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马尔可夫祖先模型在群体网络文化进化的时空动态中的应用。

Application of a Markovian ancestral model to the temporal and spatial dynamics of cultural evolution on a population network.

机构信息

Department of Biological Sciences, The University of Tokyo, Hongo 7-3-1, Bunkyoku, Tokyo 113-0033, Japan.

Department of Biological Sciences, The University of Tokyo, Hongo 7-3-1, Bunkyoku, Tokyo 113-0033, Japan.

出版信息

Theor Popul Biol. 2022 Feb;143:14-29. doi: 10.1016/j.tpb.2021.10.003. Epub 2021 Nov 12.

Abstract

Cultural macroevolution concerns a long-term evolutionary process involving transmission of non-genetic or cultural traits between populations as well as birth and death of populations. To understand the spatial dynamics of cultural macroevolution, we present a one-locus model of cultural diffusion in which a cultural trait is transmitted on a network of populations. Borrowing the method of ancestral backward process from population genetics, our model explores the lineage of a trait variant sampled in the present generation to quantify when and where the variant was invented. Mathematical analysis of the model enables us to predict the distribution of cultural age in each population of the network, estimate the frequencies of trait variants originating from given populations, and discuss the time it takes for a trait variant to diffuse between a given pair of populations. We also perform numerical analysis on random scale-free network of populations to investigate the effect of network topology and innovation rate on the age and origin of variants in each population. The result suggests that trait variants are more likely to derive from a population with higher innovation rate. Our numerical analysis also shows that trait variants invented in populations with higher network-centrality values are likely to be maintained at a higher frequency and transmitted to other populations in a shorter time period.

摘要

文化宏观进化涉及一个长期的进化过程,包括非遗传或文化特征在群体之间的传播,以及群体的产生和消亡。为了理解文化宏观进化的空间动态,我们提出了一个文化扩散的单基因座模型,其中文化特征在种群网络上传播。我们的模型借鉴了种群遗传学中的祖先回溯过程的方法,探索了在当前世代中采样的特征变体的谱系,以量化变体何时以及何地被发明。对模型的数学分析使我们能够预测网络中每个种群的文化年龄分布,估计来自给定种群的特征变体的频率,并讨论特征变体在给定的一对种群之间传播所需的时间。我们还对种群的随机无标度网络进行了数值分析,以研究网络拓扑结构和创新率对每个种群中变体的年龄和起源的影响。结果表明,特征变体更有可能源自创新率较高的种群。我们的数值分析还表明,在网络中心性值较高的种群中发明的特征变体更有可能以较高的频率保持,并在较短的时间内传播到其他种群。

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