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Related Concept Videos

Types of Selection01:46

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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Lagrange Multipliers: Problem Solving01:30

Lagrange Multipliers: Problem Solving

A silo with a cylindrical base, flat bottom, and hemispherical roof is a common design in agricultural and industrial storage due to its structural efficiency and ease of construction. Optimizing its dimensions to maximize storage capacity for a given amount of material—i.e., a fixed surface area—is a classic problem in applied calculus and engineering design. The key parameters are the radius r of the base and the height h of the cylindrical section.The total volume of the silo is obtained by...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Lagrange Multipliers: One Constraint01:29

Lagrange Multipliers: One Constraint

In constrained optimization, the objective is to maximize or minimize a quantity while satisfying a fixed condition. A standard example is a rectangular pen built against a barn wall using 100 meters of fencing. Because the wall provides one side of the enclosure, only the other three sides require fencing. The problem is to find the dimensions that produce the greatest possible area.Let L represent the length parallel to the wall and W the width perpendicular to it. The area of the pen is A =...
Frequency-dependent Selection01:21

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.Positive Frequency-Dependent SelectionIn positive...

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Related Experiment Video

Updated: Jul 17, 2026

Density Gradient Multilayered Polymerization (DGMP): A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering
12:54

Density Gradient Multilayered Polymerization (DGMP): A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering

Published on: February 12, 2013

Antagonistic multilevel selection on size and architecture in variable density settings.

Cynthia Weinig1, Jill A Johnston, Charles G Willis

  • 1University of Minnesota, Department of Plant Biology, St. Paul, Minnesota 55108, USA. cweinig@umn.edu

Evolution; International Journal of Organic Evolution
|February 16, 2007
PubMed
Summary

Individual fitness in Arabidopsis thaliana is influenced by both self and group phenotypes. Multilevel selection revealed opposing forces on traits like size, with individual selection favoring increase and group selection favoring decrease.

Related Experiment Videos

Last Updated: Jul 17, 2026

Density Gradient Multilayered Polymerization (DGMP): A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering
12:54

Density Gradient Multilayered Polymerization (DGMP): A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering

Published on: February 12, 2013

Area of Science:

  • Evolutionary biology
  • Ecology
  • Population genetics

Background:

  • Individual fitness can be shaped by both individual-level and group-level selection.
  • Understanding how these competing selection pressures interact is crucial for explaining phenotypic evolution.

Purpose of the Study:

  • To investigate multilevel selection in experimental stands of Arabidopsis thaliana.
  • To quantify the antagonistic effects of individual versus group selection on phenotypic traits.

Main Methods:

  • Contextual analysis was used to measure multilevel selection in Arabidopsis thaliana.
  • Experiments were conducted on initial stands and subsequent progeny stands.

Main Results:

  • Significant linear selection was detected, with opposing effects at individual (increase) and group (decrease) levels for size and elongation traits.
  • Stabilizing selection was observed for developmental rate, indicating group-level selection.
  • Density-dependent selection influenced the strength of individual versus group selection.

Conclusions:

  • Multilevel selection can act antagonistically on phenotypic traits, influencing evolutionary trajectories.
  • The balance between individual and group selection can be density-dependent.
  • These findings support the role of multilevel selection in driving phenotypic evolution and demographic processes.