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Related Concept Videos

Sampling Plans01:23

Sampling Plans

167
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...
167
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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Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
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...
11.6K

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Sampling Soils in a Heterogeneous Research Plot
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Optimising Sampling Design for Landscape Genomics.

Anusha P Bishop1,2, Drew E Terasaki Hart1,3,4, Ian J Wang1,2

  • 1Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California, USA.

Molecular Ecology Resources
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

Sampling strategy significantly impacts landscape genomics. Covering diverse environmental and geographic space is key for accurate genotype-environment association (GEA), isolation by distance (IBD), and isolation by environment (IBE) detection.

Keywords:
adaptationecological geneticsgenotype‐environment associationisolation by environmentpopulation geneticssimulation

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Ecology

Background:

  • Landscape genomics methods like genotype-environment association (GEA), isolation by distance (IBD), and isolation by environment (IBE) are increasingly used.
  • Few studies have analyzed how sampling strategies affect these methods under realistic conditions.

Purpose of the Study:

  • To evaluate the influence of sample number and spatial distribution on landscape genomics methods.
  • To compare sampling strategies focused on geographic versus environmental space coverage.

Main Methods:

  • Simulated 24,000 datasets across diverse scenarios with complex population dynamics and landscape structures.
  • Assessed the performance of common landscape genomics methods under varying sampling schemes.

Main Results:

  • Common analyses are robust if sampling covers sufficient environmental and geographic space.
  • Sampling strategies maximizing environmental space coverage matched or outperformed those focused solely on geographic space for detecting adaptive loci and estimating IBE.
  • Transect-based sampling led to fewer detected adaptive loci and higher error in IBD/IBE estimation.
  • IBD detection required as few as nine sites, while detecting adaptive loci and IBE needed over 100 individuals.
  • GEA methods failed to detect adaptive loci when spatial autocorrelation and migration were weak.

Conclusions:

  • Sampling strategy is crucial for landscape genomics, with environmental space coverage being particularly important.
  • Adequate sample size and spatial coverage are essential for reliable results in GEA, IBD, and IBE analyses.
  • Landscape structure and migration rates significantly influence the success of landscape genomic analyses.