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ZENODO
Dataset . 2016
License: CC 0
Data sources: ZENODO
DRYAD
Dataset . 2016
License: CC 0
Data sources: Datacite
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Data from: Inference of evolutionary jumps in large phylogenies using Lévy processes

Authors: Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M.; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel; Wegmann, Daniel;

Data from: Inference of evolutionary jumps in large phylogenies using Lévy processes

Abstract

While it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson's hypothesis.

Supplementary FiguressupInfo_resubmission.pdf

Keywords

Lévy process, Evolutionary jump, phenotypic evolution, Quantitative traits, Expectation-Maximization algorithm, Anolis, quantitative traits, Loriini

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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