The evolutionary dynamics of learned bird song

学习行为的研究很有吸引力，但通常很难比较许多物种之间这些行为的进化，主要是因为以标准化的方式衡量不同物种的复杂行为的各个方面是具有挑战性的。这组研究建议使用研究者实验室开发的新计算工具来分析自然界中记录的鸟类鸣叫，并提取所有鸣禽之间可比较的定量特征，如音节和鸣声持续时间。第一项研究将探索不同的鸣叫特征是进化得快还是慢，或者在不同的鸟类群体中以不同的速度进化。通过生成和分析新的歌曲信息数据集，本研究将测试关于选择压力在歌曲进化中的作用的进化假设。例如，这项研究将测试一个假设，即与女性偏好有关的歌曲特征将是进化得更快的歌曲特征。第二项研究将产生新的指标来量化鸟类鸣叫的时间模式，并检验一种假设，即节奏，一种可能在自然选择下进化的鸣叫特征，将显示出与其他鸣叫特征和生活史特征相关的进化。第三项研究将量化亲缘关系密切的鸟类是否可以通过它们的叫声来区分，以确定在相对较近的物种形成过程中，叫声是如何变化的。如果歌声在进化过程中以稳定的速度进化，那么物种之间的歌声可辨性将与遗传距离呈正相关。或者，如果歌声在生殖隔离中起作用，那么地理范围重叠的姐妹物种将更快地进化出可区分的歌声。这一发现表明，当物种有可能杂交时，鸣声的分化积累得更快。研究人员将与当地的观鸟者和社区科学家进行持续的互动，并将推出旨在教育中学生进化和保护的教育模块，以鸟鸣作为一个现成的观察例子。此外，研究人员将通过与菲斯克-范德比尔特大学硕士到博士的桥梁项目合作，为准备研究生院的代表性不足的学生增加计算训练。通过社区科学的努力，Birdsong的采样密度是独一无二的，但由于缺乏处理这些记录的灵活工具，大规模分析仍然具有挑战性。利用新开发的工具分析鸣禽的广泛分类样本，本研究将量化每个物种的众多个体的鸣声特征，并分析鸣声在长时间尺度上的进化动态。利用新的跨物种指标，本研究还旨在量化鸣禽的节奏和分析鸣禽的时间模式。最后，这些研究将评估学习鸣声在姐妹物种之间生殖隔离中的潜在作用，从而评估学习是否似乎促进了物种形成。总之，这些研究将在多个时间尺度上推进对行为进化的认识，提高我们对学习在进化过程中的作用的理解。拟议的研究还将进一步开发计算工具，以促进更广泛的研究界对发声的分析。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Learned behaviors are fascinating to study, but it is often difficult to compare the evolution of these behaviors across many species, primarily because it is challenging to measure aspects of complex behaviors in a standardized way for different species. This set of studies proposes to use new computational tools developed in the investigator’s lab to analyze bird songs recorded in nature and extract quantitative features that are comparable across all songbirds, such as syllable and song duration. The first study will explore whether different features of song evolve quickly or slowly, or at different rates in different groups of birds. By producing and analyzing new datasets of song information, this study will test evolutionary hypotheses about the role of selection pressures on song evolution. For example, this study will test the hypothesis that song features that have been linked to female preferences will be the song features that evolve more quickly. The second study will generate new metrics to quantify the temporal patterns of birdsong and test the hypothesis that rhythm, a song trait potentially evolving under natural selection, will show correlated evolution with other song traits and life-history traits. The third study will quantify whether closely related birds can be distinguished by their songs to determine how songs have changed over the course of relatively recent speciation events. If song evolved at a steady rate over evolutionary time, then song distinguishability between species would positively correlate with genetic distance. Alternatively, if song plays a role in reproductive isolation, then sister species with overlapping geographic ranges will evolve distinguishable songs more quickly. This finding would suggest that song differentiation accumulates more quickly when species have the potential to hybridize. The researchers will have ongoing interactions with local birders and community scientists and will launch educational modules aimed at educating middle-schoolers in evolution and conservation using birdsong as a readily observed example. In addition, the investigators will add to the computational training of underrepresented students preparing for graduate school by partnering with the Fisk-Vanderbilt Masters-to-PhD Bridge program.Birdsong is unique in how densely sampled it is through community science efforts, but large-scale analyses have remained challenging due to a lack of flexible tools for processing these recordings. Using new tools developed to analyze a broad taxonomic sample of songbirds, this study will quantify song features from numerous individuals per species and analyze the dynamics of song evolution over long timescales. With new cross-species metrics, this research also aims to quantify rhythm and analyze temporal patterns in birdsong across oscine songbirds. Finally, these studies will assess the potential role of learned song in reproductive isolation between sister species, thus assessing whether learning appears to facilitate speciation. Together, these studies will advance knowledge about the evolution of behavior on multiple timescales, improving our understanding of the role of learning in evolutionary processes. The proposed studies will also further develop computational tools to facilitate the analysis of vocalizations by the broader research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.