Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets

Smith, Martin R.

doi:10.1098/rsbl.2018.0632

Cited by 104 publications

(109 citation statements)

References 37 publications

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“…Recently, a suite of simulation studies have assessed the relative performance of Bayesian and parsimony phylogenetic inference built on categorical data ( Wright and Hillis 2014 ; O’Reilly et al 2016 ; Congreve and Lamsdell 2016 ; Puttick et al 2017b , 2019; O’Reilly et al 2018a ; Smith 2019a ). These simulation studies have followed one of two general approaches: they have utilized either a random model of character evolution ( Goloboff et al 2018 ; Puttick et al 2019 ) or likelihood-based Markov models of character evolution ( Wright and Hillis 2014 ; O’Reilly et al 2016 ; Brown et al 2017 ; Puttick et al 2017b ).…”

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confidence: 99%

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

et al. 2020

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Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here, we investigate phylogenetic methods using two novel simulation models—one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees—measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics—demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differ between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, toward identifying modes of morphological evolution and using these to build models for probabilistic search methods. [Bayesian; evolution; morphology; parsimony; phylogenetics; selection; simulation.]

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Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

et al. 2020

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“…For example, they can represent color palettes obtained by mixing three primary colors. In phylogenetics they have been used for different purposes than described here, by Strimmer and von Haeseler [1997] and Smith [2019]. Their first published use for representing quartet concordance factors was in several works closely related to this one [Baños, 2019, Mitchell et al, 2019, Allman et al, 2019b].…”

Section: Methodsmentioning

confidence: 99%

Gene tree discord, simplex plots, and statistical tests under the coalescent

Allman

Mitchell

Rhodes

2020

Preprint

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A simple graphical device, the simplex plot of quartet concordance factors, is introduced to aid in the exploration of a collection of gene trees on a common set of taxa. A single plot summarizes all gene tree discord, and allows for visual comparison to the expected discord from the multispecies coalescent model (MSC) of incomplete lineage sorting on a species tree. A formal statistical procedure is described that can quantify the deviation from expectation for each subset of four taxa, suggesting when the data is not in accord with the MSC, and thus either gene tree inference error is substantial or a more complex model such as that on a network may be required. If the collection of gene trees appears to be in accord with the MSC, the plots may reveal when substantial incomplete lineage sorting is present and coalescent based species tree inference is preferred over concatenation approaches. Applications to both simulated and empirical multilocus data sets illustrate the insights provided.

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“…-Score 3: the quartet agreement rate, derived from the quartet distance, which is defined as the number of subsets of four leaves that are not related by the same topology in the compared trees. These values, derived from the quartet divergence (Smith 2019a [40]) were calculated using the tqDist algorithm (Sand et al 2014 [41]) implemented in the R package Quartet v1.1.0 (Smith 2019b [42]).…”

Section: Measuring and Benchmarking Performancementioning

confidence: 99%

Stochastic approach to worldwide language classification: the signals and the noise towards long-range exploration

Beaufils¹,

Tomin²

2020

Preprint

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The exploration of distant language relationships reaching back to the Neolithic age remains very demanding and is often perceived as controversial. Inputs from data-driven systems have the potential to contribute substantially to the debate. We present a quantitative approach, using a very limited, but highly targeted set of language features as signals to identify and quantify language relatedness. The system processes language data and calculates distance values between languages completely automatically. The features, which are the basis of the algorithm, are themselves inferred through computer methods. The system ”learns" how to best classify languages by inferring the word list, sound change rules and other parameters that deliver the best classification on a limited amount of well-documented languages: we use undisputed classifications as a reference to “train" our system. The basis is a quantification of relatedness signals in the format of distances between pairs of languages and their statistical context, without judgement about relatedness. The enormous output of pairwise distances between thousands of languages allows a clustering process, which leads to an automatic classification. The possibility to apply statistical methods on the output data gives us the opportunity to explore and assess language relationships in a mere mathematical way and to challenge the results against chance. The scope of the project is global: the system processes 1962 languages from all families, the limit for inclusion of more languages being the availability of the material needed by our system. After exploring the methodology, we show that it leads to a reliable language classification, matching the reference listings to a high proportion. We then analyze long-range relationships and branch subgroupings the system unveils, which are not yet part of generally accepted classifications, but already subject of discussion by various scholars, so that our results add quantitative facts to existing debates. A further contribution is a series of new approaches in the field of measurement of the result accuracy as well as in the way we challenge them against chance.

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Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets

Cited by 104 publications

References 37 publications

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

Gene tree discord, simplex plots, and statistical tests under the coalescent

Stochastic approach to worldwide language classification: the signals and the noise towards long-range exploration

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