Structural Classification of Wild Boar (Sus scrofa) Vocalizations

Abstract

Determining whether a species' vocal communication system is graded or discrete requires definition of its vocal repertoire. In this context, research on domestic pig (Sus scrofa domesticus) vocalizations, for example, has led to significant advances in our understanding of communicative functions. Despite their close relation to domestic pigs, little is known about wild boar (Sus scrofa) vocalizations. The few existing studies, conducted in the 1970s, relied on visual inspections of spectrograms to quantify acoustic parameters and lacked statistical analysis. Here, we use objective signal processing techniques and advanced statistical approaches to classify 616 calls recorded from semi‐free ranging animals. Based on four spectral and temporal acoustic parameters—quartile Q25, duration, spectral flux, and spectral flatness—extracted from a multivariate analysis, we refine and extend the conclusions drawn from previous work and present a statistically validated classification of the wild boar vocal repertoire into four call types: grunts, grunt‐squeals, squeals, and trumpets. While the majority of calls could be sorted into these categories using objective criteria, we also found evidence supporting a graded interpretation of some wild boar vocalizations as acoustically continuous, with the extremes representing discrete call types. The use of objective criteria based on modern techniques and statistics in respect to acoustic continuity advances our understanding of vocal variation. Integrating our findings with recent studies on domestic pig vocal behavior and emotions, we emphasize the importance of grunt‐squeals for acoustic approaches to animal welfare and underline the need of further research investigating the role of domestication on animal vocal communication.

Acoustic Structure and Contextual Use of Calls by Captive Male and Female Cheetahs (Acinonyx jubatus)

“…red deer [ 52 ]. This is distinctive to the more “continual” vocal repertoires, e.g., in red fox ( Vulpes vulpes ) [ 36 ] and in the wild boar ( Sus scrofa ) [ 61 ], in which a noticeable number of intermediate vocalizations occurred along to distinctive call types. Transitional forms from one call type to another occurred only in 5.8% cases, similar to [ 19 ].…”

Honest signaling in domestic piglets (Sus scrofa domesticus): vocal allometry and the information content of grunt calls

“…Previous work has shown that two main call types, grunts and squeals, could be consistently identified while investigating the vocal repertoires of both domestic pigs ( Kiley, 1972 ; Tallet et al, 2013 ) and wild boars ( Garcia et al, 2016 ; Klingholz et al, 1979 ). Unlike squeals, the acoustic characteristics of grunts make them particularly well suited for highlighting formants because of their low F 0 ( Fitch and Hauser, 1995 ; Ryalls and Lieberman, 1982 ), even though the nasal production typical of this call type might slightly impair our ability to track formants compared with formants from calls of other mammalian species ( Charlton et al, 2011b ; Reby and McComb, 2003 ).…”

“…those deemed to have a high enough signal-to-noise ratio and visible formants) were annotated with ‘Individual’ and ‘Series' using the ‘Annotate: To TextGrid’ function. Care was taken to identify true grunts clearly, as opposed to ‘grunt–squeals' which have quite different acoustic characteristics ( Garcia et al, 2016 ). Annotated grunts were extracted and average formant values were retrieved from each call via a custom-written Praat script (M.G.)…”

Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

“…Research on the vocalizations of wild boar has shown that their calls can be categorized into grunts ( pulsatile, low-frequency sounds), squeals (noisy, harsh vocalizations in a broad frequency range), grunt -squeals (observations where both vocalizations were observed in a single vocalization), barks (isolated, short, high-intensity, non-harmonic vocalizations) and trumpets (harmonic calls with a high fundamental frequency) [136]. The recordings were analysed by extracting acoustic parameters and putting them through multinomial logistic regression models, and a hierarchical cluster analysis.…”