Individual signatures outweigh social group identity in contact calls of a communally nesting parrot
Individual signatures outweigh social group identity in contact calls of a communally nesting parrot
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在公共筑巢鹦鹉的联系呼叫中，个人签名超过了社会群体身份
DOI：
10.1093/beheco/arz202
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发表时间：
2020
影响因子：
2.4
通讯作者：
T. Wright
中科院分区：
文献类型：
--
作者：
Grace Smith;M. Araya‐Salas;T. Wright
尽管长期以来对发声学习的进化起源和维持感兴趣，但我们对社会动态如何影响自然人群的发声学习过程知之甚少。“群体成员信号”假说提出，社会习得的叫声是进化的，并作为群体成员的信号而保持。然而，在裂变-融合社会中，个人可以在各种社会规模的社会群体中互动。对于在多个社会尺度上发出群体成员身份信号的习得呼叫，它们必须包含关于这些尺度中的每一个的群体成员身份的信息，这一概念被称为“层次映射”。僧鹦鹉（Myiopsitta monachus）是原产于南美洲的小鹦鹉，在圈养环境中表现出声音模仿能力，在野外则表现出裂变融合的社会动态。我们研究了乌拉圭的接触呼叫声学相似性模式，以测试信号组成员假设的分层映射假设。我们还询问了地理变异模式是否与其他发声学习物种中记录的区域方言或地理倾向相匹配。我们使用视觉检查，光谱互相关和随机森林，一种机器学习方法，来评估接触呼叫的相似性。我们使用Mantel检验和空间自相关比较了社会规模和地理距离的声学相似性。我们发现高相似性的个人，低，但显着，在对，羊群和网站的社会规模的群体内的相似性。地理距离上的声学相似性模式不匹配马赛克或分级模式预期在方言或倾斜的变化。我们的研究结果表明，和尚长尾小鹦鹉的社会互动更严重地依赖于个人的认可比群体成员在更高的社会规模。
Despite longstanding interest in the evolutionary origins and maintenance of vocal learning, we know relatively little about how social dynamics influence vocal learning processes in natural populations. The “signaling group membership” hypothesis proposes that socially learned calls evolved and are maintained as signals of group membership. However, in fission–fusion societies, individuals can interact in social groups across various social scales. For learned calls to signal group membership over multiple social scales, they must contain information about group membership over each of these scales, a concept termed “hierarchical mapping.” Monk parakeets (Myiopsitta monachus), small parrots native to South America, exhibit vocal mimicry in captivity and fission–fusion social dynamics in the wild. We examined patterns of contact call acoustic similarity in Uruguay to test the hierarchical mapping assumption of the signaling group membership hypothesis. We also asked whether geographic variation patterns matched regional dialects or geographic clines that have been documented in other vocal learning species. We used visual inspection, spectrographic cross-correlation and random forests, a machine learning approach, to evaluate contact call similarity. We compared acoustic similarity across social scales and geographic distance using Mantel tests and spatial autocorrelation. We found high similarity within individuals, and low, albeit significant, similarity within groups at the pair, flock and site social scales. Patterns of acoustic similarity over geographic distance did not match mosaic or graded patterns expected in dialectal or clinal variation. Our findings suggest that monk parakeet social interactions rely more heavily upon individual recognition than group membership at higher social scales.