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Directed songs to females were also recorded, but not analyzed for this study.Imitation outcome varied across familiesWe first measured similarity27 between tutor and pupil songs based on acoustic features (pitch, frequency modulation, Wiener entropy, and spectral continuity)28. We observed considerable variability in the distribution of song similarities between pupils and their individual tutors (mean = 69%; range 20–100%; CV = 0.28, Fig. 1a). To test for family influence, we identified 24 families that had multiple clutches with males, calculated the mean song similarity between pupils and their tutors of each clutch, which allowed us to normalize out the effect of song convergence between siblings20. We then calculated the coefficients of variance across clutches within families and compared it to the coefficient of variance across families (Fig. 1b). We found that imitation similarity was much more variable across families than within families (Kruskal–Wallis chi-squared = 44.727, df = 23, p-value = 0.006).Fig. 1: Distribution of song similarity between pupils and their tutors.a Histogram of song similarities between 160 pupils and their tutors. b Analysis of variance in song similarity between and within families. Data include 24 families with more than one clutch with males. Similarity scores were averaged within clutch members and coefficient of variance (CV = 0.14 ± 0.02) of similarity scores were calculated across clutches. CV of the same data (averaged within clutches) across families = 0.24 is presented as a dotted line. Source data for this figure is in Supplementary Data File 1.Full size imageIn certain families, across clutches, song imitation tended to be almost exclusively accurate (top quartile), in some modest (middle quartile), and others generally poor (Fig. 2a). To assess whether this variance in imitation outcome was genetic, we compared song imitation between biological and foster pupils. Foster pupils imitated their tutor as well as biological ones (biological similarity: 68.2 ± 1.7%, n = 130; foster similarity: 70.0 ± 3.6%, n = 30, mean ± S.E.M. hereafter). Therefore, the variability we observed in imitation outcomes across families cannot be explained by genetic variability. Instead, we noted that variability in imitation among pupils appeared to be associated with tutor song structure. For example, tutor Aq12 had a very simple song with one syllable-type containing two notes and none of his pupils imitated this syllable or song accurately. Instead, some pupils introduced apparently novel syllable types not found in the tutor in developing their own songs (Fig. 2b). In contrast, tutor DG1 had a more complex song, with five syllable types containing six notes, and all of his pupils imitated the syllables and the sequence much more accurately, with little to no introduction of novel syllables (Fig. 2c). In both cases, pupils still produced their syllables in repeated song motifs of 2–6 syllable types, as is typical of zebra finches (Fig. 2b, c). This suggested to us that pupils might more accurately imitate tutor songs that are rich in acoustic structure (i.e., acoustically diverse), while improvising upon impoverished tutor songs.Fig. 2: Imitation outcome varies across families.a 24 song tutoring lineages. All tutors had pupils in more than a single clutch. Each node represents one individual animal. Node shape represents pupils from the same clutch. Tutor nodes are presented on the bottom and pupil nodes on the top. Similarity scores are presented as quartiles (green for best imitations and red for poorest). Lineages are sorted according to the mean similarity between tutor and pupils from highest (top) to lowest (bottom). b, c Examples of song imitations from tutor AQ12 with a low similarity family (b) and from tutor DG1 with a high similarity family (c). Imitation outcomes are presented as percent acoustic similarity estimates on each sonogram. Red bars outline the repeated song motifs of the tutors. Source data for this figure is in Supplementary Data File 1.Full size imageSyllable-type diversity is not correlated between tutor and pupil songsIf this impoverished tutor song hypothesis were true, we would expect to find that as tutor syllable diversity decreases, pupil’ imitation similarity also decreases; conversely, we would expect to see biases in the correlation between tutor syllable diversity and pupil syllable diversity at extreme ranges of tutor diversity. To test this hypothesis quantitatively, we sought a measure of syllable acoustic and syntax diversity. We selected a random group of 80 adult tutor–pupil pairs, and segmented their songs into syllable units using an amplitude threshold27. Song syllables were automatically clustered into types based on their acoustic features (Fig. 3a, b)27. We then calculated the relative frequency (abundance) of each syllable-type and used Shannon information entropy27 to measure syllable acoustic diversity produced by each bird. Specifically, for each bird’s song, we calculated the proportion ({p}_{i}) of syllables produced for each syllable-type