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Song learning accelerates allopatric speciation

机译:歌曲学习加速异源物种形成

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The songs of many birds are unusual in that they serve a role in identifying conspecific mates, yet they are also culturally transmitted. Noting the apparently high rate of diversity in one avian taxon, the songbirds, in which song learning appears ubiquitous, it has often been speculated that cultural transmission may increase the rate of speciation. Here we examine the possibility that song learning affects the rate of allopatric speciation. We construct a population-genetic model of allopatric divergence that explores the evolution of genes that underlie learning preferences (predispositions to learn some songs over others). We compare this with a model in which mating signals are inherited only genetically. Models are constructed for the cases where songs and preferences are affected by the same or different loci, and we analyze them using analytical local stability analysis combined with simulations of drift and directional sexual selection. Under nearly all conditions examined, song divergence occurs more readily in the learning model than in the nonlearning model. This is a result of reduced frequency-dependent selection in the learning models. Cultural evolution causes males with unusual genotypes to tend to learn from the majority of males around them, and thus develop songs compatible with the majority of the females in the population. Unusual genotypes can therefore be masked by learning. Over a wide range of conditions, learning therefore reduces the waiting time for speciation to occur and can be predicted to accelerate the rate of speciation.
机译:许多鸟的歌很不寻常,因为它们在识别同伴时起着一定的作用,但它们在文化上也能传播。注意到一种鸟类分类群中鸣叫的鸟类显然具有很高的多样性,在这种鸟类中,歌曲学习似乎无处不在,人们常常推测,文化传播可能会增加物种形成的速度。在这里,我们研究了歌曲学习影响异源物种形成速率的可能性。我们构建了一个异源散度的种群遗传模型,该模型探索了学习偏好(学习某些歌曲的倾向高于其他歌曲)的基因进化。我们将其与仅在基因上遗传交配信号的模型进行比较。针对歌曲和喜好受相同或不同基因座影响的情况构建了模型,我们使用分析性局部稳定性分析结合漂移和定向性选择的模拟来分析它们。在几乎所有检查的条件下,与非学习模型相比,学习模型中的歌曲发散更容易发生。这是由于学习模型中的频率相关选择减少所致。文化的演变导致具有不寻常基因型的男性倾向于向周围的大多数男性学习,从而发展出与人口中大多数女性相适应的歌曲。因此,异常基因型可以通过学习来掩盖。因此,在很宽的条件范围内,学习减少了物种形成的等待时间,可以预测为加快物种形成的速度。

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