The fundamental theorem of natural selection with mutations

Overview of attention for article published in Journal of Mathematical Biology, November 2017

• In the top 5% of all research outputs scored by Altmetric
• One of the highest-scoring outputs from this source (#4 of 364)
• High Attention Score compared to outputs of the same age (93rd percentile)
• High Attention Score compared to outputs of the same age and source (93rd percentile)

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Title The fundamental theorem of natural selection with mutations Journal of Mathematical Biology, November 2017 10.1007/s00285-017-1190-x William F. Basener, John C. Sanford The mutation-selection process is the most fundamental mechanism of evolution. In 1935, R. A. Fisher proved his fundamental theorem of natural selection, providing a model in which the rate of change of mean fitness is equal to the genetic variance of a species. Fisher did not include mutations in his model, but believed that mutations would provide a continual supply of variance resulting in perpetual increase in mean fitness, thus providing a foundation for neo-Darwinian theory. In this paper we re-examine Fisher's Theorem, showing that because it disregards mutations, and because it is invalid beyond one instant in time, it has limited biological relevance. We build a differential equations model from Fisher's first principles with mutations added, and prove a revised theorem showing the rate of change in mean fitness is equal to genetic variance plus a mutational effects term. We refer to our revised theorem as the fundamental theorem of natural selection with mutations. Our expanded theorem, and our associated analyses (analytic computation, numerical simulation, and visualization), provide a clearer understanding of the mutation-selection process, and allow application of biologically realistic parameters such as mutational effects. The expanded theorem has biological implications significantly different from what Fisher had envisioned.

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Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 33%
Other 3 25%
Student > Bachelor 2 17%
Student > Ph. D. Student 1 8%
Other 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 33%
Mathematics 3 25%
Unspecified 2 17%
Chemistry 1 8%
Medicine and Dentistry 1 8%
Other 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 23 August 2018.
All research outputs
#377,018
of 12,172,179 outputs
Outputs from Journal of Mathematical Biology
#4
of 364 outputs
Outputs of similar age
#17,926
of 284,944 outputs
Outputs of similar age from Journal of Mathematical Biology
#1
of 15 outputs
Altmetric has tracked 12,172,179 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done particularly well, scoring higher than 98% 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 284,944 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.