Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Speciation Rates

Speciation Rates

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

Formation of Species

Formation of Species

Speciation describes the formation of one or more new species from one or sometimes multiple original species. The resulting species are discrete from the parent species, and barriers to reproduction will typically exist. There are two primary mechanisms, speciation with and without geographic isolation—allopatric and sympatric speciation, respectively.

Genetic Drift

Genetic Drift

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.

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

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).

You might also read

Related Articles

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

Sort by

Same author

Summarizing Evolutionary Trajectories from Phylogenetic Character Maps of Discrete Traits.

bioRxiv : the preprint server for biology·2026

Same author

An evolving view of phylogenetic biogeography.

Systematic biology·2026

Same author

Ancestral state reconstruction with discrete characters using deep learning.

bioRxiv : the preprint server for biology·2026

Same author

A Phylogenetic Model of Established and Enabled Biome Shifts.

Systematic biology·2026

Same author

Phylogenetic estimation of diversity-dependent biogeographic rates using deep learning.

bioRxiv : the preprint server for biology·2026

Same author

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

medRxiv : the preprint server for health sciences·2025

Same journal

Effects of Seasonal Births and Predation on Disease Spread.

Bulletin of mathematical biology·2026

Same journal

Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.

Bulletin of mathematical biology·2026

Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026

Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026

Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026

Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

A Diffusion-Based Approach for Simulating Forward-in-Time State-Dependent Speciation and Extinction Dynamics.

Albert C Soewongsono¹, Michael J Landis²

¹Department of Biology, Washington University in St. Louis, Rebstock Hall, St. Louis, MO, 63130, USA. soewongsono@wustl.edu.

Bulletin of Mathematical Biology

|July 6, 2024

Summary

This summary is machine-generated.

This study introduces a diffusion approximation framework for simulating cladogenetic state-dependent speciation-extinction (ClaSSE) models. The new method allows for inferring rate parameters and calculating stationary state frequencies, aiding evolutionary pattern analysis.

Keywords:

Branching processes Diffusion processes Evolution Extinction Speciation Stationary frequencies

More Related Videos

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 21, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

Evolutionary Biology
Computational Biology
Phylogenetics

Background:

State-dependent diversification models are crucial for understanding evolutionary processes.
Simulating these models, especially cladogenetic state-dependent speciation-extinction (ClaSSE) models, can be computationally intensive.
Existing methods may not efficiently handle forward-in-time simulations or parameter inference for complex scenarios.

Purpose of the Study:

To develop a general diffusion approximation framework for simulating state counts in ClaSSE models.
To apply this framework to geographic-state speciation-extinction (GeoSSE) models.
To derive methods for inferring and computing rate parameters and stationary state frequencies.

Main Methods:

Utilized a diffusion approximation approach for forward-in-time simulations.
Applied the framework to two- and three-region GeoSSE models.
Derived analytical methods for parameter inference and stationary frequency computation.

Main Results:

Demonstrated comparability between tree-based and diffusion-based simulations of species range dynamics.
Obtained methods to infer rate parameters from observed stationary state frequencies.
Provided an analytical solution for computing stationary state frequencies given rate parameters.
Developed a procedure to determine the time to reach stationary frequencies.

Conclusions:

The diffusion framework offers a powerful and efficient tool for simulating and analyzing state-dependent diversification.
The derived methods facilitate parameter estimation and understanding of evolutionary dynamics under varying diversification rates.
This approach can formalize the relationship between evolutionary patterns and processes in state-dependent scenarios.