Ontogeny of vocal rhythms in harbor seal pups: an exploratory study

Abstract

Puppyhood is a very active social and vocal period in a harbor seal's life Phoca vitulina. An important feature of vocalizations is their temporal and rhythmic structure, and understanding vocal timing and rhythms in harbor seals is critical to a cross-species hypothesis in evolutionary neuroscience that links vocal learning, rhythm perception, and synchronization. This study utilized analytical techniques that may best capture rhythmic structure in pup vocalizations with the goal of examining whether (1) harbor seal pups show rhythmic structure in their calls and (2) rhythms evolve over time. Calls of 3 wild-born seal pups were recorded daily over the course of 1-3 weeks; 3 temporal features were analyzed using 3 complementary techniques. We identified temporal and rhythmic structure in pup calls across different time windows. The calls of harbor seal pups exhibit some degree of temporal and rhythmic organization, which evolves over puppyhood and resembles that of other species' interactive communication. We suggest next steps for investigating call structure in harbor seal pups and propose comparative hypotheses to test in other pinniped species.

Keywords: bioacoustics; pinnipeds; rhythm; timing; vocal development.

Figure 1.

Violin plots depicting the distribution of durations (top) and IOIs (bottom) in milliseconds over days.

Figure 2.

Comparison of distributions of durations (top) and IOIs (bottom) between any pair of recording days (x and y axes). A black square denotes a significant 2-sample Kolmogorov–Smirnov test, with alpha = 0.05/[days×(days−1)/2], adjusted for all multiple comparisons. The 45° lines denote adjacent days (i.e., d and d + 1). For instance, in the top-left panel, the square at the bottom-left of the graph denotes a significant difference between distributions of durations of Days 6 and 7. The whole graph suggests some heterogeneity but little divergence over time. Crucially, adjacent days are rarely statistically different, suggesting a punctuated slow change.

Figure 3.

Phase space plots of individual 201’s IOI at Days 13, 14, and 15 (top), and Days 23, 24, and 25 (bottom). Although no clear geometrical pattern emerges, consecutive days appear as a “smeared” version of the previous ones (see Ravignani 2017). The fact that most edges connect at the bottom-left of the figure suggests that short IOIs often occur in pairs, rather than an individual short IOI being followed by an individual long IOI, or pairs of long IOI.

Figure 4.

Transition matrices between centroids of duration clusters for individual 192. Each matrix represents 1 day (First row: Days 6, 7, 8, 9, etc.). Darker blue corresponds to a higher transition probability, that is, a higher probability that the durational category on the vertical axis d(t) is followed by the durational category on the horizontal axis d(t + 1). Categories were calculated via K-means clustering algorithms, computing a Silhouette score for each possible K ≤ 10, and choosing the K minimizing the Silhouette score.

Figure 5.

Transition matrices between centroids of duration clusters for individual 201. Each matrix represents 1 day (First row: Days 12, 13, 14, 15, etc.). See Figure 4 for details.

Figure 6.

Transition matrices between centroids of duration clusters for individual 292. Each matrix represents 1 day (First row: Days 9, 10, 11, 12, etc.). See Figure 4 for details.

Figure 7.

Daily and mean IOIs burstiness of the 3 pups. A value close to 0 denotes randomness. A value close to 1 denotes bursts of activity followed by periods of inactivity. A value close to −1 denotes isochrony.

Figure 8.

(Left) AF curves of the 3 seal pups (analysed here) and other species (from Kello et al. 2017). Each curve (i.e., function) consists of 11 orthonormal (independent) variances. Below 1 s, the curves show within-species similarities and between-species variability. Above 1 s, all species show different patterns, with harbor seals and killer whales exhibiting the steepest curves. (Right) AF curves plotted in terms of the linear and quadratic coefficients of a third-order polynomial fit to each individual AF function, in logarithmic coordinates. AF functions from animal vocalizations analyzed in Kello et al. (2017) are shown for comparison. Seal vocalizations have larger linear coefficients because their AF functions are steepened by the scarcity of seal calls compared with other animal vocalization recordings. Note also that calls were segmented and isolated for seal recordings, but not for other recordings. Despite their steepness, AF functions for seal vocalizations clustered with other animal vocalizations, and particularly with killer whales, relative to human speech and music recordings not plotted here but analyzed in Kello et al. (2017).