Skip to content

Uncertain-tree: Discriminating among competing approaches to the phylogenetic analysis of phenotype data

Research output: Contribution to journalArticle

Standard

Uncertain-tree : Discriminating among competing approaches to the phylogenetic analysis of phenotype data. / Puttick, Mark; O'Reilly, Joseph; Tanner, Alastair; Fleming, James; Clark, James; Holloway, Lucy; Lozano-Fernandez, Jesus; Parry, Luke; Tarver, James; Pisani, Davide; Donoghue, Philip.

In: Proceedings of the Royal Society B: Biological Sciences, Vol. 284, No. 1846, 20162290, 11.01.2017.

Research output: Contribution to journalArticle

Harvard

Puttick, M, O'Reilly, J, Tanner, A, Fleming, J, Clark, J, Holloway, L, Lozano-Fernandez, J, Parry, L, Tarver, J, Pisani, D & Donoghue, P 2017, 'Uncertain-tree: Discriminating among competing approaches to the phylogenetic analysis of phenotype data', Proceedings of the Royal Society B: Biological Sciences, vol. 284, no. 1846, 20162290. https://doi.org/10.1098/rspb.2016.2290

APA

Puttick, M., O'Reilly, J., Tanner, A., Fleming, J., Clark, J., Holloway, L., ... Donoghue, P. (2017). Uncertain-tree: Discriminating among competing approaches to the phylogenetic analysis of phenotype data. Proceedings of the Royal Society B: Biological Sciences, 284(1846), [20162290]. https://doi.org/10.1098/rspb.2016.2290

Vancouver

Puttick M, O'Reilly J, Tanner A, Fleming J, Clark J, Holloway L et al. Uncertain-tree: Discriminating among competing approaches to the phylogenetic analysis of phenotype data. Proceedings of the Royal Society B: Biological Sciences. 2017 Jan 11;284(1846). 20162290. https://doi.org/10.1098/rspb.2016.2290

Author

Puttick, Mark ; O'Reilly, Joseph ; Tanner, Alastair ; Fleming, James ; Clark, James ; Holloway, Lucy ; Lozano-Fernandez, Jesus ; Parry, Luke ; Tarver, James ; Pisani, Davide ; Donoghue, Philip. / Uncertain-tree : Discriminating among competing approaches to the phylogenetic analysis of phenotype data. In: Proceedings of the Royal Society B: Biological Sciences. 2017 ; Vol. 284, No. 1846.

Bibtex

@article{ad17a616a64c41ef8a0861e4351c6191,
title = "Uncertain-tree: Discriminating among competing approaches to the phylogenetic analysis of phenotype data",
abstract = "Morphological data provide the only means of classifying the majority of life’s history, but the choice between competing phylogenetic methods for the analysis of morphology is unclear. Traditionally, parsimony methods have been favoured but recent studies have shown that these approaches are less accurate than the Bayesian implementation of the Mk model. Here we expand on these findings in several ways: we assess the impact of tree shape and maximum-likelihood estimation using the Mk model, aswell as analysing data composed of both binary and multistate characters. We find that all methods struggle to correctly resolve deep clades within asymmetric trees, and when analysing small character matrices. The Bayesian Mk model is the most accurate method for estimating topology, but with lower resolution than other methods. Equal weights parsimony is more accurate than implied weights parsimony, and maximum-likelihood estimation using the Mk model is the least accurate method.We conclude that the Bayesian implementation of the Mk model should be the default method for phylogenetic estimation from phenotype datasets, and we explore the implications of our simulations in reanalysing several empirical morphological character matrices. A consequence of our finding is that high levels of resolution or the ability to classify species or groups with much confidence should not be expected when using small datasets. It is now necessary to depart from the traditional parsimony paradigms of constructing character matrices, towards datasets constructed explicitly for Bayesian methods.",
keywords = "phylogeny, Bayesian, parsimony, cladistics, morphology, palaeontology",
author = "Mark Puttick and Joseph O'Reilly and Alastair Tanner and James Fleming and James Clark and Lucy Holloway and Jesus Lozano-Fernandez and Luke Parry and James Tarver and Davide Pisani and Philip Donoghue",
year = "2017",
month = "1",
day = "11",
doi = "10.1098/rspb.2016.2290",
language = "English",
volume = "284",
journal = "Proceedings of the Royal Society B: Biological Sciences",
issn = "0962-8452",
publisher = "The Royal Society",
number = "1846",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Uncertain-tree

