Abstract

Vocal production learning (VPL) is the experience-driven ability to produce novel vocal signals through imitation or modification of existing vocalizations. A parallel strand of research investigates acoustic allometry, namely how information about body size is conveyed by acoustic signals. Recently, we proposed that deviation from acoustic allometry principles as a result of sexual selection may have been an intermediate step towards the evolution of vocal learning abilities in mammals. Adopting a more hypothesis-neutral stance, here we perform phylogenetic regressions and other analyses further testing a potential link between VPL and being an allometric outlier. We find that multiple species belonging to VPL clades deviate from allometric scaling but in the opposite direction to that expected from size exaggeration mechanisms. In other words, our correlational approach finds an association between VPL and being an allometric outlier. However, the direction of this association, contra our original hypothesis, may indicate that VPL did not necessarily emerge via sexual selection for size exaggeration: VPL clades show higher vocalization frequencies than expected. In addition, our approach allows us to identify species with potential for VPL abilities: we hypothesize that those outliers from acoustic allometry lying above the regression line may be VPL species. Our results may help better understand the cross-species diversity, variability and aetiology of VPL, which among other things is a key underpinning of speech in our species.This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part II)’.

Highlights

  • Understanding the vocal learning phenotype in humans and non-human animals has raised significant interest, most probably because this trait is a key prerequisite to human speech [1,2]

  • Similar to some of our previous results, we found a significantly greater proportion of vocal production learning (VPL) species as outliers to the phylogenetic generalized least square (PGLS) regressions for minimum dominant frequency’ (MinDF) and range of the dominant frequency’ (RangeDF) but not for maximum dominant frequency’ (MaxDF) (Z = −1.44, p = 0.16) and mean dominant frequency’ (MeanDF) (Z = −0.65, p = 0.58)

  • We investigated which species may have a potential for possessing VPL abilities by looking at which species were found to be outliers from acoustic 7 allometry scaling for at least one acoustic parameter in either of the PGLS regressions on the full dataset

Read more

Summary

Introduction

Understanding the vocal learning phenotype in humans and non-human animals has raised significant interest, most probably because this trait is a key prerequisite to human speech [1,2]. Animal vocalizations which make an individual sound bigger or smaller than their actual body size are examples of dishonest signalling, characterized by deviations from acoustic allometry. Species that produce sound frequencies higher or lower than expected (based on an interspecific averaging) for their body size can be considered as being allometric outliers and engaging in a form of dishonest signalling. Dishonest vocal signalling can arise both from size-exaggeration and size-reduction mechanisms, documented cases have mainly provided evidence for the former (see [6] and references therein).2 In such cases, runaway selection can lead to permanent anatomical adaptations (e.g. in red deer [16] and in koalas [17]), which typically correlate with deviations from allometric scaling. Without speculating on the role of evolutionary pressures, the focus of this paper consists of investigating whether species in clades with VPL abilities (VPL species thereafter) are ‘downward outliers’ (i.e. below an average interspecific acoustic allometry regression line, similar to what is observed for species with anatomical adaptations), upward outliers or outliers with no particular directional pattern

Methods
MinDF VPL
DUDDUU category
MeanDF D D D D
Findings
Limitations and conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call