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Related Concept Videos

Types of Selection01:46

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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...
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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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Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
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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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Mate choice—the decision about whom to mate with—is a type of natural selection, since animals must reproduce to pass down their genes. Mate choice is also called intersexual selection because the behavior occurs between the sexes.
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Redundancy-selection trade-off in phenotype-structured populations.

Leonardo Miele1, R M L Evans1, Sandro Azaele2

  • 1Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, U.K.

Journal of Theoretical Biology
|September 5, 2021
PubMed
Summary
This summary is machine-generated.

Realistic fitness landscapes show a trade-off between trait redundancy and fitness. Our study reveals that landscape asymmetries create unmodellable neutral effects, impacting evolutionary dynamics in populations like pathogens and cancer cells.

Keywords:
Fitness landscapePhenotype-structured populationsRedundancyReplication-MutationTrait space

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Area of Science:

  • Evolutionary biology
  • Theoretical biology
  • Quantitative genetics

Background:

  • Realistic fitness landscapes often exhibit a redundancy-fitness trade-off, where high fitness correlates with trait rarity and low fitness with redundancy.
  • Existing models implicitly account for redundancy using effective formulations, such as biased mutation rates.
  • The compatibility and consequences of these effective formulations with explicitly redundant landscapes remain unclear.

Purpose of the Study:

  • To investigate the impact of the redundancy-fitness trade-off on the evolution of populations with continuous quantitative traits.
  • To determine how landscape asymmetries in explicitly redundant models affect evolutionary dynamics.
  • To assess the limitations of effective formulations in capturing the complexities of redundant fitness landscapes.

Main Methods:

  • Modeling evolution in phenotype-structured populations with continuous quantitative traits.
  • Utilizing a standard replication-mutation dynamics framework.
  • Designing two-dimensional fitness landscapes incorporating both selective and neutral traits to model redundancy explicitly.

Main Results:

  • Landscape asymmetries generate neutral contributions to the marginalized fitness-level description.
  • These neutral contributions cannot be captured by effective formulations or fully resolved by analyzing the trait distribution.
  • The magnitude of these effects depends on the specific geometry of the fitness landscape.

Conclusions:

  • Explicitly redundant landscapes introduce complexities not accounted for by current effective formulations.
  • Landscape geometry plays a crucial role in generating emergent neutral effects.
  • Understanding these dynamics offers insights into sub-optimality and has implications for managing rapidly evolving populations like pathogens and cancer cells.