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Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

Speciation Rates

Speciation Rates

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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).

Genetics of Speciation

Genetics of Speciation

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

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.

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Related Experiment Video

Updated: Nov 19, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Fixation probabilities in evolutionary dynamics under weak selection.

Alex McAvoy^1,2,3, Benjamin Allen⁴

¹Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA. amcavoy@sas.upenn.edu.

Journal of Mathematical Biology

|February 3, 2021

Summary

This summary is machine-generated.

Calculating the success of a new trait (fixation probability) is complex. This study simplifies calculations for weak selection, reducing computational complexity for evolutionary dynamics.

Keywords:

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Area of Science:

Evolutionary dynamics
Population genetics
Mathematical biology

Background:

The fixation probability quantifies a mutant trait's success in a population.
Calculating fixation probability is computationally challenging due to factors like population structure and mating patterns.
Weak selection, where mutations have small effects, is a common scenario in evolutionary processes.

Purpose of the Study:

To develop a computationally tractable method for calculating fixation probability under weak selection.
To provide a general framework applicable to various stochastic evolutionary models with arbitrary population structures.
To reduce the complexity of determining selection's favorability towards a trait from exponential to polynomial in population size.

Main Methods:

Derivation of a weak-selection perturbation expansion for fixation probability.
Application of the expansion to arbitrary initial configurations of mutant and resident types.
Analysis of stochastic evolutionary models with fixed, arbitrary population sizes and spatial structures.

Main Results:

A novel perturbation expansion for fixation probability under weak selection was obtained.
The method applies to a wide range of stochastic evolutionary models.
Computational complexity for determining trait favorability is reduced to polynomial in population size.

Conclusions:

The developed methods simplify the analysis of evolutionary dynamics, particularly under weak selection.
New insights into evolutionary dynamics on graphs were achieved using these methods.
This approach offers a powerful tool for studying trait evolution in complex populations.