Vocal plasticity can occur in response to environmental and biological factors, including conspecifics' vocalizations and noise. Pinnipeds are one of the few mammalian groups capable of vocal learning, and are therefore relevant to understanding the evolution of vocal plasticity in humans and other animals. Here, we investigate the vocal plasticity of harbour seals ( Phoca vitulina ), a species with vocal learning abilities observed in adulthood but not puppyhood. To evaluate early mammalian vocal development, we tested 1–3 weeks-old seal pups. We tailored noise playbacks to this species and age to induce seal pups to shift their fundamental frequency ( f 0 ), rather than adapt call amplitude or temporal characteristics. We exposed individual pups to low- and high-intensity bandpass-filtered noise, which spanned—and masked—their typical range of f 0 ; simultaneously, we recorded pups' spontaneous calls. Unlike most mammals, pups modified their vocalizations by lowering their f 0 in response to increased noise. This modulation was precise and adapted to the particular experimental manipulation of the noise condition. In addition, higher levels of noise induced less dispersion around the mean f 0 , suggesting that pups may have actively focused their phonatory efforts to target lower frequencies. Noise did not seem to affect call amplitude. However, one seal showed two characteristics of the Lombard effect known for human speech in noise: significant increase in call amplitude and flattening of spectral tilt. Our relatively low noise levels may have favoured f 0 modulation while inhibiting amplitude adjustments. This lowering of f 0 is unusual, as most animals commonly display no such f 0 shift. Our data represent a relatively rare case in mammalian neonates, and have implications for the evolution of vocal plasticity and vocal learning across species, including humans. This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part I)’.

Sociality is an ethological need of horses that remained unchanged by domestication. Accordingly, it is essential to include horses’ social behavioural requirements and the opportunity to establish stable affiliative bonds in equine management systems and welfare assessment. Thus, this systematic review aims to provide an up-to-date analysis of equine intraspecific social ethograms. A literature review yielded 27 papers that met the inclusion criteria by studying adult (≥2 years) equine social behaviour with conspecifics using a well-defined ethogram. Social interactions were observed in 851 horses: 320 (semi-)feral free-ranging, 62 enclosed (semi-)feral and 469 domesticated, living in groups averaging 9.1 (mean +/− 6.8 s.d., range: 2–33) horses. The ethograms detailed in these 27 studies included a total of 40 (mean: 12.8/paper, range: 2–23) social behaviours, of which 60% (24/40) were agonistic, 30% (12/40) affiliative, 7.5% (3/40) investigative and 2.5% (1/40) neutral. The 27 publications included 67.7% agonistic and only 26% affiliative, 5.1% investigative and 1.2% neutral social behaviours in their methodology, thus focusing predominantly on socio-negative interactions. The strong emphasis on agonistic behaviours in equine ethology starkly contrasts with the rare occurrence of agonistic behaviours in stable horse groups and the well-established importance of affiliative interactions for equine welfare. The nuanced and complex equine social behaviour requires refinement of the ethogram with a greater focus on affiliative, ambivalent and indifferent interactions and the role of social tolerance in equine social networks to advance equine welfare assessment.

Every empirical research project includes bottlenecks at various levels. In bioacoustics, one of these time-consuming bottlenecks corresponds to the step of transforming a long stream of audio into acoustic properties of specific sounds. Here, we describe a data-extraction pipeline which integrates manual annotation with Parselmouth’s powerful computational analyses. This semi-supervised method allows extracting a large volume of sound features with limited repetitive human “point and click.” We illustrate this using recently published empirical research, where we focused on vocal production learning and plasticity in pinnipeds. Faced with a species capable of imitating sounds, fully automatic methods may misclassify individuals (because of imitation), while the large number of calls make fully manual approaches suboptimal and error-prone. Focusing on early vocal development, we tested 1–3 weeks-old harbor seal pups ( Phoca vitulina). Noise playbacks served to induce seal pups to shift their fundamental frequency. Pups' spontaneous calls were recorded while exposed to bandpass-filtered noise, which spanned and masked the animals’ fundamental frequency range. After a summary manual annotation of calls’ boundaries, Parselmouth identified these boundaries in the files, and automatically extracted multiple sound parameters. Based on this, we found that pups modified their vocalizations by lowering their fundamental frequency in response to noise.