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

What is Population Genetics?

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.

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).

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Speciation Rates

Speciation Rates

Conservation of Small Populations

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...

What are Populations and Communities?

What are Populations and Communities?

You might also read

Related Articles

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

Sort by

Same author

MKado: a toolkit for McDonald-Kreitman tests of natural selection.

G3 (Bethesda, Md.)·2026

Same author

Ecotypes, <i>Wolbachia</i>, and urbanization shape <i>Culex pipiens</i> population structure in a West Nile virus hotspot.

bioRxiv : the preprint server for biology·2026

Same author

Diversity and divergence of two sympatric, sibling octopus species.

bioRxiv : the preprint server for biology·2026

Same author

Neural posterior estimation for population genetics.

Genetics·2026

Same author

Assessing the potential of bee-collected pollen sequence data to train machine learning models for geolocation of sample origin.

bioRxiv : the preprint server for biology·2026

Same author

Protocol for genotyping cephalopod sex using a skin swab and quantitative PCR.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: May 13, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes.

Elizabeth T Chevy¹, Jiseon Min², Victoria Caudill²

¹Center for Computational Molecular Biology Brown University Providence Rhode Island USA.

Ecology and Evolution

|April 16, 2025

Summary

This guide offers practical advice for spatial simulation in population biology, focusing on realistic individual-based models in continuous landscapes. It helps researchers implement complex population dynamics and avoid common simulation pitfalls.

More Related Videos

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

Related Experiment Videos

Last Updated: May 13, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

Published on: May 19, 2019

Area of Science:

Ecology
Population Biology
Computational Biology

Background:

Individual-based simulations are vital in population biology.
Implementing these models in continuous geographic space presents significant challenges.
Realistic spatial modeling requires careful consideration of ecological mechanisms.

Purpose of the Study:

To provide a practical guide for researchers on implementing individual-based spatial simulations.
To address common difficulties in modeling population dynamics across continuous landscapes.
To demonstrate the application of these methods using the SLiM simulator.

Main Methods:

Detailed discussion of mating, reproduction, density-dependence, and dispersal mechanisms in spatial contexts.
Guidance on parameterizing simulations for specific population densities and ecological scenarios.
Demonstration of model implementation in SLiM, including natural selection effects.

Main Results:

The paper presents a framework for building robust spatial individual-based models.
It illustrates how landscape variation impacts population dynamics.
Vignettes showcase simulations of pikas, mosquitoes, cane toads, and monarch butterflies.

Conclusions:

Effective spatial simulation requires integrating ecological realism with computational efficiency.
The presented methods and SLiM facilitate advanced population modeling.
This work aids researchers in creating more accurate and insightful ecological simulations.