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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions
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Re-thinking the environment in landscape genomics.

Benjamin Dauphin1, Christian Rellstab1, Rafael O Wüest1

  • 1Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland.

Trends in Ecology & Evolution
|November 19, 2022
PubMed
Summary
This summary is machine-generated.

Landscape genomics helps understand adaptation by linking genomic variation to environmental pressures. Improved data and sampling designs can enhance the detection of selection in natural populations facing environmental change.

Keywords:
environmental datagenotype–environment associationsgeo-referenced databasessampling designspatial and temporal scales

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Ecology

Background:

  • Understanding how populations adapt to environmental changes is crucial for predicting evolutionary trajectories.
  • Landscape genomics leverages geo-referenced environmental data to link genomic variation with traits under selection.
  • Current landscape genomics approaches face limitations in data characteristics, sampling, and statistical methods, impacting the reliability of genotype-environment associations.

Purpose of the Study:

  • To highlight the importance of extrinsic selective pressures in shaping genomic variation.
  • To discuss the potential and limitations of landscape genomics in detecting local adaptation.
  • To propose strategies for improving the robustness of genotype-environment associations.

Main Methods:

  • Review of current landscape genomics methodologies and their limitations.
  • Discussion on the role of environmental data quality and sampling strategies.
  • Exploration of statistical frameworks for detecting selection.

Main Results:

  • Genomic variation is significantly influenced by extrinsic selective pressures.
  • Landscape genomics offers powerful insights into local adaptation.
  • Limitations in environmental data and sampling designs can reduce the accuracy of detecting genotype-environment associations.

Conclusions:

  • Improving environmental data sources and sampling designs is essential for robustly detecting selective pressures.
  • Enhanced landscape genomics approaches will improve our understanding of adaptation and evolutionary responses.
  • This research advocates for refined methodologies to better forecast population adaptation to environmental shifts.