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A computational framework to characterize and compare the tonal repertoires of toothed whales
Journal article   Open access   Peer reviewed

A computational framework to characterize and compare the tonal repertoires of toothed whales

Maia Austin, Julie N. Oswald, Manali Rege-Colt, Emma Gagne, Eric Angel Ramos, Joëlle De Weerdt, Nicola Ransome and Laura J. May-Collado
Methods in ecology and evolution
2025
DOI: https://doi.org/10.1111/2041-210X.70065
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CC BY V4.0,  Open Access

Abstract

bioacoustics community ecology comparative analysis diversity evolutionary biology machine learning toothed whales vocal repertoire
Toothed whales, despite being one of the most acoustically specialized lineages of vertebrates, lack detailed vocal repertoire characterizations comparable to those of songbirds and primates. Current descriptions often lack standardization, limiting interspecies and intraspecies comparisons critical for understanding the contribution of phylogenetic, social and environmental drivers of vocal repertoire evolution. This study reviews six bioacoustics software packages (Luscinia, Beluga, ARTwarp, DeepSqueak, PAMGuard and SASLab) using machine learning for toothed whale whistle detection, extraction of whistle fundamental frequency contours and categorization. We applied these tools to acoustic recordings from four dolphin species to create a primer with guidelines and scripts for those interested in replicating the analyses. The results show that manual (Luscinia) and semi-automated (Beluga, DeepSqueak) detection and extraction of whistles yielded a greater number of usable contours than the fully automated approaches (PAMGuard and SASLab). The integration of hard and fuzzy unsupervised machine learning analyses provided a multifaceted characterization of whistle repertoires based on identified patterns and structures. The hard approaches from ARTwarp and DeepSqueak were used to categorize signals based on contour similarity. Both packages produced similar numbers of categories. DeepSqueak was faster in categorizing contours, but ARTwarp is more flexible in how similarity thresholds are set. We used these categories to demonstrate estimating relative whistle repertoire size using three methods—Coupon Collector, Capture–Recapture and Hill Numbers. While the methods differ in their assumptions, there was consensus in the ranking of species based on repertoire size. We favour the application of Hill numbers to measure repertoire size and compositional similarity, as this method addresses uneven sampling. The fuzzy approach used the frequency and temporal features extracted from the traced contours in Luscinia to evaluate repertoire similarity in acoustic structure using the package's built-in multivariate tools. This primer equips researchers to generate more detailed toothed whale repertoire characterizations by integrating machine learning tools with mathematical and community ecology approaches. Supplementary material is provided to support researchers with varying levels of experience in implementing these techniques and indices in their studies.

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
3 Agriculture, Environment & Ecology
3.35 Zoology & Animal Ecology
3.35.796 Marine Mammal Ecology
Web Of Science research areas
Ecology
ESI research areas
Environment/Ecology
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