Non-vocal, or unvoiced, signals surprisingly have received very little attention until recently especially when compared to other acoustic signals. Some sounds made by terrestrial vertebrates are produced not only by the larynx but also by the syrinx. Furthermore, some birds are known to produce several types of non-syrinx sounds. Besides mechanical sounds produced by feathers, bills and/or wings, sounds can be also produced by constriction, anywhere along the pathway from the lungs to the lips or nostrils (in mammals), or to the bill (in birds), resulting in turbulent, aerodynamic sounds. These noises often emulate whispering, snorting or hissing. Even though hissing sounds have been studied in mammals and reptiles, only a few studies have analyzed hissing sounds in birds. Presently, only the hissing of small, nesting passerines as a defense against their respective predators have been studied. We studied hissing in domestic goose. This bird represents a ground nesting non-passerine bird which frequently produces hissing out of the nest in comparison to passerines producing hissing during nesting in holes e.g., parids. Compared to vocally produced alarm calls, almost nothing is known about how non-vocal hissing sounds potentially encode information about a caller’s identity. Therefore, we aimed to test whether non-vocal air expirations can encode an individual’s identity similar to those sounds generated by the syrinx or the larynx. We analyzed 217 hissing sounds from 22 individual geese. We calculated the Potential for Individual Coding (PIC) comparing the coefficient of variation both within and among individuals. In addition, we conducted a series of 15 a stepwise discriminant function analysis (DFA) models. All 16 acoustic variables showed a higher coefficient of variation among individuals. Twelve DFA models revealed 51.2–54.4% classification result (cross-validated output) and all 15 models showed 60.8–68.2% classification output based on conventional DFA in comparison to a 4.5% success rate when classification by chance. This indicates the stability of the DFA results even when using different combinations of variables. Our findings showed that an individual’s identity could be encoded with respect to the energy distribution at the beginning of a signal and the lowest frequencies. Body weight did not influence an individual’s sound expression. Recognition of hissing mates in dangerous situations could increase the probability of their surviving via a more efficient anti-predator response.

中文翻译：

---

鹅的嘶嘶声：编码在非声音声学信号中的来电者身份

令人惊讶的是，非声音或清音信号直到最近才受到关注，尤其是与其他声音信号相比时。陆生脊椎动物发出的一些声音不仅由喉部发出，也由空洞发出。此外，已知一些鸟类会产生几种类型的非鸣管声音。除了羽毛、喙和/或翅膀产生的机械声音外，声音也可以通过收缩产生，沿着从肺部到嘴唇或鼻孔（哺乳动物）或喙（鸟类）的路径的任何地方，导致湍流, 空气动力学声音。这些噪音通常模仿耳语、鼻息或嘶嘶声。尽管已经研究了哺乳动物和爬行动物的嘶嘶声，但只有少数研究分析了鸟类的嘶嘶声。目前，只有轻微的嘶嘶声，已经研究了筑巢雀类作为防御其各自捕食者的方法。我们研究了家鹅的嘶嘶声。这只鸟代表一种地面筑巢的非雀形目鸟类，与在洞中筑巢期间产生嘶嘶声的雀形目（例如鹦鹉）相比，它经常在巢外产生嘶嘶声。与声音发出的警报呼叫相比，几乎没有人知道非声音嘶嘶声如何潜在地编码有关呼叫者身份的信息。因此，我们旨在测试非声音呼气是否可以编码个人身份，类似于那些由空洞或喉头产生的声音。我们分析了 22 只鹅的 217 种嘶嘶声。我们计算了个体编码潜力 (PIC)，比较了个体内部和个体之间的变异系数。此外，我们进行了一系列 15 个逐步判别函数分析 (DFA) 模型。所有 16 个声学变量都显示出更高的个体变异系数。12 个 DFA 模型显示 51.2-54.4% 的分类结果（交叉验证输出），所有 15 个模型都显示基于传统 DFA 的 60.8-68.2% 分类输出，而随机分类的成功率为 4.5%。这表明即使使用不同的变量组合，DFA 结果的稳定性。我们的研究结果表明，可以根据信号开始时和最低频率的能量分布对个人身份进行编码。体重不会影响个人的声音表达。