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      Sexual dimorphism in trait variability and its eco-evolutionary and statistical implications

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          Abstract

          Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioural traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the ‘estrus-mediated variability hypothesis’); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the ‘greater male variability hypothesis’. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications, including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.

          eLife digest

          Males and females differ in appearance, physiology and behavior. But we do not fully understand the health and evolutionary consequences of these differences. One reason for this is that, until recently, females were often excluded from medical studies. This made it difficult to know if a treatment would perform as well in females as males. To correct this, organizations that fund research now require scientists to include both sexes in studies. This has led to some questions about how to account for sex differences in studies.

          One reason females have historically been excluded from medical studies is that some scientists assumed that they would have more variable responses to a particular treatment based on their estrous cycles. Other scientists, however, believe that males of a given species might be more variable because of the evolutionary pressures they face in competing for mates. Better understanding how males and females vary would help scientists better design studies to ensure they provide accurate answers.

          Now, Zajitschek et al. debunk both the idea that males are more variable and the idea that females are more variable. To do this, Zajitschek et al. analyzed differences in 218 traits, like body size or certain behaviors, among nearly 27,000 male and female mice. This showed that neither male mice nor female mice were universally more different from other mice of their sex across all features. Instead, sex differences in how much variation existed in male or female mice depended on the individual trait. For example, males varied more in physical features like size, while females showed more differences in their immune systems.

          The results suggest it is particularly important to consider sex-specific variability in both medical and other types of studies. To help other researchers better design experiments to factor in such variability, Zajitschek et al. created an interactive tool that will allow scientists to look at sex-based differences in individual features among male or female mice.

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          Most cited references55

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                Author and article information

                Contributors
                Role: Senior Editor
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                17 November 2020
                2020
                : 9
                : e63170
                Affiliations
                [1 ]Evolution & Ecology Research Center, School of Biological, Earth, and Environmental Sciences, University of New South Wales SydneyAustralia
                [2 ]Liverpool John Moores University, School of Biological and Environmental Sciences LiverpoolUnited Kingdom
                [3 ]European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus HinxtonUnited Kingdom
                [4 ]University of Sydney, Charles Perkins Centre, School of Life and Environmental Sciences, School of Mathematics and Statistics SydneyAustralia
                [5 ]Division of Ecology and Evolution, Research School of Biology, Australian National University CanberraAustralia
                University of St Andrews United Kingdom
                University of Sydney Australia
                University of Sydney Australia
                University of Sydney Australia
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-4676-9950
                http://orcid.org/0000-0001-6010-6112
                https://orcid.org/0000-0002-5786-6951
                https://orcid.org/0000-0001-6926-0781
                https://orcid.org/0000-0003-4080-4073
                https://orcid.org/0000-0002-9814-092X
                https://orcid.org/0000-0002-3993-6127
                https://orcid.org/0000-0002-2796-5123
                http://orcid.org/0000-0001-9805-7280
                https://orcid.org/0000-0001-9460-8743
                https://orcid.org/0000-0002-7765-5182
                Article
                63170
                10.7554/eLife.63170
                7704105
                33198888
                4a9676f2-936f-483a-a26d-b9bbaee464f6
                © 2020, Zajitschek et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 16 September 2020
                : 30 October 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Award ID: DP180100818
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: UM1-H G006370
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Award ID: DE180101520
                Award Recipient :
                Funded by: Australian Research Council;
                Award ID: FT160100113
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Ecology
                Evolutionary Biology
                Custom metadata
                Sex differences in trait variability imply that both sexes should be included in biomedical trials, using sex-specific statistical power calculations.

                Life sciences
                sex inequality,gender difference,meta regression,sexual selection,power analysis,mouse
                Life sciences
                sex inequality, gender difference, meta regression, sexual selection, power analysis, mouse

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