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Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes

Overview of attention for article published in PLoS Computational Biology, August 2014
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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 (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Title
Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003778
Pubmed ID
Authors

Anne-Florence Bitbol, David J. Schwab

Abstract

Natural selection drives populations towards higher fitness, but crossing fitness valleys or plateaus may facilitate progress up a rugged fitness landscape involving epistasis. We investigate quantitatively the effect of subdividing an asexual population on the time it takes to cross a fitness valley or plateau. We focus on a generic and minimal model that includes only population subdivision into equivalent demes connected by global migration, and does not require significant size changes of the demes, environmental heterogeneity or specific geographic structure. We determine the optimal speedup of valley or plateau crossing that can be gained by subdivision, if the process is driven by the deme that crosses fastest. We show that isolated demes have to be in the sequential fixation regime for subdivision to significantly accelerate crossing. Using Markov chain theory, we obtain analytical expressions for the conditions under which optimal speedup is achieved: valley or plateau crossing by the subdivided population is then as fast as that of its fastest deme. We verify our analytical predictions through stochastic simulations. We demonstrate that subdivision can substantially accelerate the crossing of fitness valleys and plateaus in a wide range of parameters extending beyond the optimal window. We study the effect of varying the degree of subdivision of a population, and investigate the trade-off between the magnitude of the optimal speedup and the width of the parameter range over which it occurs. Our results, obtained for fitness valleys and plateaus, also hold for weakly beneficial intermediate mutations. Finally, we extend our work to the case of a population connected by migration to one or several smaller islands. Our results demonstrate that subdivision with migration alone can significantly accelerate the crossing of fitness valleys and plateaus, and shed light onto the quantitative conditions necessary for this to occur.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Belgium 2 4%
India 1 2%
Germany 1 2%
Canada 1 2%
Unknown 47 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 27%
Researcher 11 20%
Student > Master 9 16%
Student > Doctoral Student 5 9%
Professor 4 7%
Other 7 13%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 40%
Physics and Astronomy 8 15%
Biochemistry, Genetics and Molecular Biology 5 9%
Linguistics 2 4%
Mathematics 2 4%
Other 9 16%
Unknown 7 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 01 September 2014.
All research outputs
#2,955,808
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#2,602
of 8,964 outputs
Outputs of similar age
#28,943
of 243,900 outputs
Outputs of similar age from PLoS Computational Biology
#34
of 159 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 70% 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 243,900 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 88% of its contemporaries.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.