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INTEGRATING FOSSILS WITH MOLECULAR PHYLOGENIES IMPROVES INFERENCE OF TRAIT EVOLUTION

Overview of attention for article published in Evolution, July 2012
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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431 Mendeley
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Article details
Title
INTEGRATING FOSSILS WITH MOLECULAR PHYLOGENIES IMPROVES INFERENCE OF TRAIT EVOLUTION
Published in
Evolution, July 2012
DOI 10.1111/j.1558-5646.2012.01723.x
Pubmed ID
Authors
Abstract

Comparative biologists often attempt to draw inferences about tempo and mode in evolution by comparing the fit of evolutionary models to phylogenetic comparative data consisting of a molecular phylogeny with branch lengths and trait measurements from extant taxa. These kinds of approaches ignore historical evidence for evolutionary pattern and process contained in the fossil record. In this article, we show through simulation that incorporation of fossil information dramatically improves our ability to distinguish among models of quantitative trait evolution using comparative data. We further suggest a novel Bayesian approach that allows fossil information to be integrated even when explicit phylogenetic hypotheses are lacking for extinct representatives of extant clades. By applying this approach to a comparative dataset comprising body sizes for caniform carnivorans, we show that incorporation of fossil information not only improves ancestral state estimates relative to those derived from extant taxa alone, but also results in preference of a model of evolution with trend toward large body size over alternative models such as Brownian motion or Ornstein-Uhlenbeck processes. Our approach highlights the importance of considering fossil information when making macroevolutionary inference, and provides a way to integrate the kind of sparse fossil information that is available to most evolutionary biologists.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 431 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 20 5%
Brazil 9 2%
United Kingdom 6 1%
Germany 2 <1%
Canada 2 <1%
Argentina 2 <1%
Sweden 1 <1%
Serbia 1 <1%
Portugal 1 <1%
Other 5 1%
Unknown 382 89%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 111 26%
Researcher 84 19%
Student > Master 44 10%
Student > Bachelor 32 7%
Student > Doctoral Student 31 7%
Other 76 18%
Unknown 53 12%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 250 58%
Earth and Planetary Sciences 59 14%
Biochemistry, Genetics and Molecular Biology 23 5%
Environmental Science 17 4%
Social Sciences 4 <1%
Other 13 3%
Unknown 65 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2017.
All research outputs
#4,960,459
of 34,151,039 outputs
Outputs from Evolution
#1,254
of 6,668 outputs
Outputs of similar age
#30,236
of 213,135 outputs
Outputs of similar age from Evolution
#9
of 45 outputs
Altmetric has tracked 34,151,039 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,668 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 213,135 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.