Evolutionary branching in multi-level selection models.

IF 2.2 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2024-10-09 DOI:10.1007/s00285-024-02145-1
Burton Simon, Yaroslav Ispolatov, Michael Doebeli
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Abstract

We study a model of group-structured populations featuring individual-level birth and death events, and group-level fission and extinction events. Individuals play games within their groups, while groups play games against other groups. Payoffs from individual-level games affect birth rates of individuals, and payoffs from group-level games affect group extinction rates. We focus on the evolutionary dynamics of continuous traits with particular emphasis on the phenomenon of evolutionary diversification. Specifically, we consider two-level processes in which individuals and groups play continuous snowdrift or prisoner's dilemma games. Individual game strategies evolve due to selection pressure from both the individual and group level interactions. The resulting evolutionary dynamics turns out to be very complex, including branching and type-diversification at one level or the other. We observe that a weaker selection pressure at the individual level results in more adaptable groups and sometimes group-level branching. Stronger individual-level selection leads to more effective adaptation within each group while preventing the groups from adapting according to the group-level games.

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多级选择模型中的进化分支
我们研究了一个群体结构的种群模型,其特点是个体层面的出生和死亡事件,以及群体层面的分裂和灭绝事件。个体在群体内进行博弈,群体则与其他群体进行博弈。个体层面博弈的回报会影响个体的出生率,群体层面博弈的回报会影响群体的灭绝率。我们的研究重点是连续特征的进化动态,特别强调进化多样化现象。具体来说,我们考虑了个体和群体玩连续雪地漂移或囚徒困境游戏的两级过程。个体博弈策略在来自个体和群体层面互动的选择压力下不断演化。由此产生的进化动力学结果非常复杂,包括某一层面的分支和类型多样化。我们观察到,个体层面的选择压力越弱,群体的适应性就越强,有时还会出现群体层面的分支。个体层面较强的选择会导致每个群体内部更有效的适应,同时阻止群体根据群体层面的博弈进行适应。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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