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Computing evolutionary distinctiveness indices in large scale analysis

Overview of attention for article published in Algorithms for Molecular Biology, April 2012
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Title
Computing evolutionary distinctiveness indices in large scale analysis
Published in
Algorithms for Molecular Biology, April 2012
DOI 10.1186/1748-7188-7-6
Pubmed ID
Authors

Iain Martyn, Tyler S Kuhn, Arne O Mooers, Vincent Moulton, Andreas Spillner

Abstract

We present optimal linear time algorithms for computing the Shapley values and 'heightened evolutionary distinctiveness' (HED) scores for the set of taxa in a phylogenetic tree. We demonstrate the efficiency of these new algorithms by applying them to a set of 10,000 reasonable 5139-species mammal trees. This is the first time these indices have been computed on such a large taxon and we contrast our finding with an ad-hoc index for mammals, fair proportion (FP), used by the Zoological Society of London's EDGE programme. Our empirical results follow expectations. In particular, the Shapley values are very strongly correlated with the FP scores, but provide a higher weight to the few monotremes that comprise the sister to all other mammals. We also find that the HED score, which measures a species' unique contribution to future subsets as function of the probability that close relatives will go extinct, is very sensitive to the estimated probabilities. When they are low, HED scores are less than FP scores, and approach the simple measure of a species' age. Deviations (like the Solendon genus of the West Indies) occur when sister species are both at high risk of extinction and their clade roots deep in the tree. Conversely, when endangered species have higher probabilities of being lost, HED scores can be greater than FP scores and species like the African elephant Loxondonta africana, the two solendons and the thumbless bat Furipterus horrens can move up the rankings. We suggest that conservation attention be applied to such species that carry genetic responsibility for imperiled close relatives. We also briefly discuss extensions of Shapley values and HED scores that are possible with the algorithms presented here.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 2%
Greece 1 2%
Brazil 1 2%
Unknown 61 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 30%
Student > Ph. D. Student 14 21%
Student > Postgraduate 6 9%
Student > Master 6 9%
Student > Bachelor 4 6%
Other 11 17%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 64%
Biochemistry, Genetics and Molecular Biology 4 6%
Environmental Science 4 6%
Computer Science 3 5%
Earth and Planetary Sciences 3 5%
Other 4 6%
Unknown 6 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 August 2012.
All research outputs
#14,143,704
of 22,664,267 outputs
Outputs from Algorithms for Molecular Biology
#111
of 264 outputs
Outputs of similar age
#94,674
of 161,293 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 7 outputs
Altmetric has tracked 22,664,267 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 52% 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 161,293 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.