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Identification of potential signature whistles from free-ranging common dolphins (Delphinus delphis) in South Africa.

Jack FeareySimon H ElwenBridget S JamesTess Gridley
Published in: Animal cognition (2019)
Conveying identity is important for social animals to maintain individually based relationships. Communication of identity information relies on both signal encoding and perception. Several delphinid species use individually distinctive signature whistles to transmit identity information, best described for the common bottlenose dolphin (Tursiops truncatus). In this study, we investigate signature whistle use in wild common dolphins (Delphinus delphis). Acoustic recordings were analysed from 11 encounters from three locations in South Africa (Hout Bay, False Bay, and Plettenberg Bay) during 2009, 2016 and 2017. The frequency contours of whistles were visually categorised, with 29 signature whistle types (SWTs) identified through contour categorisation and a bout analysis approach developed specifically to identify signature whistles in bottlenose dolphins (SIGID). Categorisation verification was conducted using an unsupervised neural network (ARTwarp) at both a 91% and 96% vigilance parameter. For this, individual SWTs were analysed type by type and then in a 'global' analysis whereby all 497 whistle contours were categorised simultaneously. Overall the analysis demonstrated high stereotypy in the structure and temporal production of whistles, consistent with signature whistle use. We suggest that individual identity information may be encoded in these whistle contours. However, the large group sizes and high degree of vocal activity characteristic of this dolphin species generate a cluttered acoustic environment with high potential for masking from conspecific vocalisations. Therefore, further investigation into the mechanisms of identity perception in such acoustically cluttered environments is required to demonstrate the function of these stereotyped whistle types in common dolphins.
Keyphrases
  • south africa
  • healthcare
  • machine learning
  • mental health
  • health information
  • risk assessment
  • genetic diversity
  • social media
  • climate change