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Related Concept Videos

Types of Selection01:46

Types of Selection

40.4K
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...
40.4K
Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180
Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.0K
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.
22.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

37
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: Jun 25, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Multiscale selection in spatially structured populations.

Hilje M Doekes1,2, Rutger Hermsen1,3

  • 1Theoretical Biology Group, Department of Biology, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.

Proceedings. Biological Sciences
|May 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new mathematical framework for multi-scale selection, analyzing how spatial scales influence evolutionary processes like altruism. It decomposes selection into local and interlocal components to understand their contributions.

Keywords:
Price’s equationaltruismevolutionpathogen transmissibilityself-organizationspatial structure

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Area of Science:

  • Ecology and Evolutionary Biology
  • Mathematical Biology
  • Population Dynamics

Background:

  • Spatial population structure is crucial for ecological and evolutionary dynamics.
  • The scale at which selection acts can alter its direction and strength, impacting trait evolution.
  • Multilevel selection theory addresses group-structured populations, but a broader framework is needed for other spatial patterns.

Purpose of the Study:

  • To develop a mathematical framework for multi-scale selection that accounts for diverse spatial population structures.
  • To decompose natural selection into local and interlocal components to quantify scale-dependent effects.
  • To provide a rigorous method for analyzing how different spatial scales contribute to and compete within selection.

Main Methods:

  • Developed a novel mathematical framework for multi-scale selection analysis.
  • Decomposed selection into 'local selection' (within environments) and 'interlocal selection' (among environments).
  • Applied the framework to models of altruism evolution and pathogen transmissibility.

Main Results:

  • The framework quantifies the contribution of selection across various spatial scales.
  • Demonstrated how ecological processes at different scales can drive or oppose selection.
  • Identified the relative importance of local versus broader-scale selection in specific evolutionary scenarios.

Conclusions:

  • The multi-scale selection framework offers a versatile tool for understanding spatial eco-evolutionary dynamics.
  • It provides a quantitative basis for understanding how spatial heterogeneity shapes evolutionary trajectories.
  • This approach rigorously underpins ecological intuitions about scale-dependent selection.