Death of the fittest

Written by: Bjørn Østman

Primary Source: Pleiotropy

This is imho an excessively beautiful figure! I keep staring at it getting thrills, and bliss pours over me as I explore its intricacies. This is evolution.


Click to enlarge.

(You should enjoy this figure while listening to one or several of these:)

What are we looking at? Fitness over time of all individuals in a population of size 100. The blue line is average population fitness. The red line is the lineage that leads to the most fit individual after 100,000 updates.

And the real treasure? All the black lines which are all the other lineages that died out. Only the individuals who descend from the ancestor on the red line are alive near the end (technically, the most recent common ancestor (MRCA) is close to but obviously not quite at the end of the simulation).

We see two interesting facts:

  • Offspring that have deleterious mutations (that decrease fitness) survive for quite a long time, and those individuals even have offspring of their own some times. In fact, we can see that there are even deleterious mutations on the line of descent (the red line goes down on five occasions).
  • Offspring that have beneficial mutations (that increase fitness) don’t always survive. In fact, most of them eventually die and those beneficial mutations are lost. Evolution does not imply survival of the fittest.
Deleterious mutations do not prohibit evolution; deleterious mutations hitchhike on the back of beneficial mutations and go to fixation that way (there is no epistasis in this model).
The simulation

I made this simulation in Matlab. Constant population size of 100. Mutation rate of 0.01. Effect of mutations is drawn at random from a uniform distribution of selection coefficients between -0.05 and 0.05. One individual reproduces per computational update (Moran process), and chance of reproducing is proportional to fitness.

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Bjørn Østman
Bjørn Østman is an evolutionary biologist postdoc working in the BEACON Center for the Study of Evolution in Action.
I am interested in many aspects of evolution. I work in computational biology, using various approaches to learn about fundamental processes of evolution. Bioinformatics is good for learning about real genes (data generously supplied by other researchers), and simulations are good for testing the mechanisms of evolution. I am particularly interested in how populations and organisms adapt to changing environments, both at the genetic and phenotypic level. Lately my research has focused on the evolutionary dynamics of populations evolving in rugged fitness landscapes.
Bjørn Østman

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