Jove
Visualize
Contact Us

Related Concept Videos

Life Histories01:29

Life Histories

Overview
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
Conservation of Declining Populations02:07

Conservation of Declining Populations

Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.
Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
Conservation of Small Populations02:04

Conservation of Small Populations

Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less likely to...

You might also read

Related Articles

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

Sort by
Same author

The evolution of extraordinary self-sacrifice.

Scientific reports·2022
Same author

Teaching math in real time.

Educational studies in mathematics·2021
Same author

Orb-web spiders as Bayesian learners.

Die Naturwissenschaften·2019
Same author

Environmental evolutionary graph theory.

Journal of theoretical biology·2014
Same author

Evolutionary game dynamics in populations with heterogenous structures.

PLoS computational biology·2014
Same author

Hamilton's inclusive fitness in finite-structured populations.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2014
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

Related Experiment Video

Updated: May 7, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Reproductive value in graph-structured populations.

Wes Maciejewski1

  • 1Department of Mathematics, The University of British Columbia, Vancouver, British Columbia, Canada.

Journal of Theoretical Biology
|October 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces Fisher

Keywords:
Evolutionary game theoryEvolutionary graph theoryFixation probabilityReproductive value

More Related Videos

Determination of Reproductive Competence by Confirming Pubertal Onset and Performing a Fertility Assay in Mice and Rats
06:38

Determination of Reproductive Competence by Confirming Pubertal Onset and Performing a Fertility Assay in Mice and Rats

Published on: October 13, 2018

Assessing Differences in Sperm Competitive Ability in Drosophila
09:34

Assessing Differences in Sperm Competitive Ability in Drosophila

Published on: August 22, 2013

Related Experiment Videos

Last Updated: May 7, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Determination of Reproductive Competence by Confirming Pubertal Onset and Performing a Fertility Assay in Mice and Rats
06:38

Determination of Reproductive Competence by Confirming Pubertal Onset and Performing a Fertility Assay in Mice and Rats

Published on: October 13, 2018

Assessing Differences in Sperm Competitive Ability in Drosophila
09:34

Assessing Differences in Sperm Competitive Ability in Drosophila

Published on: August 22, 2013

Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Graph Theory

Background:

  • Evolutionary graph theory is a growing field, yet it remains largely disconnected from core population genetics and evolution.
  • Understanding evolutionary dynamics in structured populations is crucial but often lacks a unified framework.

Purpose of the Study:

  • To integrate Fisher's reproductive value into evolutionary graph theory.
  • To develop a method for calculating fixation probabilities in graph-structured populations.

Main Methods:

  • Introduced Fisher's reproductive value to quantify an individual's genetic contribution to future generations.
  • Applied reproductive value to account for varying offspring numbers in heterogeneous graph-structured populations.
  • Utilized reproductive value for calculating neutral fixation probabilities in Moran birth-death and death-birth processes.

Main Results:

  • Reproductive value effectively captures differences in expected offspring due to varying connectivity in graph populations.
  • This framework allows for the calculation of fixation probabilities in any graph-structured population.
  • The method is applicable to both Moran birth-death and death-birth evolutionary models.

Conclusions:

  • Fisher's reproductive value provides a powerful tool to bridge evolutionary graph theory and population genetics.
  • This integration enables precise calculation of evolutionary dynamics, specifically neutral fixation probabilities, in complex population structures.
  • The introduced method offers a unified approach for studying evolution on graphs.