Jove
Visualize
Contact Us
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 Concept Videos

Population Growth00:57

Population Growth

26.3K
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.
26.3K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

60.4K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
60.4K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

74.8K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
74.8K
Genetic Drift03:33

Genetic Drift

41.6K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
41.6K
Conservation of Small Populations02:04

Conservation of Small Populations

15.5K
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...
15.5K
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

30
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
30

You might also read

Related Articles

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

Sort by
Same author

Phylograms better fit neutrally evolving traits than chronograms.

Evolution letters·2026
Same author

Why recombination hotspots?

PLoS genetics·2026
Same author

Empirical Validation of the Nearly Neutral Theory at Divergence and Population-Genomic Scales Using 144 Placental Mammal Genomes.

Genome biology and evolution·2026
Same author

EMPIRICAL VALIDATION OF THE NEARLY NEUTRAL THEORY AT DIVERGENCE AND POPULATION GENOMIC SCALE USING 144 PLACENTAL MAMMALS GENOMES.

bioRxiv : the preprint server for biology·2025
Same author

Structural Mutations Set an Equilibrium Noncoding Genome Fraction.

Molecular biology and evolution·2025
Same author

Evolution of GC-biased gene conversion by natural selection.

Genetics·2025
Same journal

The life history of recessive deleterious alleles as seen through the eyes of a honey bee (Apis mellifera).

Molecular biology and evolution·2026
Same journal

Severe bottleneck of ancient Homo populations: Insights from computational modeling and relevant fossil evidence.

Molecular biology and evolution·2026
Same journal

Population Epigenetics: Deciphering DNA Methylation Diversity and its Implications for Health, Disease, and Evolution.

Molecular biology and evolution·2026
Same journal

Genomic signature of repeated transitions to diurnality in spiders.

Molecular biology and evolution·2026
Same journal

Phylogenomic blind spots: The limits of UCE and BUSCO loci in the presence of gene flow.

Molecular biology and evolution·2026
Same journal

seqLens: Optimizing Language Models for Genomic Predictions.

Molecular biology and evolution·2026
See all related articles

Related Experiment Video

Updated: Oct 31, 2025

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

8.9K

Inferring Long-Term Effective Population Size with Mutation-Selection Models.

Thibault Latrille1,2, Vincent Lanore1, Nicolas Lartillot1

  • 1Laboratoire de Biométrie et Biologie Évolutive UMR 5558, Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France.

Molecular Biology and Evolution
|June 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced mutation-selection model for phylogenetic codon analysis, jointly reconstructing fitness landscapes and evolutionary trends in effective population size (Ne), mutation rate (μ), and life-history traits (LHTs). The model shows promise but suggests limitations in current assumptions about evolutionary interactions.

Keywords:
codon modelslife-history traitsmutation ratemutation–selection modelsphylogeneticpopulation geneticpopulation size

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

24.2K

Related Experiment Videos

Last Updated: Oct 31, 2025

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

8.9K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

24.2K

Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Bioinformatics

Background:

  • Mutation-selection codon models analyze mutation, selection, and drift.
  • Current models assume constant effective population size (Ne), which is unrealistic.
  • Ne, mutation rate (μ), and life-history traits (LHTs) can vary across lineages.

Purpose of the Study:

  • To develop an extended mutation-selection model accounting for joint evolutionary processes.
  • To jointly reconstruct fitness landscapes and lineage-specific trends in Ne, μ, and LHTs.
  • To investigate the interplay between molecular evolution and life-history traits.

Main Methods:

  • Developed a Bayesian Monte Carlo framework for joint reconstruction.
  • Utilized DNA coding sequences and observed LHTs in extant species.
  • Tested the model with simulated data and applied it to mammals, isopods, and primates.

Main Results:

  • Reconstructed Ne trends correlated with LHTs/ecological variables, indicating reasonable global trends.
  • Inferred Ne variation across species was narrower than expected.
  • The model successfully integrated fitness landscapes with evolutionary dynamics.

Conclusions:

  • The extended model offers a more realistic approach to phylogenetic codon modeling.
  • The narrow inferred Ne variation suggests potential issues with model assumptions, like epistasis.
  • Further refinement of models is needed to capture complex evolutionary interactions.