Analysing vocal complexity in relation to sociality in orcas of British Columbia: An application of long-term computational passive acoustics

Abstract

Orcas are both highly social and highly vocal animals. In coastal waters of the North-Eastern Pacific Ocean, the Northern Resident orca population is well monitored, providing a great opportunity to learn about their social and communicative behaviour. Here, we report a series of acoustic analyses that lead to the empirical assessment of factors that might impact vocal complexity.

Automatically processing long-term passive acoustic data, we detected and classified calls to transcribe vocal activity. Detailed post-hoc analyses show that the detection model is imperfect, especially in detecting calls of low energy. Also, diarisation is not possible with this data and transcriptions might gather a mixture of several emitters. Taking these limitations into account, we measured communicative complexity considering the groups’ vocal production as a whole. Acoustic and visual cues also enabled the identification of specific groups with estimated numbers of individuals.

Results highlight a positive correlation between vocal and social complexity, which could be due to the mere effect of having more potential emitters. Nonetheless, this brings a first demonstration of the non-trivial link between the number of emitters and complexity in the composition of sequences. We also demonstrate significant impacts of other proximate factors such as behaviour on vocal complexity measurements, and advocate for multi-factor considerations when evaluating communicative complexity.

This work demonstrates the pertinence of joint efforts between passive acoustics, visual observations and machine learning to enhance the scale of behavioural studies and assess the validity of evolutionary hypotheses of communication systems.