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PACo: A Novel Procrustes Application to Cophylogenetic Analysis

Overview of attention for article published in PLOS ONE, April 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

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6 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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199 Dimensions

Readers on

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255 Mendeley
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1 CiteULike
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Title
PACo: A Novel Procrustes Application to Cophylogenetic Analysis
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0061048
Pubmed ID
Authors

Juan Antonio Balbuena, Raúl Míguez-Lozano, Isabel Blasco-Costa

Abstract

We present Procrustean Approach to Cophylogeny (PACo), a novel statistical tool to test for congruence between phylogenetic trees, or between phylogenetic distance matrices of associated taxa. Unlike previous tests, PACo evaluates the dependence of one phylogeny upon the other. This makes it especially appropriate to test the classical coevolutionary model that assumes that parasites that spend part of their life in or on their hosts track the phylogeny of their hosts. The new method does not require fully resolved phylogenies and allows for multiple host-parasite associations. PACo produces a Procrustes superimposition plot enabling a graphical assessment of the fit of the parasite phylogeny onto the host phylogeny and a goodness-of-fit statistic, whose significance is established by randomization of the host-parasite association data. The contribution of each individual host-parasite association to the global fit is measured by means of jackknife estimation of their respective squared residuals and confidence intervals associated to each host-parasite link. We carried out different simulations to evaluate the performance of PACo in terms of Type I and Type II errors with respect to two similar published tests. In most instances, PACo performed at least as well as the other tests and showed higher overall statistical power. In addition, the jackknife estimation of squared residuals enabled more elaborate validations about the nature of individual links than the ParaFitLink1 test of the program ParaFit. In order to demonstrate how it can be used in real biological situations, we applied PACo to two published studies using a script written in the public-domain statistical software R.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 255 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
Canada 2 <1%
Germany 1 <1%
Tanzania, United Republic of 1 <1%
France 1 <1%
Brazil 1 <1%
India 1 <1%
Portugal 1 <1%
Chile 1 <1%
Other 3 1%
Unknown 240 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 78 31%
Researcher 46 18%
Student > Master 35 14%
Student > Bachelor 24 9%
Professor 15 6%
Other 31 12%
Unknown 26 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 131 51%
Biochemistry, Genetics and Molecular Biology 31 12%
Environmental Science 24 9%
Earth and Planetary Sciences 8 3%
Computer Science 6 2%
Other 22 9%
Unknown 33 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 02 July 2017.
All research outputs
#6,006,966
of 22,487,039 outputs
Outputs from PLOS ONE
#71,394
of 192,257 outputs
Outputs of similar age
#45,382
of 177,033 outputs
Outputs of similar age from PLOS ONE
#1,396
of 4,557 outputs
Altmetric has tracked 22,487,039 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 192,257 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has gotten more attention than average, scoring higher than 62% 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 177,033 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 4,557 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.