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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Sampling in landscape genomics.

Stéphanie Manel1, Cécile H Albert, Nigel G Yoccoz

  • 1Laboratoire Population Environnement Développement, UMR 151 UP/IRD, Université Aix-Marseille, Marseille, France. stephanie.manel@univ-provence.fr

Methods in Molecular Biology (Clifton, N.J.)
|June 6, 2012
PubMed
Summary
This summary is machine-generated.

Landscape genomics uses genetic markers to find environmental selection pressures. Model-based stratification in climatic and biological spaces is more efficient than geographic sampling for identifying these genomic regions.

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Area of Science:

  • Ecology
  • Genetics
  • Evolutionary Biology

Background:

  • Landscape genomics integrates ecological data with genetic information to understand adaptation.
  • Identifying genomic regions under selection is crucial for conservation and evolutionary studies.
  • Environmental factors significantly influence population genetic structure and adaptive evolution.

Purpose of the Study:

  • To review and propose efficient sampling strategies for landscape genomics studies.
  • To compare the effectiveness of different spatial sampling designs.
  • To highlight the need for methods that differentiate selection from other evolutionary processes.

Main Methods:

  • Discussion of sampling designs in landscape genomics.
  • Comparison of model-based stratification (climatic/biological space) versus geographic space sampling.
  • Review of existing literature on landscape genomics sampling strategies.

Main Results:

  • Model-based stratification using climatic and/or biological spaces is generally more efficient than geographic-based sampling.
  • Current sampling strategies may not effectively disentangle selection from other factors like population structure or range expansions.

Conclusions:

  • Sampling design is critical for successful landscape genomics.
  • Prioritizing sampling in environmental (climatic/biological) spaces can enhance the identification of adaptive loci.
  • Further research is needed to develop robust methods for distinguishing environmental selection from other evolutionary forces.