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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.

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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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...

What is Natural Selection?

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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.

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

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Related Experiment Video

Updated: Jun 17, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus

Saulo F S Chaves¹, Michelle B Damacena², Kaio Olimpio G Dias³

¹Federal University of Viçosa, Department of Agronomy, Viçosa, MG, Brazil.

Scientific Reports

|August 8, 2024

Summary

Selecting high-performance eucalyptus clones requires understanding genotype-by-environment interaction (GEI). This study used Factor Analytic Selection Tools (FAST) to identify reliable clones across diverse Brazilian environments.

Keywords:

Factor analytic mixed models Genotype-by-environment interaction environmental features reliability tree breeding

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Area of Science:

Forestry Science
Quantitative Genetics
Plant Breeding

Background:

Genotype-by-environment interaction (GEI) is crucial for eucalyptus silviculture expansion in Brazil.
Traditional selection methods may not fully capture clone performance across varied environments.

Purpose of the Study:

To develop and apply a novel selection index incorporating Factor Analytic Selection Tools (FAST) and clone reliability.
To identify high-performance and stable eucalyptus clones considering GEI.

Main Methods:

Utilized FAST across seven trials in five Brazilian states with 78 eucalyptus clones.
Employed an extended FAST index weighted by clone reliability for clonal selection.
Integrated factor loadings with 25 environmental features using principal component analysis to understand GEI drivers.

Main Results:

Selected ten eucalyptus clones demonstrating high performance, stability, and reliability.
Identified key environmental factors influencing GEI, including air temperature, radiation, and soil characteristics.
Established a strong association between latent factors derived from FAST and specific environmental features.

Conclusions:

The developed selection index effectively identifies superior eucalyptus clones for diverse commercial plantation environments.
Understanding GEI through integrated analysis of genetic factors and environmental variables enhances breeding decision-making.
This approach provides valuable insights for eucalyptus breeders to optimize clone selection strategies.