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Don't Fall Off the Adaptation Cliff: When Asymmetrical Fitness Selects for Suboptimal Traits

Vercken, Elodie; Wellenreuther, Maren LU ; Svensson, Erik LU and Mauroy, Benjamin (2012) In PLoS ONE 7(4).
Abstract
The cliff-edge hypothesis introduces the counterintuitive idea that the trait value associated with the maximum of an asymmetrical fitness function is not necessarily the value that is selected for if the trait shows variability in its phenotypic expression. We develop a model of population dynamics to show that, in such a system, the evolutionary stable strategy depends on both the shape of the fitness function around its maximum and the amount of phenotypic variance. The model provides quantitative predictions of the expected trait value distribution and provides an alternative quantity that should be maximized ("genotype fitness") instead of the classical fitness function ("phenotype fitness"). We test the model's predictions on three... (More)
The cliff-edge hypothesis introduces the counterintuitive idea that the trait value associated with the maximum of an asymmetrical fitness function is not necessarily the value that is selected for if the trait shows variability in its phenotypic expression. We develop a model of population dynamics to show that, in such a system, the evolutionary stable strategy depends on both the shape of the fitness function around its maximum and the amount of phenotypic variance. The model provides quantitative predictions of the expected trait value distribution and provides an alternative quantity that should be maximized ("genotype fitness") instead of the classical fitness function ("phenotype fitness"). We test the model's predictions on three examples: (1) litter size in guinea pigs, (2) sexual selection in damselflies, and (3) the geometry of the human lung. In all three cases, the model's predictions give a closer match to empirical data than traditional optimization theory models. Our model can be extended to most ecological situations, and the evolutionary conditions for its application are expected to be common in nature. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
7
issue
4
publisher
Public Library of Science
external identifiers
  • wos:000305336600064
  • scopus:84859626357
ISSN
1932-6203
DOI
10.1371/journal.pone.0034889
language
English
LU publication?
yes
id
bf546035-d1e2-4186-93b3-f2e95be1d9d7 (old id 2890902)
date added to LUP
2012-07-25 12:19:40
date last changed
2017-01-08 04:22:06
@article{bf546035-d1e2-4186-93b3-f2e95be1d9d7,
  abstract     = {The cliff-edge hypothesis introduces the counterintuitive idea that the trait value associated with the maximum of an asymmetrical fitness function is not necessarily the value that is selected for if the trait shows variability in its phenotypic expression. We develop a model of population dynamics to show that, in such a system, the evolutionary stable strategy depends on both the shape of the fitness function around its maximum and the amount of phenotypic variance. The model provides quantitative predictions of the expected trait value distribution and provides an alternative quantity that should be maximized ("genotype fitness") instead of the classical fitness function ("phenotype fitness"). We test the model's predictions on three examples: (1) litter size in guinea pigs, (2) sexual selection in damselflies, and (3) the geometry of the human lung. In all three cases, the model's predictions give a closer match to empirical data than traditional optimization theory models. Our model can be extended to most ecological situations, and the evolutionary conditions for its application are expected to be common in nature.},
  author       = {Vercken, Elodie and Wellenreuther, Maren and Svensson, Erik and Mauroy, Benjamin},
  issn         = {1932-6203},
  language     = {eng},
  number       = {4},
  publisher    = {Public Library of Science},
  series       = {PLoS ONE},
  title        = {Don't Fall Off the Adaptation Cliff: When Asymmetrical Fitness Selects for Suboptimal Traits},
  url          = {http://dx.doi.org/10.1371/journal.pone.0034889},
  volume       = {7},
  year         = {2012},
}