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雄性偏向的性别比例增加了交配前性选择的机会,但不会改变贝特曼梯度。

A male-biased sex ratio increases the opportunity for precopulatory sexual selection but does not change the Bateman gradient.

作者信息

McDonald Grant C, Edmunds Danielle, Morimoto Juliano, Wigby Stuart, Perry Jennifer C

机构信息

Department of Zoology, University of Veterinary Medicine Budapest, Budapest, Hungary.

Department of Biology, University of Oxford, Oxford, United Kingdom.

出版信息

Evol Lett. 2025 Feb 14;9(3):324-334. doi: 10.1093/evlett/qraf001. eCollection 2025 Jun.

Abstract

Theory predicts that the sex ratio within populations should influence the strength of sexual selection, and sex ratio is often used as a proxy for sexual selection. However, recent studies challenge this relationship. We manipulated adult sex ratios in to comprehensively investigate the relationship between sex ratio and sexual selection. Consistent with theory, we found stronger sexual selection in males than females and an increased variance in male reproductive success (the opportunity for selection) in male-biased sex ratios. In addition, males faced more intense sperm competition in male-biased sex ratios, although the structure of sexual networks was largely invariant to sex ratio. Despite this, we show that sex ratios did not influence sexual selection in males as measured by the Bateman gradient. We leverage randomized null models to reconcile these results and show that the higher male reproductive variance in male-biased sex ratios may be explained by random chance in mating, rather than competitive mechanisms. Our findings indicate that caution is warranted over the long-standing assumption that sex ratio bias is a good proxy for the strength of sexual selection.

摘要

理论预测,种群内的性别比例应会影响性选择的强度,且性别比例常被用作性选择的一个指标。然而,最近的研究对这种关系提出了挑战。我们通过操纵成年个体的性别比例,以全面研究性别比例与性选择之间的关系。与理论一致,我们发现雄性的性选择比雌性更强,且在雄性偏多的性别比例中,雄性繁殖成功率(选择机会)的方差增加。此外,在雄性偏多的性别比例中,雄性面临更激烈的精子竞争,尽管性网络的结构在很大程度上不受性别比例的影响。尽管如此,我们表明,以贝特曼梯度衡量,性别比例并未影响雄性的性选择。我们利用随机零模型来协调这些结果,并表明在雄性偏多的性别比例中,较高的雄性繁殖方差可能是由交配中的随机因素而非竞争机制所解释的。我们的研究结果表明,对于性别比例偏差是性选择强度的良好指标这一长期假设,有必要谨慎对待。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/24a8/12137052/4f2df2e85af9/qraf001_fig1.jpg

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