Extended Data Fig. 5: Repertoire dating control analyses.

a–c, Within-bout effects, analogous to Fig. 3c, d. a, Within-bout effects computed only from renditions that fall into short bouts (the bout length is less than the median). b, Analogous to a, but computed only from renditions that fall into long bouts (the bout length is more than the median). The changes in the behavioural repertoire observed within a bout are qualitatively similar for short and long bouts (compare a, b; within-bout effects are most pronounced after day 70). In particular, the song becomes more regressive shortly before the end of a bout (5th percentile, bottom curves). This suggests that the analogous effect in Fig. 3c, d occurs at the end of a bout, rather than at a fixed time after the beginning of a bout. c, Analogous to Fig. 3c, d but computed over the entire dataset without prior clustering into syllables. The changes in behavioural repertoire differ in several respects from those in Fig. 3c, d, which were computed on individual syllables and then averaged across syllables (see Supplementary Methods). Here, the increase in regressions at the bout end is less pronounced. Moreover, large within-bout changes also occur for anticipations early in development. Both differences may reflect changes in the relative frequency of renditions from each syllable (for example, introductory notes) sung throughout a bout. Such changes in frequency can affect the results in c, which were computed on the unclustered data, but not those in a, b. d, Within-day effects, analogous to Fig. 3a, b, but computed for individual syllables, and then averaged across syllables and animals. The changes in behavioural repertoire are qualitatively similar to those in Fig. 3a, b, which were computed using the unclustered data. This similarity implies that the dynamics along the direction of slow change in Fig. 3 cannot be explained by changes during the day in the relative frequency of renditions from each syllable. e, Analogous to Fig. 3a, b but computed after shuffling production times among all data points. Within-day changes of the percentile curves are small under this null hypothesis. The maximal span of within-day fluctuations is 0.2 days, compared with 3.71 for the unshuffled data in Fig. 3b. The total repertoire spread (5th to 95th percentiles) is around 40 days, compared with around 23 days for unshuffled data. The 50th percentile curve is flat, implying that the shuffled data do not undergo a systematic drift over time (that is, do not describe a DiSC). The vertical separation between percentiles, then, reflects the range of production times in the data, not the spread along the DiSC. The time course of the 5th and 95th repertoire dating percentiles should thus be interpreted as the progression of regressions and anticipations along the DiSC only over the range of repertoire times covered by typical renditions (that is, approximately the vertical range of the 50th repertoire dating percentile). f, Analogous to Fig. 3e but for different distance metrics (Euclidean; correlation; Euclidean after time warping) and feature representations (32 acoustic features; 1 acoustic feature (entropy variance)). See also Extended Data Fig. 9.