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克罗泽悖论与亲缘识别:简化模型的启示。

Crozier's paradox and kin recognition: Insights from simplified models.

机构信息

Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.

出版信息

J Theor Biol. 2024 Mar 21;581:111735. doi: 10.1016/j.jtbi.2024.111735. Epub 2024 Jan 19.

Abstract

Crozier's paradox suggests that genetic kin recognition will not be evolutionarily stable. The problem is that more common tags (markers) are more likely to be recognised and helped. This causes common tags to increase in frequency, eliminating the genetic variability that is required for genetic kin recognition. In recent years, theoretical models have resolved Crozier's paradox in different ways, but they are based on very complicated multi-locus population genetics. Consequently, it is hard to see exactly what is going on, and whether different theoretical resolutions of Crozier's paradox lead to different types of kin discrimination. I address this by making unrealistic simplifying assumptions to produce a more tractable and understandable model of Crozier's paradox. I use this to interpret a more complex multi-locus population genetic model where I have not made the same simplifying assumptions. I explain how Crozier's paradox can be resolved, and show that only one known theoretical resolution of Crozier's paradox - multiple social encounters - leads without restrictive assumptions to the type of highly cooperative and reliable form of kin discrimination that we observe in nature. More generally, I show how adopting a methodological approach where complex models are compared with simplified ones can lead to greater understanding and accessibility.

摘要

克罗泽悖论表明,遗传亲缘识别在进化上是不稳定的。问题在于,更常见的标记(标志物)更有可能被识别和帮助。这导致常见的标记在频率上增加,消除了遗传亲缘识别所需的遗传可变性。近年来,理论模型以不同的方式解决了克罗泽悖论,但它们都是基于非常复杂的多基因座种群遗传学。因此,很难确切地了解情况,以及不同的克罗泽悖论理论解决方案是否会导致不同类型的亲缘歧视。我通过做出不切实际的简化假设来解决这个问题,从而产生了一个更易于处理和理解的克罗泽悖论模型。我使用这个模型来解释一个更复杂的多基因座种群遗传模型,在这个模型中,我没有做出同样的简化假设。我解释了如何解决克罗泽悖论,并表明只有一种已知的克罗泽悖论理论解决方案——多次社交遭遇——在没有限制性假设的情况下,导致了我们在自然界中观察到的那种高度合作和可靠的亲缘歧视形式。更一般地说,我展示了如何采用一种方法,即通过比较复杂的模型和简化的模型来获得更深入的理解和可访问性。

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