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Related Experiment Video

Updated: Feb 20, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Key issues review: evolution on rugged adaptive landscapes.

Uri Obolski1, Yoav Ram1, Lilach Hadany1

  • 1Department of Molecular Biology and Ecology of Plants, Tel-Aviv University, 69978, Israel.

Reports on Progress in Physics. Physical Society (Great Britain)
|October 21, 2017
PubMed
Summary
This summary is machine-generated.

Evolutionary biologists explore how populations adapt on rugged adaptive landscapes, which map genotype to fitness. Recent empirical studies in microbes and viruses are providing new insights into evolutionary processes on these complex landscapes.

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Area of Science:

  • Evolutionary biology
  • Genetics
  • Biochemistry

Background:

  • Adaptive landscapes map genotype to fitness, with rugged landscapes featuring multiple adaptive peaks.
  • Understanding evolution on rugged landscapes has been a long-standing challenge since Sewall Wright's initial work.
  • Theoretical models have extensively explored evolutionary dynamics on these landscapes.

Purpose of the Study:

  • To introduce the concept of adaptive landscapes.
  • To review theoretical and empirical studies of evolution on rugged landscapes.
  • To discuss recent analyses and future directions in the field.

Main Methods:

  • Review of theoretical studies on evolution on rugged landscapes.
  • Analysis of recently generated empirical adaptive landscapes in various organisms.
  • Discussion of mathematical and statistical methods for analyzing empirical data.

Main Results:

  • Empirical adaptive landscapes have been constructed for bacteria, yeast, viruses, and fungi.
  • These empirical landscapes offer novel insights into evolutionary trajectories.
  • Theoretical work suggests adaptive landscapes significantly influence evolutionary outcomes.

Conclusions:

  • Recent technological advances enable empirical construction of adaptive landscapes.
  • Empirical data is crucial for validating and refining evolutionary theories.
  • The field is rapidly advancing with new data and analytical approaches.