The evolutionary dynamics of learned bird song

学习行为的研究很有趣，但通常很难比较这些行为在许多物种中的演变，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人员实验室开发的新计算工具来分析在自然界中记录的鸟类的歌声，并提取所有鸣禽都可以比较的量化特征，如音节和歌唱时长。第一项研究将探索不同的歌唱特征是快速还是缓慢地进化，或者在不同的鸟类群体中以不同的速度进化。通过产生和分析新的歌曲信息数据集，这项研究将检验关于选择压力对歌曲进化的作用的进化假说。例如，这项研究将检验这样一种假设，即与女性偏好相关的歌曲特征将是进化更快的歌曲特征。第二项研究将产生新的指标来量化鸟鸣的时间模式，并测试节奏--一种在自然选择下潜在进化的歌唱特征--将与其他歌唱特征和生活史特征显示出相关进化的假设。第三项研究将量化密切相关的鸟类是否可以通过它们的歌声来区分，以确定歌声在相对较新的物种形成事件过程中发生了怎样的变化。如果歌声在进化过程中以稳定的速度进化，那么不同物种之间的歌声分辨率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，当物种具有杂交潜力时，歌曲分化积累得更快。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，将鸟鸣作为一个容易观察到的例子。此外，调查人员将通过与菲斯克-范德比尔特硕士到博士桥项目合作，增加对准备进入研究生院的代表人数不足的学生的计算培训。BirdSong的独特之处在于，通过社区科学努力进行了密集的采样，但由于缺乏处理这些录音的灵活工具，大规模分析仍然具有挑战性。使用开发的新工具来分析广泛的鸣禽分类样本，这项研究将量化每个物种众多个体的歌唱特征，并分析长时间尺度上歌唱进化的动力学。通过新的跨物种指标，这项研究还旨在量化鸣禽的节奏并分析鸣禽的时间模式。最后，这些研究将评估习得的歌声在姐妹物种之间的生殖隔离中的潜在作用，从而评估学习是否似乎促进物种形成。总而言之，这些研究将在多个时间尺度上推进关于行为进化的知识，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社区对发声的分析。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.