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Population Growth

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.

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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.

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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).

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Related Experiment Video

Updated: May 26, 2026

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

An overview of population genetic data simulation.

Xiguo Yuan¹, David J Miller, Junying Zhang

¹School of Computer Science and Technology, Xidian University, Xi'an, P.R. China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 14, 2011

Summary

This summary is machine-generated.

Population genetics simulation studies are crucial for understanding evolutionary impacts on genetic variation. This review compares key simulation frameworks and tools, highlighting limitations and future directions for genomic data generation.

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Area of Science:

Population Genetics
Genomic Data Simulation

Background:

Simulation studies are vital for understanding evolutionary and demographic impacts on genetic variation.
Accurate simulation of complex genomic data is essential for disease genetics research.

Purpose of the Study:

To review and compare fundamental simulation frameworks (coalescent, forward, resampling).
To assess existing simulators based on evolutionary/demographic scenarios, computational complexity, and applications.
To identify limitations and future challenges in genomic data simulation tools.

Main Methods:

Review of three basic simulation frameworks: coalescent, forward, and resampling.
Comparison of existing simulators within these frameworks.
Analysis of evolutionary/demographic scenarios, computational complexity, and applications.

Main Results:

Overview of coalescent, forward, and resampling simulation frameworks.
Comparison of various simulators' capabilities and limitations.
Identification of current challenges in developing advanced simulation tools.

Conclusions:

Effective simulation tools are critical for advancing population genetics and disease association studies.
Further development is needed to overcome limitations in current genomic data simulation algorithms.
Future research should focus on creating more powerful and versatile simulation tools.