Department of Biology, New Mexico State University, Las Cruces, New Mexico, United States of America.
Laboratory of Neurogenetics of Language, Rockefeller University, New York, New York, United States of America.
PLoS Comput Biol. 2023 Jul 27;19(7):e1011231. doi: 10.1371/journal.pcbi.1011231. eCollection 2023 Jul.
Animals can actively encode different types of identity information in learned communication signals, such as group membership or individual identity. The social environments in which animals interact may favor different types of information, but whether identity information conveyed in learned signals is robust or responsive to social disruption over short evolutionary timescales is not well understood. We inferred the type of identity information that was most salient in vocal signals by combining computational tools, including supervised machine learning, with a conceptual framework of "hierarchical mapping", or patterns of relative acoustic convergence across social scales. We used populations of a vocal learning species as a natural experiment to test whether the type of identity information emphasized in learned vocalizations changed in populations that experienced the social disruption of introduction into new parts of the world. We compared the social scales with the most salient identity information among native and introduced range monk parakeet (Myiopsitta monachus) calls recorded in Uruguay and the United States, respectively. We also evaluated whether the identity information emphasized in introduced range calls changed over time. To place our findings in an evolutionary context, we compared our results with another parrot species that exhibits well-established and distinctive regional vocal dialects that are consistent with signaling group identity. We found that both native and introduced range monk parakeet calls displayed the strongest convergence at the individual scale and minimal convergence within sites. We did not identify changes in the strength of acoustic convergence within sites over time in the introduced range calls. These results indicate that the individual identity information in learned vocalizations did not change over short evolutionary timescales in populations that experienced the social disruption of introduction. Our findings point to exciting new research directions about the robustness or responsiveness of communication systems over different evolutionary timescales.
动物可以在学习到的交流信号中主动编码不同类型的身份信息,例如群体归属或个体身份。动物相互作用的社会环境可能偏向于不同类型的信息,但在短时间的进化过程中,学习到的信号中传达的身份信息是否稳健或对社会干扰做出反应,这一点还不是很清楚。我们通过结合计算工具,包括监督机器学习,以及“层次映射”的概念框架,即社会尺度上相对声学收敛的模式,来推断在声音信号中最突出的身份信息类型。我们使用一种具有发声学习能力的物种的种群作为自然实验,来测试在经历引入到世界新地区等社会干扰后,学习到的发声中强调的身份信息类型是否发生了变化。我们比较了具有最突出身份信息的社会尺度,这些信息分别是在乌拉圭和美国记录的本地和引入范围的和尚鹦鹉(Myiopsitta monachus)叫声中。我们还评估了引入范围叫声中强调的身份信息是否随时间而变化。为了将我们的发现置于进化背景下,我们将结果与另一种鹦鹉物种进行了比较,该物种表现出明确而独特的区域发声方言,这些方言与群体身份信号一致。我们发现,本地和引入范围的和尚鹦鹉叫声在个体尺度上显示出最强的收敛,而在地点内的收敛最小。我们没有发现引入范围的叫声中地点内的声学收敛强度随时间的变化。这些结果表明,在经历了引入带来的社会干扰的种群中,学习到的发声中的个体身份信息在短时间的进化过程中没有发生变化。我们的研究结果为关于不同进化时间尺度上的通讯系统的稳健性或响应性的新研究方向提供了依据。