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Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER

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Evolutionary change in continuous reaction norms.

Courtney J Murren¹, Heidi J Maclean, Sarah E Diamond

¹Department of Biology, College of Charleston, Charleston, South Carolina 29424.

The American Naturalist

|March 20, 2014

Summary

This summary is machine-generated.

Evolutionary divergence in reaction norms is common, with differences in curvature often exceeding differences in slope. Novel environments amplify these changes, highlighting the importance of plasticity in evolving traits.

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Area of Science:

Ecology and Evolutionary Biology
Quantitative Genetics

Background:

Understanding the evolution of reaction norms is crucial in ecology and evolution.
Investigating evolutionary divergence in reaction norm shapes offers insights into adaptation.
Reaction norms describe how an organism's phenotype changes with environment.

Purpose of the Study:

To compare evolutionary divergence in reaction norm shapes between populations and closely related species.
To quantify differences in overall trait means (Offset) and reaction norm shape (Slope and Curvature).
To identify factors influencing reaction norm divergence across taxa and environments.

Main Methods:

A meta-analytic approach was used to compare reaction norm divergence.
Quantified mean-standardized differences in Offset, Slope, and Curvature.
Analyzed data across animal and plant taxa, trait types, and environmental conditions.

Main Results:

Differences in reaction norm shape (Slope and Curvature) were generally greater than differences in Offset.
Divergence in Curvature was typically larger than divergence in Slope.
Congeneric species showed greater reaction norm differences than populations.
Novel environments significantly increased reaction norm differences.

Conclusions:

Evolutionary divergence in curvature is a common phenomenon and a key component of plasticity.
Factors like taxon contrast, trait type, and environmental novelty influence reaction norm evolution.
Understanding cryptic variation in novel environments is vital for predicting adaptation in changing ecosystems.