i, and computed entropy as (-sum {p}_{i}({{{log}}}_{2}({p}_{i}))). The measure weighs each vocal element (syllable) by its abundance, and presents the entropy (diversity) of the distribution in units of bits. We used the same Shannon information measure to also evaluate syllable transition diversity (song-syntax entropy29). The Shannon information entropy has limited bearing on capturing combinatorial complexity30, but it is a better estimate of diversity compared to just counting syllable types because it considers the frequencies (abundances) of each type. The more syllable types produced, and the more even their abundances are, the higher the entropy.Fig. 3: Syllable-type diversity.a Example sonograms of a tutor–pupil song pair. Syllable types are color-coded by lines above them. Color lines above each syllable indicate clusters computed separately for tutor and pupil in b. Note that syllable types are bird specific and color codes have no correspondence between tutor and pupil, e.g., green, yellow, and black labeled syllable types in tutor song merged into a single type (yellow labeled) in pupil’s song. b 2D scatter plots of syllable acoustic features: duration versus mean pitch, mean frequency modulation (FM), or mean Wiener entropy (a measure of the width of the power spectrum). The color of each marker indicates its computed syllable-type (type = cluster in feature space). Colors of clusters correspond to syllable-type colors shown in a. c Histogram of syllable-type diversity, pooled across all birds. d Regression analysis between tutor and pupil syllable-type diversity, showing no significant correlation for pupils with high or low imitation similarity of their tutors. e, f Tutor syllable diversity is not correlated with pupil song imitation similarity (e), or influence of tutors on pupils (f). g–k Examples of five tutor–pupil pairs with syllable recombination, namely merging in pupil songs. Source data for this figure is in Supplementary Data File 1.Full size imageThe distribution of syllable-type diversity of songs in the population was asymmetric, with most songs in the range of 2.5–3 bits and a left tail of rare songs with low syllable diversity (Fig. 3c). Surprisingly, there was no statistically significant correlation between tutor and pupil syllable diversity (R2 = 0.079, NS). Looking separately at pupils who imitated above (and below) average showed no correlations either (Fig. 3d). Further, there was no correlation between tutor syllable diversity and acoustic similarity between tutor and pupil songs (Fig. 3e). To better estimate how tutor syllable diversity may affect the cultural transmission, we calculated song acoustic similarity in reverse, from pupil to tutor. We call this a measure of “influence” because it tells us how much of the pupil’s song is influenced by the tutor. However, influence in pupils was not significantly correlated with tutor song (Fig. 3f). Near zero correlations were also observed for song-syntax (bigram) transitions between pairs of syllable types (Supplementary Fig. 1). In sum, our syllable-type diversity measure failed to capture any aspect of song learning, nullifying all our attempts to evaluate our impoverished tutor song hypothesis.Half of the pupils recombine syllablesPuzzled by the lack of even a weak correlation between tutor and pupil syllable and syntax diversity, we examined cases of most accurate imitation. We found frequent inconsistencies, as is typical of zebra finches in the boundaries of corresponding syllables in the songs of tutors and their pupils, even in cases of accurate imitation. This was not primarily due to measurement (segmentation) errors, but because pupils often modified or recombined the units they imitated (Fig. 3g). We assessed a lower bound estimate of similarity in the syllable boundaries of tutor and pupil songs, restricting the analysis to those syllables whose acoustic structure was clearly and fully imitated (either as a single unit or in parts) by the pupil (examples in Fig. 3g–k). With this strict criterion, analysis of syllable imitations in 33 randomly selected tutor–pupil pairs revealed modification of syllable boundaries in 47 cases (22%) of the copied syllables. Overall, 54% (18/33) of the pupils showed at least one case of altering syllables units. Interestingly, all 47 cases were of merging tutor syllables, rather than splitting. However, splitting might be more difficult to detect, and if so, our analyses would be an underestimate of the magnitude of syllable recombination (see “Methods” section).Vocal state measures capture balanced imitationGiven the extent of syllable recombination, we next sought an alternative quantitative measure that captures acoustic diversity at the sub-syllabic level, which would be, by design, insensitive to syllable recombination. For each of the 160 tutor–pupil pairs, we calculated continuously (in 10 ms FFT windows excluding silences, but without segmentation) three acoustic feature vectors: pitch, Wiener entropy (width of power spectrum), and frequency modulation28. Histograms of these features for all birdsongs in our sample reveal several