Evidence map›Paper›PMID 26272507›Full record

ArticleSystematic biology2015

A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology.

Seraina Klopfstein, Lars Vilhelmsen, Fredrik Ronquist

Abstract read
In one paragraph

Article in Systematic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Seraina KlopfsteinDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, SE-104 05 Stockholm, Sweden; The University of Adelaide, ACEBB, Adelaide SA 5005, Australia; Natural History Museum, Department of Invertebrates, CH-3005 Bern, Switzerland; klopfstein@nmbe.ch.
Lars VilhelmsenBiosystematics, Natural History Museum of Denmark, DK-2100 Copenhagen Ø, Denmark.
Fredrik RonquistDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, SE-104 05 Stockholm, Sweden;

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Directional evolution has played an important role in shaping the morphological, ecological, and molecular diversity of life. However, standard substitution models assume stationarity of the evolutionary process over the time scale examined, thus impeding the study of directionality. Here we explore a simple, nonstationary model of evolution for discrete data, which assumes that the state frequencies at the root differ from the equilibrium frequencies of the homogeneous evolutionary process along the rest of the tree (i.e., the process is nonstationary, nonreversible, but homogeneous). Within this framework, we develop a Bayesian approach for testing directional versus stationary evolution using a reversible-jump algorithm. Simulations show that when only data from extant taxa are available, the success in inferring directionality is strongly dependent on the evolutionary rate, the shape of the tree, the relative branch lengths, and the number of taxa. Given suitable evolutionary rates (0.1-0.5 expected substitutions between root and tips), accounting for directionality improves tree inference and often allows correct rooting of the tree without the use of an outgroup. As an empirical test, we apply our method to study directional evolution in hymenopteran morphology. We focus on three character systems: wing veins, muscles, and sclerites. We find strong support for a trend toward loss of wing veins and muscles, while stationarity cannot be ruled out for sclerites. Adding fossil and time information in a total-evidence dating approach, we show that accounting for directionality results in more precise estimates not only of the ancestral state at the root of the tree, but also of the divergence times. Our model relaxes the assumption of stationarity and reversibility by adding a minimum of additional parameters, and is thus well suited to studying the nature of the evolutionary process in data sets of limited size, such as morphology and ecology.

Indexed as

Biological EvolutionModels, BiologicalAnimalsComputer SimulationHymenopteraMarkov ChainsBayesian inferencecontinuous-time Markov modeldirectional selectionHymenopteramorphologyneutral evolutionnonstationarypositive selectionSymphyta

Identifiers

PMID26272507
PMCPMC4604834

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.