The Indo-Pacific bottlenose dolphin ( Tursiops aduncus ) is a critical species for coastal marine ecosystems; however, long-term monitoring remains challenging. Acoustic monitoring provides a noninvasive approach to studying dolphin populations, and in this study, signature whistles were identified from acoustic recordings collected around Jeju Island, South Korea. While conventional acoustic parameters, such as minimum and maximum frequency, duration, and frequency excursion, have been widely used to characterize dolphin whistle signals, they often are insufficient for capturing subtle within-whistle patterns critical for individual and population-level differentiation. To address this limitation, we introduce a novel symbolic contour encoding method termed DAS (Descent–Ascent–Static). Unlike traditional whole-whistle classification or scalar descriptor approaches, the DAS framework segments each whistle into discrete slope-based components, preserving fine-scale frequency modulation patterns. Comparative analyses revealed that the DAS-encoded features achieved clearer population separation in unsupervised clustering and principal component analyses, explaining nearly 90% of the variance compared with conventional parameters, which explains only approximately 60% of the variance. Additionally, we confirmed that more than half of the whistles detected were signature whistles, consistent with previous studies, and emphasized the importance of accounting for repeated signature whistle production to avoid statistical bias. This study represents the first report of signature whistles from the Jeju Island dolphin population and demonstrates that symbolic segment encoding offers a more biologically meaningful and statistically robust framework for population-level comparisons of dolphin vocal repertoires, with important implications for future ecological monitoring and conservation efforts aimed toward this species. • Introduced a lightweight ADS (Ascent–Descent–Static) method that captures dolphin whistle modulation patterns more effectively than scalar parameters. • ADS features showed much stronger population separation, explaining ~90% of variance versus ~60% for conventional metrics. • Sequential ADS motif mining revealed distinct contour-use patterns across Jeju, Sarasota, and Mediterranean dolphin populations. • ADS provides a biologically grounded, noise-tolerant framework suitable for large-scale monitoring and future automated analysis.
Kim et al. (Sun,) studied this question.