T2 - Discriminating among competing approaches to the phylogenetic analysis of phenotype data

AU - Puttick, Mark

AU - O'Reilly, Joseph

AU - Tanner, Alastair

AU - Fleming, James

AU - Clark, James

AU - Holloway, Lucy

AU - Lozano-Fernandez, Jesus

AU - Parry, Luke

AU - Tarver, James

AU - Pisani, Davide

AU - Donoghue, Philip

PY - 2017/1/11

Y1 - 2017/1/11

N2 - Morphological data provide the only means of classifying the majority of life’s history, but the choice between competing phylogenetic methods for the analysis of morphology is unclear. Traditionally, parsimony methods have been favoured but recent studies have shown that these approaches are less accurate than the Bayesian implementation of the Mk model. Here we expand on these findings in several ways: we assess the impact of tree shape and maximum-likelihood estimation using the Mk model, aswell as analysing data composed of both binary and multistate characters. We find that all methods struggle to correctly resolve deep clades within asymmetric trees, and when analysing small character matrices. The Bayesian Mk model is the most accurate method for estimating topology, but with lower resolution than other methods. Equal weights parsimony is more accurate than implied weights parsimony, and maximum-likelihood estimation using the Mk model is the least accurate method.We conclude that the Bayesian implementation of the Mk model should be the default method for phylogenetic estimation from phenotype datasets, and we explore the implications of our simulations in reanalysing several empirical morphological character matrices. A consequence of our finding is that high levels of resolution or the ability to classify species or groups with much confidence should not be expected when using small datasets. It is now necessary to depart from the traditional parsimony paradigms of constructing character matrices, towards datasets constructed explicitly for Bayesian methods.

AB - Morphological data provide the only means of classifying the majority of life’s history, but the choice between competing phylogenetic methods for the analysis of morphology is unclear. Traditionally, parsimony methods have been favoured but recent studies have shown that these approaches are less accurate than the Bayesian implementation of the Mk model. Here we expand on these findings in several ways: we assess the impact of tree shape and maximum-likelihood estimation using the Mk model, aswell as analysing data composed of both binary and multistate characters. We find that all methods struggle to correctly resolve deep clades within asymmetric trees, and when analysing small character matrices. The Bayesian Mk model is the most accurate method for estimating topology, but with lower resolution than other methods. Equal weights parsimony is more accurate than implied weights parsimony, and maximum-likelihood estimation using the Mk model is the least accurate method.We conclude that the Bayesian implementation of the Mk model should be the default method for phylogenetic estimation from phenotype datasets, and we explore the implications of our simulations in reanalysing several empirical morphological character matrices. A consequence of our finding is that high levels of resolution or the ability to classify species or groups with much confidence should not be expected when using small datasets. It is now necessary to depart from the traditional parsimony paradigms of constructing character matrices, towards datasets constructed explicitly for Bayesian methods.

KW - phylogeny

KW - Bayesian

KW - parsimony

KW - cladistics

KW - morphology

KW - palaeontology

UR - http://www.scopus.com/inward/record.url?scp=85011092063&partnerID=8YFLogxK

U2 - 10.1098/rspb.2016.2290

DO - 10.1098/rspb.2016.2290

M3 - Article

VL - 284

JO - Proceedings of the Royal Society B: Biological Sciences

JF - Proceedings of the Royal Society B: Biological Sciences

SN - 0962-8452

IS - 1846

M1 - 20162290

ER -