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Sexual dimorphism in trait variability and its eco-evolutionary and statistical implications
Susanne Rk Zajitschek1,2, Felix Zajitschek1, Russell Bonduriansky1
1Evolution & Ecology Research Center, School of Biological, Earth, and Environmental Sciences, University of New South Wales, Sydney, Australia.
Elife
|November 17, 2020
Summary
Sex differences in trait variability are complex. Neither the estrus-mediated variability hypothesis nor the greater male variability hypothesis universally explains these patterns, with variability being trait-dependent.
Area of Science:
- Biomedical sciences
- Evolutionary biology
- Zoology
Background:
- Males and females exhibit anatomical, physiological, and behavioral differences.
- Sex differences in trait variability are under-recognized in biomedical research.
- Historically, females were excluded from research due to assumptions of higher variability (estrus-mediated variability hypothesis), contrasting with evolutionary data supporting greater male variability.
Purpose of the Study:
- To investigate competing hypotheses on sex differences in trait variability.
- To determine if estrus cycles or evolutionary factors predominantly influence sex-specific trait variability.
- To analyze variability across a wide range of traits in mice.
Main Methods:
- Meta-analysis of 218 traits measured in over 26,900 mice.
- Statistical comparison of trait variability between sexes.
- Evaluation of the estrus-mediated variability hypothesis and the greater male variability hypothesis.
Main Results:
- Neither the estrus-mediated variability hypothesis nor the greater male variability hypothesis universally explained sex differences in trait variability.
- Sex bias in variability was found to be trait-dependent.
- Greater male variability was observed in morphological traits, whereas females exhibited higher variability in immunological traits.
Conclusions:
- Sex-specific variability patterns are complex and trait-dependent.
- Findings have implications for understanding eco-evolutionary processes, such as sex-dependent climate change responses.
- Recognizing sex-specific variability is crucial for statistical power analysis in research.
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