concentrations, and we used the contours of these concentrations to partition the entire acoustic space of the songs into 10 regions. To visualize these concentrations, we present 2D slices of the feature space according to four peaks in the distribution of pitch (Fig. 4a), which we labeled very low, low, medium, and high. These four slices show distinct concentrations of the 10 regions, that we will call vocal states (Fig. 4b). The two concentrations in the highest and lowest pitch regions consisted of down-modulated and up-modulated sounds, respectively (vocal states 1 and 2, for lowest pitch, and 9 and 10 for highest pitch). The two central pitch regions (low and medium) consisted of similar types of vocal states, and two additional states (4 and 7) centered at zero frequency modulation represent non-modulated harmonic sounds. With the vocal states of the population categorized, we can consider each song as a long sequence of vocal states, calculated in small (10 ms) time windows. We next analyzed the distribution of vocal states, by calculating the relative abundances of sounds within each vocal state for each bird.Fig. 4: Vocal states and diversity in zebra finch songs.a Histogram of pitch, calculated in 10 ms windows and pooled for all songs31. Shadings show partitioning into four regions according to contours of the pitch distribution. b Two-dimensional heatmaps of frequency modulation and Wiener entropy for each of the four-pitch regions. Red circles outline 10 clusters around which vocal states are defined. c Histogram of song diversity for all male birds recorded. d Song diversity in tutor songs versus pupil songs. Colors show R2 separately for high and low similarity birds. e Tutor song diversities vs. similarity with pupil songs (R2 = 0.08, t = 1.9, Linear mixed-effects model NS). f Tutor song diversities vs. the influence of tutor song on pupils (R2 = 0.25, Linear mixed-effects model t = 4.8, p = 4.2e−6). Vertically aligned markers are often birds from the same lineage. The trend remains significant after removing the lowest diversity families (ABCDEF will give us 100% imitation similarity because all of the tutor’s sounds are present in pupil’s song, but only 50% influence because half of the pupil’s song is improvised. Indeed, for tutors with high song diversity, the diversity of pupil’ songs is centered on the diagonal (identity) line (Fig. 4d). However, for tutors with low song diversity, pupil song diversity, in most cases, above the diagonal (Fig. 4d). For example, out of 34 tutor songs with diversity below 3, only 5 pupil songs are below the diagonal. That is, pupils of tutors with low song diversity often imitated them, but were less influenced by them: they often made additions that increase song diversity. These “low influence” songs did not resemble neighboring birds’ songs (Supplementary Note 1). We, therefore, suspect that these additions are improvisations, namely they are likely to be either modified versions of tutor song elements, or innate syllable types.Assuming a natural trend to develop high-diversity songs either via imitation or improvisation, we wondered why songs of low diversity were not rarer in our colony. We tested which factors may sustain songs of low diversity across generations and found that pupils that imitated poorly, regardless of tutor song diversity, tended to have low-diversity songs (Fig. 4g, R2 = 0.20, t = 5.7, p = 6.2e−8). To directly test for interaction between imitation accuracy and song diversity, we ran a linear mixed-effect model to explain pupil song diversity with two fixed effects: the diversity of the tutor song, and the acoustic similarity to the tutor song (how much of it was copied). Results confirmed that both factors contribute about equally to pupil song diversity (imitation similarity: t = 5.0, p = 1.4e−6; tutor song diversity: t = 4.6, p = 7.9e−6).In sum, although our syllable diversity measure failed to capture any relationship with song imitation, bypassing syllable recombination by measuring song diversity based on vocal states (without segmentation) revealed two effects: First, low diversity in a tutor’s song was not associated with lower imitation similarity in the pupil but with lower influence on the pupil, indicating a tendency in pupils to increase song diversity, which we call “balanced imitation”. Second, low diversity in a pupil’s song (but not in tutor song) is associated with poor imitation similarity in the pupil. Together these effects can explain the stable polymorphism in song diversity across generations: on the one hand, pupils tend to increase song diversity when tutored by a low-diversity song model, but on the other hand, poor imitation is associated with a decrease of song diversity in pupils’ songs. Consistent with this interpretation, when we plotted song diversity of each tutor against the mean song diversity of all of his pupils, the mean song diversity in pupils of low-diversity (below median) tutors was often higher than that of their tutors, and vice versa (Fig. 4h). That is, despite the overall positive correlation between tutor and pupil song diversity, we see