Jove
Visualize
Contact Us

Related Experiment Videos

Optimizing selection for function-valued traits.

Jay H Beder1, Richard Gomulkiewicz

  • 1Department of Mathematical Sciences, University of Wisconsin, Milwaukee, WI 53201, USA. beder@uwm.edu

Journal of Mathematical Biology
|August 3, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Phenotypic plasticity made simple, but not too simple.

American journal of botany·2022
Same author

Gene drive escape from resistance depends on mechanism and ecology.

Evolutionary applications·2022
Same author

Environmental Cue Integration and Phenology in a Changing World.

Integrative and comparative biology·2022
Same author

The sterile insect technique is protected from evolution of mate discrimination.

PeerJ·2022
Same author

Manipulation of Vector Host Preference by Pathogens: Implications for Virus Spread and Disease Management.

Journal of economic entomology·2022
Same author

Evading resistance to gene drives.

Genetics·2021
Same journal

Phenotypic plasticity trade-offs in an age-structured model of bacterial growth under stress.

Journal of mathematical biology·2026
Same journal

Intraspecific interactions facilitate mutualism across multilayer networks under weak selection.

Journal of mathematical biology·2026
Same journal

A two-species competition model on a compact metric graph for the invasion and competition of Aedes Aegypti and Aedes Albopictus mosquitoes in Florida.

Journal of mathematical biology·2026
Same journal

Superinfection and the hypnozoite reservoir for Plasmodium vivax: a multitype branching process approximation.

Journal of mathematical biology·2026
Same journal

Correction to: Superinfection and the hypnozoite reservoir for Plasmodium vivax: a general framework.

Journal of mathematical biology·2026
Same journal

Stoichiometric balance and sustained rhythms.

Journal of mathematical biology·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

This study shows that a Gaussian distribution remains Gaussian after optimizing selection, a key finding for evolutionary genetics. We derived formulas for selection differential and gradient under optimizing selection.

Area of Science:

  • Evolutionary biology
  • Quantitative genetics
  • Mathematical biology

Background:

  • Understanding how selection shapes trait distributions is fundamental in evolutionary quantitative genetics.
  • Traits often vary over time, and their distributions can be influenced by various selection pressures.

Purpose of the Study:

  • To investigate the impact of optimizing selection on the distribution of a function-valued trait.
  • To mathematically characterize the selection differential and gradient for such traits.

Main Methods:

  • Modeling a function-valued trait with a Gaussian pre-selection distribution.
  • Applying a fitness function that represents optimizing selection.
  • Deriving the post-selection distribution and selection parameters.

Related Experiment Videos

Main Results:

  • The post-selection distribution of the function-valued trait remains Gaussian.
  • The selection differential was computed.
  • An equation for the selection gradient was derived in terms of fitness and pre-selection distribution parameters.

Conclusions:

  • Optimizing selection preserves the Gaussian nature of the trait's distribution.
  • The derived equations provide a mathematical framework for analyzing selection on time-varying traits.
  • This work offers insights into the dynamics of trait evolution under specific selective pressures.