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Related Concept Videos

Life Histories01:29

Life Histories

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Overview
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Types of Selection01:46

Types of Selection

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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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Frequency-dependent Selection01:21

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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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Limits to Natural Selection01:38

Limits to Natural Selection

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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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Mutation, Gene Flow, and Genetic Drift01:09

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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Related Experiment Video

Updated: Aug 5, 2025

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
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Estimating Density Dependence, Environmental Variance, and Long-Term Selection on a Stage-Structured Life History.

R Lande, V Grøtan, S Engen

    The American Naturalist
    |March 23, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a new method to analyze population dynamics in changing environments, comparing species with different life histories. The approach accurately predicts population fluctuations and evolution using key demographic parameters.

    Keywords:
    demographydensity dependenceenvironmental variancegreat titselectionsensitivity

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    Area of Science:

    • Ecology
    • Population Biology
    • Evolutionary Biology

    Background:

    • Analyzing long-term population data in stochastic environments is challenging.
    • Comparing populations and species with diverse life histories requires robust methods.
    • Density dependence and environmental stochasticity significantly impact population dynamics.

    Purpose of the Study:

    • To develop a method for analyzing density-dependent, stage-structured populations in stochastic environments.
    • To facilitate comparisons across populations and species with different life histories.
    • To estimate key demographic parameters and evaluate long-term selection gradients.

    Main Methods:

    • Approximating population dynamics as a univariate stochastic process.
    • Modeling density dependence via a weighted sum of stage abundances (N).
    • Estimating parameters: density-independent growth rate, net density dependence, and environmental variance.

    Main Results:

    • The method accurately predicts the mean, coefficient of variation, and fluctuation rate of population size (N).
    • Key parameters governing population dynamics were successfully estimated.
    • Long-term selection gradients on life history traits were evaluated using sensitivities.

    Conclusions:

    • The derived method provides a robust framework for analyzing complex population dynamics.
    • It enables effective comparison of populations and species with varying life histories.
    • The approach elucidates the interplay between life history, density dependence, and environmental stochasticity in evolution.