frequent reversals such that a large proportion (42%) of pupils with low song diversity had tutors with high (above median) song diversity, and vice versa.Balanced imitation across multiple generationsWe further explored reversals across multiple generations, and analyzed 14 family branches, where we had song imitation data across two generations of pupils. We found that in the families where the first-generation pupils imitated poorly, there was often some recovery in imitation accuracy in the second-generation, the grand-pupils (Fig. 5a). For example, in the two lineages (HP10 and DG4) with the greatest number of first-generation pupils that imitated poorly, all of the second-generation (grand) pupils imitated the song of their tutor more accurately than the tutor’s imitation of the grand tutor. Sonograms revealed that, in both lineages, the grand-tutor songs were unbalanced: Tutor HP10 had a very high-pitched song (Fig. 5b), whereas tutor DG4’s song included numerous harmonic stacks (Fig. 5c). In both cases, their pupils developed songs that appear to be more acoustically “balanced,” and ones that the grand-pupils imitated accurately (Fig. 5b, c). In other cases, however, low similarity was simply due to partial imitation, e.g., in the lineage (LB12), where the song imitation became worse because a grand–pupil dropped a syllable during imitation (Fig. 5d). These findings suggest that grand-pupils of impoverished-song grand tutors imitate some elements from the deficient songs of their tutors, but they also further “balance” them, thus increasing the diversity of their songs.Fig. 5: Song diversity across generations.a Song similarity across two generations of pupils (colors represent quartiles (as in Fig. 1a)) in 14 family lineages. b An example from lineage HP10 showing a transition from poor imitation in a first-generation pupil to accurate imitation in a grand pupil. c Same as b for lineage DG4. d A counter example in lineage LB12, where the grand pupil imitated poorly. Source data for this figure is in Supplementary Data File 1.Full size imageBalanced imitation of vocal state abundancesOur measures up to now summarize the distribution of vocal states within a song. We next looked at each vocal state separately and measured how frequencies (abundances) of vocal states are imitated. In prior studies, we noted that vocal imitation in zebra finches is inversely related to model abundance. That is, too much exposure to a tutored song could inhibit learning31. Here we test if this is the case also for abundances of vocal states within a song.We partitioned the vocal state data into quartiles based on the overall acoustic similarity between tutor and pupil songs. For each tutor–pupil pair, in each quartile, we then plotted the relative abundances of all 10 corresponding vocal states in the tutor’s song versus his pupil’s song (Fig. 6a–d). We found that relative abundances of all 10 states were correlated, for each quartile. As expected, tutor–pupil vocal state abundances were more strongly associated when imitations were accurate; for example, the residual coefficient of determination was much higher in the top similarity quartile, explaining about 35% of the variance in cases of highest song similarity (Fig. 6a), and only about 9% of the variance in the bottom quartile (Fig. 6d). We noted that in all quartiles, the slope of the correlation was less than one (Fig. 6a–d), meaning that when tutor’s vocal state was low in abundance, pupil’s vocal states tended to be higher (above the diagonal) and vice versa.Fig. 6: Imitation of vocal state abundances.a–d Scatter plots of tutor vs. corresponding pupil vocal state abundances according to quartiles of song similarities. Note that each bird is represented by 10 markers, which are not statistically independent. The residual correlations were computed after removing trends with bird identities included as random factors. Dashed lines are identity, slope = 1. Colored lines are regression of the data. e Same data as in a–d combined, comparing vocal states abundances  > 20% in tutor vs. pupil songs. f Median imitation gains for all state abundances, according to imitation quartiles. Gain of 1 indicates no bias, gain of 2 indicates doubling of abundance, and gain of 0.5 halving. Y axis is log-scale. g–i Examples of song diversity balancing. We simplified the 10 vocal states into 4 groups: yellow for high pitch states 9–10; mustard for medium pitch, high entropy states 6 and 8; light blue for non-modulated states 4 and 7; and dark blue for the rest 1, 2, 3, and 5. In i, we present two generations of pupils. Note the more uniform pie charts in pupils compared to their tutors. j, Vocal state abundances in biological tutors vs. pupils’ songs. k Vocal state abundances in foster tutors vs. pupils’ songs. l Vocal state abundances in fostered pupils vs. their biological fathers, who did not raise them, which did not raise them. Dashed lines are identity, slope = 1. Red lines are regression of the data. Source data for this figure is in Supplementary Data File 1.Full size imageWe next tested for statistical significance of this bias across the