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Related Concept Videos

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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Null and Alternative Hypotheses01:16

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
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Frequency-dependent Selection01:21

Frequency-dependent Selection

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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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Clearance Models: Noncompartmental Models01:17

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Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Null Models for the Opportunity for Selection.

Robin S Waples, Thomas E Reed

    The American Naturalist
    |May 25, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study clarifies Crow's "opportunity for selection" concept by developing null models for fertility and viability selection. It emphasizes that this opportunity is not selection itself but a measure of its potential.

    Keywords:
    PoissonWright-Fisherage structuremortalityreproductive success

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    Area of Science:

    • Evolutionary Biology
    • Ecology
    • Population Genetics

    Background:

    • Crow's "opportunity for selection" is a key eco-evolutionary concept.
    • Its appropriate null models remain controversial, especially concerning demographic stochasticity.

    Purpose of the Study:

    • To comprehensively address null models for Crow's "opportunity for selection" (I).
    • To consider fertility selection (If) and viability selection (Im) across various life-cycle and data scenarios.

    Main Methods:

    • Constructed null models incorporating random demographic stochasticity for discrete generations.
    • Analyzed seasonal and lifetime reproductive success in age-structured populations.
    • Evaluated experimental designs with full/partial life cycles and complete/subsampled enumeration.

    Main Results:

    • Null models aligning with Crow's formulation can be built for all scenarios.
    • Fertility selection (If) can be adjusted for demographic stochasticity, but viability selection (Im) cannot without trait data.
    • Including pre-reproductive mortality leads to a zero-inflated Poisson null model.

    Conclusions:

    • Crow's "opportunity for selection" quantifies potential, not actual, selection.
    • Species-specific biology can cause offspring number variance to deviate from Poisson expectations.