer the mode and tempo of phenotypic 10 evolution across clades. These include summary statistics that test for the degree of 11 phylogenetic signal in trait data, such as Blomberg's K (Blomberg et al. 2003), and 12 maximum likelihood-based techniques that fit models to phylogenetic trait data and 13 estimate the rate at which traits evolve (see Pennell and Harmon (2013); Manceau et al. 14 (2016); Lewitus (2018) for a review of currently available models). These models rely on 15 the a priori formulation of a phenotypic mo...

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Section: Discussionsupporting

confidence: 83%

Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum

Lewitus

Aristide

Morlon

2019

Preprint

Self Cite

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“…It states that there exists an adaptive landscape shaping the evolution of functional traits, and that this landscape might shift, sometimes in a dramatic fashion, in response to environmental changes such as migration, or colonization of a new ecological niche. Such shifts, like the one observed in the brain shape and size of New World Monkeys in association with dietary and locomotion changes (Aristide et al 2015, 2016), need to be explicitly accounted for in models of phenotypic evolution.…”

Section: Motivationmentioning

confidence: 99%

“…We first considered the evolution of brain shape in New World Monkeys studied by Aristide et al (2016). The dataset consists of 49 species on a time-calibrated maximum-likelihood tree.…”

Section: Examplesmentioning

confidence: 99%

Inference of Adaptive Shifts for Multivariate Correlated Traits

Bastide

Ané

Robin

et al. 2017

Preprint

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Abstract.-To study the evolution of several quantitative traits, the classical phylogenetic 14 comparative framework consists of a multivariate random process running along the 15 branches of a phylogenetic tree. The Ornstein-Uhlenbeck (OU) process is sometimes 16 preferred to the simple Brownian Motion (BM) as it models stabilizing selection toward an 17 optimum. The optimum for each trait is likely to be changing over the long periods of time 18 spanned by large modern phylogenies. Our goal is to automatically detect the position of 19 these shifts on a phylogenetic tree, while accounting for correlations between traits, which 20 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/146191 doi: bioRxiv preprint first posted online Jun.

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“…The pointwise envelope method continues to be a popular method of inference, often as a diagnostic test in conjunction with the MDI test (e.g. Arbour & Lopez‐Fernandez, ; Aristide et al., ; Blackburn et al., ; Dornburg et al., ; Feilich, ; Hlusko, Schmitt, Monson, Brasil, & Mahaney, ; Ingram, ; Johnson & Omland, ; Slater et al., ; Weber, Mitko, Eltz, & Ramirez, ). The visual/graphical interpretation of the DTT curve with an envelope test has extra appeal as it can be used to identify time points where the burst of non‐Brownian evolution occurred, enabling correlation with known evolutionary or environmental events that have triggered the burst.…”

Section: Introductionmentioning

confidence: 99%

A global envelope test to detect non‐random bursts of trait evolution

Murrell

2018

Methods Ecol Evol

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The joint analysis of species’ evolutionary relatedness and their morphological evolution has offered much promise in understanding the processes that underpin the generation of biological diversity. Disparity through time (DTT) is a popular method that estimates the relative trait disparity within and between subclades, and compares this to the null hypothesis that trait values follow Brownian evolution along the time‐calibrated phylogenetic tree. To visualise the differences a confidence envelope is normally created by calculating, at every time point, the 97.5% minimum and 97.5% maximum disparity values from multiple simulations of the null model. The null hypothesis is rejected whenever the empirical DTT curve falls outside of this envelope, and these time periods may then be linked to events that may have sparked non‐random trait evolution. Here, simulated data are used to show this pointwise (ranking at each time point) method of envelope construction suffers from multiple testing and a poor, uncontrolled, false‐positive rate. As a consequence it cannot be recommended. Instead, each DTT curve can be given a single rank based upon their most extreme disparity value, relative to all other curves, and across all time points. Ordering curves this way leads to a test that avoids multiple testing, but still allows construction of a confidence envelope. The null hypothesis is rejected if the empirical DTT curve is ranked within the most extreme 5% ranked curves from the null model. Comparison of the rank envelope curve to the Morphological Disparity Index and Node Height tests shows it to have generally higher power to detect non‐Brownian trait evolution. An extension to allow simultaneous testing over multiple traits is also detailed. Overall the results suggest the new rank envelope test should be used in null model testing for DTT analyses. The rank envelope method can easily be adapted into recently developed posterior predictive simulation methods used in model selection analyses. More generally, the rank envelope test should be adopted whenever a null model produces a vector of correlated values and the user wants to determine where the empirical data are different to the null model.

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Brain shape convergence in the adaptive radiation of New World monkeys

Cited by 67 publications

References 60 publications

Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum

Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum

Inference of Adaptive Shifts for Multivariate Correlated Traits

A global envelope test to detect non‐random bursts of trait evolution

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