entire data set. Our null hypothesis is that when the abundance of a vocal state in the tutor’s song is high, his pupil is not more likely than chance to deviate from the model in a manner that “balances” his song. In other words, if deviations (imitation “errors”) are random, then the likelihood of deviations (errors) to increase or decrease song diversity should be determined by the overall distribution of errors in our sample. In a previous study2, some of us presented evidence that imitation of isolated tutors is biased: syllables with high abundance in abnormal isolate tutor song ( >20%) were often less abundant is pupil’s songs. Using the same 20% threshold we found that the distribution of tutor vs. pupil vocal state abundances is asymmetric (Fig. 6e): when tutor’s vocal state abundance is above 20%, about 14% of corresponding pupil’s states are above the diagonal (hence 86% of the errors increase song diversity). But looking in reverse, we found that when a pupil’s vocal state is above 20%, a higher proportion of corresponding tutor’s states (23%) are to the right of the diagonal. To overcome dependencies between vocal states, we treated each tutor–pupil pair as a statistic. We randomly shuffled the direction tutor- >pupil vs. pupil- >tutor (without breaking the pairs) to obtain a random distribution of biases. We found that the observed bias to increase song diversity (namely in the direction that decreases the abundance of vocal states that are already of high abundance) is higher than expected by chance (bootstrap direct p-value = 0.032).We wondered if this bias is stronger in cases of poor imitation, due to the inclusion of non-tutor syllables (via improvisation or innate vocalization). To evaluate if this was the case, we divided the tutors’ vocal states into 0.1 abundance bins, then calculated the median abundance of pupil vocal states for each bin. For each bin, we calculated the abundance ratio for that median. For example, if at the window centered at 0.1 tutor abundance, the median pupil vocal state abundance was 0.2, then the gain ratio would be 2. A gain value of 1 (y axis in Fig. 6f) represents the identical abundance of all 10 vocal states in pupil and tutor. A gain value of 2 indicates a doubling of abundances in the pupil (amplification), and a value of 0.5 halving (attenuation). Interestingly, the gain-loss curves have similar shapes and magnitude across all four quartile groups (Fig. 6f). In all cases, a gain of 1 (where abundance tends to be identical across pupils and their tutors), was at 11–12% abundance, which is fairly close to the center of the distribution (=10%, since we have 10 vocal states). These findings suggest that the regression we noted is not an entirely random effect. For example, in Q1, where the mean similarity is 93%, we see that when tutor state abundance is above 0.2, the corresponding pupil abundance is lower in 10 out of 11 cases (Fig. 6a). In all these cases, the corresponding vocal sounds were imitated, but produced either less often, or with biased features, by the pupil.To visually compare vocal state abundances in tutor vs pupil songs, we reduced the ten vocal states into four color codes, and graphed them along with the sonograms of each bird (Fig. 6g–i). In cases where the tutors’ songs included many high-pitched vocalizations (vocal states 9 and 10), their pupils imitated, but lowered the pitch, thereby decreasing the abundance of those states (Fig. 6g, h). In another example, where the tutor’s song had a high abundance of harmonic stacks (states 4 and 7), their pupil imitated only a subset of these sounds (Fig. 6i). In turn, in the following generation, the pupil’s pupil further differentiated his song to include more balanced vocal states (Fig. 6i). Taken together, song imitation appears to be highly sensitive to the relative abundances of vocal states, suggesting a balancing mechanism that prevents song diversity from becoming too low, perhaps independently of imitation.Finally, we asked whether fostered pupils imitate their tutor’s song vocal states as accurately as biological pupils. Analysis at the level of vocal states allowed us to compare how abundances of vocal states are influenced by foster vs. biological fathers. For reference, imitation of vocal state abundances between the 130 biological pupils and their fathers had a residual R2 = 0.16 (Fig. 6j; t = 5.9, p = 3.9e−09). The 30 foster pupils relative to their foster fathers had a similar R2 = 0.19 (Fig. 6k; t = 2.5, p = 0.01). This is supported by a near-zero correlation between fostered pupils and their biological fathers (Fig. 6l, residual R2 = 0.01, t = 0.46, NS). Therefore, the similarities we observed in vocal state abundances between tutors and their pupils reflect learning with no detectable genetic effect at this level of analysis.How balanced imitation constrains distributions of song featuresWe first tested if abundances of specific vocal states are similar across low-diversity and high-diversity tutor songs. We pooled together songs from tutors that had the lowest diversity (bottom quartile) and calculated the diversity of their “pooled song”. We found that the diversity increased from a mean of 2.99 bits to 3.17 bits, which is similar to the mean diversity in the top quartile (mean = 3.16 bits) but lower than the pooled diversity of the top quartile (=3.27 bits). This outcome indicates that the distribution of vocal states pooled across low-diversity songs is fairly broad, but not as broad as that across songs of high diversity. The distribution of abundances of pooled vocal states (Fig. 7a) explained this difference: As opposed to the nearly flat distribution of vocal state abundances in the high-diversity songs, low-diversity songs tend to have a higher proportion of states 9 and 10, which correspond to high pitch sounds. This is interesting because, in this respect, the low-diversity songs are structurally similar to isolate songs, which are often of higher pitch32. As expected, comparing top and bottom quartiles of influence on the pupil show a similar outcome (Fig. 7b). This outcome suggests that mean song features of low and high influence songs should differ. Further, the variance should also differ: High-diversity songs by definition cannot be extreme in their mean feature values. Low-diversity songs can, in principle, have average features that are close to the population mean, but are more likely to have extreme mean feature values. For example, a song containing mostly high-pitched sounds is both low diversity and extreme in its mean pitch (see for example tutor HP10 in Fig. 5b).Fig. 7: Song diversity versus imitation.a Vocal state abundances in pupils pooled over birds with lowest (bottom quartile) song diversity (dotted line) vs. top quartile (solid line). b Same as a for the bottom (red) and top (green) quartiles of tutor song influence. c–e Mean tutor’s song features versus pupil’s song features for pitch (c), frequency modulation (d), and Wiener entropy (e) for the top influences (green dots, top quartile) and for bottom influences (red dots, bottom quartile). Plotted at the bottom are histogram lines of tutor features for top and bottom quartiles. f–h Box plot distribution of mean song features in four colonies for pitch (f), frequency modulation (g), and Wiener entropy (h). Each marker represents the mean value for one bird. Green shaded areas correspond to top influence feature ranges in colony RU 2019 (this study), whereas red shaded areas correspond to bottom influence feature ranges in colony RU 2019 (n = 149 birds). In the box plots themselves, the red line is the median; Orange fill are the upper and lower quartiles; Blue fill is the minima and maxima. About 20% of the RU 2019 colony are descendants from the RU 2002 colony (Rockefeller Nottebohm Lab; n = 42 birds). The remainder of the 2019 colony originated from Duke University. Colony 3 is from the University of Southern California (Bottjer Lab; n = 48) and Colony 4 is from Cornell University (Regan Lab; n = 77). Source data for this figure is in Supplementary Data File 1.Full size imageWe asked whether we can predict imitation outcomes based on the mean features of a tutor song. If songs of low diversity were culturally transmitted less than high-diversity songs, then songs with extreme mean features—which are typically of low diversity—should be transmitted less. To evaluate this, we plotted the mean pitch of tutor songs against the pitch of their pupil’s songs. Indeed, the distribution of mean song pitch was tighter for the top quartile of tutor–pupil song imitation (Fig. 7c). For example, all tutor songs with a mean pitch above 2000 Hz were of low influence (Fig. 7c, histogram red symbols); these extreme songs were also of low diversity. A similar effect can be seen in Wiener entropy (Fig. 7d) and frequency modulation (Fig. 7e): in both cases the distributions were broader for low-diversity songs. Further, for mean pitch, top influence (green line) is equal or higher than low influence (red line) between 795 Hz and 1885Hz (Fig. 7c). Bottom influence is higher between 1885 and 3000 Hz (red line above green line, Fig. 7c).We superimposed these empirically determined pitch intervals (for top and bottom influence) on ranges of mean song pitches obtained in a database of four zebra finch colonies including the current one, and shaded the intervals values green (presumably top influence) and red (presumably low influence; Fig. 7f). We then did the same for frequency modulation (Fig. 7g), and Weiner entropy (Fig. 7h). Across the colonies, the distribution of mean song features was to a large extent confined within the range of high influence in our colony. Therefore, the range of mean feature values of highest imitation influences in our colony, but not of lowest influences, seems consistent across zebra finch colonies. This range, in turn, can be explained by balanced imitation as high influences are associated with high tutor song diversity. In sum, this outcome is consistent with the notion that over generations, songs of high feature diversity are more influential, and therefore shape the overall distribution of mean song features in a similar manner across colonies. More

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