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

Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

1.5K
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
1.5K
Speciation Rates01:07

Speciation Rates

22.5K
Overview
22.5K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

61.5K
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).
61.5K
Genetic Drift03:33

Genetic Drift

42.7K
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.
42.7K
Diffusion01:21

Diffusion

6.0K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.0K
Diffusion01:12

Diffusion

215.4K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
215.4K

You might also read

Related Articles

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

Sort by
Same author

Asymmetric autocatalytic reactions and their stationary distribution.

Royal Society open science·2024
Same author

Pushed to the edge: Spatial sorting can slow down invasions.

Ecology letters·2023
Same author

Topology and inference for Yule trees with multiple states.

Journal of mathematical biology·2016
Same author

How spatial heterogeneity shapes multiscale biochemical reaction network dynamics.

Journal of the Royal Society, Interface·2015
Same author

Five statistical questions about the tree of life.

Systematic biology·2011
Same author

Stochastic models for phylogenetic trees on higher-order taxa.

Journal of mathematical biology·2007

Related Experiment Video

Updated: Dec 29, 2025

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

1.3K

Diffusion dynamics on the coexistence subspace in a stochastic evolutionary game.

Lea Popovic1, Liam Peuckert2

  • 1Department of Mathematics and Statistics, Concordia University, Montreal, QC, H3G 1M8, Canada. lpopovic@mathstat.concordia.ca.

Journal of Mathematical Biology
|February 7, 2020
PubMed
Summary

Frequency-dependent selection in changing environments allows species coexistence. A new method simplifies analyzing evolutionary dynamics and extinction probabilities in these complex systems.

Keywords:
CoexistenceDegenerate diffusionDiffusion approximationExtinction probabilityExtinction timeRandom environmentStochastic evolutionary game

More Related Videos

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.5K
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.5K

Related Experiment Videos

Last Updated: Dec 29, 2025

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

1.3K
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.5K
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.5K

Area of Science:

  • Evolutionary biology
  • Theoretical ecology
  • Mathematical modeling

Background:

  • Frequency-dependent selection drives species interactions and competition for resources.
  • Stochastic evolutionary games incorporate random drift affecting species dynamics.
  • Environmental changes introduce complex selection advantages, influencing evolutionary trajectories.

Purpose of the Study:

  • To analyze a model of competing species in a random environment.
  • To investigate long-term coexistence and subsequent species fixation.
  • To develop a simplified method for approximating stochastic evolutionary dynamics.

Main Methods:

  • Utilized stability analysis of linear combinations of competing species.
  • Approximated stochastic dynamics using diffusion on a one-dimensional coexistence region.
  • Applied model reduction techniques for evaluating quasistationary properties.

Main Results:

  • The model demonstrates prolonged species coexistence in a random environment.
  • The diffusion approximation effectively simplifies the analysis of complex dynamics.
  • The method accurately approximates the probability of first extinction and its expected time.

Conclusions:

  • A simplified model reduction technique rigorously evaluates quasistationary properties of stochastic evolutionary dynamics.
  • This approach facilitates the study of species coexistence and extinction in fluctuating environments.
  • The findings offer new insights into the long-term evolutionary dynamics